Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children

Move more today, sleep better tonight? Daily associations between physical activity and sedentary behavior with sleep among young adults with and without insomnia symptoms

OBJECTIVE

At the between-person level, it is well-documented that individuals with more physical activity (PA) and less sedentary behavior (SB) tend to have better sleep outcomes than their peers. However, the associations at the within-person level remain unclear. This study investigated the daily associations between PA and SB with nighttime sleep among young adults with and without insomnia symptoms.

METHODS

Data was collected through activity trackers and online questionnaires for 7 consecutive days among 147 university students, including time spent on moderate to vigorous physical activity (MVPA), light physical activity (LPA), and SB, along with sleep duration, sleep efficiency, and sleep quality. Participants were classified into two subgroups according to the presence of insomnia symptoms, which were determined by a self-reported insomnia scale. Multilevel compositional data analysis was conducted on the total sample, and separately on subsamples characterized by the presence and absence of insomnia symptoms.

RESULTS

In the total sample and subsample without insomnia symptoms, substitutions among MVPA, LPA, and SB were not associated with changes in sleep outcomes at the daily level. However, in the subsample with insomnia symptoms, days with more MVPA or SB and less LPA were associated with higher sleep efficiency, while days with more LPA at the expense of MVPA or SB were associated with lower sleep efficiency.

CONCLUSIONS

For young adults with insomnia symptoms, replacing LPA with MVPA on a given day may improve their sleep efficiency that night.

Physical Activity and Sedentary Time Among U.S. Adolescents Before and During COVID-19: Findings From a Large Cohort Study

Introduction

Evidence suggests that adolescents engage in less physical activity during the summer break. Less is known regarding physical activity during the summer months of the COVID-19 pandemic.

Methods

Utilizing data from the Adolescent Brain Cognitive Development study, the authors examined daily activity measured by Fitbit Charge 2 devices before and after the onset of the COVID-19 pandemic during school and summer months. Linear models estimated activity during pre-COVID-19 school, pre-COVID-19 summer, COVID-19 school, and COVID-19 summer.

Results

Participants (N=7,179, aged 11.96 years, 51% female, 51% White) accumulated 8,671.0 (95% CI=8,544.7; 8,797.3) steps, 32.5 (95% CI=30.8, 32.3) minutes of moderate-to-vigorous physical activity, and 507.2 (95% CI=504.2, 510.2) minutes of sedentary time. During COVID-19 school, adolescents accumulated fewer daily steps and minutes of moderate-to-vigorous physical activity (-1,782.3 steps [95% CI= -2,052.7; -1,511.8] and -6.2 minutes [95% CI= -8.4, -4.0], respectively). Adolescents accumulated more minutes of daily sedentary time (29.6 minutes [95% CI=18.9, 40.3]) during COVID-19 school months than during the pre-COVID-19 school months. During pre-COVID-19 summer months, adolescents accumulated 1,255.1 (95% CI=745.3; 1,765.0) more daily steps than during COVID-19 months. Boys accumulated more daily steps and moderate-to-vigorous physical activity (2,011.5 steps [95% CI=1,271.9; 2,751.0] and 7.9 minutes [95% CI=1.4, 14.4], respectively) during the summer before COVID-19 than in summer during COVID-19. Both girls and boys accumulated more minutes of sedentary time during COVID-19 school months (47.4 [95% CI=27.5, 67.3] and 51.2 [95% CI=22.8, 79.7], respectively) than during COVID-19 summer months.

Conclusions

Societal restrictions during COVID-19 negatively impacted activity levels in the U.S., particularly during the summer months during COVID-19.

Predicting long-term sleep deprivation using wearable sensors and health surveys

Sufficient sleep is essential for individual well-being. Inadequate sleep has been shown to have significant negative impacts on our attention, cognition, and mood. The measurement of sleep from in-bed physiological signals has progressed to where commercial devices already incorporate this functionality. However, the prediction of sleep duration from previous awake activity is less studied. Previous studies have used daily exercise summaries, actigraph data, and pedometer data to predict sleep during individual nights. Building upon these, this article demonstrates how to predict a person's long-term average sleep length over the course of 30 days from Fitbit-recorded physical activity data alongside self-report surveys. Recursive Feature Elimination with Random Forest (RFE-RF) is used to extract the feature sets used by the machine learning models, and sex differences in the feature sets and performances of different machine learning models are then examined. The feature selection process demonstrates that previous sleep patterns and physical exercise are the most relevant kind of features for predicting sleep. Personality and depression metrics were also found to be relevant. When attempting to classify individuals as being long-term sleep-deprived, good performance was achieved across both the male, female, and combined data sets, with the highest-performing model achieving an AUC of 0.9762. The best-performing regression model for predicting the average nightly sleep time achieved an R-squared of 0.6861, with other models achieving similar results. When attempting to predict if a person who previously was obtaining sufficient sleep would become sleep-deprived, the best-performing model obtained an AUC of 0.9448.

Analysis of the Energy Expenditure of Sports School Activities in Children

(1) Background: This study explores the potential energy expenditure associated with participation in after-school sports activities among primary school children. (2) Methods: The study involved 129 children age (11.35 ± 0.55 years) recruited from eight different public after-school sport programs. (3) Results: Data analyses revealed significant differences between the eight sports in total calories per session, calories per minute, and METs (p < 0.05). All sports showed higher energy expenditure compared to chess (p < 0.05), with soccer and rugby exhibiting the highest energy expenditure per session. Team sports showed elevated energy consumption per session (p < 0.01, r > 0.30), calories per minute (p = 0.01, r > 0.40), and METs (p < 0.01, r > 0.40) in comparison with individual sports. (4) Conclusions: These findings enhance our understanding of the energy expenditure observed in primary school children following various after-school sports activities. The results indicate that team sports, in particular, are pivotal in elevating physical activity levels, thereby playing an essential role in fostering healthier lifestyles among children.

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wearing Fitbit Charge HR 2 devices in the Adolescent Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.

Sex Differences in Children with Uncomplicated Attention Deficit/Hyperactivity Disorder and Sleep Problems

BACKGROUND

Approximately 7.6% of children are diagnosed with attention deficit/hyperactivity disorder (ADHD), and sleep impairments affect 25-85%. There is a noticeable lack of research on girls and sex differences. The aim of this study was to examine sex differences in children with uncomplicated ADHD and sleep problems.

METHODS

Cross-sectional baseline data were retrieved from a randomized controlled trial with weighted blankets (55 boys and 41 girls, 6-14 years) on a cohort recently diagnosed with uncomplicated ADHD and sleep problems. Differences between boys and girls in ADHD symptoms, objectively and subjectively measured sleep, anxiety, and functioning were examined via parent- or self-reported validated instruments.

RESULTS

Girls reported significantly lower (worse) satisfaction with well-being, life overall, and school, but not for family. Parents reported more sleep anxiety and night-time wakings among boys, but no sex differences in other measures and also not in self-reported measures or objective sleep measures. Children who reported worry, sadness, or unhappiness had more sleep problems.

CONCLUSIONS

Boys with ADHD and sleep problems may need support with sleep-related anxiety and night-time wakings, while girls may require support with overall functioning. Additionally, children who express feelings of worry, sadness, or unhappiness alongside their ADHD symptoms should have attention given to their sleep.

Association of physical activity and screen time with cardiovascular disease risk in the Adolescent Brain Cognitive Development Study

BACKGROUND

According to the Physical Activity Guidelines Advisory Committee Scientific Report, limited evidence is available on sedentary behaviors (screen time) and their joint associations with physical activity (steps) for cardiovascular health in adolescence. The objective of this study was to identify joint associations of screen time and physical activity categories with cardiovascular disease (CVD) risk factors (blood pressure, hemoglobin A1c, cholesterol) in adolescence.

METHODS

This study analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study, comprising a diverse sample of 4,718 U.S. adolescents aged 10-15 years between 2018 and 2021. Steps were measured by a Fitbit wearable device and levels were categorized as low (1,000-6,000), medium (> 6,000-12,000), and high (> 12,000) averaged daily step counts. Self-reported recreational screen time hours per day were classified as low (0-4), medium (> 4-8), and high (> 8) hours per day. CVD risk factors including blood pressure, hemoglobin A1c, and cholesterol (total and HDL) were measured.

