Groningen Active Living Model (GALM): stimulating physical activity in sedentary older adults; validation of the behavioral change model

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1 Available online at R Preventive Medicine 37 (2003) Groningen Active Living Model (GALM): stimulating physical activity in sedentary older adults; validation of the behavioral change model Martin Stevens, Ph.D., a, * Koen A.P.M. Lemmink, Ph.D., b Marieke J.G. van Heuvelen, Ph.D., b Johan de Jong, M.Sc., b and Piet Rispens, Ph.D. b a Department of Orthopedics, Groningen University Hospital, Groningen, The Netherlands b Institute of Human Movement Sciences, University of Groningen, Groningen, The Netherlands Abstract Background. A significant proportion of older adults in The Netherlands do not participate regularly in leisure-time physical activity. The Groningen Active Living Model (GALM) was developed to change this situation for the better. Longitudinal results of the validation of the GALM behavioral change model are presented. Methods. We obtained data on potentially mediating variables of physical activity behavior change (self-efficacy, social support, perceived fitness, and enjoyment) from 96 participants in a prospective study during the 18 months the GALM strategy lasted. Results. Prospective analyses revealed significant differences in several potentially mediating variables, although some of these differences were contrary to our hypothesis. Discriminant analysis resulted in canonical correlations of 0.50 after 6 months and 0.66 after 18 months of program participation between adherers and nonadherers, respectively; 73.8 and 80.0% of the subjects were classified correctly. Conclusions. Based on the results, it can be concluded that we partially succeeded in manipulating the potentially mediating variables by means of our GALM strategy. Several mediating variables were identified that reliably discriminated long-term adherers from nonadherers, expanding the generalizability of social cognitive theory-driven variables to a Dutch population American Health Foundation and Elsevier Science (USA). All rights reserved. Keywords: Older adults; Leisure-time physical activity; Behavioral change Introduction As in other Western societies, sedentariness in the Netherlands is a potential burden on public health. A sedentary lifestyle is associated with premature death due to coronary heart disease, colon cancer, and non-insulin-dependent diabetes [1,2]. Regular physical activity has been linked consistently and reliably to a reduction in all-cause mortality [3] as well as lower rates of cardiovascular disease and several other debilitating conditions [4 6]. In addition to the beneficial effects of physical activity on health, regular physical This research was made possible by grants from The Netherlands Heart Foundation (Grant ), the Praeventiefonds (Grant ), and the Dutch Ministry of Public Health, Welfare and Sports. * Corresponding author. Department of Orthopedics, Groningen University Hospital, PO Box 30001, 9700 RB Groningen, The Netherlands. Fax: address: m.stevens@orth.azg.nl (M. Stevens). activity also improves older adults ability to perform their daily activities, thus enhancing their quality of life [7]. Despite these benefits, a large segment of the Dutch population of older adults does not participate regularly in leisure-time physical activity [8,9]. Sixty-nine percent of the total Dutch adult population are convinced of the importance of sufficient physical activity, but only 40% succeed in being physically active 5 days a week for 30 min at a moderate intensity [10]. Between 40 and 80% of 55- to-65- year-old Dutch adults are physically inactive, depending on the definition and measurement method used [11]. The Groningen Active Living Model (GALM) was developed as a strategy to promote physical activity in sedentary older adults between the ages of 55 and 65. The GALM stimulation strategy is based on a model of behavioral change (Fig. 1), which was structured with the help of Chen s program evaluation theory [12], which is comparable to the mediation model as proposed by Kraemer et al /$ see front matter 2003 American Health Foundation and Elsevier Science (USA). All rights reserved. doi: /j.ypmed

2 562 M. Stevens et al. / Preventive Medicine 37 (2003) Fig. 1. The Groningen Active Living Model. [13]. The GALM behavioral change model distinguishes four domains within a theoretical framework of action and conceptual domains. The four domains are 1. The treatment domain, in which the changes intended with the GALM strategy are effected. The treatment of GALM consists of three phases: recruitment, introductory phase, and follow-up. Together they make up the GALM strategy and last 1.5 years. 2. The implementation environment domain, in which the environment in which the strategy is implemented is described. 3. The outcome domain, in which the intended outcome of the GALM strategy is described. 