Search

Aims: To compare the effects of prone and supine sleep position on the main physiological responses to mild asphyxia: increase in ventilation and arousal.Methods: Ventilatory and arousal responses to mild asphyxia (hypercapnia/hypoxia) were measured in 53 healthy infants at newborn and 3 months of age, during quiet sleep (QS) and active sleep (AS), and in supine and prone sleep positions. The asphyxial test mimicked face down rebreathing by slowly altering the inspired air: CO(2), maximum 5% and O(2), minimum 13.5%. The change in ventilation with inspired CO(2) was measured over 5-6 minutes of the test. The slope of a linear curve fit relating inspired CO(2) to the logarithm of ventilation was taken as a quantitative measure of ventilatory asphyxial sensitivity (VAS). Sleep state and arousal were determined by behavioural criteria.Results: At 3 months of age, prone positioning in AS lowered VAS (0.184 prone v 0.269 supine, p = 0.050). At newborn age, sleep position had no effect on VAS. Infants aged 3 months were twice as likely to arouse to the test than newborns (p = 0.013). Placing infants prone as opposed to supine increased the chances of arousal 1.57-fold (p = 0.035).Conclusion: Our findings show 3 month old babies sleeping prone compared to supine have poorer ventilatory responses to mild asphyxia, particularly in AS, but the increased prevalence of arousal is a protective factor. [ABSTRACT FROM AUTHOR]

OBJECTIVES: We aimed to provide a quantitative analysis of the sleep arrangements and behaviors of bed-sharing families to further understand the risks and benefits as well as the effects of infant age and room temperature on bed-sharing behaviors. METHODS: Forty infants who regularly bed shared with > or = 1 parent > or = 5 hours per night were recruited. Overnight video of the family and physiological monitoring of the infant was conducted in infants' homes. Infant sleep position, potential for exposure to expired air, head covering and uncovering, breastfeeding, movements, family sleep arrangements, responses to the infant, and interactions were logged. RESULTS: All infants slept with their mother. Fathers were included in 18 studies and siblings in 4. Infants usually slept beside the mother, separated from the father/siblings (if present), facing the mother, with head at mothers' breast level, touching, or with mother cradling. Median overnight breastfeeding duration was 40.5 minutes. Mothers commonly faced their infant, but infants were rarely in a position that potentially exposed them to maternal expired air. Fathers were seldom in contact with the infant during sleep. Of the 102 head-covering episodes observed in 22 infants, 80% were because of changes in adult sleep position. Sixty-eight percent of head uncovering was facilitated by the mother; half of these events were prompted by the infant. A 1 degree C increase in room temperature decreased infant head covering by 0.2 hours. CONCLUSIONS: The mother-infant relationship is of prime importance during bed sharing, whether the father is present or not. The focus around breastfeeding often dictates the sleep position of the infant and mother, though room temperature may also influence this. In colder rooms infants tend to spend more time with their face covered by bedding. Frequent maternal interactions rely on the ability of the mother to arouse with little stimulation. Mothers, perhaps impaired by alcohol, smoking, or overtiredness, may not be able to respond appropriately. [ABSTRACT FROM AUTHOR]

Aim: To determine any variation in the respiratory responses to hypoxia/hypercapnia of infants born small for gestational age (SGA) to smoking and to non-smoking mothers.Methods: A total of 70 average for gestational age (AGA) infants (>36 weeks gestation, >2500 g, >25th centile for gestational age, and no maternal smoking), and 47 SGA infants (<10th centile for gestational age) were studied at 1 and 3 months of age, in quiet and active sleep. Respiratory test gases were delivered through a Perspex hood to simulate face down rebreathing by slowly allowing the inspired air to be altered to a CO(2) maximum of 5% and O(2) minimum of 13.5%. The change in ventilation with inspired CO(2) was measured over 5-6 minutes of the test. The slope of a linear curve fit relating inspired CO(2) to the logarithm of ventilation was taken as a quantitative measure of ventilatory asphyxial sensitivity (VAS).Results: There was no significant difference in VAS between the AGA and SGA infants (0.25 v 0.24). However within the SGA group, VAS was significantly higher (p = 0.048) in the infants whose mothers smoked during pregnancy (0.26 (0.01); n = 24) than in those that did not (0.23 (0.01); n = 23). The change in minute ventilation was significantly higher in the smokers than the non-smokers group (141% v 119%; p = 0.03) as the result of a significantly larger change in respiratory rate (8 v 4 breaths/min; p = 0.047) but not tidal volume.Conclusions: Maternal smoking appears to be the key factor in enhancing infants' respiratory responses to hypoxia/hypercapnia, irrespective of gestational age. [ABSTRACT FROM AUTHOR]

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.

