This study examined differences in motivation and physical activity levels between adolescents with visual impairments living in a residential school who met or exceeded healthy BMI criteria compared to those who did not. Those meeting BMI criteria had higher levels of external and introjected motivation but similar physical activity levels and amotivation scores. While physical activity levels were generally adequate, many students did not meet recommended daily amounts. Motivation profiles provide insight for encouraging active free time choices in this environment.

1. Copyright © 2006 Heldref Publications
Motivation and Physical
Activity in Adolescents With
Visual Impairments
FRANCIS M. KOZUB
The worldwide public health concerns about inactivity and obesity (Vincent, Pan-
grazi, Raustorp, Tomson, & Cuddihy, 2003) apply equally to individuals with visual
impairments. Although residential schools for children with visual impairments are
charged with providing both educational and leisure curriculums, the children spend
more time in structured academic activities than in less structured free time.
Background
To identify appropriate free-time experiences, it is important to know what motiva-
tional factors predict physical activity decision making (Kosma, Cardinal, & Rintala,
2002). Researchers have successfully used self-determination theory as a framework
for studying free-time motivation in adolescents (Baldwin & Caldwell, 2003). This the-
oretical framework contains intrinsic and extrinsic motivators that potentially predict
free-time decision making (Deci & Ryan, 1985). Models suggest multiple types of
motivation existing on a continuum from less to more self-determined. Intrinsic rea-
sons for participating in free-time activities include factors related to personnel enjoy-
ment. Obtaining rewards, avoiding negative consequences, or achieving competence in
social settings are extrinsic motivators (Baldwin & Caldwell, 2003). Self-determina-
tion theorists also describe amotivation, the counterproductive influence that depicts
individuals who have neither intrinsic nor extrinsic reasons for free-time decision mak-
ing (Baldwin & Caldwell; Vallerand, 2001).
149

2. 150 RE:view
Deci and Ryan (1985) found that individuals with visual impairments have levels of
intrinsic and extrinsic motivation and amotivation that influence their use of free time
and lead to adaptive or maladaptive outcomes. In physical activity, an adaptive out-
come refers to attaining levels of movement that lead to physical development that pro-
duces higher levels of independence. A maladaptive outcome is characterized by long
periods of inactivity during free time, creating lower physical skills and health-related
concerns. Inactive individuals with visual impairments, lacking motivation to engage
in physical activity, become dependent members of society who rely on others for suc-
cess in navigating the community (Skaggs & Hopper, 1996).
Kozub and Oh (2004) found that participants between the ages of 6 and 18 with visu-
al impairments had fewer average daily periods of physical activity at a moderate to vig-
orous level than reported in earlier studies of peers without disabilities. Other measures
of physical activity, such as pedometers, have also been used. Suzuki et al. (1991) found
that individuals with visual impairments from residential settings were less active than
their peers without disabilities. Additionally, using self-recall estimates of physical
activity, Longmuir and Bar-Or (2000) indicated that youth with visual impairments had
significantly lower physical activity levels than did peers without disabilities.
The Current Study
In this study, I explore differences in free-time motivation scores between adoles-
cents with visual impairments from a residential setting who are at criterion levels of
body mass indexes (BMIs) and their fellow students who score outside the healthy
zones, using Winnick and Short’s (1999) criterion-referenced standards. An assump-
tion of this study is that these individuals have levels of intrinsic and extrinsic motiva-
tion and amotivation that influence their use of free time and lead to adaptive or mal-
adaptive outcomes.
I also examine whether differences exist in the number of free-time minutes these
adolescents who have reached criterion levels of BMI spend at or above moderate lev-
els of activity as compared with the time spent by those who are outside the healthy
zones. I hypothesize that adolescents who have reached criterion levels of BMI have
higher physical activity counts, higher intrinsic motivation scores, lower extrinsic moti-
vation scores, and lower amotivation scores than do participants who scored outside the
healthy zones.
Methods
Participants
Students with visual impairments living in residential settings offer a unique oppor-
tunity for studying motivation, fitness, and physical activity. After-school programs at
many residential schools offer students choices of free-time activities in barrier-free

