what is randomized comparative experiment

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What Are Comparative Experiments?

Definitions of Control, Constant, Independent and Dependent Variables ...

Many students of science understand the basic idea of the comparative experiment because the name "comparative experiment" mostly explains itself. Students would be correct in defining a comparative experiment as one that compares the effects of two treatments. However, like most anything in science, the comparative experiment has advantages and disadvantages. Students must understand these aspects at a deep level before fully understanding the comparative experiment itself.

Asking the Right Question

According to Penn State, a comparative experiment starts with a question or hypothesis that asks how two or more treatments affect some response. When a scientist wants to know the difference between the effects of treatment A and treatment B on dependent variable C, he will run an experiment in which all of the conditions are the same except for one: the treatment -- A or B -- given to the subject. After receiving the results of the experiment, the scientist can then compare the difference in the dependent variable C for each treatment, concluding either that one treatment is more effective than the other or that both treatments have about the same effectiveness.

The keys to a comparative treatment are control and randomization. Control refers to holding constant all of the other variables that could affect the outcome. For example, a comparative experiment comparing the effects of two diets of different nutritional value on the growth of mice should ensure that the mice eat at the same time, regardless of which diet they are assigned to eat. Randomization refers to randomly assigning the experiment’s subjects, such as mice, to the two or more treatment groups. This randomization allows for valid conclusions and statistical analysis across treatments.

The Advantage

To many students of science, the comparative experiment is a time-saver. Standard, non-comparative experiments use a “control,” which refers to a group of subjects that receive no treatment or a placebo. Scientists engaging in non-comparative experiments in their research would need to run the experiment twice, once with each treatment. For many experiments, however, running just one experiment can be a remarkable expense in both time and money. Thus, a comparative experiment can save a scientist the trouble of having to allocate resources to a second run with a different treatment.

Comparative treatments do not need to include a control, which can be a problem if both treatments yield similar results. For example, if two different injections lead to a similar amount of increased activity in mice, a scientist might be tempted to conclude that both of the injected drugs are effective at inciting activity. The truth is that without a control, the scientist cannot make such a conclusion, as other factors might be influencing the enhanced activity of the mice, such as anxiety from the injection or being handled by the scientists. A comparative experiment is generally limited to conclude the relative effectiveness of one treatment compared to the other.

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• Experiment Design and Statistical Methods for Behavioural and Social Research; David Boniface
• Encyclopedia of Research Design; Neil Salkind
• East Tennessee State University: Producing Data: Experiments

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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.

Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].

Chapter 10 methods for comparative studies.

Francis Lau and Anne Holbrook .

10.1. Introduction

In eHealth evaluation, comparative studies aim to find out whether group differences in eHealth system adoption make a difference in important outcomes. These groups may differ in their composition, the type of system in use, and the setting where they work over a given time duration. The comparisons are to determine whether significant differences exist for some predefined measures between these groups, while controlling for as many of the conditions as possible such as the composition, system, setting and duration.

According to the typology by Friedman and Wyatt (2006) , comparative studies take on an objective view where events such as the use and effect of an eHealth system can be defined, measured and compared through a set of variables to prove or disprove a hypothesis. For comparative studies, the design options are experimental versus observational and prospective versus retro­­spective. The quality of eHealth comparative studies depends on such aspects of methodological design as the choice of variables, sample size, sources of bias, confounders, and adherence to quality and reporting guidelines.

In this chapter we focus on experimental studies as one type of comparative study and their methodological considerations that have been reported in the eHealth literature. Also included are three case examples to show how these studies are done.

10.2. Types of Comparative Studies

Experimental studies are one type of comparative study where a sample of participants is identified and assigned to different conditions for a given time duration, then compared for differences. An example is a hospital with two care units where one is assigned a cpoe system to process medication orders electronically while the other continues its usual practice without a cpoe . The participants in the unit assigned to the cpoe are called the intervention group and those assigned to usual practice are the control group. The comparison can be performance or outcome focused, such as the ratio of correct orders processed or the occurrence of adverse drug events in the two groups during the given time period. Experimental studies can take on a randomized or non-randomized design. These are described below.

10.2.1. Randomized Experiments

In a randomized design, the participants are randomly assigned to two or more groups using a known randomization technique such as a random number table. The design is prospective in nature since the groups are assigned concurrently, after which the intervention is applied then measured and compared. Three types of experimental designs seen in eHealth evaluation are described below ( Friedman & Wyatt, 2006 ; Zwarenstein & Treweek, 2009 ).

Randomized controlled trials ( rct s) – In rct s participants are randomly assigned to an intervention or a control group. The randomization can occur at the patient, provider or organization level, which is known as the unit of allocation. For instance, at the patient level one can randomly assign half of the patients to receive emr reminders while the other half do not. At the provider level, one can assign half of the providers to receive the reminders while the other half continues with their usual practice. At the organization level, such as a multisite hospital, one can randomly assign emr reminders to some of the sites but not others. Cluster randomized controlled trials ( crct s) – In crct s, clusters of participants are randomized rather than by individual participant since they are found in naturally occurring groups such as living in the same communities. For instance, clinics in one city may be randomized as a cluster to receive emr reminders while clinics in another city continue their usual practice. Pragmatic trials – Unlike rct s that seek to find out if an intervention such as a cpoe system works under ideal conditions, pragmatic trials are designed to find out if the intervention works under usual conditions. The goal is to make the design and findings relevant to and practical for decision-makers to apply in usual settings. As such, pragmatic trials have few criteria for selecting study participants, flexibility in implementing the intervention, usual practice as the comparator, the same compliance and follow-up intensity as usual practice, and outcomes that are relevant to decision-makers.

10.2.2. Non-randomized Experiments

Non-randomized design is used when it is neither feasible nor ethical to randomize participants into groups for comparison. It is sometimes referred to as a quasi-experimental design. The design can involve the use of prospective or retrospective data from the same or different participants as the control group. Three types of non-randomized designs are described below ( Harris et al., 2006 ).

Intervention group only with pretest and post-test design – This design involves only one group where a pretest or baseline measure is taken as the control period, the intervention is implemented, and a post-test measure is taken as the intervention period for comparison. For example, one can compare the rates of medication errors before and after the implementation of a cpoe system in a hospital. To increase study quality, one can add a second pretest period to decrease the probability that the pretest and post-test difference is due to chance, such as an unusually low medication error rate in the first pretest period. Other ways to increase study quality include adding an unrelated outcome such as patient case-mix that should not be affected, removing the intervention to see if the difference remains, and removing then re-implementing the intervention to see if the differences vary accordingly. Intervention and control groups with post-test design – This design involves two groups where the intervention is implemented in one group and compared with a second group without the intervention, based on a post-test measure from both groups. For example, one can implement a cpoe system in one care unit as the intervention group with a second unit as the control group and compare the post-test medication error rates in both units over six months. To increase study quality, one can add one or more pretest periods to both groups, or implement the intervention to the control group at a later time to measure for similar but delayed effects. Interrupted time series ( its ) design – In its design, multiple measures are taken from one group in equal time intervals, interrupted by the implementation of the intervention. The multiple pretest and post-test measures decrease the probability that the differences detected are due to chance or unrelated effects. An example is to take six consecutive monthly medication error rates as the pretest measures, implement the cpoe system, then take another six consecutive monthly medication error rates as the post-test measures for comparison in error rate differences over 12 months. To increase study quality, one may add a concurrent control group for comparison to be more convinced that the intervention produced the change.

10.3. Methodological Considerations

The quality of comparative studies is dependent on their internal and external validity. Internal validity refers to the extent to which conclusions can be drawn correctly from the study setting, participants, intervention, measures, analysis and interpretations. External validity refers to the extent to which the conclusions can be generalized to other settings. The major factors that influence validity are described below.

10.3.1. Choice of Variables

Variables are specific measurable features that can influence validity. In comparative studies, the choice of dependent and independent variables and whether they are categorical and/or continuous in values can affect the type of questions, study design and analysis to be considered. These are described below ( Friedman & Wyatt, 2006 ).

Dependent variables – This refers to outcomes of interest; they are also known as outcome variables. An example is the rate of medication errors as an outcome in determining whether cpoe can improve patient safety. Independent variables – This refers to variables that can explain the measured values of the dependent variables. For instance, the characteristics of the setting, participants and intervention can influence the effects of cpoe . Categorical variables – This refers to variables with measured values in discrete categories or levels. Examples are the type of providers (e.g., nurses, physicians and pharmacists), the presence or absence of a disease, and pain scale (e.g., 0 to 10 in increments of 1). Categorical variables are analyzed using non-parametric methods such as chi-square and odds ratio. Continuous variables – This refers to variables that can take on infinite values within an interval limited only by the desired precision. Examples are blood pressure, heart rate and body temperature. Continuous variables are analyzed using parametric methods such as t -test, analysis of variance or multiple regression.

10.3.2. Sample Size

Sample size is the number of participants to include in a study. It can refer to patients, providers or organizations depending on how the unit of allocation is defined. There are four parts to calculating sample size. They are described below ( Noordzij et al., 2010 ).

Significance level – This refers to the probability that a positive finding is due to chance alone. It is usually set at 0.05, which means having a less than 5% chance of drawing a false positive conclusion. Power – This refers to the ability to detect the true effect based on a sample from the population. It is usually set at 0.8, which means having at least an 80% chance of drawing a correct conclusion. Effect size – This refers to the minimal clinically relevant difference that can be detected between comparison groups. For continuous variables, the effect is a numerical value such as a 10-kilogram weight difference between two groups. For categorical variables, it is a percentage such as a 10% difference in medication error rates. Variability – This refers to the population variance of the outcome of interest, which is often unknown and is estimated by way of standard deviation ( sd ) from pilot or previous studies for continuous outcome.

Sample Size Equations for Comparing Two Groups with Continuous and Categorical Outcome Variables.

An example of sample size calculation for an rct to examine the effect of cds on improving systolic blood pressure of hypertensive patients is provided in the Appendix. Refer to the Biomath website from Columbia University (n.d.) for a simple Web-based sample size / power calculator.

10.3.3. Sources of Bias

There are five common sources of biases in comparative studies. They are selection, performance, detection, attrition and reporting biases ( Higgins & Green, 2011 ). These biases, and the ways to minimize them, are described below ( Vervloet et al., 2012 ).

Selection or allocation bias – This refers to differences between the composition of comparison groups in terms of the response to the intervention. An example is having sicker or older patients in the control group than those in the intervention group when evaluating the effect of emr reminders. To reduce selection bias, one can apply randomization and concealment when assigning participants to groups and ensure their compositions are comparable at baseline. Performance bias – This refers to differences between groups in the care they received, aside from the intervention being evaluated. An example is the different ways by which reminders are triggered and used within and across groups such as electronic, paper and phone reminders for patients and providers. To reduce performance bias, one may standardize the intervention and blind participants from knowing whether an intervention was received and which intervention was received. Detection or measurement bias – This refers to differences between groups in how outcomes are determined. An example is where outcome assessors pay more attention to outcomes of patients known to be in the intervention group. To reduce detection bias, one may blind assessors from participants when measuring outcomes and ensure the same timing for assessment across groups. Attrition bias – This refers to differences between groups in ways that participants are withdrawn from the study. An example is the low rate of participant response in the intervention group despite having received reminders for follow-up care. To reduce attrition bias, one needs to acknowledge the dropout rate and analyze data according to an intent-to-treat principle (i.e., include data from those who dropped out in the analysis). Reporting bias – This refers to differences between reported and unreported findings. Examples include biases in publication, time lag, citation, language and outcome reporting depending on the nature and direction of the results. To reduce reporting bias, one may make the study protocol available with all pre-specified outcomes and report all expected outcomes in published results.

10.3.4. Confounders

Confounders are factors other than the intervention of interest that can distort the effect because they are associated with both the intervention and the outcome. For instance, in a study to demonstrate whether the adoption of a medication order entry system led to lower medication costs, there can be a number of potential confounders that can affect the outcome. These may include severity of illness of the patients, provider knowledge and experience with the system, and hospital policy on prescribing medications ( Harris et al., 2006 ). Another example is the evaluation of the effect of an antibiotic reminder system on the rate of post-operative deep venous thromboses ( dvt s). The confounders can be general improvements in clinical practice during the study such as prescribing patterns and post-operative care that are not related to the reminders ( Friedman & Wyatt, 2006 ).

To control for confounding effects, one may consider the use of matching, stratification and modelling. Matching involves the selection of similar groups with respect to their composition and behaviours. Stratification involves the division of participants into subgroups by selected variables, such as comorbidity index to control for severity of illness. Modelling involves the use of statistical techniques such as multiple regression to adjust for the effects of specific variables such as age, sex and/or severity of illness ( Higgins & Green, 2011 ).

10.3.5. Guidelines on Quality and Reporting

There are guidelines on the quality and reporting of comparative studies. The grade (Grading of Recommendations Assessment, Development and Evaluation) guidelines provide explicit criteria for rating the quality of studies in randomized trials and observational studies ( Guyatt et al., 2011 ). The extended consort (Consolidated Standards of Reporting Trials) Statements for non-pharmacologic trials ( Boutron, Moher, Altman, Schulz, & Ravaud, 2008 ), pragmatic trials ( Zwarestein et al., 2008 ), and eHealth interventions ( Baker et al., 2010 ) provide reporting guidelines for randomized trials.

The grade guidelines offer a system of rating quality of evidence in systematic reviews and guidelines. In this approach, to support estimates of intervention effects rct s start as high-quality evidence and observational studies as low-quality evidence. For each outcome in a study, five factors may rate down the quality of evidence. The final quality of evidence for each outcome would fall into one of high, moderate, low, and very low quality. These factors are listed below (for more details on the rating system, refer to Guyatt et al., 2011 ).

Design limitations – For rct s they cover the lack of allocation concealment, lack of blinding, large loss to follow-up, trial stopped early or selective outcome reporting. Inconsistency of results – Variations in outcomes due to unexplained heterogeneity. An example is the unexpected variation of effects across subgroups of patients by severity of illness in the use of preventive care reminders. Indirectness of evidence – Reliance on indirect comparisons due to restrictions in study populations, intervention, comparator or outcomes. An example is the 30-day readmission rate as a surrogate outcome for quality of computer-supported emergency care in hospitals. Imprecision of results – Studies with small sample size and few events typically would have wide confidence intervals and are considered of low quality. Publication bias – The selective reporting of results at the individual study level is already covered under design limitations, but is included here for completeness as it is relevant when rating quality of evidence across studies in systematic reviews.

The original consort Statement has 22 checklist items for reporting rct s. For non-pharmacologic trials extensions have been made to 11 items. For pragmatic trials extensions have been made to eight items. These items are listed below. For further details, readers can refer to Boutron and colleagues (2008) and the consort website ( consort , n.d.).

Title and abstract – one item on the means of randomization used. Introduction – one item on background, rationale, and problem addressed by the intervention. Methods – 10 items on participants, interventions, objectives, outcomes, sample size, randomization (sequence generation, allocation concealment, implementation), blinding (masking), and statistical methods. Results – seven items on participant flow, recruitment, baseline data, numbers analyzed, outcomes and estimation, ancillary analyses, adverse events. Discussion – three items on interpretation, generalizability, overall evidence.

The consort Statement for eHealth interventions describes the relevance of the consort recommendations to the design and reporting of eHealth studies with an emphasis on Internet-based interventions for direct use by patients, such as online health information resources, decision aides and phr s. Of particular importance is the need to clearly define the intervention components, their role in the overall care process, target population, implementation process, primary and secondary outcomes, denominators for outcome analyses, and real world potential (for details refer to Baker et al., 2010 ).

10.4. Case Examples

10.4.1. pragmatic rct in vascular risk decision support.

Holbrook and colleagues (2011) conducted a pragmatic rct to examine the effects of a cds intervention on vascular care and outcomes for older adults. The study is summarized below.

Setting – Community-based primary care practices with emr s in one Canadian province. Participants – English-speaking patients 55 years of age or older with diagnosed vascular disease, no cognitive impairment and not living in a nursing home, who had a provider visit in the past 12 months. Intervention – A Web-based individualized vascular tracking and advice cds system for eight top vascular risk factors and two diabetic risk factors, for use by both providers and patients and their families. Providers and staff could update the patient’s profile at any time and the cds algorithm ran nightly to update recommendations and colour highlighting used in the tracker interface. Intervention patients had Web access to the tracker, a print version mailed to them prior to the visit, and telephone support on advice. Design – Pragmatic, one-year, two-arm, multicentre rct , with randomization upon patient consent by phone, using an allocation-concealed online program. Randomization was by patient with stratification by provider using a block size of six. Trained reviewers examined emr data and conducted patient telephone interviews to collect risk factors, vascular history, and vascular events. Providers completed questionnaires on the intervention at study end. Patients had final 12-month lab checks on urine albumin, low-density lipoprotein cholesterol, and A1c levels. Outcomes – Primary outcome was based on change in process composite score ( pcs ) computed as the sum of frequency-weighted process score for each of the eight main risk factors with a maximum score of 27. The process was considered met if a risk factor had been checked. pcs was measured at baseline and study end with the difference as the individual primary outcome scores. The main secondary outcome was a clinical composite score ( ccs ) based on the same eight risk factors compared in two ways: a comparison of the mean number of clinical variables on target and the percentage of patients with improvement between the two groups. Other secondary outcomes were actual vascular event rates, individual pcs and ccs components, ratings of usability, continuity of care, patient ability to manage vascular risk, and quality of life using the EuroQol five dimensions questionnaire ( eq-5D) . Analysis – 1,100 patients were needed to achieve 90% power in detecting a one-point pcs difference between groups with a standard deviation of five points, two-tailed t -test for mean difference at 5% significance level, and a withdrawal rate of 10%. The pcs , ccs and eq-5D scores were analyzed using a generalized estimating equation accounting for clustering within providers. Descriptive statistics and χ2 tests or exact tests were done with other outcomes. Findings – 1,102 patients and 49 providers enrolled in the study. The intervention group with 545 patients had significant pcs improvement with a difference of 4.70 ( p < .001) on a 27-point scale. The intervention group also had significantly higher odds of rating improvements in their continuity of care (4.178, p < .001) and ability to improve their vascular health (3.07, p < .001). There was no significant change in vascular events, clinical variables and quality of life. Overall the cds intervention led to reduced vascular risks but not to improved clinical outcomes in a one-year follow-up.

10.4.2. Non-randomized Experiment in Antibiotic Prescribing in Primary Care

Mainous, Lambourne, and Nietert (2013) conducted a prospective non-randomized trial to examine the impact of a cds system on antibiotic prescribing for acute respiratory infections ( ari s) in primary care. The study is summarized below.

