Background Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). Methods Thirty-nine participants with CVD, Alzheimer’s disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson’s disease, or amyotrophic lateral sclerosis (median age 68 (45–83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. Results Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17–22 min/day), with significant differences between some locations ( p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear ( p < 0.001) and there was a small but significant increase in non-wear over the collection period ( p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. Conclusion A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.
Background Accelerometery is commonly used to estimate physical activity, sleep, and sedentary behavior. In free-living conditions, periods of device removal (non-wear) can lead to misclassification of behavior with consequences for research outcomes and clinical decision making. Common methods for non-wear detection are limited by data transformations (e.g., activity counts) or algorithm parameters such as minimum durations or absolute temperature thresholds that risk over- or under-estimating non-wear time. This study aimed to advance non-wear detection methods by integrating a ‘rate-of-change’ criterion for temperature into a combined temperature-acceleration algorithm. Methods Data were from 39 participants with neurodegenerative disease (36% female; age: 45–83 years) who wore a tri-axial accelerometer (GENEActiv) on their wrist 24-h per day for 7-days as part of a multi-sensor protocol. The reference dataset was derived from visual inspection conducted by two expert analysts. Linear regression was used to establish temperature rate-of-change as a criterion for non-wear detection. A classification and regression tree (CART) decision tree classifier determined optimal parameters separately for non-wear start and end detection. Classifiers were trained using data from 15 participants (38.5%). Outputs from the CART analysis were supplemented based on edge cases and published parameters. Results The dataset included 186 non-wear periods (85.5% < 60 min). Temperature rate-of-change over the first five minutes of non-wear was − 0.40 ± 0.17 °C/minute and 0.36 ± 0.21 °C/minute for the first five minutes following device donning. Performance of the DETACH (DEvice Temperature and Accelerometer CHange) algorithm was improved compared to existing algorithms with recall of 0.942 (95% CI 0.883 to 1.0), precision of 0.942 (95% CI 0.844 to 1.0), F1-Score of 0.942 (95% CI 0.880 to 1.0) and accuracy of 0.996 (0.994–1.000). Conclusion The DETACH algorithm accurately detected non-wear intervals as short as five minutes; improving non-wear classification relative to current interval-based methods. Using temperature rate-of-change combined with acceleration results in a robust algorithm appropriate for use across different temperature ranges and settings. The ability to detect short non-wear periods is particularly relevant to free-living scenarios where brief but frequent removals occur, and for clinical application where misclassification of behavior may have important implications for healthcare decision-making.
<b><i>Background:</i></b> Independent mobility is a complex behavior that relies on the ability to walk, maintain stability, and transition between postures. However, guidelines for assessment that details <i>what</i> elements of mobility to evaluate and <i>how</i> they should be measured remain unclear. <b><i>Methods:</i></b> Performance on tests of standing, sit-to-stand, and walking were evaluated in a cohort of 135 complex, comorbid, and older adults (mean age 87 ± 5.5 years). Correlational analysis was conducted to examine the degree of association for measures within and between mobility domains on a subset of participants (<i>n</i> = 83) able to complete all tasks unaided. Participants were also grouped by the presence of risk markers for frailty (gait speed and grip strength) to determine if the level of overall impairment impacted performance scores and if among those with risk markers, the degree of association was greater. <b><i>Results:</i></b> Within-domain relationships for sit-to-stand and walking were modest (rho = 0.01–0.60). Associations either did not exist or relationships were weak for measures reflecting different domains (rho = −0.35 to 0.25, <i>p</i> > 0.05). As expected, gait speed differed between those with and without frailty risk markers (<i>p</i> < 0.001); however, balance and sit-to-stand measures did not (<i>p</i> ≥ 0.05). <b><i>Conclusions:</i></b> This study highlights the need to independently evaluate different mobility domains within an individual as a standard assessment approach. Modest within-domain relationships emphasize the need to account for multiple, unique control challenges within more complex domains. These findings have important implications for standardized mobility assessment and targeted rehabilitation strategies for older adults.
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