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.

Household food insecurity and novel complementary feeding methods in New Zealand families.

Optimal nutrition during infancy is critical given its influence on lifelong health and wellbeing. Two novel methods of infant complementary feeding, commercial baby food pouch use and baby-led weaning (BLW), are becoming increasingly popular worldwide. Household food insecurity may influence complementary feeding practices adopted by families, but no studies have investigated the use of BLW and baby food pouches in families experiencing food insecurity. The First Foods New Zealand study was a multicentre, observational study in infants 7.0-9.9 months of age. Households (n = 604) were classified into one of three categories of food insecurity (severely food insecure, moderately food insecure, and food secure). The use of complementary feeding practices was assessed via a self-administered questionnaire, both at the current age (mean 8.4 months) and retrospectively at 6 months. Mothers experiencing severe food insecurity had 5.70 times the odds of currently using commercial baby food pouches frequently (≥5 times/week) compared to food secure mothers (95% CI [1.54, 21.01]), reporting that pouches were 'easy to use' (89%) and made it 'easy to get fruits and vegetables in' (64%). In contrast, no evidence of a difference in the prevalence of current BLW was observed among mothers experiencing moderate food insecurity (adjusted OR; 1.28, 95% CI [0.73, 2.24]) or severe food insecurity (adjusted OR; 1.03, 95% CI [0.44, 2.43]) compared to food secure mothers. The high prevalence of frequent commercial baby food pouch use in food insecure households underscores the need for research to determine whether frequent pouch use impacts infant health.

The measurement of young children's nocturnal sleep health and the development of the Perception of Infant and Toddler Sleep Scale (PoITSS) in Aotearoa New Zealand whanau (families).

OBJECTIVES: To modify an existing questionnaire Brief Infant Sleep Questionnaire - Revised (BISQ-R) to ensure that it is suitable to measure nocturnal sleep health in a diverse sample of young children from Aotearoa New Zealand whanau (families), and to develop a "Perception of Infant and Toddler Sleep Scale" (PoITSS) to use as a primary outcome measurement in an upcoming trial. METHODS: Items from the BISQ-R were adapted for use among ethnically diverse whanau, and tested online with caregivers of 0-2 year old children. A PoITSS score was generated by scaling the responses from three of the questionnaire items to create a value between 0 (very poor) and 10 (very good). Caregivers provided qualitative feedback about the ease of interpreting and answering questionnaire items. RESULTS: Caregivers of 957 children (35% Maori, 12% Pacific) completed the questionnaire. Few differences in children's nocturnal sleep were observed by demographic characteristics. The mean PoITSS score was 6.9 (SD 2.3) and was slightly higher among Maori children (mean difference 0.4, 95% CI 0.1, 0.7). Test-retest indicated good reliability (ICC=0.81). While the majority (86%) of caregivers did not find it difficult to answer any of the items which formed the PoITSS, qualitative feedback indicated that simple modifications to some items would help ensure that they would be well understood by most caregivers. CONCLUSIONS: Items from the BISQ-R were successfully adapted, and the PoITSS scale was shown to be appropriate, for use in ethnically diverse Aotearoa New Zealand whanau with young children.

Evening regular activity breaks extend subsequent free-living sleep time in healthy adults: a randomised crossover trial.

Objective: To determine if performing regular 3-min bouts of resistance exercise spread over 4 hours in an evening will impact subsequent sleep quantity and quality, sedentary time and physical activity compared with prolonged uninterrupted sitting. Methods: In this randomised crossover trial, participants each completed two 4-hour interventions commencing at approximately 17:00 hours: (1) prolonged sitting and (2) sitting interrupted with 3 min of bodyweight resistance exercise activity breaks every 30 min. On completion, participants returned to a free-living setting. This paper reports secondary outcomes relating to sleep quality and quantity, physical activity and sedentary time which were assessed using wrist-worn ActiGraph GT3+ accelerometers paired with a sleep and wear time diary. Results: A total of 28 participants (women, n=20), age 25.6±5.6 years, body mass index 29.5±6.7 kg/m2 (mean±SD) provided data for this analysis. Compared with prolonged sitting, regular activity breaks increased mean sleep period time and time spent asleep by 29.3 min (95% CI: 1.3 to 57.2, p=0.040) and 27.7 min (95% CI: 2.3 to 52.4, p=0.033), respectively, on the night of the intervention. There was no significant effect on mean sleep efficiency (mean: 0.2%, 95% CI: -2.0 to 2.4, p=0.857), wake after sleep onset (1.0 min, 95% CI: -9.6 to 11.7, p=0.849) and number of awakenings (0.8, 95% CI: -1.8 to 3.3, p=0.550). Subsequent 24-hour and 48-hour physical activity patterns were not significantly different. Conclusions: Performing bodyweight resistance exercise activity breaks in the evening has the potential to improve sleep period and total sleep time and does not disrupt other aspects of sleep quality or subsequent 24-hour physical activity. Future research should explore the longer-term impact of evening activity breaks on sleep. Trial registration number: Australian New Zealand Clinical Trials Registry (ACTRN12621000250831).