Setting – A primary care research network in the United States whose members use a common emr and pool data quarterly for quality improvement and research studies. Participants – An intervention group with nine practices across nine states, and a control group with 61 practices. Intervention – Point-of-care cds tool as customizable progress note templates based on existing emr features. cds recommendations reflect Centre for Disease Control and Prevention ( cdc ) guidelines based on a patient’s predominant presenting symptoms and age. cds was used to assist in ari diagnosis, prompt antibiotic use, record diagnosis and treatment decisions, and access printable patient and provider education resources from the cdc . Design – The intervention group received a multi-method intervention to facilitate provider cds adoption that included quarterly audit and feedback, best practice dissemination meetings, academic detailing site visits, performance review and cds training. The control group did not receive information on the intervention, the cds or education. Baseline data collection was for three months with follow-up of 15 months after cds implementation. Outcomes – The outcomes were frequency of inappropriate prescribing during an ari episode, broad-spectrum antibiotic use and diagnostic shift. Inappropriate prescribing was computed by dividing the number of ari episodes with diagnoses in the inappropriate category that had an antibiotic prescription by the total number of ari episodes with diagnosis for which antibiotics are inappropriate. Broad-spectrum antibiotic use was computed by all ari episodes with a broad-spectrum antibiotic prescription by the total number of ari episodes with an antibiotic prescription. Antibiotic drift was computed in two ways: dividing the number of ari episodes with diagnoses where antibiotics are appropriate by the total number of ari episodes with an antibiotic prescription; and dividing the number of ari episodes where antibiotics were inappropriate by the total number of ari episodes. Process measure included frequency of cds template use and whether the outcome measures differed by cds usage. Analysis – Outcomes were measured quarterly for each practice, weighted by the number of ari episodes during the quarter to assign greater weight to practices with greater numbers of relevant episodes and to periods with greater numbers of relevant episodes. Weighted means and 95% ci s were computed separately for adult and pediatric (less than 18 years of age) patients for each time period for both groups. Baseline means in outcome measures were compared between the two groups using weighted independent-sample t -tests. Linear mixed models were used to compare changes over the 18-month period. The models included time, intervention status, and were adjusted for practice characteristics such as specialty, size, region and baseline ari s. Random practice effects were included to account for clustering of repeated measures on practices over time. P -values of less than 0.05 were considered significant. Findings – For adult patients, inappropriate prescribing in ari episodes declined more among the intervention group (-0.6%) than the control group (4.2%)( p = 0.03), and prescribing of broad-spectrum antibiotics declined by 16.6% in the intervention group versus an increase of 1.1% in the control group ( p < 0.0001). For pediatric patients, there was a similar decline of 19.7% in the intervention group versus an increase of 0.9% in the control group ( p < 0.0001). In summary, the cds had a modest effect in reducing inappropriate prescribing for adults, but had a substantial effect in reducing the prescribing of broad-spectrum antibiotics in adult and pediatric patients.

10.4.3. Interrupted Time Series on EHR Impact in Nursing Care

Dowding, Turley, and Garrido (2012) conducted a prospective its study to examine the impact of ehr implementation on nursing care processes and outcomes. The study is summarized below.

Setting – Kaiser Permanente ( kp ) as a large not-for-profit integrated healthcare organization in the United States. Participants – 29 kp hospitals in the northern and southern regions of California. Intervention – An integrated ehr system implemented at all hospitals with cpoe , nursing documentation and risk assessment tools. The nursing component for risk assessment documentation of pressure ulcers and falls was consistent across hospitals and developed by clinical nurses and informaticists by consensus. Design – its design with monthly data on pressure ulcers and quarterly data on fall rates and risk collected over seven years between 2003 and 2009. All data were collected at the unit level for each hospital. Outcomes – Process measures were the proportion of patients with a fall risk assessment done and the proportion with a hospital-acquired pressure ulcer ( hapu ) risk assessment done within 24 hours of admission. Outcome measures were fall and hapu rates as part of the unit-level nursing care process and nursing sensitive outcome data collected routinely for all California hospitals. Fall rate was defined as the number of unplanned descents to the floor per 1,000 patient days, and hapu rate was the percentage of patients with stages i-IV or unstageable ulcer on the day of data collection. Analysis – Fall and hapu risk data were synchronized using the month in which the ehr was implemented at each hospital as time zero and aggregated across hospitals for each time period. Multivariate regression analysis was used to examine the effect of time, region and ehr . Findings – The ehr was associated with significant increase in document rates for hapu risk (2.21; 95% CI 0.67 to 3.75) and non-significant increase for fall risk (0.36; -3.58 to 4.30). The ehr was associated with 13% decrease in hapu rates (-0.76; -1.37 to -0.16) but no change in fall rates (-0.091; -0.29 to 011). Hospital region was a significant predictor of variation for hapu (0.72; 0.30 to 1.14) and fall rates (0.57; 0.41 to 0.72). During the study period, hapu rates decreased significantly (-0.16; -0.20 to -0.13) but not fall rates (0.0052; -0.01 to 0.02). In summary, ehr implementation was associated with a reduction in the number of hapu s but not patient falls, and changes over time and hospital region also affected outcomes.

10.5. Summary

In this chapter we introduced randomized and non-randomized experimental designs as two types of comparative studies used in eHealth evaluation. Randomization is the highest quality design as it reduces bias, but it is not always feasible. The methodological issues addressed include choice of variables, sample size, sources of biases, confounders, and adherence to reporting guidelines. Three case examples were included to show how eHealth comparative studies are done.

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Appendix. Example of Sample Size Calculation

This is an example of sample size calculation for an rct that examines the effect of a cds system on reducing systolic blood pressure in hypertensive patients. The case is adapted from the example described in the publication by Noordzij et al. (2010) .

(a) Systolic blood pressure as a continuous outcome measured in mmHg

Based on similar studies in the literature with similar patients, the systolic blood pressure values from the comparison groups are expected to be normally distributed with a standard deviation of 20 mmHg. The evaluator wishes to detect a clinically relevant difference of 15 mmHg in systolic blood pressure as an outcome between the intervention group with cds and the control group without cds . Assuming a significance level or alpha of 0.05 for 2-tailed t -test and power of 0.80, the corresponding multipliers 1 are 1.96 and 0.842, respectively. Using the sample size equation for continuous outcome below we can calculate the sample size needed for the above study.

n = 2[(a+b)2σ2]/(μ1-μ2)2 where

n = sample size for each group

μ1 = population mean of systolic blood pressures in intervention group

μ2 = population mean of systolic blood pressures in control group

μ1- μ2 = desired difference in mean systolic blood pressures between groups

σ = population variance

a = multiplier for significance level (or alpha)

b = multiplier for power (or 1-beta)

Providing the values in the equation would give the sample size (n) of 28 samples per group as the result

n = 2[(1.96+0.842)2(202)]/152 or 28 samples per group

(b) Systolic blood pressure as a categorical outcome measured as below or above 140 mmHg (i.e., hypertension yes/no)

In this example a systolic blood pressure from a sample that is above 140 mmHg is considered an event of the patient with hypertension. Based on published literature the proportion of patients in the general population with hypertension is 30%. The evaluator wishes to detect a clinically relevant difference of 10% in systolic blood pressure as an outcome between the intervention group with cds and the control group without cds . This means the expected proportion of patients with hypertension is 20% (p1 = 0.2) in the intervention group and 30% (p2 = 0.3) in the control group. Assuming a significance level or alpha of 0.05 for 2-tailed t -test and power of 0.80 the corresponding multipliers are 1.96 and 0.842, respectively. Using the sample size equation for categorical outcome below, we can calculate the sample size needed for the above study.

n = [(a+b)2(p1q1+p2q2)]/χ2

p1 = proportion of patients with hypertension in intervention group

q1 = proportion of patients without hypertension in intervention group (or 1-p1)

p2 = proportion of patients with hypertension in control group

q2 = proportion of patients without hypertension in control group (or 1-p2)

χ = desired difference in proportion of hypertensive patients between two groups

Providing the values in the equation would give the sample size (n) of 291 samples per group as the result

n = [(1.96+0.842)2((0.2)(0.8)+(0.3)(0.7))]/(0.1)2 or 291 samples per group

From Table 3 on p. 1392 of Noordzij et al. (2010).

This publication is licensed under a Creative Commons License, Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0): see https://creativecommons.org/licenses/by-nc/4.0/

• Cite this Page Lau F, Holbrook A. Chapter 10 Methods for Comparative Studies. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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• Introduction
• Types of Comparative Studies
• Methodological Considerations
• Case Examples
• Example of Sample Size Calculation

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A simplified guide to randomized controlled trials

Affiliations.

• 1 Fetal Medicine Unit, St. Georges University Hospital, London, UK.
• 2 Division of Neonatology, Department of Pediatrics, Mount Sinai Hospital, Toronto, ON, Canada.
• 3 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
• 4 Department of Clinical Science, Intervention and Technology, Karolinska Institute and Center for Fetal Medicine, Karolinska University Hospital, Stockholm, Sweden.
• 5 Women's Health and Perinatology Research Group, Department of Clinical Medicine, UiT-The Arctic University of Norway, Tromsø, Norway.
• 6 Department of Obstetrics and Gynecology, University Hospital of North Norway, Tromsø, Norway.
• PMID: 29377058
• DOI: 10.1111/aogs.13309

A randomized controlled trial is a prospective, comparative, quantitative study/experiment performed under controlled conditions with random allocation of interventions to comparison groups. The randomized controlled trial is the most rigorous and robust research method of determining whether a cause-effect relation exists between an intervention and an outcome. High-quality evidence can be generated by performing an randomized controlled trial when evaluating the effectiveness and safety of an intervention. Furthermore, randomized controlled trials yield themselves well to systematic review and meta-analysis providing a solid base for synthesizing evidence generated by such studies. Evidence-based clinical practice improves patient outcomes and safety, and is generally cost-effective. Therefore, randomized controlled trials are becoming increasingly popular in all areas of clinical medicine including perinatology. However, designing and conducting an randomized controlled trial, analyzing data, interpreting findings and disseminating results can be challenging as there are several practicalities to be considered. In this review, we provide simple descriptive guidance on planning, conducting, analyzing and reporting randomized controlled trials.

Keywords: Clinical trial; good clinical practice; random allocation; randomized controlled trial; research methods; study design.

© 2018 Nordic Federation of Societies of Obstetrics and Gynecology.

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1.4.2 - Causal Conclusions

In order to control for confounding variables, participants can be randomly assigned to different levels of the explanatory variable. This act of randomly assigning cases to different levels of the explanatory variable is known as randomization . An experiment that involves randomization may be referred to as a  randomized experiment or randomized comparative experiment . By randomly assigning cases to different conditions, a  causal conclusion  can be made; in other words, we can say that differences in the response variable are caused by differences in the explanatory variable. Without randomization, an  association  can be noted, but a causal conclusion cannot be made.

Note that randomization and random sampling are different concepts. Randomization refers to the random assignment of experimental units to different conditions (e.g., different treatment groups). Random sampling refers to probability-based methods for selecting a sample from a population.

Example: Fitness Programs

Two teams have designed research studies to compare the weight loss of participants in two different fitness programs. Each team used a different research study design.

The first team surveyed people who already participate in each program. This is an observational study, which means there is no randomization . Each group is comprised of participants who made the personal decision to engaged in that fitness program. With this research study design, the researchers can only determine whether or not there is an  association  between the fitness program and participants' weight loss. A causal conclusion cannot be made because there may be  confounding variables . The people in the two groups may be different in some key ways. For example, if the cost of the two programs is different, the two groups may differ in terms of their finances. 

The second team of researchers obtained a sample of participants and randomly assigned half to participate in the first fitness program and half to participate in the second fitness program. They measured each participants' weight twice: both at the beginning and end of their study. This is a  randomized experiment  because the researchers randomly assigned each participant to one of the two programs. Because participants were randomly assigned to groups, the groups should be balanced in terms of any confounding variables and a  causal conclusion  may be drawn from this study.

A Refresher on Randomized Controlled Experiments

by Amy Gallo

In order to make smart decisions at work, we need data. Where that data comes from and how we analyze it depends on a lot of factors — for example, what we’re trying to do with the results, how accurate we need the findings to be, and how much of a budget we have. There is a spectrum of experiments that managers can do from quick, informal ones, to pilot studies, to field experiments, and to lab research. One of the more structured experiments is the randomized controlled experiment­ .

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3.3 Designing Experiments

A study is an experiment when we actually do something to people, animals, or objects in order to observe the response. Here is the basic vocabulary of experiments.

Experimental Units, Subjects, Treatment

The cases on which the experiment is done are the experimental units . When the units are human beings, they are called subjects . A specific experimental condition applied to the units is called a treatment .

Because the purpose of an experiment is to reveal the response of one variable to changes in other variables, the distinction between explanatory and response variables is important. The explanatory variables in an experiment are often called factors . Many experiments study the joint effects of several factors. In such an experiment, each treatment is formed by combining a specific value (often called a level ) of each of the factors.

level of a factor

EXAMPLE 3.15 Is the Cost Justified?

The increased costs for teacher salaries and facilities associated with smaller class sizes can be substantial. Are smaller classes really better? We might do an observational study that compares students who happened to be in smaller and larger classes in their early school years. Small classes are expensive, so they are more common in schools that serve richer communities. Students in small classes tend to also have other advantages: their schools have more resources, their parents are better educated, and so on. The size of the classes is confounded with other characteristics of the students, making it impossible to isolate the effects of small classes.

The Tennessee STAR program was an experiment on the effects of class size. It has been called “one of the most important educational investigations ever carried out.” The subjects were 6385 students who were beginning kindergarten. Each student was assigned to one of three treatments: regular class (22 to 25 students) with one teacher, regular class with a teacher and a full-time teacher’s aide, and small class (13 to 17 students) with one teacher. These treatments are levels of a single factor: the type of class. The students stayed in the same type of class for four years, then all returned to regular classes. In later years, students from the small classes had higher scores on standard tests, were less likely to fail a grade, had better high school grades, and so on. The benefits of small classes were greatest for minority students. 20

Example 3.15 illustrates the big advantage of experiments over observational studies. In principle, experiments can give good evidence for causation. In an experiment, we study the specific factors we are interested in, while controlling the effects of lurking variables. All the students in the Tennessee STAR program followed the usual curriculum at their schools. Because students were assigned to different class types within their schools, school resources and family backgrounds were not confounded with class type. The only systematic difference was the type of class. When students from the small classes did better than those in the other two types, we can be confident that class size made the difference.

EXAMPLE 3.16 Effects of TV Advertising

What are the effects of repeated exposure to an advertising message? The answer may depend both on the length of the ad and on how often it is repeated. An experiment investigates this question using undergraduate students as subjects. All subjects view a 40-minute television program that includes ads for a digital camera. Some subjects see a 30-second commercial; others, a 90-second version. The same commercial is repeated one, three, or five times during the program. After viewing, all of the subjects answer questions about their recall of the ad, their attitude toward the camera, and their intention to purchase it. These are the response variables. 21

This experiment has two factors: length of the commercial, with two levels; and repetitions, with three levels. All possible combinations of the factor levels form six treatment combinations. Figure 3.4 shows the layout of these treatments.

Experimentation allows us to study the effects of the specific treatments we are interested in. Moreover, we can control the environment of the subjects to hold constant the factors that are of no interest to us, such as the specific product advertised in Example 3.16 . In one sense, the ideal case is a laboratory experiment in which we control all lurking variables and so see only the effect of the treatments on the response. On the other hand, the effects of being in an artificial environment such as a laboratory may also affect the outcomes. The balance between control and realism is an important consideration in the design of experiments.

Another advantage of experiments is that we can study the combined effects of several factors simultaneously. The interaction of several factors can produce effects that could not be predicted from looking at the effect of each factor alone. Perhaps longer commercials increase interest in a product, and more commercials also increase interest, but if we make a commercial longer and show it more often, viewers get annoyed and their interest in the product drops. The two-factor experiment in Example 3.16 will help us find out.

Apply Your Knowledge

Question 3.42

3.42 Radiation and storage time for food products.

Storing food for long periods of time is a major challenge for those planning for human space travel beyond the moon. One problem is that exposure to radiation decreases the length of time that food can be stored. One experiment examined the effects of nine different levels of radiation on a particular type of fat, or lipid. 22 The amount of oxidation of the lipid is the measure of the extent of the damage due to the radiation. Three samples are exposed to each radiation level. Give the experimental units, the treatments, and the response variable . Describe the factor and its levels. There are many different types of lipids. To what extent do you think the results of this experiment can be generalized to other lipids?

Question 3.43

3.43 Can they use the Web?

A course in computer graphics technology requires students to learn multiview drawing concepts. This topic is traditionally taught using supplementary material printed on paper. The instructor of the course believes that a web-based interactive drawing program will be more effective in increasing the drawing skills of the students. 23 The 50 students who are enrolled in the course will be randomly assigned to either the paper-based instruction or the web-based instruction. A standardized drawing test will be given before and after the instruction. Explain why this study is an experiment, and give the experimental units, the treatments, and the response variable. Describe the factor and its levels. To what extent do you think the results of this experiment can be generalized to other settings?

It is an experiment because the instructor has assigned the students to one of two instructional methods (paper-based or web-based). The experimental units are students. The treatments are type of instruction, paper-based and web-based. The response is the change in standardized drawing test (pre vs. post). The factor is the instructional method, with levels paper-based and web-based. The results would likely not be widely generalizable because the students were all in the same course but could probably be generalized to the same or similar courses in computer graphics technology.

Question 3.44

3.44 Is the packaging convenient for the customer?

A manufacturer of food products uses package liners that are sealed by applying heated jaws after the package is filled. The customer peels the sealed pieces apart to open the package. What effect does the temperature of the jaws have on the force needed to peel the liner? To answer this question, engineers prepare 20 package liners. They seal five liners at each of four different temperatures: 250°F, 275°F, 300°F, and 325°F. Then they measure the force needed to peel each seal.

• What are the experimental units studied?
• There is one factor ( explanatory variable ). What is it, and what are its levels?
• What is the response variable?

Comparative experiments

Many experiments have a simple design with only a single treatment, which is applied to all experimental units. The design of such an experiment can be outlined as

EXAMPLE 3.17 Increase the Sales Force

A company may increase its sales force in the hope that sales will increase. The company compares sales before the increase with sales after the increase. Sales are up, so the manager who suggested the change gets a bonus.

The sales experiment of Exercise 3.17 was poorly designed to evaluate the effect of increasing the sales force. Perhaps sales increased because of seasonal variation in demand or other factors affecting the business.

In medical settings, an improvement in condition is sometimes due to a phenomenon called the placebo effect . In medicine, a placebo is a dummy or fake treatment, such as a sugar pill. Many participants, regardless of treatment, respond favorably to personal attention or to the expectation that the treatment will help them.

placebo effect

For the sales force study, we don’t know whether the increase in sales was due to increasing the sales force or to other factors. The experiment gave in conclusive results because the effect of increasing the sales force was confounded with other factors that could have had an effect on sales. The best way to avoid confounding is to do a comparative experiment. Think about a study where the sales force is increased in half of the regions where the product is sold and is not changed in the other regions. A comparison of sales from the two sets of regions would provide an evaluation of the effect of the increasing the sales force.

comparative experiment

In medical settings, it is standard practice to randomly assign patients to either a control group or a treatment group . All patients are treated the same in every way except that the treatment group receives the treatment that is being evaluated. In the setting of our comparative sales experiment, we would randomly divide the regions into two groups. One group will have the sales force increased and the other group will not.

control group

treatment group

bias , p. 131

Uncontrolled experiments in medicine and the behavioral sciences can be dominated by such influences as the details of the experimental arrangement, the selection of subjects, and the placebo effect. The result is often bias.

An uncontrolled study of a new medical therapy, for example, is biased in favor of finding the treatment effective because of the placebo effect. It should not surprise you to learn that uncontrolled studies in medicine give new therapies a much higher success rate than proper comparative experiments do. Well-designed experiments usually compare several treatments.

Question 3.45

3.45 Does using statistical software improve exam scores?

An instructor in an elementary statistics course wants to know if using a new statistical software package will improve students’ final-exam scores. He asks for volunteers, and approximately half of the class agrees to work with the new software. He compares the final-exam scores of the students who used the new software with the scores of those who did not. Discuss possible sources of bias in this study.

