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Article

Is the Behavioral Regulation in Exercise Questionnaire a Valid Measure in Older People?

by
Tommaso Palombi
1,*,
Fabio Lucidi
1,
Andrea Chirico
1,
Guido Alessandri
2,
Lorenzo Filosa
2,
Simone Tavolucci
2,
Anna M. Borghi
3,
Chiara Fini
3,
Elisa Cavicchiolo
4,
Jessica Pistella
1,
Roberto Baiocco
1 and
Fabio Alivernini
1
1
Department of Developmental and Social Psychology, Sapienza University of Rome, 00185 Rome, Italy
2
Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy
3
Department of Dynamic and Clinical Psychology, and Health Studies, Sapienza University of Rome, 00185 Rome, Italy
4
Department of Systems Medicine, Tor Vergata University of Rome, 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
Healthcare 2023, 11(20), 2707; https://doi.org/10.3390/healthcare11202707
Submission received: 21 August 2023 / Revised: 23 September 2023 / Accepted: 2 October 2023 / Published: 10 October 2023
(This article belongs to the Special Issue Instruments and Measures for Health, Education, and Sport Research)
Browse Figure
Review Reports Versions Notes

Abstract

:
Background: Despite the widely recognized benefits of physical activity for preventing physical and cognitive decline during aging, global estimates indicate that most older adults do not achieve the recommended amount of physical activity due to a lack of motivation. The current research examined the validity and psychometric properties of the Behavioral Regulation in Exercise Questionnaire (BREQ-3) among older adults. Based on Self-Determination Theory (SDT), the BREQ-3 stands out as one of the most extensively utilized tools among exercise motivation studies. Methods: A sample of older adults (N = 383; M age = 73.2 years, SD age = 7.2) completed the BREQ-3 and the Godin–Shepard Leisure-Time Physical Activity Questionnaire (GSLTPAQ). Results: Confirmatory factor analyses confirmed the six-factor structure postulated by SDT, showing good fit indices (CFI= 0.95; RMSEA = 0.05; SRMR = 0.04) and supporting the full measurement invariance of the scale across sex and age groups (65 to 74 years; over 75 years). The construct and criterion validity of the BREQ-3 was upheld through the latent correlations between its subscales and their correlations with the GSLTPAQ. Conclusions: We demonstrated for the first time the effectiveness of the BREQ-3 in assessing all forms of behavioral regulation proposed by SDT in older adults, suggesting that older adults similarly interpreted the items across sex and age groups.

1. Introduction

Participation in physical activity and exercise among older adults represents a healthy approach to reducing chronic diseases and mortality and improves overall health and quality of life [1]. Nevertheless, the latest global estimates by the World Health Organization (WHO) showed that 1.4 billion adults (27.5% of the world’s adult population) fall short of reaching the prescribed standard of physical activity, which tends to decrease among both women and men as they get older [2].
A systematic review of the literature conducted by Franco and colleagues investigated older people’s perspectives on physical activity to identify and synthesize the barriers and facilitators to physical activity participation [3]. In all, 40% of the studies reviewed reported that low motivation prevented the participation of older people in physical activity and exercise, although they acknowledged the benefits of such activities [3]. Several factors can contribute to a lack of motivation, including low interest or perceived competence in physical activity. These factors can make people unmotivated or insufficiently motivated to be more physically active [4]. Motivation is one of the most crucial variables explaining exercise and physical activity participation among older adults [3].
Nonetheless, there is a substantial lack of evidence in the scientific literature regarding the validity of measures of motivation toward physical activity in older people (i.e., people aged 65 years and above). Older adults tend to spend more time reflecting on their lives and contemplating their values and priorities, and many of them deal with chronic illnesses [5,6], suggesting that they could conceptualize physical activity differently than young adults or adults. Several literature reviews on barriers and motivators toward physical activity among older adults highlighted that factors such as health worries, fear of injury or pain, self-motivation, social support, confidence, and perceived support affected their adherence to and maintenance of physical activity [7,8,9,10].
In addition, there is evidence of sex differences in adopting exercise among older adults. Women perceived their health as being poorer, encountered more barriers to physical activity, and displayed lower self-efficacy for engaging in such activities than men [11]. Conversely, men exhibited lower motivation when it comes to weight loss or enhancing their physical appearance, in contrast to women, who prioritized these motivations [12]. Given these peculiarities, older adults might conceptualize motivation and interpret motivational items in existing measures differently across sex or age groups. Despite several studies assessing motivation in engaging in physical activity, no measures are currently appropriately validated in older adults. Instead, researchers often assume “measurement invariance” [13], a prevalent issue associated with many self-reporting instruments.
In the present paper, we investigated the psychometric proprieties for older people of a widely adopted measure of motivation in engaging in physical activity [14,15,16,17] based on Self-Determination Theory (SDT) [18,19]: the Behavioral Regulation in Exercise Questionnaire (BREQ-3) [4].