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.

Equations to estimate human milk intake in infants aged 7 to 10 months: prediction models from a cross-sectional study.

BACKGROUND: Obtaining valid estimates of nutrient intake in infants is currently limited by the difficulties of accurately measuring human milk intake. Current methods are either unsuitable for large-scale studies (i.e., the gold standard dose-to-mother stable isotope technique) or use set amounts, regardless of known variability in individual intake. OBJECTIVES: This cross-sectional study aimed to develop equations to predict human milk intake using simple measures and to carry out external validation of existing methods against the gold standard technique. METHODS: Data on human milk intake were obtained using the dose-to-mother stable isotope technique in 157 infants aged 7-10 mo and their mothers. Predictive equations were developed using questionnaire and anthropometric data (Model 1) and additional dietary data (Model 2) using lasso regression. Bland-Altman plots and intraclass correlation coefficients (ICC) also assessed the validity of existing methods (FITS and ALSPAC studies). RESULTS: The strongest univariate predictors of human milk intake in infants of 8.3 mo on average (46% female) were infant age, infant body mass index (BMI), number of breastfeeds a day, infant formula consumption, and energy from complementary food intake. Mean [95% confidence interval (CI)] differences in predicted versus measured human milk intake [mean (SD): 762 (257) mL/day] were 0.0 mL/day (-26, 26) for Model 1 (ICC 0.74) and 0.5 mL/day (-21, 22) for Model 2 (ICC 0.83). Corresponding differences were -197 mL/day (-233, -161; ICC 0.32) and -175 mL/day (-216, -134; ICC 0.41) for the methods used by FITS and ALSPAC, respectively. CONCLUSIONS: The Human Milk Intake Level Calculation provides substantial improvements on existing methods to estimate human milk intake in infants aged 7-10 mo, while utilizing data commonly collected in nutrition surveys. Although further validation in an external sample is recommended, these equations can be used to estimate human milk intake at this age with some confidence. This clinical trial was registered at http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=379436) as ACTRN12620000459921.

Baby Food Pouches, Baby-Led Weaning, and Iron Status in New Zealand Infants: An Observational Study.

Iron deficiency in infants can impact development, and there are concerns that the use of baby food pouches and baby-led weaning may impair iron status. First Foods New Zealand (FFNZ) was an observational study of 625 New Zealand infants aged 6.9 to 10.1 months. Feeding methods were defined based on parental reports of infant feeding at "around 6 months of age": "frequent" baby food pouch use (five+ times per week) and "full baby-led weaning" (the infant primarily self-feeds). Iron status was assessed using a venepuncture blood sample. The estimated prevalence of suboptimal iron status was 23%, but neither feeding method significantly predicted body iron concentrations nor the odds of iron sufficiency after controlling for potential confounding factors including infant formula intake. Adjusted ORs for iron sufficiency were 1.50 (95% CI: 0.67-3.39) for frequent pouch users compared to non-pouch users and 0.91 (95% CI: 0.45-1.87) for baby-led weaning compared to traditional spoon-feeding. Contrary to concerns, there was no evidence that baby food pouch use or baby-led weaning, as currently practiced in New Zealand, were associated with poorer iron status in this age group. However, notable levels of suboptimal iron status, regardless of the feeding method, emphasise the ongoing need for paying attention to infant iron nutrition.