For those students who volunteered, they could have attributes that lead them to volunteer and also do better on the final. For example, they might be more willing to work hard or might enjoy studying, which may make them more willing to agree to use the new software and also do better on the final.

Randomized comparative experiments

experiment design

The design of an experiment first describes the response variables, the factors(explanatory variables), and the layout of the treatments, with comparison as the leading principle. The second aspect of design is the rule used to assign the subjects to the treatments. Comparison of the effects of several treatments is valid only when all treatments are applied to similar groups of subjects. If one corn variety is planted on more fertile ground, or if one cancer drug is given to less seriously ill patients, comparisons among treatments are biased. How can we assign cases to treatments in a way that is fair to all the treatments?

Our answer is the same as in sampling: let impersonal chance make the assignment. The use of chance to divide subjects into groups is called randomization . Groups formed by randomization don’t depend on any characteristic of the subjects or on the judgment of the experimenter. An experiment that uses both comparison and randomization is a randomized comparative experiment . Here is an example.

randomization

randomized comparative experiment

EXAMPLE 3.18 Testing a Breakfast Food

A food company assesses the nutritional quality of a new “instant breakfast” product by feeding it to newly weaned male white rats. The response variable is a rat’s weight gain over a 28-day period. A control group of rats eats a standard diet but otherwise receives exactly the same treatment as the experimental group.

This experiment has one factor (the diet) with two levels. The researchers use30 rats for the experiment and so divide them into two groups of 15. To do this in an unbiased fashion, put the cage numbers of the 30 rats in a hat, mix them up, and draw 15. These rats form the experimental group and the remaining 15 make up the control group. Each group is an SRS of the available rats. Figure 3.5 outlines the design of this experiment.

Question 3.46

3.46 Diagram the food storage experiment.

Refer to Exercise 3.42 ( page 144 ). Draw a diagram similar to Figure 3.5 that describes the food for space travel experiment.

Question 3.47

3.47 Diagram the Web use.

Refer to Exercise 3.43 ( page 144 ). Draw a diagram similar to Figure 3.5 that describes the computer graphics drawing experiment.

Completely randomized designs

The design in Figure 3.5 combines comparison and randomization to arrive at the simplest statistical design for an experiment. This “flowchart” outline presents all the essentials: randomization, the sizes of the groups and which treatment they receive, and the response variable. There are, as we will see later, statistical reasons for generally using treatment groups that are approximately equal in size. We call designs like that in Figure 3.5 completely randomized.

Completely Randomized Design

In a completely randomized experimental design, all the subjects are allocated at random among all the treatments.

Completely randomized designs can compare any number of treatments. Here is an example that compares three treatments.

EXAMPLE 3.19 Utility Companies and Energy Conservation

Many utility companies have introduced programs to encourage energy conservation among their customers. An electric company considers placing electronic meters in households to show what the cost would be if the electricity use at that moment continued for a month. Will these meters reduce electricity use? Would cheaper methods work almost as well? The company decides to design an experiment.

One cheaper approach is to give customers a chart and information about monitoring their electricity use. The experiment compares these two approaches (meter, chart) and also a control. The control group of customers receives information about energy conservation but no help in monitoring electricity use. The response variable is total electricity used in a year. The company finds 60 single-family residences in the same city willing to participate, so it assigns 20 residences at random to each of the three treatments. Figure 3.6 outlines the design.

How to randomize

The idea of randomization is to assign experimental units to treatments by drawing names from a hat. In practice, experimenters use software to carry out randomization. In Example 3.19 , we have 60 residences that need to be randomly assigned to three treatments. Most statistical software will be able to do the randomization required.

We prefer to use software for randomizing but if you do not have that option available to you, a table of random digits, such as Table B can be used. Using software, the method is similar to what we used to select an SRS in Example 3.9 ( page 133 ). Here are the steps needed:

Step 1: Label . Give each experimental unit a unique label. For privacy reasons, we might want to use a numerical label and a keep a file that identifies the experimental units with the number in a separate place.

Step 2: Use the computer. Once we have the labels, we create a data file with the labels and generate a random number for each label. In Excel, this can be done with the RAND() function. Finally, we sort the entire data set based on the random numbers. Groups are formed by selecting units in order from the sorted list.

EXAMPLE 3.20 Do the Randomization for the Utility Company Experiment Using Excel

In the utility company experiment of Example 3.19 , we must assign 60 residences to three treatments. First we generate the labels. Let’s use numerical labels and keep a separate file that gives the residence address for each number. So for Step 1, we will use these labels, 1 to 60:

To illustrate Step 2, we will show several Excel files. To see what we are doing, it will be easier if we reduce the number of residences to be randomized. So, let’s randomize 12 residences to the three treatments. Our labels are

For the first part of Step 2, we create an Excel file with the numbers 1 to 12 in the first column. This file is shown in Figure 3.7(a) . Next, we use the RAND() function in Excel to generate 12 random numbers in the second column. The result is shown in Figure 3.7(b) . We then sort the file based in the random numbers. We create a third column with the following treatments: “Meter” for the first four, “Chart” for the next four, and “Control” for the last four. The result is displayed in Figure 3.7(c) .

If software is not available, you can use the random digits in Table B to do the randomization. The method is similar to the one we used to select an SRS in Example 3.8 ( page 133 ). Here are the steps that you need:

Step 1: Label. Give each experimental unit a numerical label. Each label must contain the same number of digits. So, for example, if you are randomizing 10 experimental units, you could use the labels, 0, 1, … , 8, 9; or 01, 02, … , 10. Note that with the first choice you need only one digit, but for the second choice, you need two.

Step 2: Table. Start anywhere in Table B and read digits in groups corresponding to one-digit or two-digit groups. (You really do not want to use Table B for more than100 experimental units. Software is needed here.)

EXAMPLE 3.21 Do the Randomization for the Utility Company Experiment Using Random Digits

As we did in Example 3.20 , we will illustrate the method by randomizing 12 residences to three treatments. For Step 1, we assign the 12 residences the following labels:

Compare these labels with the ones we used in Example 3.20 . Here, we need the same number of digits for each label, so we put a zero as the first digit for the first nine labels.

For Step 2, we will use Table B starting at line 118. Here are the table entries for that line:

To make our work a little easier, we rewrite these digits in pairs:

We now select the labels for the first treatment, “Meter.” Reading pairs of digits from left to write and ignoring pairs that do not correspond to any of our labels, we see the labels 03, 04, 02, and 07. The corresponding residences will receive the “Meter” treatment. We will continue the process to find four labels to be assigned to the “Chart” treatment. We continue to the next line in Table B , where we do not find any labels between 01 and 12. On line 120, we have the label 04. This label has already been assigned to a treatment so we ignore it. Line121 has two labels between 01 and 12: 07, which has already been assigned to a treatment, and 10, which we assign to “Chart.” On the next line, we have 05, 09,and 08 which we also assign to “Chart.” The remaining four labels are assigned to the “Control” treatment. In summary, 02, 03, 04, and 07 are assigned to “Meter,”05, 08, 09, and 10 are assigned to “Chart,” and 01, 06, 11, and 12 are assigned to “Control.”

As Example 3.21 illustrates, randomization requires two steps: assign labels to the experimental units and then use Table B to select labels at random. Be sure that all labels are the same length so that all have the same chance to be chosen. You can read digits from Table B in any order—along a row, down a column, and so on—because the table has no order. As an easy standard practice, we recommend reading along rows. In Example 3.21 , we needed 180 random digits from four and a half lines (118 to 121 and half of 122) to complete the randomization. If we wanted to reduce this amount, we could use more than one label for each residence. For example, we could use labels 01, 21, 41, 61, and 81 for the first residence; 02, 22,42, 62, and 82 for the second residence; and so forth.

Examples 3.18 and 3.19 describe completely randomized designs that compare levels of a single factor. In Example 3.18 , the factor is the diet fed to the rats. In Example 3.19 , it is the method used to encourage energy conservation. Completely randomized designs can have more than one factor. The advertising experiment of Example 3.16 has two factors: the length and the number of repetitions of a television commercial. Their combinations form the six treatments outlined in Figure 3.4 ( page 144 ). A completely randomized design assigns subjects at random to these six treatments. Once the layout of treatments is set, the randomization needed for a completely randomized design is tedious but straight forward.

Question 3.48

3.48 Does child care help recruit employees?

Will providing child care for employees make a company more attractive to women? You are designing an experiment to answer this question. You prepare recruiting material for two fictitious companies, both in similar businesses in the same location. Company A’s brochure does not mention child care. There are two versions of Company B’s brochure. One is identical to Company A’s brochure. The other is also the same, but a description of the company’s onsite child care facility is included. Your subjects are 40 women who are college seniors seeking employment. Each subject will read recruiting material for Company A and one of the versions of the recruiting material for Company B. You will give each version of Company B’s brochure to half the women. After reading the material for both companies, each subject chooses the one she would prefer to work for. You expect that a higher percent of those who read the description that includes child care will choose Company B.

• Outline an appropriate design for the experiment.
• The names of the subjects appear below. Use software or Table B , beginning at line 112, to do the randomization required by your design. List the subjects who will read the version that mentions child care.

Question 3.49

3.49 Sealing food packages.

Use a diagram to describe a completely randomized experimental design for the package liner experiment of Exercise 3.44 ( page 145 ).(Show the size of the groups, the treatment each group receives, and the response variable. Figures 3.5 and 3.6 are models to follow.) Use software or Table B , starting at line 140, to do the randomization required by your design.

Using labels 01–40 and line 140 the assignments are:

• Group 1: 12, 13, 04, 18, 19
• Group 2: 16, 02, 08, 17, 10
• Group 3: 05, 09, 06, 01, 20
• Group 4: 03, 07, 11, 14, 15

The logic of randomized comparative experiments

Randomized comparative experiments are designed to give good evidence that differences in the treatments actually cause the differences we see in the response. The logic is as follows:

• Random assignment of subjects forms groups that should be similar in all respects before the treatments are applied.
• Comparative design ensures that influences other than the experimental treatments operate equally on all groups.
• Therefore, differences in average response must be due either to the treatments or to the play of chance in the random assignment of subjects to the treatments.

That “either-or” deserves more thought. In Example 3.18 ( page 146 ), we cannot say that any difference in the average weight gains of rats fed the two diets must be caused by a difference between the diets. There would be some difference even if both groups received the same diet because the natural variability among rats means that some grow faster than others. If chance assigns the faster-growing rats to one group or the other, this creates a chance difference between the groups. We would not trust an experiment with just one rat in each group, for example. The results would depend on which group got lucky and received the faster-growing rat. If we assign many rats to each diet, however, the effects of chance will average out, and there will be little difference in the average weight gains in the two groups unless the diets themselves cause a difference. “Use enough subjects to reduce chance variation” is the third big idea of statistical design of experiments.

Principles of Experimental Design

• Compare two or more treatments. This will control the effects of lurking variables on the response.
• Randomize —use chance to assign subjects to treatments.
• Replicate each treatment on enough subjects to reduce chance variation in the results.

EXAMPLE 3.22 Cell Phones and Driving

Does talking on a hands-free cell phone distract drivers? Undergraduate students “drove” in a high-fidelity driving simulator equipped with a hands-free cell phone. The car ahead brakes: how quickly does the subject respond? Twenty students (the control group) simply drove. Another 20 (the experimental group) talked on the cellphone while driving. The simulator gave the same driving conditions to both groups. 24

This experimental design has good control because the only difference in the conditions for the two groups is the use of the cell phone. Students are randomized to the two groups, so we satisfy the second principle. Based on past experience with the simulators, the length of the drive and the number of subjects were judged to provide sufficient information to make the comparison. (We learn more about choosing sample sizes for experiments in starting Chapter 7 .)

We hope to see a difference in the responses so large that it is unlikely to happen just because of chance variation. We can use the laws of probability , which give a mathematical description of chance behavior, to learn if the treatment effects are larger than we would expect to see if only chance were operating. If they are, we call them statistically significant .

statistically significant

Statistical Significance

An observed effect so large that it would rarely occur by chance is called statistically significant.

If we observe statistically significant differences among the groups in a comparative randomized experiment, we have good evidence that the treatments actually caused these differences. You will often see the phrase “statistically significant” in reports of investigations in many fields of study. The great advantage of randomized comparative experiments is that they can produce data that give good evidence for a cause-and-effect relationship between the explanatory and response variables. We know that, in general, a strong association does not imply causation. A statistically significant association in data from a well-designed experiment does imply causation.

Question 3.50

3.50 Utility companies.

Example 3.19 ( page 147 ) describes an experiment to learn whether providing households with electronic meters or charts will reduce their electricity consumption. An executive of the utility company objects to including a control group. He says, “It would be simpler to just compare electricity use last year (before the meter or chart was provided) with consumption in the same period this year. If households use less electricity this year, the meter or chart must be working.” Explain clearly why this design is inferior to that in Example 3.19 .

Question 3.51

3.51 Statistical significance.

The financial aid office of a university asks a sample of students about their employment and earnings. The report says that “for academic year earnings, a significant difference was found between the sexes, with men earning more on the average. No significant difference was found between the earnings of black and white students.” Explain the meaning of “a significant difference” and “no significant difference” in plain language.

“A significant difference” means that the difference found between the sexes is unlikely to have occurred by chance alone and that sex is likely a contributor to the difference found in earnings. “No significant difference” means that the difference between black and white students is small enough that it is likely due to just chance. Whichever group happens to have more or less earnings, the difference is not due to race.

Completely randomized designs can compare any number of treatments. The treatments can be formed by levels of a single factor or by more than one factor. Here is an example with two factors.

EXAMPLE 3.23 Randomization for the TV Commercial Experiment

Figure 3.4 ( page 144 ) displays six treatments formed by the two factors in an experiment on response to a TV commercial. Suppose that we have 150 students who are willing to serve as subjects. We must assign 25 students at random to each group. Figure 3.8 outlines the completely randomized design.

To carry out the random assignment, label the 150 students 001 to 150. (Three digits are needed to label 150 subjects.) Enter Table B and read three-digit groups until you have selected 25 students to receive Treatment 1 (a 30-second ad shown once). If you start at line 140, the first few labels for Treatment 1 subjects are 129, 048, and 003.

Continue in Table B to select 25 more students to receive Treatment 2 (a 30-second ad shown three times). Then select another 25 for Treatment 3 and so on until you have assigned 125 of the 150 students to Treatments 1 through 5. The 25 students who remain get Treatment 6. The randomization is straightforward but very tedious to do by hand. We recommend software such as the Simple Random Sample applet. Exercise 3.62 ( page 158 ) shows how to use the applet to do the randomization for this example.

Question 3.52

3.52 Do the randomization.

Use computer software to carry out the randomization in Example 3.23 .

Cautions about experimentation

The logic of a randomized comparative experiment depends on our ability to treat all the subjects identically in every way except for the actual treatments being compared. Good experiments therefore require careful attention to details.

Many—perhaps most—experiments have some weaknesses in detail. The environment of an experiment can influence the outcomes in unexpected ways. Although experiments are the gold standard for evidence of cause and effect, really convincing evidence usually requires that a number of studies in different places with different details produce similar results. The most serious potential weakness of experiments is lack of realism . The subjects or treatments or setting of an experiment may not realistically duplicate the conditions we really want to study. Here are two examples.

lack of realism

EXAMPLE 3.24 Layoffs and Feeling Bad

How do layoffs at a workplace affect the workers who remain on the job? Psychologists asked student subjects to proofread text for extra course credit, then “let go” some of the workers (who were actually accomplices of the experimenters). Some subjects were told that those let go had performed poorly (Treatment 1). Others were told that not all could be kept and that it was just luck that they were kept and others let go (Treatment 2). We can’t be sure that the reactions of the students are the same as those of workers who survive a layoff in which other workers lose their jobs. Many behavioral science experiments use student subjects in a campus setting. Do the conclusions apply to the real world?

EXAMPLE 3.25 Does the Regulation Make the Product Safer?

Do those high center brake lights, required on all cars sold in the United States since 1986, really reduce rear-end collisions? Randomized comparative experiments with fleets of rental and business cars, done before the lights were required, showed that the third brake light reduced rear-end collisions by as much as 50%. Unfortunately, requiring the third light in all cars led to only a 5% drop.

What happened? Most cars did not have the extra brake light when the experiments were carried out, so it caught the eye of following drivers. Now that almost all cars have the third light, they no longer capture attention.

Lack of realism can limit our ability to apply the conclusions of an experiment to the settings of greatest interest. Most experimenters want to generalize their conclusions to some setting wider than that of the actual experiment. Statistical analysis of the original experiment cannot tell us how far the results will generalize. Nonetheless, the randomized comparative experiment, because of its ability to give convincing evidence for causation, is one of the most important ideas in statistics.

Question 3.53

3.53 Managers and stress.

Some companies employ consultants to train their managers in meditation in the hope that this practice will relieve stress and make the managers more effective on the job. An experiment that claimed to show that meditation reduces anxiety proceeded as follows. The experimenter interviewed the subjects and rated their level of anxiety. Then the subjects were randomly assigned to two groups. The experimenter taught one group how to meditate, and they meditated daily for a month. The other group was simply told to relax more. At the end of the month, the experimenter interviewed all the subjects again and rated their anxiety level. The meditation group now had less anxiety. Psychologists said that the results were suspect because the ratings were not blind—that is, the experimenter knew which treatment each subject received. Explain what this means and how lack of blindness could bias the reported results.

Because the experimenter measured their anxiety and also taught the group how to meditate, the experimenter could biasedly rate the group that meditated lower or higher in anxiety based on their expectation of whether the meditation would help or not. Also, separate from the experimenter’s possible bias, the subjects themselves could behave more or less anxiously during the final evaluation based on their interaction with the experimenter during the meditation instruction, which could also bias the results.

Question 3.54

3.54 Frustration and teamwork.

A psychologist wants to study the effects of failure and frustration on the relationships among members of a work team. She forms a team of students, brings them to the psychology laboratory, and has them play a game that requires teamwork. The game is rigged so that they lose regularly. The psychologist observes the students through a one-way window and notes the changes in their behavior during an evening of game playing. Why is it doubtful that the findings of this study tell us much about the effect of working for months developing a new product that never works right and is finally abandoned by your company?

Matched pairs designs

Completely randomized designs are the simplest statistical designs for experiments. They illustrate clearly the principles of control, randomization, and replication of treatments on a number of subjects. However, completely randomized designs are often inferior to more elaborate statistical designs. In particular, matching the subjects in various ways can produce more precise results than simple randomization.

matched pairs design

One common design that combines matching with randomization is the matched pairs design. A matched pairs design compares just two treatments. Choose pairs of subjects that are as closely matched as possible. Assign one of the treatments to each subject in a pair by tossing a coin or reading odd and even digits from Table B . Sometimes, each “pair” in a matched pairs design consists of just one subject, who gets both treatments one after the other. Each subject serves as his or her own control. The order of the treatments can influence the subject’s response, so we randomize the order for each subject, again by a coin toss.

EXAMPLE 3.26 Matched Pairs for the Cell Phone Experiment

Example 3.22 ( page 151 ) describes an experiment on the effects of talking on a cell phone while driving. The experiment compared two treatments: driving in a simulator and driving in a simulator while talking on a hands-free cell phone. The response variable is the time the driver takes to apply the brake when the car in front brakes suddenly. In Example 3.22 , 40 student subjects were assigned at random, 20 students to each treatment. Subjects differ in driving skill and reaction times. The completely randomized design relies on chance to create two similar groups of subjects.