RESULTS

The analytical sample averaged 6.6 h of screen time per day and 9,722 steps per day. In models including both screen time and steps, the high screen time category was associated with a 4.27 higher diastolic blood pressure percentile (95% CI 1.83-6.73) and lower HDL cholesterol (B= -2.85, 95% CI -4.77 to -0.94 mg/dL) compared to the low screen time category. Medium (B = 3.68, 95% CI 1.24-6.11) and low (B = 7.64, 95% CI 4.07-11.20) step categories were associated with higher diastolic blood pressure percentile compared to the high step category. The medium step category was associated with lower HDL cholesterol (B= -1.99, 95% CI -3.80 to -0.19 mg/dL) compared to the high step category. Findings were similar when screen time and step counts were analyzed as continuous variables; higher continuous step count was additionally associated with lower total cholesterol (mg/dL).

CONCLUSIONS

Combinations of low screen time and high steps were generally associated with favorable cardiovascular health markers including lower diastolic blood pressure and higher HDL cholesterol, which can inform future adolescent health guidelines.

Associations Between Adverse Childhood Experiences and Early Adolescent Physical Activity in the United States

OBJECTIVE

To determine the associations between the number of adverse childhood experiences (ACEs) and objectively-measured physical activity (PA) in a population-based, demographically diverse cohort of 9-14-year-olds and to determine which subtypes of ACEs were associated with physical activity levels.

METHODS

We analyzed data (n = 7046) from the Adolescent Brain Cognitive Development (ABCD) Study 4.0 release at baseline and year 2 follow-up. ACE (cumulative score and subtypes) and physical activity (average Fitbit daily steps assessed at Year 2) were analyzed using linear regression analyses. Covariates included race and ethnicity, sex, household income, parent education, body mass index, study site, twins/siblings, and data collection period.

RESULTS

Adjusted models suggest an inverse association between number of ACEs and Fitbit daily steps, with ≥4 (compared to 0) ACEs associated with 567 fewer daily steps (95% CI -902.2, -232.2). Of the ACEs subtypes, emotional abuse (B = -719.3, 95% CI -1430.8, -7.9), physical neglect (B = -423.7, 95% CI -752.8, -94.6), household mental illness (B = -317.1, 95% CI -488.3, -145.9), and household divorce or separation (B = -275.4, 95% CI -521.5, -29.2) were inversely and statistically significant associated with Fitbit daily steps after adjusting for confounders.

CONCLUSIONS

Our results suggest that there is an inverse, dose-dependent relationship between cumulative number of ACEs and physical activity as measured by daily steps. This work highlights the importance of screening for ACEs among young people at an early age to help identify those who could benefit from interventions or community programs that support increased physical activity.

State of the science and recommendations for using wearable technology in sleep and circadian research

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

Changes in rest-activity rhythms in adolescents as they age: associations with brain changes and behavior in the ABCD study

Background

Adolescents with disrupted rest-activity rhythms (RAR) including shorter sleep duration, later sleep timing and low physical activity levels have higher risk for mental and behavioral problems. However, it remains unclear whether the same associations can be observed for within-subject changes in RAR.

Methods

Our longitudinal investigation on RAR used Fitbit data from the Adolescent Brain Cognitive Development (ABCD) Study at the 2-year (FL2: aged 10-13 years) and 4-year follow-up (FL4: aged 13-16 years). 963 youths had good-quality Fitbit data at both time points. In this study we examined changes in RAR from FL2 to FL4, their environmental and demographic contributors as well as brain and behavioral correlates.

Results

From FL2 to FL4, adolescents showed decreases in sleep duration and physical activity as well as delayed sleep timing (Cohen's d .44-.75). The contributions of environmental and demographic factors to RAR changes were greatest to sleep timing (explained 10% variance) and least to sleep duration (explained 1% variance). Delays in sleep timing had stronger correlations with behavioral problems including greater impulsivity and poor academic performance than reductions in sleep duration or physical activity. Additionally, the various brain measures differed in their sensitivity to RAR changes. Reductions in sleep duration were associated with decreased brain functional connectivity between subcortical regions and sensorimotor and cingulo-opercular networks and with enhanced functional connectivity between sensorimotor, visual and auditory networks. Delays in sleep timing were mainly associated with grey matter changes in subcortical regions.

Conclusions

The current findings corroborate the role of sleep and physical activity in adolescent's brain neurodevelopment and behavior problems. RAR might serve as biomarkers for monitoring behavioral problems in adolescents and to serve as potential therapeutic targets for mental disorders.

Step cadence as a novel objective postoperative recovery metric in children who undergo laparoscopic appendectomy

BACKGROUND

Daily step counts from consumer wearable devices have been used to objectively assess postsurgical recovery in children. However, step cadence, defined as steps taken per minute, may be a more specific measure of physiologic status. The purpose of this study is to define objective normative physical activity recovery trajectories after laparoscopic appendectomy using this novel metric. We hypothesized that patients would have a progressive increase in peak cadence until reaching a plateau representing baseline status, and this would occur earlier for simple compared with complicated appendicitis.

METHODS

Children aged 3 to 18 years were enrolled after laparoscopic appendectomy for simple or complicated appendicitis between March 2019 and December 2022 at a tertiary children's hospital. Participants wore a Fitbit for 21 postoperative days. The peak 1-minute cadence and peak 30-minute cadence were determined each postoperative day. Piecewise linear regression was conducted to generate normative peak step cadence recovery trajectories for simple and complicated appendicitis.

RESULTS

A total of 147 children met criteria (53.7% complicated appendicitis). Patients with simple appendicitis reached plateau postoperative day 10 at a mean peak 1-minute cadence of 111 steps/minute and a mean peak 30-minute cadence of 77 steps/minute. The complicated appendicitis recovery trajectory reached a plateau postoperative day 13 at a mean peak 1-minute cadence of 106 steps/minute and postoperative day 15 at a mean peak 30-minute cadence of 75 steps/minute.

CONCLUSION

Using step cadence, we defined procedure-specific normative peak cadence recovery trajectories after laparoscopic appendectomy. This can empower clinicians to set data-driven expectations for recovery after surgery and establish the groundwork for consumer wearable devices as a post-discharge remote monitoring tool.

Evaluating reliability in wearable devices for sleep staging

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

Criterion-Related Validity of Consumer-Wearable Activity Trackers for Estimating Steps in Primary Schoolchildren under Controlled Conditions: Fit-Person Study

The purposes were to examine the criterion-related validity of the steps estimated by consumer-wearable activity trackers (wrist-worn activity trackers: Fitbit Ace 2, Garmin Vivofit Jr, and Xiomi Mi Band 5; smartphone applications: Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health) and their comparability in primary schoolchildren under controlled conditions. An initial sample of 66 primary schoolchildren (final sample = 56; 46.4% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), wore three wrist-worn activity trackers (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on their non-dominant wrist and had three applications in two smartphones (Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health for Android/iOS installed in Samsung Galaxy S20+/iPhone 11 Pro Max) in simulated front trouser pockets. Primary schoolchildren's steps estimated by the consumer-wearable activity trackers and the video-based counting independently by two researchers (gold standard) were recorded while they performed a 200-meter course in slow, normal and brisk pace walking, and running conditions. Results showed that the criterion-related validity of the step scores estimated by the three Samsung applications and the Garmin Vivofit Jr 2 were good-excellent in the four walking/running conditions (e.g., MAPE = 0.6-2.3%; lower 95% CI of the ICC = 0.81-0.99), as well as being comparable. However, the Apple applications, Fitbit Ace 2, and Xiaomi Mi Band 5 showed poor criterion-related validity and comparability on some walking/running conditions (e.g., lower 95% CI of the ICC < 0.70). Although, as in real life primary schoolchildren also place their smartphones in other parts (e.g., schoolbags, hands or even somewhere away from the body), the criterion-related validity of the Garmin Vivofit Jr 2 potentially would be considerably higher than that of the Samsung applications. The findings of the present study highlight the potential of the Garmin Vivofit Jr 2 for monitoring primary schoolchildren's steps under controlled conditions.

Associations of habitual sedentary time with executive functioning and short-term memory in 7th and 8th grade adolescents

BACKGROUND

While there is increasing evidence for negative physical health consequences of high volumes of sedentary time and prolonged sedentary time in adolescents, the association with cognition is less clear. This study investigated the association of volumes of habitual sedentary time and prolonged sedentary time with executive functions and short-term memory in adolescents.

METHODS

This study has a cross-sectional observational study design. Volumes of sedentary time and prolonged sedentary time (accumulated sedentary time spent in bouts of  ≥ 30 min) were measured using the Axivity AX3 accelerometer. Six cognitive functions (spatial and verbal short-term memory; and working memory, visuospatial working memory, response inhibition and planning as executive functions) were measured using six validated cognitive assessments. Data were analysed using generalised linear models.