Contribution of Infant Food Pouches and Other Commercial Infant Foods to the Diets of Infants: A Cross-sectional Study.

BACKGROUND: Although considerable concern has been expressed about the nutritional implications of infant food pouches, how they impact infant diet has not been examined. OBJECTIVES: The objective of this study was to determine the contribution of infant food pouches specifically, and commercial infant foods generally, to nutrient intake from complementary foods in infants. METHODS: Two multiple-pass 24-h diet recall data were collected from 645 infants (6.0-11.9 mo) in the First Foods and Young Foods New Zealand studies. Detailed information was obtained on commercial infant food use, including pouches, and nutrient composition was calculated through recipe modeling. RESULTS: The diverse sample (46.1% female; 21.1% Maori, 14.1% Asian, and 54.6% European) was aged (SD) 8.4 (0.9) mo. More than one-quarter of households had high socioeconomic deprivation. Almost half (45.3%) of infants consumed an infant food pouch on ≥1 recall day [mean (SD), 1.3 (0.9) times/d], obtaining 218 (124) kJ of energy on each eating occasion. Comparable numbers for all commercial infant and toddler foods (CITFs) were 78.0%, contributing 2.2 (1.6) and 140 (118) kJ of energy. Infant food pouches provided 25.5% of the total energy from complementary foods in those infants who consumed pouches on the recall days but just 11% in all infants. Median percentage contribution of infant food pouches to nutrient intake from complementary foods in consumers ranged from <1% (added sugars and retinol) to >30% (carbohydrate, total sugars, fiber, vitamin A, and vitamin C). CITF contributed 21.4% of energy from complementary foods for infant consumers, with median percentage contribution ranging from 0.1% (retinol) to 40.3% (iron). CONCLUSIONS: Infant food pouches make relatively small contributions to energy intake in infants but are important sources of carbohydrates, fiber, and vitamins A, C, and B-6. Almost half of the total sugars consumed from complementary foods is provided by these pouches. This trial was registered at the Australian New Zealand Clinical Trials Registry as ACTRN12620000459921.

Baby food pouches and Baby-Led Weaning: Associations with energy intake, eating behaviour and infant weight status.

Although concern is frequently expressed regarding the potential impact of baby food pouch use and Baby-Led Weaning (BLW) on infant health, research is scarce. Data on pouch use, BLW, energy intake, eating behaviour and body mass index (BMI) were obtained for 625 infants aged 7-10 months in the First Foods New Zealand study. Frequent pouch use was defined as ≥5 times/week during the past month. Traditional spoon-feeding (TSF), "partial" BLW and "full" BLW referred to the relative proportions of spoon-feeding versus infant self-feeding, assessed at 6 months (retrospectively) and current age. Daily energy intake was determined using two 24-h dietary recalls, and caregivers reported on a variety of eating behaviours. Researchers measured infant length and weight, and BMI z-scores were calculated (World Health Organization Child Growth Standards). In total, 28% of infants consumed food from pouches frequently. Frequent pouch use was not significantly related to BMI z-score (mean difference, 0.09; 95% CI -0.09, 0.27) or energy intake (92 kJ/day; -19, 202), but was associated with greater food responsiveness (standardised mean difference, 0.3; 95% CI 0.1, 0.4), food fussiness (0.3; 0.1, 0.4) and selective/restrictive eating (0.3; 0.2, 0.5). Compared to TSF, full BLW was associated with greater daily energy intake (BLW at 6 months: mean difference 150 kJ/day; 95% CI 4, 297; BLW at current age: 180 kJ/day; 62, 299) and with a range of eating behaviours, including greater satiety responsiveness, but not BMI z-score (6 months: 0.06 (-0.18, 0.30); current age: 0.06 (-0.13, 0.26)). In conclusion, neither feeding approach was associated with weight in infants, despite BLW being associated with greater energy intake compared with TSF. However, infants who consumed pouches frequently displayed higher food fussiness and more selective eating.

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.

Modelled Distributional Cost-Effectiveness Analysis of Childhood Obesity Interventions: A Demonstration.

