Motivation and PhysicalActivity 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 visualimpairments. Although residential schools for children with visual impairments arecharged with providing both educational and leisure curriculums, the children spendmore 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 frameworkfor studying free-time motivation in adolescents (Baldwin & Caldwell, 2003). This the-oretical framework contains intrinsic and extrinsic motivators that potentially predictfree-time decision making (Deci & Ryan, 1985). Models suggest multiple types ofmotivation 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 insocial settings are extrinsic motivators (Baldwin & Caldwell, 2003). Self-determina-tion theorists also describe amotivation, the counterproductive influence that depictsindividuals who have neither intrinsic nor extrinsic reasons for free-time decision mak-ing (Baldwin & Caldwell; Vallerand, 2001).

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Deci and Ryan (1985) found that individuals with visual impairments have levels ofintrinsic and extrinsic motivation and amotivation that influence their use of free timeand 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 longperiods of inactivity during free time, creating lower physical skills and health-relatedconcerns. Inactive individuals with visual impairments, lacking motivation to engagein 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 measuresof physical activity, such as pedometers, have also been used. Suzuki et al. (1991) foundthat individuals with visual impairments from residential settings were less active thantheir peers without disabilities. Additionally, using self-recall estimates of physicalactivity, Longmuir and Bar-Or (2000) indicated that youth with visual impairments hadsignificantly 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 ofbody mass indexes (BMIs) and their fellow students who score outside the healthyzones, 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 theseadolescents 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 healthyzones. I hypothesize that adolescents who have reached criterion levels of BMI havehigher physical activity counts, higher intrinsic motivation scores, lower extrinsic moti-vation scores, and lower amotivation scores than do participants who scored outside thehealthy 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 atmany residential schools offer students choices of free-time activities in barrier-free

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 ofthe participants had any coexisting cognitive or physical disabilities.

The criterion for admission into the study was residual sight that allowed the studentto use either regular or enlarged text with assistance to complete the motivation scale.Participants had to be enrolled in after-school residential programming during thestudy. Limiting the scope of the study to these participants avoided problems associat-ed with attempting to match individuals from integrated settings on age, gender, bodymass index (BMI), and level of vision. I secured consent to collect data and use theresults 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 collecteddata, 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 trainedthem on initializing and attaching RT3 activity monitors to collect physical activitydata. To insure confidentiality, the school staff collected and coded all data before Iused 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 participantscould independently circle their levels of agreement to the 20-item scale. To collectphysical activity data participants wore activity monitors fastened to their right hipsfrom 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 eachminute on the basis of a criterion value calculated for each participant; by countingonly those values that fell at or above a moderate to vigorous physical activity level, Iattained 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 andShort (1999) established criterion values for body composition for adolescents with

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visual impairments, using calf and triceps skin-fold estimates. I used this measure todivide the group: The participants who fell within Winnick and Short’s measures wereone group (BMI) and those who were outside the healthy zone (OHBMI) were another.

Free Time Motivation Scale (FTMS-A). Baldwin and Caldwell (2003) developed thescale1 using the constructs of self-determination theory (Ryan & Deci, 2000). FTMS-Acontains five subscales: amotivation (participation for unknown reasons), externalmotivation (participation to avoid negative consequences), introjected motivation (par-ticipation to maintain some perceived status), identified motivation (participation togain knowledge or skills), and intrinsic motivation (participation for pleasure). Bald-win and Caldwell demonstrated adequate estimates of reliability for adolescentsbetween the ages of 12 and 15 and provided evidence of validity for the FTMS-A. TheLikert-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 forchildren 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 onadolescents with visual impairments during physical education activities.

Data Analyses

I used descriptive statistics and displays to explore key study variables. I calculatedCronbach’s alpha values to estimate the reliability of the FTMS-A total and calculatedsubscales and chi-square tests to determine if reaching criterion levels of fitness wasdependent 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-healthyBMI (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 motivationsubscale from the MANOVA analysis because of the low Cronbach’s alpha values for thissample (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

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significant (p >. 05) indicating that the groups were proportionally similar in gender andage. In all cases, BMI values that grouped participants according to healthy or unhealthylevels of body fat were supported by skin-fold values (Winnick & Short, 1999).

