UTFacultiesEEMCSDisciplines & departmentsBSSEventsPhD Defence Marian Hurmuz | eHealth - In or out of our daily lives? - Measuring the (non-)use of eHealth in summative evaluations

PhD Defence Marian Hurmuz | eHealth - In or out of our daily lives? - Measuring the (non-)use of eHealth in summative evaluations

eHealth - In or out of our daily lives? - Measuring the (non-)use of eHealth in summative evaluations

The PhD defence of Marian Hurmuz will take place (partly) online and can be followed by a live stream.

Marian Hurmuz is a PhD student in the research group Biomedical Signals and Systems (BSS). Supervisor is prof.dr.ir. H.J. Hermens and co-supervisor is dr. S.M. Jansen-Kosterink, both from the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS).

While there are many different eHealth services (being) developed, its use among the target population is still low. eHealth services can be a solution for many problems in healthcare (e.g. long waiting lists, limited capacity of healthcare demands, rising costs). However, if the target population does not use those services in daily lives, can eHealth even be proposed as a solution to healthcare problems? In this thesis, it is being investigated why eHealth services are (not) being used by the target population. The aim of this thesis was to increase our understanding about the (non-)use of eHealth services among the target population in a real-world setting.

In the first study (Chapter 2), it was explored which demographics and personality traits of older adults can predict dropping out of an eHealth service. The results showed two factors that predict drop-out in eHealth among older adults. First, perceived computer skills influences drop-out: the higher an older adult perceives his/her computer skills, the lower the chance is to drop out. Second, the motivation type external regulation to live healthy influences drop-out: the more external regulated an older adult’s motivation is to live healthy, the higher the chance is to drop out. To prevent drop-out in these groups, training is needed to improve an older adult’s perceived computer skills, and an eHealth service needs to include options which influence the external regulated motivation type (e.g. giving compliments, educating older adults).

Now we know older adults’ demographics and personality traits that predict drop-out in eHealth, but we also need to take into account other factors that influence the use of eHealth. In the second study (Chapter 3), a model based on the Technology Acceptance Model (TAM) was developed to investigate which determinants explain older adults’ use and intention to continue use a gamified eHealth service. The analysis showed that the TAM did not fully predict the use of and intention to continue using a gamified eHealth service. The perceived ease of use influenced the use of the gamified eHealth service, and perceived usefulness influenced the intention to continue use this service. But previous use did not influenced older adults ’ intention to continue use the eHealth service.

Next, a case study was presented (Chapter 4) which shows how you can evaluate an eHealth service in a real-world setting with mixed methods. This evaluation with a virtual coaching system showed that the number of older adults using the system declined over time, that the use of this system was mostly once a week, that this system was easy to use, and that on an individual level, minimal clinical important differences were found in different health variables. Furthermore, the results in this chapter highlight the importance of the mixed methods used. Due to this research method, more in-depth data was gathered about the quantitative results found.

The following study (Chapter 5) focused on qualitatively investigating barriers and facilitators adults with neck and/or low back pain (NLBP) perceive when using an mHealth app. The top three most mentioned barriers that were perceived by these adults were mode of delivery of the mHealth app, novelty of the app, and health-related factors. The top three most mentioned facilitators to use the app were the inclusion of action plans, health-related factors, and access to technology. In this chapter, practical implications were given on how to tackle the barriers and how to reinforce the presence of the facilitators. This could increase eHealth use among this target population.

In multiple chapters different aspects of eHealth (non-)use were discussed. However, to better understand why the target population does not use eHealth in daily live, the last study (Chapter 6) identified the reasons of potential end-users to participate in eHealth studies, the influence of these reasons on the use of eHealth, and their expectations about these studies. The most mentioned reason to participate was health-related, e.g. participating to improve my health, or to feel more fit. Between two motivation categories there was a difference in use of eHealth: people with an intellectual motivation to participate in eHealth evaluations are more likely to drop out compared to people with an altruistic motivation to participate. The results showed that including altruistic motivated adults in your study population, biases the study findings.

Finally, Chapter 7 concluded this thesis by discussing the main findings. First, the use of eHealth and the intensity of use among different eHealth services is discussed. This showed that among older adults, a part of the users will stop using eHealth, but if there is enough variation in the content of the eHealth service, the remaining users will use the eHealth service more intensive. Furthermore, it showed that when using an eHealth service for a specific physical complaint, decline in users is less apparent, compared to using an eHealth service for more general health promotion or prevention. Second, different aspects which can be used to measure eHealth use, and how eHealth use can be improved were discussed. These aspects were related to the user, the technology, and the interaction of those two. Based on these aspects, strategies for improvements were given. Third, three recommendations on improving summative eHealth evaluations were given. These were: (1) conducting summative eHealth evaluations in a real-world setting, (2) evaluating the eHealth service with those people who actually need it, and (3) investigating motivations of the study population for participating in summative eHealth evaluations. Fourth, this chapter focused on future research by (1) discussing considerations for future research, (2) by discussing the two practical tools that were derived from this thesis and that can be used in future studies, and (3) by discussing a note on publishing qualitative studies in health journals. Finally, this chapter ends with concluding words.