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 whānau (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 whānau, 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% Māori, 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 Māori 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 whānau with young children., Competing Interests: Declaration of conflicts of interest None., (Copyright © 2024 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

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.)

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., (© 2024. The Author(s).)

Background: Actigraphy is often used to measure sleep in pediatric populations, despite little confirmatory evidence of the accuracy of existing sleep/wake algorithms. The aim of this study was to determine the performance of 11 sleep algorithms in relation to overnight polysomnography in children and adolescents., Methods: One hundred thirty-seven participants aged 8-16 years wore two Actigraph wGT3X-BT (wrist, waist) and three Axivity AX3 (wrist, back, thigh) accelerometers over 24-h. Gold standard measures of sleep were obtained using polysomnography (PSG; Embletta MPRPG, ST + Proxy and TX Proxy) in the home environment, overnight. Epoch by epoch comparisons of the Sadeh (two algorithms), Cole-Kripke (three algorithms), Tudor-Locke (four algorithms), Count-Scaled (CS), and HDCZA algorithms were undertaken. Mean differences from PSG values were calculated for various sleep outcomes., Results: Overall, sensitivities were high (mean ± SD: 91.8%, ± 5.6%) and specificities moderate (63.8% ± 13.8%), with the HDCZA algorithm performing the best overall in terms of specificity (87.5% ± 1.3%) and accuracy (86.4% ± 0.9%). Sleep outcome measures were more accurately measured by devices worn at the wrist than the hip, thigh or lower back, with the exception of sleep efficiency where the reverse was true. The CS algorithm provided consistently accurate measures of sleep onset: the mean (95%CI) difference at the wrist with Axivity was 2 min (-6; -14,) and the offset was 10 min (5, -19). Several algorithms provided accurate measures of sleep quantity at the wrist, showing differences with PSG of just 1-18 min a night for sleep period time and 5-22 min for total sleep time. Accuracy was generally higher for sleep efficiency than for frequency of night wakings or wake after sleep onset. The CS algorithm was more accurate at assessing sleep period time, with narrower 95% limits of agreement compared to the HDCZA (CS:-165 to 172 min; HDCZA: -212 to 250 min)., Conclusion: Although the performance of existing count-based sleep algorithms varies markedly, wrist-worn devices provide more accurate measures of most sleep measures compared to other sites. Overall, the HDZCA algorithm showed the greatest accuracy, although the most appropriate algorithm depends on the sleep measure of focus., (© 2024. The Author(s).)

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., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Advanced hybrid closed loop (AHCL) systems have the potential to improve glycemia and reduce burden for people with type 1 diabetes (T1D). Children and youth, who are at particular risk for out-of-target glycemia, may have the most to gain from AHCL. However, no randomized controlled trial (RCT) specifically targeting this age group with very high HbA 1c has previously been attempted. Therefore, the CO-PILOT trial (Closed lOoP In chiLdren and yOuth with Type 1 diabetes and high-risk glycemic control) aims to evaluate the efficacy and safety of AHCL in this group., Methods: A prospective, multicenter, parallel-group, open-label RCT, comparing MiniMed™ 780G AHCL to standard care (multiple daily injections or continuous subcutaneous insulin infusion). Eighty participants aged 7-25 years with T1D, a current HbA 1c  ≥ 8.5% (69 mmol/mol), and naïve to automated insulin delivery will be randomly allocated to AHCL or control (standard care) for 13 weeks. The primary outcome is change in HbA 1c between baseline and 13 weeks. Secondary outcomes include standard continuous glucose monitor glycemic metrics, psychosocial factors, sleep, platform performance, safety, and user experience. This RCT will be followed by a continuation phase where the control arm crosses over to AHCL and all participants use AHCL for a further 39 weeks to assess longer term outcomes., Conclusion: This study will evaluate the efficacy and safety of AHCL in this population and has the potential to demonstrate that AHCL is the gold standard for children and youth with T1D experiencing out-of-target glucose control and considerable diabetes burden., Trial Registration: This trial was prospectively registered with the Australian New Zealand Clinical Trials Registry on 14 November 2022 (ACTRN12622001454763) and the World Health Organization International Clinical Trials Registry Platform (Universal Trial Number U1111-1284-8452)., Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-024-01397-4., Competing Interests: Conflict of interestThe diabetes technology used in this study will be provided by Medtronic, in addition to partial financial support for some aspects of staffing related to study conduct. Authors BJW, MIdB, and RGP have received past speaker honoraria and compensation for travel costs from Medtronic., (© The Author(s) 2024.)

