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• Longitudinal Study | Definition, Approaches & Examples

Longitudinal Study | Definition, Approaches & Examples

Published on 5 May 2022 by Lauren Thomas . Revised on 24 October 2022.

In a longitudinal study, researchers repeatedly examine the same individuals to detect any changes that might occur over a period of time.

Longitudinal studies are a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.

While they are most commonly used in medicine, economics, and epidemiology, longitudinal studies can also be found in the other social or medical sciences.

Table of contents

How long is a longitudinal study, longitudinal vs cross-sectional studies, how to perform a longitudinal study, advantages and disadvantages of longitudinal studies, frequently asked questions about longitudinal studies.

No set amount of time is required for a longitudinal study, so long as the participants are repeatedly observed. They can range from as short as a few weeks to as long as several decades. However, they usually last at least a year, oftentimes several.

One of the longest longitudinal studies, the Harvard Study of Adult Development , has been collecting data on the physical and mental health of a group of men in Boston, in the US, for over 80 years.

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The opposite of a longitudinal study is a cross-sectional study. While longitudinal studies repeatedly observe the same participants over a period of time, cross-sectional studies examine different samples (or a ‘cross-section’) of the population at one point in time. They can be used to provide a snapshot of a group or society at a specific moment.

Both types of study can prove useful in research. Because cross-sectional studies are shorter and therefore cheaper to carry out, they can be used to discover correlations that can then be investigated in a longitudinal study.

If you want to implement a longitudinal study, you have two choices: collecting your own data or using data already gathered by somebody else.

Using data from other sources

Many governments or research centres carry out longitudinal studies and make the data freely available to the general public. For example, anyone can access data from the 1970 British Cohort Study, which has followed the lives of 17,000 Brits since their births in a single week in 1970, through the UK Data Service website .

These statistics are generally very trustworthy and allow you to investigate changes over a long period of time. However, they are more restrictive than data you collect yourself. To preserve the anonymity of the participants, the data collected is often aggregated so that it can only be analysed on a regional level. You will also be restricted to whichever variables the original researchers decided to investigate.

If you choose to go down this route, you should carefully examine the source of the dataset as well as what data are available to you.

Collecting your own data

If you choose to collect your own data, the way you go about it will be determined by the type of longitudinal study you choose to perform. You can choose to conduct a retrospective or a prospective study.

• In a retrospective study , you collect data on events that have already happened.
• In a prospective study , you choose a group of subjects and follow them over time, collecting data in real time.

Retrospective studies are generally less expensive and take less time than prospective studies, but they are more prone to measurement error.

Like any other research design , longitudinal studies have their trade-offs: they provide a unique set of benefits, but also come with some downsides.

Longitudinal studies allow researchers to follow their subjects in real time. This means you can better establish the real sequence of events, allowing you insight into cause-and-effect relationships.

Longitudinal studies also allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals.

Prospective longitudinal studies eliminate the risk of recall bias , or the inability to correctly recall past events.

Disadvantages

Longitudinal studies are time-consuming and often more expensive than other types of studies, so they require significant commitment and resources to be effective.

Since longitudinal studies repeatedly observe subjects over a period of time, any potential insights from the study can take a while to be discovered.

Attrition, which occurs when participants drop out of a study, is common in longitudinal studies and may result in invalid conclusions.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

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Longitudinal Study: Overview, Examples & Benefits

By Jim Frost Leave a Comment

What is a Longitudinal Study?

A longitudinal study is an experimental design that takes repeated measurements of the same subjects over time. These studies can span years or even decades. Unlike cross-sectional studies , which analyze data at a single point, longitudinal studies track changes and developments, producing a more dynamic assessment.

A cohort study is a specific type of longitudinal study focusing on a group of people sharing a common characteristic or experience within a defined period.

Imagine tracking a group of individuals over time. Researchers collect data regularly, analyzing how specific factors evolve or influence outcomes. This method offers a dynamic view of trends and changes.

Consider a study tracking 100 high school students’ academic performances annually for ten years. Researchers observe how various factors like teaching methods, family background, and personal habits impact their academic growth over time.

Researchers frequently use longitudinal studies in the following fields:

• Psychology: Understanding behavioral changes.
• Sociology: Observing societal trends.
• Medicine: Tracking disease progression.
• Education: Assessing long-term educational outcomes.

Learn more about Experimental Designs: Definition and Types .

Duration of Longitudinal Studies

Typically, the objectives dictate how long researchers run a longitudinal study. Studies focusing on rapid developmental phases, like early childhood, might last a few years. On the other hand, exploring long-term trends, like aging, can span decades. The key is to align the duration with the research goals.

Implementing a Longitudinal Study: Your Options

When planning a longitudinal study, you face a crucial decision: gather new data or use existing datasets.

Option 1: Utilizing Existing Data

Governments and research centers often share data from their longitudinal studies. For instance, the U.S. National Longitudinal Surveys (NLS) has been tracking thousands of Americans since 1979, offering a wealth of data accessible through the Bureau of Labor Statistics .

This type of data is usually reliable, offering insights over extended periods. However, it’s less flexible than the data that the researchers can collect themselves. Often, details are aggregated to protect privacy, limiting analysis to broader regions. Additionally, the original study’s variables restrict you, and you can’t tailor data collection to meet your study’s needs.

If you opt for existing data, scrutinize the dataset’s origin and the available information.

Option 2: Collecting Data Yourself

If you decide to gather your own data, your approach depends on the study type: retrospective or prospective.

A retrospective longitudinal study focuses on past events. This type is generally quicker and less costly but more prone to errors.

The prospective form of this study tracks a subject group over time, collecting data as events unfold. This approach allows the researchers to choose the variables they’ll measure and how they’ll measure them. Usually, these studies produce the best data but are more expensive.

While retrospective studies save time and money, prospective studies, though more resource-intensive, offer greater accuracy.

Learn more about Retrospective and Prospective Studies .

Advantages of a Longitudinal Study

Longitudinal studies can provide insight into developmental phases and long-term changes, which cross-sectional studies might miss.

These studies can help you determine the sequence of events. By taking multiple observations of the same individuals over time, you can attribute changes to the other variables rather than differences between subjects. This benefit of having the subjects be their own controls is one that applies to all within-subjects studies, also known as repeated measures design. Learn more about Repeated Measures Designs .

Consider a longitudinal study examining the influence of a consistent reading program on children’s literacy development. In a longitudinal framework, factors like innate linguistic ability, which typically don’t fluctuate significantly, are inherently accounted for by using the same group of students over time. This approach allows for a more precise assessment of the reading program’s direct impact over the study’s duration.

Collectively, these benefits help you establish causal relationships. Consequently, longitudinal studies excel in revealing how variables change over time and identifying potential causal relationships .

Disadvantages of a Longitudinal Study

A longitudinal study can be time-consuming and expensive, given its extended duration.

For example, a 30-year study on the aging process may require substantial funding for decades and a long-term commitment from researchers and staff.

Over time, participants may selectively drop out, potentially skewing results and reducing the study’s effectiveness.

For instance, in a study examining the long-term effects of a new fitness regimen, more physically fit participants might be less likely to drop out than those finding the regimen challenging. This scenario potentially skews the results to exaggerate the program’s effectiveness.

Maintaining consistent data collection methods and standards over a long period can be challenging.

For example, a longitudinal study that began using face-to-face interviews might face consistency issues if it later shifts to online surveys, potentially affecting the quality and comparability of the responses.

In conclusion, longitudinal studies are powerful tools for understanding changes over time. While they come with challenges, their ability to uncover trends and causal relationships makes them invaluable in many fields. As with any research method, understanding their strengths and limitations is critical to effectively utilizing their potential.

Newman AB. An overview of the design, implementation, and analyses of longitudinal studies on aging . J Am Geriatr Soc. 2010 Oct;58 Suppl 2:S287-91. doi: 10.1111/j.1532-5415.2010.02916.x. PMID: 21029055; PMCID: PMC3008590.

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What Is a Longitudinal Study?

Tracking Variables Over Time

Steve McAlister / The Image Bank / Getty Images

The Typical Longitudinal Study

Potential pitfalls, frequently asked questions.

A longitudinal study follows what happens to selected variables over an extended time. Psychologists use the longitudinal study design to explore possible relationships among variables in the same group of individuals over an extended period.

Once researchers have determined the study's scope, participants, and procedures, most longitudinal studies begin with baseline data collection. In the days, months, years, or even decades that follow, they continually gather more information so they can observe how variables change over time relative to the baseline.

For example, imagine that researchers are interested in the mental health benefits of exercise in middle age and how exercise affects cognitive health as people age. The researchers hypothesize that people who are more physically fit in their 40s and 50s will be less likely to experience cognitive declines in their 70s and 80s.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies, a type of correlational research , are usually observational, in contrast with cross-sectional research . Longitudinal research involves collecting data over an extended time, whereas cross-sectional research involves collecting data at a single point.

To test this hypothesis, the researchers recruit participants who are in their mid-40s to early 50s. They collect data related to current physical fitness, exercise habits, and performance on cognitive function tests. The researchers continue to track activity levels and test results for a certain number of years, look for trends in and relationships among the studied variables, and test the data against their hypothesis to form a conclusion.

Examples of Early Longitudinal Study Design

Examples of longitudinal studies extend back to the 17th century, when King Louis XIV periodically gathered information from his Canadian subjects, including their ages, marital statuses, occupations, and assets such as livestock and land. He used the data to spot trends over the years and understand his colonies' health and economic viability.

In the 18th century, Count Philibert Gueneau de Montbeillard conducted the first recorded longitudinal study when he measured his son every six months and published the information in "Histoire Naturelle."

The Genetic Studies of Genius (also known as the Terman Study of the Gifted), which began in 1921, is one of the first studies to follow participants from childhood into adulthood. Psychologist Lewis Terman's goal was to examine the similarities among gifted children and disprove the common assumption at the time that gifted children were "socially inept."

Types of Longitudinal Studies

Longitudinal studies fall into three main categories.

• Panel study : Sampling of a cross-section of individuals
• Cohort study : Sampling of a group based on a specific event, such as birth, geographic location, or experience
• Retrospective study : Review of historical information such as medical records

Benefits of Longitudinal Research

A longitudinal study can provide valuable insight that other studies can't. They're particularly useful when studying developmental and lifespan issues because they allow glimpses into changes and possible reasons for them.

For example, some longitudinal studies have explored differences and similarities among identical twins, some reared together and some apart. In these types of studies, researchers tracked participants from childhood into adulthood to see how environment influences personality , achievement, and other areas.

Because the participants share the same genetics , researchers chalked up any differences to environmental factors . Researchers can then look at what the participants have in common and where they differ to see which characteristics are more strongly influenced by either genetics or experience. Note that adoption agencies no longer separate twins, so such studies are unlikely today. Longitudinal studies on twins have shifted to those within the same household.

As with other types of psychology research, researchers must take into account some common challenges when considering, designing, and performing a longitudinal study.

Longitudinal studies require time and are often quite expensive. Because of this, these studies often have only a small group of subjects, which makes it difficult to apply the results to a larger population.

Selective Attrition

Participants sometimes drop out of a study for any number of reasons, like moving away from the area, illness, or simply losing motivation . This tendency, known as selective attrition , shrinks the sample size and decreases the amount of data collected.

If the final group no longer reflects the original representative sample , attrition can threaten the validity of the experiment. Validity refers to whether or not a test or experiment accurately measures what it claims to measure. If the final group of participants doesn't represent the larger group accurately, generalizing the study's conclusions is difficult.

The World’s Longest-Running Longitudinal Study

Lewis Terman aimed to investigate how highly intelligent children develop into adulthood with his "Genetic Studies of Genius." Results from this study were still being compiled into the 2000s. However, Terman was a proponent of eugenics and has been accused of letting his own sexism , racism , and economic prejudice influence his study and of drawing major conclusions from weak evidence. However, Terman's study remains influential in longitudinal studies. For example, a recent study found new information on the original Terman sample, which indicated that men who skipped a grade as children went on to have higher incomes than those who didn't.

A Word From Verywell

Longitudinal studies can provide a wealth of valuable information that would be difficult to gather any other way. Despite the typical expense and time involved, longitudinal studies from the past continue to influence and inspire researchers and students today.

A longitudinal study follows up with the same sample (i.e., group of people) over time, whereas a cross-sectional study examines one sample at a single point in time, like a snapshot.

A longitudinal study can occur over any length of time, from a few weeks to a few decades or even longer.

That depends on what researchers are investigating. A researcher can measure data on just one participant or thousands over time. The larger the sample size, of course, the more likely the study is to yield results that can be extrapolated.

Piccinin AM, Knight JE. History of longitudinal studies of psychological aging . Encyclopedia of Geropsychology. 2017:1103-1109. doi:10.1007/978-981-287-082-7_103

Terman L. Study of the gifted . In: The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. 2018. doi:10.4135/9781506326139.n691

Sahu M, Prasuna JG. Twin studies: A unique epidemiological tool .  Indian J Community Med . 2016;41(3):177-182. doi:10.4103/0970-0218.183593

Almqvist C, Lichtenstein P. Pediatric twin studies . In:  Twin Research for Everyone . Elsevier; 2022:431-438.

Warne RT. An evaluation (and vindication?) of Lewis Terman: What the father of gifted education can teach the 21st century . Gifted Child Q. 2018;63(1):3-21. doi:10.1177/0016986218799433

Warne RT, Liu JK. Income differences among grade skippers and non-grade skippers across genders in the Terman sample, 1936–1976 . Learning and Instruction. 2017;47:1-12. doi:10.1016/j.learninstruc.2016.10.004

Wang X, Cheng Z. Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest . 2020;158(1S):S65-S71. doi:10.1016/j.chest.2020.03.012

Caruana EJ, Roman M, Hernández-Sánchez J, Solli P. Longitudinal studies .  J Thorac Dis . 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Methodology

• Primary Research | Definition, Types, & Examples

Primary Research | Definition, Types, & Examples

Published on January 14, 2023 by Tegan George . Revised on January 12, 2024.

Primary research is a research method that relies on direct data collection , rather than relying on data that’s already been collected by someone else. In other words, primary research is any type of research that you undertake yourself, firsthand, while using data that has already been collected is called secondary research .

Primary research is often used in qualitative research , particularly in survey methodology, questionnaires, focus groups, and various types of interviews . While quantitative primary research does exist, it’s not as common.

