ReviewJMIR mHealth and uHealth2019
Smartphone Apps to Support Self-Management of Hypertension: Review and Content Analysis.
Review in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 6 of them syntheses that pooled 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.
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.
Who cites it
88 citing papers in PubMed, 6 syntheses or guidelines pooled it, 177 citations in OpenAlex.
- Smartphone application-based intervention to lower blood pressure: a systematic review and meta-analysis.Hypertension research : official journal of the Japanese Society of Hypertension · 2025Pooled it
- The effect of digital therapeutics intervention on improving hypertension management in adults: a meta-analysis of randomized controlled trial.Hypertension research : official journal of the Japanese Society of Hypertension · 2025Pooled it
- Content Analysis of Brazilian Mobile Applications Targeting Blood Pressure Management: A Systematic Search.Arquivos brasileiros de cardiologia · 2025Pooled it
- The Association Between Smartphone App-Based Self-monitoring of Hypertension-Related Behaviors and Reductions in High Blood Pressure: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2022Pooled it
- mHealth Interventions for Self-management of Hypertension: Framework and Systematic Review on Engagement, Interactivity, and Tailoring.JMIR mHealth and uHealth · 2022Pooled it
- A Systematic Review on Mobile Health Applications' Education Program for Patients Taking Oral Anticoagulants.International journal of environmental research and public health · 2021Pooled it
- Effects of Artificial Intelligence Recognition-Based Telerehabilitation on Exercise Capacity in Patients With Hypertension: Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Effectiveness of Standard vs Enhanced Self-measurement of Blood Pressure Paired With a Connected Smartphone Application: A Randomized Clinical Trial.JAMA internal medicine · 2022Trial
- Efficacy of a digital therapeutics system in the management of essential hypertension: the HERB-DH1 pivotal trial.European heart journal · 2021Trial
- Digital therapeutics for essential hypertension using a smartphone application: A randomized, open-label, multicenter pilot study.Journal of clinical hypertension (Greenwich, Conn.) · 2021Trial
- TensioBot: a Chatbot Assistant for Self-Managed in-House Blood Pressure Checking.Journal of medical systems · 2021Trial
- A WeChat-Based Self-Management Intervention for Community Middle-Aged and Elderly Adults with Hypertension in Guangzhou, China: A Cluster-Randomized Controlled Trial.International journal of environmental research and public health · 2019Trial
- A self-service response system to improve nutrition impact symptoms management among home-based patients with advanced cancer and their family caregivers.Asia-Pacific journal of oncology nursing · 2026Article
- Systematic and Collaborative Approach to Learning and Educational Content Development (SCALED) for Health Apps: An Experience-Informed Conceptual Framework.JMIR mHealth and uHealth · 2026Article
- Patients' mHealth Apps Usage and Data Privacy, Security, and Confidentiality Concerns: Exploratory Study.JMIR formative research · 2026Article
- A mini-program for high-risk of stroke population management using the intervention mapping approach: design, development process, and usability study.BMC public health · 2026Article
- A hierarchical framework to evaluate the usability of smartphone health applications.Scientific reports · 2026Article
- Involving End Users in Co-Designing Mobile Health Interventions for Hypertension Self-Management: Formative Study.JMIR formative research · 2026Article
- Digital health information-seeking behaviors and trust in AI-based physician chatbots among patients with hypertension: a cross-sectional survey in Saudi Arabia.Frontiers in public health · 2026Article
- Development and usability evaluation of HOPE: A patient-centered mHealth application for HTN self-management in Iran.PloS one · 2026Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHypertension is a widespread chronic disease, and its effective treatment requires self-management by patients. Health-related apps provide an effective way of supporting hypertension self-management. However, the increasing range and variety of hypertension apps available on the market, owing to the global growth in apps, creates the need for patients and health care professionals to be informed about the effectiveness of these apps and the levels of privacy and security that they provide.
objectiveThis study aimed to describe and assess all available apps supporting hypertension self-management in the most popular app stores and investigate their functionalities.
methodsIn January 2018, the UK Apple and Google Play stores were scanned for all free and paid apps supporting hypertension self-management. Apps were included if they were in English, had functionality supporting hypertension self-management, and targeted adult users with hypertension. The included apps were downloaded and their functionalities were investigated. Behavior change techniques (BCTs) linked with the theoretical domain framework (TDF) underpinning potentially effective apps were independently coded by two reviewers. The data privacy and security of the apps were also independently assessed.
resultsA total of 186 hypertension apps that met the inclusion criteria were included in this review. The majority of these apps had only one functionality (n=108), while the remainder offered different combinations of functionalities. A small number of apps had comprehensive functionalities (n=30) that are likely to be more effective in supporting hypertension self-management. Most apps lacked a clear theoretical basis, and 24 BCTs identified in these 30 apps were mapped to 10 TDF mechanisms of actions. On an average, 18.4 BCTs were mapped to 6 TDF mechanisms of actions that may support hypertension self-management behaviors. There was a concerning absence of evidence related to the effectiveness and usability of all 186 apps, and involvement of health care professionals in the app development process was minimal. Most apps did not meet the current standards of data security and privacy.
conclusionsDespite the widespread accessibility and availability of smartphone apps with a range of combinations of functionalities that can support the self-management of hypertension, only a small number of apps are likely to be effective. Many apps lack security measures as well as a clear theoretical basis and do not provide any evidence concerning their effectiveness and usability. This raises a serious issue, as health professionals and those with hypertension have insufficient information to make decisions on which apps are safe and effective.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.