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• Beauty sleep:...

Beauty sleep: experimental study on the perceived health and attractiveness of sleep deprived people

• Related content
• Peer review
• John Axelsson , researcher 1 2 ,
• Tina Sundelin , research assistant and MSc student 2 ,
• Michael Ingre , statistician and PhD student 3 ,
• Eus J W Van Someren , researcher 4 ,
• Andreas Olsson , researcher 2 ,
• Mats Lekander , researcher 1 3
• 1 Osher Center for Integrative Medicine, Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
• 2 Division for Psychology, Department of Clinical Neuroscience, Karolinska Institutet
• 3 Stress Research Institute, Stockholm University, Stockholm
• 4 Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, and VU Medical Center, Amsterdam, Netherlands
• Correspondence to: J Axelsson john.axelsson{at}ki.se
• Accepted 22 October 2010

Objective To investigate whether sleep deprived people are perceived as less healthy, less attractive, and more tired than after a normal night’s sleep.

Design Experimental study.

Setting Sleep laboratory in Stockholm, Sweden.

Participants 23 healthy, sleep deprived adults (age 18-31) who were photographed and 65 untrained observers (age 18-61) who rated the photographs.

Intervention Participants were photographed after a normal night’s sleep (eight hours) and after sleep deprivation (31 hours of wakefulness after a night of reduced sleep). The photographs were presented in a randomised order and rated by untrained observers.

Main outcome measure Difference in observer ratings of perceived health, attractiveness, and tiredness between sleep deprived and well rested participants using a visual analogue scale (100 mm).

Results Sleep deprived people were rated as less healthy (visual analogue scale scores, mean 63 (SE 2) v 68 (SE 2), P<0.001), more tired (53 (SE 3) v 44 (SE 3), P<0.001), and less attractive (38 (SE 2) v 40 (SE 2), P<0.001) than after a normal night’s sleep. The decrease in rated health was associated with ratings of increased tiredness and decreased attractiveness.

Conclusion Our findings show that sleep deprived people appear less healthy, less attractive, and more tired compared with when they are well rested. This suggests that humans are sensitive to sleep related facial cues, with potential implications for social and clinical judgments and behaviour. Studies are warranted for understanding how these effects may affect clinical decision making and can add knowledge with direct implications in a medical context.

Introduction

The recognition [of the case] depends in great measure on the accurate and rapid appreciation of small points in which the diseased differs from the healthy state Joseph Bell (1837-1911)

Good clinical judgment is an important skill in medical practice. This is well illustrated in the quote by Joseph Bell, 1 who demonstrated impressive observational and deductive skills. Bell was one of Sir Arthur Conan Doyle’s teachers and served as a model for the fictitious detective Sherlock Holmes. 2 Generally, human judgment involves complex processes, whereby ingrained, often less consciously deliberated responses from perceptual cues are mixed with semantic calculations to affect decision making. 3 Thus all social interactions, including diagnosis in clinical practice, are influenced by reflexive as well as reflective processes in human cognition and communication.

Sleep is an essential homeostatic process with well established effects on an individual’s physiological, cognitive, and behavioural functionality 4 5 6 7 and long term health, 8 but with only anecdotal support of a role in social perception, such as that underlying judgments of attractiveness and health. As illustrated by the common expression “beauty sleep,” an individual’s sleep history may play an integral part in the perception and judgments of his or her attractiveness and health. To date, the concept of beauty sleep has lacked scientific support, but the biological importance of sleep may have favoured a sensitivity to perceive sleep related cues in others. It seems warranted to explore such sensitivity, as sleep disorders and disturbed sleep are increasingly common in today’s 24 hour society and often coexist with some of the most common health problems, such as hypertension 9 10 and inflammatory conditions. 11

To describe the relation between sleep deprivation and perceived health and attractiveness we asked untrained observers to rate the faces of people who had been photographed after a normal night’s sleep and after a night of sleep deprivation. We chose facial photographs as the human face is the primary source of information in social communication. 12 A perceiver’s response to facial cues, signalling the bearer’s emotional state, intentions, and potential mate value, serves to guide actions in social contexts and may ultimately promote survival. 13 14 15 We hypothesised that untrained observers would perceive sleep deprived people as more tired, less healthy, and less attractive compared with after a normal night’s sleep.

Using an experimental design we photographed the faces of 23 adults (mean age 23, range 18-31 years, 11 women) between 14.00 and 15.00 under two conditions in a balanced design: after a normal night’s sleep (at least eight hours of sleep between 23.00-07.00 and seven hours of wakefulness) and after sleep deprivation (sleep 02.00-07.00 and 31 hours of wakefulness). We advertised for participants at four universities in the Stockholm area. Twenty of 44 potentially eligible people were excluded. Reasons for exclusion were reported sleep disturbances, abnormal sleep requirements (for example, sleep need out of the 7-9 hour range), health problems, or availability on study days (the main reason). We also excluded smokers and those who had consumed alcohol within two days of the protocol. One woman failed to participate in both conditions. Overall, we enrolled 12 women and 12 men.

The participants slept in their own homes. Sleep times were confirmed with sleep diaries and text messages. The sleep diaries (Karolinska sleep diary) included information on sleep latency, quality, duration, and sleepiness. Participants sent a text message to the research assistant by mobile phone (SMS) at bedtime and when they got up on the night before sleep deprivation. They had been instructed not to nap. During the normal sleep condition the participants’ mean duration of sleep, estimated from sleep diaries, was 8.45 (SE 0.20) hours. The sleep deprivation condition started with a restriction of sleep to five hours in bed; the participants sent text messages (SMS) when they went to sleep and when they woke up. The mean duration of sleep during this night, estimated from sleep diaries and text messages, was 5.06 (SE 0.04) hours. For the following night of total sleep deprivation, the participants were monitored in the sleep laboratory at all times. Thus, for the sleep deprivation condition, participants came to the laboratory at 22.00 (after 15 hours of wakefulness) to be monitored, and stayed awake for a further 16 hours. We therefore did not observe the participants during the first 15 hours of wakefulness, when they had had a slightly restricted sleep, but had good control over the last 16 hours of wakefulness when sleepiness increased in magnitude. For the sleep condition, participants came to the laboratory at 12.00 (after five hours of wakefulness). They were kept indoors two hours before being photographed to avoid the effects of exposure to sunlight and the weather. We had a series of five or six photographs (resolution 3872×2592 pixels) taken in a well lit room, with a constant white balance (×900l; colour temperature 4200 K, Nikon D80; Nikon, Tokyo). The white balance was differently set during the two days of the study and affected seven photographs (four taken during sleep deprivation and three during a normal night’s sleep). Removing these participants from the analyses did not affect the results. The distance from camera to head was fixed, as was the focal length, within 14 mm (between 44 and 58 mm). To ensure a fixed surface area of each face on the photograph, the focal length was adapted to the head size of each participant.

For the photo shoot, participants wore no makeup, had their hair loose (combed backwards if long), underwent similar cleaning or shaving procedures for both conditions, and were instructed to “sit with a straight back and look straight into the camera with a neutral, relaxed facial expression.” Although the photographer was not blinded to the sleep conditions, she followed a highly standardised procedure during each photo shoot, including minimal interaction with the participants. A blinded rater chose the most typical photograph from each series of photographs. This process resulted in 46 photographs; two (one from each sleep condition) of each of the 23 participants. This part of the study took place between June and September 2007.