Table of contents

When to use primary research, types of primary research, examples of primary research, advantages and disadvantages of primary research, other interesting articles, frequently asked questions.

Primary research is any research that you conduct yourself. It can be as simple as a 2-question survey, or as in-depth as a years-long longitudinal study . The only key is that data must be collected firsthand by you.

Primary research is often used to supplement or strengthen existing secondary research. It is usually exploratory in nature, concerned with examining a research question where no preexisting knowledge exists. It is also sometimes called original research for this reason.

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Primary research can take many forms, but the most common types are:

• Surveys and questionnaires
• Observational studies
• Interviews and focus groups

Surveys and questionnaires collect information about a group of people by asking them questions and analyzing the results. They are a solid choice if your research topic seeks to investigate something about the characteristics, preferences, opinions, or beliefs of a group of people.

Surveys and questionnaires can take place online, in person, or through the mail. It is best to have a combination of open-ended and closed-ended questions, and how the questions are phrased matters. Be sure to avoid leading questions, and ask any related questions in groups, starting with the most basic ones first.

Observational studies are an easy and popular way to answer a research question based purely on what you, the researcher, observes. If there are practical or ethical concerns that prevent you from conducting a traditional experiment , observational studies are often a good stopgap.

There are three types of observational studies: cross-sectional studies , cohort studies, and case-control studies. If you decide to conduct observational research, you can choose the one that’s best for you. All three are quite straightforward and easy to design—just beware of confounding variables and observer bias creeping into your analysis.

Similarly to surveys and questionnaires, interviews and focus groups also rely on asking questions to collect information about a group of people. However, how this is done is slightly different. Instead of sending your questions out into the world, interviews and focus groups involve two or more people—one of whom is you, the interviewer, who asks the questions.

There are 3 main types of interviews:

• Structured interviews ask predetermined questions in a predetermined order.
• Unstructured interviews are more flexible and free-flowing, proceeding based on the interviewee’s previous answers.
• Semi-structured interviews fall in between, asking a mix of predetermined questions and off-the-cuff questions.

While interviews are a rich source of information, they can also be deceptively challenging to do well. Be careful of interviewer bias creeping into your process. This is best mitigated by avoiding double-barreled questions and paying close attention to your tone and delivery while asking questions.

Alternatively, a focus group is a group interview, led by a moderator. Focus groups can provide more nuanced interactions than individual interviews, but their small sample size means that external validity is low.

Primary research can often be quite simple to pursue yourself. Here are a few examples of different research methods you can use to explore different topics.

Primary research is a great choice for many research projects, but it has distinct advantages and disadvantages.

Advantages of primary research

Advantages include:

• The ability to conduct really tailored, thorough research, down to the “nitty-gritty” of your topic . You decide what you want to study or observe and how to go about doing that.
• You maintain control over the quality of the data collected, and can ensure firsthand that it is objective, reliable , and valid .
• The ensuing results are yours, for you to disseminate as you see fit. You maintain proprietary control over what you find out, allowing you to share your findings with like-minded individuals or those conducting related research that interests you for replication or discussion purposes.

Disadvantages of primary research

Disadvantages include:

• In order to be done well, primary research can be very expensive and time consuming. If you are constrained in terms of time or funding, it can be very difficult to conduct your own high-quality primary research.
• Primary research is often insufficient as a standalone research method, requiring secondary research to bolster it.
• Primary research can be prone to various types of research bias . Bias can manifest on the part of the researcher as observer bias , Pygmalion effect , or demand characteristics . It can occur on the part of participants as a Hawthorne effect or social desirability bias .

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

The 3 main types of primary research are:

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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Longitudinal Study Design

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

A longitudinal study is a type of observational and correlational study that involves monitoring a population over an extended period of time. It allows researchers to track changes and developments in the subjects over time.

What is a Longitudinal Study?

In longitudinal studies, researchers do not manipulate any variables or interfere with the environment. Instead, they simply conduct observations on the same group of subjects over a period of time.

These research studies can last as short as a week or as long as multiple years or even decades. Unlike cross-sectional studies that measure a moment in time, longitudinal studies last beyond a single moment, enabling researchers to discover cause-and-effect relationships between variables.

They are beneficial for recognizing any changes, developments, or patterns in the characteristics of a target population. Longitudinal studies are often used in clinical and developmental psychology to study shifts in behaviors, thoughts, emotions, and trends throughout a lifetime.

For example, a longitudinal study could be used to examine the progress and well-being of children at critical age periods from birth to adulthood.

The Harvard Study of Adult Development is one of the longest longitudinal studies to date. Researchers in this study have followed the same men group for over 80 years, observing psychosocial variables and biological processes for healthy aging and well-being in late life (see Harvard Second Generation Study).

When designing longitudinal studies, researchers must consider issues like sample selection and generalizability, attrition and selectivity bias, effects of repeated exposure to measures, selection of appropriate statistical models, and coverage of the necessary timespan to capture the phenomena of interest.

Panel Study

• A panel study is a type of longitudinal study design in which the same set of participants are measured repeatedly over time.
• Data is gathered on the same variables of interest at each time point using consistent methods. This allows studying continuity and changes within individuals over time on the key measured constructs.
• Prominent examples include national panel surveys on topics like health, aging, employment, and economics. Panel studies are a type of prospective study .

Cohort Study

• A cohort study is a type of longitudinal study that samples a group of people sharing a common experience or demographic trait within a defined period, such as year of birth.
• Researchers observe a population based on the shared experience of a specific event, such as birth, geographic location, or historical experience. These studies are typically used among medical researchers.
• Cohorts are identified and selected at a starting point (e.g. birth, starting school, entering a job field) and followed forward in time. 
• As they age, data is collected on cohort subgroups to determine their differing trajectories. For example, investigating how health outcomes diverge for groups born in 1950s, 1960s, and 1970s.
• Cohort studies do not require the same individuals to be assessed over time; they just require representation from the cohort.

Retrospective Study

• In a retrospective study , researchers either collect data on events that have already occurred or use existing data that already exists in databases, medical records, or interviews to gain insights about a population.
• Appropriate when prospectively following participants from the past starting point is infeasible or unethical. For example, studying early origins of diseases emerging later in life.
• Retrospective studies efficiently provide a “snapshot summary” of the past in relation to present status. However, quality concerns with retrospective data make careful interpretation necessary when inferring causality. Memory biases and selective retention influence quality of retrospective data.

Allows researchers to look at changes over time

Because longitudinal studies observe variables over extended periods of time, researchers can use their data to study developmental shifts and understand how certain things change as we age.

High validation

Since objectives and rules for long-term studies are established before data collection, these studies are authentic and have high levels of validity.

Eliminates recall bias

Recall bias occurs when participants do not remember past events accurately or omit details from previous experiences.

Flexibility

The variables in longitudinal studies can change throughout the study. Even if the study was created to study a specific pattern or characteristic, the data collection could show new data points or relationships that are unique and worth investigating further.

Limitations

Costly and time-consuming.

Longitudinal studies can take months or years to complete, rendering them expensive and time-consuming. Because of this, researchers tend to have difficulty recruiting participants, leading to smaller sample sizes.

Large sample size needed

Longitudinal studies tend to be challenging to conduct because large samples are needed for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

Participants tend to drop out

Not only is it a struggle to recruit participants, but subjects also tend to leave or drop out of the study due to various reasons such as illness, relocation, or a lack of motivation to complete the full study.

This tendency is known as selective attrition and can threaten the validity of an experiment. For this reason, researchers using this approach typically recruit many participants, expecting a substantial number to drop out before the end.

Report bias is possible

Longitudinal studies will sometimes rely on surveys and questionnaires, which could result in inaccurate reporting as there is no way to verify the information presented.

• Data were collected for each child at three-time points: at 11 months after adoption, at 4.5 years of age and at 10.5 years of age. The first two sets of results showed that the adoptees were behind the non-institutionalised group however by 10.5 years old there was no difference between the two groups. The Romanian orphans had caught up with the children raised in normal Canadian families.
• The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents (Marques Pais-Ribeiro, & Lopez, 2011)
• The correlation between dieting behavior and the development of bulimia nervosa (Stice et al., 1998)
• The stress of educational bottlenecks negatively impacting students’ wellbeing (Cruwys, Greenaway, & Haslam, 2015)
• The effects of job insecurity on psychological health and withdrawal (Sidney & Schaufeli, 1995)
• The relationship between loneliness, health, and mortality in adults aged 50 years and over (Luo et al., 2012)
• The influence of parental attachment and parental control on early onset of alcohol consumption in adolescence (Van der Vorst et al., 2006)
• The relationship between religion and health outcomes in medical rehabilitation patients (Fitchett et al., 1999)

Goals of Longitudinal Data and Longitudinal Research

The objectives of longitudinal data collection and research as outlined by Baltes and Nesselroade (1979):

• Identify intraindividual change : Examine changes at the individual level over time, including long-term trends or short-term fluctuations. Requires multiple measurements and individual-level analysis.
• Identify interindividual differences in intraindividual change : Evaluate whether changes vary across individuals and relate that to other variables. Requires repeated measures for multiple individuals plus relevant covariates.
• Analyze interrelationships in change : Study how two or more processes unfold and influence each other over time. Requires longitudinal data on multiple variables and appropriate statistical models.
• Analyze causes of intraindividual change: This objective refers to identifying factors or mechanisms that explain changes within individuals over time. For example, a researcher might want to understand what drives a person’s mood fluctuations over days or weeks. Or what leads to systematic gains or losses in one’s cognitive abilities across the lifespan.
• Analyze causes of interindividual differences in intraindividual change : Identify mechanisms that explain within-person changes and differences in changes across people. Requires repeated data on outcomes and covariates for multiple individuals plus dynamic statistical models.

How to Perform a Longitudinal Study

When beginning to develop your longitudinal study, you must first decide if you want to collect your own data or use data that has already been gathered.

Using already collected data will save you time, but it will be more restricted and limited than collecting it yourself. When collecting your own data, you can choose to conduct either a retrospective or prospective study .

In a retrospective study, you are collecting data on events that have already occurred. You can examine historical information, such as medical records, in order to understand the past. In a prospective study, on the other hand, you are collecting data in real-time. Prospective studies are more common for psychology research.

Once you determine the type of longitudinal study you will conduct, you then must determine how, when, where, and on whom the data will be collected.

A standardized study design is vital for efficiently measuring a population. Once a study design is created, researchers must maintain the same study procedures over time to uphold the validity of the observation.

A schedule should be maintained, complete results should be recorded with each observation, and observer variability should be minimized.

Researchers must observe each subject under the same conditions to compare them. In this type of study design, each subject is the control.

Methodological Considerations

Important methodological considerations include testing measurement invariance of constructs across time, appropriately handling missing data, and using accelerated longitudinal designs that sample different age cohorts over overlapping time periods.

Testing measurement invariance

Testing measurement invariance involves evaluating whether the same construct is being measured in a consistent, comparable way across multiple time points in longitudinal research.

This includes assessing configural, metric, and scalar invariance through confirmatory factor analytic approaches. Ensuring invariance gives more confidence when drawing inferences about change over time.

Missing data

Missing data can occur during initial sampling if certain groups are underrepresented or fail to respond.

Attrition over time is the main source – participants dropping out for various reasons. The consequences of missing data are reduced statistical power and potential bias if dropout is nonrandom.

Handling missing data appropriately in longitudinal studies is critical to reducing bias and maintaining power.

It is important to minimize attrition by tracking participants, keeping contact info up to date, engaging them, and providing incentives over time.

Techniques like maximum likelihood estimation and multiple imputation are better alternatives to older methods like listwise deletion. Assumptions about missing data mechanisms (e.g., missing at random) shape the analytic approaches taken.

Accelerated longitudinal designs

Accelerated longitudinal designs purposefully create missing data across age groups.

Accelerated longitudinal designs strategically sample different age cohorts at overlapping periods. For example, assessing 6th, 7th, and 8th graders at yearly intervals would cover 6-8th grade development over a 3-year study rather than following a single cohort over that timespan.

This increases the speed and cost-efficiency of longitudinal data collection and enables the examination of age/cohort effects. Appropriate multilevel statistical models are required to analyze the resulting complex data structure.

In addition to those considerations, optimizing the time lags between measurements, maximizing participant retention, and thoughtfully selecting analysis models that align with the research questions and hypotheses are also vital in ensuring robust longitudinal research.

So, careful methodology is key throughout the design and analysis process when working with repeated-measures data.

Cohort effects

A cohort refers to a group born in the same year or time period. Cohort effects occur when different cohorts show differing trajectories over time.

Cohort effects can bias results if not accounted for, especially in accelerated longitudinal designs which assume cohort equivalence.

Detecting cohort effects is important but can be challenging as they are confounded with age and time of measurement effects.

Cohort effects can also interfere with estimating other effects like retest effects. This happens because comparing groups to estimate retest effects relies on cohort equivalence.

Overall, researchers need to test for and control cohort effects which could otherwise lead to invalid conclusions. Careful study design and analysis is required.

Retest effects

Retest effects refer to gains in performance that occur when the same or similar test is administered on multiple occasions.

For example, familiarity with test items and procedures may allow participants to improve their scores over repeated testing above and beyond any true change.

Specific examples include:

• Memory tests – Learning which items tend to be tested can artificially boost performance over time
• Cognitive tests – Becoming familiar with the testing format and particular test demands can inflate scores
• Survey measures – Remembering previous responses can bias future responses over multiple administrations
• Interviews – Comfort with the interviewer and process can lead to increased openness or recall

To estimate retest effects, performance of retested groups is compared to groups taking the test for the first time. Any divergence suggests inflated scores due to retesting rather than true change.

If unchecked in analysis, retest gains can be confused with genuine intraindividual change or interindividual differences.

This undermines the validity of longitudinal findings. Thus, testing and controlling for retest effects are important considerations in longitudinal research.

Data Analysis

Longitudinal data involves repeated assessments of variables over time, allowing researchers to study stability and change. A variety of statistical models can be used to analyze longitudinal data, including latent growth curve models, multilevel models, latent state-trait models, and more.

Latent growth curve models allow researchers to model intraindividual change over time. For example, one could estimate parameters related to individuals’ baseline levels on some measure, linear or nonlinear trajectory of change over time, and variability around those growth parameters. These models require multiple waves of longitudinal data to estimate.

Multilevel models are useful for hierarchically structured longitudinal data, with lower-level observations (e.g., repeated measures) nested within higher-level units (e.g., individuals). They can model variability both within and between individuals over time.

