Increasing Engagement in the Electronic Framingham Heart Study: Factorial Randomized Controlled Trial
Open accessAbstract
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.
Integrity signals
Registry
Cites
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.
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
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · see citing works
The mPower study, Parkinson disease mobile data collected using ResearchKit
Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.11 · Cited by 630 · see citing works
Bobrow, K., Farmer, A. J., Springer, D., Shanyinde, M., Yu, L.-M., Brennan, T., Rayner, B., Namane, M., Steyn, K., Tarassenko, L., et al. (2016) · Circulation · doi:10.1161/circulationaha.115.017530 · Cited by 295 · see citing works
The Effectiveness of Prompts to Promote Engagement With Digital Interventions: A Systematic Review
Alkhaldi, G., Hamilton, F. L., Lau, R., Webster, R., Michie, S., & Murray, E. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4790 · Cited by 273 · see citing works
Martin, S. S., Feldman, D. I., Blumenthal, R. S., Jones, S. R., Post, W. S., McKibben, R. A., Michos, E. D., Ndumele, C. E., Ratchford, E. V., Coresh, J., et al. (2015) · Journal of the American Heart Association · doi:10.1161/jaha.115.002239 · Cited by 267 · see citing works
The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit
Chan, Y.-F. Y., Wang, P., Rogers, L., Tignor, N., Zweig, M., Hershman, S. G., Genes, N., Scott, E. R., Krock, E., Badgeley, M., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3826 · Cited by 204 · see citing works
Bidargaddi, N., Almirall, D., Murphy, S., Nahum-Shani, I., Kovalcik, M., Pituch, T., Maaieh, H., & Strecher, V. (2018) · JMIR mHealth and uHealth · doi:10.2196/10123 · Cited by 189 · see citing works
Frampton, G. K., Shepherd, J., Pickett, K., Griffiths, G., & Wyatt, J. C. (2020) · Trials · doi:10.1186/s13063-020-04358-3 · Cited by 119 · see citing works
Shcherbina, A., Hershman, S. G., Lazzeroni, L., King, A. C., O'Sullivan, J. W., Hekler, E., Moayedi, Y., Pavlovic, A., Waggott, D., Sharma, A., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30129-3 · Cited by 90 · see citing works
Bobrow, K., Brennan, T., Springer, D., Levitt, N. S., Rayner, B., Namane, M., Yu, L.-M., Tarassenko, L., & Farmer, A. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-28 · Cited by 83 · see citing works
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
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-01-20
- Volume
- 25
- Pages
- e40784
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC9898831
- 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
- gold
- doi.org
- 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