1.1. Self-Determination Theory (SDT)

Self-Determination Theory (SDT) stands as a frequently theoretical framework used for assessing the influence of motivational factors and of basic psychological needs (i.e., autonomy, competence, and relatedness) on emotional, cognitive, and behavioral outcomes within the context of education [18,19,20,21,22,23,24,25,26], sports [27,28], health [29,30,31], and exercise [32,33,34,35].
The SDT postulates a multidimensional structure of motivation, categorizing diverse regulatory patterns that represent varying degrees of self-determination (the perception of being the source of one’s behavior). Within the SDT framework, a differentiation is made between intrinsic and extrinsic forms of motivation that drive an individual’s behavior. Intrinsic motivation is the most self-determined form of behavior, occurring when individuals participate in activities driven by their inherent interest and enjoyment [36]. Intrinsically motivated individuals experience feelings of enjoyment, the exercise of their abilities, personal accomplishments, and a sense of enthusiasm [4]. Physical activity and exercise can be pursued for the satisfaction of engaging in a challenging activity. Over an individual’s lifespan, intrinsic motivation plays a pivotal role in learning [19]. Several studies have suggested that intrinsic motivation yields beneficial impacts on overall well-being and life satisfaction, persistence, engagement in activities, and performance [4,30,37,38,39,40,41]. On the other hand, extrinsic motivation pertains to activities carried out for instrumental purposes, obtaining a result distinct from the activity itself [19]. The extrinsically motivated behaviors are expressed in four regulations: external regulation, which refers to behaviors carried out to fulfill external requirements or driven by rewards and punishments from an external source [18]; introjected regulation, which refers to behaviors guided by inner rewards such as self-esteem and the avoidance of feelings like anxiety, shame, or guilt [25]; identified regulation, in which personal importance and value are attributed to the behavior, thereby enhancing the willingness to engage in it, even if the activity in itself is perceived as disagreeable; and integrated regulation, when identified regulatory patterns become integrated into an individual’s sense of self [42] and the behavior aligns with the individual’s other interests and principles. These regulatory mechanisms indicate the extent to which behaviors are internalized, demonstrating the transformation of habits into personally endorsed values and self-regulation. This is especially crucial when examining physical activity behavior. During this process, individuals who engage in physical activity or exercise can move from being driven by external and introjected regulations (controlled motivation) to being motivated by identified and integrated regulations (autonomous motivation) [36]. The most autonomous forms of motivation (i.e., identified and integrated motivations) and intrinsic motivation have been recognized as crucial elements in sustaining ongoing commitment to exercise over the long term [43]. The SDT also encompasses the concept of amotivation, which pertains to the absence of intentionality [25,44]. When individuals experience amotivation, regardless of whether it is intrinsic or extrinsic, they perceive a lack of linkage between their behaviors and the ensuing outcomes, leading to difficulties in recognizing any compelling reasons to engage in a particular activity [45]. The SDT continuum is fully explained in Table 1.

