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Beauty sleep:...

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

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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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ORIGINAL RESEARCH article

Impact of weekly physical activity on stress response: an experimental study.

$\r\nRicardo de la Vega$

1 Department of Physical Education, Sport and Human Movement, Autonomous University of Madrid, Madrid, Spain
2 Didactic and Behavioral Analysis in Sport Research Group, Faculty of Sport Sciences, University of Extremadura, Cáceres, Spain
3 Sport of Studies Center, Rey Juan Carlos University, Madrid, Spain

The aim of this research is focused on analyzing the alteration of the psychophysiological and cognitive response to an objective computerized stress test (Determination Test - DT-, Vienna test System ® ), when the behavioral response is controlled. The sample used was sports science students (N = 22), with a mean age of 22.82 (M age = 22.82; SD years = 3.67; M PhysicalActivity hours/Week = 7.77; SD hours / week = 3.32) A quasi-experimental design was used in which the response of each participant to the DT test was evaluated. The variable “number of hours of physical activity per week” and the variable “level of behavioral response to stress” were controlled. Before and after this test, the following parameters were measured: activation and central fatigue (Critical Flicker Fusion Threshold (CFF Critical flicker fusion ascending and Critical flicker fusion descending; DC potential), and perceived exertion (Central Rating of Perceived Exertion and Peripheral Rating of Perceived Exertion). Significant differences were found in all of the measures indicated. The usefulness of this protocol and the measures used to analyze the stress response capacity of the study subjects are discussed.

Introduction

The analysis of psychophysiological fatigue is considered very important in different contexts ( Lohani et al., 2019 ). In this sense, the consideration of the study of humans’s response to external and internal loads ( Wijesuriya et al., 2007 ; Wilson et al., 2007 ) has become one of the most important research topics. The external loads exerted on the individual are added to their skills and coping strategies, resulting in a level of tolerance and adaptation to each situation ( Folkman and Lazarus, 1988 ). Along the last decades, distinctions are often made between physical and mental fatigue role, indicating clear methodologies for the analysis of physiological fatigue, but with clear limitations in the study of central fatigue, because this is measurable only indirectly, which emphasizes the importance of developing new central fatigue analysis procedures ( Bittner et al., 2000 ).

Throughout the decades of research on this topic, different strategies have been used to evaluate the adaptation to these external and internal loads ( Lazarus, 1990 ; Amann, 2011 ). Thus, for example, a multitude of self-reports and standardized tests have been used ( Britner et al., 2003 ), to which physiological and biological measures have been added ( Arza et al., 2019 ). However, relatively low attention is usually given to the Central Nervous System (CNS)-related mechanisms, which play a major role on the development of fatigue ( Tarvainen et al., 2014 ), but are rarely monitored in the sport and physical activity field ( Valenzuela et al., 2020 ). Most of the studies related to central fatigue to date have focused on the effect it has on performing strenuous physical tasks ( Amann and Dempsey, 2008 ), although over the last few years there has been a notable increase in interest in studying the role of central fatigue in explaining human performance ( Inzlicht and Marcora, 2016 ). In this sense, the psychobiological model based on motivational intensity theory has gained special strength ( Gendolla and Richter, 2010 ). This model emphasizes that perception of effort and potential motivation are the central determinants of task engagement. Both variables are taken into consideration in our research, controlling the involvement in the task (motivation), by applying a computerized test, and analyzing the perception of both central and peripheral effort as detailed in the methodological section.

Two of these measures, which focus the methodological attention of this research due to its great potential in the study of this topic, are the Critical Flicker Fusion Threshold (CFFT), evaluated using one Flicker Fusion instrument ( Vicente-Rodríguez et al., 2020 ), and the DC Potential, evaluated using the OmegaWave technology. The neuro-physiological basis of flicker perception is complex but well established ( Görtelmeyer and Zimmermann, 1982 ). In particular, flickering light directly influences cortical activity. The CFFT was measured using two red light- emitting diodes in binocular foveal fixation. The continuous psychophysical method of limits was employed to determine CFFT ( Woodworth and Schlosberg, 1954 ). The utility of CFFT in sport has been focused on the relationship of arousal level with CNS ( Görtelmeyer and Zimmermann, 1982 ). Increase in CFFT suggests an increase in cortical arousal and sensory sensitivity. By contrast, a decrease of CFFT suggests a reduction in the efficiency of the system to process information ( Li et al., 2004 ; Clemente and Díaz, 2019 ). On the other hand, for the evaluation of the brain’s direct current (DC) potentials -slow potentials that reflect alterations in brain excitability- OmegaWave technology has gained strength in recent years ( Naranjo-Orellana et al., 2020 ; Valenzuela et al., 2020 ). This device not only measures the Heart Rate Variability (HRV) but it also simultaneously a brainwave signal (DC potential) in order to complement the information obtained from HRV to assess the athlete’s functional state ( Naranjo-Orellana et al., 2020 ). DC potentials—frequency ranges between 0 and 0.5 Hz, are correlated with different brain processes, such as take consciousness during decision making ( Guggisberg and Mottaz, 2013 ) high alertness states ( Bachmann, 1984 ), arousal state ( Haider et al., 1981 ), or attention ( Rösler et al., 1997 ).

To date, most studies conducted in the evaluation of central fatigue have shown that the greatest disturbances are produced by tasks that require efforts at maximum speed that involve a large amount of force ( Davranche and Pichon, 2005 ; Clemente and Díaz, 2019 ). However, there are very few studies that have analyzed central fatigue through controlled analysis of a task that primarily involves central fatigue ( Fuentes et al., 2019 ). In this sense, the aim is to apply a computerized test (DT, Vienna Test System), that allows evaluating people’s tolerance to stress and central fatigue by applying a standardized protocol, in physical activity practitioners. The knowledge in this field is really limited, for this reason we developed the present research with the aim of studying the modifications in CFFT and DC potentials in a sample group of regular physical activity. The first hypothesis establishes that the computerized stress task increases the participants’ perception of central fatigue, while keeping the perception of peripheral fatigue stable. As a consequence, the second hypothesis establishes that differences will be found in the “post” situation in the CFFT measures and in the central physiological indicators, which would indicate a relationship between the subjective and objective measures of central fatigue.

Materials and Methods

This study followed a quasi-experimental design ( Montero and León, 2007 ) and it received the approval of the University Ethical Commission in compliance with the Helsinki Declaration. All subjects were informed about the procedure and gave their written consent to participate. This study was carried out complying with the Standards for Ethics in Sport and Exercise Science Research ( Harriss et al., 2019 ).

Participants

The participants included 22 individuals from Madrid (Spain), 18 of whom were male and 4 females. These participants were aged between 18 and 36 years ( M years = 22.82, SD years = 3.67). All of the participants regularly engaged in physical activity, between 4 and 14 h per week ( M hours / week = 7.77, SD hours / week = 3.32). The inclusion criteria was that they performed physical activity at least 3 times a week and 150 min of moderate/vigorous physical activity. The exclusion criteria was not correctly performing the proposed measurements. Four participants were excluded from the study for not completing the measurements correctly. Intentional sampling methods were used ( Montero and León, 2007 ). Due to the impossibility of continuing with the data collection due to the Alert State decreed by the Spanish Government as a result of COVID-19, the sample had to be closed with the participants who had passed all the tests before March 2020.

Instrumentation and Study Variables

The number of hours of physical activity per week and the scores obtained on the DT test were used as controlled variables. This allows us to know that the differences found are not due to the ability to respond to stress, or to the weekly amount of physical exercise performed. Therefore, only the subjects in which there were no statistically significant differences in their weekly level of physical exercise, nor in the scores obtained in the DT test, were used.

To carry out this research, three measurement systems have been used: OmegaWave device, Flicker Fusion Unit (Vienna Test System), and the Determination Test (Vienna Test System). OmegaWave is a device that assesses the physiological readiness of athletes by examining the autonomic balance through HRV and brain‘s energy balance via DC potential ( Gómez-Oliva et al., 2019 ), Elastic chest band MEDITRACE (dominant hand and forehead). Coach + application (OmegaWave Ltd, Espoo, Finland) was used on Ipad mini 2 32GB. The Vienna Test System is an instrument for computerized psychological assessments that allows the objective evaluation of different psychological parameters. The Determination Test (DT Vienna test system) ( Whiteside, 2002 ; Whiteside et al., 2003 ) was used to determine neuropsychological fatigue. The test studied the attentional capacity, reactive stress tolerance, reaction speed among continuously, and quickly changing acoustic and visual stimuli. The test is simple, the difficulty of the task lies in the different modality of the arriving stimuli and their speed. This way we measure those cognitive abilities of the people involved that are needed for the distinction of colors and sounds, the perception of the characteristics of stimuli, their memorization, and finally, the selection of the adequate answer. The stimuli coming during the test are not predictable. Instead, the subjects need to react to them randomly ( Schuhfried, 2013 ). We study four key variables: the average value of reaction speed (sec), the number of correct answers (raw score), which reflects the ability of the respondent to precisely and quickly select the adequate answer even under pressure. Furthermore, we also examine the number of incorrect answers (raw score) which can show us how likely the respondent is to get confused under stress and pressure; finally, the high number of missed answers (raw score) reveals that the respondent is not capable of maintaining his/her attention under stress and is prone to giving up these situations ( Neuwirth and Benesch, 2012 ). The duration of this test was 6 min.

Before and after the stress test the following parameters were analyzed in this order:

Parameters analyzed through OmegaWave Coach + device ® (OmegaWave Ltd, Espoo, Finland):

– Hear Rate Variability (HRV). Square root of the mean of the squares of successive RR interval differences (RMSSD), Standard deviation of all normal to normal RR intervals (SDNN), and Standard deviation of successive squares of intervals RR (SDSD). OmegaWave is a device that assesses the physiological readiness of athletes by examining autonomic balance through HRV and brain‘s metabolic state via DC potential ( Ilyukhina and Zabolotskikh, 2020 ). Elastic chest band MEDITRACE (dominant hand and forehead). Coach + application (Omegawave Ltd., Espoo, Finland) was used on Ipad mini 2 32GB. For calculating HRV it be used the Root Mean Square of the Successive Differences score (RMSSD) ( Ilyukhina et al., 1982 ). It was used before and after the stress test.

– DC potential dynamics. DC Potential represent changes in the brain’s metabolic balance in response to increased exercise intensity or psychological challenges and are linked to cognitive and mental load ( Wagshul et al., 2011 ; Ilyukhina, 2015 ).

– CNS System Readiness ( Ilyukhina, 1986 ). It’s indicated by a floating grade from 1.0 to 7.0, where 7.0 is the optimal state. This index represents the state of the brain’s energy level and is composed of three factors (in order of significance): stabilization point of DC potential (mV), stabilization time (reduces system readiness state of 1.0–7.0, if not optimal), and curve shape (reduces system readiness state of 1.0–7.0, if not optimal).