RESULTS

Data of 119 adolescents were analysed (49% boys, 13.4 ± 0.6 year). No evidence for an association of volumes of sedentary time and prolonged sedentary time with spatial and verbal short-term memory, working memory, and visuospatial working memory was found. Volumes of sedentary time and prolonged sedentary time were significantly related to planning. One hour more sedentary time or prolonged sedentary time per day was associated with respectively on average 17.7% (95% C.I.: 3.5-29.7%) and 12.1% (95% C.I.: 3.9-19.6%) lower scores on the planning task.

CONCLUSIONS

No evidence was found for an association of volumes of habitual sedentary time and prolonged sedentary time with short-term memory and executive functions, except for planning. Furthermore, the context of sedentary activities could be an important confounder in the association of sedentary time and prolonged sedentary time with cognition among adolescents. Future research should therefore collect data on the context of sedentary activities.

TRIAL REGISTRATION

This study was registered at ClinicalTrials.gov in January 2020 (NCT04327414; released on March 11, 2020).

Physical and mental health in adolescence: novel insights from a transdiagnostic examination of FitBit data in the ABCD study

Adolescence is among the most vulnerable period for the emergence of serious mental illnesses. Addressing this vulnerability has generated interest in identifying markers of risk for symptoms and opportunities for early intervention. Physical fitness has been linked to psychopathology and may be a useful risk marker and target for early intervention. New wearable technology has made assessing fitness behavior more practical while avoiding recall and self-report bias. Still, questions remain regarding the clinical utility of physical fitness metrics for mental health, both transdiagnostically and along specific symptom dimensions. The current study includes 5007 adolescents (ages 10-13) who participated in the Adolescent Brain Cognitive Development (ABCD) study and additional sub-study that collected fitness data from wearable technology and clinical symptom measures. Physical fitness metrics included resting heart rate (RHR- an index of cardiovascular health), time spent sedentary (associated with increased inflammation and cardiovascular disease), and time spent in moderate physical activity (associated with increased neurogenesis, neuroplasticity, and healthy neurodevelopment). Self-report clinical symptoms included measures of psychosis-like experiences (PLE), internalizing symptoms, and externalizing symptoms. Increased RHR- lower cardiovascular fitness- related only to greater internalizing symptoms (t = 3.63). More sedentary behavior related to elevated PLE severity (t = 5.49). More moderate activity related to lower PLE (t = -2.69) and internalizing (t = -6.29) symptom severity. Wearable technology fitness metrics linked physical health to specific mental health dimensions, which emphasizes the utility of detailed digital health data as a marker for risk and the need for precision in targeting physical health behaviors to benefit symptoms of psychopathology.

The Influence of Pubertal Development on Early Adolescent Sleep and Changes in Family Functioning

Pubertal development has been separately linked to adolescents' sleep problems and larger family functioning, but research connecting these inter-related processes remains sparse. This study aimed to examine how pubertal status and tempo were related to early adolescents' sleep and their family functioning. Using longitudinal data from the Adolescent Brain and Cognitive Development study, the study's sample (N = 4682) was 49.2% female, was an average of 9.94 years old at baseline, and was 60.1% white. Analyses in the current study modeled the indirect associations between pubertal change and changes in family conflict via adolescent sleep duration and variability of duration. The results suggested that pubertal status and tempo predicted shorter adolescent sleep durations and greater variability in those durations, which predicted residual increases in family conflict. The findings highlight the role of adolescents' pubertal changes in their sleep and how such changes can negatively affect family functioning.

Feasibility of wearable activity tracking devices to measure physical activity and sleep change among adolescents with chronic pain-a pilot nonrandomized treatment study

Purpose

Personal informatics devices are being used to measure engagement in health behaviors in adults with chronic pain and may be appropriate for adolescent use. The aim of this study was to evaluate the utilization of a wearable activity tracking device to measure physical activity and sleep among adolescents attending a three-week, intensive interdisciplinary pain treatment (IIPT) program. We also assessed changes in physical activity and sleep from baseline to the treatment phase.

Methods

Participants (57.1% female, average age 15.88, SD = 1.27) wore an activity tracking device three weeks prior to starting and during the treatment program.

Results

Of 129 participants contacted, 47 (36.4%) agreed to participate. However, only 30 (64%) complied with the instructions for using the device prior to programming and during program participation. Preliminary analyses comparing averages from 3-weeks pre-treatment to 3-weeks during treatment indicated increases in daily overall activity minutes, daily step counts, and minutes of moderate to vigorous physical activity (by 353%), as well as a corresponding decrease in sedentary minutes. There was more missing data for sleep than anticipated.

Conclusions

Wearable activity tracking devices can be successfully used to measure adolescent physical activity in-person, with more difficulty obtaining this information remotely. Adolescents with chronic pain experience improvements in objective measurements of physical activity over the course of a 3-week IIPT program. Future studies may want to spend more time working with pediatric patients on their understanding of how to use trackers for sleep and physical activity.

Influence of Body Mass Index, Physical Fitness, and Physical Activity on Energy Expenditure during Recess

This study aims to investigate the potential relationship between energy expenditure, physical fitness level, physical activity, and body mass index among children taking part in a 30 min school recess. A total of 259 participants from three schools were included in this study. Data on energy expenditure during recess, age, gender, anthropometric measurements, global physical fitness, and physical activity index were recorded. The evaluation sessions occurred twice a week on alternate days over two weeks. A significant gender difference was observed in energy expenditure during recess, favoring boys (p < 0.01). The participants classified as very active exhibited significantly higher scores compared to those categorized as sedentary and moderately active, respectively (p < 0.01), with a poor but significant correlation (rho: 0.208; p < 0.001). There were significant differences between energy expenditure and cardiorespiratory fitness and energy expenditure and global physical fitness (p < 0.01) with fair (rho: 0.289; p < 0.001) and poor (rho: 0.196, p = 0.001) correlation, respectively. In contrast, there were no significant differences by body mass index categories (p = 0.571). These results suggest that gender, physical activity index, and global physical fitness were found to influence energy expenditure during a 30 min recess. However, no significant relationships were found with the body mass index.

Evaluation of the Garmin Vivofit 4 for assessing sleep in youth experiencing sleep disturbances

Objective

Wearable monitors are increasingly used to assess sleep. However, validity is unknown for certain monitors and populations. We tested the Garmin Vivofit 4 in a pediatric clinical sample.

Methods

Participants (n = 25) wore the monitor on their nondominant wrist during an overnight polysomnogram. Garmin and polysomnography were compared using 95% equivalence testing, mean absolute error, and Bland-Altman analysis.

Results

On average (mean ± SD), the Garmin predicted later sleep onset (by 0.84 ± 1.60 hours) and earlier sleep offset (by 0.34 ± 0.70 hours) than polysomnography. The resulting difference for total sleep time was -0.55 ± 1.21 hours. Sleep onset latency was higher for Garmin than polysomnography (77.4 ± 100.9 and 22.8 ± 20.0 minutes, respectively), while wake after sleep onset was lower (5.2 ± 9.3 and 43.2 ± 37.9 minutes, respectively). Garmin sleep efficiency averaged 3.3% ± 13.8% lower than polysomnography. Minutes in light sleep and deep sleep (the latter including rapid eye movement) were within ±3.3% of polysomnography (both SDs = 14.9%). No Garmin means were significantly equivalent with polysomnography (adjusted p > 0.99). Mean absolute errors were 0.47 to 0.95 hours for time-based variables (sleep onset, offset, and latency, plus total sleep time and wake after sleep onset), and 8.9% to 21.2% for percentage-based variables (sleep efficiency and sleep staging). Bland-Altman analysis showed systematic bias for wake after sleep onset, but not other variables.

Conclusions

The Vivofit 4 showed consistently poor individual-level validity, while group-level validity was better for some variables (total sleep time and sleep efficiency) than others.

Latent Profiles of Sleep Patterns in Early Adolescence: Associations With Behavioral Health Risk

PURPOSE

The present study characterized sleep profiles in a national longitudinal sample of early adolescents and examined whether profiles predicted later behavioral problems.

METHODS

Three waves of data (2016-2021) were obtained from the Adolescent Behavior and Cognitive Development study, including 3,326 participants with both weekday and weekend sleep data measured by Fitbit wearables (age range 10.58-13.67 years; 49.3% female). Latent profile analysis was utilized to identify sleep profiles using multiple sleep indicators (duration, latency, efficiency, wake minutes, wake counts, and midpoint). We then explored whether demographic predictors predicted profile membership and tested the latent sleep profiles' predictive utility of internalizing and externalizing symptoms.

