ActiGraph GT3X+ and Actical Wrist and Hip Worn Accelerometers for Sleep and Wake Indices in Young Children Using an Automated Algorithm: Validation With Polysomnography

Sleep disturbance associations between parents and children with overweight and obesity

This cross-sectional study examined sleep disturbance associations between parents and their school-age children with overweight and obesity. A 7-day wrist-worn actigraph recording was performed on 246 children aged 6-9 years with overweight and obesity recruited from 10 public elementary schools in Taipei, Taiwan. Children's sleep disturbance was assessed using the Children's Sleep Habits Questionnaire. Parental subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index, with parental depressive symptoms measured using the Epidemiologic Studies-Depression Scale. General linear models were used to examine sleep disturbance associations within parent-child dyads. The results showed that 208 (84.6%) children had a clinically significant sleep disturbance score, and 123 (50%) parents had poor sleep quality. Higher children's sleep disturbance scores significantly predicted poorer parental sleep quality (b = 0.11, p < 0.001). Poorer parental sleep quality was associated with more severe sleep disturbances in children (b = 0.46, p < 0.001). This association was independent of children's actigraphic sleep (all p > 0.05) and was not attenuated by adjustment for parental depressive symptoms (b = 0.14, p < 0.001). Findings from our study suggest that sleep disturbances occur in both parents and their school-age children with overweight and obesity, with a significant bidirectional association between the two. Nurses and healthcare professionals should proactively assess and screen for sleep disturbances in parent-child dyads of children with overweight and obesity. Future studies should develop family-based sleep interventions and evaluate their effects on the sleep, health, and well-being of children with overweight and obesity and their parents.

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.

Proportion and Correlates of Children in the US-Affiliated Pacific Region Meeting Sleep, Screen Time, and Physical Activity Guidelines

INTRODUCTION

Limited data on 24-hour movement behaviors of children aged 5-8 years exist globally. We describe the prevalence and sociodemographic associations of meeting physical activity (PA), sedentary recreational screen time (ST), and sleep guidelines among children from 11 jurisdictions in the US-Affiliated Pacific region.

METHODS

Cross-sectional representative data from 1192 children aged 5-8 years living in the US-Affiliated Pacific region were drawn from the baseline 2012-2014 Children's Healthy Living Program. Sleep and moderate- to vigorous-intensity PA were calculated from accelerometry. ST and sociodemographic data were collected from caregiver surveys. The percentage of children meeting the Asia-Pacific 24-hour movement guidelines for PA (≥60 min/d of moderate- to vigorous-intensity PA), sleep (≥9 and ≤ 11 h/d) and ST (≤2 h/d) were calculated. Generalized linear mixed models were used to examine associations with adiposity and sociodemographic variables.

RESULTS

Twenty-seven percent (95% confidence interval, 24.6-30.0) of children met integrated guidelines; 98% (96.2-98.0) met PA, 78% (75.4-80.0) met sleep, and 35% (32.6-38.0) met ST guidelines. Females (adjusted odds ratio = 1.40 [95% confidence interval, 1.03-1.91]) and those living in lower-middle-income jurisdictions (2.29 [1.49-3.54]) were more likely to meet ST guidelines. Overweight children (0.62 [0.40-0.96]), those aged 8 years (0.39 [0.22-0.69]), and children with caregivers of an education level of high school or beyond (0.44 [0.29-0.68]) were less likely to achieve ST guidelines. Children from midrange annual household incomes were less likely to meet combined guidelines (0.60 [0.39-0.92]).

CONCLUSIONS

Three-quarters of children are not meeting integrated Asia-Pacific 24-hour movement guidelines. Future strategies for reducing ST and increasing integrated guidelines compliance are needed.

International study of 24-h movement behaviors of the early years (SUNRISE): A pilot study from Iran

BACKGROUND

This study examined the proportion of Iranian children who met the World Health Organization (WHO) Guidelines for physical activity, sedentary behaviour and sleep for children under 5 years. Additionally, it investigated the feasibility and acceptability of the methods to be used in the SUNRISE study.

METHODS

This pilot study was conducted among 83 children aged 3 and 4 years in preschools and health care centres in Iran, in 2022. Physical activity, sedentary behaviour and sleep (ActiGraph wGT3x-BT); fine and gross motor skills (validated activities); and executive functions (the Early Years Toolbox) were assessed.

RESULTS

Only four (4.8%) children met all recommendations of the WHO guidelines. The proportion of children who met MVPA, TPA, screen time, restrained sitting and sleep were 44.6%, 38.6%, 19.3%, 38.6% and 65.1%, respectively. Fifty-two (62.6%) children wore the ActiGraph for at least three full days. A total of 97.6%, 95.1% and 91.5% of children completed anthropometric, EF and motor skill assessments, respectively.

CONCLUSION

This pilot study was feasible and acceptable among Iranian children. Regarding the low proportion of children who met the WHO guidelines, it is recommended that long-term and practical strategies be developed to promote healthier lifestyles among preschool children in Iran.

Validation of actigraphy sleep metrics in children aged 8 to 16 years: considerations for device type, placement and algorithms

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.

Evaluation of the association between excessive screen use, sleep patterns and behavioral and cognitive aspects in preschool population. A systematic review

In this review, we analyzed the possible relationship between the excessive use of screens and sleep patterns, and how this may affect certain behavioral and cognitive factors in preschool children. The selection, extraction and synthesis of the data were conducted according to PRISMA guidelines. The search was carried out in the electronic databases Medline (PubMed), PsycINFO (American Psychological Association), Scopus and Web of Science (WOS). Of the 597 articles initially identified, 13 met the inclusion criteria. The risk of bias of the articles selected was evaluated using a specific scale created for this purpose. The results found indicate that excessive use of screens is associated with a negative impact on the duration and quality of sleep-in preschoolers, and this worsening of sleep in infancy is related with a greater probability of the appearance of internalizing and externalizing behavioral problems and certain cognitive problems. The results also suggest that sleep could play a mediating or moderating role as a bioregulatory system that attenuates or increases the onset of behavioral and cognitive difficulties in those children most exposed to digital devices.

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.