3. Volume 37, Number 4, Winter 2006 151
settings, opportunities unavailable to adolescents with disabilities who live at home.
Participants in the study were 31 students (11 women and 20 men), 12 to 21 years old,
who were involved in educational and after-school residential programs at a midwest-
ern school for the blind. All participants had residual sight but were classified as visu-
ally impaired with vision deficits that affected their educational performance. None of
the participants had any coexisting cognitive or physical disabilities.
The criterion for admission into the study was residual sight that allowed the student
to use either regular or enlarged text with assistance to complete the motivation scale.
Participants had to be enrolled in after-school residential programming during the
study. Limiting the scope of the study to these participants avoided problems associat-
ed with attempting to match individuals from integrated settings on age, gender, body
mass index (BMI), and level of vision. I secured consent to collect data and use the
results through a University Internal Review (for the protection of human subjects)
board and the superintendent of the residential school.
Procedures
The physical education staff of the school collected data, including district-wide fit-
ness testing, to isolate participants with high and low BMIs. Before the staff collected
data, they were trained on administering The Brockport Physical Fitness Test (Winnick
& Short, 1999) and the Free Time Motivation Scale for Adolescents (FTMS-A; Baldwin
& Caldwell, 2003). After the staff collected the fitness and motivation scores, I trained
them on initializing and attaching RT3 activity monitors to collect physical activity
data. To insure confidentiality, the school staff collected and coded all data before I
used them. School staff also monitored the participants’ adherence to the protocol con-
cerning wearing the monitors during after-school hours.
Students completed the FTMS-A during physical education class. All the participants
could independently circle their levels of agreement to the 20-item scale. To collect
physical activity data participants wore activity monitors fastened to their right hips
from Monday through Thursday of the same week. Staff retrieved the monitors on Fri-
day; in this way, we collected data on 4 days from dismissal at 3:00 p.m. to bedtime,
when participants removed the monitors. I sorted physical activity scores for each
minute on the basis of a criterion value calculated for each participant; by counting
only those values that fell at or above a moderate to vigorous physical activity level, I
attained a mean value for minutes per day for each participant.
Instrumentation
We used the following instruments for data collection:
Brockport Physical Fitness Test. In the Brockport Physical Fitness Test, Winnick and
Short (1999) established criterion values for body composition for adolescents with

4. 152 RE:view
visual impairments, using calf and triceps skin-fold estimates. I used this measure to
divide the group: The participants who fell within Winnick and Short’s measures were
one group (BMI) and those who were outside the healthy zone (OHBMI) were another.
Free Time Motivation Scale (FTMS-A). Baldwin and Caldwell (2003) developed the
scale1 using the constructs of self-determination theory (Ryan & Deci, 2000). FTMS-A
contains five subscales: amotivation (participation for unknown reasons), external
motivation (participation to avoid negative consequences), introjected motivation (par-
ticipation to maintain some perceived status), identified motivation (participation to
gain knowledge or skills), and intrinsic motivation (participation for pleasure). Bald-
win and Caldwell demonstrated adequate estimates of reliability for adolescents
between the ages of 12 and 15 and provided evidence of validity for the FTMS-A. The
Likert-type scaling for each of the 20 items includes five choices ranging from strong-
ly disagree to strongly agree.
Three-plane monitors. Researchers have reported using three-plane monitors to mea-
sure the physical activity of individuals with visual impairments (Kozub & Oh, 2004;
Kozub, Oh, & Rider, 2005). Earlier studies using TriTrac R3D monitors2 have demon-
strated adequate estimates of validity for long-term physical activity monitoring for
children of varied obesity levels (Kalakanis, Goldfield, Paluch, & Epstein, 2001; Jaki-
cic et al., 1998). Specific estimates of reliability (r = .90) and criterion validity (r = .89)
are found in Kozub et al. (2005) who specifically studied the use of RT3 monitors2 on
adolescents with visual impairments during physical education activities.
Data Analyses
I used descriptive statistics and displays to explore key study variables. I calculated
Cronbach’s alpha values to estimate the reliability of the FTMS-A total and calculated
subscales and chi-square tests to determine if reaching criterion levels of fitness was
dependent on age grouping or gender to rule out rival hypotheses that group demo-
graphics (other than BMI values) contributed to the results. Using multivariate analy-
sis of variance (MANOVA), I analyzed the subscale and physical activity scores for dif-
ferences between the 19 participants at criterion levels of BMI and the 12 over-healthy
BMI (OHBMI) participants.
Results
Table 1 contains means and standard deviations for key study variables, and correla-
tions between variables are found in Table 2. I decided to exclude the intrinsic motivation
subscale from the MANOVA analysis because of the low Cronbach’s alpha values for this
sample (α = .16, p > .05) in comparison with introjected, external, amotivation, and iden-
tified subscale internal consistency values (α = .58, .74, .75, & .64, respectively. Chi-
square values for age and gender proportions in the BMI and OHBMI groups were not

