Positive Exercise Experience Facilitates Behavior Change via Self-Efficacy

Abstract

Purpose. Motivational processes can be set in motion when positive consequences of physical exercise are experienced. However, relationships between positive exercise experience and determinants of the motivational and the volitional phases of exercise change have attracted only sparse attention in research. Method. This research examines direct and indirect associations between positive experience and motivational as well as volitional self-efficacy, intention, action planning, and exercise in two distinct longitudinal samples. The first one originates from an online observational study in the general population with three measurement points in time (N = 350) and the second one from a clinical intervention study in a rehabilitation context with four measurement points (N = 275). Results. Structural equation modeling revealed the following: Positive experience is directly related with motivational self-efficacy as well as intentions in both samples. In the online sample only, positive experience is associated with volitional self-efficacy. In each sample, experience is indirectly associated with action planning via motivational self-efficacy and intentions. Moreover, action planning, in turn, predicts changes in physical exercise levels. Conclusions. Findings suggest a more prominent role of positive experience in the motivational than in the volitional phase of physical exercise change. Thus, this research contributes to the understanding of how positive experience is involved in the behavior change process.

Keywords

exercise intention motivation phase-specific self-efficacy volition

Despite the physical and mental health benefits of physical activity (Saxena, Van Ommeren, Tang, & Armstrong, 2005; Warburton, Nicol, & Bredin, 2006), many adults remain physically inactive. According to the World Health Organization (2011), 44.5% of the population in the industrialized countries does not meet the recommendations of being physically active for at least 30 minutes on 5 or more days a week.

Lack of time, motivation, and energy as well as negative experience with physical activity (e.g., sweating, pain) seem to be main reasons for inactivity (Toscos, Consolvo, & McDonald, 2011). However, a recent longitudinal twin study (Aaltonen et al., 2012) concludes that perceived barriers are not as important for the initiation and maintenance of regular physical activity as expected because no difference in perceived barriers between active and inactive co-twins was identified. Instead, experiencing positive consequences such as pride as well as beneficial health effects were found to be more conducive for regular activity. Furthermore, the experience of well-being (feeling better afterward), social aspects (e.g., meeting people during exercise) and positive impact on one’s own appearance support engagement in physical activity (Sherwood & Jeffery, 2000; Taylor et al., 1999).

So far, the differential role of positive experience with exercise for motivational and volitional processes has attracted only sparse attention in research. However, a deeper understanding of how positive experience is related to physical exercise as well as to intentions to perform health behaviors is required to develop effective interventions.

In the course of health behavior change, different cognitive processes have been found important. Most theories distinguish at least two phases—a motivational and a volitional phase (Heckhausen, 1991). The motivational phase refers to the formation of a behavioral intention and is, therefore, also known as the goal-setting phase. Once the intention to change one’s behavior has been developed, the volitional or goal-pursuit phase begins.

The Health Action Process Approach (HAPA; Schwarzer, Lippke, & Luszczynska, 2011) suggests phase-specific self-efficacy beliefs. Motivational self-efficacy (e.g., “I am certain that I can do X”) is crucial to develop an intention in the motivational phase. Volitional self-efficacy (e.g., “I am capable of coping with barriers and recovering from setbacks”), however, should be instrumental in pursuing goals once they have been set (Schwarzer et al., 2011). It can comprise coping (or maintenance) and recovery self-efficacy, and individuals already intending to change their behavior should mainly benefit from such kinds of volitional self-efficacy. Furthermore, action planning is a facilitating factor in the volitional phase. It is a prospective self-regulatory strategy, and individuals who generate plans are more likely to translate their intentions into behavior (Koring et al., 2012; Sniehotta, Scholz, & Schwarzer, 2005). However, the belief in being able to change one’s behavior as well as action planning is not sufficient. Earlier experience of consequences that came along with this particular health behavior (e.g., feeling better afterward) is also meaningful in the behavior change process but the concept is not explicitly included within the HAPA, which examines outcome expectancies. The latter are prospective, whereas outcome experiences relate to the same phenomenon, however in a retrospective way.