Association of the timing of evening eating with BMI Z-score and waist-to-height ratio among preschool-aged children in Finland

Later timing of eating has been associated with higher adiposity among adults and children in several studies, but not all. Moreover, studies in younger children are scarce. Hence, this study investigated the associations of the timing of evening eating with BMI Z-score and waist-to-height ratio (WHtR), and whether these associations were moderated by chronotype among 627 preschoolers (3-6-year-olds) from the cross-sectional DAGIS survey in Finland. Food intake was measured with 3-d food records, and sleep was measured with hip-worn actigraphy. Three variables were formed to describe the timing of evening eating: (1) clock time of the last eating occasion (EO); (2) time between the last EO and sleep onset; and (3) percentage of total daily energy intake (%TDEI) consumed 2 h before sleep onset or later. Chronotype was assessed as a sleep debt-corrected midpoint of sleep on the weekend (actigraphy data). The data were analysed with adjusted linear mixed effects models. After adjusting for several confounders, the last EO occurring closer to sleep onset (estimate = -0·006, 95 % CI (-0·010, -0·001)) and higher %TDEI consumed before sleep onset (estimate = 0·0004, 95 % CI (0·00003, 0·0007)) were associated with higher WHtR. No associations with BMI Z-score were found after adjustments. Clock time of the last EO was not significantly associated with the outcomes, and no interactions with chronotype emerged. The results highlight the importance of studying the timing of eating relative to sleep timing instead of only as clock time.

A school-based physical activity intervention in primary school: effects on physical activity, sleep, aerobic fitness, and motor competence

Objective

The "Super Quinas" project evaluated the effectiveness of an intervention program to improve physical activity, aerobic fitness, sleep, and motor competence on children in primary school.

Methods

The experimental group (n = 19) enrolled in a 12-week intervention program (one more extra-curricular activity class of 60 min per week) compared to the CG (n = 19), all aged 9-10 years. Physical activity (PA) and sleep were measured by accelerometry, and aerobic fitness was measured by Children's Yo-Yo test (YYIR1C) during the 1st week (PRE), the 6th week (DUR), and the 12th week (POST) of the intervention program. Motor Competence in PRE and POST intervention was also assessed by the Motor Competence Assessment (MCA) instrument. Heart rate (HR, assessed using HR monitors), and enjoyment level were recorded during all intervention program classes. A linear mixed model analysis (i.e., within-subject analyses) was performed.

Results

Comparing the EG and CG in DUR and POST, the EG spent ~18 min and ~ 34 min more time in moderate to vigorous physical activity (MVPA) per day (p < 0.001); had ~44 min and ~ 203 min less sedentary time per day (p < 0.001); performed more 44 and 128 m in the Children's Yo-Yo test compared to CG (p < 0.001) and slept more 17 and 114 min per night (p < 0.001). In POST motor competence was significantly better (27%) in the EG compared to CG (p < 0.001). The %HRmax during the extra-curricular classes ranged between 65 and 81% (i.e., light to moderate intensities), and the enjoyment between fun and great fun.

Conclusion

Our findings suggest that adding one more extra-curricular activity class of 60 min per week for 12 weeks effectively increased the levels of physical activity, aerobic fitness, sleep duration, and motor competence in children aged 9-10 years.

Effects of exercise on sleep in children with overweight/obesity: a randomized clinical trial

OBJECTIVE

The objective of this study was to examine the chronic effects of a 20-week exercise training program on device-assessed sleep and sleep-disordered breathing; and to determine whether participating in a session of the exercise program had effects on device-assessed sleep the subsequent night in children with overweight/obesity.

METHODS

A randomized clinical trial was conducted from November 2014 to June 2016. A total of 109 children (age 8-11 years) with overweight/obesity were randomized into an exercise training or control group. The exercise program included aerobic and resistance training 3 to 5 days/week. The control group participants continued their usual lifestyle. Device-assessed sleep outcomes were measured using wrist-worn actigraphy at baseline, in the middle of the exercise program (10th week), and at postintervention for seven consecutive days (24 h/day), and sleep-disordered breathing was measured via the Pediatric Sleep Questionnaire.

RESULTS

The exercise training program had a statistically significant effect on wake after sleep onset time (-10.8 min/day, -0.5 SDs, p = 0.040). No other chronic or acute effects (i.e., the subsequent night of attending a session of the exercise training program) were observed on the remaining sleep outcomes.

CONCLUSIONS

A 20-week exercise training program reduced wake after sleep onset time in children with overweight/obesity. Future randomized trials that include a sample of children with poor sleep health at baseline are needed to better appreciate the role of exercise in sleep health.

Obesogenic sleep patterns among Chinese preschool children: A latent profile and transition analysis of the association sleep patterns and obesity risk

OBJECTIVE

This paper utilized a person-centered approach to examine whether sleep patterns on school and free days are associated with obesity risk in preschool children aged 3-6 years.

METHODS

The cross-sectional analysis included 204 children from the Wuhan Healthy Start Project with valid sleep data in at least four consecutive days gathered via Actigraph GT3X+. Based on three domains of sleep duration, sleep onset, and sleep offset, we used latent profile analysis to identify distinct sleep patterns on school and free days separately. Additionally, we conducted latent transition analysis to explore the probabilities of sleep patterns transitions between school and free days. The multivariate logistic regression model investigated the associations of sleep patterns with overweight/obesity (OWO) (BMI ≥ age- and sex-specific 85th percentile) and abdominal obesity (AO) (WC ≥ age- and sex-specific 75th percentile).

RESULTS

Two sleep patterns were identified for school days: "EL-sc" (early-to-sleep/longer-duration) (n = 119; 58.3%) and "LS-sc" (late-to-sleep/shorter-duration) (n = 85; 41.7%). Similarly, "LES-fr" (late-to-sleep/early-to-wake/shorter-duration) (n = 118; 57.8%) and "ELL-fr" (early-to-sleep/late-to-wake/longer-duration) (n = 86; 42.2%) patterns were identified for free days. LTA categorized the participants into four distinct transition groups, i.e., "EL-sc→ELL-fr" (32.9%), "EL-sc→LES-fr" (24.0%), "LS-sc→LES-fr" (33.8%), and "LS-sc→ELL-fr" (9.3%). Compared with the "ELsc→ELL-fr", the "LS-sc→LES-fr" had a higher risk of OWO (AOR 4.76; 95% CI: 1.39-20.33) and AO (AOR, 2.78; 95% CI, 1.21-6.62), respectively. Neither "EL-sc→LES-fr" (AOR, 1.11; 95% CI, 0.14-6.67) nor "LS-sc→ELL-fr" (AOR, 0.74; 95% CI, 0.03-6.14) was significantly associated with OWO. Likewise, no significant association was observed for "EL-sc→LES-fr" (AOR, 0.96; 95% CI, 0.35-2.62) and "LS-sc→ELL-fr" (AOR, 0.56; 95% CI, 0.11-2.18) with AO.

CONCLUSIONS

"LS-sc→LES-fr" pattern is significantly associated with an increased risk of general and abdominal obesity, indicating its obesogenic nature. Furthermore, although not statistically associated with obesity outcomes, "LS-sc→ELL-fr" and "EL-sc→LES-fr" patterns exhibit a semi-obesogenic characteristic. In addition, we identified a concerning trend that preschool children are at risk of transitioning to and persisting in sleep patterns characterized by delayed and shorter sleep. These findings underscore the importance of implementing interventions and strategies to address sleep patterns as a crucial step to minimize the risk of obesity.

