Evidence map›Paper›PMID 39475852›Full record

ArticleJMIR research protocols2024

Efficacy of a Multimodal Digital Behavior Change Intervention on Lifestyle Behavior, Cardiometabolic Biomarkers, and Medical Expenditure: Protocol for a Randomized Controlled Trial.

Sakeina Howard-Wilson, Jack Ching, Sherri Gentile, Martin Ho, Alex Garcia, Didem Ayturk, Peter Lazar, Nova Hammerquist, David McManus, Bruce Barton and 3 more

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in JMIR research protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04712383 (Impact of Wearable Health Devices and Health and Wellness Behavior Change Support on Health Outcomes and Healthcare Costs), which is not on this map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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.

NCT04712383 nacompletednot on this map

Impact of Wearable Health Devices and Health and Wellness Behavior Change Support on Health Outcomes and Healthcare Costs

TypeinterventionalSponsorUniversity of Massachusetts, WorcesterRan2021 to 2022Enrolled597ConditionsHealth BehaviorArmsFitbit Care intervention arm
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

13 authors.

Sakeina Howard-WilsonDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0002-7986-0585
Jack ChingGoogle LLC, Mountain View, CA, United States.ORCID 0000-0003-0752-8628
Sherri GentileClinician Experience Office, University of Massachusetts Memorial Health, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0009-0002-4566-3863
Martin HoGoogle LLC, Mountain View, CA, United States.ORCID 0000-0002-8970-5095
Alex GarciaGoogle LLC, Mountain View, CA, United States.ORCID 0000-0002-2955-044X
Didem AyturkDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0003-1309-8388
Peter LazarDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0002-0744-1461
Nova HammerquistGoogle LLC, Mountain View, CA, United States.ORCID 0009-0000-7042-4085
David McManusDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0002-9343-6203
Bruce BartonDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0001-7878-8895
Steven BirdClinician Experience Office, University of Massachusetts Memorial Health, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0002-5804-5063
John MooreGoogle LLC, Mountain View, CA, United States.ORCID 0009-0006-1046-8477
Apurv SoniDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0001-5049-3657

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe US Preventive Services Task Force recommends providers offer individualized healthy behavior interventions for all adults, independent of their risk of cardiovascular disease. While strong evidence exists to support disease-specific programs designed to improve multiple lifestyle behaviors, approaches to adapting these interventions for a broader population are not well established. Digital behavior change interventions (DBCIs) hold promise as a more generalizable and scalable approach to overcome the resource and time limitations that traditional behavioral intervention programs face, especially within an occupational setting.

objectiveWe aimed to evaluate the efficacy of a multimodal DBCI on (1) self-reported behaviors of physical activity, nutrition, sleep, and mindfulness; (2) cardiometabolic biomarkers; and (3) chronic disease-related medical expenditure.

methodsWe conducted a 2-arm randomized controlled trial for 12 months among employees of an academic health care facility in the United States. The intervention arm received a scale, a smartphone app, an activity tracker, a video library for healthy behavior recommendations, and an on-demand health coach. The control arm received standard employer-provided health and wellness benefits. The primary outcomes of the study included changes in self-reported lifestyle behaviors, cardiometabolic biomarkers, and chronic disease-related medical expenditure. We collected health behavior data via baseline and quarterly web-based surveys, biometric measures via clinic visits at baseline and 12 months, and identified relevant costs through claims datasets.

resultsA total of 603 participants were enrolled and randomized to the intervention (n=300, 49.8%) and control arms (n=303, 50.2%). The average age was 46.7 (SD 11.2) years, and the majority of participants were female (80.3%, n=484), White (85.4%, n=504), and non-Hispanic (90.7%, n=547), with no systematic differences in baseline characteristics observed between the study arms. We observed retention rates of 86.1% (n=519) for completing the final survey and 77.9% (n=490) for attending the exit visit.

conclusionsThis study represents the largest and most comprehensive evaluation of DBCIs among participants who were not selected based on their underlying condition to assess its impact on behavior, cardiometabolic biomarkers, and medical expenditure.

trial registrationClinicalTrials.gov NCT04712383; https://clinicaltrials.gov/study/NCT04712383. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/50378.

Indexed as

BiomarkersAdultBehavior TherapyCardiovascular DiseasesExerciseFemaleHealth BehaviorHealth ExpendituresHumansLife StyleMaleMiddle AgedMobile ApplicationsRandomized Controlled Trials as TopicBiomarkerscardiovascular diseasechronic diseasedigital devicesfitnesshealth behaviorlifestyle changemindfulnessnutritionphysical activitysleep

Identifiers

PMID39475852
PMCPMC11561444

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.