Protocol for a prospective, multicenter, parallel-group, open-label randomized controlled trial comparing standard care with Closed lOoP In chiLdren and yOuth with Type 1 diabetes and high-risk glycemic control: the CO-PILOT trial.

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 HbA1c 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 HbA1c ≥ 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 HbA1c 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.

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

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

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

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

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

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

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

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

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

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

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

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

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.

The OPTIMISE study protocol: a multicentre optimisation trial comparing continuous glucose monitoring, snacking habits, sleep extension and values-guided self-care interventions to improve glucose time-in-range in young people (13-20 years) with type 1 diabetes.

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).

The effect of do-it-yourself real-time continuous glucose monitoring on psychological and glycemic variables in children with type 1 diabetes: A randomized crossover trial.

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.

Protocol for the Let's Grow randomised controlled trial: examining efficacy, cost-effectiveness and scalability of a m-Health intervention for movement behaviours in toddlers.

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 (T0), mid-intervention (T1; 6 months post baseline), at intervention conclusion (T2; 12 months post baseline) and 1-year post intervention (T3; 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.

Use of intermittently scanned continuous glucose monitoring in young people with high-risk type 1 diabetes-Extension phase outcomes following a 6-month randomized control trial.

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 (HbA1c ≥75 mmol/mol [≥9.0%]). METHODS: In total, 64 young people (aged 13-20 years, 16.6 ± 2.1 years; 48% female; 41% Maori 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; HbA1c , 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 HbA1c 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 HbA1c and glucose monitoring over a 9-month time frame; however, benefits were not sustained to 12 months.

Can sleep questionnaires predict adenotonsillectomy outcome for children with sleep disordered breathing?

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.

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

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

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

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

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

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

Do sleep interventions change sleep duration in children aged 0-5 years? A systematic review and meta-analysis of randomised controlled trials.

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, I228%) 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, I2 = 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).

Impact of high-risk glycemic control on habitual sleep patterns and sleep quality among youth (13-20 years) with type 1 diabetes mellitus compared to controls without diabetes.

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.