Screen Use at Bedtime and Sleep Duration and Quality Among Youths.

Importance: Although questionnaire-based cross-sectional research suggests that screen time before bed correlates with poor sleep, self-reported data seem unlikely to capture the complexity of modern screen use, requiring objective night-by-night measures to advance this field. Objective: To examine whether evening screen time is associated with sleep duration and quality that night in youths. Design, Setting, and Participants: This repeated-measures cohort study was performed from March to December 2021 in participant homes in Dunedin, New Zealand. Participants included healthy youths aged 11 to 14.9 years. Data were analyzed from October to November 2023. Exposure: Objectively measured screen time, captured using wearable or stationary video cameras from 2 hours before bedtime until the first time the youth attempted sleep (shut-eye time) over 4 nonconsecutive nights. Video data were coded using a reliable protocol (κ = 0.92) to quantify device (8 options [eg, smartphone]) and activity (10 options [eg, social media]) type. Main Outcomes and Measures: Sleep duration and quality were measured objectively via wrist-worn accelerometers. The association of screen use with sleep measures was analyzed on a night-by-night basis using mixed-effects regression models including participant as a random effect and adjusted for weekends. Results: Of the 79 participants (47 [59.5%] male; mean [SD] age, 12.9 [1.1] years), all but 1 had screen time before bed. Screen use in the 2 hours before bed had no association with most measures of sleep health that night (eg, mean difference in total sleep time, 0 minutes [95% CI, -3 to 20 minutes] for every 10 minutes more total screen time). All types of screen time were associated with delayed sleep onset but particularly interactive screen use (mean difference, 10 minutes; 95% CI, 4 to 16 minutes for every additional 10 minutes of interactive screen time). Every 10 minutes of additional screen time in bed was associated with shorter total sleep time (mean difference, -3 minutes; 95% CI, -6 to -1 minute). The mean difference in total sleep time was -9 minutes (95% CI, -16 to -2 minutes) for every 10 minutes of interactive screen use and -4 minutes (95% CI, -7 to 0 minutes) for passive screen use. In particular, gaming (mean difference, -17 minutes; 95% CI, -28 to -7 minutes for every 10 minutes of gaming) and multitasking (mean difference, -35 minutes; 95% CI, -67 to -4 minutes on nights with vs without multitasking) were associated with less total sleep time. Conclusions and Relevance: In this repeated-measures cohort study, use of an objective method showed that screen time once in bed was associated with impairment of sleep, especially when screen time was interactive or involved multitasking. These findings suggest that current sleep hygiene recommendations to restrict all screen time before bed seem neither achievable nor appropriate.

Pooled Analysis of Physical Activity, Sedentary Behavior, and Sleep Among Children From 33 Countries.

Importance: The prevalence estimates of physical activity, sedentary behavior, and sleep (collectively known as movement behaviors) in 3- and 4-year-old children worldwide remains uncertain. Objective: To report the proportion of 3- and 4-year-old children who met the World Health Organization guidelines for physical activity, sedentary behavior, and sleep across 33 countries. Design, Setting, and Participants: Pooled analysis of data from 14 cross-sectional studies (July 2008 to September 2022) identified through systematic reviews and personal networks. Thirty-three countries of varying income levels across 6 geographical regions. Each study site needed to have at least 40 children aged 3.0 to 4.9 years with valid accelerometry and parent-/caregiver-reported screen time and sleep duration data. Data were analyzed from October 2022 to February 2023. Exposures: Time spent in physical activity was assessed by reanalyzing accelerometry data using a harmonized data-processing protocol. Screen time and sleep duration were proxy reported by parents or caregivers. Main Outcomes and Measures: The proportion of children who met the World Health Organization guidelines for physical activity (≥180 min/d of total physical activity and ≥60 min/d of moderate- to vigorous-intensity physical activity), screen time (≤1 h/d), and sleep duration (10-13 h/d) was estimated across countries and by World Bank income group and geographical region using meta-analysis. Results: Of the 7017 children (mean [SD] age, 4.1 [0.5] years; 3585 [51.1%] boys and 3432 [48.9%] girls) in this pooled analysis, 14.3% (95% CI, 9.7-20.7) met the overall guidelines for physical activity, screen time, and sleep duration. There was no clear pattern according to income group: the proportion meeting the guidelines was 16.6% (95% CI, 10.4-25.3) in low- and lower-middle-income countries, 11.9% (95% CI, 5.9-22.5) in upper-middle-income countries, and 14.4% (95% CI, 9.6-21.1) in high-income countries. The region with the highest proportion meeting the guidelines was Africa (23.9%; 95% CI, 11.6-43.0), while the lowest proportion was in North and South America (7.7%; 95% CI, 3.6-15.8). Conclusions and Relevance: Most 3- and 4-year-old children in this pooled analysis did not meet the current World Health Organization guidelines for physical activity, sedentary behavior, and sleep. Priority must be given to understanding factors that influence these behaviors in this age group and to implementing contextually appropriate programs and policies proven to be effective in promoting healthy levels of movement behaviors.