1.2. The Behavioral Regulation in Exercise Questionnaire (BREQ)

Numerous tools have been created to assess different facets of human motivation in alignment with the SDT framework. The original Behavioral Regulation in Exercise Questionnaire (BREQ), proposed by Mullan et al. [46], was the first attempt to measure the motivation domain, highlighting different forms of regulation according to SDT. Different versions of the BREQ have been developed over the years to measure motivation to exercise according to SDT. The first version of the BREQ [46] measured external, introjected, and identified regulations, along with intrinsic motivation, showing its validity and reliability in several studies [47,48,49,50]. Given the limitation of this version of the BREQ that did not include a measurement of the amotivation factor, Markland and Tobin [51] developed a second version of the questionnaire including four new items. This new measure was called BREQ-2, and it was composed of a 19-item scale in order to measure five factors (amotivation, external, introjected, identified, and intrinsic motivation). The BREQ-2 has been validated in different countries, becoming one of the extensively employed tools in the field of exercise motivation assessment [52,53,54,55,56]. Within the Italian context, the BREQ-2 was translated and validated by Costa et al. [54] in a sample of 576 gym users.
Their study assessed the internal reliability, construct validity, and criterion validity of the BREQ-2 and confirmed the factorial structure of the scale through exploratory factor analysis. Their findings demonstrated the good psychometric properties of the BREQ-2, confirming its utility as a valuable tool for assessing motivation in the exercise domain, also in the Italian context. Although the BREQ-2 presented good psychometric proprieties, it showed an inability to measure integrated regulation, the most autonomous form of extrinsic motivation proposed by the SDT framework. Given this limitation, a third version of the BREQ was developed to include items for measuring integrated regulation, thereby enhancing the comprehension of distinct motivational mechanisms operating within the realm of physical exercise. This new version was called BREQ-3, and it contained 24 items, 4 for each subscale, assessing the whole continuum of motivation according to SDT [4]. The psychometric proprieties of the scale were examined in several studies conducted in different countries [15,17,57], confirming the six-factor structure of the scale and showing acceptable model fit and invariance across sex and age in a sample of adults and young people. Although the 24-item version of the BREQ-3 revealed good psychometric proprieties, a study conducted by Cid et al. [14] reported an unsatisfactory model fit for the 24-item version of the scale. The authors removed 6 items (one for each factor), developing a shorter 18-item version of the BREQ-3 that substantially improved the fit of the model. Within the Italian context, Cavicchiolo et al. [16] analyzed the factorial structure, validity, and reliability of the short version of BREQ-3 in a sample of Italian young people and adults. Their study confirmed the six-factor structure of the scale, showing acceptable model fit and invariance across sex and age.

1.3. The Present Study

Although the BREQ-3 has demonstrated its validity as a tool for assessing motivation to engage in physical activity, according to the SDT framework, most studies have validated the instrument in adults and young adults. At present, no research has been conducted to validate any version of the BREQ among the older adult population. The BREQ’s effectiveness in measuring motivation to exercise within older adults remains an unexplored area. Given the increasing life expectancy, it has become essential to gain insights into the life changes associated with aging. The differences between the young-old (aged 60–74) and the old (aged 75 and above) can significantly affect physical, cognitive, and psychosocial aspects. Several studies have highlighted physical and psychological variations between the youngest old and the old [58,59,60] that could lead to disparities in item conceptualization.
In light of this, the main purpose of the current research was to evaluate the psychometric properties and the validity of the 18-item version of the BREQ-3 in a sample of older adults. We hypothesized the six-factor structure of the BREQ-3, as posited by SDT, and full measurement invariance across sex and age groups. Little research has been conducted on measurement invariance among different age categories of the elderly population. In accordance with other studies, we categorized individuals aged 65 to 74 as the youngest-old, while those falling within the 75 to 84 age range were considered to be in the old category [61,62]. In addition, we hypothesized stronger positive correlations between adjacent subscales of the SDT continuum to provide evidence for the construct validity of the BREQ-3. Moreover, we assessed physical activity using the Godin–Shepard Leisure-Time Physical Activity Questionnaire (GSLTPAQ) [63], expecting significant correlations between the six subscales of the SDT continuum. In line with the scientific literature, we hypothesized stronger positive correlations between the most autonomous forms of motivation (i.e., identified, integrated, and intrinsic) and physical activity [64,65].