5. Volume 37, Number 4, Winter 2006 153
TABLE 1. Descriptive Statistics for Participants (N = 31) on Key Variables
At criterion
level of Outside
healthy body healthy body
mass index mass index Total
(n = 19) (n = 12) sample
Variable M SD M SD M SD
Introjected motivationa 1.67 .45 1.08 .67 1.44 .61
External motivationa 2.43 .94 1.65 .74 2.13 .94
Amotivation 1.84 .97 2.27 1.23 2.01 1.08
Identified motivation 3.26 .54 3.04 .68 3.18 .60
Intrinsic motivation 4.82 .30 4.73 .44 4.78 .36
Minutes per day of physical
activity at or above the
moderate or vigourous levelb 27.30 15.53 26.02 13.99 26.80 14.73
a
Group differences at the p < .05. bPer day estimates for 4 days after school (Monday through Thurs-
day) during one calendar week.
TABLE 2. Correlation Matrix for Motivation and Physical Activity Estimates
(N = 31)
Variables 1 2 3 4 5 6
1. Introjected motivation — .61* –.06 .27 .01 –.05
2. External motivation — –.01 .28 –.22 .06
3. Amotivation — –.11 –.09 .01
4. Identified motivation — –.07 –.14
5. Intrinsic motivation — .27
6. Physical activity —
*p < .05.
significant (p >. 05) indicating that the groups were proportionally similar in gender and
age. In all cases, BMI values that grouped participants according to healthy or unhealthy
levels of body fat were supported by skin-fold values (Winnick & Short, 1999).
The majority of participants across age groups engaged in moderate to vigorous
physical activity after school. However, the variability in physical activity is large
(almost 16 min), demonstrating a range of scores from less than 4 min per day to as
much as 1 hr per day.

6. 154 RE:view
I inspected the raw data for potential heteroscedasticity in scores before doing the
MANOVA. A Box test supported a lack of significant differences in covariance matrix-
es, Box’s M = 23.69, p > .05. The subsequent MANOVA analysis of the variables indi-
cated no differences in physical activity between the two groups who were divided on
the basis of whether they reached criterion levels of BMI at the time of the study, F(1,
29) = .05, p > .05.
The data revealed motivational differences among these participants. Specifically,
we found significantly higher introjected scores, F(1, 29) = 8.71, p < .01, η2 = .23, and
external scores, F(1, 29) = 5.94, p < .05, η2 = .17, in adolescents reaching criterion lev-
els of BMI as compared with the OHBMI participants. No differences between groups
appeared in the final subscales of amotivation and identified motivation, F(1, 29) =
1.17, p > .05, and F(1, 29) = 1.02, p > .05. Amotivation did not correlate to any of the
other study variables, leading to a conclusion of no relationship between free-time
amotivation and physical activity levels or higher BMI values in the sample (Table 2).
Although participants as a whole had higher intrinsic and lower amotivation scores
coupled with adequate levels of physical activity, these variables were unrelated in a
large majority of participants. Figures 1 and 2 illustrate the age-related trends in these
two extrinsic motivation subscales.
Discussion
The data I collected shows potential differences in physical activity counts and moti-
vational profiles in the participants who met or failed to meet Winnick and Short’s
(1999) criterion levels of BMI. Such data are useful for practitioners interested in how
participants in a relatively barrier-free environment perceive free-time decision making
and engage in physical activity.
Physical Activity
The results indicate that some of these adolescents are at risk of not meeting the
recent U.S. Department of Health and Human Services (DHHS) [2005] daily rec-
ommendation of having 60 min of moderately intense physical activity on most days
of the week. This is especially regrettable because in their residential school they can
select from after-school, free-time programs with both active and sedentary options.
The residential school setting removed issues related to barriers in integrated set-
tings from the study, making it possible to demonstrate the potential for some ado-
lescents with visual impairments in residential settings to make active choices and
others to choose relative inactivity during free time. School-sponsored athletics,
walking on a nature trail, bowling, or other leisure activities were available at their
residential campus. In many cases, participants made active choices; however, in
relation to DHHS-recommended amounts of moderate to vigorous physical activity,