Identifying Risk Factors for Presarcopenia in Early Middle Age.

OBJECTIVE: This study aimed to determine whether risk factors for presarcopenia can be identified in a sample of early middle-aged men and women. DESIGN: Prospective study. SETTING: Longitudinal data from the Dunedin Multidisciplinary Health and Development Study were used to investigate the relationship between presarcopenia at age 45 years and selected early markers at ages 26, 32, and 38 years. PARTICIPANTS: Longitudinal data from N=899 participants from the Dunedin Multidisciplinary Health and Development Study. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Presarcopenia was defined as low relative appendicular lean mass index assessed by dual energy x-ray absorptiometry and low strength assessed by grip strength. Logistic regressions were used to describe the association between selected markers and presarcopenia at age 45 years. RESULTS: Multivariate logistic regression revealed that a higher body mass index (BMI) at ages 26, 32, and 38 years was associated with lower likelihood of presarcopenia at age 45 years in both men and women (odds ratio [OR] range, 0.46-0.64). Higher age-normative grip strength at age 38 years in both men and women (OR range, 0.88-0.92) was also associated with lower likelihood for presarcopenia. Lastly, lower self-perceived physical fitness level in men at age 38 years was associated with an increased likelihood of presarcopenia at age 45 years (OR, 9.35; 95% confidence interval, 3.28-26.70). CONCLUSIONS: BMI and strength were associated with lower likelihood of presarcopenia during middle age. A higher likelihood of presarcopenia was associated with sex-specific lower self-perceived physical fitness. These modifiable biomarkers may serve as targets for clinical screening and early intervention aimed at slowing or preventing progression to sarcopenia in old age.

The Effect of Prebedtime Behaviors on Sleep Duration and Quality in Children: Protocol for a Randomized Crossover Trial.

BACKGROUND: It is recommended that children should avoid eating dinner, being physically active, or using screens in the hour before bed to ensure good sleep health. However, the evidence base behind these guidelines is weak and limited to cross-sectional studies using questionnaires. OBJECTIVE: The aim of this randomized crossover trial was to use objective measures to experimentally determine whether recommendations to improve sleep by banning electronic media, physical activity, or food intake in the hour before bed, impact sleep quantity and quality in the youth. METHODS: After a baseline week to assess usual behavior, 72 children (10-14.9 years old) will be randomized to four conditions, which are (1) avoid all 3 behaviors, (2) use screens for at least 30 minutes, (3) be physically active for at least 30 minutes, and (4) eat a large meal, during the hour before bed on days 5 to 7 of weeks 2 to 5. Families can choose which days of the week they undertake the intervention, but they must be the same days for each intervention week. Guidance on how to undertake each intervention will be provided. Interventions will only be undertaken during the school term to avoid known changes in sleep during school holidays. Intervention adherence and shuteye latency (time from getting into bed until attempting sleep) will be measured by wearable and stationary PatrolEyes video cameras (StuntCams). Sleep (total sleep time, sleep onset, and wake after sleep onset) will be measured using actigraphy (baseline, days 5 to 7 of each intervention week). Mixed effects regression models with a random effect for participants will be used to estimate mean differences (95% CI) for conditions 2 to 4 compared with condition 1. RESULTS: Recruitment started in March 2024, and is anticipated to finish in April 2025. Following data analysis, we expect that results will be available later in 2026. CONCLUSIONS: Using objective measures, we will be able to establish if causal relationships exist between prebedtime behaviors and sleep in children. Such information is critical to ensure appropriate and achievable sleep guidelines. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12624000206527; https://tinyurl.com/3kcjmfnj. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63692.

Losing sleep influences dietary intake in children: a longitudinal compositional analysis of a randomised crossover trial.

BACKGROUND: Although inadequate sleep increases the risk of obesity in children, the mechanisms remain unclear. The aims of this study were to assess how sleep loss influenced dietary intake in children while accounting for corresponding changes in sedentary time and physical activity; and to investigate how changes in time use related to dietary intake. METHODS: A randomized crossover trial in 105 healthy children (8-12 years) with normal sleep (~ 8-11 h/night) compared sleep extension (asked to turn lights off one hour earlier than usual for one week) and sleep restriction (turn lights off one hour later) conditions, separated by a washout week. 24-h time-use behaviors (sleep, wake after sleep onset, physical activity, sedentary time) were assessed using waist-worn actigraphy and dietary intake using two multiple-pass diet recalls during each intervention week. Longitudinal compositional analysis was undertaken with mixed effects regression models using isometric log ratios of time use variables as exposures and dietary variables as outcomes, and participant as a random effect. RESULTS: Eighty three children (10.2 years, 53% female, 62% healthy weight) had 47.9 (SD 30.1) minutes less sleep during the restriction week but were also awake for 8.5 (21.4) minutes less at night. They spent this extra time awake in the day being more sedentary (+ 31 min) and more active (+ 21 min light physical activity, + 4 min MVPA). After adjusting for all changes in 24-h time use, losing 48 min of sleep was associated with consuming significantly more energy (262 kJ, 95% CI:55,470), all of which was from non-core foods (314 kJ; 43, 638). Increases in sedentary time were related to increased energy intake from non-core foods (177 kJ; 25, 329) whereas increases in MVPA were associated with higher intake from core foods (72 kJ; 7,136). Changes in diet were greater in female participants. CONCLUSION: Loss of sleep was associated with increased energy intake, especially of non-core foods, independent of changes in sedentary time and physical activity. Interventions focusing on improving sleep may be beneficial for improving dietary intake and weight status in children. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ANZCTR ACTRN12618001671257, Registered 10th Oct 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367587&isReview=true.

