Evidence map›Paper›PMID 40297355›Full record

ArticleDigital health

Effect of patient satisfaction on the utilization of mHealth services by patients with chronic disease.

Jiao Lu, Heng Zhao, Zhilin Ji, Yanan Dong

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

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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

4 citing papers in PubMed.

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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.

Jiao LuSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0000-0003-0866-8104
Heng ZhaoAcademy of Medical Sciences, Shanxi Medical University, Taiyuan, China.ORCID https://orcid.org/0009-0005-2252-087X
Zhilin JiDepartment of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, China.ORCID https://orcid.org/0000-0002-2258-9931
Yanan DongSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0009-0004-7052-8385

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Mobile health (mHealth) is considered an effective way to manage chronic disease patients' health. However, patients often do not use or continue using mHealth services due to concerns about service quality. Patient satisfaction may influence their utilization, and this effect may be moderated by platform regulations and differences in physicians' qualifications and regions. Objective: This study aims to enhance mHealth service utilization by examining how patient satisfaction affects both use and continuous use behaviors. It also explores the role of platform regulations (recommendation, commenting, and reward systems) and assesses differences due to physician proficiency and regional variations. Methods: Data were collected from Haodf, a leading mHealth platform in China, from October 2021 to March 2022 at 3-month intervals. Generalized additive models were used to verify nonlinear relationships between patient satisfaction and their utilization behaviors. The role of platform regulations and heterogeneity analysis regarding physician region was also examined. Results: Efficacy satisfaction positively influenced mHealth service utilization ( Conclusions: Efficacy satisfaction drives mHealth service utilization, while efficacy and attitude satisfaction drive continuous use. Recommendation and commenting systems exhibit a positive synergistic effect, while the reward system plays a supplementary role. Professional recommendations and high-quality evaluations enhance the evaluation system's reliability, promoting service utilization. Patients in big cities are more sensitive to physicians' proficiency and therapeutic effects. A multidimensional, transparent evaluation and feedback system should be integrated into the platform to improve service quality and increase patient utilization and loyalty.

Indexed as

chronic disease managementgeneralized additive modelsmHealthpatient satisfactionuse behavior

Identifiers

PMID40297355
PMCPMC12035501

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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.