In October 2007 the photographs were presented at a fixed interval of six seconds in a randomised order to 65 observers (mainly students at the Karolinska Institute, mean age 30 (range 18-61) years, 40 women), who were unaware of the conditions of the study. They rated the faces for attractiveness (very unattractive to very attractive), health (very sick to very healthy), and tiredness (not at all tired to very tired) on a 100 mm visual analogue scale. After every 23 photographs a brief intermission was allowed, including a working memory task lasting 23 seconds to prevent the faces being memorised. To ensure that the observers were not primed to tiredness when rating health and attractiveness they rated the photographs for attractiveness and health in the first two sessions and tiredness in the last. To avoid the influence of possible order effects we presented the photographs in a balanced order between conditions for each session.

Statistical analyses

Data were analysed using multilevel mixed effects linear regression, with two crossed independent random effects accounting for random variation between observers and participants using the xtmixed procedure in Stata 9.2. We present the effect of condition as a percentage of change from the baseline condition as the reference using the absolute value in millimetres (rated on the visual analogue scale). No data were missing in the analyses.

Sixty five observers rated each of the 46 photographs for attractiveness, health, and tiredness: 138 ratings by each observer and 2990 ratings for each of the three factors rated. When sleep deprived, people were rated as less healthy (visual analogue scale scores, mean 63 (SE 2) v 68 (SE 2)), more tired (53 (SE 3) v 44 (SE 3)), and less attractive (38 (SE 2) v 40 (SE 2); P<0.001 for all) than after a normal night’s sleep (table 1 ⇓ ). Compared with the normal sleep condition, perceptions of health and attractiveness in the sleep deprived condition decreased on average by 6% and 4% and tiredness increased by 19%.

 Multilevel mixed effects regression on effect of how sleep deprived people are perceived with respect to attractiveness, health, and tiredness

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A 10 mm increase in tiredness was associated with a −3.0 mm change in health, a 10 mm increase in health increased attractiveness by 2.4 mm, and a 10 mm increase in tiredness reduced attractiveness by 1.2 mm (table 2 ⇓ ). These findings were also presented as correlation, suggesting that faces with perceived attractiveness are positively associated with perceived health (r=0.42, fig 1 ⇓ ) and negatively with perceived tiredness (r=−0.28, fig 1). In addition, the average decrease (for each face) in attractiveness as a result of deprived sleep was associated with changes in tiredness (−0.53, n=23, P=0.03) and in health (0.50, n=23, P=0.01). Moreover, a strong negative association was found between the respective perceptions of tiredness and health (r=−0.54, fig 1). Figure 2 ⇓ shows an example of observer rated faces.

 Associations between health, tiredness, and attractiveness

Fig 1  Relations between health, tiredness, and attractiveness of 46 photographs (two each of 23 participants) rated by 65 observers on 100 mm visual analogue scales, with variation between observers removed using empirical Bayes’ estimates

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Fig 2  Participant after a normal night’s sleep (left) and after sleep deprivation (right). Faces were presented in a counterbalanced order

To evaluate the mediation effects of sleep loss on attractiveness and health, tiredness was added to the models presented in table 1 following recommendations. 16 The effect of sleep loss was significantly mediated by tiredness on both health (P<0.001) and attractiveness (P<0.001). When tiredness was added to the model (table 1) with an estimated coefficient of −2.9 (SE 0.1; P<0.001) the independent effect of sleep loss on health decreased from −4.2 to −1.8 (SE 0.5; P<0.001). The effect of sleep loss on attractiveness decreased from −1.6 (table 1) to −0.62 (SE 0.4; P=0.133), with tiredness estimated at −1.1 (SE 0.1; P<0.001). The same approach applied to the model of attractiveness and health (table 2), with a decrease in the association from 2.4 to 2.1 (SE 0.1; P<0.001) with tiredness estimated at −0.56 (SE 0.1; P<0.001).

Sleep deprived people are perceived as less attractive, less healthy, and more tired compared with when they are well rested. Apparent tiredness was strongly related to looking less healthy and less attractive, which was also supported by the mediating analyses, indicating that a large part of the found effects and relations on appearing healthy and attractive were mediated by looking tired. The fact that untrained observers detected the effects of sleep loss in others not only provides evidence for a perceptual ability not previously subjected to experimental control, but also supports the notion that sleep history gives rise to socially relevant signals that provide information about the bearer. The adaptiveness of an ability to detect sleep related facial cues resonates well with other research, showing that small deviations from the average sleep duration in the long term are associated with an increased risk of health problems and with a decreased longevity. 8 17 Indeed, even a few hours of sleep deprivation inflict an array of physiological changes, including neural, endocrinological, immunological, and cellular functioning, that if sustained are relevant for long term health. 7 18 19 20 Here, we show that such physiological changes are paralleled by detectable facial changes.

These results are related to photographs taken in an artificial setting and presented to the observers for only six seconds. It is likely that the effects reported here would be larger in real life person to person situations, when overt behaviour and interactions add further information. Blink interval and blink duration are known to be indicators of sleepiness, 21 and trained observers are able to evaluate reliably the drowsiness of drivers by watching their videotaped faces. 22 In addition, a few of the people were perceived as healthier, less tired, and more attractive during the sleep deprived condition. It remains to be evaluated in follow-up research whether this is due to random error noise in judgments, or associated with specific characteristics of observers or the sleep deprived people they judge. Nevertheless, we believe that the present findings can be generalised to a wide variety of settings, but further studies will have to investigate the impact on clinical studies and other social situations.

Importantly, our findings suggest a prominent role of sleep history in several domains of interpersonal perception and judgment, in which sleep history has previously not been considered of importance, such as in clinical judgment. In addition, because attractiveness motivates sexual behaviour, collaboration, and superior treatment, 13 sleep loss may have consequences in other social contexts. For example, it has been proposed that facial cues perceived as attractive are signals of good health and that this recognition has been selected evolutionarily to guide choice of mate and successful transmission of genes. 13 The fact that good sleep supports a healthy look and poor sleep the reverse may be of particular relevance in the medical setting, where health estimates are an essential part. It is possible that people with sleep disturbances, clinical or otherwise, would be judged as more unhealthy, whereas those who have had an unusually good night’s sleep may be perceived as rather healthy. Compared with the sleep deprivation used in the present investigation, further studies are needed to investigate the effects of less drastic acute reductions of sleep as well as long term clinical effects.

Conclusions

People are capable of detecting sleep loss related facial cues, and these cues modify judgments of another’s health and attractiveness. These conclusions agree well with existing models describing a link between sleep and good health, 18 23 as well as a link between attractiveness and health. 13 Future studies should focus on the relevance of these facial cues in clinical settings. These could investigate whether clinicians are better than the average population at detecting sleep or health related facial cues, and whether patients with a clinical diagnosis exhibit more tiredness and are less healthy looking than healthy people. Perhaps the more successful doctors are those who pick up on these details and act accordingly.

