The Use of Wireless, Smartphone App–Assisted Home Blood Pressure Monitoring Among Hypertensive Patients in Singapore: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Reliable home blood pressure monitoring (HBPM) is essential to effective hypertension management; however, manual recording is subject to underreporting and inaccuracies. Mobile health technologies hold great potential as HBPM tools, but the fidelity of a smartphone app in HBPM has not been adequately assessed. OBJECTIVE: The primary aim of the trial was to compare the fidelity of a smartphone app to that of a handwritten logbook in making HBPM data available to clinicians at follow-up visits. Fidelity was defined as the percentage of scheduled blood pressure (BP) recordings over a 3-week period that were properly recorded and reported to the clinic. The secondary aims were to investigate patient factors associated with HBPM fidelity and to explore the effect of time on the fidelity. METHODS: A 2-arm, parallel, unblinded, randomized controlled pilot trial was conducted in a government polyclinic in Singapore. Hypertensive adults, aged 40 to 70 years, who were on antihypertensive medication and owned a smartphone were recruited and randomized by a computer-generated randomization schedule to 3 weeks of either semiautomated HBPM utilizing a Bluetooth-enabled BP monitor and a smartphone app or a fully manual process utilizing a conventional handwritten logbook. The primary outcome was home BP recording fidelity. RESULTS: Of the 80 patients randomized, 79 (smartphone app: 38 and logbook: 41) were included in the final analysis. Although fidelity was higher among the app users, it did not differ significantly between study arms (smartphone app: 66.7% and logbook: 52.4%; P=.21). Chinese and Indian ethnicities were associated with higher fidelity (absolute percent and 95% CI) by 35.6% (4.27 to 66.9) and 45.0% (8.69 to 81.3), respectively, in comparison with other ethnicities (P=.03); longer smartphone ownership increased fidelity on an average of 10.5% (0.83 to 20.2) per year (P=.03); the number of apps on the smartphone decreased fidelity at a rate of -0.32% (-0.58 to -0.05) per app (P=.02); years of hypertension morbidity increased fidelity at a rate of 1.56% (0.03 to 3.09) per year (P=.046); and the number of people working in the household decreased fidelity at a rate of -8.18% (-16.3 to -0.08) per additional working person (P=.048). The fidelity of the app was significantly higher in the first week (64.4%) than the second (55.1%, P=.001) and third (58.2%, P=.03) weeks of monitoring. CONCLUSIONS: Amid the increasing integration of health technologies into clinical practice, our study demonstrates the feasibility of smartphone app-assisted HBPM in hypertensive adults of Singapore. Our pilot study found no statistically significant difference in mean BP recording fidelity between a smartphone app and conventional handwritten logbook. However, the small sample size precludes definitive conclusions and highlights the need for a larger, adequately powered trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT03209024; https://clinicaltrials.gov/ct2/show/NCT03209024 (Archived by WebCite at http://www.webcitation.org/78EVWBg0T).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".