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.

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.

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.

A single aerobic exercise session accelerates movement execution but not central processing.

Previous research has demonstrated that aerobic exercise has disparate effects on speed of processing and movement execution. In simple and choice reaction tasks, aerobic exercise appears to increase speed of movement execution while speed of processing is unaffected. In the flanker task, aerobic exercise has been shown to reduce response time on incongruent trials more than congruent trials, purportedly reflecting a selective influence on speed of processing related to cognitive control. However, it is unclear how changes in speed of processing and movement execution contribute to these exercise-induced changes in response time during the flanker task. This study examined how a single session of aerobic exercise influences speed of processing and movement execution during a flanker task using electromyography to partition response time into reaction time and movement time, respectively. Movement time decreased during aerobic exercise regardless of flanker congruence but returned to pre-exercise levels immediately after exercise. Reaction time during incongruent flanker trials decreased over time in both an aerobic exercise and non-exercise control condition indicating it was not specifically influenced by exercise. This disparate influence of aerobic exercise on movement time and reaction time indicates the importance of partitioning response time when examining the influence of aerobic exercise on speed of processing. The decrease in reaction time over time independent of aerobic exercise indicates that interpreting pre-to-post exercise changes in behavior requires caution.

A single session of exercise as a modulator of short-term learning in healthy individuals.

BACKGROUND: A single session of aerobic exercise is linked to faster motor responses; however, the effect on rate of short-term learning is less clear. The objective was to evaluate the influence of a single bout of aerobic exercise on the rate of short-term acquisition of a shape-letter association task requiring a motor response. METHODS: 23 [11 females, age 20.8±2.7years] healthy young adults were evaluated using a randomly assigned crossover design which was counterbalanced for order before and after moderate (exercise) and light (control) intensity cycle ergometry. Participants performed 3 blocks, with each block consisting of one round of training and testing. During training, participants were tasked with learning 6 unique shape-letter associations. Subsequent testing required a key press response to a visually presented shape pattern. Response time and error rates were used to assess acquisition over the 3 blocks of testing. RESULTS: Mean response time was faster post-exercise relative to the other testing periods, and approached statistical significance compared to post-control (p<0.07). However, no significant difference in response time reduction (difference between test block 1 and test block 3) was identified between the four evaluations (pre and post the exercise and control conditions). Error rate reduction (test block 1 minus test block 3) revealed that individuals had the smallest change in error rate post-exercise (p<0.05). Follow-up analyses revealed fewer errors in test block 1 and test block 2 post-exercise which approached statistical significance (p=0.06) suggesting near-perfect error rates were obtained after only 2 testing blocks post-exercise compared to 3 blocks in the other testing periods. CONCLUSIONS: Support for augmentation of short-term learning was mixed as errors were reduced post-exercise while response time was not different between evaluations. Future work should include neurophysiological evaluation and a retention test to better elucidate the influence of aerobic exercise on rate of short-term learning.

Standing still: is there a role for the cortex?

In humans, standing still appears so automatic that high-level cortical processes seem unnecessary. However, by measuring cortical activity time-locked to reactive control events arising from naturally occurring instability while standing still, we detected cortical involvement in the form of an evoked N1 potential prior to the onset of balance reactions. Peak amplitude and spectral power of this event-related activity increased as postural challenges and demand for reactive control increased.

Frequency characteristics of cortical activity associated with perturbations to upright stability.

Cortical evoked potentials are evident in the control of whole-body balance reactions in response to transient instability. The focus of this work is to continue to advance understanding of the potential cortical contributions to bipedal balance control. Temporally unpredictable postural perturbations evoke a negative potential (N1), which has drawn parallels to error-related negativity (ERN) as well as visual and auditory evoked N1 responses. The mechanism underlying the generation of event-related potentials (ERPs) has been a matter of debate for the past few decades. While the evoked model proposes that ERPs are generated by the addition of fixed latency and fixed polarity responses, the phase reorganization model suggests that ERPs are the result of stimulus-induced phase reorganization of the ongoing oscillations. Previous studies have suggested phase reorganization as a possible mechanism in auditory N1, visual N1 and error-related negativity (ERN). The purpose of the current study was to explore the frequency characteristics of the cortical responses to whole-body balance perturbations. Perturbations were evoked using a lean and release protocol. The results revealed a significant power increase and phase-locking of delta, theta, alpha, and beta band activity during perturbation-evoked N1. This may suggest that the stimulus-induced phase reorganization of the ongoing electroencephalographic (EEG) activity could account for the features of cortical ERPs in response to perturbation of upright stability.