RESULTS

Four profiles were identified: average sleep (40.39%), high duration & high wakefulness (28.58%), high efficiency, low duration &low wakefulness (16.86%), and low duration & low efficiency (14.17%). Participants with older age, males, higher body mass index, and advanced pubertal status were more likely to be classified in the low duration & low efficiency profile than the average group. Participants with lower income, minority identification, older age, and higher body mass index were more likely to be classified in the high efficiency, low duration &low wakefulness than the average group. Participants with lower parental education and males were more likely to be in the high sleep duration & high wakefulness than the average group. The low duration & low efficiency group had the highest attention problems, social problems, and rule-breaking behaviors.

DISCUSSION

Our findings highlight unique sleep patterns in early adolescence and their prospective links with internalizing and externalizing problems.

Consumer Wearables Define a Normative Recovery Trajectory Following the Modified Nuss Procedure

INTRODUCTION

The modified Nuss procedure is an elective procedure associated with a lengthy recovery, uncontrolled pain, and risk of infrequent, yet life-threatening complications. The absence of objective measures of normative postoperative recovery creates uncertainty about the postdischarge period, which remains highly dependent on the patients' and their caregivers' expectations and management of recovery. We aimed to describe an objective-normative, physical activity recovery trajectory after the modified Nuss procedure, using step counts from the Fitbit.

METHODS

This observational study enrolled children ≤18 y with pectus excavatum who underwent the modified Nuss procedure from 2021 to 2022. The Fitbit was worn for 21 postoperative days. Postdischarge outcomes and health-care utilization were evaluated. For patients without postoperative complications, piecewise linear regression analysis was conducted to generate a normative recovery trajectory model of daily step counts.

RESULTS

Of 80 patients enrolled, 66 (86%) met eligibility criteria (mean age, 15.1 ± 1.3 y; 89.4% male, 62.1% non-Hispanic White). The mean number of telephone and electronic message encounters regarding concerns with the patient's recovery within 30 d postoperatively was 2.1 (standard deviation = 2.7). Ten patients (15.2%) returned to the emergency department (ED) within the 30-d postoperative period. Seven patients (10.6%) presented to the ED one time, and three patients (4.5%) presented to the ED twice. Thirty-day readmission rate was four patients (6.0%). Piecewise regression model showed that patients without complications steadily increased their daily step count on each postoperative day and plateaued on day 18.

CONCLUSIONS

We have developed a normative recovery trajectory following the modified Nuss procedure using step count data collected by a consumer wearable device. This offers the potential to inform preoperative patient expectations and reduce avoidable health-care utilization through informed preoperative counseling, thus laying the ground work for the use of consumer wearable devices as a postdischarge remote monitoring tool.

Social epidemiology of Fitbit daily steps in early adolescence

BACKGROUND

Sociodemographic disparities in adolescent physical activity have been documented but mostly rely on self-reported data. Our objective was to examine differences in device-based step metrics, including daily step count (steps d-1), by sociodemographic factors among a diverse sample of 10-to-14-year-old adolescents in the US.

METHODS

We analyzed prospective cohort data from Year 2 (2018-2020) of the Adolescent Brain Cognitive Development (ABCD) Study (N = 6460). Mixed-effects models were conducted to estimate associations of sociodemographic factors (sex, sexual orientation, race/ethnicity, household income, parental education, and parental marital status) with repeated measures of steps d-1 over the course of 21 days.

RESULTS

Participants (49.6% female, 39.0% racial/ethnic minority) accumulated an average of 9095.8 steps d-1. In mixed-effects models, 1543.6 more steps d-1 were recorded for male versus female sex, Black versus White race (328.8 more steps d-1), heterosexual versus sexual minority sexual orientation (676.4 more steps d-1), >$200,000 versus <$25,000 household income (1003.3 more steps d-1), and having married/partnered parents versus unmarried/unpartnered parents (326.3 more steps d-1). We found effect modification by household income for Black adolescents and by sex for Asian adolescents.

CONCLUSIONS

Given sociodemographic differences in adolescent steps d-1, physical activity guidelines should focus on key populations and adopt strategies optimized for adolescents from diverse backgrounds.

IMPACT

Sociodemographic disparities in physical activity have been documented but mostly rely on self-reported data, which can be limited by reporting and prevarication bias. In this demographically diverse sample of 10-14-year-old early adolescents in the U.S., we found notable and nuanced sociodemographic disparities in Fitbit steps per day. More daily steps were recorded for male versus female sex, Black versus White race, heterosexual versus sexual minority, >$100,000 versus <$25,000 household income, and having married/partnered versus unmarried/unpartnered parents. We found effect modification by household income for Black adolescents and by sex for Asian adolescents.

Physical and Mental Health in Adolescence: Novel Insights from a transdiagnostic examination of FitBit data in the ABCD Study

Adolescence is among the most vulnerable period for the emergence of serious mental illnesses. Addressing this vulnerability has generated interest in identifying markers of risk for symptoms and opportunities for early intervention. Physical fitness has been linked to psychopathology and may be a useful risk marker and target for early intervention. New wearable technology has made assessing fitness behavior more practical while avoiding recall and self-report bias. Still, questions remain regarding the clinical utility of physical fitness metrics for mental health, both transdiagnostically and along specific symptom dimensions. The current study includes 5007 adolescents (ages 10 to 13) who participated in the Adolescent Brain Cognitive Development (ABCD) study and additional sub-study that collected fitness data from wearable technology and clinical symptom measures. Physical fitness metrics included resting heart rate (RHR- an index of cardiovascular health), time spent sedentary (associated with increased inflammation and cardiovascular disease), and time spent in moderate physical activity (associated with increased neurogenesis, neuroplasticity, and healthy neurodevelopment). Self-report clinical symptoms included measures of internalizing symptoms, externalizing symptoms, and psychosis-like experiences - PLE). Increased RHR- lower cardiovascular fitness- related only to greater internalizing symptoms (t = 3.63). More sedentary behavior related to elevated PLE severity (t = 5.49). More moderate activity related to lower PLE (t=-2.69) and internalizing (t=-6.29) symptom severity. Wearable technology fitness metrics linked physical health to specific mental health dimensions, which emphasizes the utility of detailed digital health data as a marker for risk and the need for precision in targeting physical health behaviors to benefit symptoms of psychopathology.

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.

Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights

GOAL AND AIMS

Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography.

SAMPLE

Twenty-one university students (10 females).

DESIGN

Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes.

CORE ANALYTICS

Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Variability of specificity and negative predictive value across subjects and across nights.

CORE OUTCOMES

Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively).

IMPORTANT ADDITIONAL OUTCOMES

Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights.

CORE CONCLUSION

This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

Validity of activity wristbands for estimating daily physical activity in primary schoolchildren under free-living conditions: School-Fit study

Introduction

The use of activity wristbands to monitor and promote schoolchildren's physical activity (PA) is increasingly widespread. However, their validity has not been sufficiently studied, especially among primary schoolchildren. Consequently, the main purpose was to examine the validity of the daily steps and moderate-to-vigorous PA (MVPA) scores estimated by the activity wristbands Fitbit Ace 2, Garmin Vivofit Jr 2, and the Xiaomi Mi Band 5 in primary schoolchildren under free-living conditions.

Materials and methods

An initial sample of 67 schoolchildren (final sample = 62; 50% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), participated in the present study. Each participant wore three activity wristbands (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on his/her non-dominant wrist and a research-grade accelerometer (ActiGraph wGT3X-BT) on his/her hip as the reference standard (number of steps and time in MVPA) during the waking time of one day.

Results

Results showed that the validity of the daily step scores estimated by the Garmin Vivofit Jr 2 and Xiaomi Mi Band 5 were good and acceptable (e.g., MAPE = 9.6/11.3%, and lower 95% IC of ICC = 0.87/0.73), respectively, as well as correctly classified schoolchildren as meeting or not meeting the daily 10,000/12,000-step-based recommendations, obtaining excellent/good and good/acceptable results (e.g., Garmin Vivofit Jr 2, k = 0.75/0.62; Xiaomi Mi Band 5, k = 0.73/0.53), respectively. However, the Fitbit Ace 2 did not show an acceptable validity (e.g., daily steps: MAPE = 21.1%, and lower 95% IC of ICC = 0.00; step-based recommendations: k = 0.48/0.36). None of the three activity wristbands showed an adequate validity for estimating daily MVPA (e.g., MAPE = 36.6-90.3%, and lower 95% IC of ICC = 0.00-0.41) and the validity for the MVPA-based recommendation tended to be considerably lower (e.g., k = -0.03-0.54).

Conclusions

The activity wristband Garmin Vivofit Jr 2 obtained the best validity for monitoring primary schoolchildren's daily steps, offering a feasible alternative to the research-grade accelerometers. Furthermore, this activity wristband could be used during PA promotion programs to provide accurate feedback to primary schoolchildren to ensure their accomplishment with the PA recommendations.