Taken together, our results provide important insights into judgments about health and attractiveness that are reminiscent of the anecdotal wisdom harboured in Bell’s words, and in the colloquial notion of “beauty sleep.”

What is already known on this topic

Short or disturbed sleep and fatigue constitute major risk factors for health and safety

Complaints of short or disturbed sleep are common among patients seeking healthcare

The human face is the main source of information for social signalling

What this study adds

The facial cues of sleep deprived people are sufficient for others to judge them as more tired, less healthy, and less attractive, lending the first scientific support to the concept of “beauty sleep”

By affecting doctors’ general perception of health, the sleep history of a patient may affect clinical decisions and diagnostic precision

Cite this as: BMJ 2010;341:c6614

We thank B Karshikoff for support with data acquisition and M Ingvar for comments on an earlier draft of the manuscript, both without compensation and working at the Department for Clinical Neuroscience, Karolinska Institutet, Sweden.

Contributors: JA designed the data collection, supervised and monitored data collection, wrote the statistical analysis plan, carried out the statistical analyses, obtained funding, drafted and revised the manuscript, and is guarantor. TS designed and carried out the data collection, cleaned the data, drafted, revised the manuscript, and had final approval of the manuscript. JA and TS contributed equally to the work. MI wrote the statistical analysis plan, carried out the statistical analyses, drafted the manuscript, and critically revised the manuscript. EJWVS provided statistical advice, advised on data handling, and critically revised the manuscript. AO provided advice on the methods and critically revised the manuscript. ML provided administrative support, drafted the manuscript, and critically revised the manuscript. All authors approved the final version of the manuscript.

Funding: This study was funded by the Swedish Society for Medical Research, Rut and Arvid Wolff’s Memory Fund, and the Osher Center for Integrative Medicine.

Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any company for the submitted work; no financial relationships with any companies that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: This study was approved by the Karolinska Institutet’s ethical committee. Participants were compensated for their participation.

Participant consent: Participant’s consent obtained.

Data sharing: Statistical code and dataset of ratings are available from the corresponding author at john.axelsson{at}ki.se .

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode .

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EXPERIMENTAL RESEARCH METHODS

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Experimental Research

• First Online: 25 February 2021

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• C. George Thomas 2  

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Experiments are part of the scientific method that helps to decide the fate of two or more competing hypotheses or explanations on a phenomenon. The term ‘experiment’ arises from Latin, Experiri, which means, ‘to try’. The knowledge accrues from experiments differs from other types of knowledge in that it is always shaped upon observation or experience. In other words, experiments generate empirical knowledge. In fact, the emphasis on experimentation in the sixteenth and seventeenth centuries for establishing causal relationships for various phenomena happening in nature heralded the resurgence of modern science from its roots in ancient philosophy spearheaded by great Greek philosophers such as Aristotle.

The strongest arguments prove nothing so long as the conclusions are not verified by experience. Experimental science is the queen of sciences and the goal of all speculation . Roger Bacon (1214–1294)

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An Introduction to Experimental and Exploratory Research

7 Pages Posted: 23 Feb 2021 Last revised: 25 Feb 2021

Patna University

Date Written: February 20, 2021

Experimental research is a study that strictly adheres to a scientific research design. It includes a hypothesis, a variable that can be manipulated by the researcher, and variables that can be measured, calculated and compared. Most importantly, experimental research is completed in a controlled environment. Exploratory research is a study that seeks to answer a question or address a phenomenon. The nature of the entity being studied does not allow a variable to be manipulated by the researcher, it cannot be completed in a controlled environment, or most likely, the researcher can’t determine all the influences on the entity, therefore a more exploratory look at the topic is more beneficial.

Keywords: Experimental, Exploratory, Research, Classification, Purpose, Organisation, Paper

JEL Classification: Y20

Suggested Citation: Suggested Citation

Ajit Singh (Contact Author)

Patna university ( email ).

Ashok Rajpath Patna, Bihar 800005 India

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Article Contents

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Sleep duration and mood in adolescents: an experimental study

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Stephen A Booth, Mary A Carskadon, Robyn Young, Michelle A Short, Sleep duration and mood in adolescents: an experimental study, Sleep , Volume 44, Issue 5, May 2021, zsaa253, https://doi.org/10.1093/sleep/zsaa253

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This study examines the relationship between experimentally manipulated sleep duration and mood in adolescents.

Thirty-four adolescents (20 male), aged 15–17 years, lived in a sleep laboratory for 10 days and 9 nights. They were allocated to one of three sleep “doses” for five consecutive nights for 5, 7.5, or 10 h sleep opportunity per night. Two baseline nights and two recovery nights entailed 10 h sleep opportunity per night. Mood was measured every 3 h during wake using unipolar visual analogue scales measuring the mood states “depressed,” “afraid,” “angry,” “confused,” “anxious,” “happy,” and “energetic.”

Mixed models analyses with post hoc comparisons revealed that participants in the 5-h group, but not the 7.5- or 10-h groups, reported being significantly more depressed, angry, and confused during sleep restriction than at baseline. Adolescents were significantly less happy and energetic during sleep restricted to 5 h and significantly less energetic during sleep restricted to 7.5 h. When adolescents had 10 h sleep opportunities their happiness significantly increased. No statistically significant effects of sleep restriction were found for fear or anxiety, although small-to-moderate effects of sleep restricted to 5 or 7.5 h were found. Two nights of recovery sleep was not sufficient to recover from increased negative mood states for the 5-h group, although recovery occurred for positive mood states.

Given the prevalence of insufficient sleep and the rising incidence of mood disorders and dysregulation in adolescents, these findings highlight the importance of sufficient sleep to mitigate these risks.

Adolescence is a critical maturational stage in terms of heightened risk of the onset of mood disorders. Insufficient experimental evidence exists that elucidates the effect of sleep duration on a range of positive and negative mood states in adolescents. The present study uses sleep restriction and sleep extension protocols to experimentally manipulate sleep duration in 34 adolescents. Results indicated that adolescents reported deteriorated in terms of depression, happiness, anger, confusion and energy. Two nights of recovery sleep did not eliminate mood deficits for negative mood states for the 5-h group, although recovery occurred for positive mood states. Sufficient sleep is crucial to guard against mood deficits in otherwise healthy adolescents.

Adolescence is a time of significant psychological, social, and physiological change [ 1 ] and a vulnerable developmental period during which individuals are at heightened risk of developing a mental illness [ 2 ]. Despite the importance of mood and the commonly held belief that sleep loss perturbs adolescent mood [ 3 , 4 ], rigorous experimental evidence supporting a causal relationship between sleep loss and mood deficits in adolescents is scant. The current experiment tests the causal association between sleep duration and adolescent mood.

Empirical literature on sleep duration and mood has overwhelmingly focused on adults [ 5–7 ]; however, adult findings may not generalize to adolescents. Adolescents differ in terms of their greater sleep need [ 8 , 9 ], types of affective challenges they face [ 10 ], and less mature prefrontal brain regions that are crucial to affective regulation [ 11 ]. As such, research focusing specifically on adolescents is needed. Most of the extant literature focusing on sleep duration and mood in adolescents is cross-sectional. These studies report a correlation of sleep duration and mood, with shorter sleep associated with worse mood [ 12 , 13 ]. Due to the cross-sectional nature of such studies, however, a casual relationship cannot be concluded.

