Background
Understanding mobility aid use has implications for falls risk reduction and aid prescription. However, aid use in daily life is understudied and more complex than revealed by commonly ...used yes/no self-reporting.
Aims
To advance approaches for evaluating mobility aid use among older adults using a situational (context-driven) questionnaire and wearable sensors.
Methods
Data from two cross-sectional observational studies of older adults were used: (1) 190 participants (86 ± 5 years) completed tests of standing, sit-to-stand, walking, grip strength, and self-reported fear of falling and (2) 20 participants (90 ± 4 years) wore two body-worn and one aid-mounted sensors continuously for seven days to objectively quantify aid use during walking. Situational and traditional binary reporting stratified participants into aid dependency levels (0–4) and aid-user groups, respectively. Physical performance and fear of falling were compared between aid users, and dependency levels and sensor-derived walking behaviors were compared to reported aid use.
Results
Physical performance and fear of falling differed between aid-user groups (
P
< 0.05). Sensor-derived outputs revealed differences in walking behaviors and aid use when categorized by dependency level and walking bout length (
P
< 0.05). Walking bout frequency (
rho
(18) = − 0.47,
P
= 0.038) and aid use time (
rho
(
1
3
)
= .72,
P
= 0.002) were associated with dependency level.
Discussion
Comparisons of situational aid dependency revealed heterogeneity between aid users suggesting binary aid use reporting fails to identify individual differences in walking and aid use behaviors.
Conclusions
Enhanced subjective aid use reporting and objective measurements of walking and aid use may improve aid prescription and inform intervention to support safe and effective mobility in older adults.
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.
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.
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.
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 (t
=4.24, P<.001) and had more variable wrist movement (t
=2.13, P=.049) compared to those in the lowest quartile.
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.
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.
TiO2 photocatalysis has been used to destroy microcystin-LR in aqueous solution. The destruction of this toxin was monitored by HPLC, and the disappearance was accompanied by the appearance of seven ...UV detectable compounds. Spectral analysis revealed that some of these compounds retained spectra similar to the parent compound suggesting that the Adda moiety, thought to be responsible for the characteristic spectrum, remained intact whereas the spectra of some of the other products was more radically altered. Six of the seven observed reaction products did not appear to undergo further degradation during prolonged photocatalysis (100 min). The degree to which microcystin-LR was mineralized by photocatalytic oxidation was determined. Results indicated that less than 10% mineralization occurred. Mass spectral analysis of the photocatalyzed microcystin-LR allowed tentative characterization of the reaction process and products. Reduction in toxicity due to the photocatalytic oxidation was evaluated using an invertebrate bioassay, which demonstrated that the disappearance of microcystin-LR was paralleled by a reduction in toxicity. These findings suggest that photocatalytic destruction of microcystins may be a suitable method for the removal of these potentially hazardous compounds from drinking water.
Microcystins are a family of hepatotoxic peptides produced by freshwater cyanobacteria. Their occurrence in drinking water is of concern since chronic exposure to these toxins causes tumor promotion. ...It is therefore essential to establish a reliable treatment strategy that will ensure their removal from potable water. We have previously described the rapid destruction of microcystin-LR using TiO
2 photocatalysis, however, since there are at least 70 microcystin variants it is essential that the destruction of a number of microcystins be evaluated. In this study the dark adsorption and destruction of four microcystins was followed over a range of pH. All four microcystins were destroyed although the efficiency of their removal varied. The two more hydrophobic microcystins (-LW and -LF) were found to have high dark adsorption (98 and 91% at pH 4) in contrast to microcystin-RR, which was found to have almost no (only 2–3%) dark adsorption across all pH.
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.
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.
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).
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.
Abstract
Background
Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for ...remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status.
Results & discussion
NiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting.
Conclusion
The NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual’s daily health-related behavior. NiMBaLWear’s focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health
.
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.
Introduction
Understanding synergies between neurodegenerative and cerebrovascular pathologies that modify dementia presentation represents an important knowledge gap.
Methods
This multi‐site, ...longitudinal, observational cohort study recruited participants across prevalent neurodegenerative diseases and cerebrovascular disease and assessed participants comprehensively across modalities. We describe univariate and multivariate baseline features of the cohort and summarize recruitment, data collection, and curation processes.
Results
We enrolled 520 participants across five neurodegenerative and cerebrovascular diseases. Median age was 69 years, median Montreal Cognitive Assessment score was 25, median independence in activities of daily living was 100% for basic and 93% for instrumental activities. Spousal study partners predominated; participants were often male, White, and more educated. Milder disease stages predominated, yet cohorts reflect clinical presentation.
Discussion
Data will be shared with the global scientific community. Within‐disease and disease‐agnostic approaches are expected to identify markers of severity, progression, and therapy targets. Sampling characteristics also provide guidance for future study design.
Background
Acute change in gait speed while performing a mental task dual-task gait cost (DTC), and hyperintensity magnetic resonance imaging signals in white matter are both important disability ...predictors in older individuals with history of stroke (poststroke). It is still unclear, however, whether DTC is associated with overall hyperintensity volume from specific major brain regions in poststroke.
Methods
This is a cohort study with a total of 123 older (69 ± 7 years of age) participants with history of stroke were included from the Ontario Neurodegenerative Disease Research Initiative. Participants were clinically assessed and had gait performance assessed under single- and dual-task conditions. Structural neuroimaging data were analyzed to measure both, white matter hyperintensity (WMH) and normal appearing volumes. Percentage of WMH volume in frontal, parietal, occipital, and temporal lobes as well as subcortical hyperintensities in basal ganglia + thalamus were the main outcomes. Multivariate models investigated associations between DTC and hyperintensity volumes, adjusted for age, sex, years of education, global cognition, vascular risk factors, APOE4 genotype, residual sensorimotor symptoms from previous stroke and brain volume.
Results
There was a significant positive global linear association between DTC and hyperintensity burden (adjusted Wilks’ λ = .87, P = .01). Amongst all WMH volumes, hyperintensity burden from basal ganglia + thalamus provided the most significant contribution to the global association (adjusted β = .008, η2 = .03; P = .04), independently of brain atrophy.
Conclusions
In poststroke, increased DTC may be an indicator of larger white matter damages, specifically in subcortical regions, which can potentially affect the overall cognitive processing and decrease gait automaticity by increasing the cortical control over patients’ locomotion.
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