Playfulness and New Technologies in Hand Therapy for Children With Cerebral Palsy: Scoping Review.

BACKGROUND: Innovative technologies such as game consoles and smart toys used with games or playful approaches have proven to be successful and attractive in providing effective and motivating hand therapy for children with cerebral palsy (CP). Thus, there is an increased interest in designing and implementing interventions that can improve the well-being of these children. However, to understand how and why these interventions are motivating children, we need a better understanding of the playful elements of technology-supported hand therapy. OBJECTIVE: This scoping review aims to identify the playful elements and the innovative technologies currently used in hand therapy for children with CP. METHODS: We included studies that design or evaluate interventions for children with CP that use innovative technologies with game or play strategies. Data were extracted and analyzed based on the type of technology, description of the system, and playful elements according to the Lenses of Play, a play design toolkit. A total of 31 studies were included in the analysis. RESULTS: Overall, 54 papers were included in the analysis. The results showed high use of consumer technologies in hand therapy for children with CP. Although several studies have used a combination of consumer technologies with therapeutic-specific technologies, only a few studies focused on the exclusive use of therapeutic-specific technologies. To analyze the playfulness of these interventions that make use of innovative technologies, we focused our review on 3 lenses of play: Open-ended Play, where it was found that the characteristics of ludus, such as a structured form of play and defined goals and rules, were the most common, whereas strategies that relate to paidia were less common. The most commonly used Forms of Play were physical or active form and games with rules. Finally, the most popular Playful experiences were control, challenge, and competition. CONCLUSIONS: The inventory and analysis of innovative technology and playful elements provided in this study can be a starting point for new developments of fun and engaging tools to assist hand therapy for children with CP.

Just-in-Time Prompts for Running, Walking, and Performing Strength Exercises in the Built Environment: 4-Week Randomized Feasibility Study.

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.

Translating Promoting Factors and Behavior Change Principles Into a Blended and Technology-Supported Intervention to Stimulate Physical Activity in Children With Asthma (Foxfit): Design Study.

BACKGROUND: Children with asthma can decrease the impact of their disease by improving their physical activity (PA). However, health care providers lack interventions for children with asthma that effectively increase their PA levels and achieve behavior change. A technology-supported approach can positively influence PA and physical functioning in children. OBJECTIVE: The aims of this study were to develop a technology-supported intervention that facilitates health care providers in promoting PA for children (aged 8 to 12 years) with asthma and to systematically describe this developmental process. METHODS: Intervention mapping (IM) was applied to develop a blended and technology-supported intervention in cocreation with children with asthma, their parents, and health care providers. In accordance with the IM framework, the following steps were performed: conduct a needs assessment; define the intervention outcome, performance objectives, and change objectives; select theory-based intervention methods and strategies; create components of the intervention and conduct pilot tests; create an implementation plan; and create an evaluation plan. RESULTS: We developed the blended intervention Foxfit that consists of an app with a PA monitor for children (aged 8 to 12 years) with asthma and a web-based dashboard for their health care provider. The intervention focuses on PA in everyday life to improve social participation. Foxfit contains components based on behavior change principles and gamification, including goal setting, rewards, action planning, monitoring, shaping knowledge, a gamified story, personal coaching and feedback, and a tailored approach. An evaluation plan was created to assess the intervention's usability and feasibility for both children and health care providers. CONCLUSIONS: The IM framework was very useful for systematically developing a technology-supported intervention and for describing the translational process from scientific evidence, the needs and wishes of future users, and behavior change principles into this intervention. This has led to the technology-supported intervention Foxfit that facilitates health care providers in promoting PA in children with asthma. The structured description of the development process and functional components shows the way behavior change techniques are incorporated in the intervention. TRIAL REGISTRATION: International Clinical Trial Registry Platform NTR6658; https://tinyurl.com/3rxejksf.

Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator.