A recent study investigated the effect of sleep restriction and sleep extension on a group of 48 adolescents aged 14–17 years with Attention Deficit Hyperactivity Disorder using a 3-week sleep protocol with an experimental crossover design in participants’ homes [ 14 ]. Sleep restriction involved a week with a 6.5 h sleep opportunity per night while sleep extension involved a week of 9.5 h sleep opportunities per night. Parent- and self-reported depressive symptoms were greater during sleep restriction compared to sleep extension, while positive affect was lower. Parents also reported increased negative affect and emotion dysregulation among adolescents during sleep restriction when compared to extension.

Among the limited experimental studies including adolescents, two were performed under continuous monitoring. The sleep of 113 adolescents, aged 15–19 years, was restricted to a 5-h sleep opportunity for seven consecutive nights, with or without a 1-h daytime nap opportunity [ 15 , 16 ]. Mood was assessed three times per day using Positive and Negative Affect Scales (PANAS). Compared to controls, who had 9-h sleep opportunity per night, sleep-restricted adolescents reported significantly lower positive mood scores. The detriment to positive mood was only partially ameliorated by an afternoon nap. Unexpectedly, no change was observed to negative mood. Lo et al. note that adolescents reported that many of the mood states assessed by the PANAS negative mood subscale, such as guilty, afraid, and scared, were not relevant to them [ 15 ]. Differential sensitivity to sleep loss among mood states has been reported in a recent meta-analysis of 361,505 adolescents [ 17 ], with positive mood showing the largest effect in response to shorter sleep (OR = 1.02), followed by anger (OR = .83), depression (OR = .62), and anxiety (OR = .41). The present literature is limited by the paucity of studies that have examined specific mood states [ 18 ]. Other challenges arise in those studies using home-based sleep restriction schedules where breaches of adherence to study protocols regarding sleep and abstinence from napping and caffeine may muddle interpretation [ 19 , 20 ].

The present study addresses the gaps and limitations in current literature by employing a laboratory-based experimental design to measure several discrete mood states over varying “doses” of sleep. The laboratory environment ensures adherence to study protocols and control of environmental variables, such as diet, caffeine consumption, and exercise that are known to affect sleep and mood [ 21–23 ]. The repeated-measures design whereby participants in each condition have their mood compared between baseline, the sleep dose condition, and following recovery sleep provides the opportunity for robust conclusions regarding the causal link between sleep duration and mood.

We hypothesized that self-reported mood will be significantly worse during sleep restriction when compared to extended 10-h baseline and recovery sleep opportunity, with positive moods decreasing and negative moods increasing when sleep is restricted to 5- or 7.5-h sleep opportunity per night, as neither duration allows for sleep that is within the recommended range for adolescents [ 24 ] (i.e. 8–10 h sleep per night).

Participants

Participants were 34 adolescents aged 15–17 years (20 male, M age = 15.91 years ± 0.86) from South Australian high schools. All participants were late- or postpubertal adolescents (Tanner Stage 4 or 5 on the Pubertal Development Scale) [ 25 ]. Prescreening by parent and adolescent self-report, showed that participants were physically and psychologically healthy and were medication-free, with the exception of birth control. Participants were good sleepers, with average sleep durations ≥8 h per night [ 26 ], average sleep onset latencies of ≤30 min per night [ 27 ], and weeknight/weekend bedtime discrepancy less than 2 h [ 28 ], as determined by a survey and a 7-day sleep diary during screening, to reduce confounding effects of preexisting poor sleep and/or sleep disorders. Extreme morning or evening chronotypes were not included (≥44 or ≤22 on the Composite Morningness/Eveningness Scale) [ 29 ] due to effects of chronotype on adolescent sleep and mood [ 30 ], nor were participants indicating less than 8 h sleep per night on average, weekend bedtime delay of 2 h or more, or sleep onset latencies greater than 30 min per night.

Materials and measures

Mood was measured using a series of 100 mm unipolar visual analog scale (VAS), similar to those used in a study by Stern et al. [ 31 ]. These consisted of scales with labels to demonstrate the spectrum of discrete mood experience, with “Not at all (mood)” at the left endpoint and “Extremely (mood)” on the right endpoint. Mood states included “Depressed,” “Afraid,” “Angry,” “Confused,” “Anxious,” “Happy,” and “Energetic.”

At each test administration, participants were asked to describe how they felt “RIGHT NOW” by marking a short vertical line intersecting the scale at some point, placing the mark further to the right to reflect the greater intensity of that mood. Items were scored by measuring the distance in millimeters from the left anchor of the scale to the point at which the participant intersects the scale with a line. Unipolar scales (i.e. spanning the range of not at all sad to extremely sad, as opposed to bipolar scales which may span happy to sad) were selected as they reduce participant confusion as compared to a bipolar scale [ 31 ].

Sleep was recorded prior to the laboratory experiment with sleep diaries, actigraphy, and by getting participants to call and leave a message on the sleep laboratory answering machine confirming their and waketime. During the laboratory experiment, sleep was recording using nightly polysomnography. Further information regarding sleep measurement is provided elsewhere [ 8 ].

This study used a mixed experimental design. The independent variables were sleep duration dose (5, 7.5, and 10 h groups), and sleep condition (Baseline, Experimental Sleep, and Recovery); dependent variables were the mood terms: “Depressed,” “Afraid,” “Angry,” “Confused,” “Anxious,” “Happy,” and “Energetic.”

An active recruitment process was used to recruit adolescent participants through announcements in South Australian high-school newsletters. Parents of potential participants provided an initial telephone screen using the Sleep, Medical, Educational, and Family History Survey [ 12 ]. Adolescent participants meeting preliminary inclusion criteria were sent a questionnaire package that included a modified Sleep Habits Survey, the Smith Composite Morningness/Eveningness questionnaire, the Pubertal Development Scale and the Sleep, Medical, Education and Family History Survey and a 7-day sleep diary and were invited to attend an interview. Upon confirming eligibility and obtaining parental consent and adolescent assent, adolescents were provided a sleep diary and a wrist activity monitor for the week prior to the in-lab study. Participants were allocated in blocks of four participants to one of the three sleep-dose conditions. Adolescents in each study run were in the same condition and were not informed of their sleep opportunity each night until the end of the study. Adolescents were required to maintain a 9.5 h sleep opportunity between 9:30 pm and 07:00 am for five nights prior to the study to eliminate any existing sleep debt before the start of the study. Thirty-seven participants (21 males) were recruited; however, one did not follow the required pre-study sleep protocol and was excluded from the study and two others (both females assigned to the 7.5 h condition) discontinued their participation before the study’s conclusion.

