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Marginalization in acculturation is related to objectively measured physical activity in Latina adolecents

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Marginalization in acculturation is related to objectively measured physical activity in Latina adolecents

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MARGINALIZATION IN ACCULTURATION IS RELATED TO OBJECTIVELY
MEASURED PHYSICAL ACTIVITY IN LATINA ADOLESCENTS

by

Peisheng Shi

A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOSTATISTICS)

December 2012

Copyright 2012 Peisheng Shi
ii

ACKNOWLEDGMENTS

The author wishes to acknowledge the chair of her thesis committee, Dr. Donna Spruijt-
Metz, who warmly welcomed the author to join her lab one year ago and offered valuable
and instructive advice during the whole research and writing process. The author wishes
to acknowledge the assistance and instructions from two outstanding committee
members, Dr. Stanley Azen and Dr. Chih-Ping Chou. The author wishes to acknowledge
the contributions made by all the members from Dr. Metz’s lab for their help in
preparation of the dataset. The author wishes to acknowledge the Childhood Obesity
Research Center at University of Southern California that supported this research.
iii

TABLE OF CONTENTS

ACKNOWLEDGMENTS .................................................................................................. ii

LIST OF TABLES ............................................................................................................. iv

ABBREVIATIONS ............................................................................................................ v

ABSTRACT ....................................................................................................................... vi

INTRODUCTION .............................................................................................................. 1

METHODS ......................................................................................................................... 4
Participants ...................................................................................................................... 4
Protocol ........................................................................................................................... 5
Measures.......................................................................................................................... 5
Accelerometry data ...................................................................................................... 5
Self-Reported Physical Activity, the 3-Day Physical Activity Recall ........................ 6
Acculturation ............................................................................................................... 7
Motivation for Physical Activity ................................................................................. 8
Statistical Analysis .......................................................................................................... 9

RESULTS ......................................................................................................................... 11

DISSCUSSION ................................................................................................................. 19

CONCLUSION ................................................................................................................. 22

REFERENCES ................................................................................................................. 23

iv

LIST OF TABLES

Table 1: Population Characteristics of SANO+TRANSITION Study Participants
(N=95) ............................................................................................................................... 12

Table 2: Unadjusted correlations between physical activity and anthropometric and
psychosocial variables ...................................................................................................... 14

Table 3: Multilinear regression for the physical activity levels controlling for age,
percent of body fat ............................................................................................................ 16

Table 4: Comparison of most popular activities in non-marginalized girls and
marginalized girls from 7:00am to 12:00am ..................................................................... 18
v

ABBREVIATIONS

BMI: body mass index
PA: physical activity
MVPA: moderate-to-vigorous physical activity
SED: sedentary behavior
3DPAR: 3 day physical activity recall
Acc: accelerometry
Acc-MVPA: accelerometry measured MVPA
Acc-SED: accelerometry measured SED
3DPAR-MVPA: 3- day physical activity recall measured MVPA
3DPAR-SED: 3- day physical activity recall measured SED
SDT: self-determination theory

vi

ABSTRACT

Purpose: Although physical activity (PA) is protective against obesity, diabetes, and
several other diseases, a profound decline in PA occurs during puberty. Previous studies
have shown that this pubertal decline in PA is particularly steep in Latina females. Latina
females are at high risk for obesity, pre-diabetes, and diabetes type 2. Therefore, this
pubertal decline in PA is detrimental to their current and future health. Research on
psychosocial correlates of PA in this understudied, high-risk population is needed.
Methods: Participants were 95 Latina females, with mean age of 12.1 and mean BMI
percentile of 87.0%. Physical activity was measured objectively by accelerometry and
subjectively by 3- Day physical activity recall (3DPAR). Measures also included body fat
by air plethysmography, motivation for physical activity, and acculturation status.
Regression models were used to evaluate the associations between PA and psychosocial
measures, correcting for age and percent of body fat.
Results: Participants accrued an average of 31.4 minutes of moderate to vigorous
physical activity (MVPA) per day as measured by accelerometry. Girls who reported
higher marginalization accrued significantly more accelerometry measured MVPA per
day (P <0.0001). Girls who reported higher identified regulation accrued significantly
more self reported MVPA per day (P = 0.04).
Conclusions: Higher marginalization was related to higher objectively measured MVPA.
Latina females who report higher marginalization may be less affected by cultural
vii

stereotypes of active females, and less influenced by the culture-specific barriers to PA
that females continue to experience. Higher identified regulation was related to higher
MVPA as measured by 3DPAR. Given Latina girls collectivist cultural background,
opinions and information received from trusted authorities might influence behavior more
than intrinsic, personal values. Interventions may need to take culture-specific factors
into account and include family and peers social support.