– Stabilization point of DC Potential (mV) ( Ilyukhina et al., 1982 ; Ilyukhina, 2013 ): The first priority in DC analysis is the stabilization point of DC Potential. In the literature, especially by Ilyukhina, this point is defined as Level of Operational Rest. In 1982, the combined work of Ilyukhina and Sychev was published which outlined quantitative parameters of LOR for the assessment of the healthy human’s adaptation and compensatory−adaptive abilities to physical and mental loads in sports.

– Stabilization time ( Ilyukhina and Zabolotskikh, 1997 ). The second priority of analysis is to look at the stabilization time. measured in minutes. The spontaneous relaxation speed represents neuroreflex reactivity (neural control of baroreflex arch) of cardiovascular and respiratory systems. This measure associated with psycho-emotional dynamic and stability. Normal stabilization time occurs within 2 min and represents optimal balance within stress-regulation systems.

– Curve Shape: The curve shape is composed of two elements: Difference between measurement start mV and end mV values ( Table 1 ). The optimal shape of the curve should show a smooth transition from a higher initial value (active wakefulness) to a lower stabilization value (operational rest DC potential form represents the dynamic interaction within stress-regulation systems). DC potential form can indicate the level of CNS activation balance.

Parameters analyzed though Flicker Fusion unit (Vienna Test System ® ):

– Critical flicker fusion ascending (Hz) (CFFA) and Critical flicker fusion descending (Hz) (CFFD). Cortical arousal was measured using the critical flicker fusion threshold (Hz) (CFFT) in a viewing chamber (Vienna Test System ® ), following the procedure of previous studies ( Clemente et al., 2016 ). An increase in CFFT suggests an increase in cortical arousal and information processing; a decrease in CFFT values below the baseline reflects a reduction in the efficiency of information processing and central nervous system fatigue ( Whiteside, 2002 ). It was used before and after the stress test.

Parameters analyzed though DT test (Vienna Test System ® ):

– We study four key variables: the average value of reaction speed (msec), the number of correct answers (raw score), which reflects the ability of the respondent to precisely and quickly select the adequate answer even under pressure. Furthermore, we also examine the number of incorrect answers (raw score) which can show us how likely the athlete is to get confused under stress and pressure; finally, the high number of missed answers (raw score) reveals that the respondent is not capable of maintaining his/her attention under stress and is prone to giving up these situations ( Neuwirth and Benesch, 2012 ). The duration of this test was 6 min without instructions.

Parameters analyzed by self-report instruments:

– Central Rating of Perceived Exertion (RPEC) and Peripheral Rating of Perceived Exertion (RPEP). The Rating of Perceived Exertion ( Borg, 1998 ), was used as a measure of central (cardiorespiratory) and peripheral (local-muscular, metabolic) exertion before and after the stress test ( Bolgar et al., 2010 ; Cárdenas et al., 2017 ). The RPE is a 15 point category-ratio; the odd numbered categories have verbal anchors. Beginning at 6, “no exertion at all,” and goes up to 20, “maximal exertion.” Before testing, subjects were instructed on the use of the RPE scale ( Noble and Robertson, 1996 ). We use the scale with the clear differentiation between central as peripheral perceived exertion following the recommendations of the medical staff and under the guideline of Borg ( Borg, 1982 ), for applied studies.

Table 1. Simplified curve change mV reduction algorithm.

The participants were contacted and informed about the measurement protocol and of the date and time of the data collection. All of the measurements were collected during the same day. The total data collection time per participant was approximately 45 min. The order of measurements was the following: CFFT, DC Potential, RPE, DT test, RPE, CFFT, and DC Potential.

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 21 (SPSS Inc., Chicago, Ill., United States). Means and SDs were calculated using traditional statistical techniques. Normality was tested with the Shapiro-Wilk test. As the distributions were not adjusted to the normal, non-parametric tests were used. A Wilcoxon sign ranges test for intragroup comparisons were conducted to analyze differences between pre and post-test. A Rho Spearman coefficient was used to know the correlations between variables. The Effect Size was tested using the formula = Z/ N for non-parametric tests ( Tomczak and Tomcak, 2014 ). Following the considerations of Cohen (1988) , the effect size is considered small when the value is inferior to 0.10, medium when it varies between 0.10 and 0.30 and high when it is superior to 0.50. The significance level was set at p < 0.05.

Descriptive Analysis, Normality Test According N, Wilcoxon Test, and Effect Sizes

Firstly, the normality tests were realized with the Shapiro-Wilk test. It was determined that most of the variables were not normal, due to which non-parametric statistical tests were applied. In relation to the descriptive analyzes of the study variables, shown in Table 2 , after applying the stressor via the DT test, worse values were obtained in all the variables measured. This reflects the alterations in the central response evaluated. Regarding the Wilcoxon rank test that was used to analyze whether there were differences between the scores obtained before and after applying the stressor (DT test), significant differences were found in the variables OverallDc ( p < 0.05), Flicker ascending ( p < 0.01), Flicker descending ( p < 0.01), Central RPE ( p < 0.01) and Physical RPE ( p < 0.01), while not finding significant differences in the rest of the variables ( Table 2 ).

Table 2. Descriptive analysis of the measured variables.

Correlation Analysis

A Spearman bivariate correlation analysis was performed. Spearman’s Rho coefficient was used, since the distribution was non-parametric. Note that significant correlations were found ( Table 3 ) entre OverallDC con DCSSatabilizationLevel ( p = 0.000; r = 0.791 ∗∗ ); OWCNS ( p = 0.005; r = 0.581 ∗∗ ); OWDCC ( p = 0.013; r = 0.522 ∗ ); Flicker Descending ( p = 0.044; r = 0.432 ∗ ). DCSStabilizationLevel con OWCNS ( p = 0.000; r = 0.766 ∗∗ ); Flicker Descending ( p = 0.049; r = 0.424 ∗ ). DCSStabilizationTime con OWCNS ( p = 0.005; r = 0.572 ∗ ); OWDCC ( p = 0.046; r = 0.430 ∗ ); Flicker Ascending ( p = 0.006; r = 0.563 ∗∗ ). OWCNS correlated with Flicker Ascending ( p = 0.018; r = 0.499 ∗ ), and SDSD with Flicker Descending score ( p = 0.046; r = −0.430 ∗ ).

Table 3. Rho Spearman coefficient.

The objective of the present research was to study the modification of DC potentials and the CFFT scores after the computerized stress test (DT). The analysis of the subjective cognitive responses about fatigue after DT test reveals significant differences in the participants, both at a physical and central level. As regards the first hypothesis, it is partially fulfilled. There are significant differences in central perceived fatigue, with a very high effect size, which supports the hypothesis and emphasizes the usefulness of the established research protocol. However, significant differences also appear in peripheral perceived fatigue, which is beyond the initial approaches. This result is of special interest because it allows to consider the relationship between both types of perceived fatigue ( Bittner et al., 2000 ; Clemente et al., 2016 ). These results, taking into account that the participants did the test sitting down, emphasize the effect achieved through the protocol used to generate stress in them, without significant differences in the performance achieved in the task. Previous research carried out with the DT test already points in this same direction ( Ong, 2015 ). The differences found in the perception of physical fatigue even without previous movement are interesting. Similar results are found in studies carried out in contexts such as chess ( Fuentes et al., 2019 ), where central fatigue due to the demands of each game also leads to physical fatigue of the players. This fact seems relevant insofar as the studies should incorporate measures of both dimensions to be able to explain a higher percentage of variance of the results found.

As regards the second hypothesis, the decrease of CFFD values indicates that it has a negative effect generating central fatigue and an alteration in cortical activation ( Li et al., 2004 ; Clemente, 2016 ). These results confirm the alterations in cortical activation found in physiological efforts of high intensity and of short duration, such as sprints at maximum speed ( Clemente et al., 2011 ). This same trend is also observed in research focused on generating a high level of stress in soldiers, which emphasizes the usefulness of using the DT test to create stress in the participants ( Clemente et al., 2016 ). In line with the ideas defended by Clemente (2016) , decreased in CFFD scores seem to be linked to high sympathetic autonomous nervous system activation, which could also affect higher cognitive functions, such as executive processes (i.e., making complex decisions, memory, and attention processes) ( Shields et al., 2016 ). These same considerations can also be made with respect to the significant differences found in CFFA scores. Higher scores are found after the stress test, which implies that the participants have needed more time to respond to the flicker task as consequence of central fatigue ( Fuentes et al., 2019 ; Lohani et al., 2019 ).

Regarding the results obtained in the Overall DC scores, the significant differences show a pattern of alteration as a consequence of the stress test. As Naranjo-Orellana et al. (2020) point out, the OW test obtains good reliability and validity values using the heart rate variability as a measure in conjunction with the DC Potential (stabilitation DC, stabilitation time, and curve shape). Changes in the DC potentials have been reported to be reflective of performance in different brain processes ( Haider et al., 1981 ; Valenzuela et al., 2020 ). The lower scores obtained after the stress test could indicate, as with the CFF scores, an increase in central fatigue detected by the OmegaWave system ( Valenzuela et al., 2020 ). This result, in any case, needs to be analyzed in detail in future research.

Therefore, monitoring the DC potentials and the CFF scores could be useful to control the cognitive load of the different tasks that having a high mental demand.

Due to the exceptional circumstances of data collection in the present study, some of the study limitations were the sample size and the small number of women who participated in it. Future research works should expand the sample power, as well as determine its effect in a sedentary sample.

To conclude, this is the first study that has jointly analyzed the scores obtained in the analysis of low-frequency brain waves (DC potentials), together with those obtained in the Flicker test. In this sense, although the performance in a specific task seems similar, the demand it has for the person must be evaluated, being useful the use of research protocols similar to the ones we have used. The results open a new field where both measurements could be interesting and useful to assess the cognitive demands of persons.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

Ethics Statement

The studies involving human participants were reviewed and approved by the University Ethical Commission in compliance with the Helsinki Declaration. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

RV: conceptualization, investigation, resources, writing—review and editing, and project administration. RV, ML-R, and RJ-C: methodology, data curation, writing—original draft preparation, visualization, supervision, and formal analysis. ML-R and RJ-C: software and validation.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : central fatigue, omega wave, cognitive response, psychophysiology, stress

Citation: de la Vega R, Jiménez-Castuera R and Leyton-Román M (2021) Impact of Weekly Physical Activity on Stress Response: An Experimental Study. Front. Psychol. 11:608217. doi: 10.3389/fpsyg.2020.608217

Received: 19 September 2020; Accepted: 04 December 2020; Published: 12 January 2021.

Reviewed by:

Copyright © 2021 de la Vega, Jiménez-Castuera and Leyton-Román. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marta Leyton-Román, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Experimental and quasi-experimental designs in implementation research

Affiliations.

