Effects of Telemedicine and mHealth on Systolic Blood Pressure Management in Stroke Patients: Systematic Review and Meta-Analysis of Randomized Controlled Trials
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Abstract
Background: Stroke is a common, harmful disease with high recurrence and mortality rates. Uncontrolled blood pressure is an important and changeable risk factor for stroke recurrence. Telemedicine and mobile health (mHealth) interventions may have the potential to facilitate the control of blood pressure among stroke survivors, but their effect has not been established. Objective: This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to estimate the effects of telemedicine and mHealth interventions on the control of systolic blood pressure among stroke survivors. Methods: The research literature published up to June 28, 2020, and consisting of RCTs related to telemedicine and mHealth interventions was searched in PubMed, EMBASE, Web of Science, and the Cochrane Library. The Cochrane risk of bias tool (RoB 2.0) was used to evaluate the quality of the studies. The Cochran Q test and I2 statistic were used to assess heterogeneity. Data were meta-analyzed using a random-effects model. Mean difference (MD) with 95% CI and 95% prediction interval (PI) were calculated. Results: In total, 9 RCTs with a total sample size of 1583 stroke survivors met the inclusion criteria. Compared with the usual care, telemedicine and mHealth had a significantly greater impact on the control of systolic blood pressure (MD –5.49; 95% CI –7.87 to –3.10; P<.001; 95% PI –10.46 to –0.51). A subgroup analysis showed that the intervention mode of telephone plus SMS text messaging (MD –9.09; 95% CI –12.71 to –5.46; P<.001) or only telephone (MD –4.34; 95% CI –6.55 to –2.13; P<.001; 95% PI –7.24 to –1.45) had a greater impact on the control of systolic blood pressure than usual care. Among the stroke survivors with an intervention interval ≤1 week (MD –6.51; 95% CI –9.36 to –3.66; P<.001; 95% PI –12.91 to –0.10) or a baseline systolic blood pressure ≥140 mm Hg (MD –6.15; 95% CI –9.44 to –2.86; P<.001; 95% PI –13.55 to 1.26), the control of systolic blood pressure using telemedicine and mHealth was better than that of usual care. Conclusions: In general, telemedicine and mHealth reduced the systolic blood pressure of stroke survivors by an average of 5.49 mm Hg compared with usual care. Telemedicine and mHealth are a relatively new intervention mode with potential applications for the control of systolic blood pressure among stroke survivors, especially those with hypertensive stroke.
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The findings of this review can be applied to patients with hypertension with comorbidities, as confirmed by previous meta-analyses conducted among hypertensive patients with other chronic conditions.
“Nevertheless, the findings of this review can be applied to patients with hypertension with comorbidities, as confirmed by previous meta-analyses conducted among hypertensive patients with other chronic conditions.”
From Effectiveness of digital health interventions on blood pressure control, lifestyle behaviours and adherence to medication in patients with hypertension in low-income and middle-income countries: a systematic review and meta-analysis of randomised controlled trials (2024) · cites this paper as [43]
Healthcare technology innovations frequently remain confined to small-scale demonstration initiatives.
“Healthcare technology innovations hold considerable promise for enhancing patient outcomes and service efficiency, but they frequently remain confined to small-scale demonstration initiatives [1–5].”
From The NASSS (Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability) framework use over time: A scoping review (2025) · cites this paper as [4]
The Results describe nine trials involving 1,583 stroke survivors, and the Discussion notes that the interventions studied were relatively short. Neither section says healthcare technology innovations frequently remain confined to small-scale demonstration initiatives. Searches for that phrase and for pilot projects or barriers to wider adoption found no supporting passage. (LLM, conf=0.73)
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- Authors
- Lv, M., Wu, T., Jiang, S., Chen, W., & Zhang, J.
- Affiliations
- Fujian Medical University (CN)
- Corresponding author
- Zhang, Jinhua
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2021-06-11
- Volume
- 9
- Issue
- 6
- Pages
- e24116
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC8235282
- OpenAlex
- W3172102065
- Cited by
- 37
- References
- 42
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 12 · 2023: 8 · 2022: 7
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Keywords
- telemedicine, mobile health, systolic blood pressure control, stroke survivors, hypertension, stroke recurrence, telephone-delivered intervention, safety management systems, antihypertensive therapy, remote patient monitoring, subgroup analysis, usual care comparison, secondary stroke prevention, medication adherence, lifestyle modification, diet, smoking cessation
- MeSH terms
- Blood Pressure, Humans, Hypertension, Randomized Controlled Trials as Topic, Telemedicine, Stroke
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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