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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.
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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.
Subject(s)
Cross-Over Studies , Diet , Exercise , Sedentary Behavior , Sleep , Humans , Female , Male , Child , Sleep/physiology , Diet/methods , Longitudinal Studies , Sleep Deprivation , Actigraphy , Energy Intake , Feeding BehaviorABSTRACT
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.
Subject(s)
Pediatric Obesity , Weight Gain , Infant, Newborn , Infant , Humans , Child , Body Composition , Adipose Tissue , Risk FactorsABSTRACT
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.
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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.
Subject(s)
Feeding Behavior , Sleep , Child , Humans , Cross-Over Studies , Australia , Sleep Deprivation , EatingABSTRACT
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.
Subject(s)
Quality of Life , Sleep Wake Disorders , Female , Humans , Child , Cross-Over Studies , Australia , Sleep , Sleep Deprivation/epidemiologyABSTRACT
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.
Subject(s)
Pediatric Obesity , Humans , Child , Cross-Over Studies , Sleep , Sleep Deprivation , ExerciseABSTRACT
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.
Subject(s)
Child Behavior , Feeding Behavior , Child , Eating , Humans , Hunger , Satiation , Surveys and QuestionnairesABSTRACT
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.
Subject(s)
Exercise , Guideline Adherence , Mental Health , Psychosocial Functioning , Sedentary Behavior , Sleep/physiology , Child , Child, Preschool , Female , Humans , Longitudinal Studies , Screen Time , Surveys and QuestionnairesABSTRACT
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.
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BACKGROUND/OBJECTIVES: Although sleep duration is well established as a risk factor for child obesity, how measures of sleep quality relate to body size is less certain. The aim of this study was to determine how objectively measured sleep duration, sleep timing, and sleep quality were related to body mass index (BMI) cross-sectionally and longitudinally in school-aged children. SUBJECTS/METHODS: All measures were obtained at baseline, 12 and 24 months in 823 children (51% female, 53% European, 18% Maori, 12% Pacific, 9% Asian) aged 6-10 years at baseline. Sleep duration, timing, and quality were measured using actigraphy over 7 days, height and weight were measured using standard techniques, and parents completed questionnaires on demographics (baseline only), dietary intake, and television usage. Data were analysed using imputation; mixed models, with random effects for person and age, estimated both a cross-sectional effect and a longitudinal effect on BMI z-score, adjusted for multiple confounders. RESULTS: The estimate of the effect on BMI z-score for each additional hour of sleep was -0.22 (95% CI: -0.33, -0.11) in cross-sectional analyses and -0.05 (-0.10, -0.004) in longitudinal analyses. A greater effect was observed for weekday sleep duration than weekend sleep duration but variability in duration was not related to BMI z-score. While sleep timing (onset or midpoint of sleep) was not related to BMI, children who were awake in the night more frequently (0.19; 0.06, 0.32) or for longer periods (0.18; 0.06, 0.36) had significantly higher BMI z-scores cross-sectionally, but only the estimates for total time awake (minutes) were significant longitudinally (increase in BMI z-score of 0.04 for each additional hour awake). CONCLUSION: The beneficial effect of a longer sleep duration on BMI was consistent in children, whereas evidence for markers of sleep quality and timing were more variable.
Subject(s)
Body Weight/physiology , Sleep/physiology , Body Mass Index , Child , Cross-Sectional Studies , Female , Humans , Longitudinal Studies , MaleABSTRACT
Heat is a major stressor during exercise, though its value in driving adaptation is not well understood. Muscle heating can upregulate pathways facilitating protein synthesis and could thereby enhance effects of exercise training, however, few studies have investigated this possibility. We examined whether heating active muscle during resistance training differentially affected physical and functional adaptations. Within a randomised contralateral-limb control study, ten healthy, resistance-untrained individuals (21 ± 3 y; 5 female) completed 30 sessions of progressive resistance training (12 weeks), performing 4 × 8 unilateral knee extensions at 70% of 1RM. One randomly-allocated thigh was heated during, and for 20 min after, each session using an electric pad eliciting muscle temperatures of >38 °C (HOT); the contralateral limb remained unheated (CON). Training intensity was progressed using 4-weekly strength assessments. Quadricep lean mass (measured using DXA) increased by 15 ± 7% in HOT (p = 0.00) and 15 ± 6% in CON (p = 0.00); the difference being trivial (p = 0.94). Peak isokinetic torque at 90°.s-1 increased by 30 ± 25% (HOT; p = 0.00) and 34 ± 33% (CON; p = 0.01), with no difference (p = 0.84) between limbs. Rate of torque development increased â¼40%, with no difference between limbs (p = 0.73). The increase in 3-RM strength was also similar in HOT (75 ± 16%) and CON (71 ± 14%; p = 0.80 for difference). No differences in mass or strength changes were evident between sexes. In conclusion, supplemental heating of active muscle during and after each bout of resistance training showed no clear positive (or negative) effect on training-induced hypertrophy or function.