On the first two of nine consecutive laboratory nights, a 10 h sleep opportunity was provided to extinguish any residual sleep debt and provide baseline sleep and mood data. Adolescents’ polysomnographically estimated sleep durations on the adaptation and baseline nights were not significantly different between conditions (all p < .05). Five experimental nights of 5, 7.5, or 10 h sleep opportunity followed, with wake time at 7:30 am regardless of sleep duration. This wake time was chosen to simulate typical rising early for school, as adolescents generally restrict sleep by staying up late rather than getting up early [ 32 ]. Two recovery nights of 10 h sleep opportunity concluded the experiment. The VAS was administered three-hourly across all wake periods, as shown in Figure 1 .

Schematic of the study protocol, displaying days by hours over three sleep dose conditions.

To control environmental variables, participants completed the study in a laboratory free of time-cues, without access to caffeine or to live television or Internet. Participants had access to mobile phones for one 15-min period each day, although Internet access was disabled and phone clocks were altered. Participants spent their time playing board games, doing craft activities, watching prerecorded movies and television series, and interacting with one another and with research staff. The laboratory was light- (<50 lx during wake periods and <1 lx during sleep opportunities) and temperature- (21 ○ C) controlled and sound-attenuated. Participants’ scheduled sleep episodes were recorded using polysomnography to confirm participant sleep times. Findings regarding changes to sleep and attention variables are reported elsewhere [ 8 ].

Statistical analyses

Linear mixed-effects models were used to test differences in mood across sleep conditions (baseline, sleep restriction, and recovery) for both males and females. This analytic approach accounted for both within and between-participant variance [ 33 ]. All models specified a random effect of subject ID. Models for mood specified “Depressed,” “Afraid,” “Angry,” “Confused,” “Anxious,” “Happy,” and “Energetic” as dependent variables. Each model was fully saturated, including all main and interaction effects, for sleep dose (5, 7.5, and 10 h), sleep condition (baseline, sleep restriction, and recovery), and sex. Baseline data collected on day 2 were excluded to reduce acclimatization effects as participants adjusted to a novel environment. Cohen’s d was calculated to indicate effect size.

Results are presented for the interactions between sleep dose group (i.e. 5, 7.5, or 10 h) and study phase (baseline, sleep restriction, or recovery) in Tables 1–2 and Figure 2 . Noted differences refer to statistical significance at a p < .05 level. Effect sizes are presented using Cohen’s d , where .2, .5, and .8 indicate a small, medium, and large effect size, respectively.

Inferential statistics for main effects and interactions of sleep dose and sleep condition on negative mood variables

Final column displays significant post hoc comparisons ( p < .05). Post hoc comparisons for the main effect of “dose” are between subjects’ comparisons while all remaining comparisons are within-subjects.

Note. BL, baseline sleep condition; ES, experimental sleep dose; RC, recovery sleep condition; 5 h, 5 h experimental sleep dose; 7.5 h, 7.5 h experimental sleep dose; 10 h, control group with 10 h sleep dose.

Inferential statistics for the main effects and interactions of experimental sleep dose and sleep condition on positive mood variables

Effect sizes (Cohen’s d ) of changes to mood states between baseline and experimental sleep dose for the 5-, 7.5-, and 10-h sleep dose conditions.

Sleep duration and negative mood

Descriptive statistics for all mood states are provided in the Supplementary Material , and inferential statistics with post hoc test results are provided in Table 1 . Figure 2 illustrates the effect size of the changes in mood states between baseline and experimental sleep dose across the three groups. Overall, the change to mood states across the phases of the study (baseline, experimental sleep dose, and recovery) varied between experimental sleep dose groups for depressed mood, anger, and confusion, but not fear or anxiety. Specifically, participants reported greater depressed mood, anger, and confusion during the experimental sleep dose when compared to baseline, but this effect was only seen when sleep was restricted to 5 h TIB and not during the 7.5 h sleep dose. Depressed mood and fear also increased from experimental sleep dose to recovery for participants in the 5 h group. Confusion increased during recovery when compared to baseline and experimental sleep dose for participants in the 10 h group. No significant changes to other mood states were found between experimental sleep dose and recovery in the 7.5 or 10 h sleep groups.

Sleep duration and positive mood

Inferential statistics and post hoc results are provided in Table 2 . The change to mood states across the phases of the study (baseline, experimental sleep dose, and recovery) varied between sleep dose groups for both happiness and energy. Specifically, participants reported significantly reduced happiness when sleep was restricted to 5 h. They also reported significantly less energy when sleep was restricted to either 5 or 7.5 h sleep opportunities per night. Participants’ happiness increased from baseline to experimental sleep dose in the 10 h sleep dose (control group), and there was a small but nonsignificant increase in energy. Significant increases in happiness and energy also occurred between the experimental sleep period and recovery for adolescents in the 5 and 7.5 h sleep dose groups, while happiness increased between the sleep dose phase and recovery for participants in the 7.5 and 10 h groups.

The aim of the current study was to explore the effect of five nights of sleep limited to either a 5, 7.5, or 10 h sleep opportunity per night on adolescent mood, when compared with baseline and recovery conditions which had 10-h sleep opportunities. Consistent with previous research [ 15–17 , 34 ], positive moods of happiness and energy significantly decreased when sleep was restricted to 5 h sleep opportunity per night, with large effect sizes for both moods. This finding is consistent with results found in adult studies that used a VAS to measure mood [ 5 ], establishing the sensitivity of happiness and energy to sleep loss. It is interesting to note that, although obtaining less than the recommended 8–10 h sleep per night [ 24 , 26 ], positive moods only decreased for energy but not happiness in the 7.5 h group, although a small-to-medium effect size was observed. Conversely, happiness, but not energy, increased from baseline to experimental sleep dose in the 10 h control group, suggesting that when adolescents consistently have the opportunity to obtain optimal sleep, happiness increases.

Results regarding negative mood states of depression, fear, anger, confusion, and anxiety were mixed. Participants in the 5 h group were significantly more depressed, angry, and confused when restricted to 5 h TIB compared to baseline, with large effect sizes for all changes. Likewise, extant research has found that depressed mood, anger, and confusion increased in response to less sleep [ 19 , 35 ]. Similar to results found for positive moods, no changes to negative mood states were observed in the 7.5-h group between baseline and experimental sleep dose, despite not obtaining the recommended duration of sleep.

Fear and anxiety did not increase during the experimental sleep phase for adolescents in the 5 or 7.5 h sleep dose groups. Findings in regard to the sensitivty of anxiety to sleep loss have been mixed [ 34 ]. There are several possible explanations for this discrepancy. First, experimental studies have struggled to replicate cross-sectional results linking less sleep to increased negative mood in adolescents [ 18 , 19 ]. A lack of significant findings in some moods may be a result of a differential sensitivity. It is possible that the “dosage” and chronicity of sleep restriction implemented in the current study, as well as previous experimental research, were sufficient to elicit an increase in depressed mood, anger, and confusion in adolescents, but not fear or anxiety. More chronic sleep restriction may be required to find observable effects. It is important to note that while statistical significance was not reached, the effect size for the increase in anxiety from baseline to sleep restriction was medium ( d = .51) for the 5 h group and small ( d = .27) for the 7.5 h group, thus part of this nonsignificant finding may also reflect a lack of statistical power in the current analyses.

