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.

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.