The majority of participants across age groups engaged in moderate to vigorousphysical 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 asmuch as 1 hr per day.

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TABLE 1. Descriptive Statistics for Participants (N = 31) on Key Variables

At criterionlevel of Outside

healthy body healthy bodymass 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 .61External motivationa 2.43 .94 1.65 .74 2.13 .94Amotivation 1.84 .97 2.27 1.23 2.01 1.08Identified motivation 3.26 .54 3.04 .68 3.18 .60Intrinsic motivation 4.82 .30 4.73 .44 4.78 .36Minutes 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

aGroup 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 –.052. External motivation — –.01 .28 –.22 .063. Amotivation — –.11 –.09 .014. Identified motivation — –.07 –.145. Intrinsic motivation — .276. Physical activity —

*p < .05.

I inspected the raw data for potential heteroscedasticity in scores before doing theMANOVA. 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 onthe 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, andexternal 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 groupsappeared 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 theother study variables, leading to a conclusion of no relationship between free-timeamotivation and physical activity levels or higher BMI values in the sample (Table 2).Although participants as a whole had higher intrinsic and lower amotivation scorescoupled with adequate levels of physical activity, these variables were unrelated in alarge majority of participants. Figures 1 and 2 illustrate the age-related trends in thesetwo 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 howparticipants in a relatively barrier-free environment perceive free-time decision makingand engage in physical activity.

Physical Activity

The results indicate that some of these adolescents are at risk of not meeting therecent U.S. Department of Health and Human Services (DHHS) [2005] daily rec-ommendation of having 60 min of moderately intense physical activity on most daysof the week. This is especially regrettable because in their residential school they canselect 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 andothers to choose relative inactivity during free time. School-sponsored athletics,walking on a nature trail, bowling, or other leisure activities were available at theirresidential campus. In many cases, participants made active choices; however, inrelation to DHHS-recommended amounts of moderate to vigorous physical activity,

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the average amount of activity recorded for both BMI and OHBMI participants isless than adequate.

The physical activity estimates in the current sample support the low daily physicalactivity 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 residentialstudents in the earlier study were even lower than in the present sample. Two factorsmay explain why the values in Table 1 are higher than those of Kozub and Oh’s earlierstudy. 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 studentswith lower vision levels. Second, Kozub and Oh’s inclusion of weekend days may havecontributed to higher levels of inactivity if fewer structured physical activity optionswere available on weekends. However, the data from both studies reveal less than idealamounts of physical activity in individuals with visual impairments.

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FIGURE 1. Age-related decreases in external motivation in participants(N = 31).

4.00

Mea

n E

xter

nal M

otiv

atio

n

Age

1.00

1.50

2.00

2.50

3.00

3.50

Participant reached healthy body mass criterion

Yes

No

12.00 21.0020.0019.0018.0017.0016.0015.0014.0013.00

Differences Between BMI and OHBMI Participants

A lack of difference noted in the daily physical activity estimates between BMI andOHBMI participants is interesting because it indicates that the data do not support anassumption that OHBMI participants would be less active than those with criterion BMI.These results are consistent with studies on individuals without disabilities (Kalakanis etal., 2001; Vincent et al., 2003). However, the limited sample of only residential studentsprohibits generalizing to individuals with visual impairments from integrated settings orthose who attend the residential school but return home at the end of the school day. Thedata indicate that (a) regardless of BMI, some participants were active and some wereinactive 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 offemale participants made it impossible to analyze gender interactions in these data.

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FIGURE 2. Age-related decreases in introjected motivation in participants(N = 31).