1 VA Boston Healthcare System, Center for Healthcare Organization and Implementation Research (CHOIR), United States Department of Veterans Affairs, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA. Electronic address: [email protected].
2 Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA; Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
3 VA Boston Healthcare System, Center for Healthcare Organization and Implementation Research (CHOIR), United States Department of Veterans Affairs, Boston, MA, USA.
PMID: 31255320
PMCID: PMC6923620
DOI: 10.1016/j.psychres.2019.06.027

Implementation science is focused on maximizing the adoption, appropriate use, and sustainability of effective clinical practices in real world clinical settings. Many implementation science questions can be feasibly answered by fully experimental designs, typically in the form of randomized controlled trials (RCTs). Implementation-focused RCTs, however, usually differ from traditional efficacy- or effectiveness-oriented RCTs on key parameters. Other implementation science questions are more suited to quasi-experimental designs, which are intended to estimate the effect of an intervention in the absence of randomization. These designs include pre-post designs with a non-equivalent control group, interrupted time series (ITS), and stepped wedges, the last of which require all participants to receive the intervention, but in a staggered fashion. In this article we review the use of experimental designs in implementation science, including recent methodological advances for implementation studies. We also review the use of quasi-experimental designs in implementation science, and discuss the strengths and weaknesses of these approaches. This article is therefore meant to be a practical guide for researchers who are interested in selecting the most appropriate study design to answer relevant implementation science questions, and thereby increase the rate at which effective clinical practices are adopted, spread, and sustained.

Keywords: Implementation; Interrupted time series; Pre-post with non-equivalent control group; Quasi-experimental; SMART design; Stepped wedge.

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

When time is an important factor in establishing a relationship between the cause and effect.
When there is an invariable or never-changing behavior between the cause and effect.
Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

One-shot Case Study Research Design
One-group Pretest-posttest Research Design
Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
A variable that can be manipulated by the researcher
Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

Researchers have firm control over variables to obtain results.
The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
The results are specific.
Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Sleep duration and mood in adolescents: an experimental study

Article contents
Figures & tables
Supplementary Data

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

.	.	.	Post hoc .
Depressed
Dose	4.08	.03*	5 h > 7.5 h, 10 h
Condition	4.87	.008*	RC > BL, ES
Dose × Condition	7.64	<.001*	5 h: RC > ES > BL
			7.5 h: no significant differences
			10 h: no significant differences
Afraid
Dose	4.59	.02*	5 h > 7.5 h, 10 h
Condition	6.08	.002*	RC > ES
Dose × Condition	7.07	<.001*	5 h: RC > BL, ES
			7.5 h: no significant differences
			10 h: no significant differences
Angry
Dose	3.82	.03*	5 h > 7.5 h, 10 h
Condition	1.72	.18
Dose × Condition	3.09	<.001*	5 h: ES, RC > BL
			7.5 h: no significant differences
			10 h: no significant differences
Confused
Dose	2.97	.07
Condition	2.71	.07
Dose × Condition	4.85	.001*	5 h: ES, RC > BL
			7.5 h: no significant differences
			10 h: RC > BL, ES
Anxious
Dose	1.45	.258
Condition	40.93	<.001*	RC > BL, ES
Dose × Condition	1.89	.11

.	.	.	Post hoc .
Depressed
Dose	4.08	.03*	5 h > 7.5 h, 10 h
Condition	4.87	.008*	RC > BL, ES
Dose × Condition	7.64	<.001*	5 h: RC > ES > BL
			7.5 h: no significant differences
			10 h: no significant differences
Afraid
Dose	4.59	.02*	5 h > 7.5 h, 10 h
Condition	6.08	.002*	RC > ES
Dose × Condition	7.07	<.001*	5 h: RC > BL, ES
			7.5 h: no significant differences
			10 h: no significant differences
Angry
Dose	3.82	.03*	5 h > 7.5 h, 10 h
Condition	1.72	.18
Dose × Condition	3.09	<.001*	5 h: ES, RC > BL
			7.5 h: no significant differences
			10 h: no significant differences
Confused
Dose	2.97	.07
Condition	2.71	.07
Dose × Condition	4.85	.001*	5 h: ES, RC > BL
			7.5 h: no significant differences
			10 h: RC > BL, ES
Anxious
Dose	1.45	.258
Condition	40.93	<.001*	RC > BL, ES
Dose × Condition	1.89	.11

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

.	.	.	Post hoc .
Happy
Dose	0.93	.93
Condition	17.98	<.001*	RC > BL, ES
Dose × Condition	12.12	<.001*	5 h: BL, RC > ES
			7.5 h: RC > ES
			10 h: RC > ES > BL
Energetic
Dose	0.56	.56
Condition	28.11	<.001*	BL, RC > ES
Dose × Condition	19.84	<.001*	5 h: BL, RC > ES
			7.5 h: BL, RC > ES
			10 h: no significant differences

.	.	.	Post hoc .
Happy
Dose	0.93	.93
Condition	17.98	<.001*	RC > BL, ES
Dose × Condition	12.12	<.001*	5 h: BL, RC > ES
			7.5 h: RC > ES
			10 h: RC > ES > BL
Energetic
Dose	0.56	.56
Condition	28.11	<.001*	BL, RC > ES
Dose × Condition	19.84	<.001*	5 h: BL, RC > ES
			7.5 h: BL, RC > ES
			10 h: no significant differences

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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Technique and tectonic concepts as theoretical tools in object and space production: an experimental approach to building technologies i and ii courses.

Graphical Abstract

1. Introduction

2. materials—methods and results, the concept of making as the theoretical focus of bt courses, 3. conceptual foundations and differences of the first stage of the bt i course and the second stage of the bt ii course in the context of the concept of making, 3.1. the first stage of the building technologies courses, 3.1.1. building technologies i course: technique as a concept of making objects, 3.1.2. methodology of building technologies i course: practices for technique, 3.2. the second stage of building technologies courses, 3.2.1. building technologies ii course: tectonics as a concept of architectural object production, 3.2.2. building technologies ii course methodology: tectonic practices, 4. discussion, 5. conclusions, data availability statement, acknowledgments, conflicts of interest.

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Click here to enlarge figure

The Concept of Making
Building Technologies I Course			Building Technologies II Course
Technique			Tectonics
Theory	Practice		Theory	Practice
Consciousness, knowledge, imagination (Construction of thought)	Materials	Methods	Context	Materials	Technique and Technology
Purpose, requirements (Construction of reality)	Sensed things	Overlapping, attaching side by side, fitting, interweaving, knitting, bending, piling up, reducing	Action	Grasped things	Framing
Purpose, requirements (Construction of reality)			Ground/Mound
Possibilities, choices, personalisation			Roof
Transformations, customisations			Enclosure
Size
Perception
Action and inaction
Form
Production of object			Production of space

Building Technologies, I Course 12 Week Syllabus
	Weeks	Design Problem	Content	Practice
Stage 1	1–4	Objective ✓ Form X Material X	Discovery of sub-concepts of the object (such as movement, sound, smell, size, size, texture, colour, hardness)	Lebineria Bird-2022, XQ-6 Creature-2021, Manduri Beetle-2020, Patunia Flower-2019, 23rd Tree-2018, Vooo Game Character-2017, Pereia Meatball-2016, Lindur Spider II-2015, Gundela Porridge-2014, A Creature-2013, Your Own Circle-2012
Stage 2	5–8	Objective ✓ Form X Material X	Discussion of making methods (such as overlapping, folding, intertwining, bending, piling, reducing, knitting)	Your head and neck in 1/1 scale-2022, Torso and upper part of your own body-2021, Your own body in ½ scale-2020, Wrist, elbow, and shoulder-2019, Wearable arm-2018, Your arm-2017, a Trap for the creature-2016, a Shelter for the creature-2015, a Shelter for the Lindur spider-2014, Your head-2013, a Body-2012
Stage 3	9–12	Objective ✓ Form ✓ Material X	Development and customisation of making methods concerning the material	Design of the Other-2022, 1/1 a Peacock- 2021, 1/1 Your own body-2020, Second skin-2019, 1/1 a Grasshopper or Flamingo-2018, 50/1 a Centipede-2017, 1/3 a Giraffe-2016, ½ an Elephant-2015, 1/1 Your own body-2014, Learn from nature and make yourself a shelter-2013, Make the shelter of the body you design-2012

Lebineria Bird, 2022	S-1	S-21	S-31
XQ-6 Ceature, 2021	S-4	S-5	S-6
Manduri Beetle, 2020	S-7	S-8	S-9
Patunya Flower, 2019	S-10	S-11	S-12
23. Tree, 2018	S-13	S-14	S-15

Your head and neck, 2022	S-16	S-17	S-18
Your own body 2021	S-19	S-20	S-21
½ Your own body 2020	S-22	S-7	S-8
Torso and upper part of your own body 2019	S-11	S-23	S-12
Wearable arm 2018	S-25	S-26	S-27

Design of the Other, 2022	S-16	S-17	S-18
Peacock, 2021	S-37, S-38	S-28	S-21
Your own body, 2020	S-29	S-8	S-30
Second Skin, 2019	S-12	S-31- S-32	S-11
Flamingo 2018	S-33, S-14	S-35	S-14, S-26

	Material
S-1, S-3- 2022	Material	Wooden popsicle sticks
	Secondary material	Filament
	Method	Knitting, punching, lacing
S-36, S-37- 2021	Material	1 × 1 cm wooden lath
	Secondary material	Flexible wire
	Method	Overlapping—binding
S-30- 2020	Material	Metal fly wire
	Secondary material	Metal wire
	Method	Knitting, wrapping
S-31, S-32 - 2019	Material	04 cm diameter, plastic pipette
	Secondary material	Wooden stick skewer
	Method	Nesting
S-13- 2018	Material	10 mm x 10 mm Wooden Lath
	Secondary material	Wire
	Method	Binding, placing side by side

Building Technologies II Course 12-Week Syllabus
	Weeks	Content	Practise
Stage 1	1–5	Discussion of constraints such as climate, topography, sensation, time, action	Constructing and producing the context
Stage 2	6–10	Discussion of structural elements such as floor, cover, and wall and transformation of design genes to establish holistic construction	Creation of architectural space according to context and structural elements
Stage 3	11–12	The production of space	Analogue and digital reproduction of context and construction integrity

S-1, S-3 -2022	Characteristics of the context	A place on the slope of a forested hill and by the water			Produced Image

	Action	Sitting, sun protection
	Models	1/500		1/50
S-35, S-36-2021	Characteristics of the context	A place on the cliffs and by the sea with a rainy weather			Produced Image

	Action	Taking a break during a nature walk, watching the scenery, sitting
	Models	1/500	1/50
S-30, 2020	Characteristics of the context	A rocky hill in the middle of the sea			Produced Image

	Action	Sitting, viewing the landscape
	Models	1/500		1/50

S-31, S-32 -2019	Characteristics of the Context	A place in the desert and on top of a hill		Produced Image

	Action	Taking a break, watching, standing in the shade, drinking water
	Models	1/500	1/50
S-13- 2018	Characteristics of the Context	A cave on a rocky hill by the sea		Produced Image

	Action	Swimming in the sea, mooring the boat, sunbathing
	Models	1/500	1/50

S-1, S-3-2022	A	Representation of context
	B	Gene transfer
	C	Construction
S-36, S-37 -2021	A	Representation of context
	B	Gene transfer
	C	Construction

S-30-2020


S-31, S-32-2019


S-13- 2018

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Sönmez, M. Technique and Tectonic Concepts as Theoretical Tools in Object and Space Production: An Experimental Approach to Building Technologies I and II Courses. Buildings 2024 , 14 , 2866. https://doi.org/10.3390/buildings14092866

Sönmez M. Technique and Tectonic Concepts as Theoretical Tools in Object and Space Production: An Experimental Approach to Building Technologies I and II Courses. Buildings . 2024; 14(9):2866. https://doi.org/10.3390/buildings14092866

Sönmez, Murat. 2024. "Technique and Tectonic Concepts as Theoretical Tools in Object and Space Production: An Experimental Approach to Building Technologies I and II Courses" Buildings 14, no. 9: 2866. https://doi.org/10.3390/buildings14092866

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Detection of diffusion anisotropy from an individual short particle trajectory

Kaito takanami, daisuke taniguchi, masafumi kuroda, sawako enoki, yasushi okada, and yoshiyuki kabashima, phys. rev. research 6 , 033272 – published 9 september 2024.

