Background: Lack of time for exercise is common among office workers given their busy lives. Because of occupational restrictions and difficulty in taking time off, it is necessary to suggest effective ways for workers to exercise regularly. Sustaining lifestyle habits that increase nonexercise activity in daily life can solve the issue of lack of exercise time. Healthy Lifestyle Coaching Chatbot is a messenger app based on the habit formation model that can be used as a tool to provide a health behavior intervention that emphasizes the importance of sustainability and involvement. Objective: This study aimed to assess the efficacy of the Healthy Lifestyle Coaching Chatbot intervention presented via a messenger app aimed at stair-climbing habit formation for office workers. Methods: From February 1, 2018, to April 30, 2018, a total of 106 people participated in the trial after online recruitment. Participants were randomly assigned to the intervention group (n=57) or the control group (n=49). The intervention group received cues and intrinsic and extrinsic rewards for the entire 12 weeks. However, the control group did not receive intrinsic rewards for the first 4 weeks and only received all rewards as in the intervention group from the fifth to twelfth week. The Self-Report Habit Index (SRHI) of participants was evaluated every week, and the level of physical activity was measured at the beginning and end of the trial. SPSS Statistics version 21 (IBM Corp) was used for statistical analysis. Results: After 4 weeks of intervention without providing the intrinsic rewards in the control group, the change in SRHI scores was 13.54 (SD 14.99) in the intervention group and 6.42 (SD 9.42) in the control group, indicating a significant difference between the groups (P=.04). When all rewards were given to both groups, from the fifth to twelfth week, the change in SRHI scores of the intervention and control groups was comparable at 12.08 (SD 10.87) and 15.88 (SD 13.29), respectively (P=.21). However, the level of physical activity showed a significant difference between the groups after 12 weeks of intervention (P=.045). Conclusions: This study provides evidence that intrinsic rewards are important to enhance the sustainability and effectiveness of an intervention. The Healthy Lifestyle Coaching Chatbot program can be a cost-effective method for healthy habit formation. Trial Registration: Clinical Research Information Service KCT0004009; https://tinyurl.com/w4oo7md

It is crucial to find ways to fit regular exercise into the daily lives of office workers. Non-exercise activity thermogenesis has been introduced as an effective form of daily exercise. This study aimed to develop a healthy lifestyle coaching program for office workers, to be delivered using a messenger application. The interface was developed using KakaoTalk and Plus Friend. Performance feedback was developed using the IBM Watson conversation program. Twenty office workers used the application for three weeks. Afterward, a survey was conducted to assess the usability of and participants' satisfaction with the application. The application delivered customized push alarms, provided information related to habit formation, allowed for one-on-one chats, and delivered rewards. The satisfaction measurement results for the application showed that extrinsic reward factors contributed the most to the performance of the activity, followed by reminders and intrinsic rewards. Regarding the usability test, the perceived usefulness of the Healthy Lifestyle Coaching Chatbot was highest, followed by the usage intent and the perceived ease of use. This study found that coaching programs using chatbots can improve the effectiveness of performing simple, repetitive exercises.

Wearable devices and digital health technologies have enabled the exchange of urgent clinical trial information. We developed an application to improve the functioning of decentralized clinical trials and performed a heuristic evaluation to reflect the user demands of existing clinical trial workers. The waterfall model of the software life cycle was used to guide the development. Focus group interviews (N = 7) were conducted to reflect the needs of clinical research professionals, and Wizard of Oz prototyping was performed to ensure high usability and completeness. Unit tests and heuristic evaluation (N = 11) were used. Thematic analysis was performed using the focus group interview data. Based on this analysis, the main menu was designed to include health management, laboratory test results, medications, concomitant medications, adverse reactions, questionnaires, meals, and My Alarm. Through role-playing, the functions and configuration of the prototype were adjusted and enhanced, and a heuristic evaluation was performed. None of the heuristic evaluation items indicated critical usability errors, suggesting that the revised prototype application can be practically applied to clinical trials. The application is expected to increase the efficiency of clinical trial management, and the development process introduced in this study will be helpful for researchers developing similar applications in the future.

The analysis of the electrocardiogram (ECG) is critical for the diagnosis of arrhythmias. Recent advances in information and communications technology (ICT) have led to the development of wearable ECG devices and arrhythmia-detection algorithms. This study aimed to develop an ICT-based clinical trial service platform using a self-reporting technique for real-time arrhythmia detection. To establish a clinical-trial service platform, a mobile application (app), a demilitarized zone (DMZ), an internal network, and Amazon web services virtual private cloud (AWS-VPC) were developed. The ECG data acquired by a wearable device were transmitted to the mobile app, which collected the participants’ self-reported information. The mobile app transmitted raw ECG and self-reported data to the AWS-VPC and DMZ, respectively. In the AWS-VPC, the live-streaming and playback-reviewer services were operational to display the currently and previously acquired ECG data to clinicians through the web client. All the measured data were transmitted to the internal network, in which the arrhythmia-detection algorithm was executed and all the data were saved. The self-reporting technique and arrhythmia-detection algorithm are the key elements of this platform. In particular, subjective information of participants can be easily collected using a self-reporting technique. These features are particularly of critical importance for treating painless, sparsely occurring arrhythmias.

Ryu H, et al. Healthc Inform Res. 2019 Apr;25(2):73-81. https://doi.org/10.4258/hir.2019.25.2.73