Evidence map›Paper›PMID 32477031›Full record

ReviewAmerican journal of lifestyle medicine

An Individualized, Data-Driven Digital Approach for Precision Behavior Change.

Shannon Wongvibulsin, Seth S Martin, Suchi Saria, Scott L Zeger, Susan A Murphy

Open access · bronzeAbstract readReview
In one paragraph

Review in American journal of lifestyle medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 3 pooled it
3.6field-weighted citation impact, top 7% of its field
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

21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 41 citations in OpenAlex.

  1. Pooled it
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  19. Person-specific dose-finding for a digital messaging intervention to promote physical activity.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2021
    Article
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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

5 authors at 1 institution in 1 country.

Shannon WongvibulsinJohns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland (SW).ORCID https://orcid.org/0000-0002-1390-7440
Seth S MartinJohns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland (SW).
Suchi SariaJohns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland (SW).
Scott L ZegerJohns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland (SW).
Susan A MurphyJohns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland (SW).
Harvard University · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007309 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI COX, ANDREA L · 1985 to 2019
$43.2M
Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI COLLINS, LINDA M · 2015 to 2019
$13.9M
Heart Steps: Adaptive mHealth intervention for physical-activity maintenanceR01HL125440 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Predrag Klasnja · 2015 to 2026
$3.8M
Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs SupplementU01CA229437 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NAHUM-SHANI, INBAL BILLIE, WETTER, DAVID W · 2018 to 2022
$2.8M
Data-Based Methods for Just-In-Time Adaptive Interventions in Alcohol UseR01AA023187 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MURPHY, SUSAN A · 2015 to 2019
$2.6M
PROTEIN STRUCTURE BY ANALYSIS OF SPIN LABEL INTERACTIONSR01EB002040 · NIBIB · VANDERBILT UNIVERSITY · PI HUSTEDT, ERIC J · 2003 to 2003
$151k
Sudden Cardiac Arrest (SCA): Prediction and PreventionF30HL142131 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI WONGVIBULSIN, SHANNON · 2018 to 2020
$135k
NCI NIH HHS U01 CA229437NHLBI NIH HHS F30 HL142131NHLBI NIH HHS R01 HL125440NIAAA NIH HHS R01 AA023187NIBIB NIH HHS R01 EB002040NIDA NIH HHS P50 DA039838NIGMS NIH HHS T32 GM007309
6 · The paper itself

Abstract

Chronic disease now affects approximately half of the US population, causes 7 in 10 deaths, and accounts for roughly 80% of US health care expenditure. Because the root causes of chronic diseases are largely behavioral, effective therapies require frequent, individualized interventions that extend beyond the hospital and clinic to reach patients in their day-to-day lives. However, a mismatch currently exists between what the health care system is equipped to provide and the interventions necessary to effectively address the chronic disease burden. To remedy this health crisis, we present an individualized, data-driven digital approach for chronic disease management and prevention through precision behavior change. The rapid growth of information, biological, and communication technologies makes this an opportune time to develop digital tools that deliver precision interventions for health behavior change to address the chronic disease crisis. Building on this rapid growth, we propose a framework that includes the precise targeting of risk-producing behaviors using real-time sensing technology, machine learning data analysis to identify the most effective intervention, and delivery of that intervention with health-reinforcing feedback to provide real-time, individualized support to empower sustainable health behavior change.

Indexed as

behavior changedigital therapeuticsmachine learningmobile health (mHealth)precision medicine

Identifiers

PMID32477031
PMCPMC7232899
OpenAlexW2941182042

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.