In fact, the experimenters used a matched pairs design in which all subjects drove under both conditions. They compared each subject’s reaction times with and without the phone. If all subjects drove first with the phone and then without it, the effect of talking on the cell phone would be confounded with the fact that this is the first run in the simulator. The proper procedure requires that all subjects first be trained in using the simulator, that the order in which a subject drives with and without the phone be random, and that the two drives be on separate days to reduce the chance that the results of the second treatment will be affected by the first treatment.

The completely randomized design uses chance to decide which 20 subjects will drive with the cell phone. The other 20 drive without it. The matched pairs design uses chance to decide which 20 subjects will drive first with and then without the cell phone. The other 20 drive first without and then with the phone.

Block designs

Matched pairs designs apply the principles of comparison of treatments, randomization, and replication. However, the randomization is not complete—we do not randomly assign all the subjects at once to the two treatments. Instead, we only randomize within each matched pair. This allows matching to reduce the effect of variation among the subjects. Matched pairs are an example of block designs.

Block Design

A block is a group of subjects that are known before the experiment to be similar in some way expected to affect the response to the treatments. In a block design , the random assignment of individuals to treatments is carried out separately within each block.

A block design combines the idea of creating equivalent treatment groups by matching with the principle of forming treatment groups at random. Here is a typical example of a block design.

EXAMPLE 3.27 Men, Women, and Advertising

An experiment to compare the effectiveness of three television commercials for the same product will want to look separately at the reactions of men and women, as well as assess the overall response to the ads.

A completely randomized design considers all subjects, both men and women, as a single pool. The randomization assigns subjects to three treatment groups without regard to their gender. This ignores the differences between men and women. A better design considers women and men separately. Randomly assign the women to three groups, one to view each commercial. Then separately assign the men at random to three groups. Figure 3.9 outlines this improved design.

A block is a group of subjects formed before an experiment starts. We reserve the word “treatment” for a condition that we impose on the subjects. We don’t speak of six treatments in Example 3.29 even though we can compare the responses of six groups of subjects formed by the two blocks (men, women) and the three commercials. Block designs are similar to stratified samples. Blocks and strata both group similar individuals together. We use two different names only because the idea developed separately for sampling and experiments.

Blocks are another form of control. They control the effects of some outside variables by bringing those variables into the experiment to form the blocks. The advantages of block designs are the same as the advantages of stratified samples. Blocks allow us to draw separate conclusions about each block—for example, about men and women in the advertising study in Example 3.27 . Blocking also allows more precise overall conclusions because the systematic differences between men and women can be removed when we study the overall effects of the three commercials.

The idea of blocking is an important additional principle of statistical design of experiments. A wise experimenter will form blocks based on the most important unavoidable sources of variability among the experimental subjects. Randomization will then average out the effects of the remaining variation and allow an unbiased comparison of the treatments.

Like the design of samples, the design of complex experiments is a job for experts. Now that we have seen a bit of what is involved, we will usually just act as if most experiments were completely randomized.

Question 3.55

3.55 Does charting help investors?

Some investment advisers believe that charts of past trends in the prices of securities can help predict future prices. Most economists disagree. In an experiment to examine the effects of using charts, business students trade (hypothetically) a foreign currency at computer screens. There are 20 student subjects available, named for convenience A, B, C, … , T. Their goal is to make as much money as possible, and the best performances are rewarded with small prizes. The student traders have the price history of the foreign currency in dollars in their computers. They may or may not also have software that highlights trends. Describe two designs for this experiment—a completely randomized design and a matched pairs design in which each student serves as his or her own control. In both cases, carry out the randomization required by the design.

In a completely randomized design: 10 students each are randomly assigned to two groups, then one group is randomly assigned the software that highlights trends, the other receives the regular software, and at the end you compare the money made by the two groups. In a matched pairs design: each student uses both types of software in random order for half the time, then the difference between the money made with and without the trend highlights is compared.

Randomized Experiment

Statistics Definitions >

What is a Randomized Experiment?

Randomized experiments are used extensively in a wide variety of agricultural and biological experiments, including human clinical trials. They are also, less commonly, seen in other fields such as economics.

Randomized Experiment Stages

The experiments are usually conducted in two stages [2]:

• Selection of a small sample of participants from a larger population , using a random sampling technique. This step ensures that the results will have external validity .
• Random assignment to treatment and control groups. This step ensures that the observed effects have internal validity .

Benefits of Randomized Experiments

Using randomization has several benefits [3]:

• It prevents selection bias and accidental bias , as well as bias in treatment assignments.
• Homogeneous, comparable groups are created.
• Probability methods, including hypothesis tests , can be used to ensure the results didn’t happen by chance.

[1] World Bank. Randomized Experiments. Retrieved December 29, 2021 from: http://web.worldbank.org/archive/website01397/WEB/IMAGES/EXPERI-2.PDF [2] Munck, G. & Verkuilen, J. (2005). Research Designs . In Encyclopedia of Social Measurement, Pages 385-395. [2] Suresh, K. An overview of randomization techniques: An unbiased assessment of outcome in clinical research. J Hum Reprod Sci. 2011 Jan-Apr; 4(1): 8–11.

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• Published: 13 September 2024

Does participating in online communities enhance the effectiveness and experience of micro-learning? Evidence from a randomized control trial

• Jiawen Zhu   ORCID: orcid.org/0000-0002-9260-6151 1 ,
• Yiran Zhao   ORCID: orcid.org/0000-0002-3559-3032 3 &
• Miaoting Cheng 4  

Humanities and Social Sciences Communications volume  11 , Article number:  1198 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

In the age of information explosion, people are increasingly accustomed to acquiring knowledge during fragmented periods of time, which has contributed to the growing popularity of micro-learning. However, when micro-learning takes place in non-formal or informal settings, it can be easily disrupted and may lack interaction, negatively impacting the learning experience and knowledge acquisition. This study examined the effect of learning communities on knowledge acquisition and learning experience in non-formal micro-learning settings. An 8-module micro-learning course was designed, and 80 participants were divided into an experimental group with a learning community and a control group without one. All participants completed a pre-test and post-test. The results showed significant improvements in post-test scores for both groups, with no notable difference in knowledge acquisition between them. Learners who took notes and repeatedly reviewed the learning content tended to have higher post-test scores. In addition, differences between the groups in terms of mental effort and satisfaction were insignificant. By the end of the course, the control group expressed a stronger desire to join a learning community. Community learners who made significant progress reported enjoying learning within the community, while those who did not regularly check community messages and experienced a decrease in test scores reported that excessive messaging caused distress. These findings have implications for course designers and researchers aiming to enhance micro-learning through online learning communities.

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Introduction.

In the age of information explosion, people are increasingly getting used to searching for information and acquiring knowledge in their spare and fragmented time. Individuals are now more inclined to engage with concise, bite-sized content (Emerson and Berge, 2018 ; Sankaranarayanan et al., 2023 ). Microlearning has emerged as a pivotal instructional strategy to address this shift and reduce the cognitive load, offering access to relatively small learning units. Microlearning is an instructional unit that enables a short engagement in an activity intentionally designed to elicit a specific outcome from the participant (Kapp and Defelice, 2019 , p. 21).

Microlearning’s application spans formal, non-formal, and informal learning settings, where its design plays a crucial role (Jahnke et al., 2020 ). Beyond content creation, establishing an effective learning mechanism is essential (Buchem and Hamelmann, 2010 ). Zhang and West ( 2020 ) emphasized the importance of integrating microlearning with learner interactions, including peer-to-peer, learner-to-expert, and learner-to-content engagements.

Despite recognizing interactions in learning communities as beneficial to microlearning, which is aligned with the Community of Practice theory (Wenger et al., 2002 ), integrating these learning communities with microlearning strategies remains insufficiently explored. Although Emerson and Berge ( 2018 ) advocated for leveraging online communities to enhance informal learning through microlearning modules, and Göschlberger ( 2017 ) identified social media as a potent tool for fostering learner communication, comprehensive strategies for their effective integration are scarce.

This gap is particularly pronounced in settings where microlearning occurs in informal or non-formal contexts, often characterized by learners engaging in self-directed learning in their own time, occasionally isolated from peer support. Challenges such as information overload and difficulties discerning online content further intensify the need for structured guidance (Lu et al., 2019 ). Additionally, there is a noted scarcity of research evaluating the effectiveness of microlearning within learning communities and perceptions by learners (Lee et al., 2021 ; McNeill and Fitch, 2023 ; Taylor and Hung, 2022 ), leaving unanswered questions about the role of online communities in facilitating microlearning and enhancing the learning experience. Specifically, how does using online learning communities in microlearning affect learners’ knowledge acquisition and learning experience?

In order to address these gaps, this study examines the impact of online learning communities on microlearning within a social media-based, non-formal learning context. It aims to elucidate how these communities can be integrated with microlearning strategies and to develop improved materials and activities for community-based microlearning. Through this exploration, the study contributes novel insights into the design and implementation of microlearning environments, establishing a foundation for future academic research and practical applications in digital learning spaces.

Related work

Many individuals feel pressed for time to learn in the fast-paced modern world. At the same time, there is a growing emphasis on professional development and lifelong learning. Against this backdrop, microlearning has emerged as a growing trend in lifelong learning (Giurgiu, 2017 ). Interest in microlearning has surged recently, evidenced by the substantial increase in publications on this topic, as it has garnered significant attention from scholars in the instructional design and technology disciplines (Kohnke et al., 2024 ; Sankaranarayanan et al., 2023 ). Concise learning content enables individuals to efficiently use their limited and fragmented time to access needed information.

Microlearning can be implemented in formal, informal, and non-formal settings. Most research on microlearning focuses on K-12, higher education, or corporate training contexts (Sankaranarayanan et al., 2023 ). Microlearning often serves as supplementary material to traditional classroom content. Teachers often chunk learning materials for students through infographics (Ozdamlı et al., 2016 ) or flashcards (Edge et al., 2012 ) and provide timely feedback. Students who adapt to the microlearning approach often find it a better learning experience than traditional face-to-face learning (Mohammed et al., 2018 ).

Reflecting its versatile nature, microlearning extends beyond traditional school settings. Corporations can make training content accessible to their employees through microlearning lessons. This form of work-based short-term training is known as microtraining (Buchem and Hamelmann, 2010 ). Such microlearning reduces training pressure and increases companies’ competitive advantage (Dolasinski and Reynolds, 2020 ). In addition, this learning approach does not require employees to be in a specific location or at a specific time during training, eliminating the time and physical space constraints for employees and reducing training costs.

Microlearning is often used in non-formal and informal forms, implying that learners are in a more spontaneous state to acquire knowledge. They may complete microlearning online by independently searching for information or taking some online microlearning courses. Scholars generally agree that microlearning can help learners acquire information, reduce cognitive load, achieve high satisfaction, and have a good learning experience (Buchem and Hamelmann, 2010 ; Giurgiu, 2017 ). However, studies have yet to explore the impact of peer interaction in a community on microlearning.

Unlike microlearning, which occurs in formal and corporate training settings, microlearning in non-formal and informal settings expects learners to learn spontaneously. However, research indicates that effective learning involves consuming content to replicate expert knowledge and creating content through social interaction and exploration (Buchem and Hamelmann, 2010 ). By transforming their role from consumer to producer, learners are more motivated and take greater responsibility for achieving their learning goals. This role transformation, in turn, requires learners to be more proactive in interacting with others and applying the information they learn, thus making it easier to acquire knowledge. Abed et al. ( 2024 ) and Wang et al. ( 2017 ) support that there were significant differences in scores between learners who actively interacted with the instructor and those who did not respond to instructor messages.

Online learning communities provide an environment for learners to communicate with others. They are commonly used in formal learning settings, with positive outcomes (Wu et al., 2017 ). Online learning communities promote collaboration among learners and enhance their competencies in a particular area. Based on the Community of Practice theory (Wenger et al., 2002 ), our study explored the impact of microlearning in a community on learners’ knowledge acquisition and learning experience. Researchers have found that learners who learn in communities have improved learning performance and achieve higher satisfaction (Jiménez-Zarco et al., 2015 ). Additionally, these communities facilitate microlearning beyond the classroom setting. Such learning communities are often linked to professional development (Chen et al., 2014 ). Learners are self-driven to communicate and share in learning communities.

However, the impact of joining learning communities on learners’ knowledge acquisition and learning experience in microlearning has yet to be conclusively determined. A case study showed that most learners preferred to learn independently rather than in a group, and many felt that they enjoyed receiving information more than producing it (Buchem and Hamelmann, 2010 ). Speily and Kardan ( 2018 ) also pointed out that most learners in online learning communities remained latent, and learners from different backgrounds caused a decrease in communication and information sharing. Lu et al. ( 2019 ) and Kumar et al. ( 2023 ) also noted that too much online information might affect learners’ information recognition. Given the diverse backgrounds of microlearning learners in online learning communities and the incredible amount of information generated by many learning communities on today’s social media platforms, it still needs to be determined how microlearning with learning communities impacts knowledge acquisition and learning experience. Therefore, this study investigates the impact of applying online learning communities in microlearning on learners’ knowledge acquisition and learning experience.

Research design

This study employed a mixed-methods sequential explanatory design. This approach was driven by the objective of comprehensively understanding the impact of microlearning in online communities. It allows for an initial quantitative analysis of learning outcomes, followed by qualitative investigations to explore the dynamics behind these outcomes. First, a randomized control trial (RCT) involving 100 learners openly recruited from Chinese-language online communities was conducted to evaluate the effectiveness of community-based microlearning on knowledge acquisition and learning experiences. Semi-structured interviews were then conducted with 10 participants randomly selected from the experimental and control groups (five from each group) to elucidate their learning experiences at the end of the experiment.

Participants

We recruited a diverse group of 100 participants online for the RCT. The eligibility criteria included learners older than 18 interested in the course content. At the end of the RCT, 20 participants dropped out due to time constraints or finding that the course content needed to meet their expectations. Therefore, the data analysis was conducted on 80 participants. This group, which included 13 males and 67 females, had an average age of 24.8 years. Undergraduates accounted for 16.25%, while graduate students comprised 46.25%.

Ten participants were recruited from the 80 learners for interviews. Considering their groups, age, pre-test and post-test scores, and community preferences, five learners from the experimental group and five from the control group were selected for the interviews. Their demographic information is shown in Table 1 .

Learning materials

The course design was under Gagné's ( 1985 ) learning theory. Gagné's framework identifies nine instructional events that, when effectively applied, significantly enhance the learning process. This framework was chosen for its comprehensive approach to structuring educational content, particularly its emphasis on sequencing information and providing conditions conducive to learning. By aligning the course with Gagné's principles, such as gaining attention, informing learners of objectives, stimulating recall of prior knowledge, and providing guidance for learning, we aimed to optimize the effectiveness of the microlearning modules.

The learning materials consisted of an 8-module microlearning course on conducting interviews in research studies, with 20 microlearning course videos. Each video was 3–10 min in length. The instructor appeared in the top-right corner of each video (see Fig. 1 ). The course content, specifically designed to explain qualitative research methods through the lens of interviews, included a comprehensive overview of the course, preparation strategies for conducting interviews, essential tools, various types of interviews, interview formats, procedural steps, and critical considerations. This curriculum aims to equip learners with a thorough understanding of conducting qualitative interviews as a research method.

A screenshot of the course video.

Each microlearning video typically concluded with 1–3 reflection questions. Sample questions were like, “Would your interview design be better suited to using focus group interviews or in-depth interviews?” or “What types of questions are appropriate for your research project?” The reflection questions help learners review and deepen their understanding of the course content. They could also answer the questions and send their answers to the instructor (for both groups) or the community (for the experimental group only) to discuss with the instructor or community members. Learning materials were sent to each learner on WeChat as a link through a private message (for the control group) or a group message (for the experimental group). The course lasted 20 days, with one microlearning video sent to students daily. However, the instructor did not force learners to study one lesson per day on time. In other words, learners could study at any time or anywhere. If a learner did not post anything in the group or contact the instructor by private message for more than five days, i.e., no interactive behaviors, the instructor would remind them of learning via private messages.

Data collection

The experiment was conducted online in the summer of 2022. Before data collection, all participants had to sign an informed consent form detailing the study’s purpose and agreeing to the use of their data. A pre-test on knowledge and a pre-course survey were conducted, with 80 participants completing both. Participants were then randomly assigned to two equal-sized groups: the experimental and the control. A balance test was conducted, and there were no significant differences between the groups in any of the variables measured in the pre-test and pre-survey.

Both groups received the same instructions from the same instructor using identical learning materials delivered over WeChat, a mobile chat app widely used in China. For the experimental group, a WeChat group chat was created to form a learning community where regular peer interaction activities were organized. Participants voluntarily engaged in discussions within the community. In the control group, participants could interact with the instructor individually. In this RCT, participants took an 8-module course on interview research methods. After the course, 80 participants completed a knowledge test and a learning experience survey. The attrition rate was 20%, with twenty participants (10 from each group) dropping out due to time constraints.

Given the data attrition, the researcher used t-tests and non-parametric tests to explore differences between dropouts and remaining participants. Although there was data attrition, no significant differences were found between the remaining participants in the experimental and control groups in terms of gender, age, pre-test scores, and prior knowledge. Moreover, the attrition did not significantly affect the data analysis for the follow-up study. There were no significant differences between the experimental and control groups regarding gender and age, suggesting that the study did not suffer from significant attrition bias.

Tests and surveys were designed and distributed using Tencent Survey, a widely used online survey platform in China. Before the course, learners completed a pre-course survey and a pre-knowledge test. All answers to the 20 questions were covered in the course materials. The pre-course survey collected learners’ demographic information, such as age, gender, and year of study. After the course, learners were given a link to complete the post-survey and post-test. The post-survey inquired about learners’ learning habits, satisfaction, mental effort, and preference for learning within an online community. The post-knowledge test was identical to the pre-knowledge test. Experimental group learners who joined the community were additionally asked questions about their sense of community. Interview data were collected and recorded after the post-survey. Ten semi-structured interviews were conducted through Tencent Meeting.

Measurement methods

Knowledge acquisition variable: knowledge test.

The knowledge test consisted of 20 multiple-choice questions about interview research methods, and the content of the knowledge test in the pre-and post-test was the same. The knowledge test was designed by the researchers and examined by two experts in educational technology for content validity. A pilot test was conducted among learners with and without previous learning experience using interview research methods. The learners with previous learning experience in interview research methods scored higher than those with no previous learning experience in interview research methods. Their feedback was used to refine the test. Scores for the pre-test and post-test were calculated on the number of correct answers in the pre-and post-tests. The total score was 20 points, respectively. The pre-test had acceptable internal consistency ( N  = 100, KR-20 = 0.69), and the post-test reached good internal consistency ( N  = 80, KR-20 = 0.73).

Learning experience variables

Satisfaction.

Learners’ learning experience satisfaction was measured on the post-test using a satisfaction scale adapted from Ritzhaupt et al., ( 2008 ) study. We translated it into Chinese. It is a five-point scale with 9 questions and two bipolar adjectives on both sides. For example, on the left side is the description “obscure” and on the right side is the description “clear”. The satisfaction data has good internal consistency, with a Cronbach’s alpha ( α ) equals to 0.85. The mean score of the 9 items was calculated.

Mental effort

Learners completed a 9-point scale for self-reported mental effort during multimedia learning (Paas, 1992 ) on the post-test. The mental effort scale ranges from “very, very low mental effort” to “very, very high mental effort”. The self-reported mental effort scale was coded from 1 to 9, with higher scores indicating more mental effort required by the learners.

