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INTRODUCTION: Self-Management Health Systems (SMHS) are envisioned to support older adults and contribute to ageing in place. To promote the use and acceptance of SMHS, designers may resort to using motivational design techniques (MDT). OBJECTIVES: This paper investigates how and which older adults prefer MDT in SMHS and whether these preferences are mediated by either motivation for health management (MHM) or mobile device proficiency (MDP). METHODS: Based on a user evaluation with 45 older adults (mean age=84, SD=6.75), 32 MDT, MHM, and MDP were polled through questionnaires and informal interviews. RESULTS: Findings showed that most techniques were welcomed, but also that scores vary widely, reflecting heterogeneity in the population and calling for caution in its implementation. These findings are then translated into implications for design. CONCLUSION: Overall, older adults welcome MDT in SMHS. However, MDT are no panacea; tailoring MDT to users’ needs is therefore crucial. ispartof: EAI Endorsed Transactions on Pervasive Health and Technology pages:1-19 status: published

Monitoring activities of daily life (ADLs) allows to evaluate health conditions for older adults. However, there are still a limited number of studies on bathroom activities monitoring using a wrist-mounted accelerometer. To fill this gap, in this study, researchers collected data from 15 older adults wearing a wrist-mounted accelerometer. Six bathroom activities, i.e., dressing, undressing, brushing teeth, using toilet, washing face, and washing hands, were investigated. In total, 49.4-hour data for bathroom activities were collected. A hybrid convolutional neural network (CNN) is introduced for bathroom activity recognition. This hybrid CNN model is developed using both hand-crafted and CNN-based features as input. The proposed hybrid CNN model is compared to four machine learning models, i.e., Multilayer Perceptron (MLP), Support Vector Machines (SVM), K-nearest Neighbors (KNN), and Decision Trees (DT), and a conventional CNN model. Based on the classification results of leave-one-subject-out cross-validation (LOSO), the hybrid CNN model outperformed the other models. The hybrid CNN model is also tested based on a transfer learning method. As a calibration step based on LOSO, the transfer learning method additionally trains the model with an example of each activity from the test subject. The transfer learning method obtained better classification performance than LOSO. With transfer learning, the f1-score for using toilet was improved from 0.7784 to 0.8437. This study proposes a deep learning model fusing hand-crafted features and CNN-based features. Besides, the transfer learning method offers a way to build subject-dependent models to improve the classification performance.Clinical relevance -This provides a model that helps monitoring older adults' bathroom activities using a single wrist-mounted accelerometer.

This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity. ispartof: Sensors vol:21 issue:6 ispartof: location:Switzerland status: Published online

Performance on activities of daily life (ADLs) is often used to evaluate older adults’ capability of living independently. Current sensor technology has already accomplished classifying several types of ADLs automatically. However, thus far, limited studies were performed in bathroom activity monitoring. In this study, we proposed using one wearable accelerometer to monitor six types of bathroom activities: dressing, undressing, washing hands, washing face, brushing teeth and toilet using. One hidden-layer neural network model was applied for activity classification. The acceleration signal was separated into patterns, using 16s sliding window with 50% overlapping. The model was developed both with (a) 10-fold cross validation or (b) leave-one-person-out (LOPO) of the training set. The classification result was evaluated on precision, recall and F1-score. The F1-score was varied in these bathroom activities. In 10-fold cross validation, the highest average F1-score was of 91.19%, which was for toileting, and the lowest average F1-score was of 51.91%, which was for washing face. In LOPO, toileting still had the highest F1-score of 80.00%, but washing hands had the lowest F1-score of 9.62%. Additionally, we tested whether one participant’s toileting event can be monitored in 3 consecutive days in the LOPO mode. The F1-score for classifying toileting events was 85.61%. In sum, the results show that it is possible to use one wearable accelerometer to monitor toileting activity. However, for the other bathroom activities, further study is needed and personal differences in these activities’ performance should be considered.

