Longitudinal associations of screen time, physical activity, and sleep duration with body mass index in U.S. youth.

BACKGROUND: Youth use different forms of screen time (e.g., streaming, gaming) that may be related to body mass index (BMI). Screen time is non-independent from other behaviors, including physical activity and sleep duration. Statistical approaches such as isotemporal substitution or compositional data analysis (CoDA) can model associations between these non-independent behaviors and health outcomes. Few studies have examined different types of screen time, physical activity, and sleep duration simultaneously in relation to BMI. METHODS: Data were baseline (2017-2018) and one-year follow-up (2018-2019) from the Adolescent Brain Cognitive Development Study, a multi-site study of a nationally representative sample of U.S. youth (N = 10,544, mean [SE] baseline age = 9.9 [0.03] years, 48.9% female, 45.4% non-White). Participants reported daily minutes of screen time (streaming, gaming, socializing), physical activity, and sleep. Sex-stratified models estimated the association between baseline behaviors and follow-up BMI z-score, controlling for demographic characteristics, internalizing symptoms, and BMI z-score at baseline. RESULTS: In females, isotemporal substitution models estimated that replacing 30 min of socializing (ß [95% CI] = -0.03 [-0.05, -0.002]), streaming (-0.03 [-0.05, -0.01]), or gaming (-0.03 [-0.06, -0.01]) with 30 min of physical activity was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing (-0.03 [-0.05, -0.01]), streaming (-0.02 [-0.03, -0.01]), or gaming (-0.02 [-0.03, -0.01]) with 30 min of sleep was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing with 30 min of gaming was associated with a lower follow-up BMI z-score (-0.01 [-0.03, -0.0001]). CoDA estimated that in males, a greater proportion of time spent in baseline socializing, relative to the remaining behaviors, was associated with a higher follow-up BMI z-score (0.05 [0.02, 0.08]). In females, no associations between screen time and BMI were observed using CoDA. CONCLUSIONS: One-year longitudinal associations between screen time and BMI may depend on form of screen time, what behavior it replaces (physical activity or sleep), and participant sex. The alternative statistical approaches yielded somewhat different results. Experimental manipulation of screen time and investigation of biopsychosocial mechanisms underlying the observed sex differences will allow for causal inference and can inform interventions.

Associations between actigraphy-measured sleep duration, continuity, and timing with mortality in the UK Biobank.

STUDY OBJECTIVES: To examine the associations between sleep duration, continuity, timing, and mortality using actigraphy among adults. METHODS: Data were from a cohort of 88 282 adults (40-69 years) in UK Biobank that wore a wrist-worn triaxial accelerometer for 7 days. Actigraphy data were processed to generate estimates of sleep duration and other sleep characteristics including wake after sleep onset (WASO), number of 5-minute awakenings, and midpoint for sleep onset/wake-up and the least active 5 hours (L5). Data were linked to mortality outcomes with follow-up to October 31, 2021. We implemented Cox models (hazard ratio, confidence intervals [HR, 95% CI]) to quantify sleep associations with mortality. Models were adjusted for demographics, lifestyle factors, and medical conditions. RESULTS: Over an average of 6.8 years 2973 deaths occurred (1700 cancer, 586 CVD deaths). Overall sleep duration was significantly associated with risk for all-cause (p < 0.01), cancer (p < 0.01), and CVD (p = 0.03) mortality. For example, when compared to sleep durations of 7.0 hrs/d, durations of 5 hrs/d were associated with a 29% higher risk for all-cause mortality (HR: 1.29 [1.09, 1.52]). WASO and number of awakenings were not associated with mortality. Individuals with L5 early or late midpoints (<2:30 or ≥ 3:30) had a ~20% higher risk for all-cause mortality, compared to those with intermediate L5 midpoints (3:00-3:29; p ≤ 0.01; e.g. HR ≥ 3:30: 1.19 [1.07, 1.32]). CONCLUSIONS: Shorter sleep duration and both early and late sleep timing were associated with a higher mortality risk. These findings reinforce the importance of public health efforts to promote healthy sleep patterns in adults.

