Cited by 11Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsMedication Adherence and Compliance

Increasing Engagement in the Electronic Framingham Heart Study: Factorial Randomized Controlled Trial

Open access
Trinquart, Ludovic, Liu, Chunyu, McManus, David D, Nowak, Christopher, Lin, Honghuang, Spartano, Nicole L, Borrelli, Belinda, Benjamin, Emelia J +1 more·2023·Journal of Medical Internet Research

Abstract

Background: Smartphone apps and mobile health devices offer innovative ways to collect longitudinal cardiovascular data. Randomized evidence regarding effective strategies to maintain longitudinal engagement is limited. Objective: This study aimed to evaluate smartphone messaging interventions on remote transmission of blood pressure (BP) and heart rate (HR) data. Methods: We conducted a 2 × 2 × 2 factorial blinded randomized trial with randomization implemented centrally to ensure allocation concealment. We invited participants from the Electronic Framingham Heart Study (eFHS), an e-cohort embedded in the FHS, and asked participants to measure their BP (Withings digital cuff) weekly and wear their smartwatch daily. We assessed 3 weekly notification strategies to promote adherence: personalized versus standard; weekend versus weekday; and morning versus evening. Personalized notifications included the participant’s name and were tailored to whether or not data from the prior week were transmitted to the research team. Intervention notification messages were delivered weekly automatically via the eFHS app. We assessed if participants transmitted at least one BP or HR measurement within 7 days of each notification after randomization. Outcomes were adherence to BP and HR transmission at 3 months (primary) and 6 months (secondary). Results: Of the 791 FHS participants, 655 (82.8%) were eligible and randomized (mean age 53, SD 9 years; 392/655, 59.8% women; 596/655, 91% White). For the personalized versus standard notifications, 38.9% (126/324) versus 28.8% (94/327) participants sent BP data at 3 months (difference=10.1%, 95% CI 2.9%-17.4%; P=.006), but no significant differences were observed for HR data transmission (212/324, 65.4% vs 209/327, 63.9%; P=.69). Personalized notifications were associated with increased BP and HR data transmission versus standard at 6 months (BP: 107/291, 36.8% vs 66/295, 22.4%; difference=14.4%, 95% CI 7.1- 21.7%; P<.001; HR: 186/281, 66.2% vs 158/281, 56.2%; difference=10%, 95% CI 2%-18%; P=.02). For BP and HR primary or secondary outcomes, there was no evidence of differences in data transmission for notifications sent on weekend versus weekday or morning versus evening. Conclusions: Personalized notifications increased longitudinal adherence to BP and HR transmission from mobile and digital devices among eFHS participants. Our results suggest that personalized messaging is a powerful tool to promote adherence to mobile health systems in cardiovascular research. Trial Registration: ClinicalTrials.gov NCT03516019; https://clinicaltrials.gov/ct2/show/NCT03516019

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/40784

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Authors
Trinquart, L., Liu, C., McManus, D. D., Nowak, C., Lin, H., Spartano, N. L., Borrelli, B., Benjamin, E. J., & Murabito, J. M.
Affiliations
  • Boston University (US)
  • University of Massachusetts Chan Medical School (US)
  • CareEvolution (United States) (US)
Corresponding author
Murabito, Joanne M
Venue
Journal of Medical Internet Research
Published
2023-01-20
Volume
25
Pages
e40784
ISSN
1438-8871 (ISSN-L)
OpenAlex
W4317542842
Cited by
11
References
10
Citations by year
2026: 3 · 2025: 3 · 2024: 4 · 2022: 1
FWCI
3.52
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Explorer (The University of Manchester) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Robert Wood Johnson Foundation (74624)
  • American Heart Association (HL092577, 2R01 HL092577, 18SFRN34110082)
  • National Institute on Aging (U01AG068221, 75N92019D00031, R01AG066010)
  • National Heart, Lung, and Blood Institute (R01HL141434, PI VSR 75N92019D00031, 75N92019D00031, R01HL13660, R01HL137794, R01HL126911, HL092577, 18SFRN34110082, R01HL137734, U54HL143541, R01HL092577)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
Keywords
participant engagement, personalized notifications, blood pressure measurement, heart rate monitoring, remote data transmission, smartwatches, longitudinal data collection, notification timing, participant recruitment and retention
MeSH terms
Smartphone, Blood Pressure, Electronics, Female, Humans, Longitudinal Studies, Male, Middle Aged, 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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review