Analyzing data from Ecological Momentary Assessment (EMA) via mobile phones, we found that higher outcome expectancy in the morning as associated with more physical activity that day.
Call Girls Service in Bommanahalli - 7001305949 with real photos and phone nu...
Morning Cognitive States Predict Daily Physical Activity Levels - Findings from an EMA Mobile Phone Study
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 one’s 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 people’s everyday lives
Examine whether one’s 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
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: one’s cognitive/feeling state relative to
his/her usual level in the morning
Between-person effect: one’s 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)
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
16. Conclusions
Higher outcome expectancy than one’s 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 people’s 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
people’s 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
Notes de l'éditeur
For example:
positive feelings may increase one’s motivation
negative feelings might decrease one’s intention
Theory of Planned Behavior
The TPB has been used extensively within the PA domain (7). Our review indicated that more than 200 studies have applied the model to predict and explain PA.
Self-Efficacy Theory
The SET has been presented more recently within the Social Cognitive Theory (SCT). Self-efficacy can be viewed as, and has been found to be, both a determinant and a consequence of PA participation.
Behavioral actions were regulated and influenced by the anticipation of negative emotional consequences of those actions (i.e., anxiety and fear). Such consequences amount to an outcome expectancy, the influence of which can be assuaged given stronger self-efficacy to perform the desired behavior.
Positive outcome expectancies influence responses in personal judgments of confidence.
“In addition, they can also ‘get derailed’ by situational contexts or self-states (e.g., distraction or multiple goals pursuit, inadequate and habitual responses, bad mood, etc.). Thus, the adoption of a physically active lifestyle might imply the acquisition of several skills required not only for the execution of the behaviour itself, but also for its integration into the personal daily routine.”
Prior work showed some cognitive factors/affective feelings associated with subsequent physical activity in the next 2 hours
Age ranged from 27 to 73.
The cut-point for MVPA was defined as 2,020 activity counts per minute, which is consistent with national surveillance studies (Belcher et al., 2010; Troiano et al., 2008).
Participants were more likely to miss the morning prompt vs. the other prompts.
People who with less available data did not differ with people who had more data in terms of demographics and physical activity levels.
Compare to other prompts, people reported higher self efficacy, outcome expectancy, and intention in the morning prompts.
1=not at all, 2=a little, 3=moderately, 4=quite a bit, 5=extremely
Compare to other prompts, people reported lower positive affect, lower positive affect, less energetic, and more fatigue in the morning prompts.
Higher outcome expectancy (than one’s usual level) in the morning predicted greater total MVPA minutes for that day (WP effect)
Higher self-efficacy in the morning (than other people in the sample) predicted greater total MVPA minutes for that day (BP effect)
Greater negative affect in the morning (than one’s usual level) predicted greater total MVPA minutes for that day (WP effect)
Will not reduce my time with family/friends; help me feel less stressed; not make me feel too tired to do my daily work; help me feel more energetic
Engaging in nonbouts, as opposed to bouts (i.e., >10 mins) of PA, is just as strongly associated with several biologic health outcomes, suggesting that adults who perceive themselves as having little time to exercise may still be able to enhance their health by adopting an active lifestyle approach.
“Our findings showed that for weight gain prevention, accumulated higher-intensity PA bouts of <10 minutes are highly beneficial, supporting the public health promotion message that “every minute counts.” – Fan et al.