ArticleJMIR mHealth and uHealth2023
Willingness to Use and Pay for Digital Health Care Services According to 4 Scenarios: Results from a National Survey.
Article in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- The Role of Rating Valence in AI Skin Cancer App Acceptance: Eye-Tracking and Questionnaire Study.JMIR human factors · 2026Article
- Preferences for Digital Mental Health Services in Urban China: A Discrete Choice Experiment.Applied health economics and health policy · 2026Article
- Willingness to pay for digital care for NCD in Ghana: an exploratory study of three health facilities in Ghana.BMC health services research · 2025Article
- Cost-Benefit Analysis of the Enhancing Men's Awareness of Testicular diseases (E-MAT) Feasibility Trial: A Virtual Reality Experience to Increase Testicular Knowledge and Self-Examination among Male Athletes.PharmacoEconomics - open · 2025Article
- Willingness to Use and Pay for Telemedicine and Teleconsultation Across Five Clinical Domains in South Korea: Cross-Sectional Survey.Journal of medical Internet research · 2025Article
- A choice based conjoint analysis of mobile healthcare application preferences among physicians, patients, and individuals.NPJ digital medicine · 2025Article
- Adoption of Digital Therapeutics in Europe.Therapeutics and clinical risk management · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSmartphones and their associated technology have evolved to an extent where these devices can be used to provide digital health interventions. However, few studies have been conducted on the willingness to use (WTU) and willingness to pay (WTP) for digital health interventions.
objectiveThe purpose of this study was to investigate how previous service experience, the content of the services, and individuals' health status affect WTU and WTP.
methodsWe conducted a nationwide web-based survey in 3 groups: nonusers (n=506), public service users (n=368), and private service users (n=266). Participants read scenarios about an imagined health status (such as having a chronic illness) and the use of digital health intervention models (self-management, expert management, and medical management). They were then asked to respond to questions on WTU and WTP.
resultsPublic service users had a greater intention to use digital health intervention services than nonusers and private service users: scenario A (health-risk situation and self-management), nonusers=odd ratio [OR] .239 (SE .076; P<.001) and private service users=OR .138 (SE .044; P<.001); scenario B (health-risk situation and expert management), nonusers=OR .175 (SE .040; P<.001) and private service users=OR .219 (SE .053; P<.001); scenario C (chronic disease situation and expert management), nonusers=OR .413 (SE .094; P<.001) and private service users=OR .401 (SE .098; P<.001); and scenario D (chronic disease situation and medical management), nonusers=OR .480 (SE .120; P=.003) and private service users=OR .345 (SE .089; P<.001). In terms of WTP, in scenarios A and B, those who used the public and private services had a higher WTP than those who did not (scenario A: β=-.397, SE .091; P<.001; scenario B: β=-.486, SE .098; P<.001). In scenario C, private service users had greater WTP than public service users (β=.264, SE .114; P=.02), whereas public service users had greater WTP than nonusers (β=-.336, SE .096; P<.001). In scenario D, private service users were more WTP for the service than nonusers (β=-.286, SE .092; P=.002).
conclusionsWe confirmed that the WTU and WTP for digital health interventions differed based on individuals' prior experience with health care services, health status, and demographics. Recently, many discussions have been made to expand digital health care beyond the early adapters and fully into people's daily lives. Thus, more understanding of people's awareness and acceptance of digital health care is needed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.