4. The intervening mechanism domain, which describes the underlying mediating variables (self-efficacy, social support, perceived fitness, and enjoyment) linking treatment (strategy) to outcome. The purpose of the strategy is to effect change in the potentially mediating variables of leisure-time physical activity [7,14]. The GALM strategy and behavioral change model have been described in detail elsewhere [15,16]. The purpose of our study is to gain insight into the validity of the GALM behavioral change model. A distinction is made between the validation of the action and the conceptual domains. A program s overall accomplishments require success in both domains. In validating the action domain we investigate whether the treatment effectively manipulates the potentially mediating variables. In validating the conceptual domain we investigate whether changes in the potentially mediating variables influence the outcome variables of program adherence and leisure-time physical activity. To be able to analyze the longitudinal influence and the changes in the potentially mediating variables, we executed a prospective study in which data were gathered during 18 months. Past research has shown that mediating variables influencing behavioral change may not be consistent across the various stages of behavioral change. In the GALM behavioral change model, social cognitive factors (social support, self-efficacy, and perceived fitness) are considered important initiators of health behavior and positively influence the early period of maintenance, but may become less important over time with repeated performance [17]. McAuley [18] argues that, as individuals adapt physiologically and psychologically to the demands placed on them by leisure-time physical activity participation, and as leisure-time physical activity becomes part of their daily schedule and possibly

3 M. Stevens et al. / Preventive Medicine 37 (2003) Fig. 2. Recruitment of the participants. becomes less demanding, the role of efficacy cognitions is diminished. This perspective is in keeping with Bandura s contention [19] that cognitive control systems play their most important role in the acquisition of behavioral proficiencies. When behaviors are less demanding and more easily engaged in, cognitive control systems such as selfefficacy give way to regulation by lower control systems. Enjoyment can be considered one such system. Several researchers suggest that feelings of enjoyment and wellbeing may play an important role in exercise adherence in the long term [7,20,21]. Research by Csikszentmihaly [22], Csikszentmihalyi and Rathunde [23], Wankel et al. [21], and Bult and Rispens [24] suggests that social support, self-efficacy, and perceived fitness may be key conditions for (re)gaining enjoyment in leisure-time physical activity. As a first step in evaluating our proposed mediation model, we hypothesized that the GALM strategy would succeed in enhancing self-efficacy, social support, and perceived fitness, which subsequently would result in the ability of formerly sedentary subjects to experience enjoyment in leisure-time physical activity. Self-efficacy, social support, and perceived fitness are considered conditional to the central mediating variable, which is the enjoyment of leisure-time physical activity. We also hypothesized that, during the 18 months the GALM strategy lasted, subjects would shift from the contemplation/preparation phase to the action/maintenance phase of the stages of change model [15]. As a second step in evaluating the proposed mediation model, we hypothesized that the increase in self-efficacy, social support, and perceived fitness would lead to an increase in enjoyment experienced in leisure-time physical activity, and that this increase in enjoyment experienced [15] would subsequently lead to continuing program adherence and an enduring physically active lifestyle. Method Subjects Sedentary older adults between the ages of 55 and 65 years formed the target population for the GALM strategy. The GALM physical activity program can be characterized as a moderately intensive leisure-time physical activity program with an emphasis on recreational sports activities. The 18-month program offers a variety of recreational leisuretime physical activities such as volleyball, basketball, aerobics, gymnastics, and badminton, and can be adjusted to the wishes of the participants. Sessions are held once a week and last 1 h. Potential participants were recruited in five local GALM projects by means of a special recruitment strategy which consisted of a population strategy and a network strategy. In the population strategy, older adults in a selected municipal area received a written invitation and were visited at home by a member of the local GALM team. As attending by oneself is often a barrier, potential participants were invited

4 564 M. Stevens et al. / Preventive Medicine 37 (2003) to bring someone along, even if that person were not sedentary or did not fit in the age category; this is called the network strategy [15]. Five levels of urbanization can be applied to municipalities in The Netherlands. We selected five municipalities to represent each level of urbanization, in order for the overall sample to be an illustrative cross section of the Dutch population. A total of 5,909 potential participants were recruited. Based on estimates of available data [15], about 60% (N 3,545) of those who received the invitation could be considered sedentary based on the 1998 ACSM recommendations on exercise and physical activity for older adults [25], and, in turn, half (N 1773) of these qualified for GALM. The other half was not interested in leisure-time physical activity or was unable to participate due to illness, personal circumstances, or other reasons. By