Evidence map›Paper›PMID 38787598›Full record

ArticleJMIR formative research2024

Evaluating a New Digital App-Based Program for Heart Health: Feasibility and Acceptability Pilot Study.

Kimberly G Lockwood, Priya R Kulkarni, Jason Paruthi, Lauren S Buch, Mathieu Chaffard, Eva C Schitter, OraLee H Branch, Sarah A Graham

Abstract read
In one paragraph

Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kimberly G LockwoodLark Health, Mountain View, CA, United States.ORCID https://orcid.org/0000-0002-5053-4129
Priya R KulkarniRoche Information Solutions, Santa Clara, CA, United States.ORCID https://orcid.org/0000-0003-2271-8622
Jason ParuthiAnara Health, Los Angeles, CA, United States.ORCID https://orcid.org/0009-0005-9939-0276
Lauren S BuchLark Health, Mountain View, CA, United States.ORCID https://orcid.org/0009-0006-4010-1190
Mathieu ChaffardRoche Information Solutions, Santa Clara, CA, United States.ORCID https://orcid.org/0009-0009-4687-8621
Eva C SchitterRoche Information Solutions, Santa Clara, CA, United States.ORCID https://orcid.org/0009-0000-3457-3307
OraLee H BranchLark Health, Mountain View, CA, United States.ORCID https://orcid.org/0000-0002-6720-6906
Sarah A GrahamLark Health, Mountain View, CA, United States.ORCID https://orcid.org/0000-0002-7782-8709

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiovascular disease (CVD) is the leading cause of death in the United States, affecting a significant proportion of adults. Digital health lifestyle change programs have emerged as a promising method of CVD prevention, offering benefits such as on-demand support, lower cost, and increased scalability. Prior research has shown the effectiveness of digital health interventions in reducing negative CVD outcomes. This pilot study focuses on the Lark Heart Health program, a fully digital artificial intelligence (AI)-powered smartphone app, providing synchronous CVD risk counseling, educational content, and personalized coaching.

objectiveThis pilot study evaluated the feasibility and acceptability of a fully digital AI-powered lifestyle change program called Lark Heart Health. Primary analyses assessed (1) participant satisfaction, (2) engagement with the program, and (3) the submission of health screeners. Secondary analyses were conducted to evaluate weight loss outcomes, given that a major focus of the Heart Health program is weight management.

methodsThis study enrolled 509 participants in the 90-day real-world single-arm pilot study of the Heart Health app. Participants engaged with the app by participating in coaching conversations, logging meals, tracking weight, and completing educational lessons. The study outcomes included participant satisfaction, app engagement, the completion of screeners, and weight loss.

resultsOn average, Heart Health study participants were aged 60.9 (SD 10.3; range 40-75) years, with average BMI indicating class I obesity. Of the 509 participants, 489 (96.1%) stayed enrolled until the end of the study (dropout rate: 3.9%). Study retention, based on providing a weight measurement during month 3, was 80% (407/509; 95% CI 76.2%-83.4%). Participant satisfaction scores indicated high satisfaction with the overall app experience, with an average score of ≥4 out of 5 for all satisfaction indicators. Participants also showed high engagement with the app, with 83.4% (408/489; 95% CI 80.1%-86.7%) of the sample engaging in ≥5 coaching conversations in month 3. The results indicated that participants were successfully able to submit health screeners within the app, with 90% (440/489; 95% CI 87%-92.5%) submitting all 3 screeners measured in the study. Finally, secondary analyses showed that participants lost weight during the program, with analyses showing an average weight nadir of 3.8% (SD 2.9%; 95% CI 3.5%-4.1%).

conclusionsThe study results indicate that participants in this study were satisfied with their experience using the Heart Health app, highly engaged with the app features, and willing and able to complete health screening surveys in the app. These acceptability and feasibility results provide a key first step in the process of evidence generation for a new AI-powered digital program for heart health. Future work can expand these results to test outcomes with a commercial version of the Heart Health app in a diverse real-world sample.

Indexed as

acceptability and feasibilityAIartificial intelligencecardiovascular diseasedigital healthlifestyle coachingmobile phonepilot study

Identifiers

PMID38787598
PMCPMC11161712

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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.