The experience of behavioral consequences has to be distinguished from the concept of mastery experience as defined by Bandura (1998). Mastery experience is seen as success with the performance of the behavior itself and not as a previously experienced positive outcome of this behavior. Fuchs, Goehner, and Seelig (2011) describe outcome experience as individual experience and appraisals after the adoption of a novel behavior. The authors propose that past experience of positive consequences of physical exercise (e.g., “jogging made me feel better”) affects motivational processes such as the formation of intentions. Based on this assumption, a theory-based intervention program for inpatients of an orthopedic rehabilitation clinic was developed with prompting focus on past success as a behavior change technique. More precisely, patients were asked to acknowledge past positive consequences of physical exercise in a group discussion session to set the motivational phase in motion (Fuchs et al., 2011).

Furthermore, Rothman (2000) argues that being satisfied with experienced consequences of a behavior is important to sustain an initiated behavior. His assumptions were confirmed by a study on rehabilitation patients’ positive experience with exercise (Fleig, Lippke, Pomp, & Schwarzer, 2011). In this study, the authors found that recently experienced positive consequences enhanced satisfaction with exercise and action planning, which in turn fostered exercise maintenance. Their findings point to a mediating effect of satisfaction and action planning between patients’ positive experience with exercise and their subsequent levels of exercise. However, the authors did not test whether positive experience also had an impact on patients’ levels of exercise via an increase in intentions and self-efficacy. Yet self-efficacy beliefs are suggested to be fostered because of positive experience as well, implying a mediating effect of self-efficacy between positive experience and physical exercise (Sherwood & Jeffery, 2000). This assumption was confirmed in recent studies investigating a sample of university students (Parschau et al., 2013) and a sample of older adults with chronic illnesses (Warner, Schüz, Knittle, Ziegelmann, & Wurm, 2011).

To sum up, there is some initial empirical evidence that exercise experience may be a catalyst for several social–cognitive mechanisms. However, the question of how positive experience exerts its influence on physical exercise remains.

Aims

This research aims to investigate the role of exercise experience as a starting point in the behavior change processes. Positive experience with consequences of physical exercise is assumed to stimulate motivational self-efficacy, behavioral intentions, and volitional self-efficacy. Action planning should be affected by experience indirectly via an increase in motivational self-efficacy, intentions, and volitional self-efficacy. Action planning would, in turn, predict changes in physical exercise. To evaluate the validity of this theoretical model, we will test it in two distinct samples: the first originating from a longitudinal online observation study in the general population, the second from a clinical intervention study in a rehabilitation context.

Study 1

Method

Participants and Procedure

Participants were recruited through a scientific TV show which is broadcasted weekly in Germany. Data collection started with the program about New Year’s resolutions in January 2012. The website of this TV show contained a link to the online study. At Time 1 (T1), 729 participants provided informed consent, filled in the online questionnaire, and provided their e-mail addresses for follow-up assessments. The second measurement point in time (T2) was implemented 2 weeks later by inviting participants via e-mail to respond to the follow-up questionnaire. The T2 questionnaire was completed by 555 participants. Another e-mail invitation for the second follow-up questionnaire (T3) took place 5 weeks after T1. All three online questionnaires were completed by 350 participants.

The sample consisted of 63.7% women. Participants were on average 41 years of age (SD = 12.8, range 16-90 years) and had a mean body mass index of 24.7 kg/m² (SD = 4.5, range 16.8-49.5 kg/m²). The majority of the participants were living with a partner (67.4%) and graduated from high school (81.4%).

Measures

The online-based questionnaires contained demographic and psychometric scales. All items given below were translated from German.

Self-reported strenuous physical exercise was measured twice by using two items of the Godin Leisure-Time Exercise Questionnaire (Godin & Shephard, 1985; Plotnikoff et al., 2007). At T1 and T3, participants were asked to indicate the average number of sessions per week and the average duration of a session regarding strenuous physical exercise such as intensive swimming, jogging, and cycling. Frequency and average duration per session were multiplied to form a measure of weighted duration of strenuous exercise throughout a normal week. Exercise scores 3 standard deviations larger than the mean were truncated.