means of this recruitment strategy, 392 subjects in five local GALM projects in The Netherlands were included in the overall study (Fig. 2). Each participant had to read and sign a written informed consent approved by the medical ethical board of the Groningen University Hospital. At the start of the study, 17% (N 67) of the subjects could be classified in the precontemplation stage, 42% (N 164) in the contemplation/preparation stage, and 39% (N 153) in the action/maintenance phase. Stages of change data were missing for 2% (N 8) of the subjects. In this study, participants in the contemplation/preparation phase were considered relevant to study the dynamics of behavioral change and were included in the current analyses (Fig. 2). To be able to validate the dynamic nature of the model, three measurements were planned: a measurement at the start of the project (T0); a second measurement after completing the introductory program, which ended 6 months after the start of the GALM project (T1); and a final measurement after completing the follow-up program, 18 months after the start of the GALM project (T2). Measures The questionnaire used contained items related to demographic factors (sex, age, and educational level as a reflection of socioeconomic status) and potentially mediating variables of leisure-time physical activity participation. In this study we distinguished three dimensions of selfefficacy: self-efficacy with respect to physical ability, selfefficacy for overcoming barriers, and task self-efficacy, defined as participants belief that they can continue leisuretime physical activities at a certain frequency and duration, at a specified level of intensity, and over a certain period [26]. We measured these dimensions using three subscales: a Dutch version (LIVAS) [27] of the Perceived Physical Ability subscale of the Physical Self-Efficacy scale developed by Ryckman et al. [28] to measure self-efficacy with respect to physical ability, and a Dutch translation of McAuley s self-efficacy questionnaire containing two subscales to measure self-efficacy for overcoming barriers and task selfefficacy [18]. Cronbach s s for the Dutch versions of the three scales were 0.80, 0.84, and 0.90 [29]. A Dutch version of the scales for measuring Social Support for Diet and Exercise Behaviors by Sallis et al. [30] was used to measure social support for leisure-time physical activity participation. This scale is made up of three subscales: the exercising together subscale from the Friend Support for Exercise Habits Scale, and the participation and involvement subscale and the rewards and punishment subscale, both from the Family Support for Exercise Habits Scale. Only the first two subscales were used in this study. Respective s for the two subscales of the Dutch version were 0.69 and 0.71 [31]. For this longitudinal study the Dutch version of the scales was extended with social support from group members, since this can be considered important to the process of behavioral change. The extended measure was collected at T1 and T2; the Cronbach for this scale at T1 was Lemmink s perceived fitness questionnaire [32] with a Cronbach of 0.94 was used to measure perceived fitness. The Groningen Enjoyment Questionnaire (GEQ) was used to measure subjects enjoyment in leisure-time physical activity. The Cronbach of this scale was 0.88 [33]. A Dutch translation of the stages of change questionnaire by Marcus and Owen [34] was used to obtain information about the process of behavioral change during the 18 months the GALM program lasted, as well as to obtain information about adherence to the program, as one of the outcome variables. Statistical analysis The data were analyzed with SPSS When missing values frustrated the calculation of sum scores, we worked to replace missing values with the mean score of all other items of that scale so that sum scores could be calculated. Missing values were highest for the LIVAS scale (8.3%). The size of Cronbach s combined with the total number of items of the scale limits the number of items that can be replaced by this method. To validate the first part of the proposed mediation model, descriptive statistics were used first to determine if participants shifted through the stages of change. Then a repeated measurement analysis of variance was conducted over the three data points with the potentially mediating variables as dependent variables, time of measurement as the within-subject factor, and group (adherence/non-adherence) as the between-subject factor. Main effect of time and differences between adjacent points of measurement (T0 vs T1, T1 vs T2) give information about the validation of the action domain. Time group interaction effects provided information on the validity of both parts of the proposed mediation model. Main effects of group and differences between the groups on the three points of measurement give information separately on the validation of the second portion of the model. P values lower than 0.05 were considered to indicate statistical significance. Additionally, discrimi-