OBJECTIVE: To demonstrate how distributional cost-effectiveness analyses of childhood obesity interventions could be conducted and presented for decision makers. METHODS: We conducted modelled distributional cost-effectiveness analyses of three obesity interventions in children: an infant sleep intervention (POI-Sleep), a combined infant sleep, food, activity and breastfeeding intervention (POI-Combo) and a clinician-led treatment for primary school-aged children with overweight and obesity (High Five for Kids). For each intervention, costs and socioeconomic position (SEP)-specific effect sizes were applied to an Australian child cohort (n = 4898). Using a purpose-built microsimulation model we simulated SEP-specific body mass index (BMI) trajectories, healthcare costs and quality-adjusted life years (QALYs) from age 4 to 17 years for control and intervention cohorts. We examined the distribution of each health outcome across SEP and determined the net health benefit and equity impact accounting for opportunity costs and uncertainty due to individual-level heterogeneity. Finally, we conducted scenario analyses to test the effect of assumptions about health system marginal productivity, the distribution of opportunity costs and SEP-specific effect sizes. The results of the primary analyses, uncertainty analyses and scenario analyses were presented on an efficiency-equity impact plane. RESULTS: Accounting for uncertainty, POI-Sleep and High Five for Kids were found to be 'win-win' interventions, with a 67% and 100% probability, respectively, of generating a net health benefit and positive equity impact compared with control. POI-Combo was found to be a 'lose-lose' intervention, with a 91% probability of producing a net health loss and a negative equity impact compared with control. Scenario analyses indicated that SEP-specific effect sizes were highly influential on equity impact estimates for POI-Combo and High Five for Kids, while health system marginal productivity and opportunity cost distribution assumptions primarily influenced the net health benefit and equity impact of POI-Combo. CONCLUSIONS: These analyses demonstrated that distributional cost-effectiveness analyses using a fit-for-purpose model are appropriate for differentiating and communicating the efficiency and equity impacts of childhood obesity interventions.

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.

Breaking Up Evening Sitting with Resistance Activity Improves Postprandial Glycemic Response: A Randomized Crossover Study.

INTRODUCTION: Interrupting sedentary time during the day reduces postprandial glycemia (a risk factor for cardiometabolic disease). However, it is not known if benefits exist for postprandial glucose, insulin and triglyceride responses in the evening, and if these benefits differ by body mass index (BMI) category. METHODS: In a randomized crossover study, 30 participants (25.4 ± 5.4 yr old; BMI 18.5-24.9: n = 10, BMI 25-29.9: n = 10, BMI ≥30: n = 10) completed two intervention arms, beginning at ~1700 h: prolonged sitting for 4 h, and sitting with regular activity breaks of 3 min of resistance exercises every 30 min. Plasma glucose, insulin, and triglyceride concentrations were measured in response to two meals fed at baseline and 120 min. Four-hour incremental area under the curve was compared between interventions. Moderation by BMI status was explored. RESULTS: Overall, when compared with prolonged sitting, regular activity breaks lowered plasma glucose and insulin incremental area under the curve by 31.5% (95% confidence interval = -49.3% to -13.8%) and 26.6% (-39.6% to -9.9%), respectively. No significant differences were found for plasma triglyceride area under the curve. Interactions between BMI status and intervention was not statistically significant. CONCLUSIONS: Interventions that interrupt sedentary time in the evening may improve cardiometabolic health by some magnitude in all participants regardless of bodyweight.

Prevalence of low and high BMI during the first 3 years of life: using New Zealand national electronic health data.

