Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data.

Background: Effective and sustainable interventions are needed to counteract the decline in physical function and sarcopenia in the growing aging population. The aim of this study was to determine the 6 and 12 month effectiveness of blended (e‐health + coaching) home‐based exercise and a dietary protein intervention on physical performance in community‐dwelling older adults.Methods: This cluster randomized controlled trial allocated 45 clusters of older adults already engaged in a weekly community‐based exercise programme. The clusters were randomized to three groups with ratio of 16:15:14; (i) no intervention, control (CON); (ii) blended home‐based exercise intervention (HBex); and (iii) HBex with dietary protein counselling (HBex‐Pro). Both interventions used a tablet PC with app and personalized coaching and were targeting on behaviour change. The study comprised coached 6 month interventions with a 6 month follow‐up. The primary outcome physical performance was assessed by modified Physical Performance Test (m‐PPT). Secondary outcomes were gait speed, physical activity level (PAL), handgrip muscle strength, protein intake, skeletal muscle mass, health status, and executive functioning. Linear mixed models of repeated measured were used to assess intervention effects at 6 and 12 months.Results: The population included 245 older adults (mean age 72 ± 6.5 (SD) years), 71% female, and 54% co‐morbidities observed. Dropout of the intervention was 18% at 6 months and 26% at 12 months. Participants were well functioning, based on an m‐PPT score of 33.9 (2.8) out of 36. For the primary outcome m‐PPT, no significant intervention effects (HBex, +0.03, P = 0.933; HBex‐Pro, −0.13, P = 0.730) were found. Gait speed (+0.20 m/s, P = 0.001), PAL (+0.06, P = 0.008), muscle strength (+2.32 kg, P = 0.001), protein intake (+0.32 g/kg/day, P < 0.001), and muscle mass (+0.33 kg, P = 0.017) improved significantly in the HBex‐Pro group compared with control group after 6 month intervention. The protein intake, muscle mass, and strength remained significantly improved after 12 months as compared with those of control. Health change and executive functioning improved significantly in both intervention groups after 6 months.Conclusions: This HBex and dietary protein interventions did not change the physical performance (m‐PPT) in community‐dwelling older adults. Changes were observed in gait speed, PAL, muscle mass, strength, and dietary protein intake, in response to this combined intervention.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: For older adults physical activity is vital for maintaining their health and their ability to live independently. Home-based programs can help them achieve the recommended exercise frequency. An application for a tablet computer was developed to support older adults in following a personal training. It featured goal setting, tailoring, progress tracking and remote feedback. Objective: In line with the Medical Research Council Framework, which prescribes thorough testing before evaluating the efficacy with a randomized controlled trial, the aim of the present study was to assess the usability of a tablet-based application that was designed to support older adults in doing exercises at home. Methods: A total of 15 older adults, ranging from 69 to 99 years old, participated in a usability study that utilized a mixed-method approach. Novice users were asked to complete 11 tasks as they verbalized their ongoing thoughts. The task ranged from looking up information about exercises and executing them to tailoring a weekly exercise schedule. The performance errors and time-on-task were calculated as proxies of effective and efficient usage. Overall satisfaction was assessed with a post-test interview. All responses were analyzed independently by two researchers. Results: On average the participants spend 46 seconds time-on-task. 94% of the users completed the task with either no help (43%) or after having received one or two hints (51%). During the post-test interview participants made three times more positive remarks about the app, than negative remarks. Conclusions: The app that was developed to support older adults in doing exercises at home is useable by the target audience. First-time users were able to perform common tasks in an effective and efficient manner. In general, they were satisfied with the app. Tasks that were associated with behavior execution and evaluation were performed with ease. Complex tasks like tailoring a personal training schedule needed more effort. Learning effects, usefulness and long-term satisfaction will be investigated with longitudinal follow-up studies. Clinical Trial: NL56094.029.16 / NTR (TC = 5888; registered 03-06-2016)

