How to Prevent the Drop-Out: Understanding Why Adults Participate in Summative eHealth Evaluations.

The aim of this study was to investigate why adults participate in summative eHealth evaluations, and whether their reasons for participating affect their (non-)use of eHealth. A questionnaire was distributed among adults (aged ≥ 18 years) who participated in a summative eHealth evaluation. This questionnaire focused on participants' reason to enroll, their expectations, and on whether the study met their expectations. Answers to open-ended questions were coded by two researchers independently. With the generalized estimating equations method we tested whether there is a difference between the type of reasons in use of the eHealth service. One hundred and thirty-one adults participated (64.9% female; mean age 62.5 years (SD = 10.5)). Their reasons for participating were mainly health-related (e.g., being more active). Between two types of motivations there was a difference in the use of the eHealth service: Participants with an intellectual motivation were more likely to drop out, compared to participants with an altruistic motivation. The most prevalent expectations when joining a summative eHealth evaluation were health-related (like expecting to improve one's health). 38.6% of the participants said their expectation was fulfilled by the study. In conclusion, We encourage eHealth evaluators to learn about adults' motivation to participate in their summative evaluation, as this motivation is very likely to affect their results. Including altruistically motivated participants biases the results by their tendency to continue participating in a study.

Game not over: Explaining older adults' use and intention to continue using a gamified eHealth service.

BACKGROUND: Gamification within eHealth services can increase eHealth adoption. However, little is known about factors affecting adoption of gamified eHealth among older adults. In this study, we sought to explain the (continued) use of a gamified eHealth service among older adults (55+). METHODS: Participants used a gamified eHealth service, focusing on falls prevention, for 4 weeks and completed a post-test questionnaire based on the Technology Acceptance Model. We used Partial Least Squares Structural Equation Modeling to analyse our data. RESULTS: Seventy-two older adults participated with a mean age of 65.1 years (SD = 7.0). Our results show that first, perceived ease of use affected use of the service (use duration: ß = 0.303, R2 = 0.130, and use frequency: ß = 0.304, R2 = 0.107). Second, perceived usefulness affected the intention to continue using the service (ß = 0.754, R2 = 0.640). Third, use of the service did not predict the intention to continue using it. Furthermore, enjoyment affected perceived usefulness (ß = 0.783, R2 = 0.563) and aesthetics affected perceived ease of use (ß = 0.634, R2 = 0.652). CONCLUSIONS: This study refutes the expected relation between use and intention to continue use a gamified eHealth service. Additionally, we learned that using theoretical approaches focusing on technology acceptance, are not suitable for explaining (continued) use of gamified eHealth services.

The First Introduction of Social Robotics in Rehabilitation Care.

Can you imagine to receive treatment through a robot? When talking about the future of healthcare, this is the vision many people have. Currently, the predominant role of social robots in care is entertaining patients. However, this does not have an impact on care process itself. In this paper, we focus on defining use cases other than merely keeping patients' company by implementing a Pepper robot in inpatient rehabilitation setting, and expand upon usability testing the use cases. Our findings showed that, to ensure sustainable implementation of social robots in care organizations, we need excessive collaboration with the target population.

Evaluation of a virtual coaching system eHealth intervention: A mixed methods observational cohort study in the Netherlands.

BACKGROUND: With the rise in human life expectancy, the prevalence of chronic disease has increased significantly. Adopting a healthy lifestyle can decrease the risk of chronic disease. Virtual coaching systems can help older adults adopt a healthy lifestyle.AimThe primary objective of this study was to assess the use, user experience and potential health effects of a conversational agent-based eHealth platform (Council of Coaches) implemented in a real-world setting among older adults. METHODS: An observational cohort study was conducted with older adults aged 55 years or older in the Netherlands. Participants were enrolled for 5-9 weeks during which they had access to Council of Coaches. They completed three questionnaires: pre-test, post-test, and at follow-up. After five weeks, an interview was conducted, and participants chose whether they wanted to use the eHealth intervention for another four weeks during the facultative phase. RESULTS: The study population consisted of 51 older adults (70.6% female) with a mean age of 65.3 years (SD = 7.4). Of these, 94.1% started interacting with Council of Coaches, and most participants interacted once per week. During the facultative phase, 21 participants were still interacting with Council of Coaches. Minimal clinical important differences in quality of life were found among the study population after interacting with Council of Coaches. CONCLUSION: Our results demonstrate that eHealth interventions with virtual coaching can be used among older adults. This may increase quality of life for older adults, and decrease their healthcare needs. Future research into such eHealth interventions should take into account the inclusion of sufficient personalised content and the use of a mixed methods study for assessing the eHealth intervention.

Automatic topic selection for long-term interaction with embodied conversational agents in health coaching: A micro-randomized trial.

INTRODUCTION: Embodied Conversational Agents (ECAs) can be included in health coaching applications as virtual coaches. The engagement with these virtual coaches could be improved by presenting users with tailored coaching dialogues. In this article, we investigate if the suggestion of an automatically tailored topic by an ECA leads to higher engagement by the user and thus longer sessions of interaction. METHODS: A Micro-Randomized Trial (MRT) was conducted in which two types of interaction with an ECA were compared: (a) the coach suggests a relevant topic to discuss, and (b) the coach asks the user to select a topic from a set of options. Every time the user would interact with the ECA, one of those conditions would be randomly selected. Participants interacted in their daily life with the ECA that was part of a multi-agent health coaching application for 4-8 weeks. RESULTS: In two rounds, 82 participants interacted with the micro-randomized coach a total of 1011 times. Interactions in which the coach took the initiative were found to be of equal length as interactions in which the user was allowed to choose the topic, and the acceptance of topic suggestions was high (71.1% overall, 75.8% for coaching topics). CONCLUSION: Tailoring coaching conversations with ECAs by letting the coach automatically suggest a topic that is tailored to the user is perceived as a natural variation in the flow of interaction. Future research could focus on improving the novel coaching engine component that supports the topic selection process for these suggestions or on investigating how the amount of initiative and coaching approach by the ECA could be tailored.