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Supplemental Material

INTRODUCTION
EXPERIMENTS
ACKNOWLEDGMENTS

In parallel with advances in microscale imaging techniques, the fields of biology and materials science have focused on precisely extracting particle properties based on their diffusion behavior. Although the majority of real-world particles exhibit anisotropy, their behavior has been studied less than that of isotropic particles. In this study, we introduce a method for estimating the diffusion coefficients of individual anisotropic particles using short-trajectory data on the basis of a maximum likelihood framework. Traditional estimation techniques often use mean-squared displacement (MSD) values or other statistical measures that inherently remove angular information. Instead, we treated the angle as a latent variable and used belief propagation to estimate it while maximizing the likelihood using the expectation-maximization algorithm. Compared to conventional methods, this approach facilitates better estimation of shorter trajectories and faster rotations, as confirmed by numerical simulations and experimental data involving bacteria and quantum rods. Additionally, we performed an analytical investigation of the limits of detectability of anisotropy and provided guidelines for the experimental design. In addition to serving as a powerful tool for analyzing complex systems, the proposed method will pave the way for applying maximum likelihood methods to more complex diffusion phenomena.

Received 23 May 2024
Accepted 19 August 2024

DOI: https://doi.org/10.1103/PhysRevResearch.6.033272

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Research Areas
Physical Systems

Authors & Affiliations

Department of Physics, Graduate School of Science, The University of Tokyo , Tokyo 113-0033, Japan
International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo , Tokyo 113-0033, Japan and Laboratory for Cell Polarity Regulation, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka 565-0874, Japan
Universal Biology Institute (UBI), The University of Tokyo , Tokyo 113-0033, Japan and Laboratory for Cell Polarity Regulation, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka 565-0874, Japan
Department of Physics, Graduate School of Science, The University of Tokyo , Tokyo 113-0033, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo 113-0033, Japan; Department of Cell Biology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan; Universal Biology Institute (UBI), The University of Tokyo, Tokyo 113-0033, Japan; and Laboratory for Cell Polarity Regulation, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka 565-0874, Japan
Department of Physics, Graduate School of Science, The University of Tokyo , Tokyo 113-0033, Japan; The Institute for Physics of Intelligence, The University of Tokyo, Tokyo 113-0033, Japan; and Trans-Scale Quantum Science Institute, The University of Tokyo, Tokyo 113-0033, Japan
* Contact author: [email protected]
† Contact author: [email protected]

Article Text

Vol. 6, Iss. 3 — September - November 2024

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Illustration of the traditional fitting approach and the proposed MLE approach. In the fitting approach, a single trajectory is segmented to multiple paths of varying lengths, from which the MSD and relevant cumulants are computed. In the MLE approach, on the other hand, given the initial values, the diffusion coefficients are recursively updated by the EM algorithm based on relevant moments of latent variables with respect to the posterior distribution defined by the diffusion coefficients at the time. The moment assessment is efficiently performed by BP, which is implemented by particle filters.

Results of simulations performed using the MLE method compared to those performed using the fitting method. Experiments were conducted with four different parameter sets. The results of the fitting method for N = 100 are omitted because the error bars are too large and the estimation accuracy is obviously bad. Plots of D θ are shown in log scale. The error bars represent the ± 1 σ range obtained by Gaussian approximation of the likelihood function on either side of the maximum likelihood estimate. (a) D a = 3.0 µ m 2 / s , D b = 1.0 µ m 2 / s , D θ = 0.1 rad 2 s − 1 . (b) D a = 2.5 µ m 2 / s , D b = 1.5 µ m 2 / s , D θ = 0.1 rad 2 s − 1 . (c) D a = 3.0 µ m 2 / s , D b = 1.0 µ m 2 / s , D θ = 100 rad 2 s − 1 . (d) D a = 2.5 µ m 2 / s , D b = 1.5 µ m 2 / s , D θ = 100 rad 2 s − 1 (a)–(d) Δ t = 0.01 s , ε = 0.02 µ m .

MSD and the fourth-cumulant estimated by fitting to simulation data. (a) and (b) D a = 3.0 µ m 2 s − 1 , D b = 1.0 µ m 2 s − 1 , Δ t = 0.01 s , ε = 0.02 µ m , N = 3000 . ( A ) D θ = 100.0 rad 2 s − 1 . Error bars represent 〈 Δ x ( t ) 2 + Δ y ( t ) 2 〉 / 2 n . ( b ) D θ = 0.1 rad 2 s − 1 . Error bars stand for 6 〈 [ Δ x ( t ) 2 + Δ y ( t ) 2 ] 2 〉 / 4 n . For both cases, n is the number of simulations (see Supplemental Material [ 42 ]).

Log likelihood vs D θ when D a and D b are fixed to their true values. (a) When the true value of D θ is too small, distinguishing the influence of measurement noise from that of the rotational diffusion is difficult, making the true value indistinguishable from smaller values of D θ . (b and c) The correct order of D θ can be estimated only when the true value of D θ is moderate. (d)–(f) Conversely, when the true D θ is too large, due to the ± π periodicity of the angle variables, distinguishing it from larger values is difficult. (a)–(f) D a = 2.0 µ m 2 / s , D b = 1.0 µ m 2 / s , Δ t = 0.01 s , and ε = 0.02 µ m .

Results obtained by the MLE method for four diffusion trajectories of bacteria. (a) The results obtained by dividing the N = 3000 trajectory into six subsets and estimating the diffusion coefficients in each N = 500 block are compared to the result obtained from the whole N = 3000 data. The error bars represent the ± 1 σ range obtained by Gaussian approximation of the log-likelihood function on either side of the maximum likelihood estimate. (b) Actual trajectories (left column) and estimated angles (right column). The inset in each trajectory data shows a snapshot of a bacterium with a yellow scale bar ( 1 µ m ). The angles at each time point are color coded. The blue bands indicate the 90 % confidence intervals of the estimated angles. (a) and (b) Δ t = 0.33 ms and ε = 0.00395 µ m . The measurement error ε was estimated as the positional standard deviation of the bacteria immobilized on the glass wall.

Results obtained by the MLE method for the diffusion trajectories of (a) quantum rods and (b) fluorescent spheres. The nominal major and minor axis lengths of the quantum rod are 28.4 ± 3.0 and 4.6 ± 0.7 nm, respectively. The nominal diameter of the fluorescent spheres is 200 nm. (a) and (b) N = 1000 , Δ t = 0.01 s and ε = 0.02 µ m . The measurement error ε was estimated as the positional standard deviation of the immobilized tracers.

Influence of trajectory length N and magnitude of measurement noise ε on the success rate of anisotropy detection. (a) Heat map of the success rate on the D a − D b plane. (b) We define Δ D * as the width of the interval on D a Δ t + D b Δ t = 10.0 µ m 2 on which the success rate is smaller than 75 % . The plots indicate that Δ D * vanishes as O ( N − 1 / 4 ) as N tends to infinity. (c) When the noise magnitude ε is smaller than the dashed line (corresponding to ε where the signal-to-noise ratio is 1), the measurement noise has little impact on the success rate. However, for ε larger than the dashed line, the noise magnitude significantly influences the success rate. (b) and (c) Plots are obtained for D ¯ = 10.0 µ m 2 / s and Δ t = 0.01 s .

Typical profiles of the log-likelihood functions in three cases: when a particle is isotropic but is incorrectly estimated to be anisotropic, when it is correctly estimated to be isotropic, and when a particle is anisotropic and correctly estimated to be anisotropic. The vertical lines represent the maximum likelihood estimates in each case. N = 1000 , Δ t = 0.01 s , and ε = 0.1 µ m . In the isotropic case D a = D b = 5.0 µ m 2 / s , D θ = 100 rad 2 / s and in the anisotropic case D a = 7.0 µ m 2 / s , D b = 3.0 µ m 2 / s , D θ = 100 rad 2 / s .

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Experimental and Quasi-Experimental Designs in Implementation Research

Christopher j. miller.

a VA Boston Healthcare System, Center for Healthcare Organization and Implementation Research (CHOIR), United States Department of Veterans Affairs, Boston, MA, USA

b Department of Psychiatry, Harvard Medical School, Boston, MA, USA

Shawna N. Smith

c Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA

d Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA

Marianne Pugatch

1. Background

The first documented clinical trial was conducted in 1747 by James Lind, a royal navy physician, who tested the hypothesis that citrus fruit could cure scurvy. Since then, based on foundational work by Fisher and others (1935), the randomized controlled trial (RCT) has emerged as the gold standard for testing the efficacy of treatment versus a control condition for individual patients. Randomization of patients is seen as a crucial to reducing the impact of measured or unmeasured confounding variables, in turn allowing researchers to draw conclusions regarding causality in clinical trials.

As described elsewhere in this special issue, implementation science is ultimately focused on maximizing the adoption, appropriate use, and sustainability of effective clinical practices in real world clinical settings. As such, some implementation science questions may be addressed by experimental designs. For our purposes here, we use the term “experimental” to refer to designs that feature two essential ingredients: first, manipulation of an independent variable; and second, random assignment of subjects. This corresponds to the definition of randomized experiments originally championed by Fisher (1925) . From this perspective, experimental designs usually take the form of RCTs—but implementation- oriented RCTs typically differ in important ways from traditional efficacy- or effectiveness-oriented RCTs. Other implementation science questions require different methodologies entirely: specifically, several forms of quasi-experimental designs may be used for implementation research in situations where an RCT would be inappropriate. These designs are intended to estimate the effect of an intervention despite a lack of randomization. Quasi-experimental designs include pre-post designs with a nonequivalent control group, interrupted time series (ITS), and stepped wedge designs. Stepped wedges are studies in which all participants receive the intervention, but in a staggered fashion. It is important to note that quasi-experimental designs are not unique to implementation science. As we will discuss below, however, each of them has strengths that make them particularly useful in certain implementation science contexts.

