slides - Curry School of Education

Report
Adolescent Social
Networks, Physical
Activity and Other Health
Behaviors
John R Sirard
10.20.2011
Overview
1. Background
2. Social factors related to physical activity (PA)
3. Social Networks, PA and Screen Time (crosssectional results)
4. Future Directions (Social Network Analysis and
Health – NIH FOA)
1. Background
• Physical Activity: Any bodily movement
produced by the contraction of skeletal
muscle that increases energy expenditure
above a basal level.
– Not just sports and planned exercise
– A behavior (not fitness or other physiological
outcomes)
% meeting PA recommendation: > 60 min of moderatevigorous physical activity per day
% meeting recommendation
30
25
20
15
Males
Females
10
5
0
9th
10th
11th
Grade
12th
PA Prevalence - A New Challenge
(Troiano et al. 2008)
• 2009 YRBSS : 18.4% (Males: 24.8%; Females: 11.4%)
• Measured by accelerometer…
• % meeting PA recommendation: > 60 min of moderatevigorous physical activity per day
PA and Health in Youth
•
•
•
•
Lipid profile
Blood Pressure
Overweight and obesity
Metabolic Syndrome
• CVD risk factors
• Bone health
• Depression, anxiety, mood, self-esteem
• QOL and fatigue of cancer survivors
PA and Other Health Behaviors
Trost SG. Physical Education,
Physical Activity and
Academic Performance. Fall
2007 Research Brief. Active
Living Research (RWJF)
• “Students whose time in PE or school-based physical activity
was increased maintained or improved their grades and
scores on standardized achievement tests, even though they
received less classroom instructional time than students in
control groups”.
2. Social Factors Related to PA
Attributes of Self
Genetics.
Self Efficacy impacts
motivation and attempts to
alter, modify, and maintain
health behaviors.
Policy
Systemic & Policy Factors
Unhealthy foods easily
accessible, heavily marketed
to children
Healthy foods are more costly
and difficult to obtain.
Urban sprawl, lack of
pedestrian infrastructure
Diet
Unhealthy
dietary
behaviors; low
intake of fruits
and vegetables
Physical
Activity
Lack of PA
and excess
sedentary
activities
Core
Relationships
Family, peers,
authority figures
offering different
types of support
for healthy
behaviors
Intra-personal &
Behaviors
Inter-personal
Environment
Extreme climates,
walking or biking trails,
public gyms and pools,
public transportation,
School/work environments
Behavior settings
Technology
Labor saving devices, automobiles,
communication technology
Cultural, Religious & Family Values
Reasons for eating; the quantity,
quality, and types of foods eaten;
gender roles
Social Factors and PA
• Social Support for Physical Activity
– Emotional Support (encourages you to do physical
activity)
– Informational Support
– Tangible Support (do physical activities with you, pay
for equipment / fees)
• Perceived support for PA from family and friends
positively associated with adolescent PA
– typically
Social Networks
Social Networks and Obesity
• Christakis and Fowler (NEJM 2007)
– Framingham Cohort Study with 30 years of followup
• “…obesity appears to spread through social ties”.
– National Longitudinal Study of Adolescent Health
• Strauss 2003, Trogdon 2008
• Challenges to analysis and interpretation (Lyons
2011)
3. Social Networks, PA and Screen
Time (cross-sectional results)
Project EAT 2010
• Eating and Activity among Teens
– New cohort of 2,793 middle and high school students
from 20 Mpls/St. Paul middle schools and high
schools (2009-2010)
– Weight-related behaviors (PA, screen time, diet,
weight control practices, disordered eating) and
– Related multi-level correlates (individual, peer, family,
school, neighborhood)
– Main “outcome”: weight status
Social Networks and PA in Project EAT 2010
• Purpose: To examine how
friends’ PA and screen time is
related to an individual
adolescent’s PA and screen time
by using data from nominated
friends
Best
Male
friend
Male
friend 2
Male
friend 3
• Hyp: Associations between ego
and friend PA and screen time
would be strongest between
same gender and weakest for
opposite gender associations.
• Hyp: Associations between ego
and friend behaviors would be
stronger for high school versus
middle school students.
Best
Female
friend
Female
friend 2
Female
friend 3
Example of network connections at one middle school. Triangles
indicate Girls, Circles indicate Boys, the numbers in the symbol
represent the grade level.
How many friends are enough?
• Sensitivity Analyses
1. Data from all 6 nominated friends (n=251)
•
From smaller, lower income, more ethnically diverse
schools – not representative of larger Project EAT sample
2. Data from all but one nominated friend (n=585)
•
Also not representative of larger sample
3. Data from at least 1/3 of nominated friends
(n=1655)
4. Data from at least one nominated friend (n=2126)
Table 1: Sample characteristics for n= 2126 Egos with any friends in dataset.