Latent state-trait models decompose the covariance between longitudinal measurements into time-invariant trait factors, time-specific state residuals, and error variance. This allows separating stable between-person differences from within-person fluctuations.

There are many other techniques like latent transition analysis, event history analysis, and time series models that have specialized uses for particular research questions with longitudinal data. The choice of model depends on the hypotheses, timescale of measurements, age range covered, and other factors.

In general, these various statistical models allow investigation of important questions about developmental processes, change and stability over time, causal sequencing, and both between- and within-person sources of variability. However, researchers must carefully consider the assumptions behind the models they choose.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies and cross-sectional studies are two different observational study designs where researchers analyze a target population without manipulating or altering the natural environment in which the participants exist.

Yet, there are apparent differences between these two forms of study. One key difference is that longitudinal studies follow the same sample of people over an extended period of time, while cross-sectional studies look at the characteristics of different populations at a given moment in time.

Longitudinal studies tend to require more time and resources, but they can be used to detect cause-and-effect relationships and establish patterns among subjects.

On the other hand, cross-sectional studies tend to be cheaper and quicker but can only provide a snapshot of a point in time and thus cannot identify cause-and-effect relationships.

Both studies are valuable for psychologists to observe a given group of subjects. Still, cross-sectional studies are more beneficial for establishing associations between variables, while longitudinal studies are necessary for examining a sequence of events.

1. Are longitudinal studies qualitative or quantitative?

Longitudinal studies are typically quantitative. They collect numerical data from the same subjects to track changes and identify trends or patterns.

However, they can also include qualitative elements, such as interviews or observations, to provide a more in-depth understanding of the studied phenomena.

2. What’s the difference between a longitudinal and case-control study?

Case-control studies compare groups retrospectively and cannot be used to calculate relative risk. Longitudinal studies, though, can compare groups either retrospectively or prospectively.

In case-control studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies look at a single subject or a single case, whereas longitudinal studies are conducted on a large group of subjects.

3. Does a longitudinal study have a control group?

Yes, a longitudinal study can have a control group . In such a design, one group (the experimental group) would receive treatment or intervention, while the other group (the control group) would not.

Both groups would then be observed over time to see if there are differences in outcomes, which could suggest an effect of the treatment or intervention.

However, not all longitudinal studies have a control group, especially observational ones and not testing a specific intervention.

Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade & P. B. Baltes (Eds.), (pp. 1–39). Academic Press.

Cook, N. R., & Ware, J. H. (1983). Design and analysis methods for longitudinal research. Annual review of public health , 4, 1–23.

Fitchett, G., Rybarczyk, B., Demarco, G., & Nicholas, J.J. (1999). The role of religion in medical rehabilitation outcomes: A longitudinal study. Rehabilitation Psychology, 44, 333-353.

Harvard Second Generation Study. (n.d.). Harvard Second Generation Grant and Glueck Study. Harvard Study of Adult Development. Retrieved from https://www.adultdevelopmentstudy.org.

Le Mare, L., & Audet, K. (2006). A longitudinal study of the physical growth and health of postinstitutionalized Romanian adoptees. Pediatrics & child health, 11 (2), 85-91.

Luo, Y., Hawkley, L. C., Waite, L. J., & Cacioppo, J. T. (2012). Loneliness, health, and mortality in old age: a national longitudinal study. Social science & medicine (1982), 74 (6), 907–914.

Marques, S. C., Pais-Ribeiro, J. L., & Lopez, S. J. (2011). The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents: A two-year longitudinal study. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 12( 6), 1049–1062.

Sidney W.A. Dekker & Wilmar B. Schaufeli (1995) The effects of job insecurity on psychological health and withdrawal: A longitudinal study, Australian Psychologist, 30: 1,57-63.

Stice, E., Mazotti, L., Krebs, M., & Martin, S. (1998). Predictors of adolescent dieting behaviors: A longitudinal study. Psychology of Addictive Behaviors, 12 (3), 195–205.

Tegan Cruwys, Katharine H Greenaway & S Alexander Haslam (2015) The Stress of Passing Through an Educational Bottleneck: A Longitudinal Study of Psychology Honours Students, Australian Psychologist, 50:5, 372-381.

Thomas, L. (2020). What is a longitudinal study? Scribbr. Retrieved from https://www.scribbr.com/methodology/longitudinal-study/

Van der Vorst, H., Engels, R. C. M. E., Meeus, W., & Deković, M. (2006). Parental attachment, parental control, and early development of alcohol use: A longitudinal study. Psychology of Addictive Behaviors, 20 (2), 107–116.

Further Information

• Schaie, K. W. (2005). What can we learn from longitudinal studies of adult development?. Research in human development, 2 (3), 133-158.
• Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal studies. Journal of thoracic disease, 7 (11), E537.

What (Exactly) Is A Longitudinal Study?

By:   Derek Jansen (MBA)   | June 2020

I f  you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably feeling a bit overwhelmed by all the technical lingo that’s hitting you. If you’ve landed here, chances are one of these terms is “longitudinal study”, “longitudinal survey” or “longitudinal research”.

Worry not – in this post, we’ll explain exactly:

• What a longitudinal study is (and what the alternative is)
• What the main advantages of a longitudinal study are
• What the main disadvantages of a longitudinal study are
• Whether to use a longitudinal or cross-sectional study for your research

What is a longitudinal study?

A longitudinal study or a longitudinal survey (both of which make up longitudinal research) is a study where the same data are collected more than once,  at different points in time . The purpose of a longitudinal study is to assess not just  what  the data reveal at a fixed point in time, but to understand  how (and why) things change  over time.

The opposite of a longitudinal study is a cross-sectional study , which is a design where you only collect data at one point in time.

Example: Longitudinal vs Cross-Sectional

Here are two examples – one of a longitudinal study and one of a cross-sectional study – to give you an idea of what these two approaches look like in the real world:

Longitudinal study: a study which assesses how a group of 13-year old children’s attitudes and perspectives towards income inequality evolve over a period of 5 years, with the same group of children surveyed each year, from 2020 (when they are all 13) until 2025 (when they are all 18).

Cross-sectional study: a study which assesses a group of teenagers’ attitudes and perspectives towards income equality at a single point in time. The teenagers are aged 13-18 years and the survey is undertaken in January 2020.

Additionally, in the cross-sectional group, each age group (i.e. 13, 14, 15, 16, 17 and 18) are all different people (obviously!) with different life experiences – whereas, in the longitudinal group, each the data at each age point is generated by the same group of people (for example, John Doe will complete a survey at age 13, 14, 15, and so on). 

What are the advantages of a longitudinal study?

Longitudinal studies and longitudinal surveys offer some major benefits over cross-sectional studies. Some of the main advantages are:

Patterns  – because longitudinal studies involve collecting data at multiple points in time from the same respondents, they allow you to identify emergent patterns across time that you’d never see if you used a cross-sectional approach. 

Order  – longitudinal studies reveal the order in which things happened, which helps a lot when you’re trying to understand causation. For example, if you’re trying to understand whether X causes Y or Y causes X, it’s essential to understand which one comes first (which a cross-sectional study cannot tell you).

Bias  – because longitudinal studies capture current data at multiple points in time, they are at lower risk of recall bias . In other words, there’s a lower chance that people will forget an event, or forget certain details about it, as they are only being asked to discuss current matters.

Need a helping hand?

What are the disadvantages of a longitudinal study?

As you’ve seen, longitudinal studies have some major strengths over cross-sectional studies. So why don’t we just use longitudinal studies for everything? Well, there are (naturally) some disadvantages to longitudinal studies as well.

Cost  – compared to cross-sectional studies, longitudinal studies are typically substantially more expensive to execute, as they require maintained effort over a long period of time.

Slow  – given the nature of a longitudinal study, it takes a lot longer to pull off than a cross-sectional study. This can be months, years or even decades. This makes them impractical for many types of research, especially dissertations and theses at Honours and Masters levels (where students have a predetermined timeline for their research)

Drop out  – because longitudinal studies often take place over many years, there is a very real risk that respondents drop out over the length of the study. This can happen for any number of reasons (for examples, people relocating, starting a family, a new job, etc) and can have a very detrimental effect on the study.

Which one should you use?

Choosing whether to use a longitudinal or cross-sectional study for your dissertation, thesis or research project requires a few considerations. Ultimately, your decision needs to be informed by your overall  research aims, objectives and research questions  (in other words, the nature of the research determines which approach you should use). But you also need to consider the practicalities. You should ask yourself the following:

• Do you really need a view of how data changes over time, or is a snapshot sufficient?
• Is your university flexible in terms of the timeline for your research?
• Do you have the budget and resources to undertake multiple surveys over time?
• Are you certain you’ll be able to secure respondents over a long period of time?

If your answer to any of these is no, you need to think carefully about the viability of a longitudinal study in your situation. Depending on your research objectives, a cross-sectional design might do the trick. If you’re unsure, speak to your research supervisor or connect with  one of our friendly Grad Coaches .

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• Introduction
• Conclusions
• Article Information

eFigure 1. Participant flow diagram

eFigure 2. Distribution of child ages at the pre-pandemic CBCL assessment

eFigure 3. Distribution of child ages at the mid-pandemic CBCL assessment

eFigure 4. Number of months between the pre-pandemic CBCL assessment and start of the pandemic

eFigure 5. Number of months between the start of the pandemic and the mid-pandemic CBCL assessment

eFigure 6. Number of months between the pre-pandemic and mid-pandemic CBCL assessments

eTable 1. Description of ECHO cohorts included in the analytic sample

eTable 2. Frequency of youth categorized in the borderline or clinical range on CBCL broadband composites and DSM-5 subscales before and during the COVID-19 pandemic

eTable 3. Generalized linear mixed effects model estimating the impact of child ethnicity on change in child mental health (N=1229)

eTable 4. Generalized linear mixed effects model estimating the impact of child sex on the rate of change in child mental health (N=1229)

eTable 5. Generalized linear mixed-effects model estimating the impact of child age on change in child mental health (n=1229)

eTable 6. Model-based mean scores pre- and mid-pandemic and their difference (LS means), by child age

eTable 7. Model-based mean scores pre- and mid-pandemic and their difference (LS means), by poverty level

eTable 8. Model-based mean scores pre- and mid-pandemic and their difference (LS means), by child race

eTable 9. Model-based mean scores pre- and mid-pandemic and their difference (LS means), by CBCL threshold

eTable 10. Subgroup sample sizes for 3-way interaction models

eTable 11. Generalized linear mixed effects model estimating the 3-way interaction visit×age×CBCL threshold (N=1229)

eTable 12. Model-based mean scores pre- and mid-pandemic and their difference (LS means), by CBCL threshold*child age

eTable 13. Generalized linear mixed effects model estimating the 3-way interaction visit×sex×CBCL threshold (N=1229)

eTable 14. Model-based mean scores pre- and mid-pandemic and their difference (LS means), by CBCL threshold*child sex

eTable 15. Sensitivity analysis using continuous age variable in the generalized linear mixed-effects model estimating change in child mental health (n=1229)

eTable 16. Sensitivity analysis including time between pre- and mid-pandemic assessments variable in the generalized linear mixed-effects model estimating change in child mental health (n=1229)

eReferences

Environmental influences on Child Health Outcomes Program Collaborators

Data Sharing Statement

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Blackwell CK , Wu G , Chandran A, et al. Longitudinal Changes in Youth Mental Health From Before to During the COVID-19 Pandemic. JAMA Netw Open. 2024;7(8):e2430198. doi:10.1001/jamanetworkopen.2024.30198

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Longitudinal Changes in Youth Mental Health From Before to During the COVID-19 Pandemic

• 1 Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois
• 2 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
• 3 Department of Psychiatry and Behavioral Sciences, University of California School of Medicine, San Francisco
• 4 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, Massachusetts
• 5 Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
• 6 Department of Psychology, Emory University, Atlanta, Georgia
• 7 Department of Pediatrics, Rhode Island Hospital, Providence
• 8 Department of Pediatrics, The Warren Alpert Medical School of Brown University, Providence, Rhode Island
• 9 Department of Pediatrics, University of California School of Medicine, San Francisco
• 10 Hasbro Children’s Hospital, Providence, Rhode Island
• 11 Eunice Kennedy Shriver Center, UMass Chan Medical School, Worcester, Massachusetts
• 12 Department of Psychiatry, UMass Chan Medical School, Worcester, Massachusetts
• 13 Department of Pediatrics, UMass Chan Medical School, Worcester, Massachusetts
• 14 Department of Clinical/Developmental Psychology, George Washington University, Washington, DC
• 15 College of Education, University of Oregon, Eugene
• 16 Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle
• 17 Department of Psychiatry, Columbia University Irving Medical Center, New York, New York
• 18 Community Environmental Health Program, University of New Mexico Health Sciences Center, Albuquerque
• 19 Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York
• 20 Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, New York
• 21 Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis

Question   How did the COVID-19 pandemic impact youth mental health?

Findings   In this cohort study of 1229 US youths, the pandemic was associated with minor changes in youth mental health overall. However, youth entering the pandemic with prepandemic mental health problems experienced notable improvements across all outcomes, and lower-income and Black youth had small improvements in attention-deficit/hyperactivity disorder symptoms.

Meaning   These findings suggest that not all youth responded to the COVID-19 pandemic in the same way; these differences are critical to understand for recovery and may yield novel insights into causes of youth mental health problems.

Importance   Robust longitudinal studies of within-child changes in mental health associated with the COVID-19 pandemic are lacking, as are studies examining sources of heterogeneity in such changes.

Objective   To investigate within-child changes, overall and between subgroups, in youth mental health from prepandemic to midpandemic.

Design, Setting, and Participants   This cohort study used longitudinal prepandemic and midpandemic data from the Environmental influences on Child Health Outcomes (ECHO) Program, collected between January 1, 2015, and March 12, 2020 (prepandemic), and between March 13, 2020, and August 31, 2022 (midpandemic). Data were analyzed between December 1, 2022, and June 1, 2024. The sample included 9 US-based observational longitudinal pediatric ECHO cohorts. Cohorts were included if they collected the Child Behavior Checklist (CBCL) School Age version before and during the pandemic on more than 20 participants of normal birth weight aged 6 to 17 years.

Exposure   The COVID-19 pandemic.