BACKGROUND: Data on body mass index (BMI) in infants and toddlers worldwide are lacking, relative to older age groups. OBJECTIVES: To describe the growth (weight, length/height, head circumference, and BMI z-score) of New Zealand children under the age of 3 years, and examine differences by sociodemographic characteristics (sex, ethnicity, and deprivation). METHODS: Electronic health data were collected by Whanau Awhina Plunket, who provide free 'Well Child' services for approximately 85% of newborn babies in New Zealand. Data from children under the age of 3, who had their weight and length/height measured between 2017 and 2019, were included. The prevalence of BMI (WHO child growth standards) ≤2nd, ≥85th, and ≥95th percentiles were determined. RESULTS: Between 12 weeks and 27 months of age, the percentage of infants ≥85th BMI percentile increased from 10.8% (95% CI, 10.4%-11.2%) to 35.0% (34.2%-35.9%). The percentage of infants with high BMI (≥95th percentile) also increased, particularly between 6 months (6.4%; 95% CI, 6.0%-6.7%) and 27 months (16.4%; 15.8%-17.1%). By contrast, the percentage of infants with low BMI (≤2nd percentile) appeared steady between 6 weeks and 6 months, and declined at older ages. The prevalence of infants with a high BMI appears to increase substantially from 6 months across sociodemographic characteristics, with a widening prevalence gap by ethnicity occurring from 6 months, mirroring that of infants with a low BMI. CONCLUSIONS: The number of children with high BMI increases rapidly between 6 months and 27 months of age, suggesting this is an important timeframe for monitoring and preventive action. Future work should investigate the longitudinal growth trajectories of these children to determine if any particular patterns predict later obesity and what strategies could effectively change them.

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.

Is the cost-effectiveness of an early-childhood sleep intervention to prevent obesity affected by socioeconomic position?

OBJECTIVE: This study aimed to determine whether the cost-effectiveness of an infant sleep intervention from the Prevention of Overweight in Infancy (POI) trial was influenced by socioeconomic position (SEP). METHODS: An SEP-specific economic evaluation of the sleep intervention was conducted. SEP-specific intervention costs and effects at age 5 years, derived from the trial data, were applied to a representative cohort of 4,898 4- to 5-year-old Australian children. Quality-adjusted life years and health care costs were simulated until age 17 years using a purpose-built SEP-specific model. Incremental cost-effectiveness ratios and acceptability curves were derived for each SEP group. RESULTS: The incremental cost-effectiveness ratios, in Australian dollars per quality-adjusted life year gained, were smaller in the low- ($23,010) and mid-SEP ($18,206) groups compared with the high-SEP group ($31,981). The probability that the intervention was cost-effective was very high in the low- and mid-SEP groups (92%-100%) and moderately high in the high-SEP group (79%). CONCLUSIONS: An infant sleep intervention is more cost-effective in low- and mid-SEP groups compared with high-SEP groups. Targeting this intervention to low-SEP groups would not require trade-offs between efficiency and equity.

Systematic review and meta-analysis of augmentation and combination treatments for early-stage treatment-resistant depression.

BACKGROUND: Major depressive disorder (MDD) is a highly burdensome health condition, for which there are numerous accepted pharmacological and psychological interventions. Adjunctive treatment (augmentation/combination) is recommended for the ~50% of MDD patients who do not adequately respond to first-line treatment. We aimed to evaluate the current evidence for concomitant approaches for people with early-stage treatment-resistant depression (TRD; defined below). METHODS: We systematically searched Medline and Institute for Scientific Information Web of Science to identify randomised controlled trials of adjunctive treatment of ⩾10 adults with MDD who had not responded to ⩾1 adequate antidepressant. The cochrane risk of bias (RoB) tool was used to assess study quality. Pre-post treatment meta-analyses were performed, allowing for comparison across heterogeneous study designs independent of comparator interventions. RESULTS: In total, 115 trials investigating 48 treatments were synthesised. The mean intervention duration was 9 weeks (range 5 days to 18 months) with most studies assessed to have low (n = 57) or moderate (n = 51) RoB. The highest effect sizes (ESs) were from cognitive behavioural therapy (ES = 1.58, 95% confidence interval (CI): 1.09-2.07), (es)ketamine (ES = 1.48, 95% CI: 1.23-1.73) and risperidone (ES = 1.42, 95% CI: 1.29-1.61). Only aripiprazole and lithium were examined in ⩾10 studies. Pill placebo (ES = 0.89, 95% CI: 0.81-0.98) had a not inconsiderable ES, and only six treatments' 95% CIs did not overlap with pill placebo's (aripiprazole, (es)ketamine, mirtazapine, olanzapine, quetiapine and risperidone). We report marked heterogeneity between studies for almost all analyses. CONCLUSIONS: Our findings support cautious optimism for several augmentation strategies; although considering the high prevalence of TRD, evidence remains inadequate for each treatment option.