
Background Digital health refers to the proper use of technology for improving the health and well-being of people and enhancing the care of patients through the intelligent processing of clinical and genetic data. Despite increasing interest in well-being in both health care and technology, there is no clear understanding of what constitutes well-being, which leads to uncertainty in how to create well-being through digital health. In an effort to clarify this uncertainty, Brey developed a framework to define problems in technology for well-being using the following four categories: epistemological problem, scope problem, specification problem, and aggregation problem. Objective This systematic scoping review aims to gain insights into how to define and address well-being in digital health. Methods We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Papers were identified from 6 databases and included if they addressed the design or evaluation of digital health and reported the enhancement of patient well-being as their purpose. These papers were divided into design and evaluation papers. We studied how the 4 problems in technology for well-being are considered per paper. Results A total of 117 studies were eligible for analysis (n=46, 39.3% design papers and n=71, 60.7% evaluation papers). For the epistemological problem, the thematic analysis resulted in various definitions of well-being, which were grouped into the following seven values: healthy body, functional me, healthy mind, happy me, social me, self-managing me, and external conditions. Design papers mostly considered well-being as healthy body and self-managing me, whereas evaluation papers considered the values of healthy mind and happy me. Users were rarely involved in defining well-being. For the scope problem, patients with chronic care needs were commonly considered as the main users. Design papers also regularly involved other users, such as caregivers and relatives. These users were often not involved in evaluation papers. For the specification problem, most design and evaluation papers focused on the provision of care support through a digital platform. Design papers used numerous design methods, whereas evaluation papers mostly considered pre-post measurements and randomized controlled trials. For the aggregation problem, value conflicts were rarely described. Conclusions Current practice has found pragmatic ways of circumventing or dealing with the problems of digital health for well-being. Major differences exist between the design and evaluation of digital health, particularly regarding their conceptualization of well-being and the types of users studied. In addition, we found that current methodologies for designing and evaluating digital health can be improved. For optimal digital health for well-being, multidisciplinary collaborations that move beyond the common dichotomy of design and evaluation are needed.
Design, Well-being, Computer applications to medicine. Medical informatics, R858-859.7, Technology assessment, Review, Telehealth, mHealth, Caregivers, Humans, eHealth, Public aspects of medicine, RA1-1270, Radboudumc 0: Other Research RIHS: Radboud Institute for Health Sciences, Evaluation, Digital health, Mobile phone, Delivery of Health Care, Radboudumc 10: Reconstructive and regenerative medicine RIHS: Radboud Institute for Health Sciences
Design, Well-being, Computer applications to medicine. Medical informatics, R858-859.7, Technology assessment, Review, Telehealth, mHealth, Caregivers, Humans, eHealth, Public aspects of medicine, RA1-1270, Radboudumc 0: Other Research RIHS: Radboud Institute for Health Sciences, Evaluation, Digital health, Mobile phone, Delivery of Health Care, Radboudumc 10: Reconstructive and regenerative medicine RIHS: Radboud Institute for Health Sciences
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 38 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