Positive exercise experience was measured at T1 by nine items of the revised Exercise Experience Scale (Fleig et al., 2011). The items were responded to on 4-point Likert-type scales, ranging from 1 = completely disagree to 4 = completely agree. The item stem “When I was physically active, I experienced that . . .” was followed by positive consequences such as “. . . I felt physically better afterward” (Cronbach’s α = .76). To minimize the number of single indicators for the latent variable and thereby reducing error variances simultaneously, three parcels of three items each were used as indicators for exercise experience (Little, Cunningham, Shahar, & Widaman, 2002).

The following scales were adapted from Schwarzer et al. (2011). The response format for each scale was a 4-point Likert-type scale, ranging from 1 = completely disagree to 4 = completely agree and the time frame referred to “the next weeks.”

Behavioral intention to perform physical exercise was assessed at T1 with the item stem “I strongly intend to . . .” which was followed by four items such as “. . . be physically active several days a week” (Cronbach’s α = .77).

Action planning was assessed at T2 by three items (Cronbach’s α = .81). The item stem “I have planned in detail . . .” was followed by items such as “. . . how often I will be physically active.”

Motivational self-efficacy consisted of two single-item indicators assessed at T1 (r = .68) such as “I am certain that I can be physically active even if it is difficult for me.”

Volitional self-efficacy was measured at T2 with four items such as “I am certain that I can be physically active on a regular basis even if I have to overcome myself” (Cronbach’s α = .83).

Intercorrelations, factor loadings, means, standard deviations, and ranges of all constructs are displayed in Table 1.

Table 1.

Intercorrelations, Factor Loadings, Means, Standard Deviations, and Ranges for Positive Experience, Motivational Self-Efficacy, Volitional Self-Efficacy, Behavioral Intention, Action Planning and Exercise for Study 1.

	1	2	3	4	5	6	M (SD)	Range
1. Positive experience T1	.62-.72						2.91 (0.55)	1-4
2. Motivational self-efficacy T1	.28***	.81-.84					3.12 (0.72)	1-4
3. Volitional self-efficacy T2	.34***	.54***	.59-.84				3.10 (0.65)	1-4
4. Intention T1	.31***	.48***	.45***	.42-.86			3.45 (0.59)	1-4
5. Action planning T2	.27***	.38***	.40***	.47***	.51-.91		2.84 (0.84)	1-4
6. Exercise T1^a	.14*	.36***	.27***	.31***	.29***		102.66 (109.51)	0-487
7. Exercise T3^a	.18**	.38***	.31***	.39***	.32***	.73***	120.80 (120.48)	0-458

Note. All scores are related to the manifest scales. The ranges of indicator factor loadings on latent constructs are presented in the diagonal and in boldface. Intercorrelations are presented below the diagonal. N = 350. T1 = Time 1; T2 = Time 2; T3 = Time 3.

Minutes per week.

p < .05. **p < .01. ***p < .001.

Data Analysis

SPSS 20 was used for reliability, descriptive and attrition analyses. Structural equation modeling was performed with AMOS 20. By default, missing values were treated using full information maximum likelihood. Goodness-of-fit indices to evaluate model fit were the Tucker–Lewis Index (TLI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). A satisfactory model fit is indicated by TLI and CFI greater than .90 and RMSEA lower than .08 (Kline, 2005).

The structural equation model included positive exercise experience as a distal predictor. Direct paths of positive experience on motivational self-efficacy, intention, volitional self-efficacy, and subsequent physical exercise were added. Physical exercise at T1 was included as a covariate. Furthermore, baseline exercise and positive experience were allowed to correlate. Latent constructs were created for positive experience, motivational self-efficacy, behavioral intention, volitional self-efficacy, and action planning whereas exercise was specified as a manifest outcome.