1

INTRODUCTION

Physical inactivity throughout childhood and adolescence has been identified as a risk
factor for the epidemic of childhood obesity in the United States (Dugan 2008). Obesity
during adolescence has been associated with multiple health risks in youth and adults,
including hypertension, cardiovascular disease, type 2 diabetes, and several types of
cancer (PinhasHamiel and Zeitler 1996; Calle and Kaaks 2004; Lawman, Wilson et al.
2011; Spruijt-Metz 2011). Evidence shows that regular physical activity is an important
contributor to the prevention and reduction of these adverse health outcomes (Deforche,
De Bourdeaudhuij et al. 2004). In spite of the metabolic and psychosocial benefits of
participating in regular physical activity (Lambourne and Donnelly 2011), 58% of
children (6-11yr) and 92% of adolescents (12-19 yr) fail to meet the CDC
recommendations (60 minutes/day of at least moderate-intensity activity) (Troiano,
Berrigan et al. 2008). Previous studies have shown that physical activity declines
dramatically during adolescence (Kimm, Glynn et al. 2002) particularly in Hispanic girls
(Gordon-Larsen, Adair et al. 2002). This pubertal decline in physical activity is related to
a physically inactive lifestyle in adulthood, and has a negative effect on children’s future
health (Matton, Thomis et al. 2006). Therefore, research on the decline in physical
inactivity among Hispanic/Latina girls is needed.
The mechanisms leading to the pubertal decline in physical activity remain unclear,
however previous studies show that psychosocial factors might be an important predictor
of physical activity habits among adolescents (Jelalian and Steele 2008; Lawman, Wilson
2

et al. 2011). Constructs from the self-determination theory (SDT) have been suggested as
important correlates to physical activity in youth(Wilson, Kitzman-Ulrich et al. 2008).
The self-determination theory (SDT) provides explanation of the roles of different types
of motivation in initiating and maintaining a behavioral change (Deci and Ryan 2002).
According to SDT, an individual’s behavioral engagement is motivated differently by
different kinds of motivation, often conceptualized as a continuum from amotivation
through external, introjected, and identified regulation, with intrinsic motivation as the
final point on the continuum (Deci and Ryan 1985). In terms of physical activity,
amotivation would indicate that participants have little or no motivation to engage in
physical activity. External regulation would occur if people take part in physical activity
to meet the requirement of someone. If physical activity is undertaken to avoid guilt and
shame, this is termed introjected regulation. Identified regulation occurs when people
have been made aware the benefits of being physical active and engage in an activity to
achieve a personal important goal. Finally, when people participate in physical activity
without any pressure and purely for personal enjoyment, this behavior is considered to be
intrinsically motivated.
In minority youth, physical activity levels have been shown to be related to acculturation
(Unger, Reynolds et al. 2004). Acculturation is a multi-dimensional process that people
from one cultural group interchange the attitudes, beliefs and behaviors with another
group (Berry 1990). Berry (Berry 1997) conceptualized acculturation as having four
types of patterns, including assimilation, integration, separation, and marginalization
3

Assimilation is characterized by changing cultural orientation from native culture to host
culture. Integration is characterized by combining home culture orientation with host
culture orientation. Separation is characterized by keeping home culture orientation while
refusing to accept host culture orientation. Marginalization is characterized by alienation
from both home culture and host culture.
Despite the increasing inclusion of SDT in the study of initiation and adherence of
physical activity (Shen, McCaughtry et al. 2007; Taylor, Ntoumanis et al. 2010), there is
limited research on physical activity and its related motivational characteristics in
minority adolescents. In the current study, we examined the association between
motivation, acculturation and both subjective and objective measures of physical activity
among Latina peripubertal girls.
4

METHODS

Participants

Participants were from two related childhood obesity studies that use the same core data
collection methods and measurements: SANO (Strength and Nutrition Outcomes for
Latino Adolescents), and TRANSITIONS (Insulin Resistance and Declining Physical
Activity Levels in African American and Latina girls). These secondary analyses used
baseline data from both studies. A total of 95 Latina females had complete baseline
information including 3DPAR data, while 75 girls had complete baseline data and
complete accelerometry data.

