Affordable sleep estimates using micro-electro-mechanical-systems (MEMS) accelerometry

Abstract

Although more aff¬ordable than polysomnography, actigraphic sleep estimates have disadvantages. Brand-specific differences in data reduction impede pooling of data for consortia to create large-scale cohorts, as for genome-wide-association-studies (GWAS). Secondly, online data reduction may not fully exploit movement information. Thirdly, sleep estimate reliability might improve by advanced analyses of tri-axial, linear accelerometry data sampled at a high rate. Such recordings are now feasible using affordable micro-electro-mechanical-systems (MEMS). However, it might take a while before advanced analyses are validated and available. PURPOSE: To provide lab-databases and ongoing studies with backward compatibility when switching from actigraphy to MEMS accelerometry, we designed and validated a method to transform accelerometry data into the traditional actigraphic ‘movement counts’, thus allowing for the use of validated algorithms to estimate sleep parameters. Simultaneous duplicate actigraphy and duplicate MEMS-accelerometry was recorded in fifteen healthy adults (23–36 years, 10 M, 5F) during one night spent at home. Actigraphy was recorded as ‘movement counts 15-s epoch with two Actiwatches (Cambridge Neurotechnology Ltd., Cambridge, UK and Mini Mitter, Respironics Inc., Bend, OR, USA). MEMS-accelerometry was digitized at 50 Hz with two Geneactivs (ActivInsights Ltd., Kimbolton, UK). Passing-Bablok regression was used to optimize the transformation of MEMS-accelerometry signals to ‘movement counts’. Actigraphic ‘movement counts’ and their MEMS-accelerometry estimates were used to calculate common sleep parameters. Reliability was evaluated both between and within the traditional actigraphs and MEMS-accelerometers using Bland–Altman plots. Movement counts could be estimated from MEMS-accelerometry with high precision. MEMS-accelerometry had a better reliability than actigraphy; sleep parameter estimate agreement between two MEMS-accelerometers or a MEMS-accelerometer and an actigraph was better than agreement between two actigraphs. The algorithm allows for continuity of outcome parameters in ongoing actigraphy studies that consider switching to the new generation of MEMS-accelerometers. Their affordability and the algorithm with graphical-user-interface we here provide, makes objective sleep estimates in large-scale twin-sibling and GWAS cohort designs feasible. Detailed results as well as instructions to get the free stand-alone Matlab-based interface to convert 3D signal to movement counts and obtain actigraphic sleep estimates are given in: te Lindert BHW and Van Someren EJW (2013) Affordable sleep estimates using micro electro-mechanical systems (MEMS) accelerometry. Sleep 36:781–789. Supported by: Project NeuroSIPE 10 738, of the Dutch Technology Foundation STW, which is part of the Netherlands Organization for Scientific Research (NWO) and partly funded by the Ministry of Economic Aff¬airs, Agriculture and Innovation; and by the VICI Innovation Grant 453-07-001 of the Netherlands Organization of Scientific Research (NWO); The Hague, the Netherlands.