2. Method

2.1. Sample and Procedure

The sample in the present study was composed of 383 older adults from different regions of central Italy. The participants’ average age was 73.2 years (SD = 7.2; min = 65, max = 95), with a slightly higher prevalence of females (51.4%). Before data collection, the study protocol was approved by the Ethics Committee of the Sapienza University of Rome. All participants provided informed consent to participate, in which they were informed regarding the overall aim of the study and their rights to anonymity and confidentiality. The online survey was delivered by email and was completed in around 10 min. The dataset used in this study is not accessible to the public, but interested parties can obtain it from the corresponding author upon reasonable request. The exclusion criteria were as follows: non-Italian speaker, current or past neurological disorder or major medical illness (e.g., dementia, traumatic brain injury, schizophrenia, epilepsy, active nausea, vomiting), current psychiatric disorder (e.g., major depression), or a severe sensory or motor deficit that would preclude physical activity or exercise.

2.2. Measures

The Behavioral Regulation Exercise Scale (BREQ-3) [51,66]. For this study, we used the 18-item Italian version of the BREQ-3, translated and validated by Cavicchiolo et al. (2022) in a sample of Italian young people and adults [16]. The BREQ-3 consists of 18 items rated on a five-point Likert scale, ranging from 0 (“not true for me”) to 4 (“very true for me”). The items were grouped posteriorly into the following six factors (three items per factor): amotivation, external, introjected, identified, integrated regulation, and intrinsic motivation. These factors reflect the motivational continuum of SDT [44]. We used the Italian version of the scale, which in previous validation studies [16] was established as being equivalent to the original by a team of independent judges.
The items of the Italian and English versions of the BREQ-3 are reported in Appendix A and Appendix B, respectively.
The Godin–Shepard Leisure-Time Physical Activity Questionnaire (GSLTPAQ) [63] is widely used in measuring Leisure-Time Physical Activity (LTPA). The questionnaire consists of 3 items to investigate the number of times, ranging on a Likert scale from 0 to 15, one engages in mild (minimal effort), moderate (not exhausting), and strenuous (heart beats rapidly) LTPA for at least 20 minutes’ duration in a typical 7-day period. Then, the Metabolic Equivalent of Task (MET) value (i.e., 3, 5, and 9 for mild, moderate, and strenuous intensity, respectively) is multiplied by each frequency score and summed to obtain a leisure score index (LSI) expressed in arbitrary units [63,67].
Sex was coded into two categories, with 0 indicating females and 1 indicating males. Age was grouped into the two categories of young older adults (65 to 74 years) and older adults (over 75 years) in accordance with previous studies [61,62].

2.3. Data Analysis

Mplus 8 software, version 1.6 [68], was used to estimate the proposed model consisting of six correlated factors. Confirmatory factor analysis (CFA) was performed using a Maximum Likelihood Robust (MLR) estimator, given the non-normal distribution of some variables. According to the cut-off values for well-fitted models [69], the model’s goodness-of-fit was assessed by employing the following fit indices: Comparative Fit Index (CFI), Root-Mean-Square Error of Approximation (RMSEA), and Standardized Root-Mean-Square Residual (SRMR). To investigate the measurement invariance of the scale across sex and age groups, a hierarchical series of multigroup confirmatory factor analyses (CFAs) was conducted. This involved progressively imposing more stringent equality constraints on the model’s parameters, following guidelines by Van de Schoot, Lugtig, and Hox [70]. In each step of the analysis, the fit of the nested models was compared using the change in CFI values (∆CFI ≤ 0.01) according to Cheung and Rensvold [71]. Finally, Pearson’s correlations were computed between the six subscales of the SDT continuum and the GSLTPAQ to provide evidence for the construct validity of the instrument.

3. Results

3.1. Descriptive Statistics

The descriptive statistics showed a non-normal univariate distribution for some items related to amotivation and external regulation, which presented a bias to the right; this could be explained by the tendency for the individuals to use the lowest levels of an answer (i.e., zero and one) for the non-autonomous form of motivation. The descriptive statistics for the items and each subscale are reported in Table 2.

3.2. Factor Structure of the BREQ-3

The results of the confirmatory factor analysis (CFA) confirmed the six-factor structure for the BREQ-3: χ2 (153) = 2980.527, p < 0.001; CFI = 0.95; RMSEA = 0.05; SRMR = 0.04. All of the loadings were statistically significant (p < 0.001). The results of the CFA showed that all of the fit indices indicated a good fit of the model with the empirical data [69,72]. Table 3 presents the goodness-of-fit indices for the BREQ-3 model in the current study, along with the findings for other available versions. The standardized factor loadings are presented in Figure 1.

