Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

Background: Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The timed up and go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment. Objective: In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following total hip arthroplasty (THA). Methods: Community-living elderly (n = 239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least three days. Activities of daily living (ADLs), including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n = 15, 55-75 years), during 12-weeks period following THA. Main results: In leave-one-out-cross-validation (LOOCV), a strong correlation (p = 0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-days estimates was high (ICC = 0.94). Compared to week 2 post-THA, remote TUG was significantly improved at week 6 (11.7 +/- 3.9 s versus 8.0 +/- 1.8 s,p &lt;0.001), with no further change at 12-weeks (8.1 +/- 3.9s, p = 0.37). Significance: Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.</p

Differences in Walking Pattern during 6-Min Walk Test between Patients with COPD and Healthy Subjects

BACKGROUND: To date, detailed analyses of walking patterns using accelerometers during the 6-min walk test (6MWT) have not been performed in patients with chronic obstructive pulmonary disease (COPD). Therefore, it remains unclear whether and to what extent COPD patients have an altered walking pattern during the 6MWT compared to healthy elderly subjects. METHODOLOGY/PRINCIPAL FINDINGS: 79 COPD patients and 24 healthy elderly subjects performed the 6MWT wearing an accelerometer attached to the trunk. The accelerometer features (walking intensity, cadence, and walking variability) and subject characteristics were assessed and compared between groups. Moreover, associations were sought with 6-min walk distance (6MWD) using multiple ordinary least squares (OLS) regression models. COPD patients walked with a significantly lower walking intensity, lower cadence and increased walking variability compared to healthy subjects. Walking intensity and height were the only two significant determinants of 6MWD in healthy subjects, explaining 85% of the variance in 6MWD. In COPD patients also age, cadence, walking variability measures and their interactions were included were significant determinants of 6MWD (total variance in 6MWD explained: 88%). CONCLUSIONS/SIGNIFICANCE: COPD patients have an altered walking pattern during 6MWT compared to healthy subjects. These differences in walking pattern partially explain the lower 6MWD in patients with COPD

The design of a purpose-built exergame for fall prediction and prevention for older people

Background Falls in older people represent a major age-related health challenge facing our society. Novel methods for delivery of falls prevention programs are required to increase effectiveness and adherence to these programs while containing costs. The primary aim of the Information and Communications Technology-based System to Predict and Prevent Falls (iStoppFalls) project was to develop innovative home-based technologies for continuous monitoring and exercise-based prevention of falls in community-dwelling older people. The aim of this paper is to describe the components of the iStoppFalls system. Methods The system comprised of 1) a TV, 2) a PC, 3) the Microsoft Kinect, 4) a wearable sensor and 5) an assessment and training software as the main components. Results The iStoppFalls system implements existing technologies to deliver a tailored home-based exercise and education program aimed at reducing fall risk in older people. A risk assessment tool was designed to identify fall risk factors. The content and progression rules of the iStoppFalls exergames were developed from evidence-based fall prevention interventions targeting muscle strength and balance in older people. Conclusions The iStoppFalls fall prevention program, used in conjunction with the multifactorial fall risk assessment tool, aims to provide a comprehensive and individualised, yet novel fall risk assessment and prevention program that is feasible for widespread use to prevent falls and fall-related injuries. This work provides a new approach to engage older people in home-based exercise programs to complement or provide a potentially motivational alternative to traditional exercise to reduce the risk of falling

Eight-Week Remote Monitoring Using a Freely Worn Device Reveals Unstable Gait Patterns in Older Fallers

Objectives: Develop algorithms to detect gait impairments remotely using data from freely worn devices during long-term monitoring. Identify statistical models that describe how gait performances are distributed over several weeks. Determine the data window required to reliably assess an increased propensity for falling. Methods: 1085 days of walking data were collected from eighteen independent-living older people (mean age 83 years) using a freely worn pendant sensor (housing a triaxial accelerometer and pressure sensor). Statistical distributions from several accelerometer-derived gait features (encompassing quantity, exposure, intensity, and quality) were compared for those with and without a history of falling. Results: Participants completed more short walks relative to long walks, as approximated by a power law. Walks less than 13.1 s comprised 50% of exposure to walking-related falls. Daily-life cadence was bimodal and step-time variability followed a log-normal distribution. Fallers took significantly fewer steps per walk and had relatively more exposure from short walks and greater mode of step-time variability. Conclusions: Using a freely worn device and wavelet-based analysis tools allowed long-term monitoring of walks greater than or equal to three steps. In older people, short walks constitute a large proportion of exposure to falls. To identify fallers, mode of variability may be a better measure of central tendency than mean of variability. A week's monitoring is sufficient to reliably assess the long-term propensity for falling. Significance: Statistical distributions of gait performances provide a reference for future wearable device development and research into the complex relationships between daily-life walking patterns, morbidity, and falls

Validation of Walking Speed Estimation from Trunk Mounted Accelerometers for a Range of Walking Speeds

Walking speed is a strong indicator of the health status of older people and patients. Using algorithms, the walking speed can be estimated from wearable accelerometers, which enables minimally obtrusive (longitudinal) monitoring. We evaluated the performance of two algorithms, the inverted pendulum (IP) algorithm, and a novel adaptation correcting for lateral step movement, which aimed to improve accuracy during slow walking. To evaluate robustness, we gathered data from different groups (healthy adults, elderly, and elderly patients) of volunteers (n = 159) walking under various conditions (over ground, treadmill, using walking aids) at a broad range of speeds (0.11–1.93 m/s). Both of the algorithms showed good agreement with the reference values and similar root-mean-square errors (RMSEs) for walking speeds ≥0.5 m/s, which ranged from 0.09–0.16 m/s for the different positions, in line with the results from others. However, for slower walking, RMSEs were significantly better for the new method (0.06–0.09 m/s versus 0.15–0.19 m/s). Pearson correlation improved for speeds &lt;0.5 m/s (from 0.67–0.72 to 0.73–0.82) as well as higher speeds (0.87–0.97 to 0.90–0.98) with the new method. Overall, we found that IP(-based) walking speed estimation proved to be applicable for a variety of wearing positions, conditions and speeds, indicating its potential value for health assessment applications

