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Translation of findings from laboratory studies of food and alcohol intake into behavior change interventions: The experimental medicine approach.

Matt FieldPaul ChristiansenCharlotte A HardmanAshleigh HaynesAndrew JonesAllecia ReidEric Robinson
Published in: Health psychology : official journal of the Division of Health Psychology, American Psychological Association (2020)
Objectives: Laboratory studies have contributed important information about the determinants of food and alcohol intake, and they have prompted the development of behavior change interventions that have been evaluated in randomized controlled trials conducted in the field. In this article we apply a recent experimental medicine (EM) framework to this body of research. Method: A conceptual review and focused discussion of the relevant literature is presented. Results: We illustrate how it is possible to translate findings from studies of food and alcohol intake in the laboratory into interventions that are effective for changing behavior in the real world. We go on to demonstrate how systematic failures can occur at different stages within the EM framework, and how these failures ultimately result in interventions that are ineffective for changing behavior. We also consider methodological issues that may constrain the external validity of findings from laboratory studies including demand effects, participant characteristics, and the timing and dose of behavioral interventions. Throughout, we make recommendations to improve the translation of findings from laboratory studies into behavior change interventions that are effective in the field. Conclusions: Consideration of the EM framework will help to ensure that promising candidate interventions for eating and drinking that are identified in laboratory studies can fulfill their translational promise. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Keyphrases
  • physical activity
  • case control
  • randomized controlled trial
  • alcohol consumption
  • healthcare
  • weight gain
  • climate change
  • machine learning
  • deep learning
  • human health
  • big data
  • risk assessment