Predictors for achieving optimal sleep in healthy children: Exploring sleep patterns in a sleep extension trial.

STUDY OBJECTIVES: Earlier bedtimes can help some children get more sleep, but we don't know which children, or what features of their usual sleep patterns could predict success with this approach. Using data from a randomized crossover trial of sleep manipulation, we sought to determine this. METHODS: Participants were 99 children aged 8-12years (49.5% female) with no sleep disturbances. Sleep was measured by actigraphy at baseline and over a restriction or extension week (1 hour later or earlier bedtime respectively), randomly allocated and separated by a washout week. Data were compared between baseline (week 1) and extension weeks only (week 3 or 5), using linear or logistic regression analyses as appropriate, controlling for randomization order. RESULTS: One hour less total sleep time than average at baseline predicted 29.7 minutes (95% CI: 19.4, 40.1) of sleep gained and 3.45 (95% CI: 1.74, 6.81) times higher odds of successfully extending sleep by >30 minutes. Per standardized variable, less total sleep time and a shorter sleep period time were the strongest predictors (significant odds ratios (ORs) of 2.51 and 2.28, respectively). Later sleep offset, more variability in sleep timing and lower sleep efficiency also predicted sleep gains. The sleep period time cut-point that optimized prediction of successful sleep gains was <8 hours 28 minutes with 75% of children's baseline sleep in that range. CONCLUSIONS: Children with a baseline sleep period time <8½ hours a night obtained the most sleep from earlier bedtimes maintained over a week, demonstrating experimentally the value of earlier bedtimes to improve sleep. CLINICAL TRIALS REGISTRY: Australian New Zealand Clinical Trial Registry, ACTRN12618001671257, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367587&isReview=true.

Rapid infant weight gain or point-in-time weight status: Which is the best predictor of later obesity and body composition?

OBJECTIVE: The aim of this study was to determine which growth indicator (weight, weight-for-length, BMI) and time frame (6- or 12-month intervals between 0 and 24 months) of rapid infant weight gain (RIWG) best predicted obesity risk and body composition at 11 years of age. METHODS: RIWG (increase ≥0.67 z scores between two time points) was calculated from weight and length/height at birth, 0.5, 1, 1.5, and 2 years. The predictive value of each measure and time frame was calculated in relation to obesity (BMI ≥95th percentile) and body fat (fat mass index [FMI], dual-energy X-ray absorptiometry scan) at 11 years. RESULTS: The sensitivity (1.5% to 62.1%) and positive predictive value (12.5% to 33.3%) of RIWG to predict obesity varied considerably. Having obesity at any time point appeared a stronger risk factor than any indicator of RIWG for obesity at 11 years. Obesity at any age during infancy consistently predicted a greater FMI of around 1.1 to 1.5 kg/m2 at 11 years, whereas differences for RIWG were inconsistent. CONCLUSIONS: A simple measure of obesity status at a single time point between 6 and 24 months of age appeared a stronger risk factor for later obesity and FMI than RIWG assessed by any indicator, over any time frame.

Development of a Protocol for Objectively Measuring Digital Device Use in Youth.

INTRODUCTION: Screen time is predominantly measured using questionnaires assessing a limited range of activities. This project aimed to develop a coding protocol that reliably identified screen time, including device type and specific screen behaviors, from video-camera footage. METHODS: Screen use was captured from wearable and stationary PatrolEyes video cameras in 43 participants (aged 10-14 years) within the home environment (May-December 2021, coding in 2022, statistical analysis in 2023). After extensive piloting, the inter-rater reliability of the final protocol was determined in 4 coders using 600 minutes of footage from 18 participants who spent unstructured time on digital devices. Coders independently annotated all footage to determine 8 device types (e.g., phone, TV) and 9 screen activities (e.g., social media, video gaming) using Observer XT (behavioral coding software). Reliability was calculated using weighted Cohen's κ for duration per sequence (meets criteria for total time in each category) and frequency per sequence (meets criteria for total time in each category and order of use) for every coder pair on a per-participant and footage type basis. RESULTS: Overall reliability of the full protocol was excellent (≥0.8) for both duration per sequence (κ=0.89-0.93) and the more conservative frequency per sequence (κ=0.83-0.86) analyses. This protocol reliably differentiates between different device types (κ=0.92-0.94) and screen behaviors (κ=0.81-0.87). Coder agreement ranged from 91.7% to 98.8% across 28.6-107.3 different instances of screen use. CONCLUSIONS: This protocol reliably codes screen activities in adolescents, offering promise for improving the understanding of the impact of different screen activities on health.

