Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature.

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.

Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study.

BACKGROUND: Accurate measurement of daily physical activity (PA) is important as PA is linked to health outcomes in older adults and people living with complex health conditions. Wrist-worn accelerometers are widely used to estimate PA intensity, including walking, which composes much of daily PA. However, there is concern that wrist-derived PA data in these cohorts is unreliable due to slow gait speed, mobility aid use, disease-related symptoms that impact arm movement, and transient activities of daily living. Despite the potential for error in wrist-derived PA intensity estimates, their use has become ubiquitous in research and clinical application. OBJECTIVE: The goals of this work were to (1) determine the accuracy of wrist-based estimates of PA intensity during known walking periods in older adults and people living with cerebrovascular disease (CVD) or neurodegenerative disease (NDD) and (2) explore factors that influence wrist-derived intensity estimates. METHODS: A total of 35 older adults (n=23 with CVD or NDD) wore an accelerometer on the dominant wrist and ankle for 7 to 10 days of continuous monitoring. Stepping was detected using the ankle accelerometer. Analyses were restricted to gait bouts ≥60 seconds long with a cadence ≥80 steps per minute (LONG walks) to identify periods of purposeful, continuous walking likely to reflect moderate-intensity activity. Wrist accelerometer data were analyzed within LONG walks using 15-second epochs, and published intensity thresholds were applied to classify epochs as sedentary, light, or moderate-to-vigorous physical activity (MVPA). Participants were stratified into quartiles based on the percent of walking epochs classified as sedentary, and the data were examined for differences in behavioral or demographic traits between the top and bottom quartiles. A case series was performed to illustrate factors and behaviors that can affect wrist-derived intensity estimates during walking. RESULTS: Participants averaged 107.7 (SD 55.8) LONG walks with a median cadence of 107.3 (SD 10.8) steps per minute. Across participants, wrist-derived intensity classification was 22.9% (SD 15.8) sedentary, 27.7% (SD 14.6) light, and 49.3% (SD 25.5) MVPA during LONG walks. All participants measured a statistically lower proportion of wrist-derived activity during LONG walks than expected (all P<.001), and 80% (n=28) of participants had at least 20 minutes of LONG walking time misclassified as sedentary based on wrist-derived intensity estimates. Participants in the highest quartile of wrist-derived sedentary classification during LONG walks were significantly older (t16=4.24, P<.001) and had more variable wrist movement (t16=2.13, P=.049) compared to those in the lowest quartile. CONCLUSIONS: The current best practice wrist accelerometer method is prone to misclassifying activity intensity during walking in older adults and people living with complex health conditions. A multidevice approach may be warranted to advance methods for accurately assessing PA in these groups.

User-centered design of feedback regarding health-related behaviors derived from wearables: An approach targeting older adults and persons living with neurodegenerative disease.

Objective: There has been tremendous growth in wearable technologies for health monitoring but limited efforts to optimize methods for sharing wearables-derived information with older adults and clinical cohorts. This study aimed to co-develop, design and evaluate a personalized approach for information-sharing regarding daily health-related behaviors captured with wearables. Methods: A participatory research approach was adopted with: (a) iterative stakeholder, and evidence-led development of feedback reporting; and (b) evaluation in a sample of older adults (n = 15) and persons living with neurodegenerative disease (NDD) (n = 25). Stakeholders included persons with lived experience, healthcare providers, health charity representatives and individuals involved in aging/NDD research. Feedback report information was custom-derived from two limb-mounted inertial measurement units and a mobile electrocardiography device worn by participants for 7-10 days. Mixed methods were used to evaluate reporting 2 weeks following delivery. Data were summarized using descriptive statistics for the group and stratified by cohort and cognitive status. Results: Participants (n = 40) were 60% female (median 72 (60-87) years). A total of 82.5% found the report easy to read or understand, 80% reported the right amount of information was shared, 90% found the information helpful, 92% shared the information with a family member or friend and 57.5% made a behavior change. Differences emerged in sub-group comparisons. A range of participant profiles existed in terms of interest, uptake and utility. Conclusions: The reporting approach was generally well-received with perceived value that translated into enhanced self-awareness and self-management of daily health-related behaviors. Future work should examine potential for scale, and the capacity for wearables-derived feedback to influence longer-term behavior change.

Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease.

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.

Human skeletal muscle fiber type-specific responses to sprint interval and moderate-intensity continuous exercise: acute and training-induced changes.

There are limited and equivocal data regarding potential fiber type-specific differences in the human skeletal muscle response to sprint interval training (SIT), including how this compares with moderate-intensity continuous training (MICT). We examined mixed-muscle and fiber type-specific responses to a single session (study 1) and to 12 wk (study 2) of MICT and SIT using Western blot analysis. MICT consisted of 45 min of cycling at ∼70% of maximal heart rate, and SIT involved 3 × 20-s "all-out" sprints interspersed with 2 min of recovery. Changes in signaling proteins involved in mitochondrial biogenesis in mixed-muscle and pooled fiber samples were similar after acute MICT and SIT. This included increases in the ratios of phosphorylated to total acetyl-CoA carboxylase and p38 mitogen-activated protein kinase protein content (main effects, P < 0.05). Following training, mitochondrial content markers including the protein content of cytochrome c oxidase subunit IV and NADH:ubiquinone oxidoreductase subunit A9 were increased similarly in mixed-muscle and type IIa fibers (main effects, P < 0.05). In contrast, only MICT increased these markers of mitochondrial content in type I fibers (interactions, P < 0.05). MICT and SIT also similarly increased the content of mitochondrial fusion proteins optic atrophy 1 (OPA1) and mitofusin 2 in mixed-muscle, and OPA1 in pooled fiber samples (main effects, P < 0.02). In summary, acute MICT and SIT elicited similar fiber type-specific responses of signaling proteins involved in mitochondrial biogenesis, whereas 12 wk of training revealed differential responses of mitochondrial content markers in type I but not type IIa fibers.NEW & NOTEWORTHY We examined mixed-muscle and fiber type-specific responses to a single session and to 12 wk of moderate-intensity continuous training (MICT) and sprint interval training (SIT) in humans. Both interventions elicited generally similar responses, although the training-induced increases in type I fiber-specific markers of mitochondrial content were greater in MICT than in SIT. These findings advance our understanding of the potential role of fiber type-specific changes in determining the human skeletal muscle response to intermittent and continuous exercise.

Effect of short-term, high-intensity exercise training on human skeletal muscle citrate synthase maximal activity: single versus multiple bouts per session.

The legs of 9 men (age 21 ± 2 years, 45 ± 4 mL/(kg·min)) were randomly assigned to complete 6 sessions of high-intensity exercise training, involving either one or four 5-min bouts of counterweighted, single-leg cycling. Needle biopsies from vastus lateralis revealed that citrate synthase maximal activity increased after training in the 4-bout group (p = 0.035) but not the 1-bout group (p = 0.10), with a significant difference between groups post-training (13%, p = 0.021). Novelty Short-term training using brief intense exercise requires multiple bouts per session to increase mitochondrial content in human skeletal muscle.

The effect of brief intermittent stair climbing on glycemic control in people with type 2 diabetes: a pilot study.

We examined the effect of brief intermittent stair climbing exercise on glycemic control using continuous glucose monitoring in people with type 2 diabetes (n = 7, 5 men; 2 women; age, 21-70 years). The protocol involved three 60-s bouts of vigorously ascending and slowly descending a flight of stairs. Mean 24-h blood glucose was unchanged after an acute session (p = 0.43) and following 18 sessions over 6 weeks (p = 0.13). The protocol was well tolerated by participants but seemingly insufficient to alter glycemic control.