A comparative study of different sleep assessment methods for preschool children

PURPOSE

This study aimed to examine the differences between different sleep assessment methods for preschool children.

METHODS

Preschool children (n = 54, mean age: 4.6 years) were recruited from kindergarten. Data were collected using an accelerometer, a sleep log, and sleep questionnaire. Furthermore, correlation analysis, Bland-Altman analysis, and repeated measures ANOVA were performed.

RESULTS

(1) The sleep duration evaluated by different sleep assessment methods were all significantly correlated, among which the sleep log and Sadeh algorithm showed the highest correlation (r = 0.972, p < .001), while the Tudor-Locke algorithm and the sleep questionnaire demonstrated the lowest correlation (r = 0.383, p < .01); (2) The points between different sleep assessment methods were all within 95% LoA, except for the sleep log and Tudor-Locke algorithm; (3) In various methods of sleep assessment, significant differences were observed in sleep onset (F2 (1.6,85.0)  = 32.8, p < .001, η2 : 0.38), while no significant differences were observed in sleep offset (F2 (1.5,80.1)  = 32.8, p = .05, η2 : 0.05); (4) In addition, no significant difference in sleep onset was observed between the sleep questionnaire and sleep log (p > .05), and there was also no significant difference in sleep onset between the Sadeh algorithm and the Tudor-Locke algorithm (p > .05).

CONCLUSIONS

Both the Sadeh algorithm and the Tudor-Locke algorithm can be used as effective algorithms for sleep duration assessment of Chinese preschool children, with the latter having obvious advantages in large sample surveys. Future research should pay attention to the differences between different sleep assessment methods when using these algorithms.

A Comparison of Sleep Duration Accuracy Between Questionnaire and Accelerometer in Middle Childhood

Purpose Healthy sleep is vital to children's well-being, and assessing sleep efficiently and accurately can help understand children's lifestyles. Due to the difficulty in objectively measuring sleep duration using wearable sensors in large-scale surveys of children, self-administered questionnaires are often used in Japan; however, their accuracy is uncertain. We evaluated and compared the accuracy of questionnaire-based sleep times to those of wearable sensors. Methods This observational study was conducted between November 2019 and January 2020. A self-administered questionnaire on lifestyle habits and ActiGraph GT3X+ (ActiGraph, Inc., Pensacola, USA) accelerometer data were collected from 40 fourth-grade elementary school students in Kagawa Prefecture, Japan. We analyzed measurements for 256 days out of 280 days (40 persons × 7 days) after excluding days when the rate of wearing the accelerometer was < 90%. Results The median sleep duration per accelerometry was 453 minutes, and the median time in bed was 519 minutes. Questionnaire-based time in bed was 11 minutes longer, with relatively high inter-individual variability. The difference in bedtime was 26 minutes earlier, and wake-up time was 12 minutes earlier for the questionnaire. The average sleep efficiency was 87.4%, and one-third of the children had sleep efficiency < 85%. Conclusion The difference in sleep duration by questionnaire compared to accelerometry was approximately 10 minutes, suggesting the questionnaire may determine sleep duration with accuracy.

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.

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.

Associations Between Sleep and Metabolic Outcomes in Preadolescent Children

Context

Growing evidence suggests an important role for sleep for the metabolic health of children.

Objective

We aimed to determine how sleep is related to insulin sensitivity, insulin secretion, beta-cell function, and adiposity (BMI z-scores, body fat %, waist to height ratio) using objectively measured sleep and oral glucose tolerance test (OGTT)-derived measures.

Methods

Sixty-two children aged 7-11 years, born at Kaiser Permanente Southern California, wore wrist accelerometers for 7 days to objectively measure sleep, completed an OGTT, and had anthropometric measures (height [cm], weight [kg], waist [cm], body fat [%]) collected. Using linear regression, associations between Matsuda insulin sensitivity index (ISI), insulinogenic index (IGI), disposition index (DI), BMI z-score, waist to height ratio, and body fat % with sleep parameters [total sleep time (TST; min), sleep efficiency (SE; %), time in bed (TIB; min), wake after sleep onset (WASO; min), and sleep latency (SL; min)] were assessed. Body fat % was tested as a mediator of the relationship between TST and ISI.

Results

Longer TST was associated with better insulin sensitivity (P = 0.02), but not after adjusting for body fat %. Sleep parameters were not associated with IGI or DI. Longer TST was associated with lower % body fat (P = 0.01) and lower waist-to-height-ratios (P = 0.05). Body fat % explained 62% (P = 0.01) of the relationship between TST and ISI. Longer TIB was associated with lower adiposity measures (P < 0.05). There were no associations between SE, WASO, or SL and metabolic outcomes.

Conclusion

Objectively measured sleep duration was associated with lower adiposity, and the relationship between sleep duration and ISI appeared partly through adiposity levels in preadolescent children. Longer sleep duration may be important for metabolic health.

Impact of COVID-19 Pandemic on School-Aged Children's Physical Activity, Screen Time, and Sleep in Hong Kong: A Cross-Sectional Repeated Measures Study

Despite concerns about the negative effects of social distancing and prolonged school closures on children's lifestyle and physical activity (PA) during the COVID-19 pandemic, robust evidence is lacking on the impact of the pandemic-related school closures and social distancing on children's wellbeing and daily life. This study aimed to examine changes in the PA levels, sleep patterns, and screen time of school-aged children during the different phases of the COVID-19 outbreak in Hong Kong using a repeated cross-sectional design. School students (grades 1 to 12) were asked to report their daily electronic device usage and to fill in a sleep diary, recording their daily sleep onset and wake-up time. They were equipped with a PA monitor, Actigraph wGT3X-BT, to obtain objective data on their PA levels and sleep patterns. Students were recruited before the pandemic (September 2019-January 2020; n = 577), during school closures (March 2020-April 2020; n = 146), and after schools partially reopened (October 2020-July 2021; n = 227). Our results indicated lower PA levels, longer sleep duration, and longer screen time among participants recruited during school closures than those recruited before the COVID-19 outbreak. Primary school students were found to sleep on average for an extra hour during school closures. The later sleep onset and increased screen time documented during school closures persisted when schools partially reopened. Our findings illustrate the significant impact of social distancing policies during the COVID-19 pandemic on the sleep pattern, screen time, and PA level in school-aged children in Hong Kong. Professionals should urgently reinforce the importance of improving physically activity, good sleep hygiene, and regulated use of electronic devices for parents and school-aged children during this unprecedented time.