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BACKGROUND: New physical activity guidelines for children address all movement behaviors across the 24-h day (physical activity, sedentary behavior, sleep), but how each component relates to body composition when adjusted for the compositional nature of 24-h data is uncertain. AIMS: To i) describe 24-h movement behaviors from 1 to 5 years of age, ii) determine cross-sectional relationships with body mass index (BMI) z-score, iii) determine whether movement behaviors from 1 to 5 years of age predict body composition and bone health at 5 years. METHODS: 24-h accelerometry data were collected in 380 children over 5-7 days at 1, 2, 3.5 and 5 years of age to determine the proportion of the day spent: sedentary (including wake after sleep onset), in light (LPA) and moderate-to-vigorous physical activity (MVPA), and asleep (including naps). BMI was determined at each age and a dual-energy x-ray absorptiometry (DXA) scan measured fat mass, bone mineral content (BMC) and bone mineral density (BMD) at 5 years of age. 24-h movement data were transformed into isometric log-ratio co-ordinates for multivariable regression analysis and effect sizes back-transformed. RESULTS: At age 1, children spent 49.6% of the 24-h day asleep, 38.2% sedentary, 12.1% in LPA, and 0.1% in MVPA, with corresponding figures of 44.4, 33.8, 19.8 and 1.9% at 5 years of age. Compositional time use was only related significantly to BMI z-score at 3.5 years in cross-sectional analyses. A 10% increase in mean sleep time (65 min) was associated with a lower BMI z-score (estimated difference, - 0.25; 95% CI, - 0.42 to - 0.08), whereas greater time spent sedentary (10%, 47 min) or in LPA (10%, 29 min) were associated with higher BMI z-scores (0.12 and 0.08 respectively, both p < 0.05). Compositional time use from 1 to 3.5 years was not related to future BMI z-score or percent fat. Although MVPA at 2 and 3.5 years was consistently associated with higher BMD and BMC at 5 years, actual differences were small. CONCLUSIONS: Considerable changes in compositional time use occur from 1 to 5 years of age, but there is little association with adiposity. Although early MVPA predicted better bone health, the differences observed had little clinical relevance. TRIAL REGISTRATION: ClinicalTrials.gov number NCT00892983 .
Subject(s)
Body Composition , Body Mass Index , Bone Density , Child Behavior , Exercise , Infant Behavior , Sedentary Behavior , Absorptiometry, Photon , Accelerometry , Adipose Tissue , Adiposity , Bone and Bones , Child, Preschool , Cross-Sectional Studies , Female , Humans , Infant , Male , Obesity , Schools , SleepABSTRACT
Background: Our Prevention of Overweight in Infancy (POI) study suggested that a brief sleep intervention in infancy reduced the risk of obesity at age 2 y. In contrast, we observed no benefit from the nutrition and activity intervention. Objective: The objective of the study was to determine how these interventions influenced growth at ages 3.5 and 5 y compared with usual care (Control). Design: A follow-up of a parallel, 4-arm, single-blind, 2-y, randomized controlled trial in 802 women (86% European, 48% primiparous) recruited in pregnancy (58% response rate) was undertaken. All groups received standard Well-Child care with additional support for 3 intervention groups: FAB (promotion of breastfeeding, healthy eating, physical activity: 8 contacts, antenatal, 18 mo); Sleep (prevention of sleep problems: antenatal, 3 wk); Combination (both interventions). Follow-up measures were collected by staff blinded to group allocation. The primary outcome was child body mass index (BMI) z score, and secondary outcomes were prevalence of obesity (BMI ≥95th percentile), self-regulation (psychological measures), sleep, physical activity (accelerometry, questionnaires), and dietary intake (food-frequency questionnaire). Analyses were conducted through the use of multiple imputation. Results: Retention was 77% at age 3.5 y and 69% at age 5 y. Children in the FAB group had significantly higher BMI z scores than did Controls at age 5 y (adjusted difference: 0.25; 95% CI: 0.04, 0.47) but not at age 3.5 y (0.15; 95% CI: -0.04, 0.34). Children who received the Sleep intervention (Sleep and Combination groups) had significantly lower BMI z scores at age 3.5 y (-0.24; 95% CI: -0.38, -0.10) and at age 5 y (-0.23; 95% CI: -0.38, -0.07) than children who did not (Control and FAB groups). Conclusions: A conventional intervention had unexpected adverse long-term weight outcomes, whereas positive outcomes from a less conventional sleep intervention remained promising at age 5 y. More intensive or extended sleep intervention might have larger or longer-lasting effects and should be investigated. This trial was registered at clinicaltrials.gov as NCT00892983.