Sense of community

A sense of community scale, adapted from the one produced by Rovai ( 2002 ), was used to collect learners’ perceptions of learning in communities on the post-test. This 5-point Likert scale contains 20 items, which were divided into two factors by Rovai ( 2002 ), i.e., connectedness and learning. The sense of community scale was coded as: for statements 1, 2, 3, 6, 7, 11, 12, 13, 15, 16, and 19, strongly agree = 4, agree = 3, neutral = 2, disagree = 1, and strongly disagree = 0. The remaining items were inverted: strongly agree = 0, agree = 1, neutral = 2, disagree = 3, and strongly disagree = 4 (Rovai, 2002 ). The scores of each subscale 10 items were added together, and mean scores were calculated. Learners with higher scores had more positive attitudes towards the community. Both the connectedness and learning factor in the sense of community scale had good internal consistency with Cronbach’s alpha of 0.76 and 0.80, respectively. The overall course community scale had good internal consistency ( α  = 0.84).

Preference for learning in a community

We also asked participants’ preferences for learning in a community. In the post-survey, we asked learners if they preferred to learn in a community after the 8-module course. Learners could express their opinions by choosing “yes” or “no”. Their answers with “yes” were coded as 1, and “no” was coded as 0.

Data analysis methods

A paired sample t -test was conducted to examine whether the micro-learning course was effective in enhancing learner knowledge of interview methods for all the participants. Then, to explore the impact of applying an online learning community in microlearning on learners’ knowledge acquisition, a one-way ANCOVA was conducted to examine whether there were significant differences in the post-test scores between the experimental and control groups while controlling for pre-test scores. Next, to investigate the effect of the online learning community on the learning experience, one-way ANOVA tests were conducted to analyze whether the two groups differed significantly in mental effort, preference for learning in a community, and satisfaction. Since only the experimental group had a community, there was only data from the experimental group on the sense of community. A descriptive analysis was conducted to analyze their experience and sense of community.

Interview data was transcribed in Chinese and then translated into English. We browsed the interview transcripts and extracted key information that indicated learners’ knowledge acquisition and learning experience. Then, we reviewed and reported what we found in the results.

In this section, we present our findings. We used the letter + number for interview results to refer to interviewees. For example, “E1” refers to an interviewee from the experimental group, while “C2” refers to an interviewee from the control group.

Descriptive data

Table 2 shows the means and standard deviations for experimental and control group learners by gender, age, prior knowledge, pre-test, post-test, mental effort, and satisfaction. These measures were comparable between the groups. Learners who did not study in a community reported higher post-test scores but required slightly higher mental effort to complete the course.

Differences in the acquisition of knowledge

Without considering grouping, we used a paired sample t -test to analyze learners’ knowledge acquisition through 8-module microlearning. The result showed that the learners’ post-test scores ( M  = 15.48) were significantly higher than the pre-test ( M  = 12.83), t (79) = 9.657, p  < 0.001. This suggests that the course successfully enhanced participants’ knowledge about interview methods.

A one-way ANCOVA, with the pre-test scores controlled, was conducted to examine whether having an online learning community would further enhance knowledge acquisition. Results indicated no significant difference in the post-test scores between the experimental and control groups ( F (1, 76) = 0.257, p  = 0.614). Joining a community had no significant effect on learners’ knowledge acquisition.

Findings from the interview data were in tandem with those from quantitative analysis. Learners who progressed through the course and those whose test scores slid existed in both groups. Interviews were used to explore further their mastery of the course content, which revealed that the inclusion of learners in a community did not have minimal effects on their mastery but that the way they learned played a more critical role. Learners who repeatedly studied the microlearning content and took notes during the learning process usually had higher post-test scores than pre-test scores. For example, E3, who scored 13 points on the pre-test, took notes using her iPad and scored 20 points on the post-test. Moreover, C4 mentioned that she took notes in her notebook as she watched the video. If she forgot some learning points, she would go back and watch the learning content again. C4 got 14 points on the pre-test but scored 20 points on the post-test. During the interviews, they reported that they could clearly recall the content of each module. For instance, E3 said, “Sometimes the instructor would ask us in the video if we remembered the content mentioned in the previous course. If I did not remember the content, I would immediately find the previous course video to make sure I remembered it.” C4 said, “I would find a quiet time to watch the microlearning videos without interruptions and record the content of the lessons. In this way, I could open my notes for review during the weeks.”

Other learners reported that in the learning process, they studied repeatedly to practice what they learned and searched information online for what they did not understand. For example, C1 shared an experience:

During the microlearning course, I happened to need to use the interview research method. So, I used interview methods over and over again to collect the data I needed. The course really helped me a lot…… When I encountered something I could not understand, I would go to literature and collect more information online, so I could successfully collect the interview data .

Learners whose post-test scores were lower than their pre-test scores indicated in the interviews that they might not listen well enough during online microlearning. When asked what they remembered about the course content, they only gave the general course framework or remembered only the content of a particular module that the instructor repeatedly emphasized. For instance, E1 admitted, “ Sorry, I do not really remember exactly what I learned; I just remember that the instructor focused on the steps of the interview research method and that the teacher said it many times .”

Differences in the learning experience

Learning experiences in this study included the levels of mental effort that learners believed they needed to invest in the 8-module course, their preferences for learning in a community, and their satisfaction with the learning process. One-way ANOVA was conducted to determine whether the groups had significant differences regarding their learning experience. Experimental group learners who studied in the community also reported their sense of community in the post-survey.

Though the descriptive data showed that learners from the control group ( M  = 4.55) required slightly more mental effort than learners from the experimental group ( M  = 4.38), there was no significant difference between the two groups according to one-way ANOVA results on their self-reported mental effort score, F (1,78) = 0.226, p  = 0.636 (see Table 3 ).

Both groups of learners indicated in the interviews that the microlearning content was relatively easy. Learner E5 said, “The microlearning lessons were all relatively short in length, no more than 10 min, as I remember. The instructor sent us one lesson per day, so I did not need to spend much effort to complete the course.” Learner C4 also said, “The microlearning videos were shorter, and I could keep track of learning on my own; therefore, I sometimes accumulated a few days of learning content together.” She said that she liked this self-directed learning process. Learned E2 noted, “I checked the community information to see what my peers were discussing to deepen my understanding of the course content.”

Community preferences

Interestingly, there was a significant difference between the experimental and control group learners on their community preferences, F (1,78) = 6.27, p  = 0.01 (see Table 3 ). Based on the descriptive data, learners who did not join a community ( M  = 0.82) were more likely to prefer having a learning community than learners in a community ( M  = 0.57).

Through the interviews, we found that learners had too many chat groups (learning communities) on WeChat, where they received hundreds of community messages every day, resulting in their ignoring the information. Learners in the experimental group mentioned that they did not check the community messages daily. They only skimmed through the information and may not have made much of an impression. Learner E4 said, “I took time out of my busy day to study the microlearning course. Since there are so many group messages, I often choose to mute them so that I am not disturbed by too many messages. But this may also lead to me missing much information.” Besides, some learners in the experimental group reported that community learning enabled them to enhance learning. Learner E1 said, “Sometimes there were learners in the group who asked questions that happened to be confusing to me as well, and I think it is very rewarding to learn in a community.”

Moreover, learners in the control group were often excited about community learning, even though they did not join the community during the experiment. Learner C3 told us, “I think I would have learned more if there had been a community.” However, when asked if they would choose to interact in a community if they had joined a community at the beginning of the course, many of the control group learners indicated that they probably would not have. Learner C2 explained, “I prefer to watch others speak, but I would probably not choose to speak in a community.” Learner C4 also noted, “I would be more apprehensive about speaking in public when there are many learners in a community. I am afraid of saying the wrong things.” Of course, not every learner from the control group would like to learn in a community. Learner C5 refused to learn in a community and thought that she would most likely not check the group messages often. She believed that the important thing about microlearning for her was understanding the course content and that the discussion part was not essential to her.

We found no significant difference between the experimental and control groups on their satisfaction scores, F (1,78) = 0.297, p  = 0.587 (see Table 3 ). Based on the descriptive data, learners who did not join a community ( M  = 4.08) and those who were in a community ( M  = 4.01) were similar in terms of satisfaction.

Both groups expressed high satisfaction with the microlearning course. They perceived that the microlearning content was easy to understand, the course content was useful, and the instructor would help them during the learning process. Learners in the control group mentioned that the daily private messages from the instructor made them feel valued (see Fig. 2 ). Learner C1 said, “I always felt like the instructor I received messages from every day was a robot until one day I asked a question, and the instructor answered it patiently. I was so excited that I communicated more with my instructor since that day.” Some learners in the experimental group reported that learning in a community positively impacted their learning experience. Learner E1 mentioned that she learned a lot from examples shared by other more experienced learners in the community; “They were like course assistants. Reading the messages about their experiences related to interviews helped me understand the interview research method.”

A screenshot of private messages between a learner and the instructor.

Forty learners in the experimental group reported their sense of community in the post-survey. Table 4 reports the mean and standard deviation of their sense of community scores.

Community learners who made significant progress in their performance mentioned that they enjoyed learning in the community. Communication in the community helped them better understand the learning materials, and the community provided good opportunities for them to learn comfortably (see Fig. 3 ). “I think sometimes other learners’ questions were also my questions. It made me happy to see the questions being answered in the community,” said E2. E3 explained, “I was able to see the perspective of my peers thinking through their responses. When they answered the same question from different angles, I was able to think about the same question from various perspectives. It is interesting.” E2 and E5 mentioned that by checking the community messages, they were able to discover some points they had overlooked in the study process, which helped them fill in the knowledge gaps. At the same time, E2 also raised the problem of missing information due to the large number of WeChat community messages and hoped that the managers or instructors could provide collated key information.

A screenshot of discussions in the community.

However, some learners did not check the community messages very often, and their test scores dropped. For instance, E4 said, “I do not think the presence of a community has much impact on me, and I do not really read the group messages anyway. Sometimes, too many group messages are a nuisance to me.” E1, when asked if she checked community messages, said, “I have so many communities with too much information to read that I usually muted them. But this also caused me to sometimes forget to check the group messages and miss the key information.” At the same time, she also mentioned that even if she checked the group messages, she did not remember any key information. She had not posted anything nor connected with anyone in the community.

In this study, we examined the effectiveness of community-based microlearning on knowledge acquisition and learning experiences. In particular, we engaged 80 learners, 40 of whom were in the community and another 40 who were not. This section revisits the research question with two aspects, knowledge acquisition, and learning experience, and discusses the implications.

Knowledge acquisition

The majority of participants in this study demonstrated significant improvements in learning performance through microlearning. This finding is consistent with the existing microlearning research that has reported increased knowledge (Lee et al., 2021 ). The concise format of microlearning effectively breaks down complex content into digestible chunks, facilitating clearer and easier comprehension. This also makes it easier for learners to grasp the main points. Microlearning has been recognized as a potent instructional approach or intervention across various educational contexts, including higher education (online, hybrid, and blended courses), corporate training, and professional development for K-12 teachers (Sankaranarayanan et al., 2023 ). Using microlearning as an intervention is crucial because it provides a focused, efficient, and adaptable learning experience tailored to the unique needs of learners in these diverse settings. Yet, the presence of a learning community did not significantly influence knowledge acquisition among learners. This contrasts the findings of Jiménez-Zarco et al. ( 2015 ), who found that learning in a community improved learners’ learning performance. This outcome may stem from the inherent characteristics of microlearning itself. Jiménez-Zarco et al. ( 2015 ) did not provide specific learning materials; learners joined a virtual community of practice to obtain the information they needed selectively. In contrast, our study provided learning materials and aimed to help learners master the basic knowledge points of interview research methods through micro-lessons. While learners might prefer a more relaxed and informal learning setting, it could result in a less serious engagement with microlearning content. Disengaged learning combined with too much online information may disrupt learners’ receptivity to the information. This is also similar to Lu et al. ( 2019 ) findings, which reported that overwhelming information in the online environment sometimes hinders learners’ recognition in response to online information.

Interview data revealed the seriousness with which learners approached the course content, and their learning choices significantly impacted knowledge acquisition. When learners listened carefully, took notes, and selectively played back course content, they were able actually to remember more information and receive higher test scores. Kauffman et al. ( 2011 ) found that learners with high self-monitoring prompts used matrix note-taking devices and improved learning achievement in online learning environments. In addition, playing back the course video means reviewing the learning content, which could help learners deepen their impression of the knowledge points and thus achieve higher test scores. Instructors can encourage note-taking during microlearning sessions and assist learners in reviewing content to enhance retention.

Learning experience

Despite the lack of a statistically significant difference in mental effort between groups, descriptive data indicated that community participants expended marginally less effort to complete the course compared to their non-community counterparts, perhaps because the community dissolved the cognitive load that would otherwise have to be undertaken by each learner in the experimental group. Jung et al. ( 2015 ) supported this finding that under low cognitive load conditions, an individual can adequately process activities, and learning collaboratively may generate the costs of recombination and coordination. Microlearning requires low mental effort because it is concise. Therefore, the mental effort required in microlearning is low, so whether or not learners were in a community did not significantly affect mental effort.

There was no significant difference in learning satisfaction between the two groups of learners, with most expressing satisfaction with the microlearning process. However, learners who completed the post-test demonstrated higher course satisfaction, while those who were less satisfied may have discontinued their participation earlier. Angelino et al. ( 2007 ) noted that online learning had higher dropout rates, ranging from 10 to 20 percent. Unlike traditional learning classes, learners engage in spontaneous learning in informal and non-formal settings. The microlearning activities they participated in gave them greater autonomy (Kohnke et al., 2024 ), making them more likely to discontinue learning when they encountered problems with time limits or when the content did not meet their expectations. This finding provides insight to community managers and micro-course educators that creating a community does not mean that learners will be more satisfied and that it is important to explore how to use course design to retain learners.

We found that learners who were not in a community were eager to learn in a community, but learners who were in a community might ignore or choose to mute community messages. As mentioned by Lu et al. ( 2019 ) and Kumar et al. ( 2023 ), for learners, too much information in the online environment may have a negative impact on learning. Chane et al. ( 2022 ) also noted that students preferred to receive personal attention from the teachers. In our study, the instructor sent private messages to the control group learners, and those who received the instructor’s private messages might be able to get noticed; in contrast, learners in the experimental group might miss the instructor’s public message sent to the entire community if they chose to mute the community message reminder and thus were unable to feel the instructor’s attention to them. Although learners in the control groups expressed the urge to join a community, they also received additional personal attention from the instructor. Although learners in the control group without learning communities wished to have an opportunity to communicate with others, they also expressed uncertainty when asked if they would post in an online community. Whether or not they would post in a community is influenced by many factors. A large number of messages already in the communities may cause learners to be reluctant to post in the community (Nguyen, 2021 ; Nonnecke et al., 2006 ). Beaudoin ( 2002 ) also noted that learners were often apprehensive about speaking in public. When they were unsure that their responses were helpful, they usually chose not to post.

Community learners’ sense of community may be polarized. Learners who were actively engaged in community learning and had improved their test scores enjoyed their learning in the community. They were able to learn about the perspectives of their peers from the community information, which helped them fill in the knowledge gaps (Schreurs, 2014 ). For learners who did not check the community information frequently or even mentioned in the interview that they did not need the community, they often chose to mute the community and received lower test scores. The community information may cause information overload for them. Kuo et al. ( 2017 ) also supported this finding. They found that the sense of community and perceived collaborative learning contributed significantly to learning, and most students in the groups had a positive sense of community. Moreover, Speily and Kardan ( 2018 ) mentioned that learners in online learning communities had different backgrounds, which might result in information not being applicable to all learners and learners being reluctant to share in the community. This is one of the reasons why some of the interviewees mentioned that they were reluctant to post in a community when the community is large and lacks connections. If group members are familiar with each other, they may find it easier to communicate within the group. High familiarity could contribute to online collaboration and give group members a more positive sense of community (Janssen et al., 2009 ). This suggests that community managers and educators should consider forming multiple small groups based on learners’ backgrounds to foster a more cohesive and supportive online learning environment. Kohnke et al. ( 2024 ) emphasized the importance of enhancing interactions in microlearning activities, suggesting that learners can feel more connected and engaged by improving the quality and frequency of these interactions. This helps to reduce the number of learners in each community, create communities with closer relationships, and promote community members to post in the community and gain a more positive sense of community (Speily and Kardan, 2018 ).

During the interviews, some learners expressed a desire to summarize and organize the information in the community into a document so that everyone could review the messages that had been discussed. In their study, Yang et al. ( 2004 ) indicated that creating a document through collaboration could motivate learners to share knowledge in an online learning community, help them sort out the relationships between knowledge points, and contribute to knowledge acquisition. This is also a good suggestion for microlearning designers and educators. Especially in social media-based microlearning, learners are often distracted by redundant information. Providing collaborative documents and encouraging learners to edit and contribute may motivate them to engage better in microlearning, facilitate tracking their learning progress, and keep them from missing out on important community discussions.

In light of these findings, it becomes imperative for course designers to meticulously consider the composition and management of online learning communities to optimize learner knowledge acquisition and learning experience. Specifically, designing the format of microlearning that is responsive to the learners’ feedback and preferences, as uncovered in our qualitative insights, can significantly enrich the learning experience. For researchers, these results highlight the critical need for further exploration into the effective integration of community features within microlearning environments. This study, therefore, not only contributes to the existing body of knowledge by providing empirical evidence on the efficacy of online learning communities in microlearning settings but also paves the way for future research to explore educational designs that cater to the evolving needs of learners.

Limitations and future studies

Some limitations of this study are listed as follows: First, the number of learners who participated was relatively limited. The attrition rate of the RCT was 20%. Future research could use the same learning materials designed for this study to recruit more learners and validate the experimental findings. Researchers could also take measures to prevent attrition and conduct sensitivity analysis after attrition. Second, we did not include questions about how learners chose to learn, such as whether they took notes during learning and re-played the study videos in our post-survey design. Future studies could include these questions in the experimental design to find evidence of what we found in the interview data. Third, learners were required to complete the post-test immediately after the 8-module microlearning course. Future studies can implement several tests during and long after the course to understand the changes in learners’ knowledge acquisition.

Microlearning, the latest lifelong learning trend, has attracted the public’s attention. Although many studies have been done on microlearning, researchers lack empirical findings on the impact of online learning communities on microlearning learners regarding knowledge acquisition and learning experience. Through its empirical exploration, this study illuminates the nuanced role of online learning communities in enhancing microlearning, focusing on knowledge acquisition and the learner’s experience. The conclusions of this study underline critical implications for educators, researchers, and microlearning designers, underscoring the paramount importance of delivering personalized learning experiences in the design of microlearning courses. It highlights the necessity of preemptively gathering data on learners’ preferences and their informational absorption capacity to tailor the micro-courses effectively. Furthermore, the study advises against overcrowding online learning communities, suggesting instead that community managers should foster interactive opportunities and prevent excessive lurking by learners. Significantly, the findings caution that providing online learning communities does not automatically enhance the learning experience. There is a vital need for a focused approach to providing personalized microlearning features, ensuring they align with individual learner profiles. This expanded understanding not only enriches the learning experience but also marks a significant stride in optimizing the efficacy of online learning environments through targeted, learner-centered strategies.

Data availability

Data will be made available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to thank the research participants who generously agreed to participate in this study and share their time and experience. This work is supported by the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission and the Peak Discipline Construction Project of Education at East China Normal University.