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., (Copyright © 2023 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

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/m 2 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., (© 2023 The Authors. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society.)

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.

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., (© 2022 The Authors. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society.)

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., (Copyright © 2022 American Society for Nutrition. All rights reserved.)

Purpose: The OPTIMISE study uses a Multiphase Optimisation Strategy (MOST) to identify the best combination of four interventions targeting key diabetes self-care behaviours for use in clinical practice to improve short-term glycaemic outcomes., Methods: This 4-week intervention trial will recruit 80 young people (aged 13-20 years) with type 1 diabetes ≥ 6 months duration), and pre-enrolment HbA1c ≥ 58 mmol/mol (7.5%) in the prior 6 months. Both main intervention and interaction effects will be estimated using a linear regression model with change in glucose time-in-range (TIR; 3.9-10.0 mmol/L) as the primary outcome. Participants will be randomised to one of 16 conditions in a factorial design using four intervention components: (1) real-time continuous glucose monitoring (CGM), (2) targeted snacking education, (3) individualised sleep extension, and (4) values-guided self-care goal setting. Baseline and post-intervention glucose TIR will be assessed with blinded CGM. Changes in self-care (snacking behaviours, sleep habits and duration, and psychosocial outcomes) will be assessed at baseline and post-intervention to determine if these interventions impacted behaviour change., Discussion: The study outcomes will enable the selection of effective and efficient intervention components that increase glucose TIR in young people who struggle to achieve targets for glycaemic control. The optimised intervention will be evaluated in a future randomised controlled trial and guide the planning of effective clinical interventions in adolescents and young adults living with type 1 diabetes., Trial Registration: This trial was prospectively registered with the Australian New Zealand Clinical Trials Registry on 7 October 2020 (ACTRN12620001017910) and the World Health Organisation International Clinical Trails Registry Platform on 26 July 2020 (Universal Trial Number WHO U1111-1256-1248)., Competing Interests: Conflict of interestThe authors have no conflicts of interest to declare that are relevant to the content of this article., (© The Author(s) 2022.)

Background: Continuous glucose monitoring (CGM) decreases fear of hypoglycemia (FOH) and improves glycemic control among those affected by type 1 diabetes (T1D). No studies to date have examined the impact of using do-it-yourself real-time continuous glucose monitoring (DIY RT-CGM) on psychological and glycemic outcomes., Methods: Child-parent dyads were recruited for a multicentre randomized crossover trial. Children with T1D were current intermittently scanned CGM (isCGM) users and aged 2-13 years. Families received either 6 weeks of DIY RT-CGM with parental remote monitoring (intervention) or 6 weeks of isCGM plus usual diabetes care (control), followed by a 4-week washout period, then crossed over. The primary outcome was parental FOH. Secondary outcomes were glycemic control using traditional CGM metrics, as well as a range of other psychosocial measures., Findings: Fifty five child-parent dyads were recruited. The child mean age was 9.1 ± 2.8 years. Although, there was no effect on parental FOH, -0.1 (95%CI: -0.3, 0.1, p = 0.4), time-in-range (TIR) (%3.9-10 mmol/L) was significantly higher with DIY RT-CGM over isCGM (54.3% ± 13.7 vs. 48.1% ± 13.6), mean difference, 5.7% (95%CI 1.8, 9.6, p <0.004). There was no difference for time spent in hypoglycemia. Parent diabetes treatment satisfaction was significantly higher following DIY RT-CGM compared to isCGM, mean difference 5.3 (95%CI: 2.3, 8.2, p <0.001)., Conclusion: The use of DIY RT-CGM versus isCGM did not improve parental FOH; however, TIR and parental satisfaction with diabetes treatment were significantly improved. This suggests in the short term, DIY RT-CGM appears safe and may offer families some clinically important advantages over isCGM., (© 2022 The Authors. Pediatric Diabetes published by John Wiley & Sons Ltd.)