Mobile health (mHealth) intervention systems can employ adaptive strategies to interact with users. Instead of designing such complex strategies manually, reinforcement learning (RL) can be used to adaptively optimize intervention strategies concerning the user's context. In this paper, we focus on the issue of overwhelming interactions when learning a good adaptive strategy for the user in RL-based mHealth intervention agents. We present a data-driven approach integrating psychological insights and knowledge of historical data. It allows RL agents to optimize the strategy of delivering context-aware notifications from empirical data when counterfactual information (user responses when receiving notifications) is missing. Our approach also considers a constraint on the frequency of notifications, which reduces the interaction burden for users. We evaluated our approach in several simulation scenarios using real large-scale running data. The results indicate that our RL agent can deliver notifications in a manner that realizes a higher behavioral impact than context-blind strategies.

Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study.

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.

Assessing Children's Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach.

BACKGROUND: Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem. OBJECTIVE: This study examines whether sensor-augmented toys can be used to assess children's fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction. METHODS: Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called "Futuro Cube." The game "roadrunner" focused on speed while the game "maze" focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05. RESULTS: The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046). CONCLUSIONS: The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.

Digitally Supported Dietary Protein Counseling Changes Dietary Protein Intake, Sources and Distribution in Community-Dwelling Older Adults.

Digitally supported dietary counselling may be helpful in increasing the protein intake in combined exercise and nutritional interventions in community-dwelling older adults. To study the effect of this approach, 212 older adults (72.2 ± 6.3 years) were randomised in three groups: control, exercise, or exercise plus dietary counselling. The dietary counselling during the 6-month intervention was a blended approach of face-to-face contacts and videoconferencing, and it was discontinued for a 6-month follow-up. Dietary protein intake, sources, product groups, resulting amino acid intake, and intake per eating occasion were assessed by a 3-day dietary record. The dietary counselling group was able to increase the protein intake by 32% at 6 months, and the intake remained 16% increased at 12 months. Protein intake mainly consisted of animal protein sources: dairy products, followed by fish and meat. This resulted in significantly more intake of essential amino acids, including leucine. The protein intake was distributed evenly over the day, resulting in more meals that reached the protein and leucine targets. Digitally supported dietary counselling was effective in increasing protein intake both per meal and per day in a lifestyle intervention in community-dwelling older adults. This was predominantly achieved by consuming more animal protein sources, particularly dairy products, and especially during breakfast and lunch.

Tailoring digital apps to support active ageing in a low income community.

Despite physical activity being one of the determinants of healthy aging, older people tend to become less active over the years. Maintaining physical activity levels during the life course is a motivational challenge. Digital tools have been used to change this pattern, such as smartphone applications to support physical activity; but there is a lack of in-depth research on the diversity of user's experiences, especially considering older users or non-users of information and communication technologies. OBJECTIVE: Our goal was to identify requirements for designing a mobile app to encourage physical activity in a low-income community population of older people in Brazil (i.e. over 40 years old). METHOD: We conducted a qualitative focus group study, involving by co-design of a physical activity application (Pacer)®. Seventeen volunteers were divided into 2 focus groups of physical active and insufficiently active, and 2 further 4 subgroups in each characterised by digital engagement. The following procedures were performed: (i) baseline assessments; (ii) a focus group with physically active older people and a focus group with insufficiently active older people (iii) design activities with both groups to re-design Pacer. RESULTS: Developing physical activity apps for older people should consider the following features: free application, simple interface, motivational messages using audio and visual information, sharing information among users, multimedia input and sharing and user customisation. In particular, we recommend that exercise apps in low-income communities be tailored to our four categories of users differing in baseline physical activity and digital engagement, to match the social and behavioural preferences we discovered.

Blended home-based exercise and dietary protein in community-dwelling older adults: a cluster randomized controlled trial.

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.

Evaluation of a Blended Physical Activity Intervention for Older Adults: Mixed Methods Study.

