Uses and Attitudes of Old and Oldest Adults towards Self-Monitoring Health Systems

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

Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

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