Sleep assessment using accelerometry: Not all algorithms are equal

Abstract

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.

Keywords

accelerometry / concordance / methods / sleep duration / sleep efficiency

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