BACKGROUND: Motor impairments not only lead to a significant reduction in patient activity levels but also trigger a further deterioration in motor function due to deconditioning, which is an issue that is particularly pronounced during hospitalization. This deconditioning can be countered by sustaining appropriate activity levels. Activities that occur outside of scheduled programs, often overlooked, are critical in this context. Wearable technology, such as smart clothing, provides a means to monitor these activities.
OBJECTIVE: This study aimed to observe activity levels in patients who had strokes during the subacute phase, focusing on both scheduled training sessions and other nontraining times in an inpatient rehabilitation environment. A smart clothing system is used to simultaneously measure heart rate and acceleration, offering insights into both the amount and intensity of the physical activity.
METHODS: In this preliminary cohort study, 11 individuals undergoing subacute stroke rehabilitation were enrolled. The 48-hour continuous measurement system, deployed at admission and reassessed 4 weeks later, monitored accelerometry data for physical activity (quantified with a moving SD of acceleration [MSDA]) and heart rate for intensity (quantified with percent heart rate reserve). The measurements were performed using a wearable activity monitoring system, the hitoe (NTT Corporation and Toray Industries, Inc) system comprising a measuring garment (wear or strap) with integrated electrodes, a data transmitter, and a smartphone. The Functional Independence Measure was used to assess the patients\' daily activity levels. This study explored factors such as differences in activity during training and nontraining periods, correlations with activities of daily living (ADLs) and age, and changes observed after 4 weeks.
RESULTS: A significant increase was found in the daily total MSDA after the 4-week program, with the average percent heart rate reserve remaining consistent. Physical activity during training positively correlated with ADL levels both at admission (ρ=0.86, P<.001) and 4 weeks post admission (ρ=0.96, P<.001), whereas the correlation between age and MSDA was not significant during training periods at admission (ρ=-0.41, P=.21) or 4 weeks post admission (ρ=-0.25, P=.45). Conversely, nontraining activity showed a negative correlation with age, with significant negative correlations with age at admission (ρ=-0.82, P=.002) and 4 weeks post admission (ρ=-0.73, P=.01).
CONCLUSIONS: Inpatient rehabilitation activity levels were positively correlated with ADL levels. Further analysis revealed a strong positive correlation between scheduled training activities and ADL levels, whereas nontraining activities showed no such correlation. Instead, a negative correlation between nontraining activities and age was observed. These observations suggest the importance of providing activity opportunities for older patients, while it may also suggest the need for adjusting the activity amount to accommodate the potentially limited fitness levels of this demographic. Future studies with larger patient groups are warranted to validate and further elucidate these findings.

This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method (ReSuMe), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of 83.4%, demonstrating the approach\'s efficacy in precise movement activity classification. This method\'s significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs\' superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare.

Background: The effectiveness of knee orthoses as part of conservative treatment for patients with medial knee osteoarthritis has not been fully explored. The purpose of this study was to evaluate the effects of a novel semi-rigid knee orthosis on pain, physical activity, and functional capacity. Methods: Pain levels, physical activity, and functional capacity were assessed in 24 participants experiencing symptomatic medial knee osteoarthritis one week before (i.e., pretest) initiating a six-week orthosis intervention and again during the final week of the intervention (i.e., post-test). Results: Night pain, pain during walking, pain during stair climbing, and pain during sitting consistently decreased by 41% to 48% while wearing the knee orthosis. Device-based measured physical activity showed a 20.2-min increase in vigorous physical activity during the post-test, while light and moderate physical activity did not show significant changes. After six weeks of orthosis application, there was a 5% increased distance for the six-minute walk test, and participants reported fewer limitations both in everyday and athletic activities, as well as an enhanced quality of life. Conclusions: These findings highlight the potential effectiveness of a semi-rigid knee orthosis to enhancing functional capacity and quality of life. More extensive and longer clinical trials are needed to improve confidence in these findings and understand their impact on disease progression.