Results

Attrition Analysis

Attrition analyses revealed no differences with regard to demographic characteristics (sex, age, partner status, and education) and baseline assessments of positive experience, motivational self-efficacy, and intention between participants who provided complete longitudinal data (48.1% of the initial sample) and those who did not take part in the follow-up assessment. However, the longitudinal sample was significantly more active at baseline (M_responders = 104.21 minutes, SD_responders = 115.52, M_{nonresponders} = 84.56 minutes, SD_{nonresponders} = 114.29, t(727) = −2.31, p < .05). This difference was accounted for by including baseline physical exercise as a covariate into the structural equation model.

Evaluation of the Sequential Mediator Model

The hypothesized model had an adequate fit with χ²(126) = 325.79, p < .001, χ²/df = 2.59, TLI = .90, CFI = .93 and RMSEA = .07 (90% confidence interval = [.06, .08]). Figure 1 displays its standardized parameter estimates.

Figure 1.

Path diagram of Study 1 (N = 350).

Positive experience emerged as a predictor of motivational self-efficacy and behavioral intention. Positive experience accounted for 16% of the variance in motivational self-efficacy. The amount of the explained variance in intention was 41%.

The direct path from experience to physical exercise T3 was not significant (β = .07, p > .10). Therefore, the path was omitted in Figure 1.

Positive experience and motivational self-efficacy accounted for 49% of the variance in volitional self-efficacy. The amount of explained variance in physical exercise was 54%. Within the 5 weeks between T1 and T3, exercise levels remained relatively stable.

Discussion

In an online-recruited sample, we investigated in which way positive experience is associated with determinants of the motivational and volitional phases of physical exercise. Structural equation modeling provided evidence for the relationship between experience and motivational self-efficacy on one hand as well as behavioral intentions on the other. Positive experience was also directly related to volitional self-efficacy, even though not as strongly as to motivational self-efficacy. Additionally, positive experience was not directly associated with changes in strenuous exercise.

However, by interpreting the results, a selection bias might have to be taken into account. It cannot be ruled out that the volunteer sample might have had an enhanced interest in the topic of physical exercise compared with the general population. Therefore, it might be that the rather high means in positive experiences, intention, and behavior are associated with an enhanced interest in the study’s topic. Furthermore, the investigated sample is likely to be biased toward higher educated individuals and should therefore not be generalized to individuals with a less educational background.

Although data of three measurement points in time were available, the relationship between positive experience, motivational self-efficacy, and intention, respectively, was examined only cross-sectionally at T1. An investigation of four measurement points would allow testing the assumed temporal order and the preceding role of exercise experience in more detail. Therefore, we also analyzed data of a second study with four measurement points in time.

Study 2

Method

Participants and Procedure

Data of a second study were analyzed to substantiate the previous findings in the context of tertiary prevention over four measurement points in time.

Participants were recruited in three German rehabilitation clinics between 2009 and 2011 (one inpatient cardiac, one inpatient orthopedic, and one outpatient orthopedic). The regular program within these clinics provided medical, physiotherapeutic, and psychological treatment. The first measurement point in time (T1) took place at the beginning of rehabilitation. After having provided informed consent, 461 patients completed a computer-based questionnaire. At the end of the rehabilitation program (T2), patients were asked to respond to a second computer-based questionnaire. Six weeks after discharge, a third assessment was completed via computer-assisted telephone interviews (CATI; T3). Six months after discharge, the fourth assessment (T4) was again carried out via CATI.

The longitudinal rehabilitation sample included participants who took part at least at the T1 and T4 measurements (n = 275). Of this sample, 69% were individuals in orthopedic rehabilitation and 31% were individuals in cardiac rehabilitation. The sample consisted of more women (56%) than men. The mean age was 50 years (SD = 9.3, range 19-76 years) and the mean body mass index was 28.2 kg/m² (SD = 5.8, range 16.7-49.2 kg/m²). Most of the rehabilitation patients indicated to have a partner (75.3%) and 44.7% of the participants graduated from high school.

Measures

The assessments contained demographic as well as several psychometric scales. Social-cognitive variables were partly related to the rehabilitation context and, therefore, differed from the ones in Study 1. All items given below were translated from German.

