A Conceptual Framework for Adaptive Preventive Interventions
Open accessAbstract
Recently, adaptive interventions have emerged as a new perspective on prevention and treatment. Adaptive interventions resemble clinical practice in that different dosages of certain prevention or treatment components are assigned to different individuals, and/or within individuals across time, with dosage varying in response to the intervention needs of individuals. To determine intervention need and thus assign dosage, adaptive interventions use prespecified decision rules based on each participant's values on key characteristics, called tailoring variables. In this paper, we offer a conceptual framework for adaptive interventions, discuss principles underlying the design and evaluation of such interventions, and review some areas where additional research is needed.
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Adaptive treatment methodologies have been applied to interventions that adjust over weeks or months, not hours or days, but these methodologies can be applied to mobile technology interventions.
“Adaptive treatment methodologies has been applied to interventions that adjust over the course of weeks or months, not hours or days, but these methodologies can be applied to mobile technology interventions [106].”
From Health behavior models in the age of mobile interventions: are our theories up to the task? (2011) · cites this paper as [106]
MOST has three phases: preparation, optimization, and evaluation.
“MOST has three phases.”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [32]
Fractional factorial designs can make examination of multiple implementation components feasible even when cluster randomization is necessary.
“Fractional designs can make examination of multiple components feasible, even when cluster randomization is necessary (30, 32, 37).”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [32]
Multiphase Optimization Strategy Implementation Trials (MOST) can be used to identify and test an optimized implementation intervention when testing all combinations in a single design is not feasible.
“Testing all combinations in a single design is not feasible, but MOST can be used to identify and test an optimized intervention.”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [32]
The SMART implementation design belongs to the factorial experiment family and uses multiple randomizations, allowing a site's implementation process to be altered when it is not working.
“The Sequential Multiple Assignment Randomized Implementation Trial (SMART) implementation design is a special case of the factorial experiment (32, 67, 78) involving multistage randomizations where the site-level implementation process can be modified if unsuccessful.”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [32]
Adaptive interventions resemble clinical practice, in that different interventions are assigned to different individuals and within individuals over time, with the intervention(s) ideally varying in response to patient needs and patient response.
“Adaptive interventions resemble clinical practice, in that different interventions are assigned to different individuals and within individuals over time, with the intervention(s) ideally varying in response to patient needs and patient response [62].”
From A Sequential Multiple Assignment Randomized Trial (SMART) study of medication and CBT sequencing in the treatment of pediatric anxiety disorders (2021) · cites this paper as [62]
The adaptive intervention that a SMART design produces can be incorporated into clinical practice more naturally and seamlessly than the findings of a fixed-intervention study.
“The adaptive intervention that a SMART design produces can be incorporated into clinical practice more naturally and seamlessly than the findings of a fixed-intervention study [53, 54, 62].”
From A Sequential Multiple Assignment Randomized Trial (SMART) study of medication and CBT sequencing in the treatment of pediatric anxiety disorders (2021) · cites this paper as [62]
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Self-citation
- Authors
- Collins, L. M., Murphy, S. A., & Bierman, K. L.
- Affiliations
- Pennsylvania State University (US)
- University of Michigan (US)
- Corresponding author
- Collins, Linda M.
- Venue
- Prevention Science
- Published
- 2004-09
- Volume
- 5
- Issue
- 3
- Pages
- 185-196
- ISSN
- 1389-4986 (ISSN-L)
- 1573-6695
- PMC
- PMC3544191
- OpenAlex
- W2021206292
- Cited by
- 608
- References
- 46
- Citations by year
- 2026: 19 · 2025: 18 · 2024: 19 · 2023: 34 · 2022: 27 · 2021: 27 · 2020: 23 · 2019: 33 · 2018: 30 · 2017: 33 · 2016: 38 · 2015: 30 · 2014: 42 · 2013: 37 · 2012: 34
- FWCI
- 19.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Prevention Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH050953)
- National Institute on Drug Abuse (K02DA015674, P50-DA-10075, P50DA010075, K02-DA15674)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Keywords
- adaptive interventions, preventive interventions, decision rules, intervention dosage, personalized prevention, intervention design, intervention evaluation, conceptual framework
- MeSH terms
- Child, Child Behavior Disorders, Clinical Competence, Decision Support Techniques, Forecasting, Home Care Services, Humans, Judgment, Parent-Child Relations, Parents, Patient Care Planning, Patient Participation, Primary Prevention, Research Design, Risk Factors, Time Factors, Reproducibility of Results, Confounding Factors, Epidemiologic, Outcome Assessment, Health Care, Risk Assessment, Conduct Disorder, Needs Assessment
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