ecological momentary assessment of physical activity · physical activity self-efficacy across the...
TRANSCRIPT
Ecological Momentary Assessment of Physical Activity
Genevieve F. Dunton, PhD, MPHGenevieve F. Dunton, PhD, MPH
Department of Preventive MedicineUniversity of Southern California
Recall Instruments
• Memory errors and biases
• Not completed in the environment in which
Limitations of Recall and Observational Methods
the behavior occurs
Observational Methods
• Often limited to a single setting
• Do not measure mood or subjective perceptions
Accelerometer
• Difficulty measuring activity type, load bearing, incline, bicycling, swimming
Limitations of Objective Methods
GPS
• Difficulty differentiating some modes of travel (scooter chair, wheel chair, stroller)
• Do not measure mood or subjective perceptions
• Real-time self-report responses in naturalistic settings
• Can simultaneously measure:
Ecological Momentary Assessment (EMA)
• Can simultaneously measure:
1) Activity type (e.g., soccer, watching TV)
2) Where (e.g., playground, trail, sidewalk)
3) With whom (e.g., alone, friends, siblings)
4) Perceived characteristics (e.g., safety, traffic)
5) Mood (e.g., positive affect, negative affect, stress)
Electronic EMA Equipment/Technology
• Mobile phone • Personal Digital Assistant (PDA)• Internet (Laptop, Desktop, iPad)• Internet (Laptop, Desktop, iPad)
• Event-contingent - information recorded during or after a pre-determined behavior
• Interval-contingent- information recorded according to specific pre-set time frames (e.g., at 8am and 12noon everyday)
EMA Sampling Schedule
(e.g., at 8am and 12noon everyday)
• Signal-contingent – information recorded when prompted, often at random times throughout the day
• Context-contingent – information recorded when a context or environment is sensed (GPS, heart rate, etc)
Example EMA Sampling Schedule
(Interval-Signal Contingent Hybrid)
• Monitoring occurred across 4 days (Fri-Mon) for
each wave.
• No prompts during school hours on Friday or • No prompts during school hours on Friday or
Monday.
Eco log ical Momen tary Assessment Prompting Schedule Day 8:30 -
10am 10am-12pm
12-2pm 2-4pm 4-6pm 6-8pm 8-8:30pm
Friday X X X Satu rday X X X X X X X
Sunday X X X X X X X Monday X X X
Note: Question sequences were prompted at a random time within each interval.
EMA Items
Compliance (children ages 9-13 years)
• Children answered 80% (range 7% – 100%) of
EMA surveys
• Unanswered surveys were more common among
African-American (21%), Asian (22%), Mixed/BiracialAfrican-American (21%), Asian (22%), Mixed/Biracial
(22%), and Other (25%) as compared with
White/Caucasian children (11%)
• No differences by day of the week, time of
day, sex, age, income or weight status
Dunton GF, Liao Y, Intille S, Spruijt-Metz D, and Pentz M. (2011). Investigating children’s physical activity and sedentary behavior using Ecological Momentary Assessment with mobile phones. Obesity. 1205-1212.
Physical Activity Levels During Answered and Unanswered Prompts
(Steps in the 30 minutes before the EMA prompt)
.
200
250
300
350
Adj. Wald F = 3.09, df = 1, p = .08)
Adjusted for day of the week, time of day, sex, age, race/ethnicity, income, weight status.
0
50
100
150
200
Answered Prompts Unanswered Prompts
Ste
ps
Extent to Which EMA Surveys
Disrupted Activity
(15 min. before/after the EMA prompt)
.
70
80
90
100
Sedentary Behavior
250
300
Physical Activity
Adj. Wald F = 2.29, df = 1, p = .13 Adj. Wald F = 0.15, df = 1, p = .70
Adjusted for day of the week, time of day, sex, age, race/ethnicity, income, weight status.
0
10
20
30
40
50
60
70
Before After
Ste
ps
0
50
100
150
200
Before After
Ste
ps
Validity of EMA Activity Responses
(30 minutes before the EMA prompt)
.
300
400
500
600
700
Ste
ps
Underweight/At Risk for Underweight/Normal WeightAt Risk for Overweight/ Overweight
Dunton GF, Liao Y, Intille S, Spruijt-Metz D, and Pentz M. (2011). Investigating children’s physical activity and sedentary behavior using Ecological Momentary Assessment with mobile phones. Obesity. 1205-1212.
0
100
200
• Where?
• With whom?
• Do these patterns differ according to?
Using EMA Describe the Contexts of PA and Sedentary Behavior
• Do these patterns differ according to? - Demographic factors (sex, age, ethnic,income)
- Temporal factors (time of day, day of the week, seasons)
Age and Income Differences in Physical Activity Contexts
30%
40%
50%
60%
Perc
en
t o
f P
hysic
al A
cti
vit
y
Rep
ort
ed
Ou
tdo
ors
30%
40%
50%
60%
70%
80%
Perc
en
t o
f P
hysic
al A
cti
vit
y
Rep
ort
ed
Ou
tdo
ors
Dunton, G. F., Kawabata, K., Intille, S., Wolch, J., & Pentz, M. (2012). Assessing the social and physical contexts of children’s leisure-time physical activity: An Ecological Momentary Assessment study. American Journal of Health Promotion. 26, 135-142.
0%
10%
20%
9-10 years 11-13 years
Perc
en
t o
f P
hysic
al A
cti
vit
y
Rep
ort
ed
Ou
tdo
ors
0%
10%
20%
30%
Perc
en
t o
f P
hysic
al A
cti
vit
y
Rep
ort
ed
Ou
tdo
ors
• How do physical activity levels
(e.g., intensity, duration) differ across
physical and/or social contexts?
