Looking Within: Can Studies of Intra-Individual Variability Improve Multiple Health Behavior Change Interventions?

Tyler Mason, PhD^✉ and Tammy Stump, PhD^✉; Multiple Health Behavior Change and Multi-Morbidities (MHBCM) SIG

Several decades of health behavior research have provided numerous insights into the factors that contribute to successful behavior change, including attitudes, self-efficacy, and perceived risks. For many health behaviors, though, a focus on individual-level differences is just the tip of the iceberg when it comes to understanding what predicts behavior and how best to intervene. Lifestyle behaviors, such as physical activity, eating a healthy diet, using sun protection, and smoking, must be performed, or avoided, consistently over time to improve health outcomes.¹ Such behaviors are dynamic and multiply-determined; they vary based on intra-individual variables, including the individual’s momentary physical and social environments, affect, and cognition. New insights on these intra-individual dynamics may provide a fresh perspective to ongoing questions within the area of multiple health behavior change, including which behaviors should be combined within the same intervention and whether to intervene on multiple behaviors simultaneously or sequentially.

Methodological advances (e.g., passive sensing, ecological momentary assessment) and statistical innovations (e.g., mixed-effects location-scale modeling) permit rigorous evaluation of the micro-temporal dynamics of lifestyle behaviors.² As a few examples: We now have direct evidence that periods of stress or negative affect have an immediate impact on smoking and that individuals who experience the most consistent relationship between smoking and decreases in negative affect have the highest levels of smoking.³ For physical activity, children’s higher-than-usual positive affect not only promotes their own physical activity but also that of their mother.⁴ For healthy eating, unplanned meals, experiencing negative emotions (boredom, stress), and time of day (evening) predict dietary lapses.⁵

Less explored by these micro-temporal evaluations is the dynamic interplay between various health behaviors. Linkages among health behaviors can be synergistic (e.g., eating a healthier diet can lead to healthy decreases in sedentary behavior)⁶ or antagonistic (e.g., those who are more physically active have a higher prevalence of sunburn).⁷ A focus on intra-individual variation can provide new insights explaining the associations among various health behaviors. For instance, these methods would allow us to learn more about the specific contexts in which health behaviors tend to co-occur and about potential pathways that operate on an intra-individual level, such as a change in affect or other physical state arising through the performance of one behavior having downstream consequences for other behaviors.

With most common health conditions – including cardiovascular disease and cancer – being impacted by multiple behavioral factors, behavioral scientists can make the biggest impact by using approaches that target change in multiple behaviors. Studies of intra-individual variability hold promise for informing refinements to these approaches along with applications of advanced intervention methods (e.g., just-in-time adaptive interventions, continuous tuning interventions) that maximize change to multiple behaviors.⁸

 

References

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3. Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2012). Modeling between‐subject and within‐subject variances in ecological momentary assessment data using mixed‐effects location scale models. Statistics in Medicine, 31(27), 3328-3336.
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7. Holman, D. M., Ding, H., Guy, G. P., Watson, M., Hartman, A. M., & Perna, F. M. (2018). Prevalence of sun protection use and sunburn and association of demographic and behaviorial characteristics with sunburn among US adults. JAMA Dermatology, 154(5), 561-568.
8. Chevance, G., Perski, O., & Hekler, E. B. (2021). Innovative methods for observing and changing complex health behaviors: Four propositions. Translational behavioral medicine, 11(2), 676-685.