Main Outcomes and Measures   Prepandemic to midpandemic changes in CBCL internalizing, externalizing, depression, anxiety, and attention-deficit/hyperactivity disorder (ADHD) scores were estimated, and differences in outcome trajectories by child sociodemographic characteristics (age, sex, race, ethnicity, and poverty level) and prepandemic mental health problems were examined using established CBCL clinical score thresholds.

Results   A total of 1229 participants (mean [SD] age during the pandemic, 10.68 [2.29] years; 625 girls [50.9%]) were included. The sample was socioeconomically diverse (197 of 1056 children [18.7%] lived at ≤130% of the Federal Poverty Level; 635 (51.7%) identified as White, 388 (31.6%) as Black, 147 (12.0%) as multiracial, 40 (3.3%) as another race, and 118 (9.6%) as Hispanic). Generalized linear mixed-effects models revealed minor decreases in externalizing problems (β = −0.88; 95% CI, −1.16 to −0.60), anxiety (β = −0.18; 95% CI, −0.31 to −0.05), and ADHD (β = −0.36; 95% CI, −0.50 to −0.22), but a minor increase in depression (β = 0.22; 95% CI, 0.10 to 0.35). Youth with borderline or clinically meaningful prepandemic scores experienced decreases across all outcomes, particularly externalizing problems (borderline, β = −2.85; 95% CI, −3.92 to −1.78; clinical, β = −4.88; 95% CI, −5.84 to −3.92). Low-income (β = −0.76; 95% CI, −1.14 to −0.37) and Black (β = −0.52; 95% CI, −0.83 to −0.20) youth experienced small decreases in ADHD compared with higher income and White youth, respectively.

Conclusions and Relevance   In this longitudinal cohort study of economically and racially diverse US youth, there was evidence of differential susceptibility and resilience for mental health problems during the pandemic that was associated with prepandemic mental health and sociodemographic characteristics.

Nearly 50% of youth have a mental health disorder in their lifetime. 1 The narrative emphasized by recent cross-sectional studies 2 - 6 is that the COVID-19 pandemic and related containment strategies exacerbated mental health risks. The few longitudinal studies examining within-child change using the same prepandemic and midpandemic mental health measures focused only on the first year of the pandemic and either took place outside the US 7 - 19 or primarily used data from the US-based Adolescent Brain Cognitive Development study, which included children aged 14 to 16 years. 20 - 22 These studies suggest an initial increase in mental health problems in the early phases of the pandemic 7 - 9 , 12 - 15 , 20 , 21 followed by recovery (although not necessarily to prepandemic levels 10 , 11 , 14 , 16 , 21 , 22 ), and, overall, clinically small but statistically significant increases in mental health problems, particularly depression.

Despite theoretical support for the notion that the pandemic affected subgroups of children differently, 23 - 25 few longitudinal studies have investigated such heterogeneity. 17 - 19 Work emphasizing that female individuals fared worse than male individuals has been conducted primarily in older samples, 7 - 9 , 17 and research suggests the pandemic was especially stressful for adolescents 18 , 19 but within-study age-based comparisons are limited, as is research on pandemic-related mental health changes in younger populations. For other subgroups with vulnerability to pandemic-related social disruptions, such as prepandemic mental health problems, 26 lower family income, 27 , 28 and minoritized race and ethnicity, 27 , 28 smaller studies were unable to detect subgroup differences, and results from larger studies were inconsistent. 18 , 19 A more nuanced understanding of the pandemic’s impact on youth mental health is, therefore, critically needed.

This study addresses prior limitations by leveraging individual-level data from before and during the pandemic for 6- to 17-year-olds participating in the National Institutes of Health–funded Environmental influences on Child Health Outcomes (ECHO) Program. 29 We hypothesized the following: first, the pandemic was associated with increases in youth mental health problems (hypothesis 1 [H1]), and second, greater increases in mental health problems were associated with having prepandemic mental health problems, being female, aged 12 years or older, lower family income, or being in a minoritized racial or ethnic group (hypothesis 2 [H2]).

For this cohort study, children were included if they had a Child Behavior Checklist (CBCL) School Age version (aged 6-18 years) assessment before the pandemic (from January 1, 2015, to March 12, 2020) and during the pandemic (March 13, 2020, to August 31, 2022), and were aged 6 to 17 years before the pandemic, of normal birth weight, and from an ECHO cohort with more than 20 participants contributing data. Local institutional review boards (IRBs) and/or the central ECHO IRB (Western IRB) reviewed all methods and procedures. Written informed consent or the parents’ or guardians’ permission was obtained, along with child assent as appropriate. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines for cohort studies. 30

Mental health was measured using the CBCL, a parent-report measure of youth behavior and mental health. 31 , 32 We examined the internalizing and externalizing problems composites, as well as Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition)–oriented scales for depression, anxiety, and attention-deficit/hyperactivity disorder (ADHD). These 5 outcomes are the most common pediatric mental health conditions in the US, 33 were increasing in prevalence even before the pandemic, 34 , 35 and were hypothesized as being most impacted by the pandemic. 36 In line with recommendations for examining within-person change, 37 analyses used raw CBCL scores. If children had multiple assessments during the pandemic, the earliest (closest to March 13, 2020) was selected. Child race and ethnicity were reported by the caregiver in accordance with National Institutes of Health requirements related to reporting of individual-level participant data.

We used generalized linear mixed-effects models 38 to estimate mental health changes from before to during the pandemic (ie, midpandemic). For succinctness, we use the term midpandemic to describe the second assessment but acknowledge that children were assessed throughout the pandemic. Models included 2 repeated measures of child mental health as the dependent variable. Visit occasion (prepandemic or midpandemic) was the primary independent variable, with the prepandemic measurement as the reference time point. The visit coefficient estimated change in CBCL scores between prepandemic and midpandemic (H1). Models included random intercepts both for child and cohort to allow prepandemic raw scores to vary between children and between cohorts, respectively. Models were also adjusted for covariates identified a priori. Sociodemographic covariates included child midpandemic age (6-11 years vs 12-17 years, based on the CBCL age-based norming categories), sex at birth, caregiver-identified child race (owing to sample size limitations, categories were collapsed to Black, White, and other race, which includes American Indian or Alaska Native, Asian Indian, Other Asian [Chinese, Filipino, Japanese, Korean, Vietnamese, Other Asian, and do not know], Native Hawaiian, Other Pacific Islander, some other race, prefer not to answer, and do not know) and ethnicity (Hispanic or non-Hispanic), caregiver highest educational attainment (less than high school, high school, some college, or bachelor’s degree or higher), and family income adjusted for household size and then categorized as percentage of the Federal Poverty Level (FPL) using thresholds established in prior studies (≤130%, >130% to 350%, and >350%). 39 We used income data available from January 1, 2017, to August 31, 2022, and selected the record closest to March 2020.

Caregiver covariates included mean-centered, self-reported depressive symptoms and perceived stress measured at least once from January 1, 2017, to August 31, 2022. Cohorts used different validated depression measures (ie, PROMIS version 1.0 Depressive Symptoms, Patient Health Questionnaire–9, Brief Symptom Inventory, Short Form–36 Mental Health, and Center for Epidemiological Studies Depression Scale), which were harmonized to the standard PROMIS T score metric (mean [SD], 50 [10]) using the PROsetta Stone score-linking method. 40 - 46 Caregiver-perceived stress was measured using the Perceived Stress Scale 47 , 48 and scored on the T score metric. For caregivers with multiple assessments, scores were averaged.

Models were also adjusted for number of months between March 13, 2020, and the midpandemic CBCL assessment. We used the multiple imputation by chained equations method (25 imputations, each with 10 iterations) to impute missing covariate data. 49

Building on H1 models, we included an interaction term between visit occasion and each hypothesized variable associated with mental health change (H2): prepandemic mental health, sex, age, income, race, and ethnicity. We defined prepandemic mental health problems using the nationally normed and clinically validated CBCL cutoff scores indicating normal (T score <65), borderline (T score = 65-70), and clinically meaningful (T score >70) behavior problems 31 , 32 ; because this score was not in H1 models, models testing the interaction between visit and prepandemic mental health also included the main effect estimate. We conceptualized race and ethnicity as social constructs that reflect membership in marginalized groups that have experienced inequitable burdens of COVID-19 infection and disease severity 27 , 28 and fewer returns on educational investments with respect to health and financial gains 28 , 50 that may buffer pandemic-related experiences. We examined both visit-by-race category and visit-by-ethnicity interactions using the largest subgroup as the reference (White and non-Hispanic, respectively). In post hoc exploratory analyses responsive to our findings, we tested the 3-way interactions of prepandemic mental health by age by visit and prepandemic mental health by sex by visit to examine associations within subgroups of children for whom intersectionality (ie, the intersections between individual identities that manifest in specific positions within societal structures and developmental contexts) 51 may have exacerbated or mitigated the impact of the pandemic on mental health.

Statistical significance was evaluated using a likelihood ratio test; if the interaction term was significant at P  < .05 for at least 1 outcome, least squares (LS) prepandemic and midpandemic means, as well as their differences, were estimated within groups to facilitate interpretation of interaction terms. We further contextualize effect sizes using Cohen d (small, 0.2 SD; medium, 0.5 SD; and large, 0.8 SD 52 ) according to the SD of each outcome at baseline. Given that the average pandemic impact on youth is estimated to be small (approximately 0.3 SD), we focus interpretations on effects that are small or greater. 17 , 53 , 54 Data were analyzed between December 1, 2022, and June 1, 2024, using R statistical software version 4.1.0 (R Project for Statistical Computing).

We were interested in whether the pandemic had a greater impact on the mental health of adolescents (aged 12 years or older) as indicated by prior research, and, thus, included a dichotomous indicator of age as a covariate in all our models to facilitate interpretation across models. In sensitivity analyses, we adjusted for continuous child age in main models to examine whether this altered effect estimates. In addition, we examined whether adjustment for time between the prepandemic to midpandemic assessments impacted effect estimates.

The analytic sample included 1229 participants (625 girls [50.9%]) from 9 ECHO cohorts (eFigure 1 and eTable 1 in Supplement 1 ). Children were a mean (SD) of 8.07 (1.83) years old before the pandemic and 10.68 (2.29) years old midpandemic (eFigures 2 and 3 in Supplement 1 ). The analytic sample largely reflected the characteristics of the eligible cohort sample (5319 individuals) for child age, sex, race, ethnicity, and family income ( Table 1 ); the analytic sample had somewhat higher levels of caregiver education (490 of 1180 [41.5%] vs 1350 of 4143 [32.6%] with a master’s degree or higher). Children were racially, ethnically, and economically diverse: 635 (51.7%) identified as White, 388 (31.6%) as Black, 147 (12.0%) as multiracial, 40 (3.3%) as another race, and 118 (9.6%) as Hispanic; 197 of 1056 children (18.7%) came from households at or below 130% of the FPL. Midpandemic assessments were conducted a mean (SD) of 12.52 (6.45) months (range, 0.03-29.60 months) after March 12, 2020 (see eFigures 4, 5, and 6 in Supplement 1 for assessment timing). Mean (SD) CBCL scores before the pandemic were 5.0 (5.2) for internalizing scores (median [IQR], 3 [1-7]), 5.8 (6.1) for externalizing scores (median [IQR], 4 [1-8]), 1.3 (1.8) for depression scores (median [IQR], 1 [0-2]), 2.2 (2.5) for anxiety scores (median [IQR], 1 [0-3]), and 3.4 (3.1) for ADHD scores (median [IQR], 3 [1-5]). These SDs are used to identify small, medium, and large effect sizes based on Cohen d . 52 Between 2% and 8% of children entered the pandemic with borderline or clinically meaningful CBCL scores (eTable 2 in Supplement 1 ).

Overall, youth experienced minor decreases in externalizing problems (β = −0.88; 95% CI, −1.16 to −0.60), anxiety (β = −0.18; 95% CI, −0.31 to −0.05), and ADHD (β = −0.36; 95% CI, −0.50 to −0.22), and a minor increase in depression (β = 0.22; 95% CI, 0.10 to 0.35) ( Table 2 ). All effect estimates represented changes in scores that were less than 0.2 SD.

For these models, estimated subgroup effect estimates (ie, slopes capturing change in CBCL scores) are in comparison to the reference group. Therefore, in terms of absolute change over time, a significant, negative slope for a subgroup could mean that (1) all groups are declining over time but the subgroup of interest is experiencing greater declines, (2) the reference group is not changing but the subgroup of interest is declining, or (3) the reference group is increasing while the subgroup of interest is not changing (or declining). Thus, a negative slope does not necessarily equate to improvements in mental health problems. To facilitate interpretation, we also provide the LS means to estimate absolute change within subgroups.

We did not observe differences in mental health changes by ethnicity (eTable 3 in Supplement 1 ). Compared with male individuals, female individuals experienced minor increases in externalizing scores (β = 0.56; 95% CI, 0.01 to 1.11) (eTable 4 in Supplement 1 ). Compared with older children, slopes were negative among younger children (<12 years old) for internalizing problems (β = −0.75; 95% CI, −1.40 to −0.09) and depression (β = −0.47; 95% CI, −0.76 to −0.17) (eTable 5 in Supplement 1 ; LS means are shown in eTable 6 in Supplement 1 ). With respect to income, the slope associated with change in mental health for children in families with income less than 130% FPL was negative for internalizing (β = −0.80; 95% CI, −1.56 to −0.03) and externalizing (β = −0.91; 95% CI, −1.67 to −0.15) problems, depression (β = −0.45; 95% CI, −0.79 to −0.10), and ADHD (β = −0.76; 95% CI, −1.14 to −0.37) ( Table 3 ; see eTable 7 in Supplement 1 for LS means). Black children had slopes that were negative compared with White children for internalizing problems (β = −0.87; 95% CI, −1.50 to −0.25), depression (β = −0.48; 95% CI, −0.76 to −0.20), and ADHD (β = −0.52; 95% CI, −0.83 to −0.20) ( Table 4 ; see eTable 8 in Supplement 1 for LS means). All effect sizes associated with broadband internalizing or externalizing scores in relation to sociodemographic characteristics were less than 0.15 SD; however, subscale score differences were relatively larger (all effect estimates were approximately 0.3-0.4 SD).