The effect of modest changes in sleep on dietary intake and eating behavior in children: secondary outcomes of a randomized crossover trial.

BACKGROUND: Insufficient sleep duration increases obesity risk in children, but the mechanisms remain unclear. OBJECTIVES: This study seeks to determine how changes in sleep influence energy intake and eating behavior. METHODS: Sleep was experimentally manipulated in a randomized, crossover study in 105 children (8-12 y) who met current sleep guidelines (8-11 h/night). Participants went to bed 1 h earlier (sleep extension condition) and 1 h later (sleep restriction condition) than their usual bedtime for 7 consecutive nights, separated by a 1-wk washout. Sleep was measured via waist-worn actigraphy. Dietary intake (2 24-h recalls/wk), eating behaviors (Child Eating Behavior Questionnaire), and the desire to eat different foods (questionnaire) were measured during or at the end of both sleep conditions. The type of food was classified by the level of processing (NOVA) and as core or noncore (typically energy-dense foods) foods. Data were analyzed according to 'intention to treat' and 'per protocol,' an a priori difference in sleep duration between intervention conditions of ≥30 min. RESULTS: The intention to treat analysis (n = 100) showed a mean difference (95% CI) in daily energy intake of 233 kJ (-42, 509), with significantly more energy from noncore foods (416 kJ; 6.5, 826) during sleep restriction. Differences were magnified in the per-protocol analysis, with differences in daily energy of 361 kJ (20, 702), noncore foods of 504 kJ (25, 984), and ultraprocessed foods of 523 kJ (93, 952). Differences in eating behaviors were also observed, with greater emotional overeating (0.12; 0.01, 0.24) and undereating (0.15; 0.03, 0.27), but not satiety responsiveness (-0.06; -0.17, 0.04) with sleep restriction. CONCLUSIONS: Mild sleep deprivation may play a role in pediatric obesity by increasing caloric intake, particularly from noncore and ultraprocessed foods. Eating in response to emotions rather than perceived hunger may partly explain why children engage in unhealthy dietary behaviors when tired. This trial was registered at Australian New Zealand Clinical Trials Registry; ANZCTR as CTRN12618001671257.

Effect of Sleep Changes on Health-Related Quality of Life in Healthy Children: A Secondary Analysis of the DREAM Crossover Trial.

Importance: Little is known regarding the effect of poor sleep on health-related quality of life (HRQOL) in healthy children. Objective: To determine the effect of induced mild sleep deprivation on HRQOL in children without major sleep issues. Design, Setting, and Participants: This prespecified secondary analysis focused on HRQOL, a secondary outcome of the Daily Rest, Eating, and Activity Monitoring (DREAM) randomized crossover trial of children who underwent alternating weeks of sleep restriction and sleep extension and a 1-week washout in between. The DREAM trial intervention was administered at participants' homes between October 2018 and March 2020. Participants were 100 children aged 8 to 12 years who lived in Dunedin, New Zealand; had no underlying medical conditions; and had parent- or guardian-reported normal sleep (8-11 hours/night). Data were analyzed between July 4 and September 1, 2022. Interventions: Bedtimes were manipulated to be 1 hour later (sleep restriction) and 1 hour earlier (sleep extension) than usual for 1 week each. Wake times were unchanged. Main Outcomes and Measures: All outcome measures were assessed during both intervention weeks. Sleep timing and duration were assessed using 7-night actigraphy. Children and parents rated the child's sleep disturbances (night) and impairment (day) using the 8-item Pediatric Sleep Disturbance and 8-item Sleep-Related Impairment scales of the Patient-Reported Outcomes Measurement Information System questionnaire. Child-reported HRQOL was assessed using the 27-item KIDSCREEN questionnaire with 5 subscale scores and a total score. Both questionnaires assessed the past 7 days at the end of each intervention week. Data were presented as mean differences and 95% CIs between the sleep restriction and extension weeks and were analyzed using intention to treat and an a priori difference in sleep of at least 30 minutes per night. Results: The final sample comprised 100 children (52 girls [52%]; mean [SD] age, 10.3 [1.4] years). During the sleep restriction week, children went to sleep 64 (95% CI, 58-70) minutes later, and sleep offset (wake time) was 18 (95% CI, 13-24) minutes later, meaning that children received 39 (95% CI, 32-46) minutes less of total sleep per night compared with the sleep extension week in which the total sleep time was 71 (95% CI, 64-78) minutes less in the per-protocol sample analysis. Both parents and children reported significantly less sleep disturbance at night but greater sleep impairment during the day with sleep restriction. Significant standardized reductions in physical well-being (standardized mean difference [SMD], -0.28; 95% CI, -0.49 to -0.08), coping in a school environment (SMD, -0.26; 95% CI, -0.42 to -0.09), and total HRQOL score (SMD, -0.21; 95% CI, -0.34 to -0.08) were reported by children during sleep restriction, with an additional reduction in social and peer support (SMD, -0.24; 95% CI, -0.47 to -0.01) in the per-protocol sample analysis. Conclusions and Relevance: Results of this secondary analysis of the DREAM trial indicated that even 39 minutes less of sleep per night for 1 week significantly reduced several facets of HRQOL in children. This finding shows that ensuring children receive sufficient good-quality sleep is an important child health issue. Trial Registration: Australian New Zealand Clinical Trials Registry: ACTRN12618001671257.