Another possible explanation for the lack of a significant relationship between sleep duration and fear and anxiety has been suggested [ 36 ]. It is argued that individual differences can predict mood responses to restricted sleep, such as a phenomenon where stressful life events influence the development of a person’s genes, making them more susceptible to mood disturbances upon reduced sleep. Restricting sleep reduces cognitive resources needed to dismiss negative stimuli, and those susceptible individuals, having biased attention for negativity, become less able to regulate or reappraise them. This is supported by Gregory et al. [ 37 ], who found that the greatest variance in the relationship between sleep duration and anxiety was accounted for by genetic factors. As the participants recruited for the current study underwent an extensive screening process, ensuring physical and psychological health, this may have minimized the possibility that individuals at higher risk of mood and/or sleep disorders would be included. Fuligni et al. have similarly found that adolescents with greater depressed mood and anxiety need more sleep for optimal mood [ 38 ]. Thus, a sample of adolescents with heightened depressed mood or anxiety at baseline may be less resilient to sleep loss. Although literature reporting this effect focuses on depressed mood and anxiety [ 36 , 37 ], it is possible that the same vulnerabilities apply to other mood states, such as fear. As such, the present results may underestimate the effect that sleep loss has on mood states among the general adolescent population.

As seen in the 5 h sleep dose group, positive mood states of happiness and energy significantly increased from sleep restriction to recovery. In addition, the 7.5 h sleep dose group displayed more energy but not happiness between the sleep restriction and recovery phases. These increases in positive mood states following recovery sleep provide support for the restorative effects of optimal sleep following cumulative sleep loss, allowing adolescents mood to recover to baseline values. For the 10 h sleep dose, happiness increased from baseline to the sleep dose condition, then further increased during recovery. This demonstrates that obtaining optimal sleep increases happiness, with 10 h group participants’ self-reported happiness increasing by more than 10% over the course of the study. Larger effects of sleep loss on mood were found for positive mood states, consistent with recent meta-analytic findings in adolescents [ 17 ]. This highlights the importance of considering positive mood states in research into the impact of sleep on mood and also has important clinical ramifications, given the role of anhedonia in psychological disorders such as depression.

It was expected that negative moods would recover from sleep restriction to recovery for participants in the 5 and 7.5 h sleep restriction conditions. Conversely, in the 5 h sleep dose, depressed mood and fear significantly increased from sleep restriction to recovery. It has been suggested that this increase in negative mood may be a natural effect of living in a laboratory environment, as has observed in adult participants given 9 h sleep opportunities over nine nights [ 39 ]. If so, it would be expected that this same pattern would be observed in the 10 h sleep dose group. However, the only mood to demonstrate this effect without sleep loss was confusion. It is possible that the factors leading to increased negative moods in adults placed in a laboratory environment do not affect adolescents in the same way. This may be due to differences in study protocols, or adolescents’ reduced ability to regulate mood following a period of sleep restriction [ 19 ] when faced with a mood-evoking situation (i.e. leaving the laboratory and their new-found friends). As reported elsewhere [ 8 ], salivary dim light melatonin onset showed a significant and dose–response delay in response to sleep restriction. As such, participants in the 5 h condition completed the study with a circadian rhythm that ran nearly 3 h later than it did at baseline. As a result, adolescents were likely to be waking closer to their circadian nadir, which may result in increased sleep inertia and worse mood, even following recovery sleep.

The current study was able to control for many of the confounding factors, which may have influenced the outcomes of prior adolescent sleep research. No other identified adolescent study examining the effect of sleep on mood was completed entirely under laboratory conditions. As such, previous studies were not able to control for exposure to environmental variables and diet, such as caffeine or excessive sugar to the same degree. The laboratory conditions allowed enforced bed/wake times, permitting stronger causal conclusions to be made about the effect of sleep duration on mood without having to consider response biases inflating relationships between subjective sleep and mood measures when both sleep and mood are measured subjectively. However, laboratory conditions present additional challenges, with reduced ecological validity.

Some of the challenges of the laboratory environment in measuring mood outcomes include the effect of an unfamiliar environment, socializing with peers who are not part of their normal friendship groups, and lack of contact with friends and family, which could have been confounding factors in the effect of sleep restriction on mood. It is a possibility that in a more familiar environment, such as participants’ homes, we may expect to see a more ecologically valid indication of the effect of sleep on mood; however, this comes at a cost of greater exposure to extraneous variables. Nonetheless, the inclusion of 7.5 and 10 h sleep dose groups provides a direct comparison between conditions to test the independent effects of sleep “dose” on mood and helps to distinguish between the effects of sleep loss and what may result simply from being in a laboratory environment for an extended period.

An important consideration regarding the current study is that the screening process ensured that participants were both physically and psychologically healthy. Although this is important to minimize exposure to at-risk individuals and to control for confounding variables, it is possible that the sample of the current study was more impervious or resilient to many of the mood disturbances often associated with inadequate sleep. As such, mood effects witnessed in the present study may have been felt more acutely in at-risk individuals, as indicated by prior research [ 36 , 38 ].

Patterns of cumulative sleep loss are increasingly prevalent among adolescents [ 40 ]. The current study found that, when restricted to 5 h sleep for five nights, adolescents’ happiness and energy decreased, depressed mood, anger, and confusion increased, while fear and anxiety did not change. For participants in the 7.5 sleep dose, no significant changes to positive or negative moods were observed between baseline and sleep restriction conditions, and this degree of sleep restriction may require a longer period of time to observe detrimental effects to mood. It is important to note that, while statistical significance was not reached, small-to-medium effect sizes in changes to these mood states were observed between baseline and sleep restriction in the 7.5-h condition. As such, we cannot be sure that, over an extended period of time, that sleep restricted to 7.5 h sleep opportunity per night may not be damaging to mental health.

The implications of the effect of sleep duration on mood relate to the increasing incidence of both sleep loss and mood disorders in adolescents [ 41–43 ], suggesting a greater need for awareness, support, and intervention in promoting healthy sleep for adolescents [ 44 ]. In addition, findings of the current study demonstrate the rapidity of mood decline when adolescent sleep is restricted to 5 h per night, while a more modest amount of sleep loss may require an extended period to see similar effects. Given the prevalence of insufficient sleep and the rising incidence of mood disorders and dysregulation in adolescents, these findings highlight the importance of sufficient sleep to mitigate these risks.

Financial disclosure: The authors have no financial conflicts of interest to declare.

Non-financial disclosure: The authors have no non-financial conflicts of interest to declare.

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Study/Experimental/Research Design: Much More Than Statistics

Kenneth l. knight.

Brigham Young University, Provo, UT

The purpose of study, experimental, or research design in scientific manuscripts has changed significantly over the years. It has evolved from an explanation of the design of the experiment (ie, data gathering or acquisition) to an explanation of the statistical analysis. This practice makes “Methods” sections hard to read and understand.

To clarify the difference between study design and statistical analysis, to show the advantages of a properly written study design on article comprehension, and to encourage authors to correctly describe study designs.