5 M. Stevens et al. / Preventive Medicine 37 (2003) Table 1 Stage of change at the three points of measurement a Stage nant analysis was used to validate the second portion of the model. By means of this analysis, information was obtained about the predictive value of the potentially mediating variables independently of each other. Canonical correlation was used to measure the degree of association between the discriminant scores and nonadherers versus adherers. Results T0 (N 96) T1 (N 96) T2 (N 96) Precontemplation 2 (2.1%) 8 (8.3%) Contemplation (10.4%) 19 (19.8%) 1 Preparation 2 38 (39.6%) 3 (3.1%) Action/maintenance 39 (40.6%) 66 (68.8%) } 2 Missing 7 (7.3%) a T0, baseline measurement; T1, measurement after the introductory program; T2, measurement after the follow-up program; 1 dropouts; 2 adherers. At T0, 164 participants were identified in the contemplation/preparation stage. At T1, 125 and at T2, 103 of those participants returned the questionnaire, response rates of 82 and 67%, respectively. Data from all three measurements were available for 96 subjects. Age of the subjects ranged from 49 to 79 years, with a mean of 60. Women made up 62.5% and men 37.5% of the sample. With respect to education, 42.7% of the subjects had achieved elementary education level, 35.4% had a secondary education level, and 21.9% had followed higher education. Subjects were screened for chronic diseases: 22.3% reported no chronic disease, 45.7% had one or two chronic diseases, and 32.0% had three or more. At T1 and T2, no significant differences were found for sex, age, education, and chronic diseases between subjects who appeared to be adherers and nonadherers. All subjects were categorized according to their score on the stages of change questionnaire [34] during the three measurements. Table 1 lists the number of subjects in each stage of change for the three points of measurement. Additionally the data on the stages of change questionnaire were used to categorize subjects as either adherers or nonadherers after the 18-month GALM program. Subjects still in the precontemplation, contemplation, and preparation phases after 18 months were considered nonadherers. Subjects in the action or maintenance phases were considered adherers. We hypothesized that, during the 18 months the GALM strategy lasted, subjects would shift from the contemplation/ preparation phase into the action/maintenance phase of the stages of change. From T0 through T2 indeed a shift was seen from the contemplation/preparation to the action/maintenance stage. Table 2 lists means and standard deviations of the potentially mediating variables at the three points of } measurements for nonadherers and adherers separately, as well as the results of the repeated measures analyses of variance. In Fig. 3, significant interaction effects are shown. For self-efficacy for overcoming barriers and task selfefficacy, we observed at T0 few differences between adherers and nonadherers, with a slight increase in self-efficacy for the adherers during the whole program and a strong decrease in self-efficacy for the nonadherers between T0 and T1. For both self-efficacy measures, the time group interaction effect and the main effect of group were significant. For the total sample, self-efficacy was lowest on T1, with a significant main effect of time in self-efficacy for overcoming barriers. Perceived physical ability showed no significant effects, although the nonadherers decreased on this variable while the adherers remained fairly stable (see Table 2). Perceived fitness was similar for adherers and nonadherers at T0, but increased for the adherers and decreased for the nonadherers, as demonstrated by the significant time group and group effects. For both groups, social support from friends increased strongly between T0 and T1, with a significant main effect of time. At all three measurement points, the adherers perceived more social support from friends than the nonadherers, with a significant main effect of group. For adherers, social support from friends continued to increase between T1 and T2, but decreased for nonadherers, as demonstrated by a significant time group interaction effect. For both groups, social support from family increased between T0 and T1 and remained stable between T1 and T2, with only a significant main effect of time. Social support from group members was measured only at T1 and T2. At these measurement points, adherers perceived more support from group members than the nonadherers, as shown by a significant main effect of group. Social support of group members remained stable between T1 and T2 for both groups. Finally, enjoyment was comparable at T0 for adherers and nonadherers. Both groups reported some decrease in their enjoyment levels during the program, with a smaller decrease for the adherers, resulting in significant main effects of time and group and a significant time group interaction effect. To gain insight into the relative importance of the potential mediating variables in discriminating between adherers and nonadherers, a multivariate discriminant analysis was used. Table 3 shows that task self-efficacy, self-efficacy for overcoming barriers, and enjoyment played the greatest role in explaining the difference in scores between the adherers and nonadherers at T1. We computed a canonical correlation of 0.50 and found that 73.8% of the subjects were classified correctly. The analysis at T2 revealed that task self-efficacy, social support from group members, and enjoyment explained most of the difference between adherers and nonadherers. A canonical correlation of 0.66 was computed, and 80.0% of the subjects were classified correctly.