Self-reported moderate and strenuous physical exercise was measured by using four items of the modified version of the Godin Leisure-Time Exercise Questionnaire (Godin & Shephard, 1985; Plotnikoff et al., 2007). At T1 and T4, participants were asked to indicate how many times and how long per session they performed moderate (hardly exhausting, light sweating) and strenuous physical exercise (fast heart rate, sweating) on average per week. For the analyses, a composite score (number of sessions per week multiplied by minutes per session) was formed for moderate and strenuous activities. Both scores (r = .29) were then aggregated to the total number of exercise per week. Scores larger than three standard deviations above the sample’s mean were truncated.

Based on a measure by Lippke, Fleig, Pomp, and Schwarzer (2010), behavioral intentions to perform exercise were assessed at T2 with the item stem “I intend to do the following activities . . .” which was followed for example by “. . . fitness activities (e.g., using an exercise bike).” Participants could indicate the frequency and duration they intended by choosing one of the following answering options: 1 = not at all, 2 = less than one time per week for about 40 [20] minutes, 3 = at least once per week for 40 [20] minutes, 4 = at least three times per week for 40 [20] minutes, and 5 = at least five times per week for 40 [20] minutes or more. Because of different medical recommendations, response options regarding the duration varied for orthopedic (20 minutes) and cardiac (40 minutes) rehabilitation patients. To account for this difference, the type of patient was entered as a covariate into the structural equation models. Even though the internal consistency for this scale was low (Cronbach’s α = .44), a factor analysis suggested one component for all three items (factor loadings between .56 and .80).

The response format for all following items was a 6-point Likert-type scale, ranging from 1 = completely disagree to 6 = completely agree.

Positive exercise experience was measured at T1 by five items of the revised Exercise Experience Scale (Fleig et al., 2011). The item stem “When I was physically active, I experienced that . . .” was followed by positive consequences such as “. . . it had a positive impact on my health” (Cronbach’s α = .75).

Action planning was assessed at T3 by four items (Cronbach’s α = .87). The item stem “Regarding the next four weeks, I have already planned . . .” was followed for example by the item “. . . which physical activities I will perform.”

Phase-specific self-efficacy was assessed at T2 (Lippke et al., 2010). Motivational self-efficacy was measured with the stem “I am certain that I can be physically active on a regular basis even if . . .” followed by two items such as “. . . it is difficult” (r = .83). Volitional self-efficacy consisted of two indicators such as “I am capable of exercising on a regular basis even if it takes some time until it becomes a routine” (r = .67). The time frame of the two self-efficacy scales referred to “the next weeks after the rehabilitation.”

Intercorrelations, factor loadings, means, standard deviations, and ranges for each manifest scale are displayed in Table 2. All analyses were performed in the same manner as in Study 1.

Table 2.

	1	2	3	4	5	6	M (SD)	Range
1. Positive experience T1	.41-.81						3.96 (1.11)	1-6
2. Motivational self-efficacy T2	.21**	.90-.92					4.80 (1.24)	1-6
3. Volitional self-efficacy T2	.04	.53***	.68-.99				4.15 (1.41)	1-6
4. Intention T2	.31***	.16*	.09	.32-.57			2.88 (0.72)	1-5
5. Action planning T3	.12	.27***	.24***	.24***	.74-.88		4.46 (1.45)	1-6
6. Exercise T1^a	.16*	.20**	.10	.20**	.14*		75.25 (99.26)	0-354
7. Exercise T4^a	.15*	.22**	.04	.30***	.20**	.29***	149.82 (121.87)	0-464

Note. All scores are related to the manifest scales. The ranges of indicator factor loadings on latent constructs are presented in the diagonal and in boldface. Intercorrelations are presented below the diagonal. N = 275. T1 = Time 1; T2 = Time 2; T3 = Time 3; T4 = Time 4.

Minutes per week.

p < .05.**p < .01. ***p < .001.

Results

Attrition Analysis

At baseline, 461 rehabilitation patients agreed to participate in the study. The first follow-up questionnaire (T2) was completed by 377 participants (81.8%) at the end of rehabilitation. At T3, 346 of them responded to the second follow-up questionnaire (CATI, 75.1% of the initial sample) and at T4, 275 responded to the third follow-up questionnaire (CATI, 59.7% of the initial sample).