Use of a Fitbit-like device in rats: Sex differences, relation to EEG sleep, and use to measure the long-term effects of adolescent ethanol exposure

BACKGROUND

Sleep difficulties and rhythm disturbances are some of the problems associated with adolescent binge drinking. Recently, animal models of alcohol-induced insomnia have been developed. However, studies in human subjects have recently focused not only on nighttime EEG findings but also on daytime sleepiness and disrupted activity levels as typically measured by activity tracking devices such as the "Fitbit." We sought to develop and test a Fitbit-like device (the "FitBite") in rats and use it to track rest-activity cycles following adolescent alcohol exposure.

METHODS

The effects of 5 weeks of adolescent ethanol vapor or control conditions were evaluated in 48 male and female Wistar rats using FitBite activity while intoxicated, and during acute (24 h post-vapor exposure) and chronic withdrawal (4 weeks post-vapor exposure). Data were analyzed using activity count and cosinor analyses. Fourteen rats were subsequently implanted with cortical electrodes, and data from the FitBite were compared with EEG data to determine how well the FitBite could identify sleep and activity cycles.

RESULTS

Female rats were generally more active than males, with higher circadian rhythm amplitudes and mesors (rhythm-adjusted means) across a 24-h period. There were significant correlations between EEG-estimated sleep and activity counts using the FitBite. When the rats were tested during intoxication after 4 weeks of ethanol vapor exposure, they had significantly less overall activity. Disruptions in circadian rhythm were also found with significant decreases in the circadian amplitude, mesor, and a later shift in the acrophase. At 24 h of ethanol withdrawal, rats had more episodes of activity with shorter durations during the daytime, when rats are expected to spend more of their time sleeping. This effect remained at 4 weeks following withdrawal, but circadian rhythm disruptions were no longer present.

CONCLUSIONS

A Fitbit-like device can be successfully used in rats to assess rest-activity cycles. Adolescent alcohol exposure produced circadian rhythm disturbances that were not observed after withdrawal. Fragmentation of ultradian rest-activity cycles during the light period was found at 24 h and 4 weeks after withdrawal and support data demonstrating the presence of sleep disturbance long after alcohol withdrawal.

Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children

Importance

The use of consumer-grade wearable devices for collecting data for biomedical research may be associated with social determinants of health (SDoHs) linked to people's understanding of and willingness to join and remain engaged in remote health studies.

Objective

To examine whether demographic and socioeconomic indicators are associated with willingness to join a wearable device study and adherence to wearable data collection in children.

Design, Setting, and Participants

This cohort study used wearable device usage data collected from 10 414 participants (aged 11-13 years) at the year-2 follow-up (2018-2020) of the ongoing Adolescent Brain and Cognitive Development (ABCD) Study, performed at 21 sites across the United States. Data were analyzed from November 2021 to July 2022.

Main Outcomes and Measures

The 2 primary outcomes were (1) participant retention in the wearable device substudy and (2) total device wear time during the 21-day observation period. Associations between the primary end points and sociodemographic and economic indicators were examined.

Results

The mean (SD) age of the 10 414 participants was 12.00 (0.72) years, with 5444 (52.3%) male participants. Overall, 1424 participants (13.7%) were Black; 2048 (19.7%), Hispanic; and 5615 (53.9%) White. Substantial differences were observed between the cohort that participated and shared wearable device data (wearable device cohort [WDC]; 7424 participants [71.3%]) compared with those who did not participate or share data (no wearable device cohort [NWDC]; 2900 participants [28.7%]). Black children were significantly underrepresented (-59%) in the WDC (847 [11.4%]) compared with the NWDC (577 [19.3%]; P < .001). In contrast, White children were overrepresented (+132%) in the WDC (4301 [57.9%]) vs the NWDC (1314 [43.9%]; P < .001). Children from low-income households (<$24 999) were significantly underrepresented in WDC (638 [8.6%]) compared with NWDC (492 [16.5%]; P < .001). Overall, Black children were retained for a substantially shorter duration (16 days; 95% CI, 14-17 days) compared with White children (21 days; 95% CI, 21-21 days; P < .001) in the wearable device substudy. In addition, total device wear time during the observation was notably different between Black vs White children (β = -43.00 hours; 95% CI, -55.11 to -30.88 hours; P < .001).

Conclusions and Relevance

In this cohort study, large-scale wearable device data collected from children showed considerable differences between White and Black children in terms of enrollment and daily wear time. While wearable devices provide an opportunity for real-time, high-frequency contextual monitoring of individuals' health, future studies should account for and address considerable representational bias in wearable data collection associated with demographic and SDoH factors.

Association of Physical Activity and Screen Time With Body Mass Index Among US Adolescents

IMPORTANCE

The Physical Activity Guidelines Advisory Committee Scientific Report identified important research gaps to inform future guidance for adolescents, including limited evidence on the importance of sedentary behaviors (screen time) and their interactions with physical activity for adolescent health outcomes, including overweight and obesity.

OBJECTIVE

To identify the independent associations of physical activity and screen time categories, and the interactions between physical activity and screen time categories, with body mass index (BMI) and overweight and obesity in adolescents.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) Study collected from September 10, 2018, to September 29, 2020. Data were analyzed from July 8 to December 20, 2022. A total of 5797 adolescents aged 10 to 14 years from 21 racially and ethnically diverse study sites across the US were included in the analysis.

EXPOSURES

Categories of total step count per day (with 1000 to 6000 steps per day indicating low, >6000 to 12 000 steps per day indicating medium, and >12 000 steps per day indicating high), as measured by a wearable digital device (Fitbit), and categories of self-reported screen time hours per day (with 0 to 4 hours per day indicating low, >4 to 8 hours per day indicating medium, and >8 hours per day indicating high).

MAIN OUTCOMES AND MEASURES

Participant BMI was calculated as weight in kilograms divided by height in meters squared and converted into sex- and age-specific percentiles in accordance with the Centers for Disease Control and Prevention growth curves and definitions. Individuals were classified as having overweight or obesity if their BMI was in the 85th percentile or higher for sex and age.

RESULTS

Among 5797 adolescents included in the analytic sample, 50.4% were male, 61.0% were White, 35.0% had overweight or obesity, and the mean (SD) age was 12.0 (0.6) years. Mean (SD) reported screen time use was 6.5 (5.4) hours per day, and mean (SD) overall step count was 9246.6 (3111.3) steps per day. In models including both screen time and step count, medium (risk ratio [RR], 1.24; 95% CI, 1.12-1.37) and high (RR, 1.29; 95% CI, 1.16-1.44) screen time categories were associated with higher overweight or obesity risk compared with the low screen time category. Medium (RR, 1.19; 95% CI, 1.06-1.35) and low (RR, 1.30; 95% CI, 1.11-1.51) step count categories were associated with higher overweight or obesity risk compared with the high step count category. Evidence of effect modification between screen time and step count was observed for BMI percentile. For instance, among adolescents with low screen use, medium step count was associated with a 1.55 higher BMI percentile, and low step count was associated with a 7.48 higher BMI percentile. However, among those with high screen use, step count categories did not significantly change the association with higher BMI percentile (low step count: 8.79 higher BMI percentile; medium step count: 8.76 higher BMI percentile; high step count: 8.26 higher BMI percentile).

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, a combination of low screen time and high step count was associated with lower BMI percentile in adolescents. These results suggest that high step count may not offset higher overweight or obesity risk for adolescents with high screen time, and low screen time may not offset higher overweight or obesity risk for adolescents with low step count. These findings addressed several research gaps identified by the Physical Activity Guidelines Advisory Committee Scientific Report and may be used to inform future screen time and physical activity guidance for adolescents.

Promoting Sleep Duration in the Pediatric Setting Using a Mobile Health Platform: A Randomized Optimization Trial

Objective

Determine the optimal combination of digital health intervention component settings that increase average sleep duration by ≥30 minutes per weeknight.

Methods

Optimization trial using a 25 factorial design. The trial included 2 week run-in, 7 week intervention, and 2 week follow-up periods. Typically developing children aged 9-12y, with weeknight sleep duration <8.5 hours were enrolled (N=97). All received sleep monitoring and performance feedback. The five candidate intervention components (with their settings to which participants were randomized) were: 1) sleep goal (guideline-based or personalized); 2) screen time reduction messaging (inactive or active); 3) daily routine establishing messaging (inactive or active); 4) child-directed loss-framed financial incentive (inactive or active); and 5) caregiver-directed loss-framed financial incentive (inactive or active). The primary outcome was weeknight sleep duration (hours per night). The optimization criterion was: ≥30 minutes average increase in sleep duration on weeknights.