Comparative Analysis of Physical Activity Detected via an External Accelerometer and Cardiac Implantable Electronic Devices

Background

Physical activity (PA) has become an important health issue for decades. Cardiovascular implantable electronic devices (CIEDs) have built-in PA-recording functions. We aimed to compare PA measurements using an external accelerometer (ActiGraph GT3X+) and internal accelerometers (Abbott, Biotronik, and Medtronic CIEDs).

Methods

This was a prospective, single-center observational study. The device-measured 7-day average PA was collected, and GT3X+ -measured 7-day average PA was used as the gold-standard, including all daily observations of activity. Pearson’s correlation coefficients were used to compare the correlations between GT3X+ -measured and CIED-measured PA. Bland-Altman plots were used to analyze measurement agreement, and intraclass correlation coefficients were used to analyze reliability.

Results

In total, 720 patients treated with CIEDs were surveyed between November 2020 and April 2021, 60 of them were analyzed after patient screening by our protocol. Each manufacturer included 20 patients for the final analysis. The CIED-measured PAs of Abbott, Biotronik, and Medtronic were 3.0 ± 1.5, 2.6 ± 1.8, and 3.8 ± 2.5 h per day, respectively; the GT3X+ -measured PAs were 6.9 ± 2.8, 6.0 ± 2.4, and 6.4 ± 2.5 h per day, respectively. Moderate and significant correlations were found in patients using Abbott, Biotronik, and Medtronic CIEDs (r = 0.534, p = 0.015; r = 0.465, p = 0.039; r = 0.677, p = 0.001, respectively). Bland-Altman plots and intraclass correlation coefficients both showed a significant correlation and reliability between the average PA measured by GT3X+ and CIEDs (hours per day).

Conclusion

Although the PA recording function of CIEDs includes a single-axis accelerometer, it has a moderate correlation compared with the triaxial accelerometer of the GT3X+. However, CIEDs seem to underestimate PA for 3–4 h compared to the GT3X+.

Deriving Normative Data on 24-Hour Ambulatory Blood Pressure Monitoring for South Asian Children (ASHA): A Clinical Research Protocol

Background:

The global prevalence of hypertension in children and adolescents has increased over the past 2 decades and is the strongest predictor of adult hypertension. South Asians have an increased prevalence of metabolic syndrome associated risk factors including abdominal obesity, diabetes, and hypertension. All these factors contribute to their increased cardiovascular disease burden. Accurate and early identification of hypertension in South Asian children is a necessary aspect of cardiovascular disease prevention. Ambulatory blood pressure monitoring (ABPM) is considered the gold-standard for pediatric blood pressure (BP) measurement. However, its utilization is limited due to the lack of validated normative reference data in diverse, multiethnic pediatric populations.

Objective:

The primary objective is to establish normative height-sex and age-sex-specific reference values for 24-h ABPM measurements among South Asian children and adolescents (aged 5-17 years) in Ontario and British Columbia, Canada. Secondary objectives are to evaluate differences in ABPM measurements by body mass index classification, to compare our normative data against pre-existing data from German and Hong Kong cohorts, and to evaluate relationships between habitual movement behaviors, diet quality, and ABPM measurements.

Design:

Cross-sectional study, quasi-representative sample.

Setting:

Participants will be recruited from schools, community centers, and places of worship in Southern Ontario (Greater Toronto and Hamilton area, including the Peel Region) and Greater Vancouver, British Columbia.

Participants:

We aim to recruit 2113 nonoverweight children (aged 5-17 years) for the primary objective. We aim to recruit an additional 633 overweight or obese children to address the secondary objectives.

Measurements:

Ambulatory BP monitoring measurements will be obtained using Spacelabs 90217 ABPM devices, which are validated for pediatric use. The ActiGraph GT3X-BT accelerometer, which has also been validated for pediatric use, will be used to obtain movement behavior data.

Methods:

Following recruitment, eligible children will be fitted with 24-h ABPM and physical activity monitors. Body anthropometrics and questionnaire data regarding medical and family history, medications, diet, physical activity, and substance use will be collected. Ambulatory BP monitoring data will be used to develop height-sex- and age-sex-specific normative reference values for South Asian children. Secondary objectives include evaluating differences in ABPM measures between normal weight, overweight and obese children; and comparing our South Asian ABPM data to existing German and Hong Kong data. We will also use compositional data analysis to evaluate associations between a child’s habitual movement behaviors and ABPM measures.

Limitations:

Bloodwork will not be performed to facilitate recruitment. A non-South Asian comparator cohort will not be included due to feasibility concerns. Using a convenience sampling approach introduces the potential for selection bias.

Conclusions:

Ambulatory BP monitoring is a valuable tool for the identification and follow-up of pediatric hypertension and overcomes many of the limitations of office-based BP measurement. The development of normative ABPM data specific to South Asian children will increase the accuracy of BP measurement and hypertension identification in this at-risk population, providing an additional strategy for primary prevention of cardiovascular disease.

Physical Activity and Executive Function in Children With ADHD: The Mediating Role of Sleep

This study examined the mediating role of sleep in the relationship between physical activity and executive function in children with attention deficit hyperactivity disorder (ADHD). Fifty-six children with ADHD were recruited from Shenzhen Children's Hospital. Participants wore an accelerometer for seven consecutive days to measure physical activity and sleep quality. Activity counts were analyzed to measure moderate-to-vigorous physical activity (MVPA). Four sleep parameters, including sleep latency (SL), sleep efficiency, total sleep time, and wake after sleep onset were recorded from the actigraph. Three core executive functions, inhibitory control; working memory (WM); and cognitive flexibility (CF), were assessed from computer-based tasks: the flanker task, and the Tower of London and Trail Making Tests, respectively. The regression results showed that MVPA was negatively associated with SL (-0.169; 95%CI [-0.244, -0.112]). WM (total scores) was positively related to MVPA (0.028, 95%CI [0.008, 0.048]), but negatively related to SL (-0.105, 95%CI [-0.167, -0.030]). CF (part B errors) was negatively associated with MVPA (-0.031, 95%CI [-0.055, -0.005]) and positively correlated with SL (0.184, 95%CI [0.092, -0.260]). The indirect effect of SL was found for MVPA and WM (0.018, 95%CI [0.015, 0.034]), supporting the indirect partial mediation. Similarly, the indirect effect of SL was found between MVPA and CF (-0.031, 95%CI [-0.060, -0.012]), supporting the indirect partial mediation. The mediating role of SL in children with ADHD suggests that the intensity of physical activity plays a key role in linking sleep quality and executive function in this group.