Attrition analyses revealed no baseline differences in terms of sex, type of patients, partner status, education, exercise, and positive experience between participants who stayed in the longitudinal sample and those who did not take part in the follow-up assessments. The longitudinal sample was, however, older (M_responders = 50.2, SD_responders = 9.3, M_{nonresponders} = 47.8, SD_{nonresponders} = 11.6, t(314.6) = −2.24, p < .05; effect size d = .17). Therefore, age was included as a covariate into the structural equation model.

Evaluation of the Sequential Mediator Model

The fit of the hypothesized model was satisfactory with χ²(161) = 278.60, p < .001, χ²/df = 1.73, TLI = .90, CFI = .93, and RMSEA = .05 (90% confidence interval = [.04, .06]). Figure 2 displays the standardized parameter estimates.

Figure 2.

Path diagram of Study 2 (N = 275).

Levels of motivational self-efficacy and behavioral intentions were predicted by levels of positive experience. Positive experience accounted for 6% of the variance in motivational self-efficacy. The amount of explained variance in intentions was 28%.

Neither volitional self-efficacy (β = −.06, p > .10) nor exercise at T4 (β = .12, p = .10) were significantly predicted by positive experience. The amount of explained variance in exercise was 13%.

No significant effects of type of patient (orthopedic vs. cardiac) and age were found on exercise levels.

Discussion

In Study 2, the sample consisted of rehabilitation patients, and data were assessed at four measurement points in time. Positive experience at the beginning of the rehabilitation program was associated with motivational self-efficacy as well as with behavioral intentions at the end of the rehabilitation. However, positive experience at the beginning of the rehabilitation was neither related to volitional self-efficacy 6 weeks after discharge nor related to physical exercise 6 months after discharge.

Although the setting and sample characteristics differed between both studies, all paths of the hypothesized model—apart from the path between exercise experience and volitional self-efficacy—were replicated in Study 2.

General Discussion

As an individual’s current exercise may partly be driven by past experiences with exercise consequences, we investigated the function of positive exercise experience in two distinct samples. Changes in the level of physical exercise were not directly predicted by positive experience neither in the online-recruited sample of the general population nor in the sample of rehabilitation patients. Positive exercise experience did, however, relate to motivational self-efficacy and behavioral intentions, which in turn predicted later exercise. Additionally, positive experience was associated with volitional self-efficacy in Study 1. Nevertheless, results of both studies indicate a more consistent link between positive exercise experience and motivational factors, less so with volitional factors. This finding is in accordance with the assumptions by Fuchs et al. (2011) suggesting that the experience of positive consequences affects motivational determinants of exercise.

Furthermore, both studies found intentions as well as volitional self-efficacy to be related to subsequent action planning which, in turn, was associated with exercise later on. These findings partly confirm previous evidence for the HAPA (Chiu, Lynch, Chan, & Berven, 2011; Sniehotta et al., 2005).

Strengths and Limitations

A strength of this article lies in the replication of findings because the hypothesized model was tested in two samples and distinct contexts. Although both studies showed a considerable dropout rate, the samples had an acceptable size with 350 and 275 study participants, respectively. However, the differences between responders and nonresponders were accounted for in our analyses. Although the samples were surveyed on three and four measurement points in time in Study 1 and Study 2, respectively, causality cannot be proven by these research designs.

Implications for Future Research

Previous studies found motivational self-efficacy to be a strong predictor of behavioral intentions (Parschau et al., 2012; Scholz, Sniehotta, & Schwarzer, 2005). However, evidence on how motivational self-efficacy can be fostered is rare. On the basis of the literature (Fleig et al., 2011; Fuchs et al., 2011; Parschau et al., 2013; Rothman, 2000) and the results of the present study, the construct of exercise experience is a promising candidate to be integrated and further investigated in health behavior change models.