Examining the Bidirectional Associations Between Sleep Duration, Screen Time, and Internalizing Symptoms in the ABCD Study.

PURPOSE: The likelihood of meeting sleep duration and screen time guidelines decreases as children develop toward adolescence. Simultaneously, the prevalence of internalizing symptoms increases. The purpose of this paper was to examine the bidirectional associations between sleep duration and screen time with internalizing symptoms in a one-year longitudinal study starting in late childhood. METHODS: Participants were 10,828 youth (47.8% female) enrolled in the Adolescent Brain Cognitive Development Study. At baseline (mean age 9.9 years) and one-year follow-up (mean age 10.9 years), youth self-reported screen time for weekdays and weekend days. Responses were separately dichotomized as >2 versus ≤2 hours/day (meeting behavioral guidelines). Caregiver-reported youth sleep duration was dichotomized as <9 versus 9-11 hours/night (meeting behavioral guidelines). Caregivers reported internalizing symptoms via the child behavior checklist. The withdrawn/depressed, anxious/depressed, and somatic symptom child behavior checklist subscale t-scores were separately dichotomized as ≥65 (borderline clinical levels of symptoms and above) versus <65. Analyses were gender-stratified. RESULTS: In females, longer baseline sleep duration was protective against withdrawn/depressed symptoms (odds ratio [OR] 0.6, 95% confidence interval [CI] 0.4-0.8) and somatic complaints (OR 0.8, 95% CI 0.6-0.97) one year later. In females, greater baseline weekend screen time was associated with increased risk of withdrawn/depressed symptoms (OR 1.6, 95% CI 1.1-2.2) one year later. No other significant associations were observed. DISCUSSION: Longitudinal associations between sleep duration, weekend screen time, and internalizing symptoms were unidirectional (behavior preceding internalizing symptoms), among females only, and specific to withdrawn/depressed and somatic symptoms. These prospective study findings warrant attention and inform future research in this cohort.

Actigraphy-derived measures of sleep and risk of prostate cancer in the UK Biobank.

BACKGROUND: Studies of sleep and prostate cancer are almost entirely based on self-report, with limited research using actigraphy. Our goal was to evaluate actigraphy-measured sleep and prostate cancer and to expand on findings from prior studies of self-reported sleep. METHODS: We prospectively examined 34 260 men without a history of prostate cancer in the UK Biobank. Sleep characteristics were measured over 7 days using actigraphy. We calculated sleep duration, onset, midpoint, wake-up time, social jetlag (difference in weekend-weekday sleep midpoints), sleep efficiency (percentage of time spent asleep between onset and wake-up time), and wakefulness after sleep onset. Cox proportional hazards models were used to estimate covariate-adjusted hazards ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Over 7.6 years, 1152 men were diagnosed with prostate cancer. Sleep duration was not associated with prostate cancer risk. Sleep midpoint earlier than 4:00 am was not associated with prostate cancer risk, though sleep midpoint of 5:00 am or later was suggestively associated with lower prostate cancer risk but had limited precision (earlier than 4:00 am vs 4:00-4:59 am HR = 1.00, 95% CI = 0.87 to 1.16; 5:00 am or later vs 4:00-4:59 am HR = 0.79, 95% CI = 0.57 to 1.10). Social jetlag was not associated with greater prostate cancer risk (1 to <2 hours vs <1 hour HR = 1.06, 95% CI = 0.89 to 1.25; ≥2 hours vs <1 hour HR = 0.90, 95% CI = 0.65 to 1.26). Compared with men who averaged less than 30 minutes of wakefulness after sleep onset per day, men with 60 minutes or more had a higher risk of prostate cancer (HR = 1.20, 95% CI = 1.00 to 1.43). CONCLUSIONS: Of the sleep characteristics studied, higher wakefulness after sleep onset-a measure of poor sleep quality-was associated with greater prostate cancer risk. Replication of our findings between wakefulness after sleep onset and prostate cancer are warranted.