Description:

The role of study design is explored from the introduction of the concept by Fisher through modern-day scientists and the AMA Manual of Style . At one time, when experiments were simpler, the study design and statistical design were identical or very similar. With the complex research that is common today, which often includes manipulating variables to create new variables and the multiple (and different) analyses of a single data set, data collection is very different than statistical design. Thus, both a study design and a statistical design are necessary.

Advantages:

Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.

Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping them negotiate the “Methods” section, and, thus, it improves the clarity of communication between authors and readers.

A growing trend is to equate study design with only the statistical analysis of the data. The design statement typically is placed at the end of the “Methods” section as a subsection called “Experimental Design” or as part of a subsection called “Data Analysis.” This placement, however, equates experimental design and statistical analysis, minimizing the effect of experimental design on the planning and reporting of an experiment. This linkage is inappropriate, because some of the elements of the study design that should be described at the beginning of the “Methods” section are instead placed in the “Statistical Analysis” section or, worse, are absent from the manuscript entirely.

Have you ever interrupted your reading of the “Methods” to sketch out the variables in the margins of the paper as you attempt to understand how they all fit together? Or have you jumped back and forth from the early paragraphs of the “Methods” section to the “Statistics” section to try to understand which variables were collected and when? These efforts would be unnecessary if a road map at the beginning of the “Methods” section outlined how the independent variables were related, which dependent variables were measured, and when they were measured. When they were measured is especially important if the variables used in the statistical analysis were a subset of the measured variables or were computed from measured variables (such as change scores).

The purpose of this Communications article is to clarify the purpose and placement of study design elements in an experimental manuscript. Adopting these ideas may improve your science and surely will enhance the communication of that science. These ideas will make experimental manuscripts easier to read and understand and, therefore, will allow them to become part of readers' clinical decision making.

WHAT IS A STUDY (OR EXPERIMENTAL OR RESEARCH) DESIGN?

The terms study design, experimental design, and research design are often thought to be synonymous and are sometimes used interchangeably in a single paper. Avoid doing so. Use the term that is preferred by the style manual of the journal for which you are writing. Study design is the preferred term in the AMA Manual of Style , 2 so I will use it here.

A study design is the architecture of an experimental study 3 and a description of how the study was conducted, 4 including all elements of how the data were obtained. 5 The study design should be the first subsection of the “Methods” section in an experimental manuscript (see the Table ). “Statistical Design” or, preferably, “Statistical Analysis” or “Data Analysis” should be the last subsection of the “Methods” section.

Table. Elements of a “Methods” Section

The “Study Design” subsection describes how the variables and participants interacted. It begins with a general statement of how the study was conducted (eg, crossover trials, parallel, or observational study). 2 The second element, which usually begins with the second sentence, details the number of independent variables or factors, the levels of each variable, and their names. A shorthand way of doing so is with a statement such as “A 2 × 4 × 8 factorial guided data collection.” This tells us that there were 3 independent variables (factors), with 2 levels of the first factor, 4 levels of the second factor, and 8 levels of the third factor. Following is a sentence that names the levels of each factor: for example, “The independent variables were sex (male or female), training program (eg, walking, running, weight lifting, or plyometrics), and time (2, 4, 6, 8, 10, 15, 20, or 30 weeks).” Such an approach clearly outlines for readers how the various procedures fit into the overall structure and, therefore, enhances their understanding of how the data were collected. Thus, the design statement is a road map of the methods.

The dependent (or measurement or outcome) variables are then named. Details of how they were measured are not given at this point in the manuscript but are explained later in the “Instruments” and “Procedures” subsections.

Next is a paragraph detailing who the participants were and how they were selected, placed into groups, and assigned to a particular treatment order, if the experiment was a repeated-measures design. And although not a part of the design per se, a statement about obtaining written informed consent from participants and institutional review board approval is usually included in this subsection.

The nuts and bolts of the “Methods” section follow, including such things as equipment, materials, protocols, etc. These are beyond the scope of this commentary, however, and so will not be discussed.

The last part of the “Methods” section and last part of the “Study Design” section is the “Data Analysis” subsection. It begins with an explanation of any data manipulation, such as how data were combined or how new variables (eg, ratios or differences between collected variables) were calculated. Next, readers are told of the statistical measures used to analyze the data, such as a mixed 2 × 4 × 8 analysis of variance (ANOVA) with 2 between-groups factors (sex and training program) and 1 within-groups factor (time of measurement). Researchers should state and reference the statistical package and procedure(s) within the package used to compute the statistics. (Various statistical packages perform analyses slightly differently, so it is important to know the package and specific procedure used.) This detail allows readers to judge the appropriateness of the statistical measures and the conclusions drawn from the data.

STATISTICAL DESIGN VERSUS STATISTICAL ANALYSIS

Avoid using the term statistical design . Statistical methods are only part of the overall design. The term gives too much emphasis to the statistics, which are important, but only one of many tools used in interpreting data and only part of the study design:

The most important issues in biostatistics are not expressed with statistical procedures. The issues are inherently scientific, rather than purely statistical, and relate to the architectural design of the research, not the numbers with which the data are cited and interpreted. 6

Stated another way, “The justification for the analysis lies not in the data collected but in the manner in which the data were collected.” 3 “Without the solid foundation of a good design, the edifice of statistical analysis is unsafe.” 7 (pp4–5)

The intertwining of study design and statistical analysis may have been caused (unintentionally) by R.A. Fisher, “… a genius who almost single-handedly created the foundations for modern statistical science.” 8 Most research did not involve statistics until Fisher invented the concepts and procedures of ANOVA (in 1921) 9 , 10 and experimental design (in 1935). 11 His books became standard references for scientists in many disciplines. As a result, many ANOVA books were titled Experimental Design (see, for example, Edwards 12 ), and ANOVA courses taught in psychology and education departments included the words experimental design in their course titles.

Before the widespread use of computers to analyze data, designs were much simpler, and often there was little difference between study design and statistical analysis. So combining the 2 elements did not cause serious problems. This is no longer true, however, for 3 reasons: (1) Research studies are becoming more complex, with multiple independent and dependent variables. The procedures sections of these complex studies can be difficult to understand if your only reference point is the statistical analysis and design. (2) Dependent variables are frequently measured at different times. (3) How the data were collected is often not directly correlated with the statistical design.

For example, assume the goal is to determine the strength gain in novice and experienced athletes as a result of 3 strength training programs. Rate of change in strength is not a measurable variable; rather, it is calculated from strength measurements taken at various time intervals during the training. So the study design would be a 2 × 2 × 3 factorial with independent variables of time (pretest or posttest), experience (novice or advanced), and training (isokinetic, isotonic, or isometric) and a dependent variable of strength. The statistical design , however, would be a 2 × 3 factorial with independent variables of experience (novice or advanced) and training (isokinetic, isotonic, or isometric) and a dependent variable of strength gain. Note that data were collected according to a 3-factor design but were analyzed according to a 2-factor design and that the dependent variables were different. So a single design statement, usually a statistical design statement, would not communicate which data were collected or how. Readers would be left to figure out on their own how the data were collected.

