1. Yue Liao, MPH Genevieve Dunton, PhD, MPH University of Southern California Institute for Health Promotion & Disease Prevention Research Presented at the 36th Annual Meeting of the Society of Behavioral Medicine April, 2015 How Morning Cognitive and Feeling States Predict Daily Physical Activity Levels amongAdults 2. Cognitive Factors, Affective Feelings, and Physical Activity Cognitive factors have been shown as correlates of physical activity Self-efficacy Outcome expectancy Intention Affective feelings can influence ones cognitive factors in relation to physical activity Negative affect (e.g., stress) as a barrier to habitual physical activity Rhodes & Nigg, 2011; Bauman et al., 2012; Loehr et al., 2014; Schwabe & Wolf, 3. Current Research Gap Most studies examined the inter-individual (i.e., between person) effects of cognitive/affective variables on physical activity levels Treat these variables as a static (global) construct for each person Short-term intra-individual (i.e., within person) effects might offer new insights and implications for theory and intervention development 4. Aims of Current Study Use Ecological Momentary Assessment (EMA) to capture adults cognitive and feeling states in the morning of their daily lives EMA a real-time self-report method to measure current behaviors, cognitive/feeling states repeatedly in peoples everyday lives Examine whether ones morning cognitive and feeling states predict his/her physical activity levels during that day 5. Methods 110 adults from Project MOBILE Mean age = 40.8 (SD = 9.8) 72% female 30% Hispanic 63% overweight/obese An electronic EMA survey was delivered via a mobile app each morning between 6:30-6:45 am for up to 12 days 3 waves of 4 consecutive days each wave separated by 6 months in between each wave consisted of 2 weekdays and 2 weekend days 6. Measures EMA Surveys Cognitive states for physical activity Self-efficacy (2 items, Cronbachs = .92) Outcome expectancy (4 items, Cronbachs = .63) Intention Self Efficacy Outcome Expectancy Intention 7. Positive Affect Negative Affect Energetic Fatigue Current feeling states Positive affect Happy, Cheerful, Relaxed (Cronbachs = .75) Negative affect Stressed, Angry, Anxious, Sad (Cronbachs = .80) Energetic Fatigue 8. Measures - Physical Activity Accelerometer (Actigraph GT2M) was worn around the waist during waking hours across the 12 monitoring days Activity counts were converted to total moderate- to-vigorous physical activity (MVPA) minutes for each day MVPA was defined as 2,020 activity counts per minute Only included valid days for analysis Belcher et al., 2010; Troiano et al., 2008 9. Data Analysis Multilevel linear regression model Outcome: Total MVPA minutes of each day Predictor: Morning cognitive/feeling state of that day Within-person effect: ones cognitive/feeling state relative to his/her usual level in the morning Between-person effect: ones usual cognitive/feeling state relative to the group mean All models controlled for age, gender, ethnicity, and weight status 10. Data Availability EMA Survey On average, participants received 9.5 (SD = 3.2) prompts in the morning across the 12 days Participants missed 2.4 (SD = 2.5) of these morning prompts Accelerometer Data On average, participants had 10.3 (SD = 2.9) valid accelerometer days across the 12 days Average daily MVPA minutes was 25.9 (SD = 23.8) 11. Transformation of MVPA Minutes 12. Results 1 2 3 4 5 Self Efficacy Outcome Expectancy Intention Person-Level Mean of Cognitive States Reported in the Morning Strongly disagree Somewhat disagree Neither agree nor disagree Somewhat agree Strongly agree 13. 1 2 3 4 5 Positive Affect Negative Affect Energetic Fatigue Person-Level Mean of Affective States Reported in the Morning Not at all A little Moderately Quite a bit Extremely 14. Cognitive States and Daily MVPA Minutes Beta (SE) p-value Self Efficacy Within-Person Effect 0.11 (0.08) 0.21 Between-Person Effect 0.14 (0.08) 0.07 Outcome Expectancy Within-Person Effect 0.13 (0.06) 0.04 Between-Person Effect 0.12 (0.12) 0.31 Intention Within-Person Effect 0.08 (0.07) 0.29 Between-Person Effect 0.03 (0.08) 0.71 Note: Daily MVPA minutes was log-transformed; all models controlled for age, gender, ethnicity, and weight status. 15. Affective States and Daily MVPA Minutes Beta (SE) p-value Positive Affect Within-Person Effect -0.10 (0.07) 0.15 Between-Person Effect -0.02 (0.09) 0.85 Negative Affect Within-Person Effect 0.28 (0.16) 0.09 Between-Person Effect 0.02 (0.20) 0.91 Energetic Within-Person Effect 0.09 (0.06) 0.15 Between-Person Effect 0.12 (0.09) 0.15 Fatigue Within-Person Effect -0.01 (0.05) 0.76 Between-Person Effect -0.05 (0.06) 0.47 Note: Daily MVPA minutes was log-transformed; all models controlled for age, gender, ethnicity, and weight status. 16. Conclusions Higher outcome expectancy than ones usual level in the morning is associated with more physical activity that day Short-term outcome expectancy (e.g., in the next few hours) might have a longer lasting effect on physical activity than other cognitive states Feeling states in the morning are not associated with overall activity levels for that day Feeling states might be more relevant when predicting immediate behaviors 17. Limitations Short monitoring period A pre-set morning prompting schedule might not reflect peoples different waking times Limited EMA items for each cognitive/feeling state construct 18. Future Directions Use EMA data to explore the multilevel mediational effect of cognitive state, feeling state, and physical activity level Interventions could focus on how to boost peoples short-term (e.g., in the next few hours) cognitive factors to promote daily physical activity Especially given the recent evidence that short bouts of physical activity can be health beneficial Fan et al., 2013; Loprinzi & Cardinal, 20 19. Acknowledgements Funding agency American Cancer Society 118283-MRSGT-10-012-01- CPPB (Dunton, PI) Participants App programmer Jennifer Beaudin, S. M. (Massachusetts Institute of Technology) Project staff Keito Kawabata (Project Manager) Student interns