Improving patient engagement by fostering the sharing of activity tracker data with providers: a qualitative study.

BACKGROUND: Activity trackers are becoming increasingly popular, but patients often hesitate to share the data from such devices with their health care providers. Researchers have shown that sharing everyday health data with physicians can foster greater patient engagement. OBJECTIVES: This research is intended to investigate activity tracker users' decisions regarding the sharing of their activity tracker data with physicians, as well as to build a stage based framework for improving patient engagement by fostering such data sharing. METHODS: Qualitative analysis of interview records of 12 adults, who had used Fitbit activity tracking devices for up to two years, identifying emotions and experiences surrounding their tendencies to share physical exercise data with a physician. RESULTS: This research used the subjects' emotions and considerations regarding the decision over whether to share exercise data with physicians to create a stage based framework with three stages: cognisance, tangible evidence and supportive feedback. CONCLUSION: The tendency to progress towards three stages with greater patient-physician engagement appears to increase with health risk profile and with reduced data privacy concerns. This framework contributes to ongoing discussions about establishing patient-practitioner engagement, based around patients' shared personal data collection.

Wearable activity trackers, accuracy, adoption, acceptance and health impact: A systematic literature review.

Wearable activity trackers (WAT) are electronic monitoring devices that enable users to track and monitor their health-related physical fitness metrics including steps taken, level of activity, walking distance, heart rate, and sleep patterns. Despite the proliferation of these devices in various contexts of use and rising research interests, there is limited understanding of the broad research landscape. The purpose of this systematic review is therefore to synthesize the existing wealth of research on WAT, and to provide a comprehensive summary based on common themes and approaches. This article includes academic work published between 2013 and 2017 in PubMed, Embase, Scopus, Web of Science, ACM Digital Library, and Google Scholar. A final list of 463 articles was analyzed for this review. Topic modeling methods were used to identify six key themes (topics) of WAT research, namely: (1) Technology Focus, (2) Patient Treatment and Medical Settings, (3) Behavior Change, (4) Acceptance and Adoption (Abandonment), (5) Self-monitoring Data Centered, and (6) Privacy. We take an interdisciplinary approach to wearable activity trackers to propose several new research questions. The most important research gap we identify is to attempt to understand the rich human-information interaction that is enabled by WAT adoption.

The key to an effective AI-powered digital pathology: Establishing a symbiotic workflow between pathologists and machine.

Pathology is a fundamental element of modern medicine that determines the final diagnosis of medical conditions, leads medical decisions, and portrays the prognosis. Due to continuous improvements in AI capabilities (e.g., object recognition and image processing), intelligent systems are bound to play a key role in augmenting pathology research and clinical practices. Despite the pervasive deployment of computational approaches in similar fields such as radiology, there has been less success in integrating AI in clinical practices and histopathological diagnosis. This is partly due to the opacity of end-to-end AI systems, which raises issues of interoperability and accountability of medical practices. In this article, we draw on interactive machine learning to take advantage of AI in digital pathology to open the black box of AI and generate a more effective partnership between pathologists and AI systems based on the metaphors of parameterization and implicitization.

Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use.

OBJECTIVES: Activity trackers hold the promise to support people in managing their health through quantified measurements about their daily physical activities. Monitoring personal health with quantified activity tracker-generated data provides patients with an opportunity to self-manage their health. Many have been conducted within short-time frames; makes it difficult to discover the impact of the activity tracker's novelty effect or the reasons for the device's long-term use. This study explores the impact of novelty effect on activity tracker adoption and the motivation for sustained use beyond the novelty period. MATERIALS AND METHODS: This study uses a mixed-methods approach that combines both quantitative activity tracker log analysis and qualitative one-on-one interviews to develop a deeper behavioral understanding of 23 Fitbit device users who used their trackers for at least 2 months (range of use = 69-1073 days). RESULTS: Log data from users' Fitbit devices revealed 2 stages: the novelty period and the long-term use period. The novelty period for Fitbit users in this study was approximately 3 months, during which they might have discontinued using their devices. DISCUSSION: The qualitative interview data identified various factors that users to continuously use the Fitbit devices in different stages. The discussion of these results provides design implications to guide future development of activity tracking technology. CONCLUSION: This study reveals important dynamics emerging over long-term activity tracker use, contributes new knowledge to consumer health informatics and human-computer interaction, and offers design implications to guide future development of similar health-monitoring technologies that better account for long-term use in support of patient care and health self-management.