Participants were recruited through participating clinics, schools, churches, and
community centers in Los Angeles County. To be eligible for inclusion in these
secondary analyses, participants had to be self-reported Latina females between the ages
of 8 to 18 at baseline. Inclusion criteria for the studies were: overweight or obese status
(age and gender-specific BMI ≥85th percentile according to the guidelines of Centers for
Disease Control and Prevention (Kuczmarski, Ogden et al. 2000)), or to have one
overweight parent (BMI ≥25), or one parent with diagnosed diabetes (Davis, Kelly et al.
2009; Spruijt-Metz, Emken et al. 2011). Participants were excluded if they were
previously diagnosed with a major illness, had been diagnosed with diabetes at screening
visit (assessed by fasting plasma glucose≥126 mg/dl), or under medication known to
influence metabolism, body composition, and exercise ability.
5

Both studies were conducted after receiving approval from the Institutional Review
Board at the University of Southern California. Detailed information about the study was
provided to parents and participants in English and Spanish. Written informed consent
from the parent and assent from the participant were obtained before any measurement.
Protocol

Anthropometry and questionnaire measures were taken in USC General Clinical
Research Center (GCRC) by trained professionals, or at the USC Childhood Obesity
Research Center (CORC). Weight and height were measured in triplicate and rounded to
the nearest 0.1kg and 0.1cm respectively, Body mass index was calculated as weight (kg)
divided by squared height (m) .The age and gender-specific BMI percentiles were then
determined by established Centers for Disease Control and Prevention normative curves
(Kuczmarski, Ogden et al. 2000). The mass of fat and lean tissue was measured by air
plethysmography (BodPod™). The demographic and behavioral questionnaires were
completed by participants during their visit to GCRC, or by telephone or in person after
the visit. Detailed study methods for SANO and TRANSITIONS have been discussed
elsewhere (Ventura, Davis et al. 2009; Emken, Richey et al. 2010).
Measures

Accelerometry data: Objectively measured physical activity was measured by
ActiGraph (Model GT1M; ActiGraph, LLC, Fort Walton Beach, FL). This uniaxial
6

accelerometer has been proven to be a valid and reliable device to assess physical activity
levels for adolescents (Puyau, Adolph et al. 2004). Participants were instructed to wear
the accelerometer on the right hip for seven days during waking time and take it off when
bathing and swimming. A SAS program for transferring NHANES physical activity
monitor data was used to process the accelerometer data
(http://riskfactor.cancer.gov/tools/nhanes_pam). Minutes of wear and non-wear were
identified from the raw data. A valid day of wear is defined as a day with a total of 10 or
more hours of wearing time. Participants with at least four valid days of wear were
included in analyses (n=78) (Troiano, Berrigan et al. 2008). The cut point for moderate to
vigorous physical activity (MVPA: >=4 METs MET: metabolic equivalents) was defined
by Freedson et al (Freedson, Pober et al. 2005), and the cut point for sedentary activity
(SED) was defined by Matthews et al (Matthews, Chen et al. 2008). Both criteria have
been validated in adolescents. The total minutes spent in MVPA and SED was
determined by summing minutes of each respective activity in all valid days where the
count met the criterion. The mean minutes per day spent in different levels of activity
were obtained by averaging the total minutes across all valid days.

Self-Reported Physical Activity, the 3-Day Physical Activity Recall: Three day
physical activity recall (3DPAR) was used to assess self-reported physical activity and
gather contextual data on type of activity that cannot be gathered by accelerometry as
accelerometry data is currently being used (Hsu, Belcher et al. 2011). 3DPAR has been
validated in female youth (Pate, Ross et al. 2003) and widely used in studies that include
7

