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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Miller, Allison E. Lang, Catherine E. Bland, Marghuretta D. Lohse, Keith R. |
| Abstract | Background Despite the promise of wearable sensors for both rehabilitation research and clinical care, these technologies pose significant burden on data collectors and analysts. Investigations of factors that may influence the wearable sensor data processing pipeline are needed to support continued use of these technologies in rehabilitation research and integration into clinical care settings. The purpose of this study was to investigate the effect of one such factor, sleep, on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment and across a two-day wearing period. Methods This was a secondary analysis of data collected during a prospective, longitudinal cohort study (n = 127 individuals, 62 with upper limb impairment and 65 without). Participants wore a wearable sensor on each wrist for 48 h. Five upper limb sensor variables were calculated over the full wear period (sleep included) and with sleep time removed (sleep excluded): preferred time, non-preferred time, use ratio, non-preferred magnitude and its standard deviation. Linear mixed effects regression was used to quantify the effect of sleep on each sensor variable and determine if the effect differed between people with and without upper limb impairment and across a two-day wearing period. Results There were significant differences between sleep included and excluded for the variables preferred time (p < 0.001), non-preferred time (p < 0.001), and non-preferred magnitude standard deviation (p = 0.001). The effect of sleep was significantly different between people with and without upper limb impairment for one variable, non-preferred magnitude (p = 0.02). The effect of sleep was not substantially different across wearing days for any of the variables. Conclusions Overall, the effects of sleep on sensor-derived variables of upper limb accelerometry are small, similar between people with and without upper limb impairment and across a two-day wearing period, and can likely be ignored in most contexts. Ignoring the effect of sleep would simplify the data processing pipeline, facilitating the use of wearable sensors in both research and clinical practice. |
| Related Links | https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-024-01384-z.pdf |
| Ending Page | 12 |
| Page Count | 12 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17430003 |
| DOI | 10.1186/s12984-024-01384-z |
| Journal | Journal of NeuroEngineering and Rehabilitation |
| Issue Number | 1 |
| Volume Number | 21 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-05-28 |
| Access Restriction | Open |
| Subject Keyword | Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering Stroke Sleep Wearable sensors Accelerometry Breast cancer Multiple sclerosis Fracture |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Rehabilitation |
| Journal Impact Factor | 5.2/2023 |
| 5-Year Journal Impact Factor | 5.6/2023 |
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