Discovering The Essential Characteristics That Promote Patient-Generated Health Data Collection And Selfquantification Systems Use: A Mixed-Methods Study
MetadataShow full item record
Background The combination of rapid technological innovation, high smartphone penetrance, and new viewpoints regarding population health poses a large opportunity for mobile health (mHealth) applications and devices to improve healthcare delivery. There is a substantial lack of understanding, however, of how consumers interact with these devices and what features of them most motivate long-term user engagement with self-monitoring. In order for mHealth devices to positively and sustainably improve healthcare, understanding users and designing devices around user preferences is critical. Objective The principal objective of this study is to uncover the features of selfquantification systems that users find most important in promoting their engagement with them and motivating them change their behavior towards sustained informed decision making. Methods A mixed-methods approach was planned and executed, including a literature review to scrutinize interventional studies utilizing SQS and identify and categorize what SQS characteristics study subjects felt most promoted their self-monitoring behavior (Mechanisms Promoting Long-Term Self-Monitoring, MPLTSM); a novel user survey to quantitatively assess the generalizability and relevance of these MPLTSM; and semi-structured “expert” interviews to qualitatively substantiate and complement survey results. Results from the user survey and expert interviews were used to inform the list of MPLTSM and ultimately characterize what MPLTSM were most felt to be important. Results The literature review yielded six preliminary categories of MPLTSM: Data, Use, Goal Setting and Feedback, Behavioral-Health Link, Socialness, and Smartness, as described in the text. Based on user survey results and expert interviews, preliminary MPLTSM were refined in order to identify five Essential Affordances of Self-Quantification Systems (“EA-SQS”) that all SQS should enable in order to maximize user engagement with self-monitoring and sustained informed decision making: Data, Use, Smartness, Health Consciousness, and Sharing with Healthcare Providers. Conclusion Knowledge of EA-SQS can help inform decision-making by investigators, physicians, and leaders of the HIT companies who manufacture and market these systems. Their tailoring of how these devices are studied, prescribed, or designed may motivate not only healthy users, but also sicker patients, to be more active participants in monitoring their own health. We see this actionable insight into wearable device use as a key component in transitioning self-quantification from a recreational hobby for healthy people to a clinical tool that benefits patients as well as the providers who treat them.
Apps; Digital Health; Mobile Health; Participatory Medicine; Quantified Self; Smartphones
Master of Science
Attribution-NonCommercial-NoDerivatives 4.0 International
dissertation or thesis
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International