High-intensity interval aerobic and resistance training to counteract low relative lean soft tissue mass in middle age: A randomized controlled trial.

BACKGROUND: Age-related loss of skeletal muscle mass and function begins in early middle age, yet research to date has focused on older individuals, limiting our understanding of interventions earlier in the lifespan. To date, no high-intensity interval training studies have been conducted in middle-aged adults with low relative lean soft tissue mass. METHODS: Eighty-two middle-aged adults (40-50 years of age) with low appendicular lean soft tissue mass index confirmed with dual energy x-ray absorptiometry (DXA) were randomly allocated (1:1) to group-based, 20-week, three times a week, high-intensity aerobic and resistance training (HIART) program or 60-min education session (Control). The primary outcome was change in total lean soft tissue mass measured by DXA. Secondary outcomes included cardiorespiratory fitness, physical function (handgrip strength, gait speed, 30-seconds sit-to-stand, quadriceps strength and muscle quality). Measures were obtained at baseline (0 weeks), mid-intervention (10 weeks) and post-intervention (20 weeks). RESULTS: Mean age in HIART was 44.8 (SD 3.2) and 45.4 (SD 2.9) in Control group. The majority of the participants were female with 88 % in HIART and 83 % in Control group. Mean BMI in HIART was 25.8 kg/m2 (SD 3.5) and 26.4 kg/m2 (SD 4.1) Control group. Intention to treat analysis showed that post-intervention, HIART increased significantly more total lean soft tissue mass (0.8 kg, 95%CI 0.15, 1.46), appendicular lean soft tissue mass index (0.2 kg/m2, 95%CI 0.09, 0.33), peak oxygen uptake (5.18 mL/min/kg, 2.97 to 7.39 95%CI), grip strength (2.2 kg, 95%CI 0.09, 4.32), and 30-s sit-to-stand (1.3 times, 95%CI 0.43, 2.12) with significantly greater reductions in body fat percentage (-1.1 %, 95%CI -2.03, -0.10) and maximum gait speed (-0.2 m/s, 95 % CI -0.34, -0.03) compared Control. CONCLUSION: The HIART program is an effective exercise intervention to increase total lean soft tissue mass in middle-aged adults with low relative lean soft tissue mass compared to a waitlist control group.

Results from Aotearoa New Zealand's 2022 Report Card on Physical Activity for Children and Youth: A call to address inequities in health-promoting activities.

Background: This article reports the methods and findings for Aotearoa New Zealand's 2022 Report Card on Physical Activity for Children and Youth indicators, and on inequities within these indicators. Methods: Grades were assigned to indicators using the Active Healthy Kids Global Alliance criteria depending on data availability, and inequities reported based on gender, ethnicity, disability status, area-level socioeconomic deprivation, urbanicity, and school year. Two additional indicators were included in this report card: Sleep, and Physical literacy. Results: Grades were assigned to indicators as follows: Overall physical activity: C+, Organised sport and physical activity: B-, Physical literacy: B, Active transportation: D, Sedentary behaviours: C-, Sleep: B+, Whanau (family) and peers: D, School: C+, Government: A. Inequities across all socio-demographic variables were observed. An 'inconclusive' grade was assigned to the Active play, Physical fitness, and Community and Environment indicators due to insufficient data. Conclusion: It is imperative that targeted, comprehensive, and population-specific approaches are implemented to support health-promoting physical activity behaviours and reduce inequities among children and youth in Aotearoa. There is a need to promote all dimensions of physical activity (overall activity, active play, recreation, organised sport, active transportation) and the reduction of screen time through policy, research, evidence-based social marketing campaigns, and urban design. Regular, nationally representative surveys that enable the consistent and regular measurement of key Report Card indicators are needed.

Where does the time go when children don't sleep? A randomized crossover study.

