Thoughts on Self-Monitoring on mHealth
Self-monitoring or tracking is often a key feature in mHealth. The tracking involves clinical parameters (e.g. weight, blood pressure, etc.), symptom measures and daily activities. One of the goals of tracking is to encourage the user to engage in reflective thinking. Studies claimed that self-monitoring improves the user skills in self-management of the disease, symptom management and disease regulation. (Huygens, 2017)
The question is: What are the users looking for in a health self-monitoring/tracking system?
“GIVE ME RELIABLE, USEFUL & INSIGHTFUL INFO”
One of the reasons users stop using trackers is the effort required is much higher than the reward/gain. (Harrison 2015) A seamless experience in tracking can be supported by providing automated data capture, data visualisation, and relevant insight prompting aiming to assist users with their goals. For example, the users of a self-monitoring blood glucose system, the children with Type 1 Diabetes and their parents, rated the self-monitoring experience positively for its insights (e.g. irregular blood glucose level) and reminders. The parents know about the status of their child with Type 1 Diabetes when a new blood test is updated. Although some would like to decide whether to inform the parents about their blood glucose measures, they recognised that the tracking system improved their life to some extent. (Gammon et al., 2005)
On top of the perceived value from tracking, users also considered the quality and the timeliness of the insight. Some users indicated that inaccurate data captured or irrelevant insights provided made them feel cheated. (Lazar, 2015)
“I CAN GET BETTER USING THIS mHEALTH”
Huygens (2017) found that the willingness to perform self-monitoring is associated with the plausibility to improve the situation/condition (controlability). The study surveyed 627 Dutch patients with chronic diseases discovered that the ability to control the disease is positively associated with the willingness of the users to self-monitor. For example, comparing patients with rheumatoid or patients with neurological disorders, patients with diabetes are more willing to self-monitor.
Hence, mHealth plays a role in educating and associating the potential positive outcome of self-monitoring to increase the users’ willingness to track.
“TRACK IT MY WAY “
Studies have found that a predefined tracking system lacks flexibility and often fails to support the users’ goals and emotional needs. For instance, a survey about self-tracking practice alluded that most participants who practice self-tracking had tried using various tracking apps at the beginning of tracking practice before switching to general-purpose apps (such as a notebook or excel sheet). They stopped using the tracking apps because of heavy tracking burdens, such as too many fields, and/or unsupported tracking needs. (Ayobi, 2018; Kim, 2017)
Even more challenging in designing the tracking system is — every user has different tracking needs, and the interests and targets change over time. A usability study of a user-configurable tracker demonstrated this point. The study involved 21 participants, there was 26 unique tracker schema created and tracked within three weeks. On the same tracking aspect, users created different trackers to measure. For example, two users tracked the mood, one user logged crying events and track his mood on a 10 point Likert scale; the other user used three-mood-tracker with multiple fields to upload photo and text input. (Kim, 2017)
When designing a tracking system, it is suggested to consider how does digitally tracking provide additional values and support to the user as opposed to digitised and automated analogue data. (Ayobi, 2018)
Huygens MWJ, Swinkels ICS, Jong JD, Heijmans MJWM, Friele RD, Schayck OCP, and Witte LP. Self-monitoring of health data by patients
with a chronic disease: does disease controllability matter? BMC Family Practice (2017) 18:40. DOI:10.1186/s12875–017–0615–3
Ayobi A, Sonne T, Marshall P, and Cox AL 2018. Flexible and Mindful Self-Tracking: Design Implications from Paper Bullet Journals. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, Paper 28, 1–14. DOI:10.1145/3173574.3173602
Gammon D, Årsand E, Walseth O, Andersson N, Jenssen M, Taylor T. Parent-Child Interaction Using a Mobile and Wireless System for Blood Glucose Monitoring J Med Internet Res 2005;7(5):e57. DOI: 10.2196/jmir.7.5.e57
Kim YH, Jeon JH, Lee B, Choe EK, Seo J. OmniTrack. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2017; 1(3), 1–28.
Harrison D, Marshall P, Bianchi-Berthouze N, Bird J. Activity tracking: Barriers, Workarounds and Customisation. UbiComp ’15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing September 2015 Pages 617–621.
Lazar A, Koehler C, Tanenbaum J, Nguyen DH. Why we use and abandon smart devices. UbiComp ’15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing September 2015 Pages 635–646.