6 566 M. Stevens et al. / Preventive Medicine 37 (2003) Table 2 Potential mediating variables at the three points of measurement for nonadherers and adherers and results of repeated measurement analysis of variance Adherers N Dropouts N Total sample N Main effect of time Main effect of group Interaction effect time group Self-efficacy for overcoming barriers (0 100 scale) (df 1.80) Task self-efficacy (0 100 scale) (df 1.80) Perceived fitness (10 50 scale) (df 1.85) Perceived physical ability (10 50 scale) (df 1.82) Social support from friends (10 50 scale) (df 1.70) Social support from family (10 50 scale) (df 1.68) Social support from group members (10 50 scale) (df 1.67) Enjoyment (10 70 scale) (df 1.81) T0 Mean (SD) (14.90) (19.03) (15.99) F 5.488** F *** F 8.738*** T1 Mean (SD) (15.79) (28.04) (22.18) T2 Mean (SD) (17.44) (27.45) (21.69) T0 Mean (SD) (16.26) (16.27) (16.27) F F *** F *** T1 Mean (SD) (15.63) (25.94) (21.78) T2 Mean (SD) (13.74) (21.55) (19.65) T0 Mean (SD) (5.17) (5.58) (5.27) F F 7.504** F 4.914* T1 Mean (SD) (4.92) (4.87) (4.89) T2 Mean (SD) (5.58) (6.15) (5.98) T0 Mean (SD) (4.95) (5.28) (5.03) F F F T1 Mean (SD) (4.61) (5.15) (4.74) T2 Mean (SD) (5.59) (6.59) (5.96) T0 Mean (SD) 6.94 (3.59) 5.71 (1.23) 6.59 (3.14) F *** F 5.960* F 3.689* T1 Mean (SD) (7.47) (7.81) (7.59) T2 Mean (SD) (7.89) (6.75) (7.99) T0 Mean (SD) (6.88) (4.08) (6.26) F 4.225* F F T1 Mean (SD) (7.01) (6.11) (6.80) T2 Mean (SD) (8.83) (7.39) (8.44) T0 Mean (SD) F F *** F T1 Mean (SD) (7.05) (8.77) (7.79) T2 Mean (SD) (7.02) (6.00) (7.22) T0 Mean (SD) (8.59) (8.73) (8.59) F *** F 4.706* F 5.264** T1 Mean (SD) (6.88) (9.37) (7.95) T2 Mean (SD) (6.51) (9.18) (7.83) * P ** P *** P Discussion To validate the GALM behavioral change model, a distinction was made between validating each of the two portions of the mediation model described earlier. A program s overall accomplishments require success in both portions of the model. With respect to the validation of the first portion of the model, it was our hypothesis that program participation would enhance self-efficacy, social support, and perceived fitness, which would in turn influence enjoyment experienced in leisure-time physical activity. Especially with respect to enjoyment, the GALM behavioral change model distinguishes itself from other behavioral change models in the field of social cognitive theory [15], which do not typically explicitly include or measure such constructs. Furthermore, we hypothesized that, during the 18 months the GALM strategy lasted, subjects would shift from the contemplation/preparation phase into the action/maintenance phase of the stages of change concept. The results of this study showed that main effects for time were found for self-efficacy for overcoming barriers, social support from friends and family, and enjoyment. However, for enjoyment and self-efficacy for overcoming barriers, this effect was due mainly to a decline in the scores of the nonadherers. Contrary to our expectations, self-efficacy for overcoming barriers dropped between T0 and T1 and then rose from T1 to T2. The major drop in score from T0 to T1 was seen on the item I continue to participate in the program although it was not fun or enjoyable. Enjoyment experienced dropped both from T0 to T1 and from T1 to T2. This was also contrary to our expectations, as we expected a rise in enjoyment experienced, mainly between T1 and T2. The largest drop in scores was seen on the items during leisure-time physical activity, I feel I can be myself, doing leisuretime physical activities makes me feel good, and doing leisure-time physical activities gives me satisfaction. For both variables, it is rather speculative to attempt to explain the reasons underlying the patterns observed; it could be caused by the way we measured these mediating variables or the theoretical premises we made in the first portion of our mediation model for both variables [35]. It also could be the case that participants started the program with unrealistically high ratings of self-efficacy and/or enjoyment. This phenomenon may occur more often in the case of sedentary people unfamiliar with leisure-time physical activity [36]. The pattern of social support from friends and family was in line with the hypothesis we had [15] and the fact that people tend not to experience social support to any significant degree until they face problems or challenges, such as becoming physically active [37]. Social support from friends rose mainly from T0 to T1 and only slightly from T1 to T2.

