Strong epidemiological evidence demonstrates that chronic psychological stress predicts earlier disease onset and mortality. However, there are several critical barriers that have prevented progress in the epidemiological study of stress as a risk factor for disease. Stress measurement is inherently complex and multi-level (social, psychological, physiological), is typically inconsistent and superficial, and does not capture daily stress processes or include non-self- report measures of stress or stress vulnerability. Meanwhile, there have been advances in the basic science of stress processes, which have helped unpack and define the cognitive, affective, and physiological responses to acute and chronic stress, and advances in granular measures of daily stress through ecological momentary assessments. Thus, stress science could be greatly accelerated by grounding in a multilevel model and enhancing measurement using these novel approaches. In our first cycle of the R24 Stress Measurement Network, we addressed these issues through accomplishing three aims (detailed in Past Accomplishments). 1) We published a transdisciplinary multi-level model of stress and aging that incorporates contextual factors, daily level processes, and physiological responses1; 2) We created a Stress Typology to enhance conceptualization and language of stress; and 3) We developed a Measurement Toolbox for consensus-based best validated measures. Lastly, we completed the harmonization of variables for numerous types of stress experiences across ten national epidemiological studies in partnership with the Gateway to Global Aging data harmonization initiative. We can now support researchers interested in questions related to cross-national associations among stress, aging, and health. With this foundational work, we are ideally poised to improve these products and their use in the field and develop a new set of innovative products. The proposed continuation of the network will rely on our UCSF leadership team, continuing and new scientific advisors who are experts in the area of both stress and aging, and early stage investigators who we will support as they increase their knowledge of best practices in stress measurement. Further, we will reach broadly to recruit excellent scientists to help complete the new aims, which requires deep engagement of interdisciplinary fields of population health. Our new aims will test aspects of the multi-level model. These include: 1) Initiate use of newly available harmonized stress data across 10 epidemiological cohort studies to examine cross-national associations; 2) Develop and test new measures of psychological stress, with a focus on non-self-report measures of stress vulnerability based on basic experimental research; Expand the measurement toolbox to include methods for assessing physiological indices of stress, and disseminate best practices in stress measurement; and 3) Using large scale datasets, validate daily and ecological momentary assessments of stress, and test how various psychological or behavioral manipulations alter daily psychological and autonomic stress responses. Improved stress measurement tools offer tremendous opportunities for discovering and advancing the field of basic research, enhancing the efficacy of health promotion interventions, and policies that impact social stress.
Chronic stress can impair health, and yet often epidemiological models of health do not include stressful experiences and stress responses as predictors because of the perceived difficulty in measuring such a seemingly nebulous construct. The proposed continuation of the Stress Measurement Network will advance stress science through building upon and promoting use of products from our first 5 years ? publications using the harmonized stress measures that allow us to compare stress and health in ten countries, expanding the Stress Measurement Toolbox by including physiological stress measures and developing new measures of stress vulnerability, and capitalizing on new big data opportunities examining stress management interventions in large international samples using mobile technology that will be made publicly available to researchers. These efforts in turn will lead to better measurement of stress in research studies, a deeper understanding of how stress affects aging trajectories, and ways to intervene.
| Picard, Martin; McEwen, Bruce S; Epel, Elissa S et al. (2018) An energetic view of stress: Focus on mitochondria. Front Neuroendocrinol 49:72-85 |
| Conklin, Quinn A; Crosswell, Alexandra D; Saron, Clifford D et al. (2018) Meditation, stress processes, and telomere biology. Curr Opin Psychol 28:92-101 |
| Epel, Elissa S; Crosswell, Alexandra D; Mayer, Stefanie E et al. (2018) More than a feeling: A unified view of stress measurement for population science. Front Neuroendocrinol 49:146-169 |
| Da Paz, Nikko S; Siegel, Bryna; Coccia, Michael A et al. (2018) Acceptance or Despair? Maternal Adjustment to Having a Child Diagnosed with Autism. J Autism Dev Disord 48:1971-1981 |
| Felder, Jennifer N; Epel, Elissa S; Coccia, Michael et al. (2018) Effects of daily maladaptive coping on nightly sleep in mothers. Psychol Health 33:144-157 |
| Crosswell, Alexandra D; Suresh, Madhuvanthi; Puterman, Eli et al. (2018) Advancing research on psychological stress and aging with the Health and Retirement Study: Looking back to launch the field forward. J Gerontol B Psychol Sci Soc Sci : |
| Picard, Martin; Prather, Aric A; Puterman, Eli et al. (2018) A Mitochondrial Health Index Sensitive to Mood and Caregiving Stress. Biol Psychiatry 84:9-17 |
| Kroll, Juliet L; Steele, Ashton M; Pinkham, Amy E et al. (2018) Hippocampal metabolites in asthma and their implications for cognitive function. Neuroimage Clin 19:213-221 |
| Bostock, Sophie; Crosswell, Alexandra D; Prather, Aric A et al. (2018) Mindfulness on-the-go: Effects of a mindfulness meditation app on work stress and well-being. J Occup Health Psychol : |
| Lind, Monika N; Byrne, Michelle L; Wicks, Geordie et al. (2018) The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing. JMIR Ment Health 5:e10334 |
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