Results

Average baseline sleep duration was 7.7 hours per night. The highest ranked combination included the core intervention plus the following intervention components: sleep goal (either setting was effective), caregiver-directed loss-framed incentive, messaging to reduce screen time, and messaging to establish daily routines. This combination increased weeknight sleep duration by an average of 39.6 (95% CI: 36.0, 43.1) minutes during the intervention period and by 33.2 (95% CI: 28.9, 37.4) minutes during the follow-up period.

Conclusions

Optimal combinations of digital health intervention component settings were identified that effectively increased weeknight sleep duration. This could be a valuable remote patient monitoring approach to treat insufficient sleep in the pediatric setting.

Recommendations for Identifying Valid Wear for Consumer-Level Wrist-Worn Activity Trackers and Acceptability of Extended Device Deployment in Children

BACKGROUND

Self-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data.

METHODS

Data were collected via Fitbit Charge HR continuously over the course of 3 weeks. Questionnaires were given to each child and their parent/guardian to determine the perceived usability of the device. Patterns of data were evaluated and best practice inclusion criteria recommended.

RESULTS

Best practices were established to extract, filter, and process data to evaluate device wear, r and establish minimum wear time to evaluate behavioral patterns. This resulted in usable data available from 137 (89%) of the sample.

CONCLUSIONS

Activity trackers are highly acceptable in the target population and can provide objective data over longer periods of wear. Best practice inclusion protocols that reflect physical activity in youth are provided.

Youth screen use in the ABCD® study

Adolescent screen usage is ubiquitous and influences development and behavior. Longitudinal screen usage data coupled with psychometrically valid constructs of problematic behaviors can provide insights into these relationships. We describe methods by which the screen usage questionnaire was developed in the Adolescent Brain Cognitive Development (ABCD) Study, demonstrate longitudinal changes in screen usage via child report and describe data harmonization baseline-year 2. We further include psychometric analyses of adapted social media and video game addiction scales completed by youth. Nearly 12,000 children ages 9-10 years at baseline and their parents were included in the analyses. The social media addiction questionnaire (SMAQ) showed similar factor structure and item loadings across sex and race/ethnicities, but that item intercepts varied across both sex and race/ethnicity. The videogame addiction questionnaire (VGAQ) demonstrated the same configural, metric and scalar invariance across racial and ethnic groups, however differed across sex. Video gaming and online social activity increased over ages 9/10-11/12 (p's < 0.001). Compared with boys, girls engaged in greater social media use (p < .001) and demonstrated higher ratings on the SMAQ (p < .001). Compared with girls, boys played more video games (p < .001) and demonstrated higher ratings on the VGAQ (p < .001). Time spent playing video games increased more steeply for boys than girls from age 9/10-11/12 years (p < .001). Black youth demonstrated significantly higher SMAQ and VGAQ scores compared to all other racial/ethnic groups. These data show the importance of considering different screen modalities beyond total screen use and point towards clear demographic differences in use patterns. With these comprehensive data, ABCD is poised to address critical questions about screen usage changes across adolescence.

Quantitative study of the effects of early standardized ambulation on sleep quality in patients after hepatectomy

Background

Sleep quality has been always an important problem for patients after hepatectomy. The main purpose of the study is to investigate the effects of early ambulation on sleep quality in patients after liver resection via a quantitative study.

Methods

Patients undergoing liver tumor resection were randomly divided into two groups, and the Pittsburgh Sleep Quality Index (PSQI) was used to assess the postoperative activities and sleep quality.

Results

Patients who started early ambulation after liver resection had significantly better sleep quality, faster recovery of gastrointestinal function and shorter lengths of postoperative hospital stay compared with the control group. And there was no significant difference in the incidence of postoperative complications between the two groups.

Conclusion

Early standardized physical activities are feasible for patients after liver resection, which can significantly improve patient's sleep quality, reduce patient's pain and the nursing workload, and achieve rapid recovery.

Agreement between Fitbit and ActiGraph Estimates of Physical Activity in Young Children

Physical activity (PA) estimates from the Fitbit Flex 2 were compared to those from the ActiGraph GT9X Link in 123 elementary school children. Steps and intensity-specific estimates of PA and 3-month PA change were calculated using two different ActiGraph cut-points (Evenson and Romanzini). Fitbit estimates were 35% higher for steps compared to the ActiGraph. Fitbit and ActiGraph intensity-specific estimates were closest for sedentary and light PA while estimates of moderate and vigorous PA varied substantially depending upon the ActiGraph cut-points used. Spearman correlations between device estimates were higher for steps (rs=.70) than for moderate (rs =.54 to .55) or vigorous (rs =.29 to .48) PA. There was low concordance between devices in assessing PA changes over time. Agreement between Fitbit Flex 2 and ActiGraph estimates may depend upon the cut-points used to classify PA intensity. However, there is fair to good agreement between devices in ranking children's steps and MVPA.

Accuracy and Precision of Consumer-Grade Wearable Activity Monitors for Assessing Time Spent in Sedentary Behavior in Children and Adolescents: Systematic Review

Background

A large number of wearable activity monitor models are released and used each year by consumers and researchers. As more studies are being carried out on children and adolescents in terms of sedentary behavior (SB) assessment, knowledge about accurate and precise monitoring devices becomes increasingly important.

Objective

The main aim of this systematic review was to investigate and communicate findings on the accuracy and precision of consumer-grade physical activity monitors in assessing the time spent in SB in children and adolescents.

Methods

Searches of PubMed (MEDLINE), Scopus, SPORTDiscus (full text), ProQuest, Open Access Theses and Dissertations, DART Europe E-theses Portal, and Networked Digital Library of Theses and Dissertations electronic databases were performed. All relevant studies that compared different types of consumer-grade monitors using a comparison method in the assessment of SB, published in European languages from 2015 onward were considered for inclusion. The risk of bias was estimated using Consensus-Based Standards for the Selection of Health Status Measurement Instruments. For enabling comparisons of accuracy measures within the studied outcome domain, measurement accuracy interpretation was based on group mean or percentage error values and 90% CI. Acceptable limits were predefined as –10% to +10% error in controlled and free-living settings. For determining the number of studies with group error percentages that fall within or outside one of the sides from previously defined acceptable limits, two 1-sided tests of equivalence were carried out, and the direction of measurement error was examined.

Results

A total of 8 studies complied with the predefined inclusion criteria, and 3 studies provided acceptable data for quantitative analyses. In terms of the presented accuracy comparisons, 14 were subsequently identified, with 6 of these comparisons being acceptable in terms of quantitative analysis. The results of the Cochran Q test indicated that the included studies did not share a common effect size (Q₅=82.86; P<.001). I², which represents the percentage of total variation across studies due to heterogeneity, amounted to 94%. The summary effect size based on the random effects model was not statistically significant (effect size=14.36, SE 12.04, 90% CI −5.45 to 34.17; P=.23). According to the equivalence test results, consumer-grade physical activity monitors did not generate equivalent estimates of SB in relation to the comparison methods. Majority of the studies (3/7, 43%) that reported the mean absolute percentage errors have reported values of <30%.

Conclusions

This is the first study that has attempted to synthesize available evidence on the accuracy and precision of consumer-grade physical activity monitors in measuring SB in children and adolescents. We found very few studies on the accuracy and almost no evidence on the precision of wearable activity monitors. The presented results highlight the large heterogeneity in this area of research.

Trial Registration

PROSPERO CRD42021251922; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=251922

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

BACKGROUND

Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized.

OBJECTIVE

We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk.

METHODS

We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events.

RESULTS

We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers.

CONCLUSIONS

High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.

Disparities in sleep duration among American children: effects of race and ethnicity, income, age, and sex

Children in the United States sleep less than the recommended amount and sleep deficiencies may be worse among disadvantaged children. Prior studies that compared sleep time in children of different race/ethnic groups mostly relied on questionnaires or were limited to small sample sizes. Our study takes advantage of the Adolescent Brain Cognitive Development study to compare total sleep time using a week of actigraphy data among American children (n = 4,207, 9 to 13 y old) of different racial/ethnic and income groups. We also assessed the effects of neighborhood deprivation, experience of discrimination, parent's age at child's birth, body mass index (BMI), and time the child fell asleep on sleep times. Daily total sleep time for the sample was 7.45 h and race/ethnicity, income, sex, age, BMI, were all significant predictors of total sleep time. Black children slept less than White children (∼34 min; Cohen's d = 0.95), children from lower income families slept less than those from higher incomes (∼16 min; Cohen's d = 0.44), boys slept less than girls (∼7 min; Cohen's d = 0.18), and older children slept less than younger ones (∼32 min; Cohen's d = 0.91); mostly due to later sleep times. Children with higher BMI also had shorter sleep times. Neither area deprivation index, experience of discrimination, or parent's age at child's birth significantly contributed to sleep time. Our findings indicate that children in the United States sleep significantly less than the recommended amount for healthy development and identifies significant racial and income disparities. Interventions to improve sleep hygiene in children will help improve health and ameliorate racial disparities in health outcomes.