7. Volume 37, Number 4, Winter 2006 155
4.00 Participant reached healthy body mass criterion
No
Yes
3.50
Mean External Motivation
3.00
2.50
2.00
1.50
1.00
12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00
Age
FIGURE 1. Age-related decreases in external motivation in participants
(N = 31).
the average amount of activity recorded for both BMI and OHBMI participants is
less than adequate.
The physical activity estimates in the current sample support the low daily physical
activity counts Kozub and Oh (2004) found for children with visual impairments. How-
ever, the amounts of moderate to vigorous physical activity for the full-time residential
students in the earlier study were even lower than in the present sample. Two factors
may explain why the values in Table 1 are higher than those of Kozub and Oh’s earlier
study. First, the current sample included only full-time residential students whose resid-
ual sight allowed them to complete the FTMS-A. Kozub and Oh did not exclude students
with lower vision levels. Second, Kozub and Oh’s inclusion of weekend days may have
contributed to higher levels of inactivity if fewer structured physical activity options
were available on weekends. However, the data from both studies reveal less than ideal
amounts of physical activity in individuals with visual impairments.

8. 156 RE:view
Participant reached healthy body mass criterion
No
2.20
Yes
2.00
Mean Introjected Motivation score
1.80
1.60
1.40
1.20
1.00
0.80
12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00
Age
FIGURE 2. Age-related decreases in introjected motivation in participants
(N = 31).
Differences Between BMI and OHBMI Participants
A lack of difference noted in the daily physical activity estimates between BMI and
OHBMI participants is interesting because it indicates that the data do not support an
assumption that OHBMI participants would be less active than those with criterion BMI.
These results are consistent with studies on individuals without disabilities (Kalakanis et
al., 2001; Vincent et al., 2003). However, the limited sample of only residential students
prohibits generalizing to individuals with visual impairments from integrated settings or
those who attend the residential school but return home at the end of the school day. The
data indicate that (a) regardless of BMI, some participants were active and some were
inactive after school and (b) consistent with other studies of children with visual impair-
ments, BMI is independent of activity levels (Suzuki et al., 1991). The small number of
female participants made it impossible to analyze gender interactions in these data.