Validation of actigraphy for sleep measurement in children with cerebral palsy

Objectives:

Sleep issues are common in children with cerebral palsy (CP), although there are challenges in obtaining objective data about their sleep patterns. Actigraphs measure movement to quantify sleep but their accuracy in children with CP is unknown. Our goals were to validate actigraphy for sleep assessment in children with CP and to study their sleep patterns in a cross-sectional cohort study. Methods: We recruited children with (N = 13) and without (N = 13) CP aged 2–17 years (mean age 9 y 11mo [SD 4 y 10mo] range 4–17 y; 17 males, 9 females; 54% spastic quadriplegic, 23% spastic diplegic, 15% spastic hemiplegic, 8% unclassified CP). We obtained wrist and forehead actigraphy with concurrent polysomnography for one night, and home wrist actigraphy for one week. We developed actigraphy algorithms and evaluated their accuracy (agreement with polysomnography-determined sleep versus wake staging), sensitivity (sleep detection), and specificity (wake detection).

Results:

Our actigraphy algorithms had median 72–80% accuracy, 87–91% sensitivity, and 60–71% specificity in children with CP and 86–89% accuracy, 88–92% sensitivity, and 70–75% specificity in children without CP, with similar accuracies in wrist and forehead locations. Our algorithms had increased specificity and accuracy compared to existing algorithms, facilitating detection of sleep disruption. Children with CP showed lower sleep efficiency and duration than children without CP.

Conclusions:

Actigraphy is a valid tool for sleep assessment in children with CP. Children with CP have worse sleep efficiency and duration.

Real-Time Tracking of Human Neck Postures and Movements

Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a digital platform for analyzing the impact of human neck movements on the neck musculoskeletal system. The secondary objective is to design a rehabilitation monitoring system that brings accountability in the treatment prescribed, which is shown in the use-case model. To record neck movements effectively, a Smart Neckband integrated with the Inertial Measurement Unit (IMU) was designed. The initial task was to find a suitable position to locate the sensors embedded in the Smart Neckband. IMU-based real-world kinematic data were captured from eight research subjects and were used to extract kinetic data from the OpenSim simulation platform. A Random Forest algorithm was trained using the kinetic data to predict the neck movements. The results obtained correlated with the novel idea proposed in this paper of using the hyoid muscles to accurately detect neck postures and movements. The innovative approach of integrating kinematic data and kinetic data for analyzing neck postures and movements has been successfully demonstrated through the efficient application in a rehabilitation use case with about 95% accuracy. This research study presents a robust digital platform for the integration of kinematic and kinetic data that has enabled the design of a context-aware neckband for the support in the treatment of neck musculoskeletal disorders.

Examining 24-Hour Activity and Sleep Behaviors and Related Determinants in Latino Adolescents and Young Adults With Obesity

Background. Few studies have examined 24-hour activity and sleep behaviors and their contribution to type 2 diabetes (T2D) in Latino adolescents and young adults with obesity. Aim. This study included quantitative data on T2D risk and 24-hour activity and sleep behaviors and qualitative data on individual, social, and environmental behavioral determinants. Method. A 7 day, 24-hour, wrist-worn accelerometer protocol assessed moderate-to-vigorous physical activity (PA), sedentary behaviors (SB), sleep, and sleep regularity, in adolescents (N = 38; 12-16 years) and young adults (N = 22; 18-22 years). T2D-related outcomes included adiposity (BMI, BF%, waist circumference), fasting, and 2-hour glucose. A subsample of participants (N = 16 adolescents, N = 15 young adults) completed interviews to identify behavioral determinants. Results. High levels of PA were observed among adolescents (M = 103.8 ± 67.5 minutes/day) and young adults (M = 96.8 ± 78.8 minutes/day) as well as high levels of SB across both age groups (≥10 hours/day). Sleep regularity was negatively associated with adiposity (all ps < .05) in both age groups as well as fasting and 2-hour glucose in young adults (all ps < .05). Social support was associated with PA in both age groups as well as SB in younger youth. Auditory noises, lights, and safety inhibited sleep in both age groups. Conclusion. PA is critical for disease reduction, yet reducing SB and improving sleep are also important targets for reducing T2D risk in Hispanic adolescents and young adults. Future health promotion and disease prevention strategies should leverage qualitative findings regarding behavioral determinants.

Cross-sectional examination of 24-hour movement behaviours among 3- and 4-year-old children in urban and rural settings in low-income, middle-income and high-income countries: the SUNRISE study protocol

INTRODUCTION

24-hour movement behaviours (physical activity, sedentary behaviour and sleep) during the early years are associated with health and developmental outcomes, prompting the WHO to develop Global guidelines for physical activity, sedentary behaviour and sleep for children under 5 years of age. Prevalence data on 24-hour movement behaviours is lacking, particularly in low-income and middle-income countries (LMICs). This paper describes the development of the SUNRISE International Study of Movement Behaviours in the Early Years protocol, designed to address this gap.

METHODS AND ANALYSIS

SUNRISE is the first international cross-sectional study that aims to determine the proportion of 3- and 4-year-old children who meet the WHO Global guidelines. The study will assess if proportions differ by gender, urban/rural location and/or socioeconomic status. Executive function, motor skills and adiposity will be assessed and potential correlates of 24-hour movement behaviours examined. Pilot research from 24 countries (14 LMICs) informed the study design and protocol. Data are collected locally by research staff from partnering institutions who are trained throughout the research process. Piloting of all measures to determine protocol acceptability and feasibility was interrupted by COVID-19 but is nearing completion. At the time of publication 41 countries are participating in the SUNRISE study.

ETHICS AND DISSEMINATION

The SUNRISE protocol has received ethics approved from the University of Wollongong, Australia, and in each country by the applicable ethics committees. Approval is also sought from any relevant government departments or organisations. The results will inform global efforts to prevent childhood obesity and ensure young children reach their health and developmental potential. Findings on the correlates of movement behaviours can guide future interventions to improve the movement behaviours in culturally specific ways. Study findings will be disseminated via publications, conference presentations and may contribute to the development of local guidelines and public health interventions.

Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation

BACKGROUND

Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep.

OBJECTIVE

This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest.

METHODS

The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each.

RESULTS

Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data.

CONCLUSIONS

We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments.

Wake after Sleep Onset Time Moderated Age-related Emotional Memory Bias

BACKGROUND

Numerous studies have shown that older adults have positive emotional bias. However, how sleep characteristics such as sleep continuity moderate this emotional bias in older adults is less understood.

OBJECTIVES

The present study aimed to determine whether positive emotional memory bias is associated with sleep continuity in older adults.

METHODS

We recruited 92 community-dwelling older adults for a cross-sectional study. Participants underwent 1 week of objective actigraphic sleep monitoring and completed a visuospatial associative memory task at baseline and 1 week later.

