Background: To improve workers’ health and well-being, workplace interventions have been developed, but utilization and reach are unsatisfactory, and effects are small. In recent years, new approaches such as mobile health (mHealth) apps are being developed, but the evidence base is poor. Research is needed to examine its potential and to assess when, where, and for whom mHealth is efficacious in the occupational setting. To develop interventions for workers that actually will be adopted, insight into user satisfaction and technology acceptance is necessary. For this purpose, various qualitative evaluation methods are available. Objective: The objectives of this study were to gain insight into (1) the opinions and experiences of employees and experts on drivers and barriers using an mHealth app in the working context and (2) the added value of three different qualitative methods that are available to evaluate mHealth apps in a working context: interviews with employees, focus groups with employees, and a focus group with experts. Methods: Employees of a high-tech company and experts were asked to use an mHealth app for at least 3 weeks before participating in a qualitative evaluation. Twenty-two employees participated in interviews, 15 employees participated in three focus groups, and 6 experts participated in one focus group. Two researchers independently coded, categorized, and analyzed all quotes yielded from these evaluation methods with a codebook using constructs from user satisfaction and technology acceptance theories. Results: Interviewing employees yielded 785 quotes, focus groups with employees yielded 266 quotes, and the focus group with experts yielded 132 quotes. Overall, participants muted enthusiasm about the app. Combined results from the three evaluation methods showed drivers and barriers for technology, user characteristics, context, privacy, and autonomy. A comparison between the three qualitative methods showed that issues revealed by experts only slightly overlapped with those expressed by employees. In addition, it was seen that the type of evaluation yielded different results. Conclusions: Findings from this study provide the following recommendations for organizations that are planning to provide mHealth apps to their workers and for developers of mHealth apps: (1) system performance influences adoption and adherence, (2) relevancy and benefits of the mHealth app should be clear to the user and should address users’ characteristics, (3) app should take into account the work context, and (4) employees should be alerted to their right to privacy and use of personal data. Furthermore, a qualitative evaluation of mHealth apps in a work setting might benefit from combining more than one method. Factors to consider when selecting a qualitative research method are the design, development stage, and implementation of the app; the working context in which it is being used; employees’ mental models; practicability; resources; and skills required of experts and users.@en

Objective To assess the effects of a personal robot, providing diabetes self-management education in a clinical setting on the pleasure, engagement and motivation to play a diabetes quiz of children (7–12) with type 1 diabetes mellitus (T1DM), and on their acquisition of knowledge about their illness. Methods Children with T1DM (N = 27) participated in a randomized controlled trial (RCT) in which they played a diabetes mellitus self-management education (DMSE) game, namely a diabetes quiz, with a personal or neutral robot on three occasions at the clinic, or were allocated to a control group (care as usual). Personalised robot behaviour was based on the self-determination theory (SDT), focusing on the children's needs for competence, relatedness and autonomy. The SDT determinants pleasure, motivation and diabetes knowledge were measured. Child-robot interaction was observed, including level of engagement. Results Results showed an increase in diabetes knowledge in children allocated to the robot groups and not in those allocated to the control group (P =.001). After three sessions, children working with the personal robot scored higher for determinants of SDT than children with the neutral robot (P = .02). They also found the robot to be more pleasurable (P =.04), they answered more quiz questions correctly (P =.02), and were more motivated to play a fourth time (P = .03). The analysis of audio/video recordings showed that in regard to engagement, children with the personal robot were more attentive to the robot, more social, and more positive (P < .05). Conclusion The study showed how a personal robot that plays DMSE games and applies STD based strategies (i.e., provides constructive feedback, acknowledges feelings and moods, encourages competition and builds a rapport) can help to improve health literacy in children in an pleasurable, engaging and motivating way. Using a robot in health care could contribute to self-management in children with a chronic disease and help them to cope with their illness.@en