OBJECTIVE: This study aimed to describe how mild sleep deprivation in children changes time spent physically active and sedentary. METHODS: In 2018 through 2020, children (n = 105) with normal sleep were randomized to go to bed 1 hour earlier (extension) or 1 hour later (restriction) than their usual bedtime for 1 week, each separated by a 1-week washout. Twenty-four-hour movement behaviors were measured with waist-worn actigraphy and expressed in minutes and proportions (percentages). Mixed-effects regression models determined mean differences in time use (95% CI) between conditions. Time gained from sleep lost that was reallocated to other movement behaviors in the 24-hour day was modeled using regression. RESULTS: Children (n = 96) gained ~49 minutes of awake time when sleep was restricted compared with extended. This time was mostly reallocated to sedentary behavior (28 minutes; 95% CI: 19-37), followed by physical activity (22 minutes; 95% CI: 14-30). When time was expressed as a percentage, the overall composition of movement behavior remained similar across both sleep conditions. CONCLUSIONS: Children were not less physically active when mildly sleep deprived. Time gained from sleeping less was proportionally, rather than preferentially, reallocated to sedentary time and physical activity. These findings suggest that decreased physical activity seems unlikely to explain the association between short sleep and obesity in children.

Investigating the moderators and mediators of an effective sleep intervention in the Prevention of Overweight in Infancy (POI) randomized controlled trial: Exploratory analyses.

The Prevention of Overweight in Infancy (POI) sleep intervention halved obesity risk at 2 years of age. However, the intervention mechanisms are unclear. Consequently, the objective of the current work was to use exploratory analyses to investigate potential moderators and mediators of the sleep intervention on obesity outcomes at age 2 years. Data were collected between 2009 and 2012. The effect of demographic and study design variables on body mass index z-score (BMI z-score) and obesity was compared in moderator subgroups at 2 years of age (n = 683, 85%). Mediating effects of child and parent-household variables assessed whether the sleep intervention resulted in meaningful changes in the mediating variable (defined as changes which were statistically significant [p < .05] or where the effect size was ≥0.15 SD), followed by assessing relationships with obesity outcomes. The sleep intervention appeared most effective in children in higher deprivation areas (effect on BMI z-score -0.25 [-0.53, 0.04], effect on obesity odds ratio [OR] 0.43 [0.16, 1.13]), and with mothers of non-European, non-Maori ethnicity (effect on BMI z-score -0.27 [-0.73, 0.20], effect on obesity OR 0.13 [95% confidence interval 0.01, 1.11]). This suggested moderation by deprivation and ethnicity. Aspects of sleep improved meaningfully in children after intervention but did not significantly relate to obesity outcomes, and other outcomes were not meaningfully affected by the sleep intervention. Thus, mediation was not indicated. Overall, the POI sleep intervention improved obesity outcomes at 2 years, and the current work identified some potential moderators, but no mediators.

Adherence to 24-h movement behavior guidelines and psychosocial functioning in young children: a longitudinal analysis.

BACKGROUND: A recent paradigm shift has highlighted the importance of considering how sleep, physical activity and sedentary behaviour work together to influence health, rather than examining each behaviour individually. We aimed to determine how adherence to 24-h movement behavior guidelines from infancy to the preschool years influences mental health and self-regulation at 5 years of age. METHODS: Twenty-four hour movement behaviors were measured by 7-day actigraphy (physical activity, sleep) or questionnaires (screen time) in 528 children at 1, 2, 3.5, and 5 years of age and compared to mental health (anxiety, depression), adaptive skills (resilience), self-regulation (attentional problems, hyperactivity, emotional self-control, executive functioning), and inhibitory control (Statue, Head-Toes-Knees-Shoulders task) outcomes at 5 years of age. Adjusted standardised mean differences (95% CI) were determined between those who did and did not achieve guidelines at each age. RESULTS: Children who met physical activity guidelines at 1 year of age (38.7%) had lower depression (mean difference [MD]: -0.28; 95% CI: -0.51, -0.06) and anxiety (MD: -0.23; 95% CI: -0.47, 0.00) scores than those who did not. At the same age, sleeping for 11-14 h or having consistent wake and sleep times was associated with lower anxiety (MD: -0.34; 95% CI: -0.66, -0.02) and higher resilience (MD: 0.35; 95% CI: 0.03, 0.68) scores respectively. No significant relationships were observed at any other age or for any measure of self-regulation. Children who consistently met screen time guidelines had lower anxiety (MD: -0.43; 95% CI: -0.68, -0.18) and depression (MD: -0.36; 95% CI: -0.62, -0.09) scores at 5. However, few significant relationships were observed for adherence to all three guidelines; anxiety scores were lower (MD: -0.42; 95% CI: -0.72, -0.12) in the 20.2% who adhered at 1 year of age, and depression scores were lower (MD: -0.25; 95% CI: -0.48, -0.02) in the 36.7% who adhered at 5 years of age compared with children who did not meet all three guidelines. CONCLUSIONS: Although adherence to some individual movement guidelines at certain ages throughout early childhood was associated with improved mental health and wellbeing at 5 years of age, particularly reduced anxiety and depression scores, there was little consistency in these relationships. Future work should consider a compositional approach to 24-h time use and how it may influence mental wellbeing. TRIAL REGISTRATION: ClinicalTrials.gov number NCT00892983.