RESULTS

Older adults exhibited better memory performance for positive pictures than for neutral and negative pictures at baseline, and this positive emotional bias was maintained for at least 1 week. Crucially, this effect was moderated by sleep continuity: Older adults with shorter wake after sleep onset times (WASO) exhibited this positive bias, whereas those with longer WASO did not.

DISCUSSION

The present results are the first to demonstrate that positive emotional bias is moderated by sleep continuity in older adults. These findings have implications for emotion regulation in older adults and highlight the need for targeted interventions to increase their sleep continuity, which may help to improve emotional processing in this population.

Bidirectional associations between sedentary time and sleep duration among 12- to 14-year-old adolescents

Background

The aim of this study was to investigate bidirectional associations between (prolonged) sitting time and sleep duration in 12- to 14-year-old adolescents using a between-subjects and within-subjects analyses approach.

Methods

Observational data were used from 108 adolescents (53% girls; mean age 12.9 (SD 0.7) years) from six schools in Flanders, Belgium. The Axivity AX3 triaxial accelerometer, worn on the thigh, was used to assess daily total sitting time and daily time spent in sedentary bouts of ≥30 min (as a proxy for prolonged sitting time). The Fitbit Charge 3 was used to assess nightly sleep duration. Both monitors were worn on schooldays only (ranging from 4 to 5 days). Linear mixed models were conducted to analyse the associations, resulting in four models. In each model, the independent variable (sleep duration, sitting time or prolonged sitting time) was included as within- as well as between-subjects factor.

Results

Within-subjects analyses showed that when the adolescents sat more and when the adolescents spent more time sitting in bouts of ≥30 min than they usually did on a given day, they slept less during the following night (p = 0.01 and p = 0.05 (borderline significant), respectively). These associations were not significant in the other direction. Between-subjects analyses showed that adolescents who slept more on average, spent less time sitting (p = 0.006) and less time sitting in bouts of ≥30 min (p = 0.004) compared with adolescents who slept less on average. Conversely, adolescents who spent more time sitting on average and adolescents who spent more time sitting in bouts of ≥30 min on average, slept less (p = 0.02 and p = 0.003, respectively).

Conclusions

Based on the between-subjects analyses, interventions focusing on reducing or regularly breaking up sitting time could improve adolescents’ sleep duration on a population level, and vice versa. However, the within-subjects association was only found in one direction and suggests that to sleep sufficiently during the night, adolescents might limit and regularly break up their sitting time the preceding day.

Trial registration

Data have been used from our trial registered at ClinicalTrials.gov (NCT04327414; registered on March 11, 2020).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11694-9.

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.

Sleep duration, physical activity, and caloric intake are related to weight status in Mexican American children: a longitudinal analysis

BACKGROUND

Obesity is a serious issue, spanning all ages, and, in the U.S., disproportionately affects Latinos and African Americans. Understanding sleep, physical activity and dietary behaviors that may predict childhood obesity can help identify behavioral intervention targets.

METHODS

Data were drawn from a U.S. cohort study of 323 Mexican American 8-10-year-old children and their mothers, who participated in a longitudinal study over a 2-year period. Measures were collected at baseline (BL; child mean age = 8.87, SD = 0.83), year 1 (FU1) and year 2 (FU2). Mothers reported on household income and acculturation at BL. Child height and weight were collected and BMI z-scores (BMIz) were calculated for weight status at BL, FU1, and FU2. Accelerometer-estimated sleep duration (hours) and moderate-to-vigorous physical activity (MVPA; minutes) were collected across 3 days at BL, FU1, and FU2. Two 24-h dietary recalls were performed at each time point; from these, average energy intake (EI, kcals/day) was estimated. Cross-lagged panel analysis was used to examine behavioral predictors on BMIz at each time point and across time.

RESULTS

At BL and FU1, longer sleep duration (β = - 0.22, p < 0.001; β = - 0.17, p < 0.05, respectively) and greater MVPA (β = - 0.13, p < 0.05; β = - 0.20, p < 0.01, respectively) were concurrently related to lower BMIz. At FU2, longer sleep duration (β = - 0.18, p < 0.01) was concurrently related to lower BMIz, whereas greater EI (β = 0.16, p < 0.01) was related to higher BMIz. Longer sleep duration at BL predicted lower BMIz at FU1 (β = - 0.05, p < 0.01).

CONCLUSIONS

Longer sleep duration was concurrently related to lower weight status at each time point from ages 8-10 to 10-12. Higher MVPA was concurrently related to lower weight status in earlier childhood (ages 8-10 and 9-11) and higher EI was concurrently related to higher weight status toward the end of childhood (ages 10-12 years). Furthermore, longer sleep in earlier childhood was protective of children's lower weight status 1 year later. These findings suggest that sleep duration plays a consistent and protective role against childhood obesity; in addition, MVPA and healthy EI remain important independent factors for obtaining a healthy weight.

Social jetlag is associated with obesity-related outcomes in 9-11-year-old children, independent of other sleep characteristics

INTRODUCTION

Social jetlag has been reported to predict obesity-related indices, independent of sleep duration, with associations in female adolescents but not males. However, such sex-specific relationships have not been investigated in pre-adolescents.

OBJECTIVES

To examine: (i) the relationships between sleep characteristics, including social jetlag, and obesity-related outcomes during childhood, and (ii) whether these relationships are moderated by sex.

METHODS

This cross-sectional study included 381 children aged 9-11 years (49.6% female). Average sleep duration, social jetlag, and physical activity were assessed via wrist-worn accelerometry. Sleep disturbances were quantified from the Children's Sleep Habits Questionnaire. Obesity-related outcomes included age-specific body mass index Z-scores (zBMI) and waist-to-height ratio. Additionally % fat, total fat mass, and fat mass index were assessed via bioelectrical impedance analysis. Linear mixed models that nested children within schools were used to identify relationships among sleep characteristics and obesity-related outcomes.

RESULTS

Positive associations between social jetlag with zBMI, % fat, and fat mass index were seen in univariable and unadjusted multivariable analyses. Following adjustments for known confounders, social jetlag remained significantly associated with zBMI (β = 0.12, p = 0.013). Simple slopes suggested a positive association in girls (β = 0.19, p = 0.006) but not in boys (β = 0.03, p = 0.703).

CONCLUSIONS

Obesity prevention efforts, particularly in girls, may benefit from targeted approaches to improving the consistency of sleep timing in youth.

