Recreation Program Participation by Older Adults: Its Relationship to Perceived Freedom in Leisure and Life Satisfaction

This study examined the contribution of several variables to the prediction of perceived freedom in leisure (PFL) and life satisfaction in older adults. Demographic, health and socioeconomic variables were compared with participation in recreation programs, church involvement and PFL. Church involvement was viewed as a leisure activity rather than a measure of religiosity. The survey instrument incorporated all these variables and was pretested and revised before use in the study. The sample consisted of 198 persons 60 years of age and older who were members of two Southern Baptist churches. Subjects were randomly selected, but persons considered by church staff members to be incapable of completing the survey were eliminated. Surveys were hand delivered and picked up by volunteer workers, and a 38 percent return rate was obtained. Alpha reliability for the church involvement, PFL and life satisfaction scales in the instrument were .87, .94, and .77, respectively. Frequency counts and percentages or means and standard deviations were calculated for the demographic variables. Subjects were categorized by level of involvement in church and community recreation programs. Persons involved in community but not church recreation programs were underrepresented in the sample. A selective sampling procedure was utilized to obtain more respondents in this category, but the data from these individuals were analyzed separately. Stepwise multiple regression analyses were utilized to ascertain the impact of different variables upon PFL and life satisfaction. Three predictors of PFL emerged— participation in recreation programs, church involvement and satisfaction with health. Correlations between these variables and life satisfaction were consistent with the findings in the literature. Inclusion of church involvement, participation in recreation programs and, for the life satisfaction analysis, PFL raised the percentage of variance explained. Thus, greater predictive power emerged using these variables than when only demographic, health and socioeconomic variables were included