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.

The cost of baby-led vs. parent-led approaches to introducing complementary foods in New Zealand.

Baby-led approaches to complementary feeding promote intake of family foods rather than infant specific foods, from the start of the complementary feeding period, which advocates suggest should be less expensive. However, this has never been formally examined. We recently completed a 2-year randomised controlled trial comparing baby-led (BLISS) and traditional spoon-feeding (Control) approaches to complementary feeding in 206 infants. Perceived expense was assessed at infant 7, 8, 9 and 12 months of age. The actual cost of intake (food offered, consumed and left over) was calculated from 3-day weighed diet records at 7 and 12 months of age. BLISS was perceived as less expensive than traditional feeding (P = 0.002), but comparisons of actual costs showed only small differences in total daily cost for food offered (NZ$0.20 and NZ$0.10 at 7 and 12 months, respectively), consumed (NZ$0.30, NZ$0.20) or left over (NZ$0.10, NZ$0.20). Baby-led approaches are not cheaper for families than traditional spoon-feeding.

Do differences in compositional time use explain ethnic variation in the prevalence of obesity in children? Analyses using 24-hour accelerometry.

BACKGROUND/OBJECTIVES: Whether variation in sleep and physical activity explain marked ethnic and socioeconomic disparities in childhood obesity is unclear. As time spent in one behaviour influences time spent in other behaviours across the 24-hour day, compositional analyses are essential. The aims of this study were to determine how ethnicity and socioeconomic status influence compositional time use in children, and whether differences in compositional time use explain variation in body mass index (BMI) z-score and obesity prevalence across ethnic groups. METHODS: In all, 690 children (58% European, 20% Maori, 13% Pacific, 9% Asian; 66% low-medium deprivation and 34% high deprivation) aged 6-10 years wore an ActiGraph accelerometer 24-hours a day for 5 days yielding data on sedentary time, sleep, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Height and weight were measured using standard techniques and BMI z-scores calculated. Twenty-four hour movement data were transformed into isometric log-ratio co-ordinates for multivariable regression analysis and effect sizes were back-transformed. RESULTS: European children spent more time asleep (predicted difference in minutes, 95% CI: 16.1, 7.4-24.9) and in MVPA (6.6 min, 2.4-10.4), and less time sedentary (-10.2 min, -19.8 to -0.6) and in LPA (-12.2 min, -21.0 to -3.5) than non-European children. Overall, 10% more sleep was associated with a larger difference in BMI z-score (adjusted difference, 95% CI: -0.13, -0.25 to -0.01) than 10% more MVPA (-0.06, -0.09 to -0.03). Compositional time use explained 35% of the increased risk of obesity in Pacific compared with European children after adjustment for age, sex, deprivation and diet, but only 9% in Maori and 24% in Asian children. CONCLUSIONS: Ethnic differences in compositional time use explain a relatively small proportion of the ethnic differences in obesity prevalence that exist in children.

Acute effect of fructose intake from sugar-sweetened beverages on plasma uric acid: a randomised controlled trial.

BACKGROUND/OBJECTIVE: Excessive fructose intake has been linked to hyperuricaemia. Our aim was to test whether 355 and 600 ml of commercial sugar-sweetened soft drinks would acutely raise plasma uric acid. SUBJECTS/METHODS: Forty-one participants were randomised to a control group or an intervention group. The control group consumed 600 ml of fructose and 600 ml of glucose beverages. The soft drink group consumed 355 and 600 ml of beverages in random order. The control beverages were matched for fructose content with 600 ml of soft drink (26.7 g). Blood samples were collected at baseline, 30 and 60 min and analysed for plasma uric acid. RESULTS: Plasma uric acid concentrations were 13 (95% confidence interval: (CI): 3, 23) and 17 µmol/l (95% CI: 6, 28) higher 30 and 60 min after consumption of 600 ml of soft drink compared with the glucose control. The corresponding values for the fructose beverage were 22 (95% CI: 16, 29) and 23 µmol/l (95% CI: 14, 33). There was no significant difference in the increase in uric acid following the 600-ml soft drink compared with the fructose control at 30 min (6 µmol/l; 95% CI: -4, 15) or 60 min (5 µmol/l; 95% CI: -7, 17). There was no difference in the uric-acid-raising effect between the 355 and 600 ml volumes at 30 min (-1 µmol/l; 95% CI: -9, 6) or 60 min (-5 µmol/l; 95% CI: -10, 1). CONCLUSION: Small and transient increases in plasma uric acid are likely after consumption of sucrose-sweetened commercially available single-serve soft drinks in volumes as small as 355 ml.