BACKGROUND: Physical activity can prolong the ability of older adults to live independently. Home-based exercises can help achieve the recommended physical activity levels. A blended intervention was developed to support older adults in performing home-based exercises. A tablet and a personal coach were provided to facilitate the self-regulation of exercise behavior. OBJECTIVE: In line with the Medical Research Council framework, this study aimed to carry out process evaluation of a blended intervention. The objectives were (1) to assess the long-term usability of the tablet adopted in the blended intervention and (2) to explore how the tablet, in conjunction with a personal coach, supported older adults in performing home-based exercises. METHODS: The process evaluation was conducted with a mixed-methods approach. At baseline, older adults participating in the blended intervention were asked to fill out a questionnaire about their general experience with information and communication technology (ICT) devices and rate their own skill level. After 6 months, participants filled out the Usefulness, Satisfaction, and Ease of use (USE) questionnaire to assess the usefulness, satisfaction, and ease of use of the tablet. With a random selection of participants, in-depth interviews were held to explore how the tablet and coach supported the self-regulation. The interviews were double coded and analyzed with the directed content analysis method. RESULTS: At baseline, 29% (65/224) of participants who started the intervention (mean age 72 years) filled out the ICT survey and 36% (37/103) of participants who used the tablet for 6 months (mean age 71 years) filled out the USE questionnaire. Furthermore, with 17% (18/103) of participants (mean age 73 years), follow-up interviews were held. The results of the baseline questionnaire showed that the large majority of participants already had experience with a tablet, used it regularly, and reported being skillful in operating ICT devices. After 6 months of use, the participants rated the usefulness, satisfaction, and ease of use of the tablet on average as 3.8, 4.2, and 4.1, respectively, on a 5-point scale. The analysis of the interviews showed that the participants felt that the tablet supported action planning, behavior execution, and self-monitoring. On the other hand, especially during the first few months, the personal coach added value during the goal setting, behavior execution, and evaluation phases of self-regulation. CONCLUSIONS: The results of the process evaluation showed that older adults who participated in the study were positive about the blended intervention that was designed to support them in performing home-based exercises. Participants reported that the tablet helped them to perform the exercises better, more frequently, and safely. It supported them in various phases of self-regulation. The availability of a personal coach was nevertheless crucial. To support physical activity in older adults, a blended approach is promising.

What Are Good Situations for Running? A Machine Learning Study Using Mobile and Geographical Data.

Running is a popular form of physical activity. Personal, social, and environmental determinants influence the engagement of the individual. To get insight in the relation between running behavior and external situations for different types of users, we carried out an extensive data mining study on large-scale datasets. We combined 4 years of historical running data (collected by a mobile exercise application from over 10K participants) with weather, topographical and demographical datasets. We introduce weighted frequent item mining for the analysis of the data. In this way, we capture temporal and environmental situations that frequently associate with different running performances. The results show that specific temporal and environmental situations (hour in a day, day in a week, temperature, distance to residential areas, and population density) influence the running performance of users more than other situational features. Hierarchical agglomerative clustering on the running data is used to split runners in two clusters (with sustained and less sustained running behavior). We compared the two groups of runners and found that runners with less sustained behavior are more sensitive to the environmental situations (especially several weather and location related features, such as temperature, weather type, distance to the nearest park) than regular runners. Further analysis focused on the situational features for the less sustained runners. Results show that specific feature values correspond to a better or worse running distance. Not only the influence of individual features was examined but also the interplay between features. Our findings provide important empirical evidence that the role of external situations in the running behavior of individuals can be derived from analysis of the combined historical datasets. This opens up a large potential to take those situations specifically into consideration when supporting individuals which show less sustained behavior.

The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining.

Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application. Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running. Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies "monitoring of behavior," "feedback," "goal setting," "reminders," "rewards," and "providing instruction." An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team. Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.

Promoting Factors for Physical Activity in Children with Asthma Explored through Concept Mapping.

For children with asthma, physical activity (PA) can decrease the impact of their asthma. Thus far, effective PA promoting interventions for this group are lacking. To develop an intervention, the current study aimed to identify perspectives on physical activity of children with asthma, their parents, and healthcare providers. Children with asthma between 8 and 12 years old (n = 25), their parents (n = 17), and healthcare providers (n = 21) participated in a concept mapping study. Participants generated ideas that would help children with asthma to become more physically active. They sorted all ideas and rated their importance on influencing PA. Clusters were created with multidimensional scaling and cluster analysis. The researchers labelled the clusters as either environmental or personal factors using the Physical Activity for people with a Disability model. In total, 26 unique clusters were generated, of which 17 were labelled as environmental factors and 9 as personal factors. Important factors that promote physical activity in children with asthma according to all participating groups are asthma control, stimulating environments and relatives, and adapted facilities suiting the child's needs. These factors, supported by the future users, enable developing an intervention that helps healthcare providers to promote PA in children with asthma.