3.3. Measurement Invariance across Sex and Age Groups

Table 4 reports the results of the multigroup confirmatory factor analyses (CFAs) conducted separately across sex and age groups. Regarding the multigroup CFAs across sex, when comparing the configural invariance model to the metric invariance model (i.e., the model with equal factor loadings across groups), the difference in CFI between the models was below the cutoff criterion (∆CFI = 0.003), supporting the hypothesis of metric invariance across sex. Furthermore, comparing the metric and scalar invariance models (i.e., the model with equal item intercepts across groups) provided evidence for full scalar invariance of the scales (∆CFI = 0.002). Regarding the multigroup CFAs across age groups, the comparison of the configural invariance model with the metric invariance model and the comparison of the metric invariance model with the scalar invariance model showed no differences in the CFI, demonstrating the full scalar invariance of the scales (∆CFI = 0.000). Overall, the results support the conclusion that the scales exhibit full scalar invariance across sex and age groups.

3.4. Correlations between Motivation Subscales and Physical Activity

Table 5 reports McDonald’s omega values for each subscale and a correlations matrix posited by SDT with stronger positive correlations between adjacent subscales than between subscales. In addition, the results showed a statistically significant negative correlation between amotivation and GSLTPAQ and positive correlations between GSLTPAQ and the other subscales, except for external motivation. Overall, these results provide evidence for the construct validity of the BREQ-3.

4. Discussion

Extensive research has provided robust evidence that physical inactivity plays a significant role in leading to various chronic diseases and conditions, such as depression, dementia, and the decline of functional abilities associated with aging [1,73,74,75]. Experimental studies have reported that SDT-based interventions can enhance physical activity [76,77], demonstrating that all forms of autonomous regulation predict physical activity participation across different groups and settings [4]. The assessment of motivation seems crucial to understanding the determinants of physical activity, although the scientific literature lacks significant evidence regarding the validity of measures specifically in the older population. Older adults’ needs and motivations may differ significantly from those of adults or young adults, leading to a different conceptualization of the items that could change the motivation structure. In addition, because the life conditions and priorities of the older population differ based on age, it becomes essential to classify older adults into different age categories to properly evaluate the conceptualization of motivation toward physical activity.
In light of this, the purpose of the present study was to investigate the psychometric proprieties of a widely used measure of motivation toward physical activity, the BREQ, providing evidence about the conceptualization and structure of motivation constructs related to older adults and testing its invariance across sex and age groups.
In line with previous validation studies [14,15,16,17], our findings showed that the hypothesized measurement model had a good fit to the data, highlighting the six-factor structure of the scale: amotivation, integrated, identified, introjected, external regulation, and intrinsic motivation.
Measurement invariance was assessed across all subscales of the BREQ-3, indicating that individuals of different sex and age groups similarly interpreted the items. In line with previous research [54], our results demonstrated the full scalar invariance of the scale, suggesting that the BREQ-3 scores can be reliably compared not only across different age groups (i.e., young adults, adults, and older adults) but also in different age subgroups among older people (youngest old and old). Although the scientific literature reported differences in older people’s motivations for and barriers to engaging in physical activity due to their life conditions (e.g., chronic disease) [7,8,9,10], the older adults similarly interpreted the items across sex and age groups.
Moreover, the present study provides evidence for the instrument’s construct validity. The results revealed strong correlations between the different forms of motivation, as predicted by Self-Determination Theory (SDT) and the Godin–Shepard Leisure-Time Physical Activity Questionnaire (GSLTAPQ). Specifically, all forms of autonomous regulation showed a strong correlation with the GSLTAPQ. Although these patterns aligned with the literature, highlighting the relationship between the most autonomous form of regulation and physical activity [4], integrated regulation proved to be the most strongly correlated form of motivation with physical activity. According to SDT, people naturally strive to achieve higher levels of psychological maturity and integration as they age. With time, individuals tend to develop a more authentic and unified sense of self, formed by a combination of interconnected identities based on their own approved and blended preferences, interests, and values [19,78]. As people age, they become more adept at being authentic and regulating their behavior on the basis of self-endorsed motives.
Moreover, the correlation matrix showed stronger positive correlations between adjacent subscales than between further subscales, supporting the quasi-simplex pattern posited by SDT [14,17]. These findings are crucial since construct validity analyses are not often conducted, which can lead to the reporting of biased results [79].
Our study has made a noteworthy contribution to the existing literature, validating the BREQ-3 in older adults for the first time. Measuring the invariance of the BREQ-3 across sex and different age subgroups in older people represents a novel contribution to the scientific literature. The findings support the BREQ-3 as a valid tool for assessing motivational processes based on the SDT framework within the context of exercise among older adults.
Although the results showed the validity of the BREQ-3 in older adults, the present study is not without limitations. Notably, the sample was limited to the Italian population, and to generalize our findings, future studies should be conducted across different countries and cultures.