Using a smartphone application to capture daily work activities: a longitudinal pilot study in a farming population.

OBJECTIVES: Smartphones are increasingly used to collect real-time information on time-varying exposures. We developed and deployed an application (app) to evaluate the feasibility of using smartphones to collect real-time information on intermittent agricultural activities and to characterize agricultural task variability in a longitudinal study of farmers. METHODS: We recruited 19 male farmers, aged 50-60 years, to report their farming activities on 24 randomly selected days over 6 months using the Life in a Day app. Eligibility criteria include personal use of an iOS or Android smartphone and >4 h of farming activities at least two days per week. We developed a study-specific database of 350 farming tasks that were provided in the app; 152 were linked to questions that were asked when the activity ended. We report eligibility, study compliance, number of activities, duration of activities by day and task, and responses to the follow-up questions. RESULTS: Of the 143 farmers we reached out to for this study, 16 were not reached by phone or refused to answer eligibility questions, 69 were ineligible (limited smartphone use and/or farming time), 58 met study criteria, and 19 agreed to participate. Refusals were mostly related to uneasiness with the app and/or time commitment (32 of 39). Participation declined gradually over time, with 11 farmers reporting activities through the 24-week study period. We obtained data on 279 days (median 554 min/day; median 18 days per farmer) and 1,321 activities (median 61 min/activity; median 3 activities per day per farmer). The activities were predominantly related to animals (36%), transportation (12%), and equipment (10%). Planting crops and yard work had the longest median durations; short-duration tasks included fueling trucks, collecting/storing eggs, and tree work. Time period-specific variability was observed; for example, crop-related activities were reported for an average of 204 min/day during planting but only 28 min/day during pre-planting and 110 min/day during the growing period. We obtained additional information for 485 (37%) activities; the most frequently asked questions were related to "feed animals" (231 activities) and "operate fuel-powered vehicle (transportation)" (120 activities). CONCLUSIONS: Our study demonstrated feasibility and good compliance in collecting longitudinal activity data over 6 months using smartphones in a relatively homogeneous population of farmers. We captured most of the farming day and observed substantial heterogeneity in activities, highlighting the need for individual activity data when characterizing exposure in farmers. We also identified several areas for improvement. In addition, future evaluations should include more diverse populations.

Equating NHANES Monitor-Based Physical Activity to Self-Reported Methods to Enhance Ongoing Surveillance Efforts.

PURPOSE: Harmonization of assessment methods represents an ongoing challenge in physical activity research. Previous research has demonstrated the utility of calibration approaches to enhance agreement between measures of physical activity. The present study utilizes a calibration methodology to add behavioral context from the Global Physical Activity Questionnaire (GPAQ), an established report-based measure, to enhance interpretations of monitor-based data scored using the novel Monitor Independent Movement Summary (MIMS) methodology. METHODS: Matching data from the GPAQ and MIMS were obtained from adults (20-80 yr of age) assessed in the 2011-2014 National Health and Nutrition Examination Survey. After developing percentile curves for self-reported activity, a zero-inflated quantile regression model was developed to link MIMS to estimates of moderate to vigorous physical activity (MVPA) from the GPAQ. RESULTS: Cross-validation of the model showed that it closely approximated the probability of reporting MVPA across age and activity-level segments, supporting the accuracy of the zero-inflated model component. Validation of the quantile regression component directly corresponded to the 25%, 50%, and 75% values for both men and women, further supporting the model fit. CONCLUSIONS: This study offers a method of improving activity surveillance by translating accelerometer signals into interpretable behavioral measures using nationally representative data. The model provides accurate estimates of minutes of MVPA at a population level but, because of the bias and error inherent in report-based measures of physical activity, is not suitable for converting or interpreting individual-level data. This study provides an important preliminary step in utilizing information from both device- and report-based methods to triangulate activity related outcomes; however additional measurement error modeling is needed to improve precision.

Advancing Digital Health Innovation in Oncology: Priorities for High-Value Digital Transformation in Cancer Care.

Although health care delivery is becoming increasingly digitized, driven by the pursuit of improved access, equity, efficiency, and effectiveness, progress does not appear to be equally distributed across therapeutic areas. Oncology is renowned for leading innovation in research and in care; digital pathology, digital radiology, real-world data, next-generation sequencing, patient-reported outcomes, and precision approaches driven by complex data and biomarkers are hallmarks of the field. However, remote patient monitoring, decentralized approaches to care and research, "hospital at home," and machine learning techniques have yet to be broadly deployed to improve cancer care. In response, the Digital Medicine Society and Moffitt Cancer Center convened a multistakeholder roundtable discussion to bring together leading experts in cancer care and digital innovation. This viewpoint highlights the findings from these discussions, in which experts agreed that digital innovation is lagging in oncology relative to other therapeutic areas. It reports that this lag is most likely attributed to poor articulation of the challenges in cancer care and research best suited to digital solutions, lack of incentives and support, and missing standardized infrastructure to implement digital innovations. It concludes with suggestions for actions needed to bring the promise of digitization to cancer care to improve lives.

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.

Health behavior changes, such as prevention of suicidal thoughts and behaviors, smoking, drug use, and alcohol use; and the promotion of mental health, sleep, and physical activities, and decreases in sedentary behavior, are difficult to sustain. The ILHBN is a cooperative agreement network funded jointly by seven participating units within the National Institutes of Health to collaboratively study how factors that occur in individuals' everyday life and in their natural environment influence the success of positive health behavior changes. This article discusses how information collected using smartphones, wearables, and other devices can provide helpful active and passive reflections of the participants' extent of risk and resources at the moment for an extended period of time. However, successful engagement and retention of participants also require tailored adaptations of study designs, measurement tools, measurement intervals, study span, and device choices that create hurdles in integrating (harmonizing) data from multiple studies. We describe some of the challenges, opportunities, and solutions that emerged from harmonizing intensive longitudinal data under heterogeneous study and participant characteristics within the ILHBN, and share some tools and recommendations to facilitate future data harmonization efforts.

Higher 24-h Total Movement Activity Percentile Is Associated with Better Cognitive Performance in U.S. Older Adults.

PURPOSE: This study aimed to assess the association of a wrist-worn, device-based metric of 24-h movement with cognitive function and subjective cognitive complaints among older adults, 60 yr and older. METHODS: This is a cross-sectional analysis of the 2011-2012 and 2013-2014 National Health and Nutrition Examination Survey (NHANES) cycles. A wrist-worn ActiGraph GT3X+ accelerometer captured total 24-h movement activity, analyzed as Monitor-Independent Movement Summary units (MIMS-units), and quantified into placement based on an age- and sex-standardized percentile. Cognitive tests in the domains of memory, language/verbal fluency, and executive performance were administered. Test-specific cognitive z -scores were generated. Subjective cognitive complaints included perceived difficulty remembering and confusion/memory loss. RESULTS: The analytical sample included 2708 U.S. older adults (69.5 ± 0.2 yr, 55% female, 20.9% non-White). Multivariable linear regressions revealed those in quartiles 3 (50th-74th percentile) and 4 (≥75th percentile) for their age and sex had higher cognitive function z -scores across all domains compared with those in quartile 1. Logistic regressions demonstrated those in quartiles 3 and 4 also had lower odds of reporting difficulty remembering (adjusted odds ratio [AOR] = 0.52, 95% confidence interval [CI] = 0.31-0.89; AOR = 0.57, 95% CI = 0.37-0.88) and confusion/memory loss (AOR = 0.49, 95% CI = 0.27-0.91; AOR = 0.49, 95% CI = 0.27-0.98), respectively, compared with those in quartile 1. CONCLUSIONS: In a representative sample of U.S. older adults, higher cognitive functioning occurs among those that perform total 24-h movement activity at or above the 50th percentile for their age and sex. Future studies should consider movement behaviors across a 24-h period on cognitive health outcomes in older adults. More research exploring prospective associations of MIMS-units and time-use behaviors across midlife and older adulthood that may affect cognitive functioning across diverse populations is needed.

Passive Sensing of Preteens' Smartphone Use: An Adolescent Brain Cognitive Development (ABCD) Cohort Substudy.

BACKGROUND: Concerns abound regarding childhood smartphone use, but studies to date have largely relied on self-reported screen use. Self-reporting of screen use is known to be misreported by pediatric samples and their parents, limiting the accurate determination of the impact of screen use on social, emotional, and cognitive development. Thus, a more passive, objective measurement of smartphone screen use among children is needed. OBJECTIVE: This study aims to passively sense smartphone screen use by time and types of apps used in a pilot sample of children and to assess the feasibility of passive sensing in a larger longitudinal sample. METHODS: The Adolescent Brain Cognitive Development (ABCD) study used passive, objective phone app methods for assessing smartphone screen use over 4 weeks in 2019-2020 in a subsample of 67 participants (aged 11-12 years; 31/67, 46% female; 23/67, 34% White). Children and their parents both reported average smartphone screen use before and after the study period, and they completed a questionnaire regarding the acceptability of the study protocol. Descriptive statistics for smartphone screen use, app use, and protocol feasibility and acceptability were reviewed. Analyses of variance were run to assess differences in categorical app use by demographics. Self-report and parent report were correlated with passive sensing data. RESULTS: Self-report of smartphone screen use was partly consistent with objective measurement (r=0.49), although objective data indicated that children used their phones more than they reported. Passive sensing revealed the most common types of apps used were for streaming (mean 1 hour 57 minutes per day, SD 1 hour 32 minutes), communication (mean 48 minutes per day, SD 1 hour 17 minutes), gaming (mean 41 minutes per day, SD 41 minutes), and social media (mean 36 minutes per day, SD 1 hour 7 minutes). Passive sensing of smartphone screen use was generally acceptable to children (43/62, 69%) and parents (53/62, 85%). CONCLUSIONS: The results of passive, objective sensing suggest that children use their phones more than they self-report. Therefore, use of more robust methods for objective data collection is necessary and feasible in pediatric samples. These data may then more accurately reflect the impact of smartphone screen use on behavioral and emotional functioning. Accordingly, the ABCD study is implementing a passive sensing protocol in the full ABCD cohort. Taken together, passive assessment with a phone app provided objective, low-burden, novel, informative data about preteen smartphone screen use.

US Population-referenced Percentiles for Wrist-Worn Accelerometer-derived Activity.

PURPOSE: This study aimed to present age- and sex-specific percentiles for daily wrist-worn movement metrics in US youth and adults. This metric represents a summary of all recorded movement, regardless of the purpose, context, or intensity. METHODS: Wrist-worn accelerometer data from the combined 2011-2014 National Health and Nutrition Examination Survey cycles and the 2012 National Health and Nutrition Examination Survey National Youth Fitness Survey were used for this analysis. Monitor-Independent Movement Summary units (MIMS-units) from raw triaxial accelerometer data were used. We removed the partial first and last assessment days and days with ≥5% nonwear time. Participants with ≥1 valid day were included. Mean MIMS-units were calculated across all valid days. Percentile tables and smoothed curves of daily MIMS-units were calculated for each age and sex using the Generalized Additive Models for Location Shape and Scale. RESULTS: The analytical sample included 14,705 participants age ≥3 yr. The MIMS-unit activity among youth was similar for both sexes, whereas adult females generally had higher MIMS-unit activity than did males. Median daily MIMS-units peaked at age 6 yr for both sexes (males, 20,613; females, 20,706). Lowest activity was observed for males and females 80+ yr of age: 8799 and 9503, respectively. CONCLUSIONS: Population referenced MIMS-unit percentiles for US youth and adults are a novel means of characterizing total activity volume. By using MIMS-units, we provide a standardized reference that can be applied across various wrist-worn accelerometer devices. Further work is needed to link these metrics to activity intensity categories and health outcomes.