Various accelerometry protocols have been used to quantify upper extremity (UE) activity, encompassing diverse epoch lengths and thresholding methods. However, there is no consensus on the most effective approach. The aim of this study was to delineate the optimal parameters for analyzing accelerometry data to quantify UE use in individuals with unilateral cerebral palsy (CP).
METHODS: A group of adults with CP (n = 15) participated in six activities of daily living, while a group of children with CP (n = 14) underwent the Assisting Hand Assessment. Both groups performed the activities while wearing ActiGraph GT9X-BT devices on each wrist, with concurrent video recording. Use ratio (UR) derived from accelerometry and video analysis and accelerometer data were compared for different epoch lengths (1, 1.5, and 2 s) and activity count (AC) thresholds (between 2 and 150).
RESULTS: In adults, results are comparable across epoch lengths, with the best AC thresholds being ≥ 100. In children, results are similar across epoch lengths of 1 and 1.5 (optimal AC threshold = 50), while the optimal threshold is higher with an epoch length of 2 (AC = 75).
CONCLUSIONS: The combination of epoch length and AC thresholds should be chosen carefully as both influence the validity of the quantification of UE use.

BACKGROUND: Delirium is common in geriatric inpatients and associated with poor outcomes. Hospitalization is associated with low levels of physical activity. Motor symptoms are common in delirium, but how delirium affects physical activity remains unknown.
OBJECTIVE: To investigate differences in physical activity between geriatric inpatients with and without delirium.
METHODS: We included acutely admitted patients ≥ 75 years in a prospective observational study at a medical geriatric ward at a Norwegian University Hospital. Delirium was diagnosed according to the DSM-5 criteria. Physical activity was measured by an accelerometer-based device worn on the right thigh. The main outcome was time in upright position (upright time) per 24 h (00.00 to 23.59) on the first day of hospitalization with verified delirium status. Group differences were analysed using t test.
RESULTS: We included 237 patients, mean age 86.1 years (Standard Deviation (SD) 5.1), and 73 patients (30.8%) had delirium. Mean upright time day 1 for the entire group was 92.2 min (SD 84.3), with 50.9 min (SD 50.7) in the delirium group and 110.6 min (SD 89.7) in the no-delirium group, mean difference 59.7 minutes, 95% Confidence Interval 41.6 to 77.8, p value < 0.001.
CONCLUSIONS: Low levels of physical activity in patients with delirium raise the question if immobilization may contribute to poor outcomes in delirium. Future studies should investigate if mobilization interventions could improve outcomes of delirium.
CONCLUSIONS: In this sample of geriatric inpatients, the group with delirium had lower levels of physical activity than the group without delirium.

Voluntary wheel running is a common measure of general activity in many rodent models across neuroscience and physiology. However, current commercial wheel monitoring systems can be cost-prohibitive to many investigators, with many of these systems requiring investments of thousands of dollars. In recent years, several open-source alternatives have been developed, and while these tools are much more cost effective than commercial system, they often lack the flexibility to be applied to a wide variety of projects. Here, we have developed PAW, a 3D Printable Arduino-based Wheel logger. PAW is wireless, fully self-contained, easy to assemble, and all components necessary for its production can be obtained for only $75 CAD. Furthermore, with its compact internal electronics, the 3D printed casing can be easily modified to be used with a wide variety of running wheel designs for a wide variety of rodent species. Data recorded with the PAW system shows circadian patterns of activity which is expected from mice and is consistent with results found in the literature. Altogether, PAW is a flexible, low-cost system that can be beneficial to a broad range of researchers who study rodent models.