Our goal for this manuscript is two-fold. First, we will summarize the use of experimental designs in implementation science. This will include discussion of ways that implementation-focused RCTs may differ from efficacy- or effectiveness-oriented RCTs. Second, we will summarize the use of quasi-experimental designs in implementation research. This will include discussion of the strengths and weaknesses of these types of approaches in answering implementation research questions. For both experimental and quasi-experimental designs, we will discuss a recent implementation study as an illustrative example of one approach.

1. Experimental Designs in Implementation Science

RCTs in implementation science share the same basic structure as efficacy- or effectiveness-oriented RCTs, but typically feature important distinctions. In this section we will start by reviewing key factors that separate implementation RCTs from more traditional efficacy- or effectiveness-oriented RCTs. We will then discuss optimization trials, which are a type of experimental design that is especially useful for certain implementation science questions. We will then briefly turn our attention to single subject experimental designs (SSEDs) and on-off-on (ABA) designs.

The first common difference that sets apart implementation RCTs from more traditional clinical trials is the primary research question they aim to address. For most implementation trials, the primary research question is not the extent to which a particular treatment or evidence-based practice is more effective than a comparison condition, but instead the extent to which a given implementation strategy is more effective than a comparison condition. For more detail on this pivotal issue, see Drs. Bauer and Kirchner in this special issue.

Second, as a corollary of this point, implementation RCTs typically feature different outcome measures than efficacy or effectiveness RCTs, with an emphasis on the extent to which a health intervention was successfully implemented rather than an evaluation of the health effects of that intervention ( Proctor et al., 2011 ). For example, typical implementation outcomes might include the number of patients who receive the intervention, or the number of providers who administer the intervention as intended. A variety of evaluation-oriented implementation frameworks may guide the choices of such measures (e.g. RE-AIM; Gaglio et al., 2013 ; Glasgow et al., 1999 ). Hybrid implementation-effectiveness studies attend to both effectiveness and implementation outcomes ( Curran et al., 2012 ); these designs are also covered in more detail elsewhere in this issue (Landes, this issue).

Third, given their focus, implementation RCTs are frequently cluster-randomized (i.e. with sites or clinics as the unit of randomization, and patients nested within those sites or clinics). For example, consider a hypothetical RCT that aims to evaluate the implementation of a training program for cognitive behavioral therapy (CBT) in community clinics. Randomizing at the patient level for such a trial would be inappropriate due to the risk of contamination, as providers trained in CBT might reasonably be expected to incorporate CBT principles into their treatment even to patients assigned to the control condition. Randomizing at the provider level would also risk contamination, as providers trained in CBT might discuss this treatment approach with their colleagues. Thus, many implementation trials are cluster randomized at the site or clinic level. While such clustering minimizes the risk of contamination, it can unfortunately create commensurate problems with confounding, especially for trials with very few sites to randomize. Stratification may be used to at least partially address confounding issues in cluster- randomized and more traditional trials alike, by ensuring that intervention and control groups are broadly similar on certain key variables. Furthermore, such allocation schemes typically require analytic models that account for this clustering and the resulting correlations among error structures (e.g., generalized estimating equations [GEE] or mixed-effects models; Schildcrout et al., 2018 ).

1.1. Optimization trials

Key research questions in implementation science often involve determining which implementation strategies to provide, to whom, and when, to achieve optimal implementation success. As such, trials designed to evaluate comparative effectiveness, or to optimize provision of different types or intensities of implementation strategies, may be more appealing than traditional effectiveness trials. The methods described in this section are not unique to implementation science, but their application in the context of implementation trials may be particularly useful for informing implementation strategies.

While two-arm RCTs can be used to evaluate comparative effectiveness, trials focused on optimizing implementation support may use alternative experimental designs ( Collins et al., 2005 ; Collins et al., 2007 ). For example, in certain clinical contexts, multi-component “bundles” of implementation strategies may be warranted (e.g. a bundle consisting of clinician training, technical assistance, and audit/feedback to encourage clinicians to use a new evidence-based practice). In these situations, implementation researchers might consider using factorial or fractional-factorial designs. In the context of implementation science, these designs randomize participants (e.g. sites or providers) to different combinations of implementation strategies, and can be used to evaluate the effectiveness of each strategy individually to inform an optimal combination (e.g. Coulton et al., 2009 ; Pellegrini et al., 2014 ; Wyrick, et al., 2014 ). Such designs can be particularly useful in informing multi-component implementation strategies that are not redundant or overly burdensome ( Collins et al., 2014a ; Collins et al., 2009 ; Collins et al., 2007 ).

Researchers interested in optimizing sequences of implementation strategies that adapt to ongoing needs over time may be interested in a variant of factorial designs known as the sequential, multiple-assignment randomized trial (SMART; Almirall et al., 2012 ; Collins et al., 2014b ; Kilbourne et al., 2014b ; Lei et al., 2012 ; Nahum-Shani et al., 2012 ; NeCamp et al., 2017 ). SMARTs are multistage randomized trials in which some or all participants are randomized more than once, often based on ongoing information (e.g., treatment response). In implementation research, SMARTs can inform optimal sequences of implementation strategies to maximize downstream clinical outcomes. Thus, such designs are well-suited to answering questions about what implementation strategies should be used, in what order, to achieve the best outcomes in a given context.

One example of an implementation SMART is the Adaptive Implementation of Effective Program Trial (ADEPT; Kilbourne et al., 2014a ). ADEPT was a clustered SMART ( NeCamp et al., 2017 ) designed to inform an adaptive sequence of implementation strategies for implementing an evidence-based collaborative chronic care model, Life Goals ( Kilbourne et al., 2014c ; Kilbourne et al., 2012a ), into community-based practices. Life Goals, the clinical intervention being implemented, has proven effective at improving physical and mental health outcomes for patients with unipolar and bipolar depression by encouraging providers to instruct patients in self-management, and improving clinical information systems and care management across physical and mental health providers ( Bauer et al., 2006 ; Kilbourne et al., 2012a ; Kilbourne et al., 2008 ; Simon et al., 2006 ). However, in spite of its established clinical effectiveness, community-based clinics experienced a number of barriers in trying to implement the Life Goals model, and there were questions about how best to efficiently and effectively augment implementation strategies for clinics that struggled with implementation.

The ADEPT study was thus designed to determine the best sequence of implementation strategies to offer sites interested in implementing Life Goals. The ADEPT study involved use of three different implementation strategies. First, all sites received implementation support based on Replicating Effective Programs (REP), which offered an implementation manual, brief training, and low- level technical support ( Kilbourne et al., 2007 ; Kilbourne et al., 2012b ; Neumann and Sogolow, 2000 ). REP implementation support had been previously found to be low-cost and readily scalable, but also insufficient for uptake for many community-based settings ( Kilbourne et al., 2015 ). For sites that failed to implement Life Goals under REP, two additional implementation strategies were considered as augmentations to REP: External Facilitation (EF; Kilbourne et al., 2014b ; Stetler et al., 2006 ), consisting of phone-based mentoring in strategic skills from a study team member; and Internal Facilitation (IF; Kirchner et al., 2014 ), which supported protected time for a site employee to address barriers to program adoption.

The ADEPT study was designed to evaluate the best way to augment support for these sites that were not able to implement Life Goals under REP, specifically querying whether it was better to augment REP with EF only or the more intensive EF/IF, and whether augmentations should be provided all at once, or staged. Intervention assignments are mapped in Figure 1 . Seventy-nine community-based clinics across Michigan and Colorado were provided with initial implementation support under REP. After six months, implementation of the clinical intervention, Life Goals, was evaluated at all sites. Sites that had failed to reach an adequate level of delivery (defined as those sites enrolling fewer than ten patients in Life Goals, or those at which fewer than 50% of enrolled patients had received at least three Life Goals sessions) were considered non-responsive to REP and randomized to receive additional support through either EF or combined EF/IF. After six further months, Life Goals implementation at these sites was again evaluated. Sites surpassing the implementation response benchmark had their EF or EF/IF support discontinued. EF/IF sites that remained non-responsive continued to receive EF/IF for an additional six months. EF sites that remained non-responsive were randomized a second time to either continue with EF or further augment with IF. This design thus allowed for comparison of three different adaptive implementation interventions for sites that were initially non-responsive to REP to determine the best adaptive sequence of implementation support for sites that were initially non-responsive under REP:

An external file that holds a picture, illustration, etc.
Object name is nihms-1533574-f0001.jpg

SMART design from ADEPT trial.

Provide EF for 6 months; continue EF for a further six months for sites that remain nonresponsive; discontinue EF for sites that are responsive;
Provide EF/IF for 6 months; continue EF/IF for a further six months for sites that remain non-responsive; discontinue EF/IF for sites that are responsive; and
Provide EF for 6 months; step up to EF/IF for a further six months for sites that remain non-responsive; discontinue EF for sites that are responsive.

While analyses of this study are still ongoing, including the comparison of these three adaptive sequences of implementation strategies, results have shown that patients at sites that were randomized to receive EF as the initial augmentation to REP saw more improvement in clinical outcomes (SF-12 mental health quality of life and PHQ-9 depression scores) after 12 months than patients at sites that were randomized to receive the more intensive EF/IF augmentation.

1.2. Single Subject Experimental Designs and On-Off-On (ABA) Designs

We also note that there are a variety of Single Subject Experimental Designs (SSEDs; Byiers et al., 2012 ), including withdrawal designs and alternating treatment designs, that can be used in testing evidence-based practices. Similarly, an implementation strategy may be used to encourage the use of a specific treatment at a particular site, followed by that strategy’s withdrawal and subsequent reinstatement, with data collection throughout the process (on-off-on or ABA design). A weakness of these approaches in the context of implementation science, however, is that they usually require reversibility of the intervention (i.e. that the withdrawal of implementation support truly allows the healthcare system to revert to its pre-implementation state). When this is not the case—for example, if a hypothetical study is focused on training to encourage use of an evidence-based psychotherapy—then these designs may be less useful.

2. Quasi-Experimental Designs in Implementation Science

In some implementation science contexts, policy-makers or administrators may not be willing to have a subset of participating patients or sites randomized to a control condition, especially for high-profile or high-urgency clinical issues. Quasi-experimental designs allow implementation scientists to conduct rigorous studies in these contexts, albeit with certain limitations. We briefly review the characteristics of these designs here; other recent review articles are available for the interested reader (e.g. Handley et al., 2018 ).

2.1. Pre-Post with Non-Equivalent Control Group

The pre-post with non-equivalent control group uses a control group in the absence of randomization. Ideally, the control group is chosen to be as similar to the intervention group as possible (e.g. by matching on factors such as clinic type, patient population, geographic region, etc.). Theoretically, both groups are exposed to the same trends in the environment, making it plausible to decipher if the intervention had an effect. Measurement of both treatment and control conditions classically occurs pre- and post-intervention, with differential improvement between the groups attributed to the intervention. This design is popular due to its practicality, especially if data collection points can be kept to a minimum. It may be especially useful for capitalizing on naturally occurring experiments such as may occur in the context of certain policy initiatives or rollouts—specifically, rollouts in which it is plausible that a control group can be identified. For example, Kirchner and colleagues (2014) used this type of design to evaluate the integration of mental health services into primary care clinics at seven US Department of Veterans Affairs (VA) medical centers and seven matched controls.