Off-training physical activity and training responses as determinants of sleep quality in young soccer players

This study aimed to quantify and assess the relationship of young soccer players' off-training physical activity (PA) and training responses on sleep quality. Eleven adolescent soccer players (13 ± 0.5 years old) were monitored during weekdays for four consecutive weeks, throughout soccer practice days. Off-training PA and sleep quality were assessed using 100 Hz tri-axial accelerometers and training responses analyzed using 20 Hz global positioning measurement units. A cluster analysis classified all cases into three different dimensions, (1) off-training PA, (2) training responses and (3) sleep quality. For each dimension, the most important variables for classifying the cases into clusters were sedentary PA and moderate-to-vigorous PA; total distance covered and impacts; and sleep onset latency and sleep fragmentation index, respectively. Afterwards, a correspondence analysis was used to identify whether off-training PA and training responses affected sleep quality. Results exposed that high to medium off-training PA combined with medium to high training responses may have decreased sleep quality. Conversely, no correspondence was observed between off-training PA and training responses, with higher sleep quality. This study emphasizes the importance of sports organizations adopting a holistic approach to youth soccer players' development, that appropriately considers the inter-relationship between lifestyle, performance and health-related information.

Relationship between chewing features and body mass index in young adolescents

BACKGROUND

Behavioural aspects of chewing may influence food intake, nutritional status and in turn body weight.

OBJECTIVES

The current study aimed to study chewing features in adolescents as they naturally occur in home-based settings, and to test for a possible association with weight status.

METHODS

Forty-two adolescents (15.3 ± 1.3 years) were recruited (21 with healthy-weight/21 with overweight). Using a smartphone-assisted wearable electromyographic device, the chewing features of each participant were assessed over one evening, including the evening meal, in their natural home setting.

RESULTS

The mean (±SD) for chewing pace was 1.53 ± 0.22 Hz, chewing power 30.1% ± 4.8%, number of chewing episodes 63.1 ± 36.7 and chewing time 11.0 ± 7.7 minutes. The chewing pace of the group with overweight was slower than that of healthy weight (-0.20 Hz; 95% CI, -0.06 to -0.33; P = .005) while their chewing time was shorter (-4.9 minutes; 95% CI, 0.2-9.7; P = .044). A significant negative correlation was observed between BMI z-score and chewing pace (R = -.41; P = .007), and between BMI z-score and chewing time (R = -0.32; P = .039).

CONCLUSION

The current study suggests that adolescents who are overweight eat at a slower pace for a shorter period of time than their counterparts who are a healthy weight. This unexpected finding based on objective data appears to conflict with existing questionnaire findings but provides impetus for further work testing the effectiveness of changing eating behaviour as a weight-management intervention in youth.

A meta-analysis of accelerometer sleep outcomes in healthy children based on the Sadeh algorithm: the influence of child and device characteristics

STUDY OBJECTIVES

Children often experience sleep problems, with a negative impact on mood, behavior, cognitive function, and other aspects of mental and physical health. Accelerometers are widely used to assess sleep, but general reference values for healthy children do not yet exist. The aim of this meta-analysis was to determine mean values for wake after sleep onset (WASO), sleep efficiency (SE), total sleep time (TST) and sleep onset latency (SOL), and to determine the effect of child and accelerometer-characteristics.

METHODS

A search included studies with healthy children, 0-18 years, reporting WASO, SE, TST, and/or SOL, calculated with the Sadeh algorithm. Meta-analyses with random effects produced pooled estimate means per outcome. Meta-regression analyses determined the effect of age, sex, placement site and accelerometer type.

RESULTS

Eighty-three studies (9,068 participants) were included. Pooled means were 63 min (95% CI 57 to 69) for WASO, 88% (95% CI 87 to 89) for SE, 477 min (95% CI 464 to 491) for TST and 19 min (95% CI 17 to 22) for SOL. Heterogeneity was high (95%-99%). TST decreased with age and there was an age-effect on SOL. SE differed between wrist and ankle (used in age 0-24 months) placement, and between piezoelectric and MEMS-type accelerometers. No differences were found between boys and girls, although this number of studies was small.

CONCLUSIONS

We found differences in almost all investigated outcomes and heterogeneity was high. Therefore, we advise to use a study-specific control sample until more robust reference values are available. Future research should narrow the methodological heterogeneity and produce larger datasets, needed to establish these reference values.

Validity of a Novel Research-Grade Physical Activity and Sleep Monitor for Continuous Remote Patient Monitoring

In the midst of the COVID-19 pandemic, Remote Patient Monitoring technologies are highly important for clinicians and researchers. These connected-health technologies enable monitoring of patients and facilitate remote clinical trial research while reducing the potential for the spread of the novel coronavirus. There is a growing requirement for monitoring of the full 24 h spectrum of behaviours with a single research-grade sensor. This research describes a free-living and supervised protocol comparison study of the Verisense inertial measurement unit to assess physical activity and sleep parameters and compares it with the Actiwatch 2 actigraph. Fifteen adults (11 males, 23.4 ± 3.4 years and 4 females, 29 ± 12.6 years) wore both monitors for 2 consecutive days and nights in the free-living study while twelve adults (11 males, 23.4 ± 3.4 years and 1 female, 22 ± 0 years) wore both monitors for the duration of a gym-based supervised protocol study. Agreement of physical activity epoch-by-epoch data with activity classification of sedentary, light and moderate-to-vigorous activity and sleep metrics were evaluated using Spearman's rank-order correlation coefficients and Bland-Altman plots. For all activity, Verisense showed high agreement for both free-living and supervised protocol of r = 0.85 and r = 0.78, respectively. For physical activity classification, Verisense showed high agreement of sedentary activity of r = 0.72 for free-living but low agreement of r = 0.36 for supervised protocol; low agreement of light activity of r = 0.42 for free-living and negligible agreement of r = -0.04 for supervised protocol; and moderate agreement of moderate-to-vigorous activity of r = 0.52 for free-living with low agreement of r = 0.49 for supervised protocol. For sleep metrics, Verisense showed moderate agreement for sleep time and total sleep time of r = 0.66 and 0.54, respectively, but demonstrated high agreement for determination of wake time of r = 0.83. Overall, our results showed moderate-high agreement of Verisense with Actiwatch 2 for assessing epoch-by-epoch physical activity and sleep, but a lack of agreement for activity classifications. Future validation work of Verisense for activity cut-point potentially holds promise for 24 h continuous remote patient monitoring.

The MiaoMiao study: can do-it-yourself continuous glucose monitoring technology improve fear of hypoglycaemia in parents of children affected by type 1 diabetes?