Hispanic children. Participants were provided with a list of 71 activities and instructed to
describe their activity in blocks of 30 minutes during a day from 7:00 a.m. to 12:00 a.m.
for three consecutive days and rated the intensity of each activity (light, moderate, hard,
or very hard). Activity types were converted into half hour blocks of light, moderate, or
vigorous physical activity using a combination of the compendium of physical activities
(Ainsworth, Haskell et al. 2000) and intensity ratings provided by participants. According
to the equation provided by Weston et al (Weston, Petosa et al. 1997), each of the half-
hour blocks was assigned a rate of relative energy expenditure based on MET levels
obtained. MVPA (>=4METs) for self-reported activities was then generated using the
same criterion as accelerometry variable. Half hours spent watching TV/movie, playing
video games, and surfing the internet was coded separately as sedentary activities (SED)
(Pate, Ross et al. 2003). Total minutes spent in MVPA and SED was calculated by
summing the corresponding 30-minute blocks. Mean minutes per day for each level of
activity was obtained by averaging the total minutes across 3 days. Participants
completed 1 day of the 3-day physical activity recall questionnaire during the first GCRC
visit. Data for the other two days were collected either over telephone or through home
visit by trained staff during the week after first GCRC visit.

Acculturation: Among the most widely used acculturation scales, some are language-
based single dimension scale (Negy and Woods 1992), some are too complicated to apply
in adolescent surveys (Hazuda, Haffner et al. 1988), and some are designed for a specific
ethnic group (Marín and Gamba 1996; Garrett and Pichette 2000; Klonoff and Landrine
8

2000). Here, acculturation was measured by The Acculturation, Habits, and Interests
Multicultural Scale for Adolescents (AHIMSA) (Unger, Gallaher et al. 2002). The
AHIMSA was developed specifically for multiethnic adolescent research was used to
measure acculturation (Unger, Gallaher et al. 2002). The AHIMSA contains 8 items and
generates four subscales: Assimilation (United States Orientation), Separation (Home
Country Orientation), Integration (Both Countries Orientation), and Marginalization
(Neither Country Orientation). An example of an item from the AHIMSA scale is: “I am
most comfortable being with people from….”, with response options for this and all
items from the scale including: “The United States”, “The country my family is from”,
“Both”, and “Neither”. The acculturation orientation of participants was determined by
the sum of each category of responses, with item responses of “United States” indicating
Assimilation, item responses of “The country my family is from” indicating Separation,
item responses of “Both” indicating Integration, item responses of “Neither” indicating
Marginalization. AHIMSA has been validated in Hispanic adolescents (Unger, Gallaher
et al. 2002)

Motivation for Physical Activity: Motivation for physical activity was measured by the
Exercise Self-Regulation Questionnaire (SRQ-E) (Williams, Grow et al. 1996; Williams,
Minicucci et al. 2002). The SRQ-E contains 16 questions and employs a 3-point Likert
scale response format ranging from ”not at all true” to “very true.” This scale assesses
four types of motivation of regular exercise: external regulation, introjected regulation,
identified regulation, and intrinsic motivation. SRQ-E was derived from Perceived Locus
9

of Causality Scale (PLOC) (Ryan and Connell 1989), and revised in context of physical
activity for children by Goudas et al (Goudas, Biddle et al. 1994). SRQ-E has been used
in minority youth extensively by our group. The internal reliability of each SRQ-E
subscale was acceptable in our sample (External Regulation: α=0.69, Introjected
Regulation: α=0.64, Identified Regulation α=0.83, Intrinsic Motivation α=0.72.)
Statistical Analysis

Four sets of analyses were conducted among the analytical sample (n = 95). First, the
demographic and psychosocial variables were compared between those with and without
accelerometer-measured physical activity data using independent sample t-tests or χ
2

tests. Second, univariate analyses between each physical activity level and demographic
characteristics were performed to examine the linear correlation. Third, multivariate
regression analyses were performed to examine the association between physical activity
acculturation, and motivation for physical activity. Potential confounding factors
including age, percent of body fat were included in all models. The fourth analysis was
conducted in order to further elucidate findings from the multivariate regression. Using
self-reported physical activity data, a count of each activity item across ½ hour time units
was generated. The total count for each activity item was calculated as summing the
count for each time interval across the day. Ad hoc analyses were used to explore
differences in types of activity by marginalization status. The percent of time that the
participant spent on each activity during their waking time was calculated by dividing the
count for each activity by the total count of all the activities across three days. Separate
10

tables for the activity count were generated for girls who reported some feelings of
marginalization (Marginalization scores not equal to zero) as opposed to those who
reported no feelings of marginalization (Marginalization score equal to zero). An analysis
that compares the top twenty activities in marginalized girls and non-marginalized girls
were conducted to find out whether there is any potential difference in activity preference
between the two groups. All analyses were conducted using SAS v.9.2 (SAS Institute,
Cary, NC) at the 0.05 significance level.