MULTIVARIATE RESEARCH AND THE NEED FOR STUDY DESIGNS

With the advent of electronic data gathering and computerized data handling and analysis, research projects have increased in complexity. Many projects involve multiple dependent variables measured at different times, and, therefore, multiple design statements may be needed for both data collection and statistical analysis. Consider, for example, a study of the effects of heat and cold on neural inhibition. The variables of H max and M max are measured 3 times each: before, immediately after, and 30 minutes after a 20-minute treatment with heat or cold. Muscle temperature might be measured each minute before, during, and after the treatment. Although the minute-by-minute data are important for graphing temperature fluctuations during the procedure, only 3 temperatures (time 0, time 20, and time 50) are used for statistical analysis. A single dependent variable H max :M max ratio is computed to illustrate neural inhibition. Again, a single statistical design statement would tell little about how the data were obtained. And in this example, separate design statements would be needed for temperature measurement and H max :M max measurements.

As stated earlier, drawing conclusions from the data depends more on how the data were measured than on how they were analyzed. 3 , 6 , 7 , 13 So a single study design statement (or multiple such statements) at the beginning of the “Methods” section acts as a road map to the study and, thus, increases scientists' and readers' comprehension of how the experiment was conducted (ie, how the data were collected). Appropriate study design statements also increase the accuracy of conclusions drawn from the study.

CONCLUSIONS

The goal of scientific writing, or any writing, for that matter, is to communicate information. Including 2 design statements or subsections in scientific papers—one to explain how the data were collected and another to explain how they were statistically analyzed—will improve the clarity of communication and bring praise from readers. To summarize:

• Purge from your thoughts and vocabulary the idea that experimental design and statistical design are synonymous.
• Study or experimental design plays a much broader role than simply defining and directing the statistical analysis of an experiment.
• A properly written study design serves as a road map to the “Methods” section of an experiment and, therefore, improves communication with the reader.
• Study design should include a description of the type of design used, each factor (and each level) involved in the experiment, and the time at which each measurement was made.
• Clarify when the variables involved in data collection and data analysis are different, such as when data analysis involves only a subset of a collected variable or a resultant variable from the mathematical manipulation of 2 or more collected variables.

Acknowledgments

Thanks to Thomas A. Cappaert, PhD, ATC, CSCS, CSE, for suggesting the link between R.A. Fisher and the melding of the concepts of research design and statistics.

Watch CBS News

Here's what a Sam Altman-backed basic income experiment found

By Megan Cerullo

Edited By Anne Marie Lee

Updated on: July 23, 2024 / 10:33 AM EDT / CBS News

A recent study on basic income, backed by OpenAI founder Sam Altman, shows that giving low-income people guaranteed paydays with no strings attached can lead to their working slightly less, affording them more leisure time. 

The study, which is one of the largest and most comprehensive of its kind, examined the impact of guaranteed income on recipients' health, spending, employment, ability to relocate and other facets of their lives.

Altman first announced his desire to fund the study in a 2016 blog post on startup accelerator Y Combinator's site.

Some of the questions he set out to answer about how people behave when they're given free cash included, "Do people sit around and play video games, or do they create new things? Are people happy and fulfilled?" according to the post. Altman, whose OpenAI is behind generative text tool ChatGPT, which threatens to take away some jobs, said in the blog post that he thinks technology's elimination of "traditional jobs"  could make universal basic income necessary in the future. 

How much cash did participants get?

For OpenResearch's Unconditional Cash Study , 3,000 participants in Illinois and Texas received $1,000 monthly for three years beginning in 2020. The cash transfers represented a 40% boost in recipients' incomes. The cash recipients were within 300% of the federal poverty level, with average incomes of less than $29,000. A control group of 2,000 participants received $50 a month for their contributions.

Basic income recipients spent more money, the study found, with their extra dollars going toward essentials like rent, transportation and food.

Researchers also studied the free money's effect on how much recipients worked, and in what types of jobs. They found that recipients of the cash transfers worked 1.3 to 1.4 hours less each week compared with the control group. Instead of working during those hours, recipients used them for leisure time. 

"We observed moderate decreases in labor supply," Eva Vivalt, assistant professor of economics at the University of Toronto and one of the study's principal investigators, told CBS MoneyWatch. "From an economist's point of view, it's a moderate effect." 

More autonomy, better health

Vivalt doesn't view the dip in hours spent working as a negative outcome of the experiment, either. On the contrary, according to Vivalt. "People are doing more stuff, and if the results say people value having more leisure time — that this is what increases their well-being — that's positive." 

In other words, the cash transfers gave recipients more autonomy over how they spent their time, according to Vivalt. 

"It gives people the choice to make their own decisions about what they want to do. In that sense, it necessarily improves their well-being," she said. 

Researchers expected that participants would ultimately earn higher wages by taking on better-paid work, but that scenario didn't pan out. "They thought that if you can search longer for work because you have more of a cushion, you can afford to wait for better jobs, or maybe you quit bad jobs," Vivalt said. "But we don't find any effects on the quality of employment whatsoever."

Uptick in hospitalizations

At a time when even Americans with insurance say they have trouble staying healthy because they struggle to afford care , the study results show that basic-income recipients actually increased their spending on health care services. 

Cash transfer recipients experienced a 26% increase in the number of hospitalizations in the last year, compared with the average control recipient. The average recipient also experienced a 10% increase in the probability of having visited an emergency department in the last year.

Researchers say they will continue to study outcomes of the experiment, as other cities across the U.S. conduct their own tests of the concept.

Megan Cerullo is a New York-based reporter for CBS MoneyWatch covering small business, workplace, health care, consumer spending and personal finance topics. She regularly appears on CBS News 24/7 to discuss her reporting.

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Pathogenicity of <i>meloidogyne</i> species on tomato (<i>solanum lycopersicum</i>) in soil amended with different sources of green manure in ishiagu, southeast nigeria, s.i ogwulumba, e.a. nwankwo, a. i. mkpuma.

Pathogenicity of root-knot nematodes on tomato ( Solanum lycopersicum L.) in soil amended with different sources of green manure was investigated at the research and teaching farm of Federal College of Agriculture Ishiagu, Ebonyi State, Nigeria, during the 2022 and 2023 cropping seasons. Hedge fig plants, African peach plants, and banana leaves at 10t/ha were used as green manure sources while the control plots did not receive any treatment. The experimental design used was a Randomized Complete Block Design (RCBD) with the four treatments replicated three times. Growth and yield parameters were evaluated from plant height, number of leaves, number of fruits, and weight of fruit at harvest while disease parameters were obtained from number of galled roots and number of galls per root. Data collected were averaged over the two cropping seasons and analyzed using analysis of variance (ANOVA) and significant treatment means were separated using least significant difference (LSD). All inferences were made at a 5% level of probability. The results showed that the treatments significantly (P<0.05) increased the plant heights at 6 and 9 weeks after transplanting. The treatments did not have any significant (P>0.05) effect on the number of leaves produced by the plants. The number and weight of fruits at harvest were significantly (P<0.05) increased by the treatments. The number of galled roots was significantly (P<0.05) reduced at the application of hedge plant leaves at 10 t/ha while there was no significant (P>0.05) effect on the gall index of the treatments at harvest when compared with other treatments and it was recommended for tomato farmers as an alternative to inorganic nematicide . 

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