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Faculty of Education, East China Normal University, Shanghai, China

School of Library and Information Management, Emporia State University, Emporia, KS, USA

Department of Educational Studies, Academy of Future Education, Xi’an Jiaotong-Liverpool University, Suzhou, China

Department of Educational Technology, Faculty of Education, Shenzhen University, Shenzhen, China

Miaoting Cheng

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Zhu, J., He, H., Zhao, Y. et al. Does participating in online communities enhance the effectiveness and experience of micro-learning? Evidence from a randomized control trial. Humanit Soc Sci Commun 11 , 1198 (2024). https://doi.org/10.1057/s41599-024-03719-6

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• Published: 11 September 2024

Why is Antactic krill (Euphasia superba) oil on the spotlight? A review

• Fereidoon Shahidi   ORCID: orcid.org/0000-0002-9912-0330 1 &
• Abrehem Abad 1  

Food Production, Processing and Nutrition volume  6 , Article number:  88 ( 2024 ) Cite this article

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Antarctic krill ( Euphausia superba ) oil is attracting more interest for its nutritional as well as functional potentials. Nevertheless, its potential as new and innovative food component remains largely unexplored. This review aims to outline the chemical composition, extraction methods, and health advantages of krill oil, offering insights for its utilization and provides evidence why it is now on the spotlight. Krill oil presents a distinctive fat profile, rich in lipid classes, with phospholipids (PLs) comprising a significant portion (38.93—79.99%) with high levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Additionally, it includes several minor bioactive components like astaxanthin, tocopherols, sterols, flavonoids, and vitamin A. Various extraction technics, including solvent and solvent-free extraction, enzyme-assisted pretreatment extraction, super/subcritical fluid extraction, significantly influence both output as well as standard of the resulting product. Furthermore, the oil had been linked to a number of health advantages, including prevention of cardiovascular disease (CVD), anti-inflammatory effects, support for women's physiology, anticancer activities, as well as neuroprotection, among others. Despite the commercial availability of krill oil products as dietary supplement, there is a scarcity of studies exploring the underlying molecular mechanisms responsible for its various biological activities. Despite this, apply krill oil as an innovative food ingredient has not been thoroughly investigated. This review consolidates information on the chemical composition, extraction techniques, possible health advantages, as well as existing uses as applications, aiming to offer insights for its complete exploitation. In addition, it attempts to unravel the fundamental molecular mechanisms that being investigated to deeply understand how krill oil produces various biological effects.

Graphical Abstract

Introduction

Krill, scientifically known as Euphausia superba , is a tiny crustacean found in the seas of the Antarctic Ocean, its also holds significant ecological importance as a primary food source for many fish species (Zhou et al.,  2021 ). Although quantifying krill biomass poses challenges, estimates suggest approximately 379 million metric tons. In an effort to preserve the marine ecosystem, the agency of Antarctic Marine Living has implemented the catch maximum per season which is < 619,000 tons (Zeb et al.,  2021 ). However, the actual catch less than 250,000 tons, which is below the allowed limits, likely due to challenges in preserving krill due to its delicate nature (Fuxing et al.,  2017 ). Krill composition has a water content of 77.9—83.1%, lipids from 0.5 to 3.6%, protein from 11.9 to 15.4%, chitin at 2%, along with carbohydrates, and 3% ash (Xie et al.,  2017 ).

Krill oil (KO) finds its way in the aquaculture sectors and as a dietary supplement to enhance health. This is attributed to its nutritional profile, that is high in omega-3 fatty acids as triacylglycerols (TAGs) also phospholipids (PLs), astaxanthin, and vitamins (A and E) (Cicero and Colletti,  2015 ).

Krill has a lipid content of 0.5–3.6% (Xie et al.,  2019 ), notably phospholipids (PLs) that account for 30 to 65% of the total. Unlike fish oils (FO), which primarily occur as TAGs, the oil has a significant portion of phosphatidylcholine. With roughly 40% of its overall fatty acids are due to EPA (C20:5) and DHA (C22:6) (Ramprasath et al.,  2013 ). The C20:5 and C22:6 in KO exhibit various beneficial pharmacological properties, hence serve a major role in the management of several chronic conditions like CVD as well as inflammatory diseases (Cicero et al.,  2012 ; Costanzo et al.,  2016 ). Moreover, they contribute to cancer prevention and improve the gut health (Saravanan et al.,  2010 ). Research findings indicate that C20:5 and C22:6 obtained from KO shown better bioaccessibility when compared to different n-3 PUFA sources and varieties (Rossmeisl et al.,  2012 ).

Krill oil received approval from the U.S. Food and Drug Administration (FDA) in 2008, designated as Generally Recognized as Safe (GRAS) status. It was also granted approval as a novel food by the European Food Safety Authority (EFSA) in 2009 and was granted authorization in China in 2014. Additionally, EFSA approved the use of KO for pregnant women in 2014.

This write up offers a comprehensive account of krill oil with regard to its chemical composition, bioavailability, health benefits, mechanisms of action, extraction methods (both traditional and unconventional), and existing applications. Additionally, it explores the future prospects of krill oil as nutraceutical and why it has captured the spotlight.

Composition of Antarctic krill oil ( Euphausia superba )

Lipid class composition.

Unlike typical edible oils, which predominantly comprising mostly TAGs (over 95%) (Shahidi and Abad,  2019 ; Shahidi et al.,  2020 ), krill oil consists of a broader range of lipid classes. Phospholipids constitute the primary class, followed by TAGs, DAGs, MAGs, FFAs, and other constituents (Ahmmed et al.,  2020 ; Phleger et al.,  2002 ). Various factors affect the composition of each lipid portion in krill oil. These include year-to-year environmental changes, seasonal fluctuations, the maturity level of KO, and storage conditions, transportation means, and preparation techniques (Xie et al.,  2019 ). For instance, dehydrating krill through hot air yields higher amounts of free fatty acids (Gang et al.,  2019 ). Moreover, several studies have employed different analytical methods to analyze the composition of krill oil, thus comparison of the results may not always be straightforward (Han and Liu,  2019 ).

Numerous studies have documented varying lipid compositions in different krill samples. Krill oils typically exhibit a substantial content of PLs, varying from 39.89 to 80.69%, which can vary based on the sample type as well as the analytical method employed (Table  1 ). Discrepancies in lipid compositions observed in different years and regions may be due to fluctuations in feeding behavior (Phleger et al.,  2002 ). Clarke ( 1980 ) noted that oil extracted from krill ovarian tissues had higher phospholipid levels compared to that from muscle tissues, similar to that observed in fish (Takama et al.,  1994 ). Thus, females tended to possess higher PL levels than males due to the presence of developing ovaries (Kołakowska,  1991 ). Moreover, the extracted KO's PL content was greatly impacted by the extraction solvent selection, with mix of ethanol and isopropanol extraction resulting in elevated levels of phospholipids compared to solvents like hexane and acetone (Xie et al.,  2017 ).

Table 2 provides an overview of the phospholipid (PL) composition of Antarctic krill oil, indicating that phosphatidylcholine (PC) constitutes the majority, 44.58 to 99.80%, followed by phosphatidylethanolamine (PE) at 0.20 to 24.74%. Additionally, lysophosphatidylcholine (LPC) is also present in significant proportions, possibly attributed to PC hydrolysis due to either incorrect storage or preparation of KO (Lim et al.,  2015 ). Other PLs types such as lysophosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, cardiolipin, sphingomyelin, phosphatidic acid, and phosphatidylglycerol have also been observed in smaller amounts, typically not surpassing 15%, in certain studies (Fricke et al.,  1984 ; Kołakowska,  1991 ). PLs, particularly PC, have long been used as food additives and nutritional supplements, mainly derived from sources such as egg yolk, plant oils, and milk products (Chen et al.,  2023 ). The abundance of PC in KO provides a promising marine source for supplying PLs.

Lipid fraction (TAGs and PLs)

As already noted, KO composition consists primarily phospholipids (PLs) followed by triacylglycerols (Abad and Shahidi,  2023 ; Ahmmed et al.,  2020 ). These components significantly contribute to in absorption, and metabolism (Zhang et al.,  2021 ).

More than 64 molecular species of triacylglycerols have been identified in krill oil, with carbon number (CN) 42 to 60 and with one to eleven double bonds. Among these, the primary prevalent TAG species are 16:0/16:0/18:1, 14:0/16:0/18:1, 16:0/18:1/18:1, and 16:0/16:1/16:1 (Castro-Gómez et al.,  2015 ), constituting relative proportions of 4.8, 8, 5.5, and 6% of all TAG species found, in that order. This observation is consistent with the primary fatty acid profiles of TAG that contain 14:0, 16:0, 16:1, and 18:1. While the TAG fraction in krill oil contains relatively low levels of C20:5 and C22:6, the majority of residues are EPA found in the sn-1,3 positions, whereas C22:6 residues are located mainly in the sn-2 position (Fuller et al.,  2020 ). This distribution pattern mirrors that observed in FO (Akanbi et al.,  2013 ; Standal et al.,  2009 ). Moreover, another study found that about 21% of n-3 PUFAs were in TAGs fraction of krill oil, notably found in the sn -2 location (Araujo et al.,  2014 ).

Choline-containing PLs are dominant in krill oil. Using HPLC–ESI–MS, Winther et al. ( 2011 ) 69 PL species in krill oil have choline-head groups, comprising 60 PC species and nine LPC species. Notably, PC (16:0/22:6), PC (16:0/18:1), also PC (16:0/20:5) were the species with the highest abundance based on relative intensity, consistent with findings reported by Castro-Gómez et al. ( 2015 ) as well as Le Grandois et al. ( 2009 ). Although differing numbers of PC and LPC species were reported, it was discovered that about seven species of PC have omega-3 fatty acid in both two positions ( sn -1 and sn -2). These species included PC, (18:4/22:6), (18:4/20:5), (20:5/22:6) (20:5/20:5), (20:5/23:5), (20:5/22:5), as well as (22:6/22:6) (Winther et al.,  2011 ), highlighting the prevalence of omega-3 fatty acid within PC molecules. Furthermore, NMR analysis (Hupfeld,  2018 ) indicated that in contrast to the first position ( sn -1), the majority of omega-3 fatty acids in krill phospholipids were located in the sn -2 positions.

Fatty acid composition

Krill oil is rich in PUFAs, including C20:5 and C22:6, together with high amounts of C14:0, C16:1, C16:0, C18:1, and C20:1(Sun et al.,  2018 ). In addition, n-3 PUFAs, in particular C20:5 and C22:6 derived from dietary lipids, are recognized for their vital role in health (Marventano et al.,  2015 ). While other marine oil including fish known for their high C20:5 and C22:6 content and have traditionally been used as supplements of n-3 PUFAs, similar composition could be offered by KO. Table 3 provides a comparative overview of the composition of fatty acid in KO (Xie et al.,  2018 ) along with other marine oils including algal oil (DHASCO) (Abuzaytoun and Shahidi,  2006 ), cod liver oil (Dalheim et al.,  2021 ), as well as tuna and menhaden oils (Codex Standard 329–2017, WHO, Food and Agriculture Organization of the United Nations,  2017 ). Furthermore, KO has similar levels of C20:5 and C22:6 as other marine oils, although a significant portion of these fatty acids in KO are linked to PLs rather than TAGs found in other oil like fish.

The levels of C20:5 and C22:6 in krill oil are similar to what been found in FO (Table  3 ), but occurring mainly in the PLs rather than TAGs. Clarke ( 1980 ) found that the PLs portion of KO exhibited substantially greater proportions of PUFAs, as well as n-3 PUFAs, along with lower concentrations of monounsaturated and saturated fatty acids. Specifically, 31.13% of C20:5 and 14.87% of C22:6 been identified in PL portion, compared to about 3.17% of C20:5 and 1.5% of C22:6 in TAG portion. Those findings were supported by several additional research (Cicero and Colletti,  2015 ; Laidlaw et al.,  2014 ), which indicate that KO with elevated PL levels have higher amount of C20:5 and C22:6 (Sun et al.,  2019 ). The latest studies have indicated that omega-3 fatty acid that located in PLs demonstrate notably enhanced bioavailability compared to other omega-3 that located in TAGs (Jiang et al.,  2020 ). Consequently, krill oil may offer superior bioavailability of C20:5 and C22:6 compared to FO.

Minor components

Astaxanthin.

Astaxanthin, consider as a primary carotenoid present in certain marine organisms as well as algae, exhibits potent antioxidant properties as well as significant biological advantages (Table  4 ) (Ambati et al.,  2014 ). Miki ( 1991 ) highlighted astaxanthin's antioxidant potency that is ten times stronger than zeaxanthin, lutein, canthaxanthin, and β-carotene; furthermore, hundred times superior to alpha-tocopherol. Moreover, existence of astaxanthin responsable the deep red color in KO (Zeng et al.,  2024 ). The astaxanthin content in krill oilKO varies from 4 to 500 mg/100 g, also is affected by factors such as extraction techniques and analytical methods (Ali-Nehari et al.,  2012 ; Sun et al.,  2017a ; Tandyet al.,  2009 ). Extraction by acetone solvent has been shown to yield krill oil with higher levels of astaxanthin (Ahmadkelayeh and Hawboldt,  2020 ).

In KO astaxanthin predominantly exists as form of fatty acid esters. Foss et al. ( 1987 ) reported that astaxanthin diesters (51%), monoesters (43%), as well as free astaxanthin (6%), which coincides with findings of other studies (Lambertsen and Braekkan,  1971 ; Yamaguchi et al.,  1983 ) and similar to that of other shellfish. Subsequently, research has identified C14:0, C16:0, C16:1, C18:1, C20:0, C20:5, as well as C22:6 as primary fatty acids present as astaxanthin esters form (Cao et al.,  2023 ). Furthermore, astaxanthin was found to exist as three astaxanthin isomers in KO, namely all- trans , 9- cis , and 13- cis astaxanthin, with the all- trans isomer being most abundant.

KO have a considerable proportion of sterols, ranging from 2.3 to 3.9% of total lipids, mainly as cholesterol and desmosterol (Table  1 ) (Huenerlage et al.,  2016 ). Cholesterol constitutes approximately 81.31—82.57% of total sterols at concentrations ranging from 1895 to 3196 mg/100 g (Colletti et al.,  2021 ). These concentrations exceed those found in certain fish oils like tuna oil (204 mg/100 g) and hoki oil (515 mg/100 g) (Huenerlage et al.,  2016 ), also those present in egg yolk (1181 mg/100 g) (Albalat et al.,  2016 ). Since diet that including high cholesterol is associated with CVD (Nissinen et al.,  2008 ), concerns have been expressed regarding the consumption of krill oil. Bruheim et al. ( 2017 ) suggested that employing one solvent for extraction like ethanol may end to reduced cholesterol levels in KO in contrast to ethanol–water mixtures. Nonetheless, there is a need to explore novel extraction techniques to further mitigate cholesterol amounts in KO.

Desmosterol, recognized as the forerunner to cholesterol which constitutes 1.70—18.63% of all sterols (Fricke et al.,  1984 ). Additionally, Phleger et al. ( 2002 ), identified several other sterols in KO like brassica-sterol brassica-sterol (0.5—1.7%), 24-nordehydrocholesterol (0.1—1.7%), 24-methylenecholesterol (0.1—0.4%), transdehydrocholesterol (1.1—1.5%), and stanols (0.1—0.2%). Minor discrepancies in sterol composition may arise from variations in krill diet over different years, as crustaceans rely heavily on dietary sources or phytosterol dealkylation for sterol acquisition, rather than de novo synthesis (Xie et al.,  2018 ).

Vitamin E encompasses all tocopherols and tocotrienols, comprising four pairs of homologues (α-, β-, γ-, δ-), each possessing antioxidant properties and biological advantages, with α-tocopherol being the most potent (Valk and Hornstra,  2000 ). Similar to many other marine organisms (Ackman and Cormier,  1967 ), α-tocopherol predominates in krill oil, ranging from 14.74 to 63.0 mg/100 (Xie et al.,  2017 ; Tilseth,  2010 ). Some studies have also identified γ-tocopherol vary in concentration from 0.25 to 3.67 mg/100; however, traces of δ-tocopherol from 0 to 0.65 mg/100 g (Sun et al.,  2018 ; Xie et al.,  2017 ). Typically, in KO over 90% of tocopherols exist as α-tocopherol. Tocopherols in KO may enhance antioxidant capacity also potentially synergize with some bioactive constituents.

Vitamin A, crucial for human nutrition, is essential for both immune function as well as the management of certain infectious diseases (Mayo-Wilson et al.,  2011 ). According to wet weight basis, frozen krill normally contains 0.11 mg/100 g of vitamin A, (Suzuki and Shibata,  1990 ). a nutrient that is fat-soluble and may concentrate in the oil during the lipid extraction process. Xie et al. ( 2017 ) reported vitamin A contents of 16.40—28.55 mg/100 g of krill oil, with variations attributed to the extraction solvents used. Tilseth ( 2010 ) noted that oil extracted from cookedd krill had a vitamin A content of about 18 mg/100 g. Krill oil has a higher content of vitamin A than some FO, like menhaden oil (0.1–0.6 mg/100 g) as well as tuna oil (11.09 mg/100 g), but less than the 99.76 mg/100 g found in hoki oil.

Flavonoids exhibit various biological activities, including antioxidant, antibacterial, immunomodulatory, antitumor, also anti-inflammatory properties (Ullah et al.,  2020 ). While fruits, vegetables, and grains are primary sources of flavonoids (Merken and Beecher,  2000 ; Shahidi and Yeo,  2018 ), krill oil contains a novel flavonoid, 6,8-di- C -glucosyl luteolin. Sampalis ( 2013 ) patented a KO extract containing approximately 40% phospholipids (PLs) and about 7 mg/100 mL of flavonoids. This extract demonstrated efficacy in protecting the skin against harmful ultraviolet B (UVB) radiation also in improving dyslexia and abnormal motor function. According to certain research (Omar et al.,  2011 ), flavonoids' ability to function as antioxidants is increased when they are C -glycosylated at particular locations as well as their antidiabetic properties (Matsuda et al.,  2003 ). However, information about the characteristics of flavonoids found in KO are currently unavailable.

Whole krill possesses significant levels of minerals that are necessary for bone health, including magnesium, calcium, and phosphorus 360, 1322 1140 mg/100 g respectively, that fulfill the recommended amount for adults (Colletti et al.,  2021 ). Even though processing of krill may lead to loss of some minerals, using the Sampalis ( 2011 ) patented process, a krill lipid extract enriched with multiple minerals including potassium, calcium, selenium, and zinc may be obtained. Moreover, KO also have a great amount of fluoride which is 2,400 mg/kg (Soevik and Braekkan,  1979 ). However, fluoride is recognized as a global health concern (Barbier et al.,  2010 ). While fluoride in krill predominantly accumulates in the exoskeleton, there is a potential chance that it gets released in deceased krill. Hence, careful consideration of fluoride transfer is essential during krill oil extraction to prevent excessive fluoride levels in the oil. Typically, removing the exoskeleton from krill prior to extract the oil yields KO with low concentration of fluoride < 0.5 mg/kg, whereas extract by use whole body of krill exhibit a great concentration of fluoride from 3—5 mg/kg (Bruheim et al.,  2016 ; Jansson et al.,  2018 ).