For all outcomes, children entering the pandemic with borderline or clinically meaningful CBCL scores experienced large decreases in scores compared with those with prepandemic scores in the normal range. Most notably, children experienced medium to large (changes of 0.5 to 1 SD) decreases in externalizing problems (β = −4.88; 95% CI, −5.84 to −3.92) and ADHD symptoms (β = −1.40; 95% CI, −1.91 to −0.89) ( Table 5 ) from prepandemic to midpandemic (see LS means in eTable 9 in Supplement 1 ). A similar pattern of medium effect sizes was observed among children with CBCL scores in the borderline range (externalizing problems, β = −2.85; 95% CI, −3.92 to −1.78; ADHD, β = −1.28; 95% CI, −1.85 to −0.72). Children entering the pandemic with clinically meaningful internalizing problems, depression, and anxiety also experienced declines (medium effect sizes) in symptoms (internalizing problems, β = −2.87; 95% CI, −3.88 to −1.87; depression, β = −0.81; 95% CI, −1.26 to −0.35; and anxiety, β = −1.16; 95% CI, −1.63 to −0.70). For post hoc exploratory analyses, we report results for internalizing and externalizing broadband scores only given sample size limitations (see eTable 10 in Supplement 1 for 3-way interaction sample sizes). For those entering the pandemic with clinically meaningful internalizing scores, the slope for younger children (<12 years old) was negative compared with older children (β = −3.22; 95% CI, −5.58 to −0.86; medium to large effect size) (eTable 11 in Supplement 1 ); younger children experienced a notable reduction in symptoms (3.4 points) whereas older children increased (0.5 points) on average (eTable 12 in Supplement 1 ). For youth entering the pandemic with internalizing problems, we also observed differences by sex. For those with borderline scores, female individuals had a positive slope compared with male individuals (β = 3.35; 95% CI, 1.09 to 5.96; a medium to large effect size) (eTable 13 in Supplement 1 ); on average, male individuals experienced a 2-point decline, and female individuals experienced a 1.6-point increase (eTable 14 in Supplement 1 ). For female individuals entering the pandemic with clinically meaningful internalizing scores, the slope was negative compared with male individuals (β = 2.78; 95% CI, 1.09 to 5.61; medium effect size) such that female individuals experienced a 4-point decline where male individuals experienced 1.4-point decline (eTable 14 in Supplement 1 ). We observed no significant differences by age or sex for externalizing problems among those entering the pandemic with borderline or clinical scores. In sensitivity analyses, adjustment for age modeled continuously (eTable 15 in Supplement 1 ) and time between the prepandemic and midpandemic assessments (eTable 16 in Supplement 1 ) did not impact our findings.

Contrary to our hypotheses and prior cross-sectional studies, in this cohort study we found no association of the COVID-19 pandemic with average changes in internalizing problems and observed very small decreases in externalizing problems. Slight increases in depression reported here are consistent with findings from several longitudinal studies and meta-analyses, 17 , 20 and collectively, this work converges on statistically significant but likely not clinically meaningful increases in youth depression, on average, associated with the pandemic. Our finding of a lack of a meaningful impact of the pandemic on child mental health in our full study sample is an important contribution, given that our within-child analysis accounts for stable characteristics of the child (eg, genetic predisposition, stable aspects of the family environment, and prior mental health–related exposures such as prior adverse childhood experiences). Prior longitudinal studies that only include outcomes measured during the pandemic (eg, Xiao et al 21 and Ravens-Sieberer et al 16 ) are more vulnerable to residual confounding by factors associated with poor mental health and are either unmeasured or not included as covariates, greatly limiting internal validity and likely overestimating the average impact of the pandemic on mental health. Although addressing youth mental health is warranted given the increasing prepandemic rates, such efforts may be better focused on subgroups. As shown here, although average effect sizes were small, some subgroups of youth experienced moderate changes.

We found that children entering the pandemic with clinically meaningful mental health problems experienced notable improvements in their mental health, suggesting that average associations of the pandemic with mental health tell only part of the story. Youth entering the pandemic with borderline or clinical range CBCL scores experienced medium-to-large decreases in all scores (approximately 0.5-1.0 SDs), particularly for externalizing problems, suggesting real and meaningful reductions in mental health problems for these children.

Limited work has examined mental health changes for youth with preexisting mental health problems, but those that do suggest similar results as those found here. Several studies with older children and adolescents found symptom improvement during the early months of the pandemic for youth who entered with preexisting mental health problems. 15 , 20 , 22 , 55 We extend this work across a larger age range and beyond the first year of the pandemic, suggesting that these unexpected decreases in mental health problems may persist. One potential explanation is that pandemic-related social restrictions (eg, school closures) represented a break from stressful social environments that may have been more impactful for youth with preexisting mental health problems. 2 , 56 - 59 Academic stress and school-related pressures, as well as negative social interactions such as bullying, social comparison, and academic and extracurricular competition, can contribute to psychological distress 60 - 62 ; for youth lacking the necessary coping skills and resources, such as those with mental health problems, these school and social stressors can have detrimental psychological impacts. Indeed, seasonal patterns in psychiatric hospital admissions and diagnoses, suicidality, and depression track with the school year but only in youth, not adults, suggesting school-related stressors contribute to poor youth mental health. 59 Thus, a pause during COVID-19 from such stressful contexts may have improved mental health for these youth. The current study cannot draw finite conclusions as to whether school closures or social restrictions in particular were associated with improvements in youth mental health, and additional investigations into this potential pathway to improved outcomes for these vulnerable youth is warranted to clarify these relationships. 36 , 63 Such work can help inform parents, practitioners, and policymakers on potential adaptive choices and strategies to combat the current youth mental health crisis.

Although exploratory analyses should be interpreted with caution, we found preliminary evidence to suggest that decreases in internalizing scores among those who entered the pandemic in the clinically meaningful range were more notable in younger children. With respect to sex, female individuals with borderline internalizing symptoms experienced increases in symptoms during the pandemic, whereas male individuals experienced decreases. In contrast, female individuals with clinically meaningful internalizing problems experienced the largest decreases in symptoms. It is worth noting that we did not observe clinically meaningful main effect estimates of either age or sex on pandemic-related changes in mental health, but this preliminary evidence suggests that both characteristics, in combination with preexisting mental health problems, may have influenced how youth experienced the pandemic. Future work with larger, clinically enriched samples is needed to further investigate heterogeneous effects of the pandemic on youth mental health.

We also found that other groups of children who might be considered more vulnerable to mental health problems in the context of pandemic-related disruptions generally fared better compared with their peers. Low-income and Black youth experienced decreases (0.3-0.4 SD) in ADHD symptoms, whereas higher income and White youth largely stayed the same. In contrast, high-income and White youth experienced increases in depression symptoms, whereas low-income and Black youth largely stayed the same. Although low-income and Black families experienced disproportionately larger financial hardships, COVID-19 infection rates, and reduced health care access, 27 our results suggest the pandemic had null or small positive outcomes for these youth. One possible reason for such findings is that schools in lower-income communities are more likely to be underresourced, overcrowded, 64 and have more school-based violence 65 , 66 ; furthermore, schools can be threatening spaces for Black youth. 67 , 68 Thus, observed improvements in mental health, although small, may be related to reduced exposure to these perpetually stressful environments during school closures and remote schooling. However, these youth may also have experienced substantial academic setbacks due to economic and racial and ethnic digital divides and lacking the technology infrastructure necessary to participate in remote learning. 69 School closures also resulted in limited access to school-based services such as free or reduced price lunch, which are disproportionately used by lower income and minoritized youth. 69 Understanding subgroup-specific impacts of school closures and other pandemic-related social restrictions on a broad range of neurodevelopmental and academic outcomes is essential for accurately capturing the pandemic’s impact on youth.

Although these sociodemographic differences were small according to Cohen d criteria, these estimates were observed using a robust, within-child design. Burgeoning evidence suggests application of less-robust study designs in child mental health research has overestimated well-studied relationships. For example, a recent meta-analysis 70 examining the association between childhood maltreatment and child mental health estimated in robust, quasi-experimental studies (including within-child study designs) concluded that these associations were smaller (Cohen d  = 0.31, small effect size) compared with estimates from less-robust designs (Cohen d  = 0.56, medium effect size). Thus, effect estimates observed here can be considered comparable in magnitude to the impact of childhood maltreatment on child mental health. Finally, such differences are important to interrogate in future research as they may both point to children who are particularly vulnerable to depression during the pandemic as well as to potential pathways by which to reduce inequalities in ADHD outcomes.

This study has limitations that should be mentioned. The ECHO Cohort is not nationally representative, and our analytic sample had a smaller proportion of Hispanic youth and families with lower education compared with our eligible sample, similar to other studies that continued during the pandemic. 71 Thus, our results may not generalize to the most disadvantaged youth. Second, our outcome data are parent-reported, and parents may underreport youth mental health symptoms. 72 However, parent report enabled reliable and valid measurement across a large age range, and cross-informant CBCL analyses suggest moderate correlations between parent and youth report that are larger than other instruments. 32 The CBCL also has high test-retest reliability and scale score stability, 32 suggesting that parent informants are internally consistent across time and, therefore, the absolute change in mental health problems is reliably estimated, even if individual time points are subject to potential underestimation. In addition, because our analysis includes only 2 measurement occasions, we are unable to completely address concerns that our findings, particularly those focused on children with prepandemic mental health problems, are due to regression to the mean. However, we do find evidence that scores in subgroups of these at-risk children increase and decrease (eg, male individuals vs female individuals with borderline internalizing symptoms prepandemic) in a manner supported by prior evidence. This increases our confidence that these results are not a statistical artifact.

Overall, our results suggest the pandemic had a minimal impact on child mental health on average. Importantly, however, changes in mental health—some positive, some negative—depended on individual characteristics, such as prepandemic mental health and sociodemographic characteristics. These differences are critical for understanding how best to support different youth during social disruptions and future related research may yield novel insights into causes of youth mental health problems.

Accepted for Publication: June 21, 2024.

Published: August 26, 2024. doi:10.1001/jamanetworkopen.2024.30198

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Blackwell CK et al. JAMA Network Open .

Corresponding Authors: Courtney K. Blackwell, PhD, Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, 420 E Superior St, Chicago, IL 60611 ( [email protected] ); Kaja Z. LeWinn, ScD, Department of Psychiatry and Behavioral Sciences, University of California School of Medicine, San Francisco, 675 18th St, San Francisco, CA 94143 ( [email protected] ).

Author Contributions: Mr Wu and Dr Chandran had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Blackwell, Arizaga, Bush, Ganiban, Koinis-Mitchell, R. O. Wright, LeWinn.

Acquisition, analysis, or interpretation of data: Blackwell, Wu, Chandran, Arizaga, Bosquet Enlow, Brennan, Burton, Bush, Cella, Cummins, D’Sa, Frazier, Ganiban, Gershon, Leve, Loftus, Lukankina, Margolis, Nozadi, R. J. Wright, R. O. Wright, Zhao, LeWinn.

Drafting of the manuscript: Blackwell, Chandran, Ganiban, Koinis-Mitchell, R. O. Wright, LeWinn.

Critical review of the manuscript for important intellectual content: Blackwell, Wu, Arizaga, Bosquet Enlow, Brennan, Burton, Bush, Cella, Cummins, D’Sa, Frazier, Ganiban, Gershon, Koinis-Mitchell, Leve, Loftus, Lukankina, Margolis, Nozadi, R. J. Wright, R. O. Wright, Zhao, LeWinn.

Statistical analysis: Blackwell, Wu, Chandran, Arizaga, Gershon.

Obtained funding: Bosquet Enlow, Brennan, Bush, D’Sa, Ganiban, Koinis-Mitchell, Leve, Margolis, R. J. Wright, R. O. Wright, Zhao, LeWinn.

Administrative, technical, or material support: Blackwell, Arizaga, Bush, Cella, Frazier, R. O. Wright, Zhao, LeWinn.

Supervision: R. O. Wright, LeWinn.

Conflict of Interest Disclosures: Dr Bush reported receiving grants from University of California San Francisco during the conduct of the study. Dr Frazier reported receiving grants from Tetra Discovery Partners for a clinical trial for Fragile X outside the submitted work. No other disclosures were reported.

Funding/Support: Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) Program, Office of The Director, National Institutes of Health (NIH), under Award Nos. U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center, Drs Chandran and Wu), U24OD023319 with cofunding from the Office of Behavioral and Social Sciences Research (PRO Core; Drs Cella, Gershon, and Blackwell), UH3OD023318 (Dr Brennan), UH3OD023348 (Dr Frazier), UH3OD023285 (Drs Koinis-Mitchell, D’Sa, Burton, Cummins, and Lukankina), UH3OD023290 (Dr Margolis), UH3OD023271 (Drs LeWinn, Zhao, Bush, Arizaga, and Loftus), UH3OD023389 (Drs Leve and Ganiban), UH3OD023344 (Dr Nozadi), UH3OD023337 (Drs R. J. Wright, R. O. Wright, and Bosquet Enlow), and T32MH018261 (Dr Arizaga).

Role of the Funder/Sponsor: The sponsor, NIH, participated in the overall design and implementation of the ECHO Program, which was funded as a cooperative agreement between NIH and grant awardees. The sponsor approved the Steering Committee–developed ECHO protocol and its amendments including COVID-19 measures. The sponsor had no access to the central database, which was housed at the ECHO Data Analysis Center. Data management and site monitoring were performed by the ECHO Data Analysis Center and Coordinating Center. All analyses for scientific publication were performed by the study statistician, independently of the sponsor. The lead author wrote all drafts of the manuscript and made revisions based on coauthors and the ECHO Publication Committee (a subcommittee of the ECHO Operations Committee) feedback without input from the sponsor. The study sponsor did not review nor approve the manuscript for submission to the journal.

Group Information: Members of the Environmental Influences on Child Health Outcomes Program Collaboraotes are listed in Supplement 2 .

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Data Sharing Statement: See Supplement 3 .

Additional Contributions: We thank our ECHO colleagues; the medical, nursing, and program staff; and the children and families participating in the ECHO cohorts.

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Locally prepared therapeutic food for treatment of severely underweight children in rural india: an interventional prospective controlled community-based study with long follow-up:—‘samman’ trial.