5. Conclusions

The BREQ-3 demonstrated its effectiveness in assessing all forms of behavioral regulation proposed by SDT in older adults. Moreover, our study established the invariance of the BREQ-3 across sex and age subgroups (i.e., youngest old and old), suggesting that older people interpret and conceptualize the different forms of exercise motivation similarly. Gaining insights into the various types of motivation that older adults experience during exercise holds the potential to predict possible outcomes and to help develop interventions to foster motivation to engage in physical activity among the older population.

Author Contributions

All the authors contributed to the study’s conception and design. All the authors participated in the material preparation and data collection. Methodology, formal analyses, and investigation: T.P., F.A., A.C., E.C. and F.L.; Writing—original draft preparation: T.P., F.A. and A.C.; Writing—review and editing: R.B., A.M.B., G.A., J.P., S.T., C.F., L.F., F.A., T.P. and F.L. All the authors reviewed the manuscript. Supervision: F.A., R.B., G.A., A.M.B. and F.L. All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported with funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8–Project Age-It: “Ageing Well in an Ageing Society”. This resource was co-financed by Next Generation EU [DM 1557 11.10.2022]. The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.

Institutional Review Board Statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Approval was granted by the Ethics Committee of the Department of Developmental and Social Psychology, Faculty of Medicine and Psychology, Sapienza University of Rome (Prot. N. 0000465).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request to the first author.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

BREQ-3 Items—Italian Version
1.Fare attività fisica è importante per me
2.Non vedo il motivo per cui dovrei svolgere attività fisica
3.Svolgo attività fisica perché è divertente
4.Mi sento in colpa se non faccio attività fisica
5.Faccio attività fisica perché è coerente con i miei obiettivi di vita
6.Faccio attività fisica perché gli altri mi dicono che dovrei farlo
7.Perché riconosco i benefici di svolgere attività fisica
8.Mi vergogno quando salto una sessione di attività fisica
9.Considero l’attività fisica parte della mia identità
10.Svolgo attività fisica perché i miei amici/familiari/partner dicono che dovrei
11.Credo che sia importante fare lo sforzo di fare attività fisica regolarmente
12.Non vedo la ragione per svolgere attività fisica
13.Ritengo che fare attività fisica sia molto piacevole
14.Mi sento un fallimento quando non faccio attività fisica per un po’
15.Considero l’attività fisica una parte fondamentale di me
16.Svolgo attività fisica perché gli altri non sarebbero contenti di me se non lo facessi
17.Penso che svolgere attività fisica sia una perdita di tempo
18.Traggo piacere e soddisfazione dal fare attività fisica

Appendix B

BREQ-3 Items—English Version
1.It’s important to me to exercise regularly
2.I don’t see why I should have to exercise
3.I exercise because it’s fun
4.I feel guilty when I don’t exercise
5.I exercise because it is consistent with my life goals
6.I exercise because other people say I should
7.I value the benefits of exercise
8.I feel ashamed when I miss an exercise session
9.I consider exercise part of my identity
10.I take part in exercise because my friends/family/partner say I should
11.I think it is important to make the effort to exercise regularly
12.I don’t see the point in exercising
13.I find exercise a pleasurable activity
14.I feel like a failure when I haven’t exercised in a while
15.I consider exercise a fundamental part of who I am
16.I exercise because others will not be pleased with me if I don’t
17.I think exercising is a waste of time
18.I get pleasure and satisfaction from participating in exercise