National Institutes of Health Support of Digital Health Behavior Research.

The National Institutes of Health (NIH) has increasingly supported research in digital health technologies to advance research and deliver behavior change interventions. We highlight some of the research supported by the NIH in eHealth, mHealth, and social media as well as research resources supported by the NIH to accelerate research in this area. We also describe some of the challenges and opportunities in the digital health field and the need to balance the promise of these technologies with rigorous scientific evidence.

Activity Tracking with Momentary Assessments.

Task and activity tracking has been an effective industrial management and research technique for generations. It is applied to workflow optimization, group coordination, task sequencing, individual time management and environmental exposures. Appropriately, task tracking technologies are migrating to personal mobile devices. At the same time, individual survey approaches have been advanced tremendously as mobile apps. We report on a method of dynamic task registration with momentary assessment systems in natural environments that apply knowledge of context. We describe how the app was refined by a user acceptance study and its deployment in studies on agricultural exposure and industrial operations.

Characterizing user engagement with health app data: a data mining approach.

The use of mobile health applications (apps) especially in the area of lifestyle behaviors has increased, thus providing unprecedented opportunities to develop health programs that can engage people in real-time and in the real-world. Yet, relatively little is known about which factors relate to the engagement of commercially available apps for health behaviors. This exploratory study examined behavioral engagement with a weight loss app, Lose It! and characterized higher versus lower engaged groups. Cross-sectional, anonymized data from Lose It! were analyzed (n = 12,427,196). This dataset was randomly split into 24 subsamples and three were used for this study (total n = 1,011,008). Classification and regression tree methods were used to identify subgroups of user engagement with one subsample, and descriptive analyses were conducted to examine other group characteristics associated with engagement. Data mining validation methods were conducted with two separate subsamples. On average, users engaged with the app for 29 days. Six unique subgroups were identified, and engagement for each subgroup varied, ranging from 3.5 to 172 days. Highly engaged subgroups were primarily distinguished by the customization of diet and exercise. Those less engaged were distinguished by weigh-ins and the customization of diet. Results were replicated in further analyses. Commercially-developed apps can reach large segments of the population, and data from these apps can provide insights into important app features that may aid in user engagement. Getting users to engage with a mobile health app is critical to the success of apps and interventions that are focused on health behavior change.

Surveillance of Youth Physical Activity and Sedentary Behavior With Wrist Accelerometry.

INTRODUCTION: Accurate tracking of physical activity (PA) and sedentary behavior (SB) is important to advance public health, but little is known about how to interpret wrist-worn accelerometer data. This study compares youth estimates of SB and moderate to vigorous PA (MVPA) obtained using raw and count-based processing methods. METHODS: Data were collected between April and October 2014 for the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Study: a cross-sectional Internet-based study of youth/family cancer prevention behaviors. A subsample of 628 adolescents (aged 12-17 years) wore the ActiGraph GT3X+ on the wrist for 7 days. In 2015-2016, SB and MVPA time were calculated from raw data using R-package GGIR and from activity counts data using published cutpoints (Crouter and Chandler). Estimates were compared across age, sex, and weight status to examine the impact of processing methods on behavioral outcomes. RESULTS: ActiGraph data were available for 408 participants. Large differences in SB and MVPA time were observed between processing methods, but age and gender patterns were similar. Younger children (aged 12-14 years) had lower sedentary time and greater MVPA time (p-values <0.05) than older children (aged 15-17 years), consistent across methods. The proportion of youth with ≥60 minutes of MVPA/day was highest with the Crouter methods (~50%) and lowest with GGIR (~0%). CONCLUSIONS: Conclusions about youth PA and SB are influenced by the wrist-worn accelerometer data processing method. Efforts to harmonize processing methods are needed to promote standardization and facilitate reporting of monitor-based PA data.