Purpose

Type 1 diabetes (T1D) is one of the most common chronic diseases of childhood and comes with considerable management and psychological burden for children and their families. Fear of hypoglycaemia (FOH), particularly nocturnal hypoglycaemia, is a common worry. Continuous glucose monitoring (CGM) is a tool that may help reduce FOH, as well as reduce overall diabetes burden. However, CGM systems are expensive and often not publicly funded or subsidised. MiaoMiao (MM) is a novel relatively affordable third-party add-on technology to intermittently scanned CGM (isCGM). MM allows users to convert their isCGM to a form of "Do-it-yourself" (DIY)-CGM. Our hypothesis is that MM-CGM will result in significant reduction in parental fear from hypoglycaemia. The primary objective is to determine the impact of real-time DIY-CGM on parental fear of hypoglycaemia using Hypoglycaemia Fear Survey (HFS).

Methods

This is a multisite randomised cross-over study of 55 New Zealand children (ages 2-13 years) with established T1D and current users of isCGM (Abbott FreeStyle Libre). DIY-CGM will be compared to usual care with isCGM. Participants will be randomised to either arm of the study for 6 weeks followed by a 4-week wash-out period before crossing over to the other study arm for a further 6 weeks.

Discussion

The results of this study will provide much needed clinical trial data regarding DIY-CGM effectiveness in reducing parental FOH, as measured by HFS, as well as various other secondary outcomes including traditional glycaemic metrics, and child and caregiver sleep. The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN 12619001551189) on 18 November 2019, and the World Health Organisation International Clinical Trial Registry Platform (Universal Trial Number U1111-1236-9189).

The association of actigraphic sleep measures and physical activity with excess weight and adiposity in kindergarteners

Insufficient sleep duration and physical activity (PA) are known risk factors for overweight and obesity in children; however, there are no studies on comprehensive associations of objectively-measured sleep parameters and PA with excess weight and excess adiposity in kindergarteners. Therefore, the aim of this study was to determine the associations between objectively measured sleep parameters and PA with excess weight and excess adiposity, defined as BMI ≥ 85th percentile and body fat percentage (BFP) ≥ 85th percentile, respectively. Sleep parameters and PA were measured in 676 subjects aged 5–6 years using accelerometers for 7 days, worn at the participant’s hip. Bioelectrical impedance analysis was used to estimate BFP. In the total sample, lower sleep duration, sleep efficiency, vigorous PA and the number of steps per day were associated with excess weight. However, excess adiposity was associated with lower sleep duration, total PA, vigorous PA, moderate-to-vigorous physical activity (MVPA) and the number of steps per day. Logistic regression by the stepwise progressive method showed that the strongest predictor of excess adiposity in boys and girls was vigorous PA, while the strongest predictor of excess weight in boys was sleep efficiency. A holistic approach to health targeting all of these factors synergistically is needed to optimize the effectiveness of obesity prevention and treatment interventions.

Association between objectively measured body composition, sleep parameters and physical activity in preschool children: a cross-sectional study

OBJECTIVE

Associations between self-reported sleep duration and obesity indices in children are well recognised; however, there are no studies on associations between objectively measured other sleep parameters and physical activity with body composition in preschoolers. Therefore, the aim of this study was to determine the associations between sleep parameters and moderate-to-vigorous physical activity (MVPA) with body composition indices in preschoolers using objective measures.

DESIGN

A cross-sectional study.

PARTICIPANTS

The study group consisted of 676 children aged 5-6 years, who were enrolled in kindergartens in the 2017/2018 school year.

OUTCOME MEASURES

Sleep parameters and MVPA were measured using accelerometers for 7 days. Bioelectrical impedance analysis was used to estimate body composition.

RESULTS

Sleep duration and sleep efficiency were inversely associated with body fat percentage (BFP) (β=-0.013 and β from -0.311 to -0.359, respectively) and body mass index (BMI) (β from -0.005 to -0.006 and from -0.105 to -0.121, respectively), and directly associated with fat-free mass (FFM) (β from 0.010 to 0.011 and from 0.245 to 0.271, respectively) and muscle mass (β from 0.012 to 0.012 and from 0.277 to 0.307, respectively) in unadjusted and adjusted models. BFP was inversely associated with MVPA and positively associated with number of awakenings and sleep periods. Number of sleep periods was inversely associated with FFM, and positively with BMI and muscle mass. Correlation matrix indicated significant correlation between BFP, FFM and muscle mass with sleep duration, sleep efficiency, number of sleep periods and MVPA.

CONCLUSIONS

Periodic assessment of sleep parameters and MVPA in relation to body composition in preschool children may be considered, especially in those who are at risk for obesity.

Machine learning sleep duration classification in Preschoolers using waist-worn ActiGraphs

OBJECTIVE

To create a sleep duration classification technique for waist-worn ActiGraph accelerometers in preschool-aged children.

METHODS

Children wore ActiGraph wGT3X-BT accelerometers on their right hip for 7 days (24 h/day). Ground truth nap, sleep, and wake were estimated through visual inspection of accelerometer data, guided by sleep log-sheets and previously published visual inspection heuristics. Raw accelerometer data (30Hz) were used to generate 144 features aggregated to 1-min epochs. Machine learning classification (ie, Random Forest and Hidden Markov Modeling [HMM]) predicted nap, sleep, and wake. A simplified prediction formula was also created using features (n = 10) with the highest mean decrease in Gini index during training of Random Forests, and temporally smoothed with rolling median calculations.

RESULTS

Children (n = 89, mean age = 4.5 years, 67% boys) contributed >600,000 min of accelerometer data. Overall classification accuracy of the Random Forest and HMM classifier was 96.2% (95%CI: 96.1, 96.2%), with a Kappa score of 0.93. Additionally, overall classification accuracy for the temporally smoothed simplified formula was 93.7% (95%CI: 93.6, 93.7%) with Kappa = 0.87. Nap prediction accuracy was 99.8% for the final machine learning model, and 86.1% for the simplified formula. For participant-level daily summaries, generally small but statistically significant differences were found between machine learning and ground truth behaviour predictions, whereas non-significant differences were found between the simplified formulas and ground truth predictions.

CONCLUSIONS

Predictions for both machine learning and the simplified formula had almost perfect agreement with visual inspection ground truth measurements. Future research is needed to confirm these findings using polysomnography ground truth sleep measurements.

Examining the effects of applying ActiGraph low-frequency extension feature to analyze the sleeping behaviours of preschool-aged children

This study compares sleep outcome measures obtained using normal- and low-frequency extension (LFE) settings (Actilife). Forty-two children (aged 3–6 years) were instructed to wear an ActiGraph GT3X+ accelerometer on their hip for 7 days, 24 h/day. Total sleep time (min), sleep efficiency (%), and number and cumulative length (min) of awakening were used to compare the settings. Results suggest that the LFE setting results in significant but relatively small reductions in the sleep metrics of children. Trial registration no.: clincialtrials.gov (ID no. NCT02223234)

Novelty LFE setting, available through ActiGraph, estimates a significantly reduced total sleep time and efficiency.