11

RESULTS

Table 1 shows the baseline population characteristics of participants included in analytic
sample. No statistical differences in BMI percentile, percent fat mass, percent lean tissue
mass, SRQ-E scores and AHIMSA scores were found between participants with and
without four days of valid accelerometry data. However, those who provided four days of
valid accelerometry data were younger (P=0.01) and were in lower pubertal tanner stages
(P=0.04). Objectively measured MVPA and sedentary behavior were 31.4 minutes/day
and 467.0 minutes/day, respectively. Self-reported MVPA and sedentary behavior were
93.1 minutes/day and 192.8 minutes/day, respectively. Variance in the self-report
measures was considerably larger than in the objective measures.

12

Table 1: Population Characteristics of SANO+TRANSITION Study Participants
(N=95)
All(N=95) With
Accelerometer
data(N=75)
Without
Accelerometer
Data(N=20)
P
a

Age, mean±SD 12.1 ± 3.1 11.7 ± 3.0 13.89 ± 3.0 0.01
BMI Percentile, mean±SD 87.0 ± 20.0 86.9 ± 19.7 87.3 ± 21.9 0.94
BMI Cut Point, n (%)

0.71
Under Weight 1 (1.0) 1 (1.3) 0 (0.0)

Normal 21 (22.1) 18 (24.0) 3 (15.0)

Overweight 22 (23.2) 16 (21.3) 6 (30.0)

Obese 51 (53.7) 40 (53.3) 11 (55.0)

Pubertal Tanner stage, n (%)

0.04
1 27 (28.4) 24 (32.0) 3 (15.0)

2 23 (24.2) 21 (28.0) 2 (10.0)

4 14 (14.7) 10 (13.3) 4 (20.0)

5 31 (32.6) 20 (26.7) 11 (55.0)

Percent Body Fat, mean±SD 32.5 ± 11.2 32.3 ± 11.0 33.5 ± 12.1 0.67
AHIMSA Score, mean±SD

Assimilation 2.5 ± 2.0 2.7 ± 2.1 2.0 ± 1.6 0.16
Integration 3.8 ± 2.3 3.9 ± 2.3 3.7 ± 2.5 0.78
Marginalization 0.4 ± 1.1 0.3 ± 0.6 0.9 ± 2.0 0.19
Separation 1.2 ± 1.6 1.2 ± 1.5 1.4 ± 1.9 0.63
SRQ-E Score, mean±SD

External Regulation 1.8 ± 1.1 1.8 ± 1.1 1.9 ± 1.2 0.75
Introjected Regulation 2.3 ± 1.2 2.3 ± 1.2 2.4 ± 1.2 0.66
Identified Regulation 4.8 ± 1.6 4.8 ± 1.6 4.8 ± 1.8 0.95
Intrinsic Motivation 4.8 ± 1.6 4.9 ± 1.6 4.4 ± 1.6 0.23
Acc- MVPA - 31.4 ± 21.1 - -
Acc- SED - 467.0 ± 96.0 - -
3DPAR-MVPA 93.1 ± 75.4 96.9 ± 74.2 78.8 ± 79.9 0.34
3DPAR- SED 192.8 ± 125.0 187.7 ± 107.7 211.8 ± 177.9 0.57

a
χ
2
tests or Fisher exact test were used for categorical variables and independent t-tests for continuous variables.