Predictors to Use Mobile Apps for Monitoring COVID-19 Symptoms and Contact Tracing: Survey Among Dutch Citizens.

BACKGROUND: eHealth apps have been recognized as a valuable tool to reduce COVID-19's effective reproduction number. The factors that determine the acceptance of COVID-19 apps remain unknown. The exception here is privacy. OBJECTIVE: The aim of this article was to identify antecedents of acceptance of (1) a mobile app for COVID-19 symptom recognition and monitoring and (2) a mobile app for contact tracing, both by means of an online survey among Dutch citizens. METHODS: Next to the demographics, the online survey contained questions focusing on perceived health, fear of COVID-19, and intention to use. We used snowball sampling via posts on social media and personal connections. To identify antecedents of the model for acceptance of the 2 mobile apps, we conducted multiple linear regression analyses. RESULTS: In total, 238 Dutch adults completed the survey; 59.2% (n=141) of the responders were female and the average age was 45.6 years (SD 17.4 years). For the symptom app, the final model included the predictors age, attitude toward technology, and fear of COVID-19. The model had an r2 of 0.141. The final model for the tracing app included the same predictors and had an r2 of 0.156. The main reason to use both mobile apps was to control the spread of the COVID-19 virus. Concerns about privacy was mentioned as the main reason to not use the mobile apps. CONCLUSIONS: Age, attitude toward technology, and fear of COVID-19 are important predictors of the acceptance of COVID-19 mobile apps for symptom recognition and monitoring and for contact tracing. These predictors should be taken into account during the development and implementation of these mobile apps to secure acceptance.

User Experience and Potential Health Effects of a Conversational Agent-Based Electronic Health Intervention: Protocol for an Observational Cohort Study.

BACKGROUND: While the average human life expectancy has increased remarkably, the length of life with chronic conditions has also increased. To limit the occurrence of chronic conditions and comorbidities, it is important to adopt a healthy lifestyle. Within the European project "Council of Coaches," a personalized coaching platform was developed that supports developing and maintaining a healthy lifestyle. OBJECTIVE: The primary aim of this study is to assess the user experience with and the use and potential health effects of a fully working Council of Coaches system implemented in a real-world setting among the target population, specifically older adults or adults with type 2 diabetes mellitus or chronic pain. METHODS: An observational cohort study with a pretest-posttest design will be conducted. The study population will be a dynamic cohort consisting of older adults, aged ≥55 years, as well as adults aged ≥18 years with type 2 diabetes mellitus or chronic pain. Each participant will interact in a fully automated manner with Council of Coaches for 5 to 9 weeks. The primary outcomes are user experience, use of the program, and potential effects (health-related factors). Secondary outcomes include demographics, applicability of the virtual coaches, and user interaction with the virtual coaches. RESULTS: Recruitment started in December 2019 and is conducted through mass mailing, snowball sampling, and advertisements in newspapers and social media. This study is expected to conclude in August 2020. CONCLUSIONS: The results of this study will either confirm or reject the hypothesis that a group of virtual embodied conversational coaches can keep users engaged over several weeks of interaction and contribute to positive health outcomes. TRIAL REGISTRATION: The Netherlands Trial Register: NL7911; https://www.trialregister.nl/trial/7911. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/16641.

Changes in medicine prescription following a medication review in older high-risk patients with polypharmacy.

Background The more (inappropriate) drugs a patient uses, the higher the risk of drug related problems. To reduce these risks, medication reviews can be performed. Objective To report changes in the prescribed number of (potentially inappropriate) drugs before and after performing a medication review in high-risk polypharmacy patients. A secondary objective was to study reasons for continuing potentially inappropriate drugs (PIDs). Setting Dutch community pharmacy and general medical practice. Methods A retrospective longitudinal intervention study with a pre-test/post-test design and follow-up of 1 week and 3 months was performed. The study population consisted of 126 patients with polypharmacy and with additional risk for drug related problems that underwent a medication review in five community pharmacies. The medication review was performed by the pharmacist in close cooperation with the general practitioner of each corresponding patient. Main outcome measure Number of (potentially inappropriate) drugs, and appropriateness of prescribed medicines. Results The average number of drugs a patient used 1 day before the review was 8.7 (SD = 2.9), which decreased (p < 0.05) to 8.3 (SD = 2.7) 1 week after the review, and to 8.4 (SD = 2.6) 3 months after the review. The average number of PIDs was initially 0.6 (SD = 0.8) per patient and decreased to 0.4 (SD = 0.6, p < 0.05). Twenty-two of the 241 initial drug changes (9%) were deprescribed during follow-up. Registered reasons for continuing PIDs are clinical or patients' preferences. Conclusions Performing medication reviews in polypharmacy patients seems useful to continue at least in high-risk patients in The Netherlands. The time-consuming reviews could be limited to patients who are willing to change their medication.