Motivating Adherence to Exercise Plans Through a Personalized Mobile Health App: Enhanced Action Design Research Approach.

BACKGROUND: Physical inactivity is a global issue that affects people's health and productivity. With the advancement of mobile technologies, many apps have been developed to facilitate health self-management. However, few studies have examined the effectiveness of these mobile health (mHealth) apps in motivating exercise adherence. OBJECTIVE: This study aims to demonstrate the enhanced action design research (ADR) process and improve the design of mHealth apps for exercise self-management. Specifically, we investigate whether sending motivational messages improves adherence to exercise plans, whether the motivational effect is affected by personality, the impact of message type and repetition, and the process of involving a field experiment in the design process and learning new design principles from the results. METHODS: This formative research was conducted by proposing an enhanced ADR process. We incorporated a field experiment into the process to iteratively refine and evaluate the design until it converges into a final mHealth app. We used the Apple ResearchKit to develop the mHealth app and promoted it via trainers at their gyms. We targeted users who used the app for at least two months. Participants were randomly assigned to 1 of the 12 groups in a 2×3×2 factorial design and remained blinded to the assigned intervention. The groups were defined based on personality type (thinking or feeling), message type (emotional, logical, or none), and repetition (none or once). Participants with different personality types received tailored and repeated messages. Finally, we used the self-reported completion rate to measure participants' adherence level to exercise plans. By analyzing users' usage patterns, we could verify, correct, and enhance the mHealth app design principles. RESULTS: In total, 160 users downloaded the app, and 89 active participants remained during the 2-month period. The results suggest a significant main effect of personality type and repetition and a significant interaction effect between personality type and repetition. The adherence rate of people with feeling personality types was 18.15% higher than that of people with thinking types. Emotional messages were more effective than logical messages in motivating exercise adherence. Although people received repeated messages, they were more likely to adhere to exercise plans. With repeated reminders, the adherence rates of people with thinking personality types were significantly improved by 27.34% (P<.001). CONCLUSIONS: This study contributes to the literature on mHealth apps. By incorporating a field experiment into the ADR process, we demonstrate the benefit of combining design science and field experiments. This study also contributes to the research on mHealth apps. The principles learned from this study can be applied to improve the effectiveness of mHealth apps. The app design can be considered a foundation for the development of more advanced apps for specific diseases, such as diabetes and asthma, in future research.

Evaluating self-management behaviors of diabetic patients in a telehealthcare program: longitudinal study over 18 months.

BACKGROUND: Self-management is an important skill for patients with diabetes, and it involves frequent monitoring of glucose levels and behavior modification. Techniques to enhance the behavior changes of diabetic patients have been developed, such as diabetes self-management education and telehealthcare. Although the patients are engaged in self-management activities, barriers to behavior changes remain and additional work is necessary to address the impact of electronic media and telehealthcare on patient self-care behaviors. OBJECTIVE: The aims of this study were to (1) explore the behaviors of diabetic patients interacting with online applications, (2) determine the impact of a telehealthcare program among 7 self-care behaviors of the patients, and (3) determine the changes in glycosylated hemoglobin (HbA1c) levels. METHODS: A telehealthcare program was conducted to assist the patients with 7 self-care activities. The telehealthcare program lasted for 18 months and included the use of a third-generation mobile telecommunications glucometer, an online diabetes self-management system, and a teleconsultant service. We analyzed the data of 59 patients who participated in the telehealthcare program and 103 who did not. The behavioral assessments and the HbA1c data were collected and statistically analyzed to determine whether the telehealthcare services had an impact on the patients. We divided the 18-month period into 3 6-month intervals and analyzed the parameters of patients assisted by the telehealthcare service at different time points. We also compared the results of those who were assisted by the telehealthcare service with those who were not. RESULTS: There was a significant difference in monitoring blood glucose between the beginning and the end of the patient participation (P=.046) and between the overall period and the end of patient participation (P<.001). Five behaviors were significantly different between the intervention and control patients: being active (P<.001), healthy eating (P<.001), taking medication (P<.001), healthy coping (P=.02), and problem solving (P<.001). Monitoring of blood glucose was significantly different (P=.02) during the 6-12 month stage of patient participation between the intervention and control patients. A significant difference between the beginning and the 6-12 month stage of patient participation was observed for the mean value of HbA1c level (P=.02), and the differences between the overall HbA1c variability and the variability of each 6-month interval was also significant. CONCLUSIONS: Telehealthcare had a positive effect on diabetic patients. This study had enhanced blood glucose monitoring, and the patients in the program showed improvements in glycemic control. The self-care behaviors affect patient outcomes, and the changes of behavior require time to show the effects.

U-Health: an example of a high-quality individualized healthcare service.

National Taiwan University Hospital (Taipei, Taiwan), which is one of the largest hospitals in Taiwan, is undergoing healthcare service reforms: it is combining the home healthcare service network and sensors network into a new service known as the Ubiquitous Healthcare (U-Health) service system in order to continually provide a personalized healthcare service to patients who live in remote areas. The U-Health service system is comprised of six service components that are introduced in an integrated manner: tele-education, telediagnosis, telemonitoring, teleconsultation, teletherapy and lifetime health. The target patient segments are postdischarge patients, patients suffering from cardiovascular diseases, patients suffering from chronic diseases and patients in hospice care. Since its launch in October 2009, the U-Health service system has effectively reduced the rehospitalization rate from 12% in October 2009 to 6% in May 2010 and the rate of visiting the emergency room from 21% in October 2009 to 9% in May 2010, during the 14-day postdischarge follow-up period.