One overarching drawback of this design is that it is especially vulnerable to threats to internal validity ( Shadish, 2002 ), because pre-existing differences between the treatment and control group could erroneously be attributed to the intervention. While unmeasured differences between treatment and control groups are always a possibility in healthcare research, such differences are especially likely to occur in the context of these designs due to the lack of randomization. Similarly, this design is particularly sensitive to secular trends that may differentially affect the treatment and control groups ( Cousins et al., 2014 ; Pape et al., 2013 ), as well as regression to the mean confounding study results ( Morton and Torgerson, 2003 ). For example, if a study site is selected for the experimental condition precisely because it is underperforming in some way, then regression to the mean would suggest that the site will show improvement regardless of any intervention; in the context of a pre-post with non-equivalent control group study, however, this improvement would erroneously be attributed to the intervention itself (Type I error).

There are, however, various ways that implementation scientists can mitigate these weaknesses. First, as mentioned briefly above, it is important to select a control group that is as similar as possible to the intervention site(s), which can include matching at both the health care network and clinic level (e.g. Kirchner et al., 2014 ). Second, propensity score weighting (e.g. Morgan, 2018 ) can statistically mitigate internal validity concerns, although this approach may be of limited utility when comparing secular trends between different study cohorts ( Dimick and Ryan, 2014 ). More broadly, qualitative methods (e.g. periodic interviews with staff at intervention and control sites) can help uncover key contextual factors that may be affecting study results above and beyond the intervention itself.

2.2. Interrupted Time Series

Interrupted time series (ITS; Shadish, 2002 ; Taljaard et al., 2014 ; Wagner et al., 2002 ) designs represent one of the most robust categories of quasi-experimental designs. Rather than relying on a non-equivalent control group, ITS designs rely on repeated data collections from intervention sites to determine whether a particular intervention is associated with improvement on a given metric relative to the pre-intervention secular trend. They are particularly useful in cases where a comparable control group cannot be identified—for example, following widespread implementation of policy mandates, quality improvement initiatives, or dissemination campaigns ( Eccles et al., 2003 ). In ITS designs, data are collected at multiple time points both before and after an intervention (e.g., policy change, implementation effort), and analyses explore whether the intervention was associated with the outcome beyond any pre-existing secular trend. More formally, ITS evaluations focus on identifying whether there is discontinuity in the trend (change in slope or level) after the intervention relative to before the intervention, using segmented regression to model pre- and post-intervention trends ( Gebski et al., 2012 ; Penfold and Zhang, 2013 ; Taljaard et al., 2014 ; Wagner et al., 2002 ). A number of recent implementation studies have used ITS designs, including an evaluation of implementation of a comprehensive smoke-free policy in a large UK mental health organization to reduce physical assaults ( Robson et al., 2017 ); the impact of a national policy limiting alcohol availability on suicide mortality in Slovenia ( Pridemore and Snowden, 2009 ); and the effect of delivery of a tailored intervention for primary care providers to increase psychological referrals for women with mild to moderate postnatal depression ( Hanbury et al., 2013 ).

ITS designs are appealing in implementation work for several reasons. Relative to uncontrolled pre-post analyses, ITS analyses reduce the chances that intervention effects are confounded by secular trends ( Bernal et al., 2017 ; Eccles et al., 2003 ). Time-varying confounders, such as seasonality, can also be adjusted for, provided adequate data ( Bernal et al., 2017 ). Indeed, recent work has confirmed that ITS designs can yield effect estimates similar to those derived from cluster-randomized RCTs ( Fretheim et al., 2013 ; Fretheim et al., 2015 ). Relative to an RCT, ITS designs can also allow for a more comprehensive assessment of the longitudinal effects of an intervention (positive or negative), as effects can be traced over all included time points ( Bernal et al., 2017 ; Penfold and Zhang, 2013 ).

ITS designs also present a number of challenges. First, the segmented regression approach requires clear delineation between pre- and post-intervention periods; interventions with indeterminate implementation periods are likely not good candidates for ITS. While ITS designs that include multiple ‘interruptions’ (e.g. introductions of new treatment components) are possible, they will require collection of enough time points between interruptions to ensure that each intervention’s effects can be ascertained individually ( Bernal et al., 2017 ). Second, collecting data from sufficient time points across all sites of interest, especially for the pre-intervention period, can be challenging ( Eccles et al., 2003 ): a common recommendation is at least eight time points both pre- and post-intervention ( Penfold and Zhang, 2013 ). This may be onerous, particularly if the data are not routinely collected by the health system(s) under study. Third, ITS cannot protect against confounding effects from other interventions that begin contemporaneously and may impact similar outcomes ( Eccles et al., 2003 ).

2.3. Stepped Wedge Designs

Stepped wedge trials are another type of quasi-experimental design. In a stepped wedge, all participants receive the intervention, but are assigned to the timing of the intervention in a staggered fashion ( Betran et al., 2018 ; Brown and Lilford, 2006 ; Hussey and Hughes, 2007 ), typically at the site or cluster level. Stepped wedge designs have their analytic roots in balanced incomplete block designs, in which all pairs of treatments occur an equal number of times within each block ( Hanani, 1961 ). Traditionally, all sites in stepped wedge trials have outcome measures assessed at all time points, thus allowing sites that receive the intervention later in the trial to essentially serve as controls for early intervention sites. A recent special issue of the journal Trials includes more detail on these designs ( Davey et al., 2015 ), which may be ideal for situations in which it is important for all participating patients or sites to receive the intervention during the trial. Stepped wedge trials may also be useful when resources are scarce enough that intervening at all sites at once (or even half of the sites as in a standard treatment-versus-control RCT) would not be feasible. If desired, the administration of the intervention to sites in waves allows for lessons learned in early sites to be applied to later sites (via formative evaluation; see Elwy et al., this issue).

The Behavioral Health Interdisciplinary Program (BHIP) Enhancement Project is a recent example of a stepped-wedge implementation trial ( Bauer et al., 2016 ; Bauer et al., 2019 ). This study involved using blended facilitation (including internal and external facilitators; Kirchner et al., 2014 ) to implement care practices consistent with the collaborative chronic care model (CCM; Bodenheimer et al., 2002a , b ; Wagner et al., 1996 ) in nine outpatient mental health teams in VA medical centers. Figure 2 illustrates the implementation and stepdown periods for that trial, with black dots representing primary data collection points.

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BHIP Enhancement Project stepped wedge (adapted form Bauer et al., 2019).

The BHIP Enhancement Project was conducted as a stepped wedge for several reasons. First, the stepped wedge design allowed the trial to reach nine sites despite limited implementation resources (i.e. intervening at all nine sites simultaneously would not have been feasible given study funding). Second, the stepped wedge design aided in recruitment and retention, as all participating sites were certain to receive implementation support during the trial: at worst, sites that were randomized to later- phase implementation had to endure waiting periods totaling about eight months before implementation began. This was seen as a major strength of the design by its operational partner, the VA Office of Mental Health and Suicide Prevention. To keep sites engaged during the waiting period, the BHIP Enhancement Project offered a guiding workbook and monthly technical support conference calls.

Three additional features of the BHIP Enhancement Project deserve special attention. First, data collection for late-implementing sites did not begin until immediately before the onset of implementation support (see Figure 2 ). While this reduced statistical power, it also significantly reduced data collection burden on the study team. Second, onset of implementation support was staggered such that wave 2 began at the end of month 4 rather than month 6. This had two benefits: first, this compressed the overall amount of time required for implementation during the trial. Second, it meant that the study team only had to collect data from one site at a time, with data collection periods coming every 2–4 months. More traditional stepped wedge approaches typically have data collection across sites temporally aligned (e.g. Betran et al., 2018 ). Third, the BHIP Enhancement Project used a balancing algorithm ( Lew et al., 2019 ) to assign sites to waves, retaining some of the benefits of randomization while ensuring balance on key site characteristics (e.g. size, geographic region).

Despite their utility, stepped wedges have some important limitations. First, because they feature delayed implementation at some sites, stepped wedges typically take longer than similarly-sized parallel group RCTs. This increases the chances that secular trends, policy changes, or other external forces impact study results. Second, as with RCTs, imbalanced site assignment can confound results. This may occur deliberately in some cases—for example, if sites that develop their implementation plans first are assigned to earlier waves. Even if sites are randomized, however, early and late wave sites may still differ on important characteristics such as size, rurality, and case mix. The resulting confounding between site assignment and time can threaten the internal validity of the study—although, as above, balancing algorithms can reduce this risk. Third, the use of formative evaluation (Elwy, this issue), while useful for maximizing the utility of implementation efforts in a stepped wedge, can mean that late-wave sites receive different implementation strategies than early-wave sites. Similarly, formative evaluation may inform midstream adaptations to the clinical innovation being implemented. In either case, these changes may again threaten internal validity. Overall, then, stepped wedges represent useful tools for evaluating the impact of health interventions that (as with all designs) are subject to certain weaknesses and limitations.

3. Conclusions and Future Directions

Implementation science is focused on maximizing the extent to which effective healthcare practices are adopted, used, and sustained by clinicians, hospitals, and systems. Answering questions in these domains frequently requires different research methods than those employed in traditional efficacy- or effectiveness-oriented randomized clinical trials (RCTs). Implementation-oriented RCTs typically feature cluster or site-level randomization, and emphasize implementation outcomes (e.g. the number of patients receiving the new treatment as intended) rather than traditional clinical outcomes. Hybrid implementation-effectiveness designs incorporate both types of outcomes; more details on these approaches can be found elsewhere in this special issue (Landes, this issue). Other methodological innovations, such as factorial designs or sequential, multiple-assignment randomized trials (SMARTs), can address questions about multi-component or adaptive interventions, still under the umbrella of experimental designs. These types of trials may be especially important for demystifying the “black box” of implementation—that is, determining what components of an implementation strategy are most strongly associated with implementation success. In contrast, pre-post designs with non-equivalent control groups, interrupted time series (ITS), and stepped wedge designs are all examples of quasiexperimental designs that may serve implementation researchers when experimental designs would be inappropriate. A major theme cutting across each of these designs is that there are relative strengths and weaknesses associated with any study design decision. Determining what design to use ultimately will need to be informed by the primary research question to be answered, while simultaneously balancing the need for internal validity, external validity, feasibility, and ethics.

New innovations in study design are constantly being developed and refined. Several such innovations are covered in other articles within this special issue (e.g. Kim et al., this issue). One future direction relevant to the study designs presented in this article is the potential for adaptive trial designs, which allow information gleaned during the trial to inform the adaptation of components like treatment allocation, sample size, or study recruitment in the later phases of the same trial ( Pallmann et al., 2018 ). These designs are becoming increasingly popular in clinical treatment ( Bhatt and Mehta, 2016 ) but could also hold promise for implementation scientists, especially as interest grows in rapid-cycle testing of implementation strategies or efforts. Adaptive designs could potentially be incorporated into both SMART designs and stepped wedge studies, as well as traditional RCTs to further advance implementation science ( Cheung et al., 2015 ). Ideally, these and other innovations will provide researchers with increasingly robust and useful methodologies for answering timely implementation science questions.

