Study objectives:Accelerometry devices are increasingly used to assess sleep.However,whether different algorithms provide consistent estimates remains uncertain.This study compared sleep parameters derived from two ac...Study objectives:Accelerometry devices are increasingly used to assess sleep.However,whether different algorithms provide consistent estimates remains uncertain.This study compared sleep parameters derived from two accelerometry-based algorithms and a self-reported sleep journal.Methods:Data were obtained from the second(2014-2017;n=2724;53.3% women;62.0±10.0 years)and third(2018-2021;n=2087;53.5% women;65.1±9.6 years)follow-ups of the CoLaus|PsyCoLaus study.Seven-day accelerometry data were analysed using GGIR(R-based)and MACRO(Excel-based)algorithms.A subset of participants also completed an ecological momentary assessment(EMA)sleep diary.Sleep onset(<22:00,22:00-23:59,≥24:00),average sleep duration,and average sleep efficiency were compared.Results:In both surveys,GGIR estimated longer sleep duration than MACRO(406±103 vs.378±79 min;397±60 vs.366±84 min;p<0.001).Sleep duration correlations were moderate(Spearman r=0.592)with Lin's concordance correlation of 0.269 and 0.513,respectively.GGIR estimates were closer to EMA than MACRO.For sleep onset,GGIR classified>80% of participants before 22:00,compared with 38%-64%(MACRO)and 8%-12%(EMA).GGIR also provided higher sleep efficiency(72±17 vs.70±14%;70±7 vs.67±15%;p<0.001;r=0.383).Conclusion:When assessing sleep from accelerometry,algorithm choice strongly influences estimates,highlighting the need for standardisation.展开更多
基金ResMed FoundationGlaxoSmithKline+1 种基金the Faculty of Biology and Medicine of Lausanne,the Swiss National Science Foundation,Grant/Award Numbers:33CSCO-122661,33CS30-139468,33CS30-148401,33CS30_177535,324730_204523,320030_220190the Swiss Personalised Health Network,Grant/Award Number:2018DRI01。
文摘Study objectives:Accelerometry devices are increasingly used to assess sleep.However,whether different algorithms provide consistent estimates remains uncertain.This study compared sleep parameters derived from two accelerometry-based algorithms and a self-reported sleep journal.Methods:Data were obtained from the second(2014-2017;n=2724;53.3% women;62.0±10.0 years)and third(2018-2021;n=2087;53.5% women;65.1±9.6 years)follow-ups of the CoLaus|PsyCoLaus study.Seven-day accelerometry data were analysed using GGIR(R-based)and MACRO(Excel-based)algorithms.A subset of participants also completed an ecological momentary assessment(EMA)sleep diary.Sleep onset(<22:00,22:00-23:59,≥24:00),average sleep duration,and average sleep efficiency were compared.Results:In both surveys,GGIR estimated longer sleep duration than MACRO(406±103 vs.378±79 min;397±60 vs.366±84 min;p<0.001).Sleep duration correlations were moderate(Spearman r=0.592)with Lin's concordance correlation of 0.269 and 0.513,respectively.GGIR estimates were closer to EMA than MACRO.For sleep onset,GGIR classified>80% of participants before 22:00,compared with 38%-64%(MACRO)and 8%-12%(EMA).GGIR also provided higher sleep efficiency(72±17 vs.70±14%;70±7 vs.67±15%;p<0.001;r=0.383).Conclusion:When assessing sleep from accelerometry,algorithm choice strongly influences estimates,highlighting the need for standardisation.