References

  1. Piercy, K.L.; Troiano, R.P.; Ballard, R.M.; Carlson, S.A.; Fulton, J.E.; Galuska, D.A.; George, S.M.; Olson, R.D. The physical activity guidelines for Americans. JAMA 2018, 320, 2020–2028. [Google Scholar] [CrossRef] [PubMed]
  2. WHO. Global Status Report on Physical Activity 2022; WHO: Geneva, Switzerland, 2022. [Google Scholar]
  3. Franco, M.R.; Tong, A.; Howard, K.; Sherrington, C.; Ferreira, P.H.; Pinto, R.Z.; Ferreira, M.L. Older people’s perspectives on participation in physical activity: A systematic review and thematic synthesis of qualitative literature. Br. J. Sports Med. 2015, 49, 1268–1276. [Google Scholar] [CrossRef] [PubMed]
  4. Teixeira, P.J.; Carraça, E.V.; Markland, D.; Silva, M.N.; Ryan, R.M. Exercise, physical activity, and self-determination theory: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2012, 9, 78. [Google Scholar] [CrossRef]
  5. Carstensen, L.L.; Fung, H.H.; Charles, S.T. Socioemotional selectivity theory and the regulation of emotion in the second half of life. Motiv. Emot. 2003, 27, 103–123. [Google Scholar] [CrossRef]
  6. Hess, T.M. Selective Engagement of Cognitive Resources: Motivational Influences on Older Adults’ Cognitive Functioning. Perspect. Psychol. Sci. 2014, 9, 388–407. [Google Scholar] [CrossRef] [PubMed]
  7. Costello, E.; Kafchinski, M.; Vrazel, J.; Sullivan, P. Motivators, barriers, and beliefs regarding physical activity in an older adult population. J. Geriatr. Phys. Ther. 2011, 34, 138–147. [Google Scholar] [CrossRef] [PubMed]
  8. Baert, V.; Gorus, E.; Mets, T.; Geerts, C.; Bautmans, I. Motivators and barriers for physical activity in the oldest old: A systematic review. Ageing Res. Rev. 2011, 10, 464–474. [Google Scholar] [CrossRef]
  9. Van Stralen, M.M.; de Vries, H.; Mudde, A.N.; Bolman, C.; Lechner, L. Determinants of initiation and maintenance of physical activity among older adults: A literature review. Health Psychol. Rev. 2009, 3, 147–207. [Google Scholar] [CrossRef]
  10. Bauman, A.E.; Reis, R.S.; Sallis, J.F.; Wells, J.C.; Loos, R.J.F.; Martin, B.W.; Lancet Physical Activity Series Working Group. Correlates of physical activity: Why are some people physically active and others not? Lancet 2012, 380, 258–271. [Google Scholar] [CrossRef]
  11. Lee, Y.S. Gender differences in physical activity and walking among older adults. J. Women Aging 2005, 17, 55–70. [Google Scholar] [CrossRef]
  12. Van Uffelen, J.G.Z.; Khan, A.; Burton, N.W. Gender differences in physical activity motivators and context preferences: A population-based study in people in their sixties. BMC Public Health 2017, 17, 624. [Google Scholar] [CrossRef] [PubMed]
  13. Erber, J.T. Life-Span Developmental Psychology: Introduction to Research Methods. J. Gerontol. 1979, 34, 448–449. [Google Scholar] [CrossRef]
  14. Cid, L.; Monteiro, D.; Teixeira, D.; Teques, P.; Alves, S.; Moutão, J.; Silva, M.; Palmeira, A. The Behavioral Regulation in Exercise Questionnaire (BREQ-3) Portuguese-version: Evidence of reliability, validity and invariance across gender. Front. Psychol. 2018, 9, 1940. [Google Scholar] [CrossRef] [PubMed]
  15. Guedes, D.; Sofiati, S. Tradução e validação psicométrica do Behavioral Regulation in Exercise Questionnaire para uso em adultos brasileiros. Rev. Bras. Atividade Fís. Saúde 2015, 20, 397. [Google Scholar] [CrossRef]
  16. Cavicchiolo, E.; Sibilio, M.; Lucidi, F.; Cozzolino, M.; Chirico, A.; Girelli, L.; