Table · dataset · 2026
Supplementary file 1_Comparative efficacy of digital health intervention delivery modalities on quality of life, anxiety, and depression in coronary heart disease: a systematic review and network meta-analysis.docx
Listed in HKU DataHub and figshare and Loughborough Research Repository and UP Research Data Repository — shown once because both records carry DOI 10.3389/fpsyt.2026.1912762.s001
Background<p>Nonadherence to traditional cardiac rehabilitation and persistent psychological distress among patients with coronary heart disease (CHD) severely undermine desired therapeutic outcomes.
Description
Digital health interventions (DHIs) emerge as promising strategies to effectively tackle this adherence and mental health issue remotely.</p>Objective<p>The aim of this study was to conduct a network meta-analysis (NMA) to compare and rank the comparative efficacy of various DHI delivery modalities in improving quality of life (QoL), anxiety, and depression among patients with CHD.</p>Methods<p>A systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane, CNKI, VIP, WanFang, and SinoMed databases to search for randomized controlled trials (RCTs)published from their inception to February 1,2026.We carried out a frequentist NMA using Stata 18.0to compare the efficacy of various DHIs against standard care.
The quality of literature was assessed using the Cochrane Risk of Bias tool (version2.0).The certainty of evidence was evaluated using the Confidence in Network Meta-Analysis (CINeMA) framework, and interventions were ranked utilizing the surface under the cumulative ranking (SUCRA)curve.</p>Results<p>A total of 44RCTs involving 8,495 patients were enrolled. The NMA revealed that for improving QoL, telephone follow-up (standardized mean difference 2.98, 95%CI 1.75-4.20) and social media (SMD 1.13, 95%CI 0.42-1.84) were statistically superior to usual care.
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Notably, telephone follow-up, with a SUCRA of 99.5%, appeared to be the most effective option. Regarding psychological outcomes, web-based platforms demonstrated the highest probability of being the best intervention for reducing both anxiety (SMD -1.56,95%CI-2.34 to -0.79;SUCRA 98.6%) and depression (SMD-1.07,95%CI-1.94to-0.20;SUCRA 91.4%).Safety analyses showed interactive modalities significantly reduced adverse events, whereas one-way SMS carried a significantly higher risk than interactive telephone support(OR 3.38,95%CI 1.13-10.13).</p>Conclusions<p>The research suggests that telephone follow-up ranked highest among the included studies for improving short-term QoL, while web-based platforms showed a high probability of being effective for alleviating long-term anxiety and depression.
However, these rankings should be interpreted cautiously, as intervention content, duration, and patient populations varied considerably across trials, and the small number of studies for certain modalities limits the robustness of direct comparisons. Social media interventions also emerged as a versatile option with benefits across all three outcomes, though their modest effect sizes warrant further optimization of engagement strategies.
Passive SMS interventions present potential safety concerns, as their unidirectional nature may attenuate timely symptom recognition and delay appropriate care-seeking—a hypothesis supported by their significantly higher adverse event rates compared with interactive modalities. These results provide crucial evidence-based support for healthcare providers to tailor specific DHIs to individual patient needs.</p>Systematic review registration<p>crd.york.ac.uk/PROSPERO/, identifier CRD420261380157.</p>
Links
Where it is published
- DOI doi.org/10.3389/fpsyt.2026.1912762.s001 ↗
DOI / persistent id · from datahub hku hk
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from datahub hku hk
Topics
- From keywords
- Astronomy & Astrophysics · Chemistry · Chemistry · Computer Science & AI · Computer Science & AI · Digital health · Digital health · Digital health · Digital health · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Economics & Finance · Economics & Finance · Engineering · Engineering · Engineering · Humanities · Humanities · Humanities · Life Sciences · Life Sciences · Life Sciences · Materials Science · Mathematics & Statistics · Medicine & Health · Medicine & Health · Medicine & Health · Medicine & Health · Ocean & Atmospheric Science · Psychology & Behavioral Science · Social Science · Social Science · Social Science
- Inferred from text
- Cardiovascular disease 65% · Disease 75% · Heart 75%
Provenance · 4 source records, 43 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| HKU DataHub | oai:figshare.com:article/34013412 | 6 d ago | JSON v1 |
| figshare | oai:figshare.com:article/34013412 | 5 d ago | JSON v1 |
| Loughborough Research Repository | oai:figshare.com:article/34013412 | 5 d ago | JSON v1 |
| UP Research Data Repository | oai:figshare.com:article/34013412 | 5 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[anatomy].local:anatomy:heart | enrichment · datahub hku hk | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[disease].local:disease:cardiovascular-disease | enrichment · datahub hku hk | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[disease].local:disease:disease | enrichment · datahub hku hk | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:field:420302 | mapping · repository lboro ac uk | vocabulary-mapper@1.0.0 | keywords['digital health'] |
| concepts[field].anzsrc:field:420302 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['digital health'] |
| concepts[field].anzsrc:field:420302 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['digital health'] |
| concepts[field].anzsrc:field:420302 | mapping · datahub hku hk | vocabulary-mapper@1.0.0 | keywords['digital health'] |
| concepts[field].local:field:astronomy | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:engineering | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:engineering | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:materials-science | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:mathematics-statistics | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:social-science | mapping · figshare com | connector:figshare_com@1.0.0 | |
| description | source · datahub hku hk | connector:datahub_hku_hk@1.0.0 | /metadata/dc/description |
| license | source · datahub hku hk | connector:datahub_hku_hk@1.0.0 | /metadata/dc/rights |
| publication_date | source · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| title | source · datahub hku hk | connector:datahub_hku_hk@1.0.0 | /metadata/dc/title |