Evidence map›Paper›PMID 39303282›Full record

ArticleJournal of medical Internet research2024

Perceptions and Satisfaction With the Use of Digital Medical Services in Urban Older Adults of China: Mixed Methods Study.

Ning Wang, Siyu Zhou, Zhuo Liu, Ying Han

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ning Wang *School of Management, Shanxi Medical University, Jinzhong, China.ORCID 0009-0006-3487-6889
Siyu Zhou *School of Public Health, Hangzhou Normal University, Hangzhou, China.ORCID 0000-0001-7946-8172
Zhuo LiuSchool of Management, Shanxi Medical University, Jinzhong, China.ORCID 0009-0008-6858-6331
Ying HanSchool of Management, Shanxi Medical University, Jinzhong, China.ORCID 0009-0000-2426-6418

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn an aging and information-driven society, older adults have distinct perceptions of and specific demands for digital medical services. It is essential for society to understand these needs and develop a more thoughtful approach to digital health care.

objectiveThis study aims to evaluate the behavioral intention and satisfaction of older adults with digital medical services by identifying the perceived factors and the pathways through which these factors influence their behavior.

methodsThis study used a mixed methods approach, combining qualitative and quantitative analyses. A focus group interview was conducted with 30 randomly selected older adults, and the interviews were transcribed verbatim and coded using grounded theory. In addition, 876 valid questionnaires were collected to describe older adults' perceptions of and satisfaction with digital medical care. Then, t tests and ANOVA were used to explore differences among various demographic groups, while hierarchical multiple regression was conducted to identify the factors most closely related to satisfaction. Structural equation modeling was used to identify multiple mediating effects.

resultsThe qualitative study identified the core category of "medical service relief and transformation paths for older adults in the context of digital reform." Quantitative analysis revealed that more than half of the older adults were satisfied with digital medical services, and behavioral intentions were higher among those with higher incomes and education levels. Structural equation modeling confirmed that external variables, such as digital skills training, positively influenced perceived ease of use (β=.594, P<.001), perceived usefulness (β=.544, P<.001), and promoted digital medical behavioral intentions (β=.256, P<.001), while also reducing perceived risk (β=-.295, P<.001). Additionally, perceived ease of use (β=.168, P<.001) and perceived usefulness (β=.508, P<.001) positively impacted behavioral intention, whereas perceived risk (β=-.05, P=.037) exerted a negative influence. Furthermore, behavioral intention (β=.641, P<.001) significantly and positively affected older adults' satisfaction with digital medical care. The mediation test identified 4 significant paths: (1) external variables → perceived ease of use → behavioral intention (effect size of 13.9%); (2) external variables → perceived usefulness → behavioral intention (effect size of 38.4%); (3) external variables → perceived ease of use → perceived usefulness → behavioral intention (effect size of 10.1%); and (4) a direct effect (35.5%) from external variables to behavioral intention.

conclusionsBased on the study's findings, addressing the needs of older adults and enhancing perceived usefulness are the most effective ways to encourage the use of digital health care devices. Community support plays a crucial role in helping older adults integrate into digital health care, and adapting the design of services and products to suit their needs improves their perceptions of digital health care. This, in turn, promotes usage behavior and satisfaction, while the negative impact of perceived risk remains minimal.

Indexed as

Patient SatisfactionAgedAged, 80 and overChinaFemaleFocus GroupsHumansMaleMiddle AgedPerceptionPersonal SatisfactionSurveys and QuestionnairesTelemedicineUrban Populationbehavioral intentiondigital medical servicesolder adultsperceptionsatisfactionTechnology Acceptance Model

Identifiers

PMID39303282
PMCPMC11452758

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.