Aims: To describe the impact of a 12-month intervention using intermittently scanned continuous glucose monitoring (isCGM) on glycaemic control and glucose test frequency in adolescents and young adults with type 1 diabetes (T1D) and high-risk glycaemic control (HbA 1c ≥75 mmol/mol [≥9.0%])., Methods: In total, 64 young people (aged 13-20 years, 16.6 ± 2.1 years; 48% female; 41% Māori or Pacific ethnicity; mean diabetes duration 7.5 ± 3.8 years) with T1D were enrolled in a 6-month, randomized, parallel-group study comparing glycaemic outcomes from the isCGM intervention (n = 33) to self monitoring blood glucose (SMBG) controls (n = 31). In this 6-month extension phase, both groups received isCGM; HbA 1c , glucose time-in-range (TIR), and combined glucose test frequency were assessed at 9 and 12 months., Results: At 12 months, the mean difference in HbA 1c from baseline was -4 mmol/mol [-0.4%] (95% confidence interval, CI: -8, 1 mmol/mol [-0.8, 0.1%]; p = 0.14) in the isCGM intervention group, and -7 mmol/mol [-0.7%] (95% CI: -16, 1 mmol/mol [-1.5, 0.1%]; p = 0.08) in the SMBG control group. No participants achieved ≥70% glucose TIR (3.9-10.0 mmol/L). The isCGM intervention group mean rate of daily glucose testing was highest at 9 months, 2.4 times baseline rates (p < 0.001), then returned to baseline by 12 months (incidence rate ratio = 1.4; 95% CI: 0.9, 2.1; p = 0.091)., Conclusions: The use of isCGM in young people with high-risk T1D resulted in transient improvements in HbA 1c and glucose monitoring over a 9-month time frame; however, benefits were not sustained to 12 months., (© 2021 Diabetes UK.)