Extraction methods

Krill oil extraction uses dried material as well as fresh krill (Katevas et al.,  2014 ; Ronen et al.,  2017 ). High concentrations of active proteolytic enzymes in krill allow for quick autolysis following catch. Therefore, it is imperative to commence on-board processing as soon as krill are captured in order to extract oil from fresh krill (Beaudoin et al.,  2004 ). The krill biomass serves as a more suitable material for on-shore krill oil extraction, particularly in regions lacking onboard or offshore processing capabilities (Yoshitomi et al.,  2003 ). Various extraction techniques, such as solvent extraction, mechanical pressing (nonsolvent extraction), enzyme-assisted extraction, and super/subcritical fluid extraction, are well-documented for krill oil extraction (see Table  5 ). Every approach has pros and disadvantages of its own, which are discussed below.

Traditional extraction methods

Solvent extraction.

Solvent extraction, a traditional method in oil production (Abad and Shahidi,  2017 , 2021 ), remains prevalent for krill oil production. Since one type of solvent cannot effectively extract all of the lipids from krill because of presence of different lipid classes with differing polarities, Xie et al. ( 2017 ) found that alcohols like ethanol as well as isopropanol could extract substantial volumes of PLs from krill meal but yield KO has less minor components. Conversely, acetone efficiently extracts minor components but fails to fully extract PLs. Hexane is widely used in oil extraction from seeds and is cost-effective with high extraction efficiency (Abad and Shahidi,  2020a , 2020b ); as such, it shows moderate capabilities to extract PLs as well as minor components (Li et al.,  2013 ). Combining solvents such a polar with a nonpolar can help balance the extraction efficiencies of PLs as well as minor component (Ronen et al.,  2017 ). Although the Folch method (Folch et al.,  1957 ) is widely applied to extract lipids from animal tissues with high lipid (Bruheim et al.,  2016 ), its commercial feasibility is limited due to the solvent’s toxicity such as chloroform as well as methanol.

Presently, the most used technique for extracting KO involves a two-step process using ethanol and acetone (Beaudoin et al.,  2004 ) which yields better lipid extraction (2.62%) compared to a single solvent extraction (acetone, 2.15%). Alternatively, a simpler one-step strategy, using a mixture of ethanol and acetone (1:1, v/v), can also achieve high lipid yields (Gigliotti et al.,  2011 ). Furthermore, Yin et al. ( 2015 ) found that combining solvent extraction with extrusion pretreatment enhances lipid extraction efficiency from krill. Defatted krill remains a valuable resource for protein recovery (Chen et al.,  2009 ), including enzymatic hydrolysis for producing peptides (Zhao et al.,  2013 ) or by fermentation (Sun and Mao,  2016 ). While solvent extraction is cost-effective and scalable, it necessitates large quantities of solvents, thus posing potential environmental concerns. Moreover, the process takes a lot of time also labor-intensive.

Mechanical pressing (solvent-free extraction)

Solvent-free extraction, unlike solvent-based methods, does not rely on organic solvents for extracting KO. Mechanical pressing, which is known as classic solvent-free extraction technique, is commonly used for oilseeds with great content of oil including sesame oil 49 to 58% and sunflower oil 40 to 43% (Khan and Hanna,  1983 ). Although less efficient compared to solvent extraction, mechanical pressing is often employed to remove the majority of the oil before recovering the remaining oil via solvent extraction. Fresh krill is not inherently suited for conventional mechanical pressing due to its relatively and 17.24% in krill meal (Yin et al.,  2015 ) using this method, fresh or thawed material should be ground as slurry in fluid medium, facilitating lipid release during subsequent mechanical disruption procedures, followed by oil recovery using centrifugation (Larsen et al.,  2007 ). However, the resultant slurry during grinding could lead to emulsification because of nature of phospholipids, hence complicating the removal of the fat off the mixture. To address this concern, Katevas et al. ( 2014 ) introduced an alternative method, which includes cooking, drain, and centrifuging. The approach allows simultaneous extraction of PLs-enriched KO as well as neutral lipid-enriched KO. It is worth noting that the initial cooking step take place at 90 °C with no agitation to prevent emulsification. Additionally, it is preferable to process krill when fresh, because ice crystals that grow as a result of freezing might harm krill tissues, resulting in emulsification during processing and yielding low-quality products.

Solvent-free extraction offers the advantage of providing a safer and more environmentally friendly process compared to solvent extraction methods. However, it presents significant drawbacks such as the need for investing in equipment purchase and high energy requirements. Additionally, the high operating temperatures involved may lead to product oxidation. Furthermore, solvent-free extraction may not efficiently extract all oil that exist in krill, as indicated by Katevas et al. ( 2014 ) who reported a yield of only 2.1%. Consequently, some krill manufacturers opt to use mechanical separation consider as very first step to extract a portion of oil while at the same time producing krill meal. Thereafter, some techniques like solvent extraction or supercritical fluid extraction are employed to extract oil from the remaining krill meal (Tilseth et al.,  2015 ).

Other extraction techniques

Enzymatic extraction.

Enzyme pretreatment represents an efficient method for releasing bound compounds and increasing lipid yield during the extraction process (Dom´ınguez et al.,  1994 ). By using specific enzymes, the extractability of oil can be improved. Moreover, the gentle nature of this process guarantees better-quality meal and oil. These advantages render enzyme pretreatment an attractive option for KO extraction.

Oil has been extracted from raw krill using proteases, as demonstrated by Bruheim et al. ( 2016 ). The typical process involves disintegrating the krill into small particles, followed by the addition of water then heating. Subsequently, enzymes are added to hydrolyze resultant material, after which the enzymes are deactivated. The solids, primarily the exoskeleton, are removed and then PL-protein complex is separated then dried. KO is then extracted from this complex (Bruheim et al.,  2016 ). It is worth noting that the remove the exoskeleton from material can lead to a reduction in fluoride content in the resulting products. Lee ( 2014 ) patented an alternative enzyme assisted extraction method by using ultra high-pressure reactor ranging from 10 to 300 MPa to liquefy krill and assure effective interaction with enzyme (proteases). Following undergoing enzymatic processing for a duration of 4 to 24 h, the krill that had turned into liquid was thereafter subjected to filtration in order to separate the resulting filtrate from the solid residue known as sludge via centrifugation. moreover, the astaxanthin-enriched oils were separated from sludge using another solvent such as ethanol.

This method's main benefit is gentle operating conditions, that enable the extraction of high-quality protein and oil from krill at the same time. Additionally, the enzymatic hydrolysis process facilitates the recovery useful byproduct like krill peptides. These peptides are gaining attention and recognized as bioactive compounds in functional foods and nutraceuticals, that have positive effects on health and low the risk of disease. Nevertheless, importantly, the longer hydrolysis time restricts the enzymes' potential for large-scale industrial applications, Furthermore, the high coast compared to other extraction method.

Supercritical fluid extraction

Lipid extraction via supercritical extraction has attracted a lot of attention recently for its solvent-free nature, environmental friendliness, and gentle operating conditions. Among supercritical solvents, supercritical carbon dioxide (SC-CO 2 ) is preferred for its chemical inertness, safety, non-toxicity, and moderate critical properties (Friedrich and Pryde,  1984 ). Despite its advantages, SC-CO 2 is not optimal for extracting all krill lipids, particularly phospholipids (PLs) (Yamaguchi et al.,  1986 ). Nevertheless, extracting lipids using SC-CO 2 yields good quality as well as more thermally stable proteins from krill compared to the traditional solvent extraction methods. The addition of ethanol at 5 to 20% in SC-CO2 could enhance PL solubility, thereby improving lipid recovery. However, because ethanol is liquid at ambient temperature, use it not be ideal. on the other hand, for commercial of extraction by supercritical fluid remains limited because of the restricted processing capacity and expensive high-pressure equipment (Bruheim et al.,  2018 ).

Although it works at lower pressure and temperature levels than supercritical extraction, subcritical fluid extraction has many of the same benefits. Liu et al. ( 2015 ) reported that propane as well as butane are primary subcritical fluids used in extraction due to their colorless nature also easy removal from the extracted products. Extracting krill oil using subcritical butane at 30 °C and 0.3—0.8 MPa conditions (Xie et al.,  2017 ) yielded similar oil quantity and quality as hexane but in a faster process with less solvent usage. A study by Sun et al. ( 2018 ) showed that KO extracted with subcritical butane contained great levels of tocopherols also astaxanthin while maintaining lower oxidation level compared to solvent extraction methods. However, similar to supercritical fluid extraction is also not yet cost-effective for routine applications.

Health benefits of Antarctic krill oil

Krill oil contains a number of nutrients and bioactives like C20:5, C22:6, PLs, astaxanthin, vitamin A, also tocopherols (vitamin E), all of which contribute to human health support. Multiple research studies have examined the potential health benefits of Antarctic krill oil, encompassing cardiovascular disease prevention, anti-inflammatory activities, potential anti-cancer properties, effects on diabetes and obesity, neuroprotection, and benefits for women's physiology. These findings are summarized in Table  6 .

Cardiovascular health

CVD is recognized as a significant worldwide health challenge and a leading cause of mortality among adults and the elderly. Research by Harris et al. ( 1988 ), also Rizos et al. ( 2012 ) has indicated that incorporating n-3PUFAs into the diet can help mitigate CVD risks. Fish oil consumption, for having a high n-3 PUFA content, is widely acknowledged for its positive impact on CVD prevention. Studies are presently underway to investigate any possible connection between the use of KO and CVD prevention.

Elevation of triacylglycerols (TAG), total cholesterol (TC), also low-density lipoprotein cholesterol (LDL-C) usually linked to increased risk of CVD disease and are commonly considered as CVD risk indicators. Papakonstantinou et al. ( 2013 ) have demonstrated this association. Several studies (Batetta et al.,  2009 ; Hals et al.,  2017 ; Sun et al.,  2017b ; Zhu et al.,  2008 ) utilizing animal models have assessed the impact KO on CVD risk factor in both tissues and blood. Through eight weeks feeding trial, where mice were supplemented with 1.25, 2.50, or 5.0% KO in their diet, significant reductions in hepatic TAG and TC levels were observed, along with a decrease in serum TAG levels in mice fed with diet contains lots of fat (Tandy et al.,  2009 ). Additionally, in the diet that has a highest dosage of 5%, rise in serum adiponectin level was noted in mice fed with krill oil, supporting its anti-atherogenic properties (Lu et al.,  2008 ).

In another investigation, revealed that KO supplementation (5% in the diet) caused the drop in serum LDL-C after twelve weeks (0.45 mol/L) and TC (reaching 2.50 mol/L) levels in mice fed with diet contains lots of fat, compared to control group (0.65 mol/L, 3.70 mol/L, respectively). Additionally, Zhu et al. ( 2008 ) also Batetta et al. ( 2009 ) showed that KO can lower TAGs, TC, as well as LDL levels in mice with metabolic dysfunction induced by diet contains lots of fat. Similar findings observed in a separate study, using cynomolgus monkeys as a model Hals et al. ( 2017 ), where KO effectively improved various CVD risk factors, including HDL-C, LDL, TC, TAG, apolipoprotein, as well as A1, apolipoprotein B100 in dyslipidemic nonhuman primates with diabetes type 2.

Further support for the preventive effects of KO against CVD has emerged from human clinical trials conducted by, Cicero et al. ( 2016 ), also Rundblad et al. ( 2017 ). Bunea et al. ( 2004 ) examined the relationship between the consumption of KO and level of lipid in blood in 120 hyperlipidemic patients with moderately very high level of TC as well as high TAGs. Patients receiving 1- 3 g/day of KO for 3 months exhibited significantly elevated levels of HDL also decreased levels of glucose in blood, TC, LDL, as well as TAG compared to others were given placebo. In a similar vein, Berge et al. ( 2014 ) noted reduced risk of CVD in 300 adults with extremely high or high fasting serum TAG levels after consuming KO capsules. An about 10 percentage reduction in serum TAG level (relative to the placebo group) was observed in subjects administered krill oil at doses ranging from 0.5 – 4.0 g/day for 3 months. Similar improvements in lipid profiles following KO treatment were observed in overweight subjects as well as healthy individuals with fasting serum TAG levels range from 1.3 to 4.0 mmol/l (Rundblad et al.,  2017 ).

Anti-inflammatory properties

Chronic inflammation is strongly linked to numerous illnesses, including inflammatory bowel disease, asthma, psoriasis, and rheumatoid arthritis (Barnes and Karin,  1997 ). Additionally, systemic inflammation might be contributed to development of exacerbated like atherosclerosis, obesity, cachexia, osteoporosis, as well as anorexia (Gan,  2004 ; Monteiro and Azevedo,  2010 ). Hence, it is crucial to focus on controlling inflammation for overall health improvement. The anti-inflammatory properties of KO have been validated through in vivo as well as vitro studies, as detailed in Table  6 .

In laboratory experiments, it has been demonstrated that krill oil can markedly reduce the tumor necrosis factor α (TNF-α). This reduction is achieved by blocking the attachment of lipopolysaccharide (LPS) to toll-like receptor 4 (TLR4) (Bonaterra et al.,  2017 ) in LPS induced inflammatory human acute monocytic leukemia cell line THP-1, in a manner that depends on the dosage of KO used. At a concentration of 49 μg/ml in the medium, KO totally prevented the irrevocable of LPS to TLR4 and decreased TNF-α production by 75%. Similarly, Batetta et al. ( 2009 ) observed reduced TNF-α release in LPS-treated peritoneal macrophages from obese Zucker rats, given KO supplements in their diets compared to control. The n-3PUFAs induced modifications in the endocannabinoid (EC) system which affects these anti-inflammatories.

The endocannabinoids (ECs) derived from n-3PUFAs have been shown to possess anti-inflammatory properties (Calder,  2009 ). Additionally, KO effectively reduced the mRNA expression levels of pro-inflammatory cytokines such as interleukin-8 (IL-8) and TNF-α in inflammatory cells exposed to LF82 bacteria or cytomix. Treatment with about 250 (mg/L) of krill oil in the medium also inhibited bacterial adhesion/invasion in epithelial cells as well as promoted wound healing. Those findings provide support for the health benefits associated with KO, particularly in the process of mitigating epithelial restitution and enhancing intestinal barrier integrity (Costanzo et al.,  2016 ).

The main focus of in vivo investigations has been on investigating the anti-inflammatory effects of krill oil on conditions such as arthritis or colitis in both humans and mice, as summarized in Table  6 . One study investigated the anti-inflammatory properties of KO using an experimental model of collagen-induced arthritis in mice (DBA/1) (Ierna et al.,  2010 ). They found that administering KO at a daily dosage approximately 0.45 g of C20:5 and C22:6/100 g of diet for 8 weeks (in mice) improved arthritis pathology. This improvement was evidenced by cartilage erosion, synovial membrane thickening, and reductions in cell influx. Moreover, krill oil demonstrated potential in mitigating inflammation in a rat model of colitis. Rats supplemented with KO at amount of 4.9% in diet for one month exhibited preserved colon length and favorable changes in prostaglandin (PG) and interleukin (IL) levels associated with inflammation.

The plasma level of CRP notably increases during inflammatory states and serves as a marker for multiple forms of inflammation (Young et al.,  1991 ). Deutsch ( 2007 ) observed that everyday intake of 300 mg of KO over 2 weeks led to a significant decrease in CRP concentration and relief of arthritic symptoms in patients with chronic inflammatory conditions. Similarly, another study reported that supplementing with 500 mg of KO two time a day for one month resulted in a significant reduction in high-sensitivity CRP levels in plasma, decreased from 2.15 to 0.43 mg/L in overweight subjects (Cicero et al.,  2016 ).

Anti-cancer

Cancer has emerged as a primary cause of death worldwide in both developed and developing nations (Jemal et al.,  2011 ) The worldwide prevalence of cancer is steadily increasing due to factors such as population growth, aging, as well as lifestyle habits, including smoking, also food rich in fat, sugar, and salt (Torre et al.,  2015 ) While chemotherapy as well as radiotherapy known as crucial in cancer treatment to slow down disease progression or inducing apoptosis to halt tumor formation, they are often come with undesirable consequences like diarrhea, myelosuppression, mucositis, as well as dermatitis (Jayathilake et al.,  2016 ). There is an increasing interest in exploring the potential of dietary factors to modulate apoptosis as a means of anticancer therapy (Block et al.,  1992 ). Although there are relatively few experimental studies examining the in vivo anticancer effect of KO in the literature, several in vitro studies have evaluated its impact on the growth of certain cancer cell lines (Xie et al.,  2019 ).

Colon cancer ranks as the second most common cause of cancer related deaths the United States (Jemal et al.,  2005 ). Research indicates that KO exhibits dose- duration of treatment effects on the growth of colon cancer cells, specifically SW480 cells (Jemal et al.,  2005 ). Application of KO at dose of 20 μg/ml in Dulbecco’s modified Eagle’s medium for two days led to a 29.9% suppression of SW480 cell growth (Zhu et al.,  2008 ). Jayathilake et al. ( 2016 ) investigated impact of KO free fatty acid (FFA) extracts on cell proliferation and apoptosis in three human colon adenocarcinoma cell lines. Treatment use 0.12 μL of FFA from KO in 100 μl of DMEM for two inhibited the proliferation of HCT-15 as well as SW-480 cells. Additionally, The FFA extract elicited markedly elevated levels of apoptosis in all three colon cell lines (Su et al.,  2018 ) compared to control. Furthermore, the anticancer activity of FFA that extracted from KO was validated in human osteosarcoma cells, where the inhibitory effect of 1.89 μM of FFA from KO comparable to 0.5 to 1.0 μM of doxorubicin which is commonly used anticancer drug (Su et al.,  2018 ).

Zheng et al. ( 2017 ) conducted research to isolate and identify the trans (E) -configuration of certain FAs detected in KO, such as C20:5 and C22:6. They discovered that these FAs exhibited significantly stronger inhibitory effects on the growth of various cancer cell lines (including K562, PC-3, HL60, MCF-7, and U937) compared to C20:5 and C22:6 from FO. Additionally, astaxanthins as well as tocopherols have shown anti-cancer effects (Constantinou et al.,  2008 ; Rao et al.,  2013 ). It is plausible that the combined effects of the bioactive compounds in KO contribute to its potent anti-cancer capabilities. However, further in vivo studies are necessary to elucidate the underlying molecular mechanisms and validate these anti-cancer effects.

Anti-diabetic and anti-obesity effects

Imbalanced intake of energy could disrupt the endocannabinoid (EC) system, leading to excessive accumulation of visceral fat and reduced release of adiponectin, thereby the chances of type 2 diabetes and obesity. Anandamide (AEA) and 2-arachidonoylglycerol (2-AG) are the primary ECs studied, known for their roles in regulating fat as well as glucose metabolism (Di Marzo,  2008 ). Obese people's tissues have been found to contain elevated quantities of 2-AG and AEA (Batetta et al.,  2009 ; Di Marzo et al.,  2010 ). Diets supplemented with KO have been shown to reduce the concentration of AEA as well as 2-AG in various tissues, including the kidneys, heart, as well as adipose tissues in high fat-fed C57BL/6 mice (Piscitelli et al., 2011 ). Furthermore, krill oil diets decreased body weight gain in obese mice (Sun et al.,  2017b ; Yang et al.,  2016 ) also hyperlipidemic rats (Zhu et al.,  2008 ). The n-3PUFAs diminished the biosynthesis of arachidonic acid and its integration into phospholipids, possibly decreasing the quantity available of biosynthetic precursors for anandamide as well as 2-arachidonoylglycerol (Matias et al.,  2008 ). Effect of the anti-obesity associated with KO are likely due to its high content of n-3 PUFAs. Maki et al. ( 2009 ) also, observed that one month of KO supplementation at amount of 2 g/day led to increased the plasma level of C20:5 and C22:6 in obese as well as overweight individuals.

