Consumer Devices for Patient-Generated Health Data Using Blood Pressure Monitors for Managing Hypertension: Systematic Review
Bibliographic record
Abstract
BACKGROUND: In the era of digital health information technology, there has been a proliferation of devices that collect patient-generated health data (PGHD), including consumer blood pressure (BP) monitors. Despite their widespread use, it remains unclear whether such devices can improve health outcomes. OBJECTIVE: We performed a systematic review of the literature on consumer BP monitors that collect PGHD for managing hypertension to summarize their clinical impact on health and surrogate outcomes. We focused particularly on studies designed to measure the specific effect of using a BP monitor independent of cointerventions. We have also summarized the process and consumer experience outcomes. METHODS: An information specialist searched PubMed, MEDLINE, and Embase for controlled studies on consumer BP monitors published up to May 12, 2020. We assessed the risk of bias using an adapted 9-item appraisal tool and performed a narrative synthesis of the results. RESULTS: We identified 41 different types of BP monitors used in 49 studies included for review. Device engineers judged that 38 (92%) of those devices were similar to the currently available consumer BP monitors. The median sample size was 222 (IQR 101-416) participants, and the median length of follow-up was 6 (IQR 3-12) months. Of the included studies, 18 (36%) were designed to isolate the clinical effects of BP monitors; 6 of the 18 (33%) studies evaluated health outcomes (eg, mortality, hospitalizations, and quality of life), and data on those outcomes were unclear. The lack of clarity was due to low event rates, short follow-up duration, and risk of bias. All 18 studies that isolated the effect of BP monitors measured both systolic and diastolic BP and generally demonstrated a decrease of 2 to 4 mm Hg in systolic BP and 1 to 3 mm Hg in diastolic BP compared with non-BP monitor groups. Adherence to using consumer BP monitors ranged from 38% to 89%, and ease of use and satisfaction ratings were generally high. Adverse events were infrequent, but there were a few technical problems with devices (eg, incorrect device alerts). CONCLUSIONS: Overall, BP monitors offer small benefits in terms of BP reduction; however, the health impact of these devices continues to remain unclear. Future studies are needed to examine the effectiveness of BP monitors that transmit data to health care providers. Additional data from implementation studies may help determine which components are critical for sustained BP improvement, which in turn may improve prescription decisions by clinicians and coverage decisions by policy makers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".