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BackgroundThe 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. ObjectiveThe 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. MethodsWe 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). ResultsThis 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. ConclusionsLong-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 RegistrationClinicalTrials.gov NCT00892983; https://tinyurl.com/y3xepvxf International Registered Report Identifier (IRRID)DERR1-10.2196/24968

Background: Although evening screen time is thought to impair subsequent sleep, current measures are limited to questionnaires which seem unlikely to accurately assess screen time in youth. Given the ubiquitous nature of digital devices, improving measurement of screen time is required before related health effects can be appropriately determined. The aim of this study was to objectively quantify screen time before sleep using video camera footage. Methods: This repeated-measures observational study in healthy adolescents (11–14 years) from Dunedin, New Zealand measured screen time on four evenings over one week in the home environment from March-December 2021. Wearable and stationary PatrolEyes video cameras captured screen time from two hours before bedtime until sleep and manually coded for device type (phone, tablet, laptop computer, desktop computer, handheld gaming console, gaming console, television and other) and screen activity (watching, listening, reading, educational/creative, browsing, communication, social media, video gaming, multitasking) using a reliable coding schedule (κ ≥ 0.8). Descriptive findings are reported. Findings: Among the 83 participants (mean 12.3 [SD 1.0] years, 42% female, 52% New Zealand European, 37% Māori [indigenous]), 82 used screens in the two hours before bed on 308 of 344 (90%) nights for a mean of 54.4 min (SD 25.5). Televisions (median 37 min, 56% of nights), phones (19 min, 64% nights), and multitasking using multiple devices (19 min, 48% nights) were most commonly used (> 75% of adolescents). Once in bed but before trying to sleep, 58% of adolescents engaged in screen time for 17 (26.3) minutes on 36% of nights. The most common screen activities were watching (32.5%), social media (26.5%) and communication (20.5%). Even after attempting sleep, 32.5% of participants used screens for 8.0 min (median) on 16% of nights, mostly listening on phones. Conclusions: Objective video cameras offer detailed insight into evening screen habits, capturing frequency, content, and duration. Youth frequently engage with screens before bed and throughout the night on a range of activities, despite recommendations to restrict screen time prior to sleep. Trial registration: Australian New Zealand Clinical Trials Registry (anzctr.org.au), AACTRN12621000193875, Registered 23 February 2021, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380926&isReview=true. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background. The bidirectional relationship between sleep and blood glucose levels may particularly affect adolescents and young adults (AYA), who are more likely to experience less healthy glycemic outcomes and more disrupted sleep patterns. To date, few data exist describing the impact of intermittently scanned continuous glucose monitoring (isCGM) on habitual sleep patterns and sleep quality in AYA with type 1 diabetes (T1D). Objective. To evaluate the impact of 6-month use of isCGM on habitual sleep and wake timing, sleep duration, frequency, and duration of night-time awakenings, sleep efficiency, and perceived sleep quality in young people with T1D and HbA1c ≥ 75 mmol/mol. Participants. The study recruited 64 participants aged 13–20 years (mean 16.6 ± 2.1), 48% female, diabetes duration 7.5 ± 3.8 years, 41% Māori or Pasifika, and a mean HbA1c 96.0 ± 18.0 mmol/mol [10.9 ± 3.8%]; 33 were allocated to an isCGM plus self-monitoring blood glucose [SMBG] intervention, and 31 were allocated to the SMBG control group. Methods. Participants completed 7-day actigraphy measures and the Pittsburgh Sleep Quality Index questionnaire at the baseline and at 6 months. Regression analyses were used to model between-group comparisons, adjusted for baseline sleep measures. Results. At 6 months, subjective measures for overall sleep quality, latency, duration, efficiency, night-time disturbances, use of sleep medications, and daytime dysfunction were similar between the groups. Regression analyses of actigraphy found no significant differences in objectively measured sleep timing and duration across the week after adjusting for age, the period of the school year, and baseline sleep values. Conclusions. The use of first-generation isCGM in addition to finger-prick testing did not impact objective or subjective sleep measures in AYA with T1D, elevated HbA1c, and highly variable sleep patterns. Research using alternative interventions for improving glycemic outcomes and habitual sleep-wake timing, duration, and perceived sleep quality is warranted in this population group. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Several early childhood obesity prediction models have been developed, but none for New Zealand's diverse population. We aimed to develop and validate a model for predicting obesity in 4–5-year-old New Zealand children, using parental and infant data from the Growing Up in New Zealand (GUiNZ) cohort. Obesity was defined as body mass index (BMI) for age and sex ≥ 95th percentile. Data on GUiNZ children were used for derivation (n = 1731) and internal validation (n = 713). External validation was performed using data from the Prevention of Overweight in Infancy Study (POI, n = 383) and Pacific Islands Families Study (PIF, n = 135) cohorts. The final model included: birth weight, maternal smoking during pregnancy, maternal pre-pregnancy BMI, paternal BMI, and infant weight gain. Discrimination accuracy was adequate [AUROC = 0.74 (0.71–0.77)], remained so when validated internally [AUROC = 0.73 (0.68–0.78)] and externally on PIF [AUROC = 0.74 [0.66–0.82)] and POI [AUROC = 0.80 (0.71–0.90)]. Positive predictive values were variable but low across the risk threshold range (GUiNZ derivation 19–54%; GUiNZ validation 19–48%; and POI 8–24%), although more consistent in the PIF cohort (52–61%), all indicating high rates of false positives. Although this early childhood obesity prediction model could inform early obesity prevention, high rates of false positives might create unwarranted anxiety for families. [ABSTRACT FROM AUTHOR]