Utility of Fitbit devices among children and adolescents with chronic health conditions: a scoping review

Background

While Fitbit® devices were initially intended for leisurely, consumer use, there has been recent interest among scientific and medical communities in the prospective use of Fitbit devices for clinical and research purposes. Those who have chronic health conditions are often required to spend considerable amounts of money and time undergoing physiological tests and activity monitoring to support, stabilize, and manage their health. This disease burden is only amplified in pediatric populations. Devices that are used to collect these data can be invasive, uncomfortable, and disconcerting. Using the Fitbit tracker to acquire such biometric data could ease this burden. Our scoping review seeks to summarize the research that has been conducted on the utilization of Fitbit devices in studies of children and adolescents with chronic health conditions and the feasibility, accuracy, and potential benefits of doing so.

Methods

Searches were conducted on PubMed for articles relating pediatric health to Fitbit device usage (using a Boolean search strategy). The eligibility criteria included trials being clinical and/or randomized controlled and articles being in English. Once articles were obtained, they underwent screening and exclusion processes and were charted for their titles, authors, objectives, results, and respective chronic illnesses. In the subsequent full-text review, further charting was conducted, collecting study designs, Fitbit parameters, feasibility, accuracy, and related health and clinical outcomes.

Results

Fitbit trackers were unanimously demonstrated to be feasible devices in this population for physical activity monitoring and were determined to be potentially beneficial in measuring and improving overall wellbeing and physical health in children with chronic illness. Nevertheless, sufficient evidence was not found in support of Fitbit accuracy. Additional biases were identified against the population of children with chronic health conditions that may further enable inaccurate data.

Conclusions

While Fitbit devices may be beneficial for those interested in improving physical health, discretion is advised for those seeking to collect accurate and/or medically necessitated data. Given the existing literature evaluated, medical-grade technologies are preferred in instances of the latter, as Fitbit devices have not been found to provide reliably accurate data.

Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies

Objectives

Studies that assess all three dimensions of the integrative 24-hour physical behaviour (PB) construct, namely, intensity, posture/activity type and biological state, are on the rise. However, reviews on validation studies that cover intensity, posture/activity type and biological state assessed via wearables are missing.

Design

Systematic review. The risk of bias was evaluated by using the QUADAS-2 tool with nine signalling questions separated into four domains (ie, patient selection/study design, index measure, criterion measure, flow and time).

Data sources

Peer-reviewed validation studies from electronic databases as well as backward and forward citation searches (1970–July 2021).

Eligibility criteria for selecting studies

Wearable validation studies with children and adolescents (age <18 years). Required indicators: (1) study protocol must include real-life conditions; (2) validated device outcome must belong to one dimension of the 24-hour PB construct; (3) the study protocol must include a criterion measure; (4) study results must be published in peer-reviewed English language journals.

Results

Out of 13 285 unique search results, 76 articles with 51 different wearables were included and reviewed. Most studies (68.4%) validated an intensity measure outcome such as energy expenditure, but only 15.9% of studies validated biological state outcomes, while 15.8% of studies validated posture/activity type outcomes. We identified six wearables that had been used to validate outcomes from two different dimensions and only two wearables (ie, ActiGraph GT1M and ActiGraph GT3X+) that validated outcomes from all three dimensions. The percentage of studies meeting a given quality criterion ranged from 44.7% to 92.1%. Only 18 studies were classified as ‘low risk’ or ‘some concerns’.

Summary

Validation studies on biological state and posture/activity outcomes are rare in children and adolescents. Most studies did not meet published quality principles. Standardised protocols embedded in a validation framework are needed.

PROSPERO registration number

CRD42021230894.

Effects of Sleep Restriction on Self-Reported Putting Performance in Golf

In the present study, we aimed to explore the effects of sleep restriction (SR) on self-reported golf putting skills. Eleven collegiate golfers participated in a self-reported, counterbalanced experimental study under two conditions: (a) a SR condition in which sleep on the night prior to putting was restricted to 4-5 hours, and (b) a habitual normal sleep (NS) condition on the night before the putting test. Following each sleep condition, participants engaged in ten consecutive putting tests at 7 am, 11 am, and 3 pm. Participants reported their subjective sleepiness before each time frame, and their chronotype, defined as their individual circadian preference, was scored based on a morningness-eveningness questionnaire (MEQ). Participants restricted sleep to an average period of 267.6 minutes/night (SD = 51.2) in the SR condition and 426.2 (SD =38.0) minutes/night in the NS condition. A two-way analysis of variance revealed a significant main effect of the sleep condition on the lateral displacement of putts from the target (lateral misalignment) (p = 0.002). In addition, there was a significant main effect of time on distance from the target (distance misalignment) (p = 0.017), indicating less accuracy of putting in the SR condition. In the SR condition, the MEQ score was positively correlated with distance misalignment at 3 pm (ρ = 0.650, p = 0.030), suggesting that morningness types are susceptible to the effects of SR on putting performance. Our findings suggest that golfers should obtain sufficient sleep to optimize putting performance.

Reference Values for Wristband Accelerometry Data in Children Aged 6-11 Years of Age

OBJECTIVES

Wristband activity trackers (accelerometers) could serve as a convenient monitoring tool to continuously quantify physical activity throughout the day. We aim to provide reference values for the use of these devices in healthy children.

METHODS

Children were recruited at a local school and provided with activity trackers (Fitbit Charge 2). Pupils were instructed to wear devices during all normal daytime activities over a period of 11-15 days. Demographic data, total number of daily steps and heart rate were recorded. In addition, all children/parents were asked to complete a questionnaire providing information about daily physical routine (mode of transport to school, sporting activities as well as sport club memberships).

RESULTS

Three hundred two children (54.6% boys; median age 8.7 years) participated in this prospective study. Median wearing time of the device was 12.1 h/day. Overall, the median daily total step count was 12,095. Median step counts/day were significantly higher in boys compared to girls (13,015 vs. 11,305 steps/day; p < 0.0001). In addition, step counts were significantly higher during the week, compared to weekend days. The effect of age on daily step count was found to be non-linear: the total daily step count increased from 6 to 8.5 years of age, while older children (aged >8.5 years) had lower step counts compared to the younger children. Significant predictors of the daily step count were male gender (+1,324.9 steps, p = 0.0008), mode of transportation to school (walking, bicycle, scooter: +865.5 steps p = 0.049), active membership in a sports club (+1,324.9 steps/day, p = 0.0008), and number of structured units of physical exercise performed (+336.5/per 45 min, p < 0.0001). Severe obesity was associated with a significant reduction in total daily step count (-3037.7 steps/day, p = 0.015).

CONCLUSION

Our prospective cohort study of healthy school children provides reference values for wristband accelerometers in normal individuals. In addition, it clarifies the effect of age, body weight and lifestyle on normal daily step counts in school children. This data should be helpful to judge the degree of physical limitation of patients compared to healthy peers.

Impact of the COVID-19 Pandemic on Objectively Measured Physical Activity and Sedentary Behavior Among Overweight Young Adults: Yearlong Longitudinal Analysis

BACKGROUND

The COVID-19 pandemic has impacted multiple aspects of daily living, including behaviors associated with occupation, transportation, and health. It is unclear how these changes to daily living have impacted physical activity and sedentary behavior.

OBJECTIVE

In this study, we add to the growing body of research on the health impact of the COVID-19 pandemic by examining longitudinal changes in objectively measured daily physical activity and sedentary behavior among overweight or obese young adults participating in an ongoing weight loss trial in San Diego, California.

METHODS

Data were collected from 315 overweight or obese (BMI: range 25.0-39.9 kg/m²) participants aged from 18 to 35 years between November 1, 2019, and October 30, 2020, by using the Fitbit Charge 3 (Fitbit LLC). After conducting strict filtering to find valid data on consistent wear (>10 hours per day for ≥250 days), data from 97 participants were analyzed to detect multiple structural changes in time series of physical activity and sedentary behavior. An algorithm was designed to detect multiple structural changes. This allowed for the automatic identification and dating of these changes in linear regression models with CIs. The number of breakpoints in regression models was estimated by using the Bayesian information criterion and residual sum of squares; the optimal segmentation corresponded to the lowest Bayesian information criterion and residual sum of squares. To quantify the changes in each outcome during the periods identified, linear mixed effects analyses were conducted. In terms of key demographic characteristics, the 97 participants included in our analyses did not differ from the 210 participants who were excluded.