1. Introduction

2.1. study design, 2.2. setting and participants, 2.3. phases of study, 3. statistical methods, 3.1. sample size, 3.2. patient and public involvement (ppi), recovery with/without relapse and associated factors, 5. discussion, 5.1. recovery with/without relapse and associated factors, 5.2. value addition of this study, 6. conclusions, limitations, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Satav, A.R.; Dani, V.S.; Pendharkar, J.S.; Satav, K.A.; Raje, D.; Jain, D.; Khirwadkar, S.S.; Simões, E.A.F. Locally Prepared Therapeutic Food for Treatment of Severely Underweight Children in Rural India: An Interventional Prospective Controlled Community-Based Study with Long Follow-Up:—‘SAMMAN’ Trial. Nutrients 2024 , 16 , 2872. https://doi.org/10.3390/nu16172872

Satav AR, Dani VS, Pendharkar JS, Satav KA, Raje D, Jain D, Khirwadkar SS, Simões EAF. Locally Prepared Therapeutic Food for Treatment of Severely Underweight Children in Rural India: An Interventional Prospective Controlled Community-Based Study with Long Follow-Up:—‘SAMMAN’ Trial. Nutrients . 2024; 16(17):2872. https://doi.org/10.3390/nu16172872

Satav, Ashish Rambhau, Vibhawari S. Dani, Jayashri S. Pendharkar, Kavita Ashish Satav, Dhananjay Raje, Dipty Jain, Shubhada S. Khirwadkar, and Eric A. F. Simões. 2024. "Locally Prepared Therapeutic Food for Treatment of Severely Underweight Children in Rural India: An Interventional Prospective Controlled Community-Based Study with Long Follow-Up:—‘SAMMAN’ Trial" Nutrients 16, no. 17: 2872. https://doi.org/10.3390/nu16172872

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Qualitative longitudinal research in health research: a method study

Åsa audulv.

1 Department of Nursing, Umeå University, Umeå, Sweden

Elisabeth O. C. Hall

2 Faculty of Health, Aarhus University, Aarhus, Denmark

3 Faculty of Health Sciences, University of Faroe Islands, Thorshavn, Faroe Islands Denmark

Åsa Kneck

4 Department of Health Care Sciences, Ersta Sköndal Bräcke University College, Stockholm, Sweden

Thomas Westergren

5 Department of Health and Nursing Science, University of Agder, Kristiansand, Norway

6 Department of Public Health, University of Stavanger, Stavanger, Norway

Mona Kyndi Pedersen

7 Center for Clinical Research, North Denmark Regional Hospital, Hjørring, Denmark

8 Department of Clinical Medicine, Aalborg University, Aalborg, Denmark

Hanne Aagaard

9 Lovisenberg Diaconale Univeristy of College, Oslo, Norway

Kristianna Lund Dam

Mette spliid ludvigsen.

10 Department of Clinical Medicine-Randers Regional Hospital, Aarhus University, Aarhus, Denmark

11 Faculty of Nursing and Health Sciences, Nord University, Bodø, Norway

Associated Data

The datasets used and analyzed in this current study are available in supplementary file  6 .

Qualitative longitudinal research (QLR) comprises qualitative studies, with repeated data collection, that focus on the temporality (e.g., time and change) of a phenomenon. The use of QLR is increasing in health research since many topics within health involve change (e.g., progressive illness, rehabilitation). A method study can provide an insightful understanding of the use, trends and variations within this approach. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

This method study used an adapted scoping review design. Articles were eligible if they were written in English, published between 2017 and 2019, and reported results from qualitative data collected at different time points/time waves with the same sample or in the same setting. Articles were identified using EBSCOhost. Two independent reviewers performed the screening, selection and charting.

A total of 299 articles were included. There was great variation among the articles in the use of methodological traditions, type of data, length of data collection, and components of longitudinal data collection. However, the majority of articles represented large studies and were based on individual interview data. Approximately half of the articles self-identified as QLR studies or as following a QLR design, although slightly less than 20% of them included QLR method literature in their method sections.

Conclusions

QLR is often used in large complex studies. Some articles were thoroughly designed to capture time/change throughout the methodology, aim and data collection, while other articles included few elements of QLR. Longitudinal data collection includes several components, such as what entities are followed across time, the tempo of data collection, and to what extent the data collection is preplanned or adapted across time. Therefore, there are several practices and possibilities researchers should consider before starting a QLR project.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01732-4.

Health research is focused on areas and topics where time and change are relevant. For example, processes such as recovery or changes in health status. However, relating time and change can be complicated in research, as the representation of reality in research publications is often collected at one point in time and fixed in its presentation, although time and change are always present in human life and experiences. Qualitative longitudinal research (QLR; also called longitudinal qualitative research, LQR) has been developed to focus on subjective experiences of time or change using qualitative data materials (e.g., interviews, observations and/or text documents) collected across a time span with the same participants and/or in the same setting [ 1 , 2 ]. QLR within health research may have many benefits. Firstly, human experiences are not fixed and consistent, but changing and diverse, therefore people’s experiences in relation to a health phenomenon may be more comprehensively described by repeated interviews or observations over time. Secondly, experiences, behaviors, and social norms unfold over time. By using QLR, researchers can collect empirical data that represents not only recalled human conceptions but also serial and instant situations reflecting transitions, trajectories and changes in people’s health experiences, personal development or health care organizations [ 3 – 5 ].

Key features of QLR

Whether QLR is a methodological approach in its own right or a design element of a particular study within a traditional methodological approach (e.g., ethnography or grounded theory) is debated [ 1 , 6 ]. For example, Bennett et al. [ 7 ] describe QLR as untied to methodology, giving researchers the flexibility to develop a suitable design for each study. McCoy [ 6 ] suggests that epistemological and ontological standpoints from interpretative phenomenological analysis (IPA) align with QLR traditions, thus making longitudinal IPA a suitable methodology. Plano-Clark et al. [ 8 ] described how longitudinal qualitative elements can be used in mixed methods studies, thus creating longitudinal mixed methods. In contrast, several researchers have argued that QLR is an emerging methodology [ 1 , 5 , 9 , 10 ]. For example, Thomson et al. [ 9 ] have stated “What distinguishes longitudinal qualitative research is the deliberate way in which temporality is designed into the research process, making change a central focus of analytic attention” (p. 185). Tuthill et al. [ 5 ] concluded that some of the confusion might have arisen from the diversity of data collection methods and data materials used within QLR research. However, there are no investigations showing to what extent QLR studies use QLR as a distinct methodology versus using a longitudinal data collection as a more flexible design element in combination with other qualitative methodologies.

QLR research should focus on aspects of temporality, time and/or change [ 11 – 13 ]. The concepts of time and change are seen as inseparable since change is happening with the passing of time [ 13 ]. However, time can be conceptualized in different ways. Time is often understood from a chronological perspective, and is viewed as fixed, objective, continuous and measurable (e.g., clock time, duration of time). However, time can also be understood from within, as the experience of the passing of time and/or the perspective from the current moment into the constructed conception of a history or future. From this perspective, time is seen as fluid, meaning that events, contexts and understandings create a subjective experience of time and change. Both the chronological and fluid understanding of time influence QLR research [ 11 ]. Furthermore, there is a distinction between over-time, which constitutes a comparison of the difference between points in time, often with a focus on the latter point or destination, and through-time, which means following an aspect across time while trying to understand the change that occurs [ 11 ]. In this article, we will mostly use the concept of across time to include both perspectives.

Some authors assert that QLR studies should include a qualitative data collection with the same sample across time [ 11 , 13 ], whereas Thomson et al. [ 9 ] also suggest the possibility of returning to the same data collection site with the same or different participants. When a QLR study involves data collection in shorter engagements, such as serial interviews, these engagements are often referred to as data collection time points. Data collection in time waves relates to longer engagements, such as field work/observation periods. There is no clear-cut definition for the minimum time span of a QLR study; instead, the length of the data collection period must be decided based upon what processes or changes are the focus of the study [ 13 ].

Most literature describing QLR methods originates from the social sciences, where the approach has a long tradition [ 1 , 10 , 14 ]. In health research, one-time-data collection studies have been the norm within qualitative methods [ 15 ], although health research using QLR methods has increased in recent years [ 2 , 5 , 16 , 17 ]. However, collecting and managing longitudinal data has its own sets of challenges, especially regarding how to integrate perspectives of time and/or change in the data collection and subsequent analysis [ 1 ]. Therefore, a study of QLR articles from the health research literature can provide an insightful understanding of the use, trends and variations of how methods are used and how elements of time/change are integrated in QLR studies. This could, in turn, provide inspiration for using different possibilities of collecting data across time when using QLR in health research. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

More specifically, the research questions were:

• What methodological approaches are described to inform QLR research?
• What methodological references are used to inform QLR research?
• How are longitudinal perspectives articulated in article aims?
• How is longitudinal data collection conducted?

In this method study, we used an adapted scoping review method [ 18 – 20 ]. Method studies are research conducted on research studies to investigate how research design elements are applied across a field [ 21 ]. However, since there are no clear guidelines for method studies, they often use adapted versions of systematic reviews or scoping review methods [ 21 ]. The adaptations of the scoping review method consisted of 1) using a large subsample of studies (publications from a three-year period) instead of including all QLR articles published, and 2) not including grey literature. The reporting of this study was guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist [ 20 , 22 ] (see Additional file 1 ). A (unpublished) protocol was developed by the research team during the spring of 2019.

Eligibility criteria

In line with method study recommendations [ 21 ], we decided to draw on a manageable subsample of published QLR research. Articles that were eligible for inclusion were health research primary studies written in English, published between 2017 and 2019, and with a longitudinal qualitative data collection. Our operating definition for qualitative longitudinal data collection was data collected at different time points (e.g., repeated interviews) or time waves (e.g., periods of field work) involving the same sample or conducted in the same setting(s). We intentionally selected a broad inclusion criterion for QLR since we wanted a wide variety of articles. The selected time period was chosen because the first QLR method article directed towards health research was published in 2013 [ 1 ] and during the following years the methodological resources for QLR increased [ 3 , 8 , 17 , 23 – 25 ], thus we could expect that researchers publishing QLR in 2017–2019 should be well-grounded in QLR methods. Further, we found that from 2012 to 2019 the rate of published QLR articles were steady at around 100 publications per year, so including those from a three-year period would give a sufficient number of articles (~ 300 articles) for providing an overview of the field. Published conference abstracts, protocols, articles describing methodological issues, review articles, and non-research articles (e.g., editorials) were excluded.

Search strategy

Relevant articles were identified through systematic searches in EBSCOhost, including biomedical and life science research and nursing and allied health literature. A librarian who specialized in systematic review searches developed and performed the searches, in collaboration with the author team (LF, TW & ÅA). In the search, the term “longitudinal” was combined with terms for qualitative research (for the search strategy see Additional file 2 ). The searches were conducted in the autumn of 2019 (last search 2019-09-10).

Study selection

All identified citations were imported into EndNote X9 ( www.endnote.com ) and further imported into Rayyan QCRI online software [ 26 ], and duplicates were removed. All titles and abstracts were screened against the eligibility criteria by two independent reviewers (ÅA & EH), and conflicting decisions were discussed until resolved. After discussions by the team, we decided to include articles published between 2017 and 2019, that selection alone included 350 records with diverse methods and designs. The full texts of articles that were eligible for inclusion were retrieved. In the next stage, two independent reviewers reviewed each full text article to make final decisions regarding inclusion (ÅA, EH, Julia Andersson). In total, disagreements occurred in 8% of the decisions, and were resolved through discussion. Critical appraisal was not assessed since the study aimed to describe the range of how QLR is applied and not aggregate research findings [ 21 , 22 ].

Data charting and analysis

A standardized charting form was developed in Excel (Excel 2016). The charting form was reviewed by the research team and pretested in two stages. The tests were performed to increase internal consistency and reduce the risk of bias. First, four articles were reviewed by all the reviewers, and modifications were made to the form and charting instructions. In the next stage, all reviewers used the charting form on four other articles, and the convergence in ratings was 88%. Since the convergence was under 90%, charting was performed in duplicate to reduce errors in the data. At the end of the charting process, the convergence among the reviewers was 95%. The charting was examined by the first author, who revised the charting in cases of differences.

Data items that were charted included 1) the article characteristics (e.g., authors, publication year, journal, country), 2) the aim and scope (e.g., phenomenon of interest, population, contexts), 3) the stated methodology and analysis method, 4) text describing the data collection (e.g., type of data material, number of participants, time frame of data collection, total amount of data material), and 5) the qualitative methodological references used in the methods section. Extracted text describing data collection could consist of a few sentences or several sections from the articles (and sometimes figures) concerning data collection practices, rational for time periods and research engagement in the field. This was later used to analyze how the longitudinal data collection was conducted and elements of longitudinal design. To categorize the qualitative methodology approaches, a framework from Cresswell [ 27 ] was used (including the categories for grounded theory, phenomenology, ethnography, case study and narrative research). Overall, data items needed to be explicitly stated in the articles in order to be charted. For example, an article was categorized as grounded theory if it explicitly stated “in this grounded theory study” but not if it referred to the literature by Glaser and Strauss without situating itself as a grounded theory study (See Additional file 3 for the full instructions for charting).

All charting forms were compiled into a single Microsoft Excel spreadsheet (see Supplementary files for an overview of the articles). Descriptive statistics with frequencies and percentages were calculated to summarize the data. Furthermore, an iterative coding process was used to group the articles and investigate patterns of, for example, research topics, words in the aims, or data collection practices. Alternative ways of grouping and presenting the data were discussed by the research team.

Search and selection

A total of 2179 titles and abstracts were screened against the eligibility criteria (see Fig.  1 ). The full text of one article could not be found and the article was excluded [ 28 ]. Fifty full text articles were excluded. Finally, 299 articles, representing 271 individual studies, were included in this study (see additional files 4 and 5 respectively for tables of excluded and included articles).

PRISMA diagram of study selection]

General characteristics and research areas of the included articles

The articles were published in many journals ( n  = 193), and 138 of these journals were represented with one article each. BMJ Open was the most prevalent journal ( n  = 11), followed by the Journal of Clinical Nursing ( n  = 8). Similarly, the articles represented many countries ( n  = 41) and all the continents; however, a large part of the studies originated from the US or UK ( n  = 71, 23.7% and n  = 70, 23.4%, respectively). The articles focused on the following types of populations: patients, families−/caregivers, health care providers, students, community members, or policy makers. Approximately 20% ( n  = 63, 21.1%) of the articles collected data from two or more of these types of population(s) (see Table  1 ).

Characteristics of the included QLR articles

Approximately half of the articles ( n  = 158, 52.8%) articulated being part of a larger research project. Of them, 95 described a project with both quantitative and qualitative methods. They represented either 1) a qualitative study embedded in an intervention, evaluation or implementation study ( n  = 66, 22.1%), 2) a longitudinal cohort study collecting both quantitative and qualitative material ( n  = 23, 7.7%), or 3) qualitative longitudinal material collected together with a cross sectional survey (n = 6, 2.0%). Forty-eight articles (16.1%) described belonging to a larger qualitative project presented in several research articles.