Many implementation science questions can be addressed by fully experimental designs (e.g. randomized controlled trials [RCTs]).
Implementation trials differ in important ways, however, from more traditional efficacy- or effectiveness-oriented RCTs.
Adaptive designs represent a recent innovation to determine optimal implementation strategies within a fully experimental framework.
Quasi-experimental designs can be used to answer implementation science questions in the absence of randomization.
The choice of study designs in implementation science requires careful consideration of scientific, pragmatic, and ethical issues.

Acknowledgments

This work was supported by Department of Veterans Affairs grants QUE 15–289 (PI: Bauer) and CIN 13403 and National Institutes of Health grant RO1 MH 099898 (PI: Kilbourne).

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Published: 12 September 2024

Experimental study on dynamic elastic modulus loss of concrete broken by high voltage pulse discharge based on orthogonal design

Long Che 1 ,
Linlin Pan 1 &
Xiaohui Gu 2

Scientific Reports volume 14 , Article number: 21299 ( 2024 ) Cite this article

Metrics details

Civil engineering
Environmental impact

High pulse discharge breakage has a vast prospect as a fresh crushing mechanism for it has the capability to enhance the comminuting effect, however, the breaking mechanism is not yet well studied. In this orthogonal designed research, 27 indoor tests of high voltage pulse discharge (HVPD) for breaking concrete together with the determination of dynamic elastic modulus of concrete based on three variables, i.e. applied voltage, pulse number, and discharge electrode gap, were carried out at three levels. The effects of these factors were studied by using significance and range analysis. The results showed that among these factors, the pulse number has the greatest impact on the dynamic elastic modulus loss (DEML) of concrete, while the applied voltage has the least influence. By changing the value of pulse number and applied voltage, the DEML can be increased to 12.9% and 26.7%, respectively. The impact of the factors’ combination was experimentally proven, and the resulting DEML of concrete broken by HVPD was obtained as 219.73 ± 9.58 MPa, which was 25.19% higher than the maximum of the DEML of concrete broken by HVPD in the orthogonal experiment under various individual factors. These findings provide technical references for improving the crushing efficiency of concrete materials and the engineering application of HVPD crushing technology.

Experimental study on mechanical properties and breakage of high temperature carbon fiber-bar reinforced concrete under impact load

Study on the freeze–thaw effect on concrete arch dam using an improved response surface method

Seismic performance evaluation of precast post-tensioned high-performance concrete frame beam-column joint under cyclic loading

Introduction.

With the rapid development of urban construction and the increasing demand for demolition of old building projects year by year. approximately 460 million square meters of buildings are demolished every year 1 . The annual amount of building demolitions in the UK is about 120 million tons 2 , while in Japan it is 76 million tons 3 . Concrete is the most widely used material in civil construction facilities and buildings, and the mainly used concrete breaking modes nowadays are mechanical breaking, ejection shock wave breaking, and high-pressure water jet breaking 4 , 5 , 6 , 7 . However, the intensive progress in civil engineering and the pursuit of controllable and environmentally friendly technics essentially require the improvements of the current concrete breakage technologies or the development of the new ones. The technology of breaking concrete by high voltage pulse discharge (HVPD) was developed and implemented in recent twenty years. HVPD fragmentation is a novel technology that turns electrical energy into shock waves, which may successfully break concrete in aquatic conditions. It entails generating a pulse voltage with a rising edge of less than 500ns through a high-voltage pulse discharge device, injecting it into the interior of the concrete through an electrode rod in contact with the concrete surface, and requiring the concrete to be completely submerged in the aqueous medium. When supplied energy generates an ionization effect within the concrete, the number of charge carriers rapidly increases, forming a discharge channel. At this point, the high temperature and high voltage environment created by the high-voltage pulse discharge device encourage discharge. The channel rapidly widens, causing an explosion. The resulting shock wave forces the concrete in the water to break 8 , 9 , 10 . Due to the complexity of the concrete breaking process by HVPD and lots of affecting factors, the mechanism of concrete broken by HVPD is still not clear.

To gain insights into the mechanism of concrete breakage by HVPD, several studies have been done. Generally, the influence of single factor on the effectiveness of concrete crushing by HVPD has been studied in the literature, like the size, strength, composition and nature of the sample 11 , 12 , 13 , 14 , 15 , its structure and porosity 16 , 17 , discharge voltage parameters 18 , 19 , structure, material and the position of the electrodes 20 , 21 , 22 , and destruction media influence 23 . It is shown that under different conditions, the influence of these factors can be positive or negative 24 , 25 , 26 .

Simulation studies successfully allow predicting the optimal parameters of the electrodes for the fragmentation of hard rock 15 , and it was also revealed an electric field distortion existing in the rock due to the naturally occurring air gaps, which can enhance the internal electric field strength 22 .It can be assumed that the joint simultaneous change of two or more factors can lead to the process improvement, as well as to adverse consequences 11 , 20 , 23 . However, there are currently no works investigating the effect of a joint change in these factors.

The dynamic elastic modulus is often utilized as the damage variable to characterize the deterioration degree of concrete under several varied loads 27 , 28 . Some researchers have taken the loss of the relative dynamic modulus of elasticity as the damage variable when investigating the deterioration of concrete under different conditions 29 , 30 . However, There is almost no research on HVPD crushing concrete based on dynamic elastic modulus. Therefore, this paper uses the dynamic elastic modulus loss (DEML) as an index to examine the non-destructive effect of concrete material. Based on the orthogonal scheme, the effects of different applied voltage, pulse number, and discharge electrodes gap on the DEML of concrete broken by HVPD were experimentally analyzed. The cumulative effect of different factors on the DEML of crushing concrete was obtained through the method of mathematical modeling. Consequently, to improve the breaking mechanism of rock breakage by HVPD, we provide theoretical and practical guidance for the selection of fragmentation parameters, promoting the progress of breaking concrete materials' technology and contribute the city's sustainable development.

Materials and methods

Experimental system.

The schematic of the experimental system of breaking concrete by HVPD is shown in Fig. 1 a. It contains the high voltage pulse power supply, output electrodes, crushing container, experimental sample, and insulating medium, which was water. The high voltage pulse power supply based on ten stages impulse generator (Shenyang Ligong University, China) was used for further experiments.

Schematic of ( a ) the HVPD crushing concrete experimental system and ( b ) the experimental system for concrete dynamic elastic modulus measurements.

During the process, a DC (Direct Current) source slowly charged the capacitor until the spherical spark switch closed, then switched to the self-breakdown mode. The high voltage pulse power supply was discharged once per experiment; the capacity was 5uF, the maximum output voltage was up to 450kV, and the maximum energy output of a single electric pulse was 100J. Output electrodes were composed of two stainless steel rods with an implemented needle-needle structure. One of the output electrodes was connected to the high voltage pulse power supply by positive output pole and the other by negative one. To avoid the breakdown outside the hard rock, a ceramic sleeve insulated the electrodes’ outer surface, and the electrodes/hard rock contact was constantly kept. The electrode gap ranged from 1 to 10cm. The crushing cuboid container was made of plexiglass to observe the experiment flow. The experimental sample used was a C45 concrete standard block which has a compressive strength grade of 45MPa. Insulating medium was tap water. Before the experiment, the concrete surface was cleaned and dried to avoid the dust affection on the crushing test results. During the experiment, the concrete sample was completely submerged by water to avoid any breakdown and breakage in the air.

The experimental system (Tianjin Sansitrang Test Equipment Manufacturing, China) used to measure the dynamic elastic modulus of concrete is shown in Fig. 1 b. It contained two parts: dynamic elastic modulus tester (on the right) and the concrete target holder with two test probes (on the left). The dynamic elastic modulus tester consisted of tester host, launcher, receiver support frame and processing software of dynamic elasticity tester of concrete. The DEML of concrete crushed by HVPD was calculated as the difference between the dynamic elastic modulus value measured before and after crushing.

Experimental preparation

For this research, the self-made C45 cubic concrete sample with a side length of 150mm was made by mixing cement (PO42.5, Xuzhou Fengdu material Trade Co., Ltd, China), fly ash (Class F II, China Railway 15th Bureau Group Materials Co., Ltd, China), sand (d av = 0.5–0.25mm), spalls (30% of d av = 5–10mm, 20% of d av = 10–20mm, and 50% of d a = 20–31.5mm), additives (Polycarboxylic acid, Shanxi Sangmusi Building Materials Chemical Co., Ltd, China) and water at the proportion of 1 : 0.43 : 2.12 : 3.93 : 0.01 : 0.62, respectively. Samples were cured up to 28 days under standard conditions (20 ± 2 ºC, relative humidity > 95%). According to the standards for mechanical testing methods of ordinary concrete (GB/T50081-2002), the strength of the concrete sample was analyzed by such parameters as mass, density, compressive strength, and elastic modulus. The experiment contained 27 cubic-shaped concrete samples. The mean values of relevant parameters are presented in Table 1 , each parameter was calculated from the measurements of five individual samples. These data are consistent with concrete classification by compressive strength 31 .

Experimental scheme

In the light of the literature 32 , the parameters of applied voltage, pulse number, and electrode spacing three factors affecting the DEML of concrete broken by HVPD, and the selected experimental conditions were as follows in Table 2 . The L 9 (3 3 ) orthogonal table was selected for experimental analysis of the DEML. All the experiments were repeated three times at different levels of each factor. In the following discussion, we chose factors as A-applied voltage, B-pulse number, and C-discharge electrodes gap.

Significance analysis

To precisely estimate the variance scope of the experiment's results of the DEML of concrete fractured through HVPD, along with properly distinguish data fluctuation caused by experimental errors and variations of the experimental conditions, a significance analysis of the impact of the three variables considered in the tests on the DEML of concrete crushed through HVPD is carried out. Due to the orthogonal design used in this experiment, there are only three influencing factors, namely applied voltage, number of pulses, and electrode spacing, and each combination is only repeated 3 times, resulting in limited sample data. Therefore, the significance level of 0.1 is chosen in this article to increase the significance. According to the ANOVA (Analysis of Variance) statistics model, the degree of freedom is equal to the factor level number minus 1, which is 2 for the current experiments; f 0.1 is the critical value of the F test when the significant level is 0.1; f 0.1 can be obtained by querying the upper sub-table of the F distribution. Test statistic F was determined as the ratio of inter-group to intra-group variation 33 .

After generating the test statistic F, the significance of each factor is determined by comparing it to the test critical value f 0.1 . When the F value is bigger than f 0.1 = 9, this factor has a considerable effect on the experimental results; on the contrary, the influence is insignificant.