Introduction: Despite being an important period for the development of movement behaviours (physical activity, sedentary behaviour and sleep), few interventions commencing prior to preschool have been trialled. The primary aim of this trial is to assess the 12-month efficacy of the Let's Grow mHealth intervention, designed to improve the composition of movement behaviours in children from 2 years of age. Let's Grow is novel in considering composition of movement behaviours as the primary outcome, using non-linear dynamical approaches for intervention delivery, and incorporating planning for real-world implementation and scale-up from its inception., Methods and Analysis: A randomised controlled trial will test the effects of the 12-month parental support mHealth intervention, Let's Grow , compared with a control group that will receive usual care plus electronic newsletters on unrelated topics for cohort retention. Let's Grow will be delivered via a purpose-designed mobile web application with linked SMS notifications. Intervention content includes general and movement-behaviour specific parenting advice and incorporates established behaviour change techniques. Intervention adherence will be monitored by app usage data. Data will be collected from participants using 24-hour monitoring of movement behaviours and parent report at baseline (T 0 ), mid-intervention (T 1 ; 6 months post baseline), at intervention conclusion (T 2 ; 12 months post baseline) and 1-year post intervention (T 3 ; 2 years post baseline). The trial aims to recruit 1100 families from across Australia during 2021. In addition to assessment of efficacy, an economic evaluation and prospective scalability evaluation will be conducted., Ethics and Dissemination: The study was approved by the Deakin University Human Ethics Committee (2020-077). Study findings will be disseminated through publication in peer-reviewed journals, presentation at scientific and professional conferences, and via social and traditional media., Trial Registration Number: ACTRN12620001280998; U1111-1252-0599., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Adenotonsillar hypertrophy is the main cause of childhood sleep disordered breathing (SDB) and adenotonsillectomy (TA) the most common treatment. Polysomnography (PSG) for diagnosing SDB is often difficult to obtain with Otolaryngologists usually relying on history and examination when recommending TA. Questionnaires assessing quality of life (QoL) may assist the Otolaryngologists decision making., Aims: To explore changes in QoL tools following TA for SDB in children aged 3 to 15 with the aim of identifying whether the Pediatric Sleep Questionnaire (PSQ) or Obstructive Sleep Apnoea -18 (OSA-18) is a better predictor of outcome following TA., Methods: QoL was assessed using OSA-18, PSQ and the Pediatric Quality of Life Inventory™ (PedsQL™). Four groups were recruited from three research databases, those with: SDB, recurrent tonsillitis (RT), SDB and RT, or no disease (controls). Children either received TA or underwent observation. QoL questionnaires were administered at recruitment and 3 months later. Test-retest reliability was assessed using Bland-Altman plots. Pre-intervention scores were plotted against changes in scores, with pre-established cut-offs and cut-offs indicated by control group variability., Results: There were 120 children, 25 had no intervention, and 19 were controls. All questionnaires showed test-retest reliability over time. Using the distribution of scores from the control group we estimated the 95th percentile to redefine the cut-off for OSA-18 (reduced from 60 to 46) and PSQ (unchanged from 0.33). Higher pre-operative scores predicted greater reduction following TA, with OSA-18 the most consistent predictor of QoL change. The PSQ classified 86.8% of children undergoing TA above the 0.33 cut-off; whereas OSA-18 classified 73.7% above the 46 cut-off. Of these, 71.2% and 87.5% showed improvement after TA, respectively. Using the 95% confidence interval for change in the control group to identify a 'meaningful' change in score, children with OSA-18 scores >46 had a 93% chance of a meaningful improvement, whereas PSQ scores >0.33 were associated with an 80% chance of a meaningful improvement., Conclusions: OSA-18 is a better predictor of improved QoL than PSQ for TA in children with SDB. We propose a new cut off score (>46) for OSA-18. This may assist Otolaryngologists' decision making when assessing a child with SDB., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: While insufficient sleep duration has emerged as a strong, independent risk factor for obesity, the mechanisms remain unclear. One possibility is greater "eating in the absence of hunger" (EAH) or energy intake beyond the point of satiety, when tired., Objective: The aim was to determine whether mild sleep loss increases EAH in children., Methods: A crossover study was undertaken in 105 healthy children (8-12 y) with normal sleep (∼8-11 h/night). After randomization, children went to bed 1 h earlier (sleep extension) or 1 h later (sleep restriction) than their usual bedtime, over 2 intervention weeks separated by a 1-wk washout. At the end of each intervention week, children underwent an EAH feeding experiment involving a preloading meal until satiation, followed by an ad libitum buffet (of highly palatable snacks) to measure EAH, with each food item weighed before and after consumption., Results: Ninety-three children completed the EAH experiment. There was no evidence of a difference in energy intake from EAH between sleep restriction and extension conditions when analyzed as a crossover design. However, a learning effect was found, with children eating significantly less (-239 kJ; 95% CI: -437, -41 kJ; P = 0.018) during the preload phase and significantly more (181 kJ; 95% CI: 38, 322 kJ; P = 0.013) in the ad libitum phase in the second week. No significant differences were seen using an underpowered parallel analysis for energy intake during the ad libitum phase when sleep deprived (106 kJ; 95% CI: -217, 431 kJ; P = 0.514)., Conclusions: Our findings suggest that measuring a difference in eating behavior in relation to sleep proved unsuitable using the EAH experiment in a crossover design in children, due to a learning effect. This trial was registered at the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367587&isReview=true) as ACTRN12618001671257 ., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