Measuring short-term eating behaviour and desire to eat: Validation of the child eating behaviour questionnaire and a computerized 'desire to eat' computerized questionnaire.

The Child Eating Behaviour Questionnaire (CEBQ) is designed to measure 'usual' eating behaviour, with no time period attached, thus may not be suitable for assessing the effectiveness of short-term experimental studies. The aim of this study was to validate i) the CEBQ adapted to measure 'past week' rather than 'usual' eating behaviour, and ii) a computerized questionnaire assessing desire to eat core and non-core foods, against an objective measure of eating behaviour and food intake (eating in the absence of hunger (EAH) experiment). Children (n = 103) aged 8-12 years completed the desire to eat questionnaire followed by the EAH experiment while primary caregivers completed the adapted CEBQ. Results from the CEBQ showed that children with greater 'satiety responsiveness' (1-point higher) consumed less energy (-342 kJ; 95% CI -574, -110) whereas those with greater 'enjoyment of food' scale consumed more energy (380 kJ; 95% CI 124, 636) during the ad-libitum phase of the EAH experiment. Higher scores for slowness in eating (-705 kJ; 95% CI -1157, -254), emotional undereating (-590 kJ; 95% CI -1074, -106) and food fussiness (-629 kJ; 95% CI -1103, -155) were associated with lower total energy intake. Children who expressed greater desire to eat non-core foods consumed more energy in total (275 kJ; 95% CI 87, 463). Overall, this adapted CEBQ appears valid for measuring several short-term eating behaviours in children. The desire to eat questionnaire may be useful for identifying short-term susceptibility to overeating, however further investigation into how ratings of desire relate to the intake of highly palatable, energy dense foods is warranted.

Social jetlag is associated with obesity-related outcomes in 9-11-year-old children, independent of other sleep characteristics.

INTRODUCTION: Social jetlag has been reported to predict obesity-related indices, independent of sleep duration, with associations in female adolescents but not males. However, such sex-specific relationships have not been investigated in pre-adolescents. OBJECTIVES: To examine: (i) the relationships between sleep characteristics, including social jetlag, and obesity-related outcomes during childhood, and (ii) whether these relationships are moderated by sex. METHODS: This cross-sectional study included 381 children aged 9-11 years (49.6% female). Average sleep duration, social jetlag, and physical activity were assessed via wrist-worn accelerometry. Sleep disturbances were quantified from the Children's Sleep Habits Questionnaire. Obesity-related outcomes included age-specific body mass index Z-scores (zBMI) and waist-to-height ratio. Additionally % fat, total fat mass, and fat mass index were assessed via bioelectrical impedance analysis. Linear mixed models that nested children within schools were used to identify relationships among sleep characteristics and obesity-related outcomes. RESULTS: Positive associations between social jetlag with zBMI, % fat, and fat mass index were seen in univariable and unadjusted multivariable analyses. Following adjustments for known confounders, social jetlag remained significantly associated with zBMI (ß = 0.12, p = 0.013). Simple slopes suggested a positive association in girls (ß = 0.19, p = 0.006) but not in boys (ß = 0.03, p = 0.703). CONCLUSIONS: Obesity prevention efforts, particularly in girls, may benefit from targeted approaches to improving the consistency of sleep timing in youth.

Eating in the absence of hunger in children with mild sleep loss: a randomized crossover trial with learning effects.

BACKGROUND: While insufficient sleep duration has emerged as a strong, independent risk factor for obesity, the mechanisms remain unclear. One possibility is greater "eating in the absence of hunger" (EAH) or energy intake beyond the point of satiety, when tired. OBJECTIVE: The aim was to determine whether mild sleep loss increases EAH in children. METHODS: A crossover study was undertaken in 105 healthy children (8-12 y) with normal sleep (∼8-11 h/night). After randomization, children went to bed 1 h earlier (sleep extension) or 1 h later (sleep restriction) than their usual bedtime, over 2 intervention weeks separated by a 1-wk washout. At the end of each intervention week, children underwent an EAH feeding experiment involving a preloading meal until satiation, followed by an ad libitum buffet (of highly palatable snacks) to measure EAH, with each food item weighed before and after consumption. RESULTS: Ninety-three children completed the EAH experiment. There was no evidence of a difference in energy intake from EAH between sleep restriction and extension conditions when analyzed as a crossover design. However, a learning effect was found, with children eating significantly less (-239 kJ; 95% CI: -437, -41 kJ; P = 0.018) during the preload phase and significantly more (181 kJ; 95% CI: 38, 322 kJ; P = 0.013) in the ad libitum phase in the second week. No significant differences were seen using an underpowered parallel analysis for energy intake during the ad libitum phase when sleep deprived (106 kJ; 95% CI: -217, 431 kJ; P = 0.514). CONCLUSIONS: Our findings suggest that measuring a difference in eating behavior in relation to sleep proved unsuitable using the EAH experiment in a crossover design in children, due to a learning effect. This trial was registered at the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367587&isReview=true) as ACTRN12618001671257 .

