A Framework for Understanding the Role of Psychological Processes in Disease Development, Maintenance, and Treatment: The 3P-Disease Model.

Health psychology is multidisciplinary, with researchers, practitioners, and policy makers finding themselves needing at least some level of competency in a variety of areas from psychology to physiology, public health, and others. Given this multidisciplinary ontology, prior attempts have been made to establish a framework for understanding the role of biological, psychological, and socio-environmental constructs in disease development, maintenance, and treatment. Other models, however, do not explain how factors may interact and develop over time. The aim here was to apply and adapt the 3P model, originally developed and used in the treatment of insomnia, to couch the biopsychosocial model in a way that explains how diseases develop, are maintained, and can be treated. This paper outlines the role of predisposing, precipitating, and perpetuating factors in disease states and conditions (the 3Ps) and provides examples of how this model may be adapted and applied to a number of health-related diseases or disorders including chronic pain, gastrointestinal disorders, oral disease, and heart disease. The 3P framework can aid in facilitating a multidisciplinary, theoretical approach and way of conceptualizing the study and treatment of diseases in the future.

Bedtime Variability and Metabolic Health in Midlife Women: The SWAN Sleep Study.

STUDY OBJECTIVES: Circadian misalignment, as seen in shift workers, can disrupt metabolic processes. Associations between sleep timing in nonshift workers and metabolic health are unknown. We examined sleep timing and indices of metabolic health in a community sample of midlife women. METHODS: Caucasian (n = 161), African American (n = 121) and Chinese (n = 56) non-shift-working women aged 48-58 y who were not taking insulin-related medications, participated in the Study of Women's Health Across the Nation (SWAN) Sleep Study and were subsequently examined approximately 5.39 (standard deviation = 0.71) y later. Daily diary-reported bedtimes were used to calculate four measures of sleep timing: mean bedtime, bedtime variability, bedtime delay and bedtime advance. Body mass index (BMI) and insulin resistance (homeostatic model assessment-insulin resistance, HOMA-IR) were measured at two time points. Linear regressions evaluated whether sleep timing was associated with BMI and HOMA-IR cross-sectionally and prospectively. RESULTS: In cross-sectional models, greater variability in bedtime and greater bedtime delay were associated with higher HOMA-IR (ß = 0.128; P = 0.007, and ß = 0.110; P = 0.013, respectively) and greater bedtime advance was associated with higher BMI (ß = 0.095; P = 0.047). Prospectively, greater bedtime delay predicted increased HOMA-IR at Time 2 (ß = 0.152; P = 0.003). Results were partially explained by shifted sleep timing on weekends. CONCLUSION: Frequent shifts in sleep timing may be related to metabolic health among non-shift working midlife women. COMMENTARY: A commentary on this article appears in this issue on page 269.