Evidence map›Paper›PMID 40268986›Full record

ArticleScientific reports2025

Enhancing long-term adherence in elderly stroke rehabilitation through a digital health approach based on multimodal feedback and personalized intervention.

Xintong Wen, Yuxuan Li, Qi Zhang, Zhiwei Yao, Xijie Gao, Zhibo Sun, Xing Fang, Wei Huang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

8 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

8 authors.

Xintong Wen *Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yuxuan Li *Department of Information Design, Wuhan University of Techology, Wuhan, Hubei, China. simon2016@whut.edu.cn.
Qi ZhangDepartment for Public Health, Wuhan Jinyintan Hospital, Wuhan, Hubei, China.
Zhiwei YaoDepartment of Information Design, Wuhan University of Techology, Wuhan, Hubei, China.
Xijie GaoDepartment of Information Design, Wuhan University of Techology, Wuhan, Hubei, China.
Zhibo Sun *Department of Orthopedics, Renmin Hospital, Wuhan University, Wuhan, Hubei, China.
Xing FangDepartment of Information Design, Wuhan University of Techology, Wuhan, Hubei, China.
Wei HuangIntelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. tongtongzai2023@163.com.

Funding

Natural Science Foundation of HubeiProvince 2024AFB705)The National Natural Science Foundationof China 82002318
6 · The paper itself

Abstract

Multimodal digital health technologies aim to improve long-term adherence in stroke patients through personalized feedback and psychological monitoring. However, the interactive effects of physiological and psychological factors on rehabilitation adherence remain unclear. This study evaluates personalized feedback, physiological and psychological factors, and their independent and synergistic effects to optimize rehabilitation adherence in elderly stroke patients. This study was designed as a longitudinal study, with data collected from 180 participants across two central hospitals between March and September 2024. A linear mixed effects model (LMM) was used to analyze the impact of physiological monitoring, psychological monitoring, and personalized feedback mechanisms on long-term patient adherence. Data were gathered through structured questionnaires, resulting in a final sample size of 540 data points. The time effect has a significant positive effect on rehabilitation compliance. The rehabilitation plan completion rate (A1) increases by 1.25 (t = 34.25) and 2.28 units (t = 62.56) in the mid-term follow-up (T2) and long-term follow-up (T3) respectively; the self-reported compliance score (A2) increases by 1.12 (t = 31.39) and 2.3 points (t = 64.27) at T2 and T3 respectively; the completion of specific activities (A3) increases by 1.37 (t = 50.34) and 2.34 units (t = 86.26); the number of interruptions (A4) decreases by 0.89 (t = -17.31) and 2.11 times (t = -41.17) respectively. In personalized feedback, high-quality feedback (D2) significantly promotes compliance (β = 0.0318, t = 2.08), while excessively frequent feedback (D1) showes a negative impact (β=-0.0914, t=-1.93 ). In terms of psychological factors, positive emotion (C3) has a significant positive effect on compliance (β = 0.1572, t = 2.695), while depressed emotion (C1) significantly reduces interruption behavior (β=-0.0885). The interaction effect between physiological factors and psychological factors is not significant, indicating that their influence is relatively independent. This study demonstrates that personalized feedback, psychological support, and time effects are essential for enhancing rehabilitation adherence in elderly stroke patients. High-quality, relevant feedback significantly improves adherence, while ineffective feedback may have adverse effects. Positive emotions within psychological factors promote adherence, whereas depressive emotions hinder recovery, underscoring the importance of psychological support. Although physiological and psychological factors lack significant interactive effects, their independent influences merit attention and optimization in rehabilitation interventions.

Indexed as

Patient ComplianceStrokeStroke RehabilitationAgedAged, 80 and overDigital HealthFeedbackFemaleHumansLongitudinal StudiesMaleMiddle AgedSurveys and QuestionnairesTelemedicineAdherenceElderly patientsLinear mixed effects modelPersonalized feedback interventionRehabilitationStroke

Identifiers

PMID40268986
PMCPMC12019390

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LicenceCC BY-NC-ND
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Registered trials

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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.