Day-night locomotor activities are the most readily observed outputs of the circadian (~24-h period) clock in many animals. Temporal patterns of the light-dark schedule serve as input to the clock. While circadian activity patterns under various lighting conditions have been observed and documented, the full extent of circadian locomotor activities by genotype and entrainment remains uncharacterized. To facilitate large-scale, parallel cataloging of circadian input-output patterns, we created the LocoBox, an easy-to-construct and easy-to-operate system that can control environmental light with flexible entrainment scenarios combined with the T-cycle and measure locomotor activities in individual home cages. The LocoBox is made using economical, common components, and normal breeding cages can be used for long-term recording. We provide details of the components and blueprints, along with software programs for Arduino and a Python-based graphical user interface (GUI), so that the system can be easily replicated in other laboratories.

Inactive behavior is common in hospitalized patients. This study investigated the effectiveness of using a smartphone app with an accelerometer (Hospital Fit) in addition to usual care physiotherapy on increasing patients\' physical activity (PA) behavior. A randomized controlled trial was performed at Maastricht University Medical Centre. Patients receiving physiotherapy while hospitalized at the department of Pulmonology or Internal Medicine were randomized to usual care physiotherapy or using Hospital Fit additionally. Daily time spent walking, standing, and upright (standing/walking) (min) and daily number of postural transitions were measured with an accelerometer between the first and last treatment. Multiple linear regression analysis was performed to determine the association between PA behavior and Hospital Fit use, corrected for functional independence (mILAS). Seventy-eight patients were included with a median (IQR) age of 63 (56-68) years. Although no significant effects were found, a trend was seen in favor of Hospital Fit. Effects increased with length of use. Corrected for functional independence, Hospital Fit use resulted in an average increase of 27.4 min (95% CI: -2.4-57.3) standing/walking on day five and 29.2 min (95% CI: -6.4-64.7) on day six compared to usual care. Hospital Fit appears valuable in increasing PA in functionally independent patients.

Activity monitoring is a necessary technique to ensure stroke survivors\' activity levels in the hospital are within optimal levels as this is important for enhanced motor recovery. However, this could be time-consuming for healthcare professionals like physiotherapists. Activity monitoring by caregivers could be an alternate option. Therefore, our aim was to compare the activity monitoring of stroke survivors by caregivers and physiotherapists during early phase in a hospital setting.
An observation study was carried out in the neuroscience ward in a tertiary care hospital among 17 stroke survivors. Physiotherapist and caregivers were instructed to use an activity log chart that was developed during previous research conducted by the same authors for observing the activities performed by the patients every 15 minutes from 8 AM to 5 PM across one day. Data collected were analysed using Stata 15. Kappa statistics were carried out to determine the agreement of the observations between the two raters.
A total of 10 male and seven female caregivers of stroke survivors with a mean age of 40.11 ± 9.2 years and a trained physiotherapist participated in the study. A total of 272 observations of caregivers were in agreement with that of the physiotherapist. Inter-rater Kappa statistics showed 60% agreement between the physiotherapist and the caregivers (p<0.05).
There was moderate agreement between the physiotherapist and caregiver for activity monitoring of stroke survivors. This suggests behavioural mapping by caregivers may be a potential alternative solution in healthcare settings.

Demographic changes and an ageing population require more effective methods to confront the increased prevalence of chronic diseases which generate dependence in older adults as well as an important rise in social expenditure. The challenge is not only to increase life expectancy, but also to ensure that the older adults can fully enjoy that moment in their lives, living where they wish to (private home, nursing home, …). Physical activity (PA) is a representative parameter of a person\'s state of health, especially when we are getting older, because it plays an important role in the prevention of diseases, and that is the reason why it is promoted in older adults. One of the goals of this work is to assess the feasibility of objectively measuring the PA levels of older adults wherever they live. In addition, this work proposes long-term monitoring that helps to gather daily activity patterns. We fuse inertial measurements with other technologies (WiFi- and ultrasonic-based location) in order to provide not only PA, but also information about the place where the activities are carried out, including both room-level location and precise positioning (depending on the technology used). With this information, we would be able to generate information about the person\'s daily routines which can be very useful for the early detection of physical or cognitive impairment.