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Background: Psychological stress and physical activity (PA) are believed to be reciprocally related; however, most research examining the relationship between these constructs is devoted to the study of exercise and/or PA as an instrument to mitigate distress., Objective: The aim of this paper was to review the literature investigating the influence of stress on indicators of PA and exercise., Methods: A systematic search of Web of Science, PubMed, and SPORTDiscus was employed to find all relevant studies focusing on human participants. Search terms included "stress", "exercise", and "physical activity". A rating scale (0-9) modified for this study was utilized to assess the quality of all studies with multiple time points., Results: The literature search found 168 studies that examined the influence of stress on PA. Studies varied widely in their theoretical orientation and included perceived stress, distress, life events, job strain, role strain, and work-family conflict but not lifetime cumulative adversity. To more clearly address the question, prospective studies (n = 55) were considered for further review, the majority of which indicated that psychological stress predicts less PA (behavioral inhibition) and/or exercise or more sedentary behavior (76.4 %). Both objective (i.e., life events) and subjective (i.e., distress) measures of stress related to reduced PA. Prospective studies investigating the effects of objective markers of stress nearly all agreed (six of seven studies) that stress has a negative effect on PA. This was true for research examining (a) PA at periods of objectively varying levels of stress (i.e., final examinations vs. a control time point) and (b) chronically stressed populations (e.g., caregivers, parents of children with a cancer diagnosis) that were less likely to be active than controls over time. Studies examining older adults (>50 years), cohorts with both men and women, and larger sample sizes (n > 100) were more likely to show an inverse association. 85.7 % of higher-quality prospective research (≥ 7 on a 9-point scale) showed the same trend. Interestingly, some prospective studies (18.2 %) report evidence that PA was positively impacted by stress (behavioral activation). This should not be surprising as some individuals utilize exercise to cope with stress. Several other factors may moderate stress and PA relationships, such as stages of change for exercise. Habitually active individuals exercise more in the face of stress, and those in beginning stages exercise less. Consequently, stress may have a differential impact on exercise adoption, maintenance, and relapse. Preliminary evidence suggests that combining stress management programming with exercise interventions may allay stress-related reductions in PA, though rigorous testing of these techniques has yet to be produced., Conclusions: Overall, the majority of the literature finds that the experience of stress impairs efforts to be physically active. Future work should center on the development of a theory explaining the mechanisms underlying the multifarious influences of stress on PA behaviors.

Background: During quarantine, both physical and mental health are a concern. To the same extent that physicians are a scarce resource during this crisis, psychiatrists and psychologists are also limited in number. To help practitioners and public managers decide where to invest their resources, the present research investigated the relationships of stress, depression and state anxiety levels with sociodemographic and behavioural variables. Methods: Data were collected in Brazil between March, 18 and 22, 2020 in 1,468 volunteers during quarantine. Participants with a history of or current mental health illnesses were excluded leaving 1,460 individuals in the final sample. The online assessment included instruments for psychological stress, depression and state anxiety. A sociodemographic and behavioural questionnaire with 15 items was used to assess other factors. Multiple linear regression was performed for each psychological outcome to determine a hierarchy of significant predictors. Findings: Stress, depression and state anxiety levels were all predicted by gender (women higher than men), quality of nutrition, attendance in tele-psychotherapy, exercise frequency, presence of elderly persons in quarantine with the person, obligation to work outside the home, level of education (more educated, lesser risk for mental illness) and age (younger age, greater risk). Having a perceived risk factor for COVID-19 predicted depression and state anxiety, but not stress. Finally, the presence of children in quarantine with the participant was a protective factor for depression. Interpretation: Even though this research is limited by its cross-sectional design, it is possible to infer that mental health varies by demographic attributes, obligations and health behaviours. Those who report higher distress must work outside the home during quarantine, live with an elderly person and carry a risk factor for COVID-19, among other factors. Identifying those who are most vulnerable would help to prioritize those who may need the greatest psychological aid and assist public health practitioners in developing support strategies. [ABSTRACT FROM AUTHOR]