As both studies investigated a selection of motivational and volitional components taken from established models only (e.g., HAPA), the function and location of exercise experience in health behavior change models is not yet sufficiently clarified. In future studies, direct and indirect associations between positive experience and volitional cognitions such as action planning, coping planning, and action control might be investigated in more detail by differentiating between the initiation and maintenance of physical exercise. Moreover, examining whether long-term or short-term exercise experience has different effects on subsequent exercise might be of further interest. The thematic content of the experience (e.g., health, emotion, or appearance related) might also offer implications for exercise interventions.

These two studies bear several practical implications for the promotion of exercise self-efficacy, exercise intentions, and exercise behavior in the general population and in rehabilitation patients. To raise individuals’ awareness of positive exercise experience, a recall of positive effects of previous exercise could be prompted in interviews or recorded in diaries (Fleig et al., 2011; Pomp, Fleig, Schwarzer, & Lippke, 2012). Providing individuals with feedback on their positive exercise outcomes has been found effective in previous studies (Ashford, Edmunds, & French, 2010) and could be another straightforward strategy to be implemented, for example, in the rehabilitation context.

Conclusion

According to the present results, positive exercise experience seems to be relevant in the motivational phase of changes in exercise behavior. Together with future research on positive experience in the context of other health behaviors, this construct is a promising candidate to be integrated into the motivational phase of health behavior process models.

Footnotes

Acknowledgements

We thank the rehabilitation clinics and their rehabilitants for participating in this study. We especially appreciate the support of Mrs. Pimmer, Dr. Kiwus, Dr. Glatz, Dr. Milse, and Dr. Johnigk.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interests with respect to the research, authorship, and/or publication of this article.

Funding

The authors declared the following financial support for the research, authorship, and/or publication of this article: Study 2 was supported by the Deutsche Rentenversicherung Bund (DRV; German Pension Insurance) within the project FABA (Project ID 8011-106-31/31.91).

References

Aaltonen

Leskinen

Morris

Alen

Kaprio

Liukkonen

Kujala

(2012). Motives for and barriers to physical activity in twin pairs discordant for leisure time physical activity for 30 years. International Journal of Sports Medicine, 33, 157-163. doi:10.1055/s-0031-1287848

Ashford

Edmunds

French

D. P.

(2010). What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis. British Journal of Health Psychology, 15, 265-288. doi:10.1348/135910709X461752

Bandura

(1998). Health promotion from the perspective of social cognitive theory. Psychology & Health, 13, 623-649. doi:10.1080/08870449808407422

Chiu

C. Y.

Lynch

R. T.

Chan

Berven

N. L.

(2011). The Health Action Process Approach as a motivational model for physical activity self-management for people with multiple sclerosis: A path analysis. Rehabilitation Psychology, 56, 171-181. doi:10.1037/a0024583

Fleig

Lippke

Pomp

Schwarzer

(2011). Exercise maintenance after rehabilitation: How experience can make a difference. Psychology of Sport and Exercise, 12, 293-299. doi:10.1016/j.psychsport.2011.01.003

Fuchs

Goehner

Seelig

(2011). Long-term effects of a psychological group intervention on physical exercise and health: The MoVo concept. Journal of Physical Activity and Health, 8, 794-803.

Godin

Shephard

R. J.

(1985). A simple method to assess exercise behavior in the community. Canadian Journal of Applied Sport Sciences, 10, 141-146.

Heckhausen

(1991). Motivation and action. Berlin, Germany: Springer.

Kline

R. B.

(2005). Principles and practice of structural equation modeling. New York, NY: Guilford Press.

10.

Koring

Richert

Lippke

Parschau

Reuter

Schwarzer

(2012). Synergistic effects of planning and self-efficacy on physical activity. Health Education & Behavior, 39, 152-158. doi:10.1177/1090198111417621

11.

Lippke

Fleig

Pomp

Schwarzer

(2010). Validity of a stage algorithm for physical activity in participants recruited from orthopedic and cardiac rehabilitation clinics. Rehabilitation Psychology, 55, 398-408. doi:10.1037/a0021563

12.

