Evidence map›Paper›PMID 26327939›Full record

ArticleTranslational behavioral medicine2015

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.

Donna Spruijt-Metz, Eric Hekler, Niilo Saranummi, Stephen Intille, Ilkka Korhonen, Wendy Nilsen, Daniel E Rivera, Bonnie Spring, Susan Michie, David A Asch and 4 more

Abstract read
In one paragraph

Article in Translational behavioral medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 4 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

81 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  14. Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA).Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025
    Article
  15. Article
  16. Exploring novel determinants of exercise behavior: a lagged exposure-wide approach.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2025
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21 more citing papers are in PubMed but not listed here.

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

14 authors.

Donna Spruijt-MetzUniversity of Southern California, 635 Downey Way, Suite 305 Building Code: VPD 3332, Los Angeles, CA 90089-3332 USA.ORCID 0000-0002-3884-4300
Eric HeklerArizona State University, Tempe, AZ USA.
Niilo SaranummiVTT Technical Research Centre of Finland, Espoo, Finland.
Stephen IntilleNortheastern University, Boston, MA USA.
Ilkka KorhonenTampere University of Technology, Tampere, Finland.
Wendy NilsenNational Institutes of Health, Bethesda, MD USA.
Daniel E RiveraArizona State University, Tempe, AZ USA.
Bonnie SpringNorthwestern University, Evanston, IL USA.
Susan MichieUniversity College London, London, UK.
David A AschWharton School, University of Pennsylvania, Philadelphia, PA USA.
Alberto SannaScientific Institute Hospital San Raffaelle, Milano, Italy.
Vicente Traver SalcedoValencia Polytechnical University, Valencia, Spain.
Rita KukakfaColumbia University, New York, NY USA.
Misha PavelVTT Technical Research Centre of Finland, Espoo, Finland.

Funding

Tumor Environment and Metastasis (TEAM) Research ProgramP30CA060553 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Daniela E Matei · 1993 to 2026
$153.9M
The Penn Roybal Center on Behavioral Economics and Health: Solving for Scale and Impact with Design Thinking, Digital Platforms, and Dynamic AdaptationP30AG034546 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Alison Meredith Buttenheim · 2009 to 2026
$10.6M
Virtual Sprouts: Web-based Gardening Games to Teach Nutrition and Combat ObesityR25OD011113 · OD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI RAGUSA, GISELE, SPRUIJT-METZ, DONNA D · 2012 to 2015
$874k
Medical Research Council MR/K023195/1NCI NIH HHS P30 CA060553NIA NIH HHS P30 AG034546NIH HHS R25 OD011113
6 · The paper itself

Abstract

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

Indexed as

Computational models of behaviorConnected healthHealth-related behaviorJust-in-time adaptive interventionsmHealthMobile healthReal-time interventions

Identifiers

PMID26327939
PMCPMC4537459

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.