Effectiveness of sensor monitoring in a rehabilitation programme for older patients after hip fracture: a three-arm stepped wedge randomised trial.

OBJECTIVES: to test the effects of an intervention involving sensor monitoring-informed occupational therapy on top of a cognitive behavioural treatment (CBT)-based coaching therapy on daily functioning in older patients after hip fracture. DESIGN, SETTING AND PATIENTS: three-armed randomised stepped wedge trial in six skilled nursing facilities, with assessments at baseline (during admission) and after 1, 4 and 6 months (at home). Eligible participants were hip fracture patients ≥ 65 years old. INTERVENTIONS: patients received care as usual, CBT-based occupational therapy or CBT-based occupational therapy with sensor monitoring. Interventions comprised a weekly session during institutionalisation, followed by four home visits and four telephone consultations over three months. MAIN OUTCOMES AND MEASURES: the primary outcome was patient-reported daily functioning at 6 months, assessed with the Canadian Occupational Performance Measure. RESULTS: a total of 240 patients (mean[SD] age, 83.8[6.9] years were enrolled. At baseline, the mean Canadian Occupational Performance Measure scores (range 1-10) were 2.92 (SE 0.20) and 3.09 (SE 0.21) for the care as usual and CBT-based occupational therapy with sensor monitoring groups, respectively. At six months, these values were 6.42 (SE 0.47) and 7.59 (SE 0.50). The mean patient-reported daily functioning in the CBT-based occupational therapy with sensor monitoring group was larger than that in the care as usual group (difference 1.17 [95% CI (0.47-1.87) P = 0.001]. We found no significant differences in daily functioning between CBT-based occupational therapy and care as usual. CONCLUSIONS AND RELEVANCE: among older patients recovering from hip fracture, a rehabilitation programme of sensor monitoring-informed occupational therapy was more effective in improving patient-reported daily functioning at six months than to care as usual. TRIAL REGISTRATION: Dutch National Trial Register, NTR 5716.

How Is the Caregiver Doing? Capturing Caregivers' Experiences With a Reflective Toolkit.

BACKGROUND: This paper describes the Co-Care-KIT, a reflective toolkit designed to provide insights into the diverse experiences of home-based informal caregivers during the delivery of care to a relative or loved one. OBJECTIVE: The aim of this study was to evaluate the toolkit, including a custom-designed journal, tools for photography-based experience sampling, and heart rate tracking, which enables caregivers to collect and reflect on their positive and negative daily experiences in situ. METHODS: A 2-week field study with informal caregivers (N=7) was conducted to evaluate the Co-Care-KIT and to capture their daily personal emotional experiences. The collected data samples were analyzed and used for collaborative dialogue between the researcher and caregiver. RESULTS: The results suggest that the toolkit (1) increased caregivers' awareness of their own well-being through in situ reflection on their experiences; (2) empowered caregivers to share their identities and experiences as a caregiver within their social networks; (3) enabled the capturing of particularly positive experiences; and (4) provided caregivers reassurance with regards to their own mental health. CONCLUSION: By enabling capturing and collaborative reflection, the kit helped to gain a new understanding of caregivers' day-to-day needs and emotional experiences.

Supporting Older Adults in Exercising With a Tablet: A Usability Study.

BACKGROUND: For older adults, physical activity is vital for maintaining their health and 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 program. 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 this study was to assess the usability of a tablet-based app that was designed to support older adults in doing exercises at home. METHODS: A total of 15 older adults, age ranging from 69 to 99 years old, participated in a usability study that utilized a mixed-methods approach. In a laboratory setting, novice users were asked to complete a series of tasks while verbalizing their ongoing thoughts. The tasks ranged from looking up information about exercises and executing them to tailoring a weekly exercise schedule. Performance errors and time-on-task were calculated as proxies of effective and efficient usage. Overall satisfaction was assessed with a posttest interview. All responses were analyzed independently by 2 researchers. RESULTS: The participants spent 13-85 seconds time-on-task. Moreover, 79% (11/14)-100% (14/14) participants completed the basic tasks with either no help or after having received 1 hint. For expert tasks, they needed a few more hints. During the posttest interview, the participants made 3 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 usable by the target audience. First-time users were able to perform basic 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 such as tailoring a personal training schedule needed more effort. Learning effects, usefulness, and long-term satisfaction will be investigated through longitudinal follow-up studies.