This review investigated whether randomised controlled trials attempting to improve sleep or prevent sleep problems in 0-5 year olds influenced nocturnal sleep duration, day-time naps, or 24-h sleep. Medline (Ovid), EMBASE, and CINAHL were searched from inception until 9 July 2020 and supplemented with hand searching. Search results were screened, eligible data were extracted, and risk of bias was assessed by at least two reviewers. Of 8571 publications considered, 32 trials which used a variety of subjective and objective sleep measurements were included in generic inverse variance random effects meta-analysis of nocturnal (n = 24), day-time (n = 14), and 24-h (n = 13) sleep duration. Overall, sleep interventions increased nocturnal sleep duration by a mean of 9 min (95% CI 4.1 to 13.8, I 2 28%) per night when compared with no sleep intervention. Increases were predominantly seen in sleep-only, rather than multi-component interventions. Total 24-h sleep duration tended to increase by a similar amount (8.6 min (95% CI -2.7 to 19.8, I 2  = 59%)), but this was mainly only seen in studies that assessed sleep using diaries. There was no evidence that interventions changed day-time sleep duration. Future studies should involve sleep-only rather than multi-component interventions, and use objective sleep measures (reviewregistry857)., Competing Interests: Conflicts of interest The authors do not have any conflicts of interest to disclose., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Context: Although dietary advice has long been a cornerstone of a healthy lifestyle, how sleep quality and quantity may interact with dietary intake or eating behaviors remains unclear., Objective: To consider a bidirectional relationship between sleep and diet in children aged 6-12 years via a systematic review following PRISMA guidelines., Data Sources: Relevant trials and observational studies were identified by searching the PubMed, Medline, Embase, and CENTRAL databases up to June 1, 2019, without language or date restrictions and supplemented with hand searching. Recognized procedures and reporting standards were applied., Data Extraction: Data on participant characteristics, study parameters, diet measures, sleep measures, and findings of study quality assessment criteria were collected., Data Analysis: Forty-five articles involving 308 332 participants on a diverse range of topics were included. Meta-analyses were planned but were impossible to perform due to high study heterogeneity. Most studies (82%) were cross-sectional, which prevented examining directionality of the observed associations. Risk of bias was assessed for trial, cohort studies, and cross-sectional studies, using the Cochrane Risk of Bias Tool or Newcastle Ottawa Scale., Results: Of 16 studies in which the effect of sleep on dietary intake was investigated, 81% (n = 13) reported a significant association. All studies (n = 8) of sugar-sweetened or caffeinated beverages reported a negative association with sleep, and in 6 of 7 studies in which eating behaviors were investigated, associations with sleep were reported. The use of objective measures of sleep and diet were scarce, with most trials and studies relying on subjective measures of sleep (68%) or diet (93%)., Conclusion: Because most studies investigating the relationship between sleep and diet in this age group are cross-sectional, temporality could not be determined. Additional randomized controlled trials and long-term cohort studies in middle childhood, particularly those using objective rather than questionnaire measures of sleep, are required to better understand interactions between diet and sleep., Systematic Review Registration: Prospectively registered with PROSPERO International Prospective Register of Systematic Reviews (CRD42018091647)., (© The Author(s) 2021. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© 2021. The Author(s).)

Background: In type 1 diabetes mellitus (T1D), glycemic control and sleep have a bidirectional relationship, with unhealthy glycemic control impacting sleep, and inadequate sleep impacting diabetes management. Youth are at risk for poor quality sleep; however, little is known about sleep among youth with high-risk glycemic control., Objective: To assess differences in habitual sleep timing, duration, and quality among youth with T1D and controls., Subjects: Two-hundred-thirty youth (13-20 years): 64 with T1D (mean age 16.6 ± 2.1 years, 48% female, diabetes duration 7.5 ± 3.8 years, HbA1c 96 ± 18.0 mmol/mol [10.9 ± 1.7%]), and 166 controls (mean age 15.3 ± 1.5, 58% female)., Methods: Comparison of data from two concurrent studies (from the same community) using subjective and objective methods to assess sleep in youth: Pittsburgh Sleep Quality Index evaluating sleep timing and quality; 7-day actigraphy measuring habitual sleep patterns. Regression analyses were used to compare groups., Results: When adjusted for various confounding factors, youth with T1D reported later bedtimes (+36 min; p < 0.05) and shorter sleep duration (-53 min; p < 0.05) than controls, and were more likely to rate subjective sleep duration (OR 3.57; 95% CI 1.41-9.01), efficiency (OR 4.03; 95% CI 1.43-11.40), and quality (OR 2.59; 95% CI 1.16-5.76) as "poor" (p < 0.05). However, objectively measured sleep patterns were similar between the two groups., Conclusions: Youth with high-risk T1D experience sleep difficulties, with later bedtimes contributing to sleep deficit. Despite a lack of objective differences, they perceive their sleep quality to be worse than peers without diabetes., (© 2021 John Wiley & Sons A/S . Published by John Wiley & Sons Ltd.)

