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.

Self-monitoring has no adverse effect on disordered eating in adults seeking treatment for obesity.

OBJECTIVES: Although monitoring is considered a key component of effective behaviour change, the development of apps has allowed consumers to constantly evaluate their own diet, with little examination of what this might mean for eating behaviour. The aim of this study was to investigate whether self-monitoring of diet using the app MyFitnessPal or daily self-weighing increases the reported occurrence of eating disorders in adults with overweight/obesity following a weight loss programme. METHODS: Two hundred fifty adults with body mass index ≥ 27 kg/m2 received diet and exercise advice and were randomized to one of four monitoring strategies (daily self-weighing, MyFitnessPal, brief monthly consults or self-monitoring hunger) or control for 12 months. The Eating Disorder Examination Questionnaire 6.0 was used to assess eating disorder symptoms and behaviours for the previous 28 d at 0 and 12 months. RESULTS: There were no significant differences in the global Eating Disorder Examination Questionnaire score or the subscales between those in the four monitoring groups and the control at 12 months (all p ≥ 0.164), nor were there differences in binge eating, self-induced vomiting, laxative misuse or excessive exercise at 12 months (p ≥ 0.202). The overall prevalence of one or more episodes of binge eating was 53.6% at baseline and 50.6% at 12 months, with no change over time (p = 0.662). CONCLUSIONS: There was no evidence that self-monitoring, including using diet apps like MyFitnessPal or daily self-weighing, increases the reported occurrence of eating disorder behaviours in adults with overweight/obesity who are trying to lose weight.

Bioelectrical impedance as a measure of change in body composition in young children.

BACKGROUND AND OBJECTIVES: The ability of bioelectrical impedance (BIA) to measure change in body composition in children has rarely been examined. METHODS: Body composition was estimated by BIA (Tanita BC-418) and dual-energy x-ray absorptiometry (DXA) in 187 children aged 4-8 years at baseline and at 12 months. Change in body composition was compared between the two methods using mixed models. RESULTS: Estimates of change in fat mass did not differ between BIA and DXA for overweight girls (mean difference between methods, 95% confidence interval: 0.04 kg, -0.19 to 0.28) or boys (0.07 kg, -0.14 to 0.27). BIA was also able to accurately detect change in fat-free mass, with no significant differences between methods (-0.14 kg, -0.10 to 0.38 in girls and -0.07 kg, -0.35 to -0.20 in boys). Change in percentage fat produced similar estimates in both genders (0.18%, -0.82 to 0.46 in girls and 0.38%, -0.37 to 1.13 in boys). BIA/DXA comparisons in normal weight children were also not significantly different, with the exception of percentage fat in girls, where BIA slightly underestimated change compared with DXA (0.7%, 0.02-0.37). CONCLUSION: BIA performed well as a measure of change in body composition, providing confidence for its use as an outcome measure in children.