Everyday life after a hip fracture: what community-living older adults perceive as most beneficial for their recovery.

OBJECTIVE: to gain insight into what older adults after hip fracture perceive as most beneficial to their recovery to everyday life. DESIGN: qualitative research approach. SETTING: six skilled nursing facilities. PARTICIPANTS: 19 older community dwelling older adults (aged 65-94), who had recently received geriatric rehabilitation after hip fracture. METHODS: semi-structured interviews were conducted with 19 older adults after hip fracture. Coding techniques based on constructivist grounded theory were applied. RESULTS: four categories were derived from the data: 'restrictions for everyday life', 'recovery process', 'resources for recovery' and 'performing everyday activities'. Physical and psychological restrictions are consequences of hip fracture that older adults have struggled to address during recovery. Three different resources were found to be beneficial for recovery; 'supporting and coaching', 'myself' and 'technological support'. These resources influenced the recovery process. Having successful experiences during recovery led to doing everyday activities in the same manner as before; unsuccessful experiences led to ceasing certain activities altogether. CONCLUSION: participants highlight their own role ('myself') as essential for recovery. Additionally, coaching provides emotional support, which boosts self-confidence in performing everyday activities. Furthermore, technology can encourage older adults to become more active and being engaged in the recovery process. The findings suggest that more attention should be paid to follow-up interventions after discharge from inpatient rehabilitation to support older adults in finding new routines in their everyday activities.A conceptual model is presented and provides an understanding of the participants' experiences and perspectives concerning their process of recovery after hip fracture to everyday life.

A digitally supported home-based exercise training program and dietary protein intervention for community dwelling older adults: protocol of the cluster randomised controlled VITAMIN trial.

BACKGROUND: Increased physical activity and dietary protein intake are promising interventions to prevent or treat the age-related decline in physical performance in older adults. There are well-controlled exercise as well as dietary intervention studies that show beneficial effects on physical performance in older adults. In practice, however, weekly group based exercise or nutritional programs may not be as effective. To optimise these exercise programs for community dwelling older adults, a digitally supported and personalised home-based exercise training program has been designed aiming to improve physical performance in older adults. In addition, a protein intervention in combination with the training program may further improve physical performance in older adults. METHODS: The VITAMIN study will be a cluster randomised controlled trial with three parallel arms. In total, 240 community dwelling older adults (≥ 55 years) participating in weekly group exercise are randomly allocated into: 1) regular weekly exercise program (Control group, n = 80), 2) digitally supported personalised home-based exercise training program group (VITA group, n = 80) and 3) digitally supported personalised home-based exercise training program group plus dietary protein counselling (VITA-Pro group, n = 80). The VITAMIN study aims to evaluate effectiveness of the digitally supported personalised home-based exercise training program as well as the additional value of dietary protein on physical performance after 6 months. In addition, a 12 month follow-up measurement will assess the retaining effect of the interventions. Primary outcome is physical performance measured by the Modified Physical Performance Test (M-PPT) and relevant secondary and observational outcomes include habitual physical activity and dietary intake, body composition, cognitive performance, quality of life, compliance and tablet usage. Data will be analysed by Linear Mixed Models. DISCUSSION: To our knowledge, the VITAMIN study is the first study that investigates the impact of home-based exercise, protein intake as well as use of persuasive technology in the population of community dwelling older adults. TRIAL REGISTRATION: NL56094.029.16 / NTR ( TC = 5888 ; registered 03-06-2016).

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry.

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.

Translating Behavior Change Principles Into a Blended Exercise Intervention for Older Adults: Design Study.

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.