Background: The literature regarding flash glucose monitoring (FGM)-associated cutaneous adverse events (AE) is limited., Objectives: This study among youth participating in a 6 month randomized controlled trial aimed to compare cutaneous AE between FGM and self-monitored blood glucose (SMBG) use and evaluate premature FGM sensor loss., Methods: Patients aged 13 to 20 years with type 1 diabetes were randomized to intervention (FGM and usual care) or control (SMBG and usual care). Participants self-reported cutaneous AEs electronically every 14 days. Reports were analyzed to determine frequency, type, and severity of cutaneous AEs, and evaluate premature sensor loss., Results: Sixty-four participants were recruited; 33 randomized to FGM and 31 to control. In total, 80 cutaneous AEs were reported (40 in each group); however, the proportion of participants experiencing cutaneous AEs was greater in the FGM group compared to control (58% and 23% respectively, P = .004). FGM participants most frequently reported erythema (50% of AEs), while controls most commonly reported skin hardening (60% of AEs). For FGM users, 80.0% of cutaneous AEs were mild, 17.5% moderate, and 2.5% severe. Among controls, 82.5% of cutaneous AEs were mild and 17.5% moderate. One participant ceased using FGM due to recurring cutaneous AEs. Additionally, over 6 months, 82% of FGM participants experienced at least one premature sensor loss, largely unrelated to a cutaneous AE., Conclusions: Cutaneous FGM-associated AEs are common, and mostly rated as mild. However, the majority of users continued FGM despite cutaneous AEs. Awareness of cutaneous complications and mitigation measures may reduce cutaneous AEs and improve the overall experience of FGM., (© 2020 John Wiley & Sons A/S . Published by John Wiley & Sons Ltd.)

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., (©Taiwo O Adebowale, Barry J Taylor, Andrew R Gray, Barbara C Galland, Anne-Louise M Heath, Sarah Fortune, Kim A Meredith-Jones, Trudy Sullivan, Deborah McIntosh, Bradley Brosnan, Rachael W Taylor. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 30.11.2020.)

Aims: Although strategies to prevent premature sensor loss for flash glucose monitoring (FGM) systems may have substantial benefit, limited data are available. This study among youth with high-risk type 1 diabetes evaluated whether an additional adhesive patch over FGM sensors would reduce premature sensor loss frequency and not cause additional cutaneous adverse events (AEs)., Methods: This is a six-month, open-label, randomized crossover trial. Participants were recruited at completion of prior 'Managing Diabetes in a Flash' randomized controlled trial and allocated to three months of Freestyle Libre FGM sensors with either standard adhesive (control) or additional adhesive patches (RockaDex, New Zealand) (intervention), before crossing over to the opposite study arm. Participants self-reported patch use or non-use, premature sensor loss and cutaneous AEs fortnightly via an electronic questionnaire., Results: Thirty-four participants were enrolled: mean age (± SD) 17.0 (± 2.2) years; mean HbA1c (± SD) 89 (± 16) mmol/mol (10.3% ± 1.4%). The response rate of questionnaires was 77% (314/408). Premature sensor loss was reported in 18% (58/314) of questionnaires: 20% (32/162) from intervention and 17% (26/152) from control (p = 0.56). Thirty-eight percent (118/314) of questionnaires were non-compliant to protocol allocation. However, per-protocol analysis showed similar findings. No significant difference in AEs was reported between compliant adhesive patch use and non-use (6% [5/78] and 3% [3/118], respectively, p = 0.27)., Conclusions: The adhesive patch investigated in this study does not appear to prevent premature FGM sensor loss. However, the low risk of AEs and low cost of an adhesive patch suggest an individualized approach to their use may still be warranted. Further research is needed to explore alternative strategies to prevent sensor loss.

Objective: To investigate whether intermittently scanned continuous glucose monitoring (isCGM) significantly improves glycemic control compared with capillary self-monitored blood glucose (SMBG) in youth with type 1 diabetes and high-risk glycemic control., Research Design and Methods: This multicenter 6-month randomized, controlled, parallel-arm trial included 64 participants aged 13-20 years with established type 1 diabetes and glycated hemoglobin (HbA 1c ) ≥9% (≥75 mmol/mol). Participants were allocated to 6-month intervention (isCGM; FreeStyle Libre; Abbott Diabetes Care, Witney, U.K.) ( n = 33) or control (SMBG; n = 31) using minimization. The primary outcome was the difference in change in HbA 1c from baseline to 6 months., Results: There was no evidence of a difference between groups for changes in HbA 1c at 6 months (adjusted mean 0.2% greater improvement for isCGM [95% CI -0.9 to 0.5] [-2.1 mmol/mol (95% CI -9.6 to 5.4)]; P = 0.576). However, glucose-monitoring frequency was 2.83 (95% CI 1.72-4.65; P < 0.001) times higher in the isCGM group compared with that in the SMBG group at 6 months. The change in the Diabetes Treatment Satisfaction Questionnaire mean item score also favored isCGM at 6 months ( P = 0.048), with no significant differences between groups for fear of hypoglycemia and quality of life (both general and diabetes specific) (all P > 0.1)., Conclusions: For youth with high-risk glycemic control, isCGM led to improvements in glucose testing frequency and diabetes treatment satisfaction. However, these did not translate to greater improvement in glycemic control over usual care with SMBG at 6 months., (© 2020 by the American Diabetes Association.)