Validation of Walking Speed Estimation from Trunk Mounted Accelerometers for a Range of Walking Speeds

Walking speed is a strong indicator of the health status of older people and patients. Using algorithms, the walking speed can be estimated from wearable accelerometers, which enables minimally obtrusive (longitudinal) monitoring. We evaluated the performance of two algorithms, the inverted pendulum (IP) algorithm, and a novel adaptation correcting for lateral step movement, which aimed to improve accuracy during slow walking. To evaluate robustness, we gathered data from different groups (healthy adults, elderly, and elderly patients) of volunteers (n = 159) walking under various conditions (over ground, treadmill, using walking aids) at a broad range of speeds (0.11–1.93 m/s). Both of the algorithms showed good agreement with the reference values and similar root-mean-square errors (RMSEs) for walking speeds ≥0.5 m/s, which ranged from 0.09–0.16 m/s for the different positions, in line with the results from others. However, for slower walking, RMSEs were significantly better for the new method (0.06–0.09 m/s versus 0.15–0.19 m/s). Pearson correlation improved for speeds <0.5 m/s (from 0.67–0.72 to 0.73–0.82) as well as higher speeds (0.87–0.97 to 0.90–0.98) with the new method. Overall, we found that IP(-based) walking speed estimation proved to be applicable for a variety of wearing positions, conditions and speeds, indicating its potential value for health assessment applications

Analysis of effects and usage indicators for a ICT-based fall prevention system in community dwelling older adults

Falls are a serious problem in aging societies. A sedentary life style and low levels of physical activity are major factors aggravating older adults’ fall risk. Information and communication technology (ICT)-based fall prevention interventions are a promising approach to counteract the fall risk of this target group. For some time now, fall prevention interventions have put emphasize to video game based solutions, as video games have become more popular and accepted among older adults. Studies show that such ICT-based fall prevention interventions significantly reduce fall risk in older adults. Nevertheless, the population of older adults is fairly heterogeneous, and factors like gender, age, fitness, sociability, and so on may influence the use of such systems. Therefore, the analysis of subgroups is a common procedure to investigate the affects of various factors on the effectiveness of ICT-based systems. Many of these studies analyze the effectiveness of the system with quantitative measures only. However, the effectiveness of ICT-based fall prevention systems always depends on the sustainable system use by the target group. Qualitative analyses is generally the prime selection to identify determining usage indicators for system usage. Therefore, it seems likely that combined quantitative and qualitative investigations will generate detailed information about system effectiveness and relevant usage indicators for respective target groups. Here, we analyze the ICT-based fall prevention system, iStoppFalls, incorporating exergames and a mobility monitor as well, targeting three aims, (1) is the system effective for different subgroups of older adults, (2) what are the factors influencing fall risk reduction in older adults using the system and are there combined effects of exergaming and activity monitoring on fall risk reduction, and (3) which usage indicators explain the usage of such a system by older adults. This paper will provide a better understanding of the effectiveness of ICT-based fall prevention for different subgroups and the indicators that determine the use of such technologies by older adults

Feasibility and Patient Experience of a Home-Based Rehabilitation Program Driven by a Tablet App and Mobility Monitoring for Patients After a Total Hip Arthroplasty

Background: Recent developments in technology are promising for providing home-based exercise programs. Objective: The objective of this study was to evaluate the feasibility and patient experience of a home-based rehabilitation program after total hip arthroplasty (THA) delivered using videos on a tablet personal computer (PC) and a necklace-worn motion sensor to continuously monitor mobility-related activities. Methods: We enrolled 30 independently living patients aged 18-75 years who had undergone THA as a treatment for primary or secondary osteoarthritis (OA) between December 2015 and February 2017. Patients followed a 12-week exercise program with video instructions on a tablet PC and daily physical activity registration through a motion sensor. Patients were asked to do strengthening and walking exercises at least 5 days a week. There was weekly phone contact with a physiotherapist. Adherence and technical problems were recorded during the intervention. User evaluation was done in week 4 (T1) and at the end of the program (T2). Results: Overall, 26 patients completed the program. Average adherence for exercising 5 times a week was 92%. Reasons mentioned most often for nonadherence were vacation or a day or weekend off 25% (33/134) and work 15% (20/134). The total number of technical issues was 8. The average score on the user evaluation questionnaire (range 0-5) was 4.6 at T1 and 4.5 at T2. The highest score was for the subscale "coaching" and the lowest for the subscale "sensor." Conclusions: A home-based rehabilitation program driven by a tablet app and mobility monitoring seems feasible for THA patients. Adherence was good and patient experience was positive. The novel technology was well accepted. When the home-based rehabilitation program proves to be effective, it could be used as an alternative to formal physiotherapy. However, further research on its effectiveness is needed

Accelerometer features.

<p>Value expressed as mean ± standard deviation (SD).</p><p>Abbreviations: AC-AP: autocorrelation coefficient in anterior-posterior direction, AC-V: autocorrelation coefficient in vertical direction, AC-ML: autocorrelation coefficient in medio-lateral direction.</p>*<p>: significantly different from healthy subjects. (p<0.05).</p>#<p>: significantly different compared to GOLD 1/2. (p<0.05).</p