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.
Sarah HargreavesMark S HawleyAnnette HaywoodPamela M EnderbyPublished in: Journal of medical Internet research (2017)
The study highlights the importance of careful development of LM technology to identify changes in activities 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 personalized 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 targeted approach is likely to yield more clinically meaningful data and address some of the ethical issues of remote monitoring.
Keyphrases
- healthcare
- public health
- heart failure
- mental health
- health information
- machine learning
- big data
- cancer therapy
- metabolic syndrome
- health promotion
- left ventricular
- cardiovascular disease
- deep learning
- atrial fibrillation
- human health
- type diabetes
- drug delivery
- quantum dots
- artificial intelligence
- risk assessment
- loop mediated isothermal amplification
- climate change
- label free
- sensitive detection