BACKGROUND: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. PURPOSE: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. METHODS: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X. RESULTS: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. CONCLUSIONS: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.
BACKGROUND: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. PURPOSE: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. METHODS: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X. RESULTS: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. CONCLUSIONS: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.
Authors: Julie B Wang; Lisa A Cadmus-Bertram; Loki Natarajan; Martha M White; Hala Madanat; Jeanne F Nichols; Guadalupe X Ayala; John P Pierce Journal: Telemed J E Health Date: 2015-06-02 Impact factor: 3.536
Authors: Charles E Matthews; Kong Y Chen; Patty S Freedson; Maciej S Buchowski; Bettina M Beech; Russell R Pate; Richard P Troiano Journal: Am J Epidemiol Date: 2008-02-25 Impact factor: 4.897
Authors: Barbara E Ainsworth; William L Haskell; Stephen D Herrmann; Nathanael Meckes; David R Bassett; Catrine Tudor-Locke; Jennifer L Greer; Jesse Vezina; Melicia C Whitt-Glover; Arthur S Leon Journal: Med Sci Sports Exerc Date: 2011-08 Impact factor: 5.411
Authors: Predrag Klasnja; Eric B Hekler; Saul Shiffman; Audrey Boruvka; Daniel Almirall; Ambuj Tewari; Susan A Murphy Journal: Health Psychol Date: 2015-12 Impact factor: 4.267
Authors: Erin E Dooley; Priya Palta; Dana L Wolff-Hughes; Pablo Martinez-Amezcua; John Staudenmayer; Richard P Troiano; Kelley Pettee Gabriel Journal: Med Sci Sports Exerc Date: 2022-04-06
Authors: Ruonan Li; Luo Xiao; Ekaterina Smirnova; Erjia Cui; Andrew Leroux; Ciprian M Crainiceanu Journal: Stat Med Date: 2022-05-01 Impact factor: 2.497
Authors: Pablo Martinez-Amezcua; Erin E Dooley; Nicholas S Reed; Danielle Powell; Bjoern Hornikel; Justin S Golub; Kelley Pettee Gabriel; Priya Palta Journal: JAMA Netw Open Date: 2022-03-01
Authors: Jairo H Migueles; Pablo Molina-Garcia; Lucia V Torres-Lopez; Cristina Cadenas-Sanchez; Alex V Rowlands; Ulrich W Ebner-Priemer; Elena D Koch; Andreas Reif; Francisco B Ortega Journal: Sci Rep Date: 2022-04-01 Impact factor: 4.379
Authors: Britni R Belcher; Dana L Wolff-Hughes; Erin E Dooley; John Staudenmayer; David Berrigan; Mark S Eberhardt; Richard P Troiano Journal: Med Sci Sports Exerc Date: 2021-11-01