Informing the design of ‘lifestyle monitoring’ technology for the detection of health deterioration in long-term conditions: a qualitative study of people living with heart failure

Background

Health technologies are being developed to help people living at home manage long-term conditions. One such technology is “lifestyle monitoring” (LM) a telecare technology based on the idea that home activities may be monitored unobtrusively via sensors to give an indication of changes in health-state. However questions remain about LM technology: how home activities change when participants experience differing health-states; and how sensors might capture clinically important changes to inform timely interventions.

Objective

This paper reports the findings of a study aiming to identify changes in activity indicative of important changes in health in people with long-term conditions, particularly those changes indicative of exacerbation - by exploring the relationship between home activities and health amongst people with heart failure (HF). We aim to add to the knowledge base informing the development of home monitoring technologies designed to detect health deterioration in order to facilitate early intervention and avoid hospital admissions.

Method

This qualitative study utilised semi-structured interviews to explore everyday activities undertaken during the three health-states of HF: normal days, bad days, and exacerbations. Potential recruits were identified by specialist nurses and from attendees at a HF support group. The sample was purposively selected to include a range of experience of living with HF.

Results

The sample comprised twenty people with HF aged fifty years and older, and eleven spouses/partners of the individuals with HF. All resided in Northern England. Participant accounts revealed that home activities are in-part shaped by the degree of intrusion from HF symptoms. During an exacerbation participants undertook activities specifically to ease symptoms, and detailed activity changes were identified. Everyday activity was also influenced by a range of factors other than health.

Conclusion

The study highlights the importance of careful development of lifestyle monitoring technology if it is to identify changes in activity that occur during clinically important changes in health. These detailed activity changes need to be considered by developers of LM sensors, platforms, and algorithms intended to detect early signs of deterioration. Results suggest that for LM to move forward sensor set-up should be personalised both to individual circumstances and targeted at individual health conditions. LM needs to take account of the uncertainties that arise from placing technology within the home, in order to inform sensor set-up and data interpretation. This more targeted approach is likely to yield more clinically meaningful data, and in addition addresses some of the ethical issues of remote monitoring.