Methodological traditions

Approximately one-third ( n  = 109, 36.5%) of the included articles self-identified with one of the qualitative traditions recognized by Cresswell [ 27 ] (case study: n  = 36, 12.0%; phenomenology: n  = 35, 11.7%; grounded theory: n  = 22, 7.4%; ethnography: n  = 13, 4.3%; narrative method: n = 3, 1.0%). In nine articles, the authors described using a mix of two or more of these qualitative traditions. In addition, 19 articles (6.4%) self-identified as mixed methods research.

Every second article self-identified as having a qualitative longitudinal design ( n  = 156, 52.2%); either they self-identified as “a longitudinal qualitative study” or “using a longitudinal qualitative research design”. However, in some articles, this was stated in the title and/or abstract and nowhere else in the article. Fifty-two articles (17.4%) self-identified both as having a QLR design and following one of the methodological approaches (case study: n  = 8; phenomenology: n  = 23; grounded theory: n  = 9; ethnography: n  = 6; narrative method: n  = 2; mixed methods: n  = 4).

The other 143 articles used various terms to situate themselves in relation to a longitudinal design. Twenty-seven articles described themselves as a longitudinal study (9.0%) or a longitudinal study within a specific qualitative tradition (e.g., a longitudinal grounded theory study or a longitudinal mixed method study) ( n  = 64, 21.4%). Furthermore, 36 articles (12.0%) referred to using longitudinal data materials (e.g., longitudinal data or longitudinal interviews). Nine of the articles (3.0%) used the term longitudinal in relation to the data analysis or aim (e.g., the aim was to longitudinally describe), used terms such as serial or repeated in relation to the data collection design ( n  = 2, 0.7%), or did not use any term to address the longitudinal nature of their design ( n  = 5, 1.7%).

Use of methodological references

The mean number of qualitative method references in the methods sections was 3.7 (range 0 to 16), and 20 articles did not have any qualitative method reference in their methods sections. 1 Commonly used method references were generic books on qualitative methods, seminal works within qualitative traditions, and references specializing in qualitative analysis methods (see Table  2 ). It should be noted that some references were comprehensive books and thus could include sections about QLR without being focused on the QLR method. For example, Miles et al. [ 31 ] is all about analysis and coding and includes a chapter regarding analyzing change.

Most frequently used method references (8 most used) and QLR method references (5 most used). Citations in Google Scholar were used as an indication of how widely used the references are; searches conducted in Google Scholar 2022-01-02

Only approximately 20% ( n  = 58) of the articles referred to the QLR method literature in their methods sections. 2 The mean number of QLR method references (counted for articles using such sources) was 1.7 (range 1 to 6). Most articles using the QLR method literature also used other qualitative methods literature (except two articles using one QLR literature reference each [ 39 , 40 ]). In total, 37 QLR method references were used, and 24 of the QLR method references were only referred to by one article each.

Longitudinal perspectives in article aims

In total, 231 (77.3%) articles had one or several terms related to time or change in their aims, whereas 68 articles (22.7%) had none. Over one hundred different words related to time or change were identified. Longitudinally oriented terms could focus on changes across time (process, trajectory, transition, pathway or journey), patterns of how something changed (maintenance, continuity, stability, shifts), or phenomena that by nature included change (learning or implementation). Other types of terms emphasized the data collection time period (e.g., over 6 months) or a specific changing situation (e.g., during pregnancy, through the intervention period, or moving into a nursing home). The most common terms used for the longitudinal perspective were change ( n  = 63), over time ( n  = 52), process ( n  = 36), transition ( n  = 24), implementation ( n  = 14), development ( n  = 13), and longitudinal (n = 13). 3

Furthermore, the articles varied in what ways their aims focused on time/change, e.g., the longitudinal perspectives in the aims (see Table  3 ). In 71 articles, the change across time was the phenomenon of interest of the article : for example, articles investigating the process of learning or trajectories of diseases. In contrast, 46 articles investigated change or factors impacting change in relation to a defined outcome : for example, articles investigating factors influencing participants continuing in a physical activity trial. The longitudinal perspective could also be embedded in an article’s context . In such cases, the focus of the article was on experiences that happened during a certain time frame or in a time-related context (e.g., described experiences of the patient-provider relationship during 6 months of rehabilitation).

Different longitudinal perspectives in the articles’ aims and objectives

Types of data and length of data collection

The QLR articles were often large and complex in their data collection methods. The median number of participants was 20 (range from one to 1366, the latter being an article with open-ended questions in questionnaires [ 46 ]). Most articles used individual interviews as the data material ( n  = 167, 55.9%) or a combination of data materials ( n  = 98, 32.8%) (e.g., interviews and observations, individual interviews and focus group interviews, or interviews and questionnaires). Forty-five articles (15.1%) presented quantitative and qualitative results. The median number of interviews was 46 (range three to 507), which is large in comparison to many qualitative studies. The observation materials were also comprehensive and could include several hundred hours of observations. Documents were often used as complementary material and included official documents, newspaper articles, diaries, and/or patient records.

The articles’ time spans 4 for data collection varied between a few days and over 20 years, with 60% of the articles’ time spans being 1 year or shorter ( n  = 180) (see Fig.  2 ). The variation in time spans might be explained by the different kinds of phenomena that were investigated. For example, Jensen et al. [ 47 ] investigated hospital care delivery and followed each participant, with observations lasting between four and 14 days. Smithbattle [ 48 ] described the housing trajectories of teen mothers, and collected data in seven waves over 28 years.

Number of articles in relation to the time span of data collection. The time span of data collection is given in months

Three components of longitudinal data collection

In the articles, the data collection was conducted in relation to three different longitudinal data collection components (see Table  4 ).

Components of longitudinal data collection

Entities followed across time

Four different types of entities were followed across time: 1) individuals, 2) individual cases or dyads, 3) groups, and 4) settings. Every second article ( n  = 170, 56.9%) followed individuals across time, thus following the same participants through the whole data collection period. In contrast, when individual cases were followed across time, the data collection was centered on the primary participants (e.g., people with progressive neurological conditions) who were followed over time, and secondary participants (e.g., family caregivers) might provide complementary data at several time points or only at one-time point. When settings were followed over time, the participating individuals were sometimes the same, and sometimes changed across the data collection period. Typical settings were hospital wards, hospitals, smaller communities or intervention trials. The type of collected data corresponded with what kind of entities were followed longitudinally. Individuals were often followed with serial interviews, whereas groups were commonly followed with focus group interviews complemented with individual interviews, observations and/or questionnaires. Overall, the lengths of data collection periods seemed to be chosen based upon expected changes in the chosen entities. For example, the articles following an intervention setting were structured around the intervention timeline, collecting data before, after and sometimes during the intervention.

Tempo of data collection

The data collection tempo differed among the articles (e.g., the frequency and mode of the data collection). Approximately half ( n  = 154, 51.5%) of the articles used serial time points, collecting data at several reoccurring but shorter sequences (e.g., through serial interviews or open-ended questions in questionnaires). When data were collected in time waves ( n  = 50, 16.7%), the periods of data collection were longer, usually including both interviews and observations; often, time waves included observations of a setting and/or interviews at the same location over several days or weeks.

When comparing the tempo with the type of entities, some patterns were detected (see Fig.  3 ). When individuals were followed, data were often collected at time points, mirroring the use of individual interviews and/or short observations. For research in settings, data were commonly collected in time waves (e.g., observation periods over a few weeks or months). In studies exploring settings across time, time waves were commonly used and combined several types of data, particularly from interviews and observations. Groups were the least common studied entity ( n  = 9, 3.0%), so the numbers should be interpreted with caution, but continuous data collection was used in five of the nine studies. The continuous data collection mode was, for example, collecting electronic diaries [ 62 ] or minutes from committee meetings during a time period [ 63 ].

Tempo of data collection in relation to entities followed over time

Preplanned or adapted data collection

A large majority ( n  = 224, 74.9%) of the articles used preplanned data collection (e.g., in preplanned data collection, all participants were followed across time according to the same data collection plan). For example, all participants were interviewed one, six and twelve months’ post-diagnosis. In contrast to the preplanned data collection approach, 44 articles had a participant-adapted data collection (14.7%), and participants were followed at different frequencies and/or over various lengths of time depending on each participant’s situation. Participant-adapted data collection was more common among articles following individuals or individual cases (see Fig.  4 ). To adapt the data collection to the participants, the researchers created strategies to reach participants when crucial events were happening. Eleven articles used a participant entry approach to data collection ( n  = 11, 6.7%), and the whole or parts of the data were independently sent in by participants in the form of diaries, questionnaires, or blogs. Another approach to data collection was using theoretical or analysis-driven ideas to guide the data collection ( n  = 19, 6.4%). In these articles, the analysis and data collection were conducted simultaneously, and ideas arising in the analysis could be followed up, for example, returning to some participants, recruiting participants with specific experiences, or collecting complementary types of data materials. This approach was most common in the articles following settings across time, which often included observations and interviews with different types of populations. Articles using theoretical or analysis driven data collection were not associated with grounded theory to a greater extent than the other articles in the sample (e.g., did not self-identify as grounded theory or referred to methodological literature within grounded theory traditions to a greater proportion).

Preplanned or adapted data collection in relation to entities followed over time

According to our results, some researchers used QLR as a methodological approach and other researchers used a longitudinal qualitative data collection without aiming to investigate change. Adding to the debate on whether QLR is a methodological approach in its own right or a design element in a particular study we suggest that the use of QLR can be described as layered (see Fig.  5 ). Namely, articles must fulfill several criteria in order to use QLR as a methodological approach, and that is done in some articles. In those articles QLR method references were used, the aim was to investigate change of a phenomenon and the longitudinal elements of the data collection were thoroughly integrated into the method section. On the other hand, some articles using a longitudinal qualitative data collection were just collecting data over time, without addressing time and/or change in the aim. These articles can still be interesting research studies with valuable results, but they are not using the full potential of QLR as a methodological approach. In all, around 40% of the articles had an aim that focused on describing or understanding change (either as phenomenon or outcome); but only about 24% of the articles set out to investigate change across time as their phenomenon of interest.

The QLR onion. The use of QLR design can be described as layered, where researchers use more or less elements of a QLR design. The two inmost layers represents articles using QLR as a methodological approach

Regarding methodological influences, about one-third of the articles self-identify with any of the traditional qualitative methodologies. Using a longitudinal qualitative data collection as an element integrated with another methodological tradition can therefore be seen as one way of working with longitudinal qualitative materials. In our results, the articles referring to methodologies other than QLR preferably used case study, phenomenology and grounded theory methodologies. This was surprising since Neale [ 10 ] identified ethnography, case studies and narrative methods as the main methodological influences on QLR. Our findings might mirror the profound impacts that phenomenology and grounded theory have had on the qualitative field of health research. Regarding phenomenology, the findings can also be influenced by more recent discussions of combining interpretative phenomenological analysis with QLR [ 6 ].

Half of the articles self-identified as QLR studies, but QLR method references were used in less than 20% of the identified articles. This is both surprising and troublesome since use of appropriate method literature might have supported researchers who were struggling with for example a large quantity of materials and complex analysis. A possible explanation for the lack of use of QLR method literature is that QLR as a methodological approach is not well known, and authors might not be aware that method literature exists. It is quite understandable that researchers can describe a qualitative project with longitudinal data collection as a qualitative longitudinal study, without being aware that QLR is a specific form of study. Balmer [ 64 ] described how their group conducted serial interviews with medical students over several years before they became aware of QLR as a method of study. Within our networks, we have met researchers with similar experiences. Likewise, peer reviewers and editorial boards might not be accustomed to evaluating QLR manuscripts. In our results, 138 journals published one article between 2017 and 2019, and that might not be enough for editorial boards and peer reviewers to develop knowledge to enable them to closely evaluate manuscripts with a QLR method.

In 2007, Holland and colleagues [ 65 ] mapped QLR in the UK and described the following four categories of QLR: 1) mixed methods approaches with a QLR component; 2) planned prospective longitudinal studies; 3) follow-up studies complementing a previous data collection with follow-up; and 4) evaluation studies. Examples of all these categories can be found among the articles in this method study; however, our results do paint a more complex picture. According to our results, Holland’s categories are not multi-exclusive. For example, studies with intentions to evaluate or implement practices often used a mixed methods design and were therefore eligible for both categories one and four described above. Additionally, regarding the follow-up studies, it was seldom clearly described if they were planned as a two-time-point study or if researchers had gained an opportunity to follow up on previous data collection. When we tried to categorize QLR articles according to the data collection design, we could not identify multi-exclusive categories. Instead, we identified the following three components of longitudinal data collection: 1) entities followed across time; 2) tempo; and 3) preplanned or adapted data collection approaches. However, the most common combination was preplanned studies that followed individuals longitudinally with three or more time points.

The use of QLR differs between disciplines [ 14 ]. Our results show some patterns for QLR within health research. Firstly, the QLR projects were large and complex; they often included several types of populations and various data materials, and were presented in several articles. Secondly, most studies focused upon the individual perspective, following individuals across time, and using individual interviews. Thirdly, the data collection periods varied, but 53% of the articles had a data collection period of 1 year or shorter. Finally, patients were the most prevalent population, even though topics varied greatly. Previously, two other reviews that focused on QLR in different parts of health research (e.g., nursing [ 4 ] and gerontology [ 66 ]) pointed in the same direction. For example, individual interviews or a combination of data materials were commonly used, and most studies were shorter than 1 year but a wide range existed [ 4 , 66 ].

Considerations when planning a QLR project

Based on our results, we argue that when health researchers plan a QLR study, they should reflect upon their perspective of time/change and decide what part change should play in their QLR study. If researchers decide that change should play the main role in their project, then they should aim to focus on change as the phenomenon of interest. However, in some research, change might be an important part of the plot, without having the main role, and change in relation to the outcomes might be a better perspective. In such studies, participants with change, no change or different kinds of change are compared to explore possible explanations for the change. In our results, change in relation to the outcomes was often used in relation to intervention studies where participants who reached a desired outcome were compared to individuals who did not. Furthermore, for some research studies, change is part of the context in which the research takes place. This can be the case when certain experiences happen during a period of change; for example, when the aim is to explore the experience of everyday life during rehabilitation after stroke. In such cases a longitudinal data collection could be advisable (e.g., repeated interviews often give a deep relationship between interviewer and participants as well as the possibility of gaining greater depth in interview answers during follow-up interviews [ 15 ]), but the study might not be called a QLR study since it does not focus upon change [ 13 ]. We suggest that researchers make informed decisions of what kind of longitudinal perspective they set out to investigate and are transparent with their sources of methodological inspiration.