13

Table 2 shows correlations between objectively measured and self-reported physical
activity data, anthropometric measures and psychosocial scores. Based on the
accelerometer data, the mean minutes spent in moderate to vigorous physical activity
correlated negatively with age, percent of fat mass, and participant’s integration score
(All P<0.01) and correlated positively with participant’s marginalization score (P<0.01)
and participant’s external regulation score (P=0.01). The mean minutes spent in sedentary
behavior correlated negatively with marginalization score (P=0.01), and external
regulation score (P<0.01) and correlated positively with age, percent of fat mass, and
participant’s integration score (All P<0.01). Based on the three day physical activity
recall data, the mean minutes spent in moderate to vigorous physical activity correlated
negatively with age (P<0.01). The mean minutes spent in sedentary behavior correlated
negatively with participant’s integration score (P=0.04) and correlated positively with
participant’s marginalization score (P=0.02). Subjective and objective measures of
MVPA showed a significant positive correlation (r = 0.38, P<0.01). There was no
association between subjective and objective measures of sedentary behavior (r = -0.13,
P=0.28).

14

Table 2: Unadjusted correlations between physical activity and anthropometric and
psychosocial variables

Acc-MVPA Acc-SED 3DPAR-MVPA 3DPAR-SED

r P-value r P-value r P-value r P-value
Age -0.69 <0.01 0.48 <0.01 -0.27 <0.01 -0.11 0.24
BMI Percentile -0.33 <0.01 0.26 0.02 -0.02 0.87 -0.01 0.90
BMI Cut Point -0.39 <0.01 0.30 0.01 -0.12 0.23 -0.03 0.72
Pubertal Tanner stage -0.65 <0.01 0.54 <0.01 -0.28 0.01 -0.08 0.41
Total fat mass -0.62 <0.01 0.40 <0.01 -0.20 0.05 -0.08 0.46
Percent fat mass -0.59 <0.01 0.37 <0.01 -0.14 0.17 -0.06 0.57
Total lean tissue mass -0.63 <0.01 0.50 <0.01 -0.17 0.10 -0.09 0.39
Percent lean tissue mass 0.59 <0.01 -0.37 <0.01 0.14 0.17 0.06 0.57
AHIMSA Score

Assimilation 0.20 0.09 -0.22 0.06 0.13 0.21 0.07 0.52
Integration -0.36 <0.01 0.34 <0.01 -0.08 0.42 -0.22 0.04
Marginalization 0.51 <0.01 -0.29 0.01 -0.17 0.09 0.24 0.02
Separation 0.10 0.39 -0.11 0.32 0.09 0.36 0.09 0.37
SRQ-E Score

External Regulation 0.31 0.01 -0.32 <0.01 0.08 0.47 0.14 0.17
Introjected Regulation 0.02 0.87 -0.13 0.27 -0.03 0.76 0.11 0.31
Identified Regulation 0.04 0.77 -0.07 0.54 0.14 0.17 -0.05 0.63
Intrinsic Motivation 0.13 0.25 -0.10 0.40 0.05 0.64 -0.07 0.52
Acc-MVPA
- - - - - - - -
Acc-SED -0.45 <0.01
- - - - - -
3DPAR-MVPA 0.38 <0.01 -0.29 0.01
- - - -
3DPAR-SED -0.04 0.76 -0.13 0.28 -0.27 <0.01
- -

15

Table 3 shows the results of linear regression analysis for physical activity. After
adjusting for age and percent of fat mass, intrinsic motivation for physical activity was
not a significant predictor of either the MVPA or SED. Identified motivation was a
significant predictor of self-reported MVPA (β = 14.2, P=0.04). Marginalization scores
significantly predicted accelerometry measured MVPA (β = 12.6, P<0.0001), and self-
reported SED (β = 29.1, P=0.02). Marginalization score also trended toward significance
as a predictor of accelerometry measured SED (β = -12.8, P=0.08) and self-reported
MVPA (β = -31.4, P=0.10). Assimilation was not a significant predictor any of the four
models.
16

Table 3: Multilinear regression for the physical activity levels controlling
for age, percent of body fat

Acc-MVPA Acc-SED

β
a
P-value

β
a
P-value
Age -3.3 <0.0001

11.0 0.02
Percent of body fat -0.4 0.05

0.9 0.49
Assimilation 0.0 0.99

-4.2 0.42
Marginalization 12.6 <0.0001

-31.4 0.10
Intrinsic Motivation -1.2 0.43

6.3 0.51
Identified Motivation 0.8 0.58

-7.3 0.42

Overall Model

<0.0001

<0.005

3DPAR-MVPA

3DPAR-SED

β
a
P-value

β
a
P-value
Age -7.2 0.03

-3.9 0.50
Percent of body fat -0.1 0.90

0.2 0.91
Assimilation 1.3 0.74

4.9 0.49
Marginalization -12.8 0.08

29.1 0.02
Intrinsic Motivation -12.3 0.10

-8.3 0.51
Identified Motivation 14.2 0.04

1.6 0.89

Overall Model <0.005 0.30
a
β coefficients are unstandardized.