Managing continence in nursing homes comprises a large economic and emotional factor, not only in residents’ Quality of Life but also in staffs’ Quality of Care. Notwithstanding increased engineering efforts in this area, nursing home continence care remains underexplored in HCI. To lay the groundwork for the technological design of human-centered continence care, we conducted a qualitative study involving 27 stakeholders—ranging from residents, relatives, caregivers and nurses to managers. By inductively analysing our data, we identified different perspectives on incontinence and surrounding care practices. We found continence care to be intricately interwoven within a complex ecosystem that is deeply rooted in an ideal of promoting dignity, which technological infrastructures might fall at risk of introducing miscommunication and installing distrust between stakeholders. Using the Dignity in Continence Care Framework, we provide suggestions for the design of technologies centered on supporting dignity in nursing home continence care.

Our study investigated how to design a user-friendly tablet-application to support nursing home residents in assessing their quality of life, by completing a questionnaire. A user-centred design research study was conducted together with 27 residents, six healthcare professionals, 148 master students and five researchers. The design space was explored by 22 designs, which were evaluated by means of a thematic analysis. The outcome resulted in first tentative considerations for developers of tablet-applications for nursing home residents.

http://www.ict4ageingwell.org/TEG.aspx For full Book of Proceedings, please see: http://www.ict4ageingwell.org/BooksPublishedScitepress.aspx ispartof: pages:269-280 ispartof: International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE) vol:6 ispartof: International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE) location:Online Streaming [Prague, Czech Republic] date:3 May - 5 May 2020 status: Published online

Recently, a shift from curative to preventive care is promoted, by which patients are expected to become active and use diverse forms of self-management health systems (SMHS), i.e., integrated solutions that present data from multiple sensors and/or self-reports, possibly enhanced with risk assessment and decision support, to perform health-related actions. This is promoted as contributing to living longer and healthier, assisting ageing-in-place. Hence, older adults have also become a potential target of SMHS. While studies are performed on uses and attitudes of specific patient groups towards SMHS, studies that focus on older, including the oldest, adults are scarce. Therefore, we report on a qualitative study with 20 older adults (mean age = 80). Through thematic analysis, we identified four themes (i.e., enforced use of technology; need for support in technology use; equivocal stance towards sharing data; hypothetical value of technology for healthy ageing) to provide a deeper understanding of older adults' attitudes and engagement with information- and communication technologies (ICT) in general and SMHS in particular. We also present four pitfalls, unified by a central concept "Ageing is not a disease'', along with design considerations for future SMHS.

Oldest adults (80 years and over) are the fastest growing group in the total world population. This is putting pressure on national healthcare budgets, as the distribution of healthcare expenses is strongly age-dependent. One way of mitigating this burden may be to let older adults contribute to their own health directly by using self-management health systems (SMHS). SMHS might help older, including oldest, adults gain insight into their health status, and invite them to take action. However, while many studies report on user evaluations of older adults with one specific sensor system, fewer studies report on older adults’ uses and attitudes towards integrated SMHS. Moreover, most studies include participants with mean ages of 65 rather than 80. In this paper, we report on a qualitative study, consisting of a focus group interview and a user evaluation of an SMHS by 12 participants with a median age of 85 years. Three main findings were derived: Older adults (1) showed heterogeneity in computer skills, (2) found health technologies useful for others – not yet for themselves, and (3) perceived health technologies as a threat to social interaction. These findings suggest that health technologies are not ready for adoption by older adults yet, and further research on making them more accessible and desirable is required.

With the rise in smartphones, many mobile health apps are released that are not grounded in theory [1]. Several theories that catalog persuasive and/or behavior change principles exist, however these theories are not adapted for mHealth apps. The principles or techniques cataloged in these theories are described and often interpreted in an additive deterministic manner, more use of techniques means more persuasiveness, which is in stark contrast with how these techniques should be interpreted. In this paper we present 26 lenses for motivational design of mHealth apps and an online tool to assess the motivational capability of mobile health applications. This online tool allows designers to be inspired and researchers to code the presence of 26 motivational principles. Next, it provides tailored feedback regarding underlying psychological drivers that empower the user in his or her actions.