To better explain daily fluctuations in physical activity and sedentary behavior, investigations of motivation are turning from social cognitive frameworks to those centered on affect, emotion and automaticity, such as the Affect and Health Behavior Framework (AHBF), Integrated Framework and Affective-Reflective Theory (ART). This shift has necessitated: (a) re-examination of older theories and their constructs, such as drives, needs and tensions and (b) an inspection of competing theories from other fields that also attempt to explain dynamic changes in health behaviors. The Dynamical Model of Desire, Elaborated Intrusion Theory and others commonly share with AHBF the idea that human behavior is driven strongly by desires and/or the similar concepts of wants, urges, and cravings. These affectively-charged motivation states (ACMS) change quickly and may better explain physical activity behavior from one moment to the next. Desires for movement predominantly derive from negative but also positive reinforcement. Data from clinical populations with movement dysfunction or psychiatric disorders provides further evidence of these drivers of movement. Those with Restless Legs Syndrome, akathisia, tic disorders and exercise dependence all report strong urges to move and relief when it is accomplished. Motor control research has identified centers of the brain responsible for wants and urges for muscular movement. Models elaborated herein differentiate between wants, desires, urges and cravings. The WANT model (Wants and Aversions for Neuromuscular Tasks) conceptualizes desires for movement and rest as varying by magnitude, approach or avoidance-orientation (wants versus aversions) and as occupying independent dimensions instead of opposite ends of the same axis. For instance, one hypothetically might be in a state of both high desire for movement and rest simultaneously. Variations in motivation states to move and rest may also be associated with various stress states, like freezing or fight and flight. The first validated instrument to measure feelings of desire/want for movement and rest, the CRAVE Scale (Cravings for Rest and Volitional Energy Expenditure) is already shedding light on the nature of these states. With these advances in theory, conceptual modeling and instrumentation, future investigations may explore the effects of desires and urges for movement and sedentary behavior in earnest. [ABSTRACT FROM AUTHOR]

Objective: Pilot-test personalized digital health information to substantiate human-delivered exercise support for adults with type 1 diabetes (T1D). Design: Single-group, 2-week baseline observation, then 10-week intervention with follow-up observation. Setting: Community-based sample participating remotely with physician oversight. Participants: Volunteers aged 18 to 65 years with T1D screened for medical readiness for exercise intervention offerings. N = 20 enrolled, and N = 17 completed all outcomes with 88% to 91% biosensor adherence. Intervention: Feedback on personalized data from continuous glucose monitoring (CGM), its intersection with other ecological data sets (exercise, mood, and sleep), and other informational and motivational elements (exercise videos, text-based exercise coach, and self-monitoring diary). Main Outcome Measures: Feasibility (use metrics and assessment completion), safety (mild and severe hypoglycemia, and diabetic ketoacidosis), acceptability (system usability scale, single items, and interview themes), and standard clinical and psychosocial assessments. Results: Participants increased exercise from a median of 0 (Interquartile range, 0-21) to 64 (20-129) minutes per week (P = 0.001, d = 0.71) with no severe hypoglycemia or ketoacidosis. Body mass index increased (29.5 ± 5.1 to 29.8 ± 5.4 kg/m2, P = 0.02, d = 0.57). Highest satisfaction ratings were for CGM use (89%) and data on exercise and its intersection with CGM and sleep (94%). Satisfaction was primarily because of improved exercise management behavioral skills, although derived motivation was transient. Conclusions: The intervention was feasible, safe, and acceptable. However, there is a need for more intensive, sustained support. Future interventions should perform analytics upon the digital health information and molecular biomarkers (eg, genomics) to make exercise support tools that are more personalized, automated, and intensive than our present offerings. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: To examine the relationships between stress events/intensity and exercise frequency, intensity, and duration, and to determine whether stage of change moderates these relationships. Design: Prospective, 6-week longitudinal observational study. Method: College-aged females (N = 95) were recruited from undergraduate classes and completed a battery of questionnaires before a 6-week evaluation period. During this period, participants were asked to return a daily exercise log and to return a weekly stress events scale (Weekly Stress Inventory: ). Results: HLM analyses revealed that exercise stage of change interacted with stress (both event frequency and intensity) to effect: (1) the amount of exercise minutes/day reported per week, (2) the number of days of exercise/week, and, (3) mean RPE for exercise/week. For those in the maintenance stage, the number of stressful events were positively related to exercise frequency, intensity, and duration. This relationship was negative or nonexistent for all other stages combined. Likewise, stress intensity showed a positive relationship with exercise duration and a minimally positive relationship with exercise frequency for maintenance stage exercisers, compared to a negative relationship for all other stages combined. Conclusions: These results suggest differential patterns of exercise engagement based on stage of change – with those in maintenance responding to stress with enhanced activity, while those in other stages were more likely to reduce physical activity. Implications for intervention design and theories of exercise behavior change are discussed. [Copyright &y& Elsevier]