17

Table 4 shows the comparison of most popular activities between marginalized
participants (AHIMM>0) and non-marginalized participants (AHIMM=0) during the day.
Sitting in class and watching TV/Movie are the top two activities during waking time,
followed by hanging around, eating a meal, riding in a car/bus, doing house chores and
doing homework. Participants who reported some marginalization spent more time in
lunch/free time/study hall, working , watching TV or movie, doing homework, sleeping,
and less time in walking, hanging around, playing video games/ surfing internet, sitting in
class and riding a car/bus compared to participants who reported no feelings of
marginalization.

18

Table 4: Comparison of most popular activities in non-marginalized
girls and marginalized girls from 7:00am to 12:00am

AHIMM>0 %

Sleeping 17.77

Sitting in class 15.34

Watching TV or movie 11.40

Hanging around 5.58

Eating a meal 5.40

Riding in a car/bus 4.31

Playing video games/ surfing Internet while sitting 4.08

Doing house chores 2.87

Travel by walking 2.85

Homework 2.78
AHIMM=0

Sleeping 19.16

Sitting in class 14.02

Watching TV or movie 13.05

Eating a meal 4.73

Homework 4.18

Hanging around 3.81

Lunch/free time/study hall 3.68

Riding in a car/bus 3.03

Doing house chores 2.94

Working (e.g., part-time job, child care) 2.62

Rank of AHIMM=0 minus AHIMM>0

Travel by walking 1.97

Hanging around 1.77

Lunch/free time/study hall -1.75

Working (e.g., part-time job, child care) -1.72

Watching TV or movie -1.66

Playing video games/ surfing Internet while sitting 1.60

Homework -1.40

Sleeping -1.40

Sitting in class 1.32
Riding in a car/bus 1.28
19

DISSCUSSION

The current study examined the effect of motivation and acculturation on objectively and
subjectively measured physical activity in Latina adolescent females. The results using
objectively measured physical activity indicated that higher marginalization was
significantly associated with more minutes spent in MVPA. Higher marginalization was
also significantly associated with more minutes spent in sedentary behavior as measured
by self-report. The associations held after controlling for age and percent body fat. These
findings might seem counterintuitive; wouldn’t more time spent in physical activity lead
to less time spent in sedentary pursuits? However, recent literature suggests that more
time spent in moderate to vigorous activity doesn't necessarily lead to less time spent in
sedentary activity (Uijtdewilligen, Nauta et al. 2011). Thus, marginalized girls seem to be
participating in more activity as well as more self-reported sedentary behaviors. Previous
findings on the relationship between assimilation and physical activity in 6
th
and 7
th
grade
Hispanic adolescent have suggested that assimilation to American culture was associated
with lower frequency of participating in physical activity (Unger, Reynolds et al. 2004).
However, this association was not found in our study. These seemingly inconsistent
findings may be due to the differences in activity measures between the two studies.
Unger et al measured frequency of participation by brief questionnaire, while we
measured intensity using both objective and in-depth self-report measures.

20

As seen in Table 4, Marginalized girls spend more time in lunch/free time/study hall,
working, watching TV or movie, doing homework, sleeping, and less time in walking,
hanging around, playing video games/ surfing internet, sitting in class and riding a
car/bus compared to marginalized girls. This result is consistent with the findings above
that one's acculturation status is an important predictor of physical activity habits. In the
Hispanic culture, females are not generally encouraged to be active (Amesty 2003). Thus,
this finding can be interpreted as girls who are neither United States oriented nor home
country oriented are less affected by barriers in home culture that discourage intense
physical activity in adolescent females.
Using self-reported physical activity data, we found that higher identified regulation was
associated with significantly more minutes spent in MVPA, while intrinsic motivation
was unrelated to MVPA. Identified regulation reflects the degree to which information
gleaned from others on the importance and benefits associated with physical activity are
recognized. The Hispanic/Latino culture is considered a collectivist culture, in which the
opinions of trusted authorities are highly valued (Iyengar and Lepper 1999; Raeff,
Greenfield et al. 2000). Therefore, opinions and information received from trusted
sources such as teachers and family members might influence behavior more than
intrinsic, personal values.