Subject(s)
Exercise , Nutritional Physiological Phenomena , Pediatric Obesity/prevention & control , Sleep , Body Mass Index , Child, Preschool , Female , Follow-Up Studies , Humans , Male , Single-Blind MethodABSTRACT
PURPOSE: Although high-intensity interval training (HIIT) and moderate-intensity continuous exercise have comparable health outcomes in the laboratory setting, effectiveness studies in real-world environments are lacking. The aim of this study was to determine the effectiveness of an unsupervised HIIT program in overweight/obese adults over 12 months. METHODS: Two hundred and fifty overweight/obese adults could choose HIIT or current exercise guidelines of 30 min·d moderate-intensity exercise. HIIT participants received a single training session and were advised to independently perform HIIT three times per week using a variety of protocols. Mixed models, with a random effect for participant, compared differences in weight, body composition, blood pressure, aerobic fitness, physical activity, and blood indices at 12 months, adjusting for relevant baseline variables. RESULTS: Forty-two percent (n = 104) of eligible participants chose HIIT in preference to current guidelines. At 12 months, there were no differences between exercise groups in weight (adjusted difference HIIT vs conventional = -0.44 kg; 95% confidence interval [CI] = -2.5 to 1.6) or visceral fat (-103 cm; -256 to 49), although HIIT participants reported greater enjoyment of physical activity (P = 0.01). Evidence of adherence to ≥2 sessions per week of unsupervised HIIT (from HR monitoring) declined from 60.8% at baseline to 19.6% by 12 months. Participants remaining adherent to HIIT over 12 months (23%) were more likely to be male (67% vs 36%, P = 0.03), with greater reductions in weight (-2.7 kg; -5.2 to 0.2) and visceral fat (-292 cm; -483 to -101) than nonadherent participants. CONCLUSIONS: HIIT was well accepted by overweight adults, and opting for HIIT as an alternative to standard exercise recommendations led to no difference in health outcomes after 12 months. Although regular participation in unsupervised HIIT declined rapidly, those apparently adherent to regular HIIT demonstrated beneficial weight loss and visceral fat reduction. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ACTRN12615000010594), retrospectively registered.
Subject(s)
High-Intensity Interval Training , Obesity/therapy , Overweight/therapy , Adult , Blood Pressure , Body Composition , Body Weight , Exercise , Female , Humans , Intra-Abdominal Fat , Male , Middle Aged , Patient Compliance , Weight LossABSTRACT
OBJECTIVES: To determine whether sleep patterns (duration, timing, efficiency) differ by ethnicity. DESIGN: Longitudinal study. SETTING: Dunedin, New Zealand. PARTICIPANTS: A total of 939 children (48% male) aged 4-12 years (572 European, 181 Maori, 111 Pacific, 75 Asian). MEASUREMENTS: All measurements were obtained at months 0, 12, and 24. Anthropometry was obtained using standard techniques, and parents completed questionnaires assessing demographics, dietary intake, and television habits of children. Sleep and physical activity were measured using Actigraph accelerometers over 1 week. Differences in sleep outcomes according to ethnicity were adjusted for demographics, weight status, and behavioral variables using mixed models. RESULTS: Pacific children had greater body mass index and were more likely to live in deprived areas than children from other ethnic groups (all P<.001), but few differences were observed in behavioral variables. Pacific Island children slept 16 (95% confidence interval, 7-25) minutes less per night than New Zealand European children, predominantly as a result of later bedtimes (29; 20-38 minutes). By contrast, sleep efficiency did not differ by ethnicity or over time (all P≥.118). Maori children did not show the same relative deficits in sleep, displaying similar results to European children. Sleep duration decreased by 8 minutes (95% confidence interval, 6-10) a night each year over 2 years, and change over time did not differ by ethnicity (all P≥.165). CONCLUSIONS: From a young age, Pacific children had poorer sleep patterns than European children, and these patterns were maintained over 2 years.