We would argue that length of data collection period, type of entities, and data materials should be in accordance with the type of change/changing processes that a study focuses on. Individual change is important in health research, but researchers should also remember the possibility of investigating changes in families, working groups, organizations and wider communities. Using these types of entities were less common in our material and could probably grant new perspectives to many research topics within health. Similarly, using several types of data materials can complement the insights that individual interviews can give. A large majority of the articles in our results had a preplanned data collection. Participant-adapted data collection can be a way to work in alignment with a “time-as-fluid” conceptualization of time because the events of subjective importance to participants can be more in focus and participants (or other entities) change processes can differ substantially across cases. In studies with lengthy and spaced-out data collection periods and/or uncertainty in trajectories, researchers should consider participant-adapted or participant entry data collection. For example, some participants can be followed for longer periods and/or with more frequency.

Finally, researchers should consider how to best publish and disseminate their results. Many QLR projects are large, and the results are divided across several articles when they are published. In our results, 21 papers self-identified as a mixed methods project or as part of a larger mixed methods project, but most of these did not include quantitative data in the article. This raises the question of how to best divide a large research project into suitable pieces for publication. It is an evident risk that the more interesting aspects of a mixed methods project are lost when the qualitative and quantitative parts are analyzed and published separately. Similar risks occur, for example, when data have been collected from several types of populations but are then presented per population type (e.g., one article with patient data and another with caregiver data). During the work with our study, we also came across studies where data were collected longitudinally, but the results were divided into publications per time point. We do not argue that these examples are always wrong, there are situations when these practices are appropriate. However, it often appears that data have been divided without much consideration. Instead, we suggest a thematic approach to dividing projects into publications, crafting the individual publications around certain ideas or themes and thus using the data that is most suitable for the particular research question. Combining several types of data and/or several populations in an analysis across time is in fact what makes QLR an interesting approach.

Strengths and limitations

This method study intended to paint a broad picture regarding how longitudinal qualitative methods are used within the health research field by investigating 299 published articles. Method research is an emerging field, currently with limited methodological guidelines [ 21 ], therefore we used scoping review method to support this study. In accordance with scoping review method we did not use quality assessment as a criterion for inclusion [ 18 – 20 ]. This can be seen as a limitation because we made conclusions based upon a set of articles with varying quality. However, we believe that learning can be achieved by looking at both good and bad examples, and innovation may appear when looking beyond established knowledge, or assessing methods from different angles. It should also be noted that the results given in percentages hold no value for what procedures that are better or more in accordance with QLR, the percentages simply state how common a particular procedure was among the articles.

As described, the included articles showed much variation in the method descriptions. As the basis for our results, we have only charted explicitly written text from the articles, which might have led to an underestimation of some results. The researchers might have had a clearer rationale than described in the reports. Issues, such as word restrictions or the journal’s scope, could also have influenced the amount of detail that was provided. Similarly, when charting how articles drew on a traditional methodology, only data from the articles that clearly stated the methodologies they used (e.g., phenomenology) were charted. In some articles, literature choices or particular research strategies could implicitly indicate that the researchers had been inspired by certain methodologies (e.g., referring to grounded theory literature and describing the use of simultaneous data collection and analysis could indicate that the researchers were influenced by grounded theory), but these were not charted as using a particular methodological tradition. We used the articles’ aims and objectives/research questions to investigate their longitudinal perspectives. However, as researchers have different writing styles, information regarding the longitudinal perspectives could have been described in surrounding text rather than in the aim, which might have led to an underestimation of the longitudinal perspectives.

The experience and diversity of the research team in our study was a strength. The nine authors on the team represent ten universities and three countries, and have extensive experience in different types of qualitative research, QLR and review methods. The different level of experiences with QLR within the team (some authors have worked with QLR in several projects and others have qualitative experience but no experience in QLR) resulted in interesting discussions that helped drive the project forward. These experiences have been useful for understanding the field.

Based on a method study of 299 articles, we can conclude that QLR in health research articles published between 2017 and 2019 often contain comprehensive complex studies with a large variation in topics. Some research was thoroughly designed to capture time/change throughout the methodology, focus and data collection, while other articles included a few elements of QLR. Longitudinal data collection included several components, such as what entities were followed across time, the tempo of data collection, and to what extent the data collection was preplanned or adapted across time. In sum, health researchers need to be considerate and make informed choices when designing QLR projects. Further research should delve deeper into what kind of research questions go well with QLR and investigate the best practice examples of presenting QLR findings.

Acknowledgments

The authors wish to acknowledge Ellen Sejersted, librarian at the University of Agder, Kristiansand, Norway, who conducted the literature searches and Julia Andersson, research assistant at the Department of Nursing, Umeå University, Sweden, who supported the data management and took part in the initial screening phases of the project.

Authors’ contributions

ÅA conceived the study. ÅA, EH, TW, LF, MKP, HA, and MSL designed the study. ÅA, TW, and LF were involved in literature searches together with the librarian. ÅA and EH performed the screening of the articles. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) took part in the data charting. ÅA performed the data analysis and discussed the preliminary results with the rest of the team. ÅA wrote the 1st manuscript draft, and ÅK, MSL and EH edited. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) contributed to editing the 2nd draft. MSL and LF provided overall supervision. All authors read and approved the final manuscript.

Authors’ information

All authors represent the nursing discipline, but their research topics differ. ÅA and ÅK have previously worked together with QLR method development. ÅA, EH, TW, LF, MKP, HA, KLD and MSL work together in the Nordic research group PRANSIT, focusing on nursing topics connected to transition theory using a systematic review method, preferably meta synthesis. All authors have extensive experience with qualitative research but various experience with QLR.

Open access funding provided by Umea University. This project was conducted within the authors’ positions and did not receive any specific funding.

Availability of data and materials

Declarations.

Not applicable.

The authors declare that they have no competing interests.

1 Qualitative method references were defined as a journal article or book with a title that indicated an aim to guide researchers in qualitative research methods and/or research theories. Primary studies, theoretical works related to the articles’ research topics, protocols, and quantitative method literature were excluded. References written in a language other than English was also excluded since the authors could not evaluate their content.

2 QLR method references were defined as a journal article or book that 1) focused on qualitative methodological questions, 2) used terms such as ‘longitudinal’ or ‘time’ in the title so it was evident that the focus was on longitudinal qualitative research. Referring to another original QLR study was not counted as using QLR method literature.

3 Words were charted depending on their word stem, e.g., change, changes and changing were all charted as change.

4 It should be noted that here time span refers to the data collection related to each participant or case. Researchers could collect data for 2 years but follow each participant for 6 months.

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• Review Article
• Published: 28 August 2024

Pathophysiological insights into HFpEF from studies of human cardiac tissue

• Ahmed U. Fayyaz 1 , 2 ,
• Muhammad Eltony   ORCID: orcid.org/0009-0009-5253-4411 1 ,
• Larry J. Prokop 3 ,
• Katlyn E. Koepp 1 ,
• Barry A. Borlaug   ORCID: orcid.org/0000-0001-9375-0596 1 ,
• Surendra Dasari 4 ,
• Melanie C. Bois 2 ,
• Kenneth B. Margulies   ORCID: orcid.org/0000-0002-8093-4465 5 ,
• Joesph J. Maleszewski 2 ,
• Ying Wang   ORCID: orcid.org/0000-0002-7852-386X 1 &
• Margaret M. Redfield 1  

Nature Reviews Cardiology ( 2024 ) Cite this article

Metrics details

• Heart failure
• Pathogenesis

Heart failure with preserved ejection fraction (HFpEF) is a major, worldwide health-care problem. Few therapies for HFpEF exist because the pathophysiology of this condition is poorly defined and, increasingly, postulated to be diverse. Although perturbations in other organs contribute to the clinical profile in HFpEF, altered cardiac structure, function or both are the primary causes of this heart failure syndrome. Therefore, studying myocardial tissue is fundamental to improve pathophysiological insights and therapeutic discovery in HFpEF. Most studies of myocardial changes in HFpEF have relied on cardiac tissue from animal models without (or with limited) confirmatory studies in human cardiac tissue. Animal models of HFpEF have evolved based on theoretical HFpEF aetiologies, but these models might not reflect the complex pathophysiology of human HFpEF. The focus of this Review is the pathophysiological insights gained from studies of human HFpEF myocardium. We outline the rationale for these studies, the challenges and opportunities in obtaining myocardial tissue from patients with HFpEF and relevant comparator groups, the analytical approaches, the pathophysiological insights gained to date and the remaining knowledge gaps. Our objective is to provide a roadmap for future studies of cardiac tissue from diverse cohorts of patients with HFpEF, coupling discovery biology with measures to account for pathophysiological diversity.

Few studies of cardiac tissue from patients with heart failure with preserved ejection fraction (HFpEF) and comparator groups have been published.

Most of these studies were small and showed variability in tissue source, case–control ascertainment and analytical approaches.

Cardiac tissue samples from patients with HFpEF show variable degrees of myocardial fibrosis, hypertrophy, microvascular rarefaction, T-tubule disruption, systolic and diastolic dysfunction and impaired metabolism.

Only eight candidate pathophysiological pathways have been examined in hypothesis-driven studies of cardiac tissue from patients with HFpEF, and these studies have not led to consensus on its pathophysiology.

Only four studies used discovery transcriptomics or proteomic technologies in cardiac tissue from patients with HFpEF and comparators, and showed intriguing, but highly variable, findings.

Studies of heart tissue in large and diverse cohorts of patients with HFpEF are urgently needed, with discovery multiomics, appropriate bioinformatic analyses and rigorous validation to address pathophysiological diversity and gain novel therapeutic insights.

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Acknowledgements

A.U.F. and K.E.K. are supported by T32 HL007111. B.A.B. is supported by R01 HL128526, R01 HL162828, U01 HL160226 and W81XWH2210245 from the US Department of Defense, and a grant from the Accelerating Medicines Partnership for Heart Failure through the Foundation for the National Institutes of Health (FNIH). S.D. is supported by HL162828 and U01HL160226. K.B.M. is supported by R01 HL149891 and a grant from the Accelerating Medicines Partnership for Heart Failure through the FNIH. Y.W. is supported by HL 148339, DK 117910 and a grant from the Cardiovascular Department, Mayo Clinic, Rochester, MN. M.M.R. is supported by HL162828, U01 HL160226 and a grant from the Accelerating Medicines Partnership for Heart Failure through the FNIH.

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Ahmed U. Fayyaz, Muhammad Eltony, Katlyn E. Koepp, Barry A. Borlaug, Ying Wang & Margaret M. Redfield

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A.U.F., M.E., L.J.P., K.E.K. and M.M.R. researched data for the article. A.U.F. and M.M.R. contributed substantially to discussion of the content and wrote the manuscript. All authors reviewed or edited the manuscript before submission.

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Review criteria

The review strategy to identify all studies of human myocardial tissue in heart failure with preserved ejection fraction (HFpEF) was performed in accordance with the systematic scoping review guidelines 150 , 151 , 152 . Databases, including Ovid MEDLINER, Ovid EMBASE, Scopus and Web of Science, were searched in English from their inception until 17 August 2022. An experienced librarian (L.J.P.), with input from the rest of the authors, designed and executed the search strategy using controlled vocabulary and keywords for human tissue and HFpEF or diastolic heart failure or dysfunction. Inclusion criteria included: (1) original investigation, (2) study of human cardiac tissue solely or for validation of findings observed in animal models, and (3) tissue obtained from patients with clinical diagnosis of HFpEF or rigorously documented diastolic dysfunction. Use of human cardiac tissue as non-HFpEF comparator was recorded but not required for inclusion. Studies that relied solely on imaging or other non-tissue collection procedures to characterize myocardial properties were excluded. Two investigators (A.U.F. and M.E.) reviewed titles, abstracts and figures from the search results and excluded articles clearly not meeting the predefined eligibility criteria. Three investigators (A.U.F., M.E. and M.M.R.) independently reviewed the remaining studies in detail and excluded those that did not meet the selection criteria or that were restricted to specific heart failure aetiologies (infiltrative or hypertrophic cardiomyopathy). Abstracted data included study type (human-only versus animal model plus human tissue), heart failure diagnostic criteria, ejection fraction criteria for HFpEF diagnosis, and comparator groups (heart failure with reduced ejection fraction (HFrEF) or comparators without heart failure (non-failing comparators)). Within HFpEF, other comparators were noted (Supplementary Box  1 ). Additionally, group sizes, age, whether both sexes were included, type of tissue (myocardium versus adipose), biopsy site and biopsy acquisition method were recorded. When human tissue studies were performed as part of an animal model-based study, only findings pertinent to the human tissue studies were presented. A total of 6,465 articles were identified from the database search and 14 from other sources (such as authors’ previous knowledge or included in the reference list of identified articles) and 4,083 duplicates were removed (Supplementary Fig.  1a ). After title, abstract and figure review, 2,254 articles not meeting the inclusion criteria were excluded. After full-text assessment, another 86 studies did not meet inclusion criteria. Ultimately, 56 studies qualified for inclusion 20 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 66 , 73 , 78 , 79 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , which included 11 (refs. 37 , 38 , 42 , 45 , 52 , 55 , 58 , 61 , 64 , 66 , 114 ) identified in cited references or by investigator pre-existing knowledge, and three 47 , 115 , 124 that were published subsequent to the literature search end-date. The included studies were from 2004 to 2023 (Supplementary Fig.  1b ), with 28 (50%) studies published since 2018. Most studies were published in high-impact journals (Supplementary Fig.  1c ). Some relevant studies might have been missed by our search and review strategies, and new studies might have emerged during the review and publication process. Our summaries were brief and focused on mechanistic insights and did not detail the strengths and weaknesses of each study.

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Fayyaz, A.U., Eltony, M., Prokop, L.J. et al. Pathophysiological insights into HFpEF from studies of human cardiac tissue. Nat Rev Cardiol (2024). https://doi.org/10.1038/s41569-024-01067-1

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