Little

T. D.

Cunningham

W. A.

Shahar

Widaman

K. F.

(2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151-173. doi:10.1207/S15328007SEM0902_1

13.

Parschau

Fleig

Koring

Lange

Knoll

Schwarzer

Lippke

(2013). Positive experience, self-efficacy, and action control predict physical activity changes: A moderated mediation analysis. British Journal of Health Psychology, 18, 395-406. doi:10.1111/j.2044-8287.2012.02099.x

14.

Parschau

Richert

Koring

Ernsting

Lippke

Schwarzer

(2012). Changes in social-cognitive variables are associated with stage transitions in physical activity. Health Education Research, 27, 129-140. doi:10.1093/her/cyr085

15.

Plotnikoff

R. C.

Lippke

Reinbold-Matthews

Courneya

K. S.

Karunamuni

Sigal

R. J.

Birkett

(2007). Assessing the validity of a stage measure on physical activity in a population-based sample of individuals with type 1 or type 2 diabetes. Measurement in Physical Education and Exercise Science, 11, 73-91. doi:10.1080/10913670701294062

16.

Pomp

Fleig

Schwarzer

Lippke

(2012). Depressive symptoms interfere with post-rehabilitation exercise: Outcome expectancies and experience as mediators. Psychology, Health, & Medicine, 17, 698-708. doi:10.1080/13548506.2012.661864

17.

Rothman

A. J.

(2000). Toward a theory-based analysis of behavioral maintenance. Health Psychology, 19(1 Suppl.), 64-69. doi:10.1037/0278-6133.19.Suppl1.64

18.

Saxena

Van Ommeren

Tang

K. C.

Armstrong

T. P.

(2005). Mental health benefits of physical activity. Journal of Mental Health, 14, 445-451. doi:10.1080/09638230500270776

19.

Scholz

Sniehotta

F. F.

Schwarzer

(2005). Predicting physical exercise in cardiac rehabilitation: The role of phase-specific self-efficacy beliefs. Journal of Sport & Exercise Psychology, 27, 135-151.

20.

Schwarzer

Lippke

Luszczynska

(2011). Mechanisms of health behavior change in persons with chronic illness or disability: The Health Action Process Approach (HAPA). Rehabilitation Psychology, 56, 161-170. doi:10.1037/a0024509

21.

Sherwood

N. E.

Jeffery

R. W.

(2000). The behavioral determinants of exercise: Implications for physical activity interventions. Annual Review of Nutrition, 20, 21-44. doi:10.1146/annurev.nutr.20.1.21

22.

Sniehotta

F. F.

Scholz

Schwarzer

(2005). Bridging the intention-behaviour gap: Planning, self-efficacy, and action control in the adoption and maintenance of physical exercise. Psychology & Health, 20, 143-160. doi:10.1080/08870440512331317670

23.

Taylor

W. C.

Yancey

A. K.

Leslie

Murray

N. G.

Cummings

S. S.

Sharkey

S. A.

. . . McCarthy

W. J.

(1999). Physical activity among African American and Latino middle school girls: Consistent beliefs, expectations, and experiences across two sites. Women & Health, 30(2), 67-82. doi:10.1300/J013v30n02_05

24.

Toscos

Consolvo

McDonald

D. W.

(2011). Barriers to physical activity: A study of self-revelation in an online community. Journal of Medical Systems, 35, 1225-1242. doi:10.1007/s10916-011-9721-2

25.

Warburton

D. E.

Nicol

C. W.

Bredin

S. S.

(2006). Health benefits of physical activity: The evidence. Canadian Medical Association Journal, 174, 801-809. doi:10.1503/cmaj.051351

26.

Warner

L. M.

Schüz

Knittle

Ziegelmann

J. P.

Wurm

(2011). Sources of perceived self-efficacy as predictors of physical activity in older adults. Applied Psychology: Health and Well-Being, 3, 172-192. doi:10.1111/j.1758-0854.2011.01050.x

27.

World Health Organization. (2011). Global status report on noncommunicable diseases 2010. Description of the global burden of NCDs, their risk factors and determinants. Geneva, Switzerland: Author.