Subject(s)
Ethnicity/statistics & numerical data , Health Status Disparities , Sleep , Actigraphy , Child , Child, Preschool , Cross-Sectional Studies , Female , Humans , Longitudinal Studies , Male , New Zealand , Time FactorsABSTRACT
OBJECTIVE: To determine the effectiveness of various monitoring strategies on weight loss, body composition, blood markers, exercise, and psychosocial indices in adults with overweight and obesity following a 12-month weight loss program. METHODS: Two hundred fifty adults with BMI ≥ 27 were randomized to brief, monthly, individual consults, daily self-monitoring of weight, self-monitoring of diet using MyFitnessPal, self-monitoring of hunger, or control over 12 months. All groups received diet and exercise advice, and 171 participants (68.4%) remained at 12 months. RESULTS: No significant differences in weight, body composition, blood markers, exercise, or eating behavior were apparent between those in the four monitoring groups and the control condition at 12 months (all P ≥ 0.053). Weight differences between groups ranged from -1.1 kg (-3.8 to 1.6) to 2.2 kg (-1.0 to 5.3). However, brief support and hunger training groups reported significantly lower scores for depression (difference [95% CI]: -3.16 [-5.70 to -0.62] and -3.05 [-5.61 to -0.50], respectively) and anxiety (-1.84, [-3.67 to -0.02]) scores than control participants. CONCLUSIONS: Although adding a monitoring strategy to diet and exercise advice did not further increase weight loss, no adverse effects on eating behavior were observed, and some monitoring strategies may even benefit mental health.
Subject(s)
Overweight/therapy , Weight Loss , Weight Reduction Programs/methods , Adult , Anxiety , Body Composition , Body Mass Index , Body Weight , Counseling , Depression , Diet , Exercise , Feeding Behavior , Female , Humans , Male , Mental Health , Middle Aged , Obesity/therapyABSTRACT
Necrotizing meningoencephalitis (NME) in the Pug is an invariably fatal disease with an early age of onset whose cause remains unknown. Breed predilection strongly suggests genetic component(s), and viral etiology proves negative in studied cases. The current study was undertaken as the first analysis of the heritable component(s) involved in NME in the Pug. Complete medical records, individual characteristics, and pedigree information were collected for 58 affected dogs with data pertaining to 4698 dogs analyzed. A high inbreeding coefficient with differences across gender and significant differences across coat color classes and variable expression was evident. Median onset age was 19months and median survival time 23days. Screening for herpes-, adeno-, and parvoviruses was negative. The data demonstrate a strong familial inheritance of NME in the Pug. This investigation provides parameters of disease from the largest Pug NME cohort analyzed to date and offers evidence of previously unrecognized familial inheritance.
Subject(s)
Dog Diseases/genetics , Meningoencephalitis/genetics , Animals , Central Nervous System Diseases/genetics , Central Nervous System Diseases/veterinary , DNA/genetics , DNA/isolation & purification , Dogs , Female , Hair Color/genetics , Male , Meningoencephalitis/mortality , Meningoencephalitis/veterinary , Phenotype , Polymerase Chain Reaction , Sex Characteristics , Survival RateABSTRACT
The authors compared separation-individuation and psychological separation from fathers of 25 adolescent boys who were living with both biological parents with that of 25 boys who were living with their biological mothers in homes in which the fathers did not reside. The results showed that the boys in the 2 groups did not differ on measures of separation-individuation and that the quality of the mother-son relationship mediated several of the assessed separation-individuation manifestations. The authors' initial data analysis with regard to psychological separation showed that boys who lived in homes in which the father was a nonresident were more separated on 2 of the 4 dimensions assessed; however, when controlled for quality of mother-son and father-son relationship, these differences were not significant. The frequency of father contact in homes in which the father did not reside was positively correlated with healthy separation but negatively correlated with functional, attitudinal, and emotional independence from the father. The results of the study support the importance of the quality of a child's relationship with his or her mother and father as a mediator of several dimensions of the separation-individuation process.