RESULTS

After the initiation of the shelter-in-place order in California on March 19, 2021, there were significant decreases in step counts (-2872 steps per day; 95% CI -2734 to -3010), light physical activity times (-41.9 minutes; 95% CI -39.5 to -44.3), and moderate-to-vigorous physical activity times (-12.2 minutes; 95% CI -10.6 to -13.8), as well as significant increases in sedentary behavior times (+52.8 minutes; 95% CI 47.0-58.5). The decreases were greater than the expected declines observed during winter holidays, and as of October 30, 2020, they have not returned to the levels observed prior to the initiation of shelter-in-place orders.

CONCLUSIONS

Among overweight or obese young adults, physical activity times decreased and sedentary behavior times increased concurrently with the implementation of COVID-19 mitigation strategies. The health conditions associated with a sedentary lifestyle may be additional, unintended results of the COVID-19 pandemic.

The impact of social networks on sleep among a cohort of college students

Background

Sleep duration and quality are associated with physical and mental wellbeing. This paper examines social network effects on individual level change in the sleep quantity and quality from late adolescence to emerging adulthood and its associated factors, including the influence of peers on sleep behavior and the impact of changes in network size.

Methods

We use sleep data from 619 undergraduates at the University of Notre Dame obtained via Fitbit devices as part of the NetHealth project. The data were collected between August 16, 2015 and May 13, 2017. We model trends in sleep behaviors using latent growth-curve models.

Results

Controlling for the many factors known to impact sleep quantity and quality, we find two social network effects: increasing network size is associated with less sleep and a student's sleep levels are influenced by his or her peers. While we do not find any consistent decline in sleep quantity over the 637 days, daily fluctuations in sleep quantity are associated with changes in network size and the composition of a student's network. As a student's network gets bigger, s/he sleeps less, and when a student's contacts sleep more (or less) than s/he does, the student becomes more like his or her contacts and sleeps more (or less).

Conclusions

Social networks can and do impact sleep, especially sleep quantity. In contexts where students want to have larger networks, the difficulties of increasing network size and maintaining larger networks negatively impact sleep. Because of peer influence, the effectiveness of interventions designed to improve sleep practices could be increased by leveraging student social networks to help diffuse better sleep habits.

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The sleep habits of 619 undergraduates were traced with Fitbit devices for 637 days.

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Their sleep quantity measures were relatively stable but affect by peers.

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Their sleep quality was slightly getting worse but not affected by peers.

Feasibility, acceptability and validation of wearable devices for climate change and health research in the low-resource contexts of Burkina Faso and Kenya: Study protocol

As the epidemiological transition progresses throughout sub-Saharan Africa, life lived with diseases is an increasingly important part of a population's burden of disease. The burden of disease of climate-sensitive health outcomes is projected to increase considerably within the next decades. Objectively measured, reliable population health data is still limited and is primarily based on perceived illness from recall. Technological advances like non-invasive, consumer-grade wearable devices may play a vital role in alleviating this data gap and in obtaining insights on the disease burden in vulnerable populations, such as heat stress on human cardiovascular response. The overall goal of this study is to investigate whether consumer-grade wearable devices are an acceptable, feasible and valid means to generate data on the individual level in low-resource contexts. Three hundred individuals are recruited from the two study locations in the Nouna health and demographic surveillance system (HDSS), Burkina Faso, and the Siaya HDSS, Kenya. Participants complete a structured questionnaire that comprises question items on acceptability and feasibility under the supervision of trained data collectors. Validity will be evaluated by comparing consumer-grade wearable devices to research-grade devices. Furthermore, we will collect demographic data as well as the data generated by wearable devices. This study will provide insights into the usage of consumer-grade wearable devices to measure individual vital signs in low-resource contexts, such as Burkina Faso and Kenya. Vital signs comprising activity (steps), sleep (duration, quality) and heart rate (hr) are important measures to gain insights on individual behavior and activity patterns in low-resource contexts. These vital signs may be associated with weather variables-as we gather them from weather stations that we have setup as part of this study to cover the whole Nouna and Siaya HDSSs-in order to explore changes in behavior and other variables, such as activity, sleep, hr, during extreme weather events like heat stress exposure. Furthermore, wearable data could be linked to health outcomes and weather events. As a result, consumer-grade wearables may serve as a supporting technology for generating reliable measurements in low-resource contexts and investigating key links between weather occurrences and health outcomes. Thus, wearable devices may provide insights to better inform mitigation and adaptation interventions in these low-resource settings that are direly faced by climate change-induced changes, such as extreme weather events.

Bidirectional associations between sedentary time and sleep duration among 12- to 14-year-old adolescents

Background

The aim of this study was to investigate bidirectional associations between (prolonged) sitting time and sleep duration in 12- to 14-year-old adolescents using a between-subjects and within-subjects analyses approach.

Methods

Observational data were used from 108 adolescents (53% girls; mean age 12.9 (SD 0.7) years) from six schools in Flanders, Belgium. The Axivity AX3 triaxial accelerometer, worn on the thigh, was used to assess daily total sitting time and daily time spent in sedentary bouts of ≥30 min (as a proxy for prolonged sitting time). The Fitbit Charge 3 was used to assess nightly sleep duration. Both monitors were worn on schooldays only (ranging from 4 to 5 days). Linear mixed models were conducted to analyse the associations, resulting in four models. In each model, the independent variable (sleep duration, sitting time or prolonged sitting time) was included as within- as well as between-subjects factor.

Results

Within-subjects analyses showed that when the adolescents sat more and when the adolescents spent more time sitting in bouts of ≥30 min than they usually did on a given day, they slept less during the following night (p = 0.01 and p = 0.05 (borderline significant), respectively). These associations were not significant in the other direction. Between-subjects analyses showed that adolescents who slept more on average, spent less time sitting (p = 0.006) and less time sitting in bouts of ≥30 min (p = 0.004) compared with adolescents who slept less on average. Conversely, adolescents who spent more time sitting on average and adolescents who spent more time sitting in bouts of ≥30 min on average, slept less (p = 0.02 and p = 0.003, respectively).

Conclusions

Based on the between-subjects analyses, interventions focusing on reducing or regularly breaking up sitting time could improve adolescents’ sleep duration on a population level, and vice versa. However, the within-subjects association was only found in one direction and suggests that to sleep sufficiently during the night, adolescents might limit and regularly break up their sitting time the preceding day.

Trial registration

Data have been used from our trial registered at ClinicalTrials.gov (NCT04327414; registered on March 11, 2020).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11694-9.

Koshu GRoup Activity, Active Play and Exercise (GRAPE) Study: A Cluster Randomised Controlled Trial Protocol of a School-Based Intervention among Japanese Children

School-based programmes need to be effective, easy for all, easy to perform within a short duration, and inexpensive. However, no studies have reported whether voluntarily and very short-time active play programmes contribute to improved health outcomes. This study aims to describe the GRoup activity, Active Play and Exercise (GRAPE) cluster randomised controlled trial that examined whether active play interventions of very short durations contribute to increasing physical activity (PA) and bone mass among school-aged children. The trial was conducted in 2018 from January to June, and the activity comprised ≥2 children jumping together for approximately 10 s per session, at least five times a day (approximately 1 min/day). School clusters, pair-matched as per school size (total number of children) and region, were randomly allocated to either intervention or wait-list control groups. The primary outcomes comprised objectively measured changes in PA levels (moderate-to-vigorous PA) evaluated using wrist-worn activity trackers from baseline to the one-year follow-up (six-month post-intervention follow-up) and changes in bone mass evaluated using calcaneus quantitative ultrasound parameters. This study could describe the problems and challenges in school-based PA intervention studies and present findings that could make a potentially important contribution to health education and PA promotion.

The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings

MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland–Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes.

It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography

PURPOSE

There is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring.

METHODS

We developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders.

RESULTS

The classifier achieved substantial agreement on four-class sleep staging with an average Cohen's kappa of 0.62 and accuracy of 76.4%. For children/adolescents, it achieved even higher agreement with an average kappa of 0.66 and accuracy of 77.9%. Performance was significantly higher in non-REM parasomnias (kappa = 0.69, accuracy = 80.1%) and significantly lower in REM parasomnias (kappa = 0.55, accuracy = 72.3%). A weak correlation was found between age and kappa (ρ = -0.30, p<0.001) and age and accuracy (ρ = -0.22, p<0.001).

CONCLUSION

This study shows the feasibility of automatic wearable sleep staging in patients with a broad variety of sleep disorders and a wide age range. Results demonstrate the potential for ambulatory long-term monitoring of clinical populations, which may improve diagnosis, estimation of severity and follow up in both sleep medicine and research.