The digital prescription: a systematic review and meta-analysis of smartphone apps for blood pressure control
Open accessAbstract
OBJECTIVE: Assess the effectiveness of digital health interventions (DHIs) in reducing blood pressure (BP) among individuals with high blood pressure and identify the impact of age, sex, and phone-based delivery methods on BP. METHODS: A systematic review and meta-analysis was undertaken according to the PRISMA and JBI. A comprehensive search was conducted across multiple databases. Randomised controlled trials (RCTs), mixed methods, descriptive, and experimental studies enrolling adult patients (≥18 years) with high BP and containing DHIs with blood pressure management aspect were included. We used a random-effects meta-analysis weighted mean difference (MD) between the comparison groups to pool data from the included studies. The outcome included the pooled MD reflecting systolic (SBP) or diastolic (DBP) change from baseline to 6-month period. Risk of bias was assessed using standardised tools. RESULTS: Thirty-six studies with 33,826 participants were included in the systematic review. The pooled estimate (26 RCTs) showed a significant reduction in SBP (MD = -1.45 mmHg, 95 % CI: -2.18 to -0.71) but not in DBP (MD = -0.50 mmHg, 95 % CI: -1.03 to 0.03), with evidence of some heterogeneity. Subgroup analysis indicated that smartphone app interventions were more effective in lowering SBP than short message services (SMS) or mobile phone calls. Additionally, the interventions significantly reduced the SBP compared with the control, regardless of participant sex. CONCLUSION: Our findings indicate that DHIs, particularly smartphone apps, can lower SBP after 6 months in individuals with hypertension or high-risk factors, although changes might not be clinically significant. Further research is needed to understand the long-term impact and optimal implementation of DHIs for BP management across diverse populations.
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There remains a critical knowledge gap regarding how technical modalities directly influence clinical workflow friction and human factor barriers at scale.
“there remains a critical knowledge gap regarding how technical modalities directly influence clinical workflow friction and human factor barriers at scale [1,12].”
From Clinical Efficacy and Patient Adherence Hurdles of Remote Patient Monitoring (RPM) Frameworks in Managing Hypertension and Type 2 Diabetes: A Scoping Review (2026) · cites this paper as [12]
The Discussion (p. 7) calls for research on long-term effectiveness, diverse populations, and co-design to improve usability. It does not identify a knowledge gap about how technical modalities affect clinical workflow friction or human-factor barriers at scale; searches for workflow, implementation barriers, and human factors found no passage addressing that point. (LLM, conf=0.72)
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Poblete, J. Y., Vawter, N. L., Lewis, S. V., Felisme, E. M., Mohn, P. A., Shea, J., Northrup, A. W., Liu, J., Al-Rousan, T., & Godino, J. G. (2023) · JMIR Human Factors · doi:10.2196/46313 · Cited by 9 · see citing works
Mancheno, C., Asch, D. A., Klinger, E. V., Goldshear, J. L., Mitra, N., Buttenheim, A. M., Barg, F. K., Ungar, L., Yang, L., & Merchant, R. M. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.020596 · Cited by 8 · see citing works
MOBILE Intervention Pilot Study in College Students With Elevated Blood Pressure
Tran, D.-M. T., Martinez, I., Cross, C. L., & Feng, Y. (2022) · The Journal of Cardiovascular Nursing · doi:10.1097/jcn.0000000000000917 · Cited by 5 · see citing works
Self‐management program and Black women with hypertension: Randomized controlled trial substudy
Abel, W. M., Efird, J. T., Crane, P. B., & Ferdinand, K. C. (2023) · Research in Nursing & Health · doi:10.1002/nur.22301 · Cited by 5 · see citing works
Lifestyle Modifications to Promote Healthy Blood Pressure
Bushman, B. A. (2018) · ACSMʼs Health & Fitness Journal · doi:10.1249/fit.0000000000000410 · Cited by 4 · see citing works
Fang, X., Guo, Y., Xia, N., Zhang, S., Yin, J., Qin, L., Zhu, C., Gao, Q., Jia, J., Zhao, Y., et al. (2023) · BMC Family Practice · doi:10.1186/s12875-023-01965-2 · Cited by 4 · see citing works
Nimi, B. M., Bompeka, F. L., Mbenza, B. L., Vangu, R. V., Longo, G. L., Nkodila, A. N., Nimi, M. M., Kalambayi, P. K., & Buyamba-Kabangu, E. R. M. (2020) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt20jun081 · Cited by 1 · see citing works
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Flagged first, then by citations
2 of the 93 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
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Cited by
Sorted by citations
Liu, Y., Qin, Y., Chen, Q., Liu, Y., Wang, Z., Jia, A., Liu, M., Ji, N., Zhang, Z., Shao, R., et al. (2025) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2025.105225 · Cited by 6 · see citing works
Motta-Yanac, E., Riley, V., Ellis, N., & Gidlow, C. J. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22101487 · Cited by 1 · see citing works
Leveraging the Electronic Health Record to Identify and Manage Resistant Hypertension
Shankar, B., Ben-Shlomo, A., Cheng, S. C., & Ebinger, J. E. (2026) · Current Hypertension Reports · doi:10.1007/s11906-026-01371-w
Ma, Y., zhang, X., Shi, J., Guo, C., Tian, C., Wan, Z., Li, Y., Li, Y., Guo, L., Lang, Y., et al. (2026) · doi:10.2196/preprints.102956
- Authors
- Motta-Yanac, E., Riley, V., Ellis, N. J., Mankoo, A., & Gidlow, C. J.
- Affiliations
- University of Staffordshire (GB)
- Midlands Partnership NHS Foundation Trust (GB)
- Corresponding author
- Motta-Yanac, Emily
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 195
- Pages
- 105755
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W4405103231
- Cited by
- 5
- References
- 93
- Citations by year
- 2026: 3 · 2025: 2
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Medical Informatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Staffordshire Online Repository (Staffordshire University) (submittedVersion)
- Funders
- Staffordshire University
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile application, digital health interventions, blood pressure control, hypertension, systolic blood pressure, diastolic blood pressure, short message service, mobile phone calls, sex differences, age differences
- MeSH terms
- Smartphone, Adult, Blood Pressure, Female, Humans, Hypertension, Male, Middle Aged, Telemedicine, Mobile Applications
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.
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