7 M. Stevens et al. / Preventive Medicine 37 (2003) Fig. 3. Significant time group effects. Social support from family rose from T0 to T1 and remained almost stable from T1 to T2. Significant group time interaction effects were found for self-efficacy for overcoming barriers, task selfefficacy, perceived fitness, social support from friends, and enjoyment. However, with respect to self-efficacy for overcoming barriers and enjoyment, these effects were due mainly to the decrease in scores of the nonadherers, while the adherers remained fairly stable over time. With respect to task self-efficacy, perceived fitness, and social support from friends, these effects were due to a rise in scores from the adherers and a decrease in scores among the nonadherers. Based on these results, it can be concluded that we partially succeeded in manipulating the potential mediating variables of interest by means of our GALM strategy. This was the case with respect to task self-efficacy, perceived fitness, and exercise-related social support from friends. The fact that we succeeded in manipulating only a few of our mediating variables is in line with other studies in the field [35]. Baranowski et al. [38] reported that it is a rare occurrence for most of the hypothesized mediators to be actually changed by the intervention in the health promotion field. In a study by Bock et al. [39], it was even concluded that the intervention had no significant effect at all on the mediating variables.

8 568 M. Stevens et al. / Preventive Medicine 37 (2003) Table 3 Discriminant analysis using potentially mediating variables to explain adherers from dropouts after 6 months (T1) and 18 months (T2) (standardized coefficients) T1 a T2 b Task self-efficacy 0.84 Task self-efficacy 0.85 Self-efficacy for overcoming barriers 0.72 Social support from group members 0.52 Enjoyment 0.47 Enjoyment 0.49 Social support from group members 0.39 Self-efficacy for overcoming barriers 0.43 Social support from family 0.29 Social support from friends 0.39 Social support from friends 0.15 Perceived fitness 0.39 Perceived physical ability 0.74 Perceived physical ability 0.31 Perceived physical fitness 0.30 Social support from family 0.17 a Wilks 0.75; canonical r 0.50; classified correctly 73.8%. b Wilks 0.56; canonical r 0.66; classified correctly 80.0%. With respect to the stages of change variable, it can be concluded that subjects shifted from the contemplation/ preparation phase to the action/maintenance phase, confirming that participants indeed move through these stages of change during the 18-month period. This result is an indication that the formerly sedentary participants did generally change their lifestyle into a physically active one. In this respect, this study is one of the few that has been specifically designed to change the lifestyle of older adults [40]. The effect of this lifestyle change on health and fitness is the objective of a parallel study that is being currently carried out. With respect to the second portion of the proposed mediation model, we hypothesized that the changes in the potential mediating variables (self-efficacy, social support, perceived fitness, and enjoyment) would eventually lead to an enduring physically active lifestyle [15]. As a consequence of this hypothesis, it should have been possible to detect differences between people with less than adequate adherence to the GALM physical activity program and those who adequately adhered. In this study, main group effects for adherers and nonadherers were found for self-efficacy for overcoming barriers, task selfefficacy, perceived fitness, social support from friends and group members, and enjoyment. For self-efficacy for overcoming barriers, task self-efficacy, and perceived fitness, an increase was seen in the scores for adherers while a decrease was observed for nonadherers. For the social support from friends variable, an increase was seen in scores for both the adherers and nonadherers, although the largest increase was seen in the adherers. With respect to both social support from group members and enjoyment, a decrease was seen in the scores of both groups, although the scores among the nonadherers decreased the most. From the results of the discriminant analysis, it can be concluded that enjoyment plays a relatively important