9. Volume 37, Number 4, Winter 2006 157
Motivational Differences in BMI and OHBMI Participants
As was the case in Baldwin and Caldwell (2003), intrinsic motivation (participating
for pleasure) received the highest scores of the motivational subscales. However, I
eliminated this variable from the analyses and further discussion because in the present
sample the intrinsic motivation subscale had inadequate estimates of reliability. In
comparison with the other dimensions, the intrinsic motivation subscale had a low
number of items and that factor plus the small sample size may have affected reliabil-
ity estimates for the scale in the current study.
Differences existed between BMI and OHBMI participants in introjected and exter-
nal motivation subscales. These differences between the groups on the extrinsic moti-
vation subscale support the hypothesis that participants within the healthy range of
body composition are more interested in free-time decisions that allow them to be per-
ceived positively by others and to feel positively about themselves. Items on the exter-
nal motivation scale, such as avoiding negative consequences, were scored differently
by the two groups. In these external motivation subscales, the BMI participants had the
higher scores, contrary to my hypothesis of higher scores in this category for OHBMI
participants. This result, coupled with the problems of internal consistency from the
intrinsic subscale, make it impossible to reject the null hypothesis in support of the
notion that higher intrinsic motivation and lower extrinsic motivation are related to
body composition or physical activity levels. It is important to note that this may be a
sample-specific effect that is heavily influenced by the context in which children with
visual impairments are educated. In the setting used for the current study, no attempts
were made to secure participants who varied in educational services or had experiences
in more integrated settings. More study is needed to determine if this inference is sup-
ported in integrated contexts and in comparison with peers without disabilities.
Group differences in motivational subscales coupled with the age trends found in
Figure 1 warrant additional discussion. Although not a research question before the
study, the trend lines show a pattern of the cross-section of participants who reached
criterion levels of BMI, placing increased value on external motivation as age increas-
es. In this regard, substantive differences between perceptions by older participants
may warrant consideration. Furthermore, the older participants who failed to reach cri-
terion levels clearly place little to no agreement with items related to rules and exter-
nal expectations.
Discussion of introjected motivation is somewhat problematic given the specific nature
of the sample and the lack of an age-related pattern consistent with external motivation
(Figure 2). The moderate correlation between these two extrinsic motivation subscales
somewhat belies the inconsistent patterns found in Figures 1 and 2. On a conceptual level,
it makes sense that others or external rules would also be related to social or tangible
rewards (introjected motivation) in this sample. However, to determine the nature of
extrinsic motivation changes with age, additional study on a more diverse group of ado-
lescents with visual impairments, including individuals from integrated educational set-

10. 158 RE:view
tings, is needed. Possibly, social motivational factors are different for the BMI and
OHBMI group, and future studies of the potential for a curvilinear trend throughout ado-
lescence would help determine if this pattern warrants concern.
A weakness of the current study is that I did not study free-time decision making. I
used the construct of free-time motivation and levels of physical activity without any
regard for what participants actually did during after-school time. Decisions to engage
in specific options of physical activity may differ from actually participating in mod-
erate to vigorous physical activity. Some children may choose active options (such as
recreational programs monitored by the residential school) yet not engage at a moder-
ate to vigorous level. Furthermore, some children may choose sedentary options and
move around at a moderate to vigorous level during these less structured activities. The
latter is unlikely but points to the potential problem with assuming that these activity
choices of high and low BMI adolescents were studied conclusively.
Applications for Practice
Combining the findings related to external motivation with other studies of adolescents
with visual impairments provides a means for discussion and some speculation (given the
content of FTMS-A items) about the influence of parents on free-time decision making in
adolescents with visual impairments. Research indicates that parental influences are
important in a study of physical activity patterns within a family systems framework
(Ayvazoglu, Oh, & Kozub, 2006) and in the subscale used in the present study that cites
rules and parental expectations (Baldwin & Caldwell, 2003). Although I did not ask the
participants in this study about physical activity at home or with parents, it may be that
these influences during weekends and summers are a potential resource to help in
addressing unhealthy values of BMI and free-time decision making at school. The exter-
nal motivation items cited rules or expectations in four of the five items with “others
won’t get mad at me” as the fifth. This could be an indicator that these children would be
more active at home if parents, or perhaps physical educators, were more persistent in
monitoring children’s physical activity counts. This could be a curriculum-planning issue
where assessment of daily physical activity levels is necessary to help some adolescents
with visual impairments reach criterion levels of BMI. At the least, it is important for
practitioners to note the potential external influences and their role in adolescent free-
time decision making related to engaging in physical activity.
Summary
Physical activity continues to be a recommended course of action to alleviate obesi-
ty and improve physical functioning, and the data from this inquiry present interesting
results that need further study. After-school programs at this residential school appear
to result in levels of moderate to vigorous physical activity, but the total time that many

