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.

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.

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.