Using EMA to Examine Differences inPhysical Activity Across Contexts
physical and/or social contexts?
• How do physical activity experiences
(e.g., enjoyment, positive and negative
affect) differ across physical and/or
social contexts?
Physical Activity Level by Social Context(30-min. before EMA prompt)
200
250
300
350
400
Ste
ps
Dunton, G. F., Liao, Y., Intille, S., Wolch, J., & Pentz, M. (2011). Social and physical contextual influences on children’s leisure-time physical activity: An Ecological Momentary Assessment study. Journal of Physical Activity and Health, 8(Suppl 1), S103-S108.
.
0
50
100
150
200
Family and friends
Friends only Family only Alone
Ste
ps
Mood During Physical Activity by
Physical Context
1.5
2
2.5
3
Avera
ge M
oo
d R
ati
ng
Positive Affect
1.5
2
2.5
3
Avera
ge M
oo
d R
ati
ng
Enjoyment
Dunton, G. F., Liao, Y., Intille, S., Wolch, J., & Pentz, M. (2011). Social and physical contextual influences on children’s leisure-time physical activity: An Ecological Momentary Assessment study. Journal of Physical Activity and Health, 8(Suppl 1), S103-S108.
0
0.5
1
1.5
Outdoors Yard Other Home Someone else's house
Avera
ge M
oo
d R
ati
ng
0
0.5
1
1.5
Outdoors Yard Other Home Someone else's house
Avera
ge M
oo
d R
ati
ng
• Is the likelihood of participating in a physical activity bout related to prior or current mood, stress, pain, fatigue, etc?
Using EMA to Examine Antecedents,
Concomitants, Consequences of PA Episodes
mood, stress, pain, fatigue, etc?
• Does participating in a physical activity bout influence subsequent predict subsequent mood, stress, pain, fatigue, etc?
Conceptual Model of
Temporal Relationships
7:45am 11:45am 3:45pm 7:45pm
MVPA MVPA MVPA MVPA
SE SE SE SE
PA PA PA PA
NA NA NA NANA NA NA NA
Energy Energy Energy Energy
Fatigue Fatigue Fatigue Fatigue
Demand Demand Demand Demand
Control Control Control Control
Lagged
Effects
Variable Group Avg.
Coeff. (SE)
p
Self-EfficacyT-1 0.08 (0.02) <.001
Positive AffectT-1 0.06 (0.02) .003
Negative AffectT-1 -0.11 (0.03) <.001
EnergyT-1 0.04 (0.02) .066
Associations with MVPAT
FatigueT-1 -0.02 (0.01) .135
ControlT-1 0.05 (0.02) .004
DemandT-1 -0.01 (0.01) .978
Pos. Soc. Inter. T 0.17 (0.05) .001
Prob. Soc. Inter. T 0.02 (0.06) .692
Stressful EventT -0.01 (0.06) .875
Dunton, G. F., Atienza, A., Castro, C. M., & King, A. C. (2009). Using ecological momentary assessment to examine antecedents and correlates of physical activity bouts in adults age 50+ years: A pilot study. Annals of Behavioral Medicine, 38, 249-255.
• Do some people show stable patterns of physical activity self-efficacy across the day or from day to day whereas other people’s patterns are variable?
Using EMA to Examine Intraindividual Variability
patterns are variable?
• Are between-person differences in degree of intraindividual variability in self-efficacy related to physical activity levels?
0
1
2
3
4
5
6
Se
lf-E
ffic
ac
y
Time
Low Variability Case
0
1
2
3
4
5
6
7
8
9
Se
lf-E
ffic
ac
y
Time
High Variability Case
Intraindividual Variability in
Self-Efficacy and Physical Activity
Intraindividual variability in self-efficacy was lower during weeks when
brisk walking was higher (τ = -0.01, SE = 0.002, p < .01)
Dunton, G. F., Atienza, A. A., Huh, J., Castro, C., Hedeker, D., King, A. C. (under review). Examining within-person variability in physical activity self-efficacy: An Ecological Momentary Assessment study.
Future Areas of Research
Context-Sensitive EMA with Environmental, Biological and Behavioral Triggers
-Integration of data from internal/external
sensors (GPS, accelerometers, salivary
amylase, air pollution monitors, instrumented
asthma inhalers)
Challenges and Limitations
• Missing data
• Reactance
Data
• Participant burden
• Costs
Acknowledgments• Collaborators: Donna Spruijt-Metz, Ph.D (USC)
Mary Ann Pentz, Ph.D (USC) Jennifer Wolch, Ph.D (UCB)
Audie Atienza, Ph.D (NCI/DHHS)
Abby King, Ph.D (Stanford)Abby King, Ph.D (Stanford)
Cynthia Castro, Ph.D (Stanford)
Stephen Intille, Ph.D (Northeastern)
Alex Rothman, Ph.D (Univ. of MN)
Don Hedeker, Ph.D (Univ. of IL, Chicago)
• Programming: Jennifer Beaudin (MIT)
• Project Manager: Keito Kawabata, B.A.
• Ph.D Student: Yue Liao, MPH
Acknowledgments (Cont.)
• Robert Wood Johnson Foundation #65837 (Dunton, PI)
• American Cancer Society #118283-MRSGT-10-012-01-
CPPB (Dunton, PI)CPPB (Dunton, PI)
• National Cancer Institute #R01-CA-123243 (Pentz, PI).
• National Institute of Aging #R01-AG-12358 (King, PI)
Thank You