Cited by 209Behavioral Health and InterventionsHealth Policy Implementation ScienceBehavioral and Psychological Studies

The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index; none of the 61 sources its claims cite is checked for support yet.

Agile science: creating useful products for behavior change in the real world

Open access
Hekler, Eric B., Klasnja, Predrag, Riley, William T., Buman, Matthew P., Huberty, Jennifer, Rivera, Daniel E., Martin, Cesar A.·2016·Translational Behavioral Medicine

Abstract

Evidence-based practice is important for behavioral interventions but there is debate on how best to support real-world behavior change. The purpose of this paper is to define products and a preliminary process for efficiently and adaptively creating and curating a knowledge base for behavior change for real-world implementation. We look to evidence-based practice suggestions and draw parallels to software development. We argue to target three products: (1) the smallest, meaningful, self-contained, and repurposable behavior change modules of an intervention; (2) "computational models" that define the interaction between modules, individuals, and context; and (3) "personalization" algorithms, which are decision rules for intervention adaptation. The "agile science" process includes a generation phase whereby contender operational definitions and constructs of the three products are created and assessed for feasibility and an evaluation phase, whereby effect size estimates/casual inferences are created. The process emphasizes early-and-often sharing. If correct, agile science could enable a more robust knowledge base for behavior change.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper proposes “agile science” as a process and three product types—behavior change modules, computational behavioral models, and personalization algorithms—for creating and curating real-world behavior change interventions. It argues for early-and-often sharing of operations, constructs, and failed tests, drawing parallels to agile software development and optimization methods, but presents a first-draft conceptual process rather than empirical validation.

Key findings

  • Defines three target products: behavior change modules (smallest, meaningful, self-contained, repurposable; structured as inputs, processes, outputs), computational behavioral models (mathematically specified theories including dynamics and threshold conditions), and personalization algorithms (decision rules for selecting interventions for a person/unit at a time and context).
  • Argues that sharing operations and constructs early and often—prior to effect size estimation—could reduce “reinventing the wheel,” facilitate specialization, and enable more robust tests of external validity across populations, treatments, outcomes, and settings.
  • Outlines agile science process v0.1 with two iterative phases: a generate phase (formative work, simulation studies, prototype testing; feasibility criteria from Bowen et al.) and an evaluate phase (effect size estimates/causal inferences using optimization methods such as MOST, SMART, and micro-randomized trials).
  • Describes ongoing complementary physical activity projects as a case study: developing “ambitious but doable” step goals, context-relevant cues and daily planning modules, and a Paco-based system for rapid creation and sharing of behavior change modules.

Methods

  • Conceptual/position paper; no empirical sample, primary data, or original quantitative results are reported.
  • Draws on evidence-based practice literature, optimization methods (MOST, SMART, micro-randomized trials), and analogies to agile software development, open-source software, APIs, and lean start-up methods.
  • Proposes a first-draft agile science process (v0.1) and illustrates it with ongoing, descriptive case-study projects focused on physical activity.

Limitations

  • The paper states that future work is needed on the generate phase, resource-efficient experimental designs and statistics, cognitive biases (e.g., sunk cost), and academic incentives for early-and-often sharing; curation strategies are not fully articulated.
  • The paper labels the process as first draft (v0.1) and generate-phase products as “contender” rather than evidence-based; no empirical validation of the agile science process is reported.
  • Evident from the text: the case study is ongoing and descriptive, and the paper provides no original effect sizes or formal evaluation of the proposed products/process.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1007/s13142-016-0395-7

Cites

62 of 62 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig 1 Computational model structure of a just in time adaptive intervention. Adapted from Martin, Deshpande, Hekler, and Rivera [38]
Fig 1 Computational model structure of a just in time adaptive intervention. Adapted from Martin, Deshpande, Hekler, and Rivera [38]CC BY · PubMed Central
Fig 2 Diagram of the model predictive controller that utilizes the computational model in Fig. 2. Adapted from Martin, Hekler, and Rivera [43]
Fig 2 Diagram of the model predictive controller that utilizes the computational model in Fig. 2. Adapted from Martin, Hekler, and Rivera [43]CC BY · PubMed Central
Fig 3 Agile Science Process v0.1
Fig 3 Agile Science Process v0.1CC BY · PubMed Central

Verification

Claims the paper makes

49 Own interpretation · 48 Not checked yet · 12 No basis given

Verify all 61 references — free account
  • Probably not a substantial claim. 64
  • It may state two separate claims as one. 32
80 of 80
  • The paper relies on this without support.

    Many behavior change operations, particularly interventions, are currently treated as the “secret sauce” of research and only shared after years of evaluation.

    “At present, many of our operations, particularly our interventions, are often treated as the “secret sauce” of our research and only shared after years of evaluation.”
  • The paper relies on this without support.

    A single research group often conducts the evaluation of a behavior change intervention.

    “As a single research group often does the evaluation, this results in limited variability related to populations, treatments, outcomes, and settings, thus providing limited data for external validity claims.”
2 of 2

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

2 of 2

Citations

Cites

Show all 64 references

Research works printed but not linked: 3

  1. Hovell MF, Wahlgren DR, Adams MA. The logical and empirical basis for the behavioral ecological model. In: DiClemente RJ, Crosby RA, Kegler M, eds. Emerging theories and models in health promotion research and practice: Strategies for enhancing public health. San Francisco, CA: Jossey-Bass Publishers; 2009: 415–450.
  2. Rogers Y, Sharp H, Preece J. Interaction design: beyond human-computer interaction. 3rd ed. West Sussex: Wiley; 2012.
  3. National Research Council. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. US: National Academies Press; 2011.

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Metadata

Authors
Hekler, E. B., Klasnja, P., Riley, W. T., Buman, M. P., Huberty, J., Rivera, D. E., & Martin, C. A.
Affiliations
  • Arizona State University (US)
  • University of Michigan (US)
  • National Institutes of Health (US)
Corresponding author
  • Hekler, Eric B.
  • Klasnja, Predrag
  • Riley, William T.
  • Buman, Matthew P.
  • Huberty, Jennifer
  • Rivera, Daniel E.
Venue
Translational Behavioral Medicine
Published
2016-02-26
Volume
6
Issue
2
Pages
317-328
ISSN
  • 1613-9860 (ISSN-L)
  • 1869-6716
OpenAlex
W2293484899
Cited by
208
References
64
Citations by year
2026: 8 · 2025: 13 · 2024: 15 · 2023: 15 · 2022: 24 · 2021: 30 · 2020: 18 · 2019: 28 · 2018: 30 · 2017: 22 · 2016: 3
FWCI
42.50
Type
journal article
Language
en
Open access
Locations
  • Translational Behavioral Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Robert Wood Jonson Foundation (71995)
  • Division of Information and Intelligent Systems (US) (1449751)
  • National Heart, Lung, and Blood Institute (US) (R01HL125440)
Topics
  • Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
  • Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
  • Social Sciences › Psychology › Developmental and Educational Psychology › Behavioral and Psychological Studies
Keywords
behaviour change interventions, evidence-based practice, real-world implementation, intervention module, computational model, personalization algorithms, intervention adaptation, knowledge base curation, operational definitions
MeSH terms
Algorithms, Computer Simulation, Humans, Software, Health Behavior, Evidence-Based Medicine, Evidence-Based Practice

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