11. Volume 37, Number 4, Winter 2006 159
of these adolescents with visual impairments spend in that way warrants concern in
light of the DHHS guidelines. The role that motivational factors play in body compo-
sition is an interesting phenomenon for further examination. The motivational differ-
ences between BMI and OHBMI participants related to external regulation need to be
investigated with a more diverse group of participants who are visually impaired,
including adolescents from integrated settings and those with more severe visual
impairments. Of considerable concern in these data are the age-related trends noted in
previous studies and the high number of OHBMI participants in the current study.
NOTES
1. For a complete description of item content and psychometric properties, see Baldwin and
Caldwell (2003).
2. TriTrac monitors and RT3 monitors are manufactured by Stayhealthy, Inc., 222 E. Hunt-
ington Drive, Suite 313, Monrovia, CA 91016.
REFERENCES
Ayvazoglu, N., Oh, H., & Kozub, F. M. (2006). Explaining physical activity in children with
visual impairments: A family systems approach. Exceptional Children, 72, 235–248.
Baldwin, C. K., & Caldwell, L. L. (2003). Development of the free time motivation scale for ado-
lescents. Journal of Leisure Research, 35, 129–151.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behav-
ior. New York: Plenum.
Department of Health and Human Services. (2005). Dietary guidelines for Americans 2005.
Washington, DC: Author.
Jakicic, J. M., Winters, C., Lagally, K., Ho, J., Robertson, R. J., & Wing, R. R. (1998). The accu-
racy of the TriTrac-R3D accelerometer to estimate energy expenditure. Medicine & Science
in Sports & Exercise, 30, 747–754.
Kalakanis, L. E., Goldfield, G. S., Paluch, R. A., & Epstein, L. H. (2001). Parental activity as a
determinant of activity level and patterns of activity in obese children. Research Quarterly for
Exercise and Sport, 72, 202–209.
Kosma, M., Cardinal, B. J., & Rintala, P. (2002). Motivating individuals with disabilities to be
physically active. QUEST, 54, 116–132.
Kozub, F. M., & Oh, H. (2004). An exploratory study of physical activity levels in children and
adolescents with visual impairments. Clinical Kinesiology, 58, 1–7.
Kozub, F. M., Oh, H., & Rider, R. A. (2005). Short term physical activity assessment in children
with visual impairments: Validity and reliability of RT3 activity monitors. Adapted Physical
Activity Quarterly, 20, 347–358.
Longmuir, P. E., & Bar-Or, O. (2000). Factors influencing the physical activity levels of youths
with physical and sensory disabilities. Adapted Physical Activity Quarterly, 17, 40–53.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic
motivation, social development, and well-being. American Psychologist, 55, 68–78.
Skaggs, S., & Hopper, C. (1996). Individuals with visual impairments: A review of psychomo-
tor behavior. Adapted Physical Activity Quarterly, 13, 16–26.
Suzuki, M., Saitoh, S., Tasaki, Y., Shimonmura, Y., Makishima, R., & Hosoya, N. (1991). Nutri-
tional status and daily physical activity of handicapped students in Tokyo metropolitan

12. 160 RE:view
schools for deaf, blind, mentally retarded, and physically handicapped individuals. American
Journal of Clinical Nutrition, 54, 1101–1111.
Vallerand, R. J. (2001). A hierarchical model of intrinsic and extrinsic motivation in sport and
exercise. In G. Roberts (Ed.), Advances in motivation in sport and exercise (pp. 263–320).
Champaign, IL: Human Kinetics.
Vincent, S. D., Pangrazi, R. P., Raustorp, A., Tomson, L. M., & Cuddihy, T. F. (2003). Activity
levels and body mass index of children in the United States, Sweden, and Australia. Medicine
& Science in Sports & Exercise, 35, 1367–1373.
Winnick, J. P., & Short, F. X. (1999). The Brockport Physical Fitness Test manual: A health-
related test for youths with physical disabilities and mental disabilities. Champaign, IL:
Human Kinetics.