A significant strength of our study is the combined use of both objective and subjective
measures of physical activity. Accelerometry provides relatively accurate measurement
of activity intensity under most circumstances, but to date, accelerometers are not
21

waterproof and thus do not capture swimming, nor are they particularly accurate at
capturing such activities as bicycling or weight lifting. Accelerometry currently does not
capture contextual variables such as type or location of activity (Hsu, Belcher et al.
2011). On the other hand, self-report by activity recall offers detailed information on the
types of activity participants undertake during their waking hours, while it is unable to
provide intensity information as accurately as accelerometer data. A combination of both
measurement types provides a more complete picture of participants’ physical activity.
Another strength of our study is the focus on Latina adolescent females and the inclusion
of participants from a wide age-range. In addition, we used a fine-grained measurement
of acculturation. Most of the previous studies adopt a proxy measure of acculturation by
using language preferences. The proxy measure regards acculturation as a single
dimensional process. The AHIMSA, used here, provides more information on this
complicated multidimensional process.

The current study has several limitations that should be addressed by future research.
Only 75 children provided acceptable accelerometer data. A larger sample size might be
necessary to study the relationship between motivation and physical activity. Finally,
because our study focused on Latina girls; it might not generalize to minority females
from other ethnicities or to boys.

22

CONCLUSION

The results of the current study show that acculturation status is related to physical
activity and sedentary behavior in Latina females. Latina females are at high risk for
obesity, Type 2 diabetes, and other metabolic disease (Narayan, Boyle et al. 2003;
Ogden, Carroll et al. 2010). Therefore, identifying and removing culture-specific barriers
to physical activity is essential in designing successful interventions targeted at Latina
females, as is identifying cultural strengths that can be capitalized upon to tailor
interventions. Given the Hispanic collectivist cultural background, developing health
promotion programs for Latina girls should include support from family and peers.

23

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Abstract (if available)

Abstract Purpose: Although physical activity (PA) is protective against obesity, diabetes, and several other diseases, a profound decline in PA occurs during puberty. Previous studies have shown that this pubertal decline in PA is particularly steep in Latina females. Latina females are at high risk for obesity, pre-diabetes, and diabetes type 2. Therefore, this pubertal decline in PA is detrimental to their current and future health. Research on psychosocial correlates of PA in this understudied, high-risk population is needed. ❧ Methods: Participants were 95 Latina females, with mean age of 12.1 and mean BMI percentile of 87.0%. Physical activity was measured objectively by accelerometry and subjectively by 3- Day physical activity recall (3DPAR). Measures also included body fat by air plethysmography, motivation for physical activity, and acculturation status. Regression models were used to evaluate the associations between PA and psychosocial measures, correcting for age and percent of body fat. ❧ Results: Participants accrued an average of 31.4 minutes of moderate to vigorous physical activity (MVPA) per day as measured by accelerometry. Girls who reported higher marginalization accrued significantly more accelerometry measured MVPA per day (P <0.0001). Girls who reported higher identified regulation accrued significantly more self reported MVPA per day (P = 0.04). ❧ Conclusions: Higher marginalization was related to higher objectively measured MVPA. Latina females who report higher marginalization may be less affected by cultural stereotypes of active females, and less influenced by the culture-specific barriers to PA that females continue to experience. Higher identified regulation was related to higher MVPA as measured by 3DPAR. Given Latina girls collectivist cultural background, opinions and information received from trusted authorities might influence behavior more than intrinsic, personal values. Interventions may need to take culture-specific factors into account and include family and peers social support.

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Asset Metadata

Creator Shi, Peisheng (author)

Core Title Marginalization in acculturation is related to objectively measured physical activity in Latina adolecents

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Publication Date 06/05/2012

Defense Date 05/08/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag acculturation,OAI-PMH Harvest,physical activity

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Spruijt-Metz, Donna (committee chair), Azen, Stanley P. (committee member), Chou, Chih-Ping (committee member)

Creator Email jinglingsps@hotmail.com,peishens@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-45120

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