Background: Type 1 diabetes mellitus (T1DM) is a common chronic illness of childhood, with parents assuming considerable responsibility for night-time diabetes caregiving. This qualitative study explored diabetes-related factors affecting, and solutions proposed to improve, parental sleep., Participants: 10 mothers and 10 fathers of children ≤18 years of age with T1DM in Otago, New Zealand., Methods: Semi-structured individual interviews were audio-recorded, transcribed, and systematically coded for themes. Parents completed the Pittsburgh Sleep Quality Index (PSQI) and habitual sleep of parents and children were assessed via 7-day actigraphy., Results: Parents (n = 20) and their children with T1DM (n = 16) were aged between 32 and 54 years, and 1 and 17 years, respectively. PSQI revealed poor quality sleep in 13/20 parents. A range of diabetes-related factors, including glucose monitoring and fear of hypoglycemia, contributed to parental sleep disturbance, including awakenings and the perception of "sleeping lightly". Two distinct time periods resulted in greater sleep disturbance, notably, following T1DM diagnosis and when transitioning to using a new diabetes technology. Factors influencing maternal and paternal sleep were similar, but, generally, mothers described greater night-time care burden and sleep disturbance. While the use of diabetes technologies was generally advocated to improve parental sleep and the provision of nocturnal T1DM care, they were also perceived to potentially contribute to parental sleep disturbance., Conclusions: Pediatric diabetes care teams should be aware of diabetes-related factors potentially affecting parental sleep, the mixed impacts of diabetes technologies, and consider tailored parental support and education to reduce the burden of nocturnal care.

The gut microbiota harvests energy from indigestible plant polysaccharides, forming short-chain fatty acids (SCFAs) that are absorbed from the bowel. SCFAs provide energy-presumably after easily digested food components have been absorbed from the small intestine. Infant night waking is believed by many parents to be due to hunger. Our objective was to determine whether faecal SCFAs are associated with longer uninterrupted sleep in infants. Infants (n = 57) provided faecal samples for determining SCFAs (7 months of age), and questionnaire data for determining infant sleep (7 and 8 months). Linear regression determined associations between SCFAs-faecal acetate, propionate and butyrate-and sleep. For each 1% higher propionate at 7 months of age, the longest night sleep was 6 (95% CI: 1, 10) minutes longer at both 7 and 8 months. A higher proportion of total faecal SCFA as propionate was associated with longer uninterrupted infant sleep.

Purpose: This study aimed to investigate (1) access to screens and screen use in the hour before bedtime and in bed; (2) attitudes and barriers to reducing screen time, and (3) perceptions of potential interventions to reduce screen time to benefit the sleep of New Zealand adolescents., Methods: Adolescents (aged 13-17 years, n = 4,811) living in New Zealand were recruited online between November 2015 and March 2016 with a direct link to complete a questionnaire about screen use in the hour before bedtime (including the types of screens used and activities) and screen use when in bed. Sleep patterns and quality were measured, and attitudes to reducing screen time using potential interventions were explored., Results: The most common prebedtime screen activities reported on most nights were social media (88%) and texting/instant messaging (77%). The majority of participants reported they used phones in bed (86%) and "agreed" they spent too much time on screens (70%), a perception that increased with age (p = .008). Being unable to communicate with friends was the most common barrier to reducing screen time (67% agreed)., Conclusions: Screen use in adolescents is complex; however, there is scope to improve sleep by reducing the impact of in-bed use of portable devices and addressing barriers to reduce screen time in adolescents., (Copyright © 2020 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.)

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% Māori, 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.