Age- and sex-specific visceral fat reference cutoffs and their association with cardio-metabolic risk.

BACKGROUND: Although excess visceral fat (VAT) is associated with numerous cardio-metabolic risk factors, measurement of this fat depot has historically been difficult. Recent dual X-ray absorptiometry approaches have provided an accessible estimate of VAT that has shown acceptable validity against gold standard methods. The aims of this study were to (i) evaluate DXA measured VAT as a predictor of elevated blood lipids and blood pressure and (ii) calculate thresholds associated with these cardio-metabolic risk factors. SUBJECTS/METHODS: The sample comprised 1482 adults (56.4% women) aged 18-66 years. Total body scans were performed using a GE Lunar Prodigy, and VAT analyses were enabled through Corescan software (v 16.0). Blood pressure and blood lipids were measured by standard procedures. Regression models assessed how VAT mass was associated with each cardio-metabolic risk factor compared to other body composition measures. Measures of sensitivity and specificity were used to determine age- and sex-specific cut points for VAT mass associated with high cardio-metabolic risk. RESULTS: Similar to waist circumference, VAT mass was a strong predictor of cardio-metabolic risk especially in men over age 40. Four cut-offs for VAT mass were proposed, above which the cardio-metabolic risk increased: 700 g in women <40 yrs; 800 g in women 40+ yrs; 1000g in men <40 yrs; and 1200 g in men 40+ yrs. In general, these cut-offs discriminated well between those with high and low cardio-metabolic risk. CONCLUSIONS: In both sexes, DXA measured VAT was associated with traditional cardio-metabolic risk factors, particularly high blood pressure in those 40+ yrs and low HDL < 40 yrs. These reference values provide a simple, accessible method to assess cardio-metabolic risk in adults.

Long-Term Follow-Up of a Randomized Controlled Trial to Reduce Excessive Weight Gain in Infancy: Protocol for the Prevention of Overweight in Infancy (POI) Follow-Up Study at 11 Years.

BACKGROUND: The Prevention of Overweight in Infancy (POI) randomized controlled trial assessed the effect of a more conventional food, physical activity, and breastfeeding intervention, with a more novel sleep intervention on weight outcomes at 2 years of age. The trial had 58% uptake at recruitment, and retention was 86% at age 2 years, 77% at age 3.5 years, and 69% at age 5 years. Children who received the brief sleep intervention in infancy had just half the risk of obesity at 2 years of age compared to those who did not receive the sleep intervention. Importantly, this substantially reduced risk was still apparent at our follow-up at 5 years of age. OBJECTIVE: The primary aim of this follow-up at age 11 years is to determine whether differences in BMI z-score and obesity risk remain apparent now that it is at least 9 years since cessation of the sleep intervention. Several secondary outcomes of interest will also be examined including 24-hour movement patterns, mental health and wellbeing, and use of electronic media, particularly prior to sleep. METHODS: We will seek renewed consent from all 734 of the original 802 POI families who expressed interest in further involvement. Children and parent(s) will attend 2 clinics and 1 home appointment to obtain measures of anthropometry and body composition (dual-energy x-ray absorptiometry scan), 24-hour movement patterns (sleep, sedentary time, and physical activity measured using an AX3 accelerometer), mental health and wellbeing (validated questionnaires), family functioning (validated questionnaires), use of electronic media (wearable and stationary cameras, questionnaires), and diet and eating behaviors (24-hour recall, questionnaires). RESULTS: This follow-up study has full ethical approval from the University of Otago Human Ethics Committee (H19/109) and was funded in May 2019 by the Health Research Council of New Zealand (grant 19/346). Data collection commenced in June 2020, and first results are expected to be submitted for publication in 2022. CONCLUSIONS: Long-term outcomes of early obesity intervention are rare. Despite the growing body of evidence linking insufficient sleep with an increased risk of obesity in children, interventions targeting improvements in sleep have been insufficiently explored. Our initial follow-up at 5 years of age suggested that an early sleep intervention may have long-term benefits for effective weight management in children. Further analysis in our now preteen population will provide much-needed evidence regarding the long-term effectiveness of sleep interventions in infancy as an obesity prevention approach. TRIAL REGISTRATION: ClinicalTrials.gov NCT00892983; https://tinyurl.com/y3xepvxf. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24968.