Background: Physical activity can prevent or delay age-related impairments and prolong the ability of older adults to live independently. Community-based programs typically offer classes where older adults can exercise only once a week under the guidance of an instructor. The health benefits of such programs vary. Exercise frequency and the duration of the program play a key role in realizing effectiveness. An auxiliary home-based exercise program can provide older adults the opportunity to exercise more regularly over a prolonged period of time in the convenience of their own homes. Furthermore, mobile electronic devices can be used to motivate and remotely guide older adults to exercise in a safe manner. Such a blended intervention, where technology is combined with personal guidance, needs to incorporate behavior change principles to ensure effectiveness. Objective: The aim of this study was to identify theory-based components of a blended intervention that supports older adults to exercise at home. Methods: The Medical Research Council framework was used to develop the blended intervention. Insights from focus group, expert panels, and literature were combined into leading design considerations. Results: A client-server system had been developed that combined a tablet app with a database in the cloud and a Web-based dashboard that can be used by a personal coach to remotely monitor and guide older adults. The app contains several components that facilitate behavior change-an interactive module for goal setting, the ability to draw up a personal training schedule from a library containing over 50 exercise videos, progress monitoring, and possibilities to receive remote feedback and guidance of a personal coach. Conclusions: An evidence-based blended intervention was designed to promote physical activity among older adults. The underlying design choices were underpinned by behavior change techniques that are rooted in self-regulation. Key components of the tablet-supported intervention were a tailored program that accommodates individual needs, demonstrations of functional exercises, monitoring, and remote feedback. The blended approach combines the convenience of a home-based exercise program for older adults with the strengths of mobile health and personal guidance.

Background: App-based mobile health exercise interventions can motivate individuals to engage in more physical activity (PA). According to the Fogg Behavior Model, it is important that the individual receive prompts at the right time to be successfully persuaded into PA. These are referred to as just-in-time (JIT) interventions. The Playful Active Urban Living (PAUL) app is among the first to include 2 types of JIT prompts: JIT adaptive reminder messages to initiate a run or walk and JIT strength exercise prompts during a walk or run (containing location-based instruction videos). This paper reports on the feasibility of the PAUL app and its JIT prompts. Objective: The main objective of this study was to examine user experience, app engagement, and users’ perceptions and opinions regarding the PAUL app and its JIT prompts and to explore changes in the PA behavior, intrinsic motivation, and the perceived capability of the PA behavior of the participants. Methods: In total, 2 versions of the closed-beta version of the PAUL app were evaluated: a basic version (Basic PAUL) and a JIT adaptive version (Smart PAUL). Both apps send JIT exercise prompts, but the versions differ in that the Smart PAUL app sends JIT adaptive reminder messages to initiate running or walking behavior, whereas the Basic PAUL app sends reminder messages at randomized times. A total of 23 participants were randomized into 1 of the 2 intervention arms. PA behavior (accelerometer-measured), intrinsic motivation, and the perceived capability of PA behavior were measured before and after the intervention. After the intervention, participants were also asked to complete a questionnaire on user experience, and they were invited for an exit interview to assess user perceptions and opinions of the app in depth. Results: No differences in PA behavior were observed (Z=−1.433; P=.08), but intrinsic motivation for running and walking and for performing strength exercises significantly increased (Z=−3.342; P<.001 and Z=−1.821; P=.04, respectively). Furthermore, participants increased their perceived capability to perform strength exercises (Z=2.231; P=.01) but not to walk or run (Z=−1.221; P=.12). The interviews indicated that the participants were enthusiastic about the strength exercise prompts. These were perceived as personal, fun, and relevant to their health. The reminders were perceived as important initiators for PA, but participants from both app groups explained that the reminder messages were often not sent at times they could exercise. Although the participants were enthusiastic about the functionalities of the app, technical issues resulted in a low user experience. Conclusions: The preliminary findings suggest that the PAUL apps are promising and innovative interventions for promoting PA. Users perceived the strength exercise prompts as a valuable addition to exercise apps. However, to be a feasible intervention, the app must be more stable.

Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.