Unpacking habit with Bayesian mixed models: Dynamic approach to health behaviors with interchangeable elements, illustrated through multiple sun protection behaviors Journal Article


Authors: Li, Y.; Schofield, E.; Hay, J. L.
Article Title: Unpacking habit with Bayesian mixed models: Dynamic approach to health behaviors with interchangeable elements, illustrated through multiple sun protection behaviors
Abstract: Analytics for behavioral habit typically model one behavior at a time, despite the fact that habit often involves multiple cooccurring behaviors, such as food choices and physical activities, where interrelated behaviors are often equally recommended. We propose a novel Mixed-Effects Dynamic hAbit model (MEDA) to simultaneously model multiple related, habitual behaviors. As an illustrative example, MEDA was applied to real-time assessments of sun protection (sunscreen, shade, hat, and protective clothing) reported twice daily by first-degree relatives of melanoma patients who are themselves at an elevated risk of skin cancer. MEDA aims to explicate habits in sun protection under varying environmental cues (e.g., sunny and hot weather). We found consistent between-group differences (e.g., men responding to weather cues more consistently than women) and interactions between cooccurring behaviors (e.g., being in shade discourages sunscreen wear-ing, more so in men than women). Moreover, MEDA transcends conventional methods to address longstanding challenges-how cue to action and volitional choices differ by groups or even by in-dividual persons. Such nuances in interrelated habitual behaviors are relevant in numerous other applications, such as recommended dietary or physical activity behaviors. These methods best in-form personalized behavioral interventions targeting individual preferences for precision behavioral intervention.
Keywords: variance; smoking; prediction; stress; sun protection; adults; heterogeneity; 1st-degree relatives; habit; ecological momentary assessment; alcohol-use; eating behavior; bayesian hierarchical models; condi-tional probability; assessment ema
Journal Title: Quantitative Methods for Psychology
Volume: 19
Issue: 3
ISSN: 1913-4126
Publisher: Univ Montreal, Dept Psychologie  
Date Published: 2023-01-01
Start Page: 265
End Page: 280
Language: English
ACCESSION: WOS:001157234500004
DOI: 10.20982/tqmp.19.3.p265
PROVIDER: wos
PMCID: PMC11423781
PUBMED: 39323564
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF. Corresponding MSK author is Yuelin Li -- Source: Wos
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MSK Authors
  1. Yuelin Li
    219 Li
  2. Jennifer L Hay
    265 Hay
  3. Elizabeth A Schofield
    161 Schofield