Mea

n In

troj

ecte

d M

otiv

atio

n sc

ore

Age

0.80

Participant reached healthy body mass criterion

Yes

No

12.00 21.0020.0019.0018.0017.0016.0015.0014.0013.00

2.20

2.00

1.80

1.60

1.40

1.20

1.00

Motivational Differences in BMI and OHBMI Participants

As was the case in Baldwin and Caldwell (2003), intrinsic motivation (participatingfor pleasure) received the highest scores of the motivational subscales. However, Ieliminated this variable from the analyses and further discussion because in the presentsample the intrinsic motivation subscale had inadequate estimates of reliability. Incomparison with the other dimensions, the intrinsic motivation subscale had a lownumber 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 ofbody 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 differentlyby the two groups. In these external motivation subscales, the BMI participants had thehigher scores, contrary to my hypothesis of higher scores in this category for OHBMIparticipants. This result, coupled with the problems of internal consistency from theintrinsic subscale, make it impossible to reject the null hypothesis in support of thenotion that higher intrinsic motivation and lower extrinsic motivation are related tobody composition or physical activity levels. It is important to note that this may be asample-specific effect that is heavily influenced by the context in which children withvisual impairments are educated. In the setting used for the current study, no attemptswere made to secure participants who varied in educational services or had experiencesin 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 inFigure 1 warrant additional discussion. Although not a research question before thestudy, the trend lines show a pattern of the cross-section of participants who reachedcriterion levels of BMI, placing increased value on external motivation as age increas-es. In this regard, substantive differences between perceptions by older participantsmay 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 natureof the sample and the lack of an age-related pattern consistent with external motivation(Figure 2). The moderate correlation between these two extrinsic motivation subscalessomewhat 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 tangiblerewards (introjected motivation) in this sample. However, to determine the nature ofextrinsic motivation changes with age, additional study on a more diverse group of ado-lescents with visual impairments, including individuals from integrated educational set-

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tings, is needed. Possibly, social motivational factors are different for the BMI andOHBMI 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. Iused the construct of free-time motivation and levels of physical activity without anyregard for what participants actually did during after-school time. Decisions to engagein specific options of physical activity may differ from actually participating in mod-erate to vigorous physical activity. Some children may choose active options (such asrecreational programs monitored by the residential school) yet not engage at a moder-ate to vigorous level. Furthermore, some children may choose sedentary options andmove around at a moderate to vigorous level during these less structured activities. Thelatter is unlikely but points to the potential problem with assuming that these activitychoices of high and low BMI adolescents were studied conclusively.

Applications for Practice

Combining the findings related to external motivation with other studies of adolescentswith visual impairments provides a means for discussion and some speculation (given thecontent of FTMS-A items) about the influence of parents on free-time decision making inadolescents with visual impairments. Research indicates that parental influences areimportant 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 citesrules and parental expectations (Baldwin & Caldwell, 2003). Although I did not ask theparticipants in this study about physical activity at home or with parents, it may be thatthese influences during weekends and summers are a potential resource to help inaddressing 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 “otherswon’t get mad at me” as the fifth. This could be an indicator that these children would bemore active at home if parents, or perhaps physical educators, were more persistent inmonitoring children’s physical activity counts. This could be a curriculum-planning issuewhere assessment of daily physical activity levels is necessary to help some adolescentswith visual impairments reach criterion levels of BMI. At the least, it is important forpractitioners 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 interestingresults that need further study. After-school programs at this residential school appearto result in levels of moderate to vigorous physical activity, but the total time that many

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of these adolescents with visual impairments spend in that way warrants concern inlight 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 beinvestigated with a more diverse group of participants who are visually impaired,including adolescents from integrated settings and those with more severe visualimpairments. Of considerable concern in these data are the age-related trends noted inprevious 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 andCaldwell (2003).

2. TriTrac monitors and RT3 monitors are manufactured by Stayhealthy, Inc., 222 E. Hunt-ington Drive, Suite 313, Monrovia, CA 91016.

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Kosma, M., Cardinal, B. J., & Rintala, P. (2002). Motivating individuals with disabilities to bephysically active. QUEST, 54, 116–132.

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schools for deaf, blind, mentally retarded, and physically handicapped individuals. AmericanJournal of Clinical Nutrition, 54, 1101–1111.

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Vincent, S. D., Pangrazi, R. P., Raustorp, A., Tomson, L. M., & Cuddihy, T. F. (2003). Activitylevels and body mass index of children in the United States, Sweden, and Australia. Medicine& Science in Sports & Exercise, 35, 1367–1373.

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