Values are mean+SD or n (percent)
Age (years)
Grade
Middle School (n=1114)
High School (n=1012)
Ethnicity group
White (n=426)
African American/Black (n=567)
Latino/Hispanic (n=373)
Asian American (n=409)
Native American (n=87)
Mixed/Other (n=264)
Socioeconomic Status
Missing (n= 64)
Low (n=611)
Low-middle(n=526)
Middle(n=717)
Upper-Middle + High (n=208)
Males (n=983)
14.29±2.01
Females (n=1143) p-value
14.11±1.88
0.03
504 (51.3)
479 (48.7)
610 (53.4)
533 (46.6)
0.34
224 (22.8)
260 (26.5)
164 (16.7)
195 (19.8)
40 (4.1)
100 (10.2)
202 (17.7)
307 (26.9)
209 (18.3)
214 (18.7)
47 (4.1)
164 (14.4)
0.01
31 (3.2)
286 (29.1)
249 (25.3)
320 (32.6)
33 (2.9)
325 (28.4)
277 (24.2)
397 (34.7)
97 (9.8)
111 (9.7)
0.61
Table 1 (cont’d): Sample characteristics for n= 2126 Egos with any
friends in dataset. Values are mean+SD or n (percent)
Males (n=983)
Weight Status *
Not overweight (n=1272)
Overweight (n=854)
Physical Activity (hrs/wk)
Screen Time (hrs/wk)
Females (n=1143) p-values
566 (57.6)
417 (42.4)
706 (61.8)
437 (38.2)
0.67
6.83±4.84
45.15±28.85
4.98±4.40
36.51±23.92
<.001
<.001
Number of friends
2.64+sd
2.62+sd
0.XX
Number of Male friends
1.49+sd
1.01+sd
0.XX
Number of Female friends
1.15+sd
1.61+sd
0.XX
Overweight status is defined as > 85th percentile on the Centers for Disease Control and
Prevention’s age and gender specific Body Mass Index curves
BMI = kg / m2
Table 2. Estimated change in male’s physical activity or screen
time for every 1 hour increase in friends’ mean (or best
friend’s) physical activity or screen time
Male Ego
Physical Activity
p-value
Screen Time
p-value
Male
Friends
Female
Friends
Male Best
Friend
Female Best
Friend
n=838
n=668
n=483
n=389
0.059 (.037) 0.114 (.042)
0.111
0.007 *
?
0.061 (.042) 0.034 (.045)
0.149
0.448
0.024 (.042) 0.111 (.054) -0.025 (.043) 0.110 (.072)
0.559
0.038
0.558
0.129
Adjusted for Ego’s age, SES, race/ethnicity, BMI, and number of friends
available
* Significant interaction with school level (middle vs. high school)
Table 3. Estimated change in female’s physical activity or screen
time for every 1 hour increase in friends’ mean (or best
friend’s) physical activity or screen time
Female Ego
Physical Activity
p-value
Screen Time
p-value
Male
Friends
n=723
Female
Friends
n=1007
0.122 (.025) .182 (.0439)
Male Best
Friend
n=404
Female Best
Friend
n=612
.086 (.026)
.128 (.052)
<.0001
<.0001
0.0009
0.0142 *
.097 (.039)
.091 (.043)
-.010 (.032)
.022 (.056)
0.012
0.032
0.75
0.696
Adjusted for Ego’s age, SES, race/ethnicity, BMI, and number of friends
available
* Significant interaction with school level (middle vs. high school)
Strengths and Limitations
• Strengths
– Large, diverse sample
– Novel method and analyses
• Limitations
– Cross-sectional
– Incomplete network data
Conclusions
• Several associations between adolescent and
friend PA and screen time
• Consistent associations for female PA support
previous research on youth PA and sports
participation
• Limited associations between Male’s PA and
screen time vs. friends’ PA and screen time
4. Future Directions
NIH Funding Opportunity
Announcement
• PAR-10-145, “Social Network Analysis and
Health”
• Mechanism: R01
• Due: May 11, 2012
• Start: April 2013
Overall Research Goal
• Do changes in friend networks from 8th through 10th
grade predict changes in weight-related behaviors of
individuals as well as sub-networks (cliques) within a
school?
– Longitudinal, multiple time points
– More complete network data
• Include multi-level predictors of PA and screen time as
mediators of the hypothesized behavior changes
– individual-level psychosocial factors, family/home
environment, school, social and physical environments of
neighborhood.
Possible Research Questions
• How are these weight-related behaviors transmitted within schoolbased peer networks?
– If an active person in 8th grade becomes friends with a group of
inactive individuals in 9th grade, does this individual become less
active themselves or do they maintain their activity level?
– Does the network become more like the new friend or is there no
effect?
– Why?
– What role do PA, sport, TV viewing, video games play in selecting new
friend(s)
– What are the characteristics of the individual and those in the network
and how do those factors mediate behavior change of the individual or
the network?
poorly written
Specific Aims
1. To determine if changes in an individual’s social network
(to a network with different weight-related behaviors)
results in changes in the individual’s weight-related
behavior that is similar to the network’s behavior
2. To determine if changes in the weight-related behaviors of
an individual(s) within a clique influences the behaviors of
others in the clique.
3. To identify the…
A.
B.
Individual and network characteristics that promote, maintain,
or increase PA within the network
Individual and network characteristics that promote, maintain,
or increase screen time within the network
Additional Research Questions
(School-level Analysis)
• Does the “spread” of student athletes within non-sports
clubs/organizations and across sub-networks (cliques)
affect the overall physical activity level of the school?
• Collect rosters for school-based sports teams and selected
formal non-sports clubs (e.g., student government,
newspaper, yearbook, band).
– Hyp: School-level physical activity will be greater in schools with
a greater “spread” of student athletes within non-sport
groups/clubs, and across cliques due to the added exposure of
other students to those student athletes, compared to schools
where student athletes stay within limited insular cliques.

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