role in explaining the difference between adherers and nonadherers, at both T1 and T2. Besides the role of enjoyment, the dominant role of task self-efficacy and self-efficacy for overcoming barriers at T1 and T2 is in line with earlier research with respect to the role of self-efficacy and behavioral change [41]. At T2, besides task self-efficacy and enjoyment, social support from group members played an important role in this sample. The relative importance of social support from group members found in the discriminant analyses at T2 justifies this extension of the original social support scale by Sallis et al. [30] with an additional subscale for group members to cover the full range of social support that subjects can receive during the process of behavioral change. Especially for older adults, social support from group members can be important, given that with increasing age loved ones (mainly spouses) pass away. The GALM behavioral change model was especially designed to provide a theoretical framework for the GALM strategy. In this longitudinal study, only participants fitting in the contemplation/preparation phase were included. With respect to behavioral change, this group was considered of primary interest. Subjects in the maintenance/action phase are already active, while subjects in the precontemplation phase report little intention or motivation to become more active currently. In terms of stimulation of a physically active lifestyle, this latter group is likely to be the most relevant but also the most difficult to reach. In that sense, it remains a challenging and laborious task to develop effective strategies by which subjects in the precontemplation phase can also be effectively persuaded to become physically active. A promising step in this direction is the fact that our recruitment strategies yielded a reasonably large percentage of precontemplators (17%) who signed up for the GALM program. In general, it is assumed that physical activity behavior models and theories of behavioral change account for somewhere between 20 and 40% of the explained variance in physical activity participation. Some researchers are more conservative and give percentages of between 20 and 30%, but they argue that with the development of models specifically designed for physical activity behavior it should be possible to reach explanations of higher variance percentages [42]. For the time being, it looks as if we succeeded in this respect with the GALM behav-

9 M. Stevens et al. / Preventive Medicine 37 (2003) ioral change model with canonical correlations of 0.50 and 0.66 (percentages of explained variances of 25 and 44%, respectively). On the other hand, we did not succeed in manipulating all of the potential mediating variables being targeted, and we have only some indications about the relative importance of the different mediating variables during the process of behavioral change. Follow-up research is needed to make definitively clear if the hypothesis as formulated in the conceptual domain is a valid one, and whether, ultimately, enjoyment is the main mediating variable for a lasting physically active lifestyle or whether self-efficacy, social support, and perceived fitness also remain important. Especially with respect to enjoyment, the GALM behavioral change model distinguishes itself from other behavioral change models in the field of social cognitive theory [15]. Acknowledgment For critical suggestions during preparation of the article the authors thank Dr. A.C. King of Stanford Medical School, Palo Alto, CA, USA. References [1] Powell KE, Blair SN. The public health burdens of sedentary living habits: theoretical but realistic estimates. Med Sci Sports Exerc 1994; 26: [2] Bijnen FCH, Mosterd WL, Caspersen CJ. Physical inactivity: a risk factor for coronary heart disease; a position statement for the World Health Organization, governments, heart foundations, societies of cardiology and other health professionals. Geneva: International Society for the World Health Organization; [3] Blair SN, Kohl HW, Paffenbarger RS, Clark DG, Cooper KH, Gibbons LW. Physical fitness and all-cause mortality: a prospective study of healthy men and women. JAMA 1989;262: [4] Bouchard C, Shephard RJ, Stephens TS Physical activity, fitness, and health. International proceedings and consensus statement. Champaign, IL: Human Kinetics. 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