Results and discussion

The resulting DEML of breaking concrete is shown in Table 3 . It can be seen that the various combinations of applied voltage, pulse number, and discharge electrodes gap have a certain impact on the DEML of the concrete broken by HVPD: maximum of DEML can be observed in experiment #1, and that under #6 is minimum. For the further study of the factors’ effect on the loss of DEML of broken concrete, the following range and significance analysis are done.

Range analysis

Firstly, compute the sum of the DEML for each factor based on its level, then the average value of DEML for each factor and level was found, and the results are presented in Table 4 . Secondly, the range of the DEML of concrete for each factor according to its level was calculated as the difference between the maximum and minimum average DEML value under the certain factor (Table 4 ).

The range indicates the change of the DEML of concrete under the impact of a certain factor, which characterizes the influence of this factor on the dynamic elastic modulus loss. Taking the maximum range as 1, it can be seen from the obtained data that the largest impact on the average value of the DEML is in the raw of the pulse number, discharge electrodes gap and applied voltage with the rate of 1, 0.8 and 0.41. According to the relationship between energy and voltage, when the capacitance is constant, the output energy of high voltage pulse power supply is determined by the output voltage. In high-voltage pulse discharge crushing, when the input voltage is not large enough, one discharge cannot break, so it needs multiple pulse discharges to break it. For the discharge electrode gap, when the input voltage is constant, the value directly determines the electric field strength between the two electrodes, and then determines the breaking performance.

A comparison of the DEML under different levels demonstrates that applied voltage impact on the DEML exhibits the direct dependency (92.2192.21 ± 4.47MPa, 93.79 ± 6.27MPa and 105.85 ± 7.40MPa), pulse number—reverse (118.73 ± 5.05MPa, 87.00 ± 5.10MPa and 86.12 ± 7.82MPa), and for electrodes gap—the DEML decreases from the maximum at the gap of 3cm—108.70 ± 5.23MPa—to a minimum value at the 5cm—82.41 ± 5.15MPa, within the subsequent growth at 7cm—100.73 ± 7.77MPa (Table 4 ). The maximum average value of the DEML of concrete in the tests of the individual influence of factors are achieved at applied voltage 415 kV (A3), pulse number factor of 1 time (B1), and discharge electrodes gap of 3cm (C1), and equal to 105.85 ± 7.40MPa, 118.73 ± 5.05MPa, and 108.70 ± 5.23MPa, respectively.

Considering the above classification, the DEML of crushing concrete under the combination of factors A 3 B 1 C 1 is expected to be the most significant. The impact of A 3 B 1 C 1 factors combination was experiment-ally proven, and the resulting DEML of concrete broken by HVPD was obtained as 219.73 ± 9.58MPa, which is 25.19% higher than the maximum of the DEML of concrete broken by HVPD in the orthogonal experiment under various individual factors (Table 3 ).

Based on the analysis of Table 4 , we can identify primary and secondary factors affecting the DEML of concrete broken by HVPD. If the factor has a great influence on the DEML of crushing concrete, the difference of the DEML under different levels of this factor will be significant, and the factor is considered to be the primary. Otherwise, this is the secondary factor. According to the above definition, the pulse number is the primary factor affecting the DEML, inter-electrode gap and applied voltage are considered to be secondary factors. The order of impact for these three factors on the DEML of concrete broken by HVPD is: pulse number—> discharge electrodes gap—> applied voltage. In the point of this finding, the DEML of concrete can be increased by adjusting sensitive factors, and the damaging of concrete building materials' problem can be further improved. In the experimental system of concrete crushed by HVPD, if the discharge electrodes gap is fixed, the distance between the electrodes can be regarded as a fixed value. Therefore, in the design of demolition of concrete building materials, the DEML of broken by HVPD concrete can be controlled by adjusting the applied voltage and the pulse number. Under these two factors, the maximum DEML of concrete is at the factor levels of A 3 B 1 .

The change in the DEML of concrete-broken by HVPD with different applied voltage under discharge electrodes gap of 3cm, 5cm, and 7cm and maintained pulse number is shown in Fig. 2 a. The DEML increases with the increase of the applied voltage, and this is consistent with Wang's research result 34 . Within the increase of voltage from 360 to 415kV, the DEML of concrete broken by HVPD increases by 15.4%, 12.9% and 12.8% for electrode gap of 3cm, 5cm and 7cm, respectively. The change of overall average compressive strength of concrete increases with the increase of the applied voltage. This is because as the applied voltage increases, so does the amount of energy released into the interior of the concrete samples per unit time via the electrodes, resulting in a greater crushing force of shock waves on the concrete samples, and then contributes to a growth of the volume of voids, cracks, and micropores in concrete samples.

Variation curves of the DEML of concrete under different ( a ) applied voltages and ( b ) pulse numbers.

The variation curves of the DEML of concrete broken by HVPD with different pulse numbers under the condition that of discharge electrodes gap of 3cm, 5cm, and 7cm, and fixed applied voltage of 360kV are shown in Fig. 2 b. It can be seen that the DEML of concrete broken decreases with the increase of the pulse number, and the loss of dynamic elastic modulus decreases significantly when the pulse number increases. When the pulse number changes from one to five times, the DEML of concrete decreases by 26.7%. When the electrode spacing is constant, with the increase of pulse number, the particle size of broken concrete decreases, and the influence on un-crushed concrete decreases 11 . This is because when high-voltage pulse discharge breaks concrete, the energy effect is mainly concentrated between the two electrodes 35 . Therefore, as the number of pulse number increases, when the concrete between the electrodes is completely broken, if the spacing and position of the electrodes do not change, the effect on the concrete will be very small.

In this experiment, according to mathematical and statistical methods, it can be calculated that the obvious impact of the pulse number on the DEML of concrete broken by HVPD can be seen with pulse number changing (F B = 9.8 > f 0.1 ), it has a decisive role, then followed by the discharge electrodes gap (F C = 5.6 < f 0.1 ), while the effect of the applied voltage is weak (F A = 1.4 < < f 0.1 ).

Conclusions

The orthogonal scheme experiment showed that the studied parameters have an obvious effect on the DEML of concrete broken by HVPD at the order from the highest impact to the lowest as: pulse number, discharge electrodes gap, and applied voltage. Because the distance of discharge electrodes is fixed during the breaking process, the DEML can be controlled more easily by changing the applied voltage and pulse number. Under the varying of these two factors, the combination of A 3 B 1 is the most significant. Adjusting the applied voltage and pulse value could increase the DEML by 12.9% and 26.7%, respectively. The F-test results showed that the impact of the pulse number on the DEML of concrete broken by HVPD is the most significant. Thus, the crushing effect of concrete building materials can be improved by increasing the pulse number of HVPD power supply, and finely controlled the applied voltage, which provides data support for the optimal design and engineering application of a HVPD concrete crushing experimental system. The factors affecting the DEML of crushed concrete are not only the applied voltage, pulse number, and electrode spacing, but also include concrete strength and composition, output electrode material, rise time of applied voltage, insulation liquid properties, and other requires further research.

Data availability

The datasets generated during the current study are not publicly available but are available from the corresponding author on reasonable request.

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Acknowledgements

This research is supported by the Basic Research Projects of Liaoning Provincial Department of Education (LJKMZ20220607), National Foreign Experts Program (DL2023006001) and Research Support Program Project of Shenyang Ligong University High Level Talent (1010147001246).

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Che, L., Pan, L. & Gu, X. Experimental study on dynamic elastic modulus loss of concrete broken by high voltage pulse discharge based on orthogonal design. Sci Rep 14 , 21299 (2024). https://doi.org/10.1038/s41598-024-71905-2

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Experimental Research of Cement Mortar With Incorporated Lauric Acid/Expanded Perlite Phase-Change Materials

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Yu, Y. Y., Liu, J. S., Xing, S. S., Zuo, J., and He, X. (July 1, 2017). "Experimental Research of Cement Mortar With Incorporated Lauric Acid/Expanded Perlite Phase-Change Materials." ASTM International. J. Test. Eval. . July 2017; 45(4): No Pagination Specified. https://doi.org/10.1520/JTE20160021

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The study presented in this paper focuses on an experimental investigation of cement mortar incorporated lauric acid/expanded perlite phase-change materials (PCMs). The physical properties of mortar incorporated shape-stable PCMs (SSPCMs) were evaluated. The results showed that the addition of SSPCM increased the water absorption of mortar, reduced the bulk density, and thermal conductivity coefficient of SSPCM mortar. Microstructure was studied for SSPCM composite in mortar. Scanning electron microscope (SEM) imaging indicated that most SSPCM granular particles were evenly distributed into the cement matrix after the mechanical strength test, and the SSPCM are in good bond with the cement binder. The temperature-control test revealed that the temperature adjustment properties of samples incorporating SSPCM was significantly greater than that of the reference mortar, and SSPCM-integrated mortar is thermally reliable because of the fact that the SSPCM mortars showed no degradation in thermal performance after multiple thermal cycles.

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Optimization of FSW Parameters Using SA Algorithm and ANFIS-Based Models to Maximize Mechanical Properties of AZ80A Mg Alloy Joints

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Published: 12 September 2024

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J. Gunasekaran 1 ,
P. Sevvel ORCID: orcid.org/0000-0003-4557-6444 2 ,
I. John Solomon 1 &
J. Vasanthe Roy 2

This paper deals with the experimental research, modeling and parametric-based optimization of the mechanical properties of the friction stir welded AZ80A Mg alloy. A four-factor, five-level-based central composite design matrix was employed to minimize the experimental runs. Adaptive neuro-fuzzy inference system (i.e., ANFIS) was employed to map the relationship amid the parameters of FSW process (namely tool pin geometry, traverse speed, axial force, and rotational speed) and mechanical properties (including yield strength, tensile strength and hardness) of the joints. Later, the formulated ANFIS model was used along with simulated annealing (SA) algorithm determining the optimized parameters of FSW process so as to attain flaw free AZ80A Mg alloy joints. Formulated ANFIS model-SA algorithm anticipated that the friction stir welded AZ80A Mg alloy joints will possess a tensile strength of 240.52 MPa during the single-response optimization scenario and a tensile strength of 240.522 MPa during the multiple-response optimization scenario. Experimental results announced that the FSW process parameter combination of tool rotational speed of 1250 rpm, tool traverse speed of 1.75 mm/sec, axial force of 3 kN and tool possessing a threaded cylindrical pin geometry have contributed for attainment of largest values of mechanical properties during both the single-response and multiple-response optimization scenarios. During the confirmatory experimental work, the flaw free friction stir welded AZ80A Mg alloy joints exhibited a tensile strength of 242.16 MPa and the results of confirmatory experiment revealed that the ANFIS-SA system had exhibited superiority in modeling and optimization of the FSW process during joining of AZ80A Mg alloys.

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Gunasekaran, J., Sevvel, P., Solomon, I.J. et al. Optimization of FSW Parameters Using SA Algorithm and ANFIS-Based Models to Maximize Mechanical Properties of AZ80A Mg Alloy Joints. J. of Materi Eng and Perform (2024). https://doi.org/10.1007/s11665-024-10062-z

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