Insulin resistance caused by fat is a prevalent issue. Ivanova et al. ( 2015 ) demonstrated that consuming a diet supplemented with KO, has about 600 mg of n-3PUFAs daily for one month, resulted in reduced the fasting blood glucose levels also improved the glucose tolerance in obese rabbits (New Zealand with rabbits). Similarly, healthy subjects experienced a decrease in fasting blood glucose after consuming about 4 g per day of KO for two months, indicating its potential as an anti-diabetic agent (Rundblad et al.,  2017 ) suggested that n-3PUFAs from KO had improved insulin sensitivity as well as secretion and altered the expression level of key enzymes involved in β-oxidation and lipogenesis in muscles as well as liver (Ivanova et al.,  2015 ).

Neuroprotective effects

Alzheimer’s disease (AD) is a neurological condition that progresses over time, frequently seen in the elderly (Francis et al.,  1999 ). It manifests as a gradual decline in cognitive function, often accompanied by behavioral changes like wandering, aggression as well as depression, significantly affecting both patients and their caregivers' quality of life. Various studies in animal and human models have explored the neuroprotective properties of KO (Table  6 ).

Using the Aversive Light Stimulus Avoidance Test (ALSAT), the Unavoidable ALSAT, as well as the Forced Swimming Test (FST), rats were treated with 1.25 g/100 g of food containing KO for around two months exhibited a positive impact on memory processes and learning. Additionally, rats supplemented with krill oil showed elevated expression levels of mRNA for brain-derived neurotrophic factor (Bdnf), a gene linked to neuronal growth also differentiation in the hippocampus. These results are consistent with those of Tome-Carneiro ( 2018 ). Moreover, Cheong et al. ( 2017 ) noted a correlation between KO consumption and alterations in the proteome of aged mice's brain tissues. They observed that giving elderly mice KO at doses ranging from 150 to 600 mg/kg daily for seven weeks dramatically changed the expression levels of 28 different proteins in their brain regions. Notably, the group receiving KO showed a significant increase in the expression levels of Celsr3 as well as Ppp1r1b mRNA, that linked to working memory, brain development, and learning acquisition. KO was found to enhance oxidative stress biomarkers in serum like malondialdehyde (MDA) as well as superoxide dismutase (SOD). Furthermore, Li et al. ( 2018 ) demonstrated that KO has a beneficial impact on Alzheimer's disease in animal model using senescence-accelerated prone mouse strain 8 mice, providing evidence for its preventive properties. Over three months, using supplement have 1% of KO in the diet effectively improved the memory capabilities and learning while alleviating nervousness in SAMP8 old mice, as determined through the open field test as well as Morris water. The accumulation of β-amyloid (Aβ) is implicated in cognitive decline as well as AD pathology. Moreover, KO mitigated the accumulation of Aβ in the hippocampus, along with reducing oxidative stress in the brain.

The decline in estrogen levels among aging women may heighten risk of AD (Janicki and schupf,  2010 ). Research conducted on ovariectomized rats have revealed significantly reduced levels of serotonin, insulin growth factor, estrogen, and dopamine, alongside alterations in the gene expression of amyloid precursor protein, glycogen synthase-3beta, Bdnf, as well as selective AD indicator-1, all of which are associated with AD in rats. Mansour et al. ( 2017 ) observed that supplementation with KO at 200 mg/kg per day for 8 weeks resulted in the normalization of all these parameters in ovariectomized rats, suggesting the effects of KO in inhibition of AD development and neurodegeneration in old women.

Human studies have also provided evidence supporting the favorable impacts of KO on cognitive function. Oxyhemoglobin, which is mostly linked to cerebral blood flow, acts as a measure of regional brain function activation during cognitive tasks (Hibino et al.,  2013 ). The P300 event-related potential is a cognitive component utilized to objectively evaluate neuroelectrical activity-linked cognitive behaviors as well as activities (Alvarenga et al.,  2005 ; Hansenne,  2000 ). Konagai et al. ( 2013 ) assessed the effects of dietary KO on cognitive function in more than 45 healthy elderly males during calculation and memory tasks by monitoring oxyhemoglobin variations as well as P300 event-related potential components in the cerebral cortex. Following the administration of KO about 1.98 g/day for three months, subjects exhibited important alterations in oxyhemoglobin concentration during working memory tasks as well as reduced differential value of P300 latency during calculation tasks compared to the control. These findings demonstrate the positive effects of KO in enhancing cognitive function among the elderly.

Women’s physiology

Premenstrual syndrome (PMS) is a cyclic disorder commonly experienced by young as well as middle aged women during the luteal phase of menstruation, characterized by psychological, emotional, also behavioral symptoms (Dickerson et al.,  2003 ; Stevinson and Ernst,  2001 ). While the exact cause of PMS is unclear, around 75% of women encounter some PMS symptoms (Barnhart et al.,  1995 ) during their reproductive years. Various dietary supplements, such as multivitamin/mineral supplements, vitamins (A, E, and B 6 ), and minerals (magnesium and calcium) have been suggested for alleviating certain PMS symptoms (Bendich,  2000 ; Dickerson et al.,  2003 ). Additionally, it has been noted thatn-3 PUFAs (Sohrabi et al.,  2013 ) may help reduce both psychiatric as well as somatic of PMS.

Krill oil as source of n-3 PUFAs as well as vitamins (E and A), has demonstrated beneficial effects in managing both emotional and physical symptoms associated with PMS. Individuals who took KO soft gels for three menstrual cycles reported decreased usage of pain relievers for menstrual pain as well as lower scores on the self assessment questionnaire for PMS, based on the American College of Obstetricians and Gynecologists (ACOG) diagnostic criteria for PMS, which ranged from zero to no symptoms to ten for unbearable. Moreover, KO exhibited greater efficacy in managing PMS and dysmenorrhea compared to FO (Sampalis et al.,  2003 ). This superior performance is attributed to the unique profile of krill oil which includes a combination of phospholipids, n-3 PUFAs as well as antioxidative substances. The n-3 PUFAs linked with PLs in KO are believed to offer higher bioavailability than TAGs in FO, thus potentially playing a more active role in regulating emotional symptoms (Sampalis et al.,  2003 ; Schuchardt et al.,  2011 ). Nevertheless, additional inquiries are required to elucidate the underlying mechanisms involved.

Postmenopausal women commonly encounter cerebrovascular dysfunction as a result of estrogen deficiency (Serock et al.,  2008 ). Key regulatory components like KCa channels, KATP channels, as well as Na + /Ca 2+ exchanger 1 (NCX1) are crucial in maintaining cerebral blood flow autoregulation; however, they are susceptible to disruption in cases of ovarian dysfunction. In a study involving ovariectomized rats, the administration of KO (providing 182 mg EPA + 118 mg DHA sourced from KO) for a duration of 2 weeks was found to beneficially regulate the expression of NCX1 mRNA, KCa channels, as well as KATP channels in the basilar artery, leading to an enhancement in cerebral blood circulation. The results indicate that KO may serve as a beneficial supplement for women who gone through menopause (Sakai et al.,  2014 ).

Effect on depression

The impact of KO supplementation on cognitive function as well as depression like behaviors was assessed through both preclinical and clinical studies. One of the initial investigations in this area involved a one and half month trial conducted on rats which received either krill oil at a dosage of 0.2 g/rat per day, imipramine at 20 mg/kg per day (utilized as a reference drug for antidepressant effects), or a placebo. Following the treatment period, cognitive abilities were evaluated using the ALSAT, while the potential antidepressant effects were assessed through FST and the Unavoidable Aversive Light Stimulus Test (UALST). The rats treated with krill oil demonstrated a notable ability to differentiate between active as well as inactive levers in the ALSAT test from the initial day of training. Furthermore, rats receiving KO and imipramine showed high improvements in behavioral aspects, including reduced level in the UALST test from the third day onwards as well as decreased immobility time in the FST test. Moreover, study examined the expression of Bdnf (Zadeh-Ardabili et al.,  2019 ), which was observed to be elevated in the hippocampus of rats treated with KO.

Zadeh-Ardabili et al. ( 2019 ) conducted a study involving mice subjected to treatments with FO, KO, vitamin B12, imipramine, or over two weeks, beginning after seven days of exposure to the Chronic Unpredictable Stress (CUS) paradigm overnight procedure. During the CUS procedures, mice were exposed to stress overnight using 10W LED light at a frequency of 15 Hz for 12 h over 21 days. The potential therapeutic benefits of the treatments on depression were assessed using the tail suspension test (TST) as well as FST. After the animals were sacrificed, oxidation markers were assessed in the brain tissue. Both KO and FO were high decreased immobility factors as well as increased climbing and swimming time, similar to the effects observed with imipramine. Moreover, both KO and FO reduced levels of MDA and hydrogen peroxide, decreased catalase activity, increased glutathione peroxidase levels, and increased superoxide dismutase activities as well as glutathione levels in hippocampal tissue (Mendoza et al.,  2018 ).

In another pre-clinical study, Mendoza et al. ( 2018 ) examined the impact of krill oil on restraint stress in mice following reduced mobility. The study investigated the effects of KO on the response to restraint stress in mice after experiencing limited mobility. Following 14 days of handling and acclimation, the mice were immobilized for one month, and then behavioral test took place for seven days. Over the course of the one month study, mice orally received either PBS, nicotine derivative cotinine about 5 mg, or combination of cotinine with 140 mg/kg of KO. Although cotinine by itself reduced the loss of memory deficiencies and the behaviors associated with anxiety and depression, cotinine in combination with KO proved to be more beneficial. This underscores the role of KO in mechanisms related to depression (van der Wurff et al.,  2016 ). Subsequently, these authors conducted, over the course of one year employed a double-blind, randomized, and controlled methodology to investigate the impact of KO supplementation on the learning and cognitive function of teenagers, mental well-being, also visual processing. The study involved 260 adolescents aged 13 to 15 years, divided into two cohorts. The first cohort initially received 400 mg/day of C20:5 and C22:6 or a placebo, with dose increased to 800 mg of C20:5 and C22:6 per day after 12 weeks. The second cohort started directly with 800 mg of C20:5 and C22:6 per day (Zheng et al.,  2017 ). The efficacy of these treatments was evaluated through omega-3 index finger-prick blood measurements, using the Centre for Epidemiologic Studies Depression Scale, and the Rosenberg Self-Esteem questionnaire. The authors did not find any evidence supporting the effectiveness of KO in reducing depressive feeling (van der Wurff et al.,  2020 ).

Exercise and bodily performance

Krill oil enhances exercise performance and reduces oxidative stress and inflammation, leading to the initiation of several clinical trials. One of the initial studies involved a small double blind trial conducted on 16 members of Polish National Rowing Team. Participants were divided into 2 groups, first one had received 1 g of KO per day for one and half month, while the other received a placebo. Various parameters were assessed before, after 1 min, and after 24 h of exercise, with the latter representing maximum effort after rowing 2000 m. Exercise increased levels of certain markers including superoxide dismutase, TNF-α, and TBARS, which indicate lipid oxidation. While there were generally no significant differences between the control and KO groups in most parameters, during the recovery period, TBARS levels kept rising in the control, while the KO group displayed notably lower levels of lipid oxidation (Da Boit et al.,  2015 ). Thus, krill oil supplementation helped reducing exercise-induced free radical mediated injuries.

The effects of KO on exercise performance as well as post effort immune function were investigated in small randomized clinical trial involving 37 athletes (Average age of 25.8 years). Participants, in two groups, first one has receiving about 2 g/day of KO for one and half month. On the other hand, second group receiving a placebo. A cycling time test was conducted before and after the supplementation period, during which blood samples were collected for five time test as follow: prior to supplementation, immediately post-exercise, after 1 h, after 3 h, as well as at rest. The results showed that after one and half month of supplementation, athletes who received krill oil showed significant increases in peripheral blood mononuclear cell IL-2 production and natural killer cell cytotoxic activity 3 h post exercise (Georges et al.,  2018 ). Based on these findings, additional research examined the potential of KO to increase body mass. In vitro experiments utilized C2C12 rat myoblasts (skeletal muscle) treated with KO, PC derived from soy, or control. Only KO was capable of stimulating the mTOR pathway. Subsequently, a double-blind, placebo controlled clinical trial was conducted on resistance-trained athletes who received 3 g per day of KO or placebo over two months resistance trained program. The findings showed no significant differences in complete blood count, comprehensive metabolic panel, and urine analysis between the two groups. Nevertheless, KO supplementation resulted in an increase in lean body mass by approximately 2.2% compared to the baseline (Barenie et al.,  2022 ).

In another study, a specific mixed formulation called ESPO-572®, consisting of 75% PCSO-524® (mussel oil) at 600 mg/day and 25% KO for 26 days, was found to alleviate exercise-induced muscle damage as well as cytokine-induced tissue degradation in untrained men who underwent a running test As choline is associated with maintaining muscle function and exercise performance, a reduction in choline level may occur after high resistance or high intensity exercise. To investigate whether krill oil could offer any protective effect against this choline loss, Storsve et al. ( 2020 ) conducted a study involving 47 triathletes randomly divided into 2 groups. First one received 4 g per day of KO called SuperbaBoost™ for 40 days leading up to the race, while the other group received a placebo. Blood test results showed high decrease in choline levels after the race; however, athletes in KO group had higher choline levels compared to those in placebo group. Thus, krill oil supplementation may help mitigating the negative effects on exercise performance, particularly during high-resistance activities by preventing choline decline (Ibrahim et al.,  2016 ).

Comparison with fish oil (bioavailability and bioaccessibility)

When KO and FO supplements compared, it has been found that KO had more pronounced effects in managing cognitive function (Konagai et al.,  2013 ), PMS (Sampalis et al.,  2003 ), and hyperlipidemia (Bunea et al.,  2004 ). Several studies carried out by Ulven et al. ( 2011 ), Rossmeisl et al. ( 2012 ), Ramprasath et al. ( 2013 ), and Laidlaw et al. ( 2014 ) attributed the superior performance of KO to its higher bioavailability of EPA and DHA in phospholipid form. However, these studies failed to administer identical doses of EPA and DHA from KO and FO as well as the existing differences arising from other components present, thus additional work is needed to verify these findings. In this connection, problems noted may be exemplified by the work of Ramprasath et al. ( 2013 ) who found that KO can elevate plasma n-3 PUFA concentrations more effectively than fish oil, but used daily amount of EPA and DHA of 777 mg for KO group and about 664 mg for FO group. In addition, Ulven et al. ( 2011 ) used dosages of EPA and DHA of around 543 mg for KO group with ratio of EPA (1.74) also 864 mg for FO with ratio o EPA/DHA (1.12). As already mentioned, most studies did not account for the minor components present in two olis (Bunea et al.,  2004 ; Konagai et al.,  2013 ; Sampalis et al.,  2003 ). Meanwhile, Köhler et al. ( 2015 ) reported that EPA and DHA in krill meal had a lower bioavailability compared to KO, but similar to FO. Thus, EPA and DHA in phospholipid form alone may not fully explain the superior performance of KO.

The exact mechanism of superior effect of KO compared to FO effect remains elusive. This may arise from the fact that KO contain high concentration of various biological active components such as astaxanthin, tocopherols, and vitamin A, hence the effects may be multifactorial. Therefore, more investigation is required to comprehend the underlying mechanisms and to use better controlled human trials to determine the performance as well as the efficacy of KO and FO after prolonged administration (Tillander et al.,  2014 ).

Applications and future perspectives of KO

Production and use of KO has emerged as a highly appealing within the food industry. Recognized as unique food ingredient, KO shows promise for applications in food, pharmaceutical, and nutraceuticals because of its wide range of health advantages.

Presently, KO products can be easily obtained as supplements in various forms including capsules, soft gels, tablets, and gummies. There are leading global producers of krill oil, with their products enjoying popularity in European and American health markets. Patents and patent applications for krill oil highlight its potential in preventing inflammation, CVD, PMS, cognitive diseases, and enhancing brain function. Some KO products are formulated with additional beneficial additives including various carotenoids, conjugated linoleic acid, and vitamin D, in order to offer enhanced benefits (Rockway,  2006 ; Derohanes et al.,  2018 ). For instance, a combination of KO, vitamin D, and probiotic Lactobacillus reuteri is proposed to alleviate gut inflammation by promoting epithelial restitution and modulation the gut microbiota (Costanzo et al.,  2018 ). Additionally, intranasal administration of KO along with cotinine (a product found in tobacco) exhibits potential in treating depressive symptoms associated with recurrent associative trama memories in patients with posttraumatic stress disorder (Alvarez-Ricartes et al.,  2018 ).

While numerous studies have explored the various functionalities as well as commercial applications of KO only a limited number have detailed molecular mechanisms involved in its diverse activities. For instance, Xie et al. ( 2018 ) observed significant differences in antioxidant activity among krill oils with distinct chemical compositions. Nevertheless, the majority of studies that examined the health advantages in KO (Table  6 ) do not provide detailed information abut the composition of KO which used in these studies. Therefore, future research must clarify the relationship between the content and health benefits of KO in order to facilitate the development of more specialized and a variety of useful KO products with specific health benefits. This would accelerate broader application of KO by taking advantage of its multicomponent feature.

Krill oil derived from Antarctic krill ( Euphausia superba ) garnered growing interest due to its unique benefits. This contribution explored the chemical composition, health benefits, extraction methods as well as some of current application of KO. As noted, KO is abundant in EBA, DHA, tocopherols, vitamin A, astaxanthin, and flavonoids, all of which offer significant health benefits. Notably, a substantial portion of EPA and DHA in KO exists in the phospholipid (PL) form, prompting extensive research comparing its benefits with those of fish oil. Four primary extraction technologies are employed in KO production, namely solvent extraction, solvent-free extraction, enzyme-assisted pretreatment extraction, as well as super/subcritical fluid extraction, each with its own advantages as well as limitations.

At present, commercially available KO products are utilized as supplements. Numerous researches inclusive in vivo as well as in vitro experiments, have indicated that KO offers a range of benefits, including CVD, anti-inflammatory, anti-cancer, anti-obesity and antidiabetic effects, neuroprotective effects, benefits for women’s physiology, and effect on depression. In spite of that, the precise mechanisms underlying these effects require further investigation. It is widely acknowledged that the functionalities of product are closely linked to its chemical composition. Hence, further research is imperative to help understanding of the intricate relationship between the chemical composition and functional properties of KO. This enhanced knowledge would not only enable the refinement of extraction techniques but also empower the creation of a wider range of KO products to address the requirements of specific markets and health objectives.

Availability of data and materials

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Fereidoon Shahidi thanks the NSERC of Canada for Grant. Abrehem abad thank Libyan Ministry of Higher Education & Scientific Research provided a scholarship as well as thnk Food and Drug Control Center- Libya.

The author FS thank the Natural Science and Engineering Research Council (NSERC) of Canada for support in the form of a Discovery Grant (RGPIN-2016–04468). The author AA achnowledges a scholarship from the Libyan Ministry of Higher Education and Scientific Research as well as Food and Drug Control Center – Libya.

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Shahidi, F., Abad, A. Why is Antactic krill (Euphasia superba) oil on the spotlight? A review. Food Prod Process and Nutr 6 , 88 (2024). https://doi.org/10.1186/s43014-024-00260-6

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DOI : https://doi.org/10.1186/s43014-024-00260-6

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