Evidence map›Paper›PMID 27856407›Full record

ArticleJournal of medical Internet research2016

Self-Monitoring Utilization Patterns Among Individuals in an Incentivized Program for Healthy Behaviors.

Ju Young Kim, Nathan E Wineinger, Michael Taitel, Jennifer M Radin, Osayi Akinbosoye, Jenny Jiang, Nima Nikzad, Gregory Orr, Eric Topol, Steve Steinhubl

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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.

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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

10 authors.

Ju Young KimDepartment of Family Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic Of Korea.ORCID 0000-0001-6018-3337
Nathan E WineingerScripps Translational Science Institute, La Jolla, CA, United States.ORCID 0000-0003-4517-228X
Michael TaitelHealth Analytics, Research & Reporting, Walgreen Company, Deerfield, IL, United States.ORCID 0000-0002-3582-4451
Jennifer M RadinDepartment of Digital Health, Scripps Translational Science Institute, La Jolla, CA, United States.ORCID 0000-0003-3843-0842
Osayi AkinbosoyeHealth Analytics, Research & Reporting, Walgreen Company, Deerfield, IL, United States.ORCID 0000-0003-1869-9908
Jenny JiangHealth Analytics, Research & Reporting, Walgreen Company, Deerfield, IL, United States.ORCID 0000-0002-6136-1841
Nima NikzadDepartment of Digital Health, Scripps Translational Science Institute, La Jolla, CA, United States.ORCID 0000-0002-0223-0534
Gregory OrrDigital Health, Walgreen Company, Bellevue, WA, United States.ORCID 0000-0003-0472-6477
Eric TopolScripps Translational Science Institute, La Jolla, CA, United States.ORCID 0000-0002-1478-4729
Steve SteinhublDepartment of Digital Health, Scripps Translational Science Institute, La Jolla, CA, United States.ORCID 0000-0002-9256-7914

Funding

Scripps Translational Science InstituteUL1TR001114 · NCATS · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2013 to 2017
$24.0M
NCATS NIH HHS UL1 TR001114
6 · The paper itself

Abstract

backgroundThe advent of digital technology has enabled individuals to track meaningful biometric data about themselves. This novel capability has spurred nontraditional health care organizations to develop systems that aid users in managing their health. One of the most prolific systems is Walgreens Balance Rewards for healthy choices (BRhc) program, an incentivized, Web-based self-monitoring program.

objectiveThis study was performed to evaluate health data self-tracking characteristics of individuals enrolled in the Walgreens' BRhc program, including the impact of manual versus automatic data entries through a supported device or apps.

methodsWe obtained activity tracking data from a total of 455,341 BRhc users during 2014. Upon identifying users with sufficient follow-up data, we explored temporal trends in user participation.

resultsThirty-four percent of users quit participating after a single entry of an activity. Among users who tracked at least two activities on different dates, the median length of participating was 8 weeks, with an average of 5.8 activities entered per week. Furthermore, users who participated for at least twenty weeks (28.3% of users; 33,078/116,621) consistently entered 8 to 9 activities per week. The majority of users (77%; 243,774/315,744) recorded activities through manual data entry alone. However, individuals who entered activities automatically through supported devices or apps participated roughly four times longer than their manual activity-entering counterparts (average 20 and 5 weeks, respectively; P<.001).

conclusionsThis study provides insights into the utilization patterns of individuals participating in an incentivized, Web-based self-monitoring program. Our results suggest automated health tracking could significantly improve long-term health engagement.

Indexed as

Health BehaviorAdolescentAdultAgedFemaleHumansMaleMiddle AgedMotivationSelf-AssessmentTelemedicineYoung Adultblood glucose self-monitoringhealth behaviormobile appsmobile healthrewardself blood pressure monitoring

Identifiers

PMID27856407
PMCPMC5133453

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.