Background: The current study aimed to examine the changes following a sleep hygiene intervention on sleep hygiene practices, sleep quality, and daytime symptoms in youth. Methods: Participants aged 10-18 years with self-identified sleep problems completed our age-appropriate F.E.R.R.E.T (an acronym for the categories of Food, Emotions, Routine, Restrict, Environment and Timing) sleep hygiene programme; each category has three simple rules to encourage good sleep. Participants (and parents as appropriate) completed the Adolescent Sleep Hygiene Scale (ASHS), Pittsburgh Sleep Quality Index (PSQI), Sleep Disturbance Scale for Children (SDSC), Pediatric Daytime Sleepiness Scale (PDSS), and wore ActicalW monitors twice before (1 and 2 weeks) and three times after (6, 12 and 20 weeks) the intervention. Anthropometric data were collected two weeks before and 20 weeks post-intervention. Results: Thirty-three youths (mean age 12.9 years; M/F = 0.8) enrolled, and retention was 100%. ASHS scores significantly improved (p = 0.005) from a baseline mean (SD) of 4.70 (0.41) to 4.95 (0.31) post-intervention, as did PSQI scores [7.47 (2.43) to 4.47 (2.37); p < 0.001] and SDSC scores [53.4 (9.0) to 39.2 (9.2); p < 0.001]. PDSS scores improved from a baseline of 16.5 (6.0) to 11.3 (6.0) post- intervention (p < 0.001). BMI z-scores with a baseline of0.79 (1.18) decreased significantly (p = 0.001) post-intervention to 0.66 (1.19). Despite these improvements, sleep duration as estimated by Actical accelerometry did not change. There was however a significant decrease in daytime sedentary/light energy expenditure. Conclusions: Our findings suggest the F.E.R.R.E.T sleep hygiene education programme might be effective in improving sleep in children and adolescents. However because this was a before and after study and a pilot study with several limitations, the findings need to be addressed with caution, and would need to be replicated within a randomised controlled trial to prove efficacy. [ABSTRACT FROM AUTHOR]

The reported association of cot death and sleeping prone could be due to rebreathing of expired gases. A mechanical model simulating the respiratory system of an infant, exhaling warm humidified air with an end tidal carbon dioxide of 5%, has been used to investigate this. Some commonly used bedding materials caused an accumulation of carbon dioxide of 7% to over 10% with the model lying face down. This phenomenon persisted even with the head inclined at 45 degrees, but only on very soft materials, and could be a cause of cot death in a baby unresponsive to asphyxial blood gas changes. A coir fibre mattress allowed complete dispersal of exhalate as did a rubber sheet between any mattress and the covering sheet. [ABSTRACT FROM AUTHOR]

Background: The introduction of complementary foods during the first year of life influences the diversity of the gut microbiome. How this diversity affects immune development and health is unclear., Objective: This study evaluates the effect of consuming kūmara or kūmara with added banana powder (resistant starch) compared to a reference control at 4 months post randomization on the prevalence of respiratory tract infections and the development of the gut microbiome., Methods: This study is a double-blind, randomized controlled trial of mothers and their 6-month-old infants (up to n=300) who have not yet started solids. Infants are randomized into one of 3 groups: control arm (C), standard kūmara intervention (K), and a kūmara intervention with added banana powder product (K+) to be consumed daily for 4 months until the infant is approximately 10 months old. Infants are matched for sex using stratified randomization. Data are collected at baseline (prior to commencing solid food) and at 2 and 4 months after commencing solid food (at around 8 and 10 months of age). Data and samples collected at each timepoint include weight and length, intervention adherence (months 2 and 4), illness and medication history, dietary intake (months 2 and 4), sleep (diary and actigraphy), maternal dietary intake, breast milk, feces (baseline and 4 months), and blood samples (baseline and 4 months)., Results: The trial was approved by the Health and Disability Ethics Committee of the Ministry of Health, New Zealand (reference 20/NTA/9). Recruitment and data collection did not commence until January 2022 due to the COVID-19 pandemic. Data collection and analyses are expected to conclude in January 2024 and early 2025, respectively. Results are to be published in 2024 and 2025., Conclusions: The results of this study will help us understand how the introduction of a specific prebiotic complementary food affects the microbiota and relative abundances of the microbial species, the modulation of immune development, and infant health. It will contribute to the expanding body of research that aims to deepen our understanding of the connections between nutrition, gut microbiota, and early-life postnatal health., Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12620000026921; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378654., International Registered Report Identifier (irrid): DERR1-10.2196/56772., (©Clare R Wall, Nicole C Roy, Jane A Mullaney, Warren Charles McNabb, Olivier Gasser, Karl Fraser, Eric Altermann, Wayne Young, Janine Cooney, Robyn Lawrence, Yannan Jiang, Barbara C Galland, Xiaoxi Fu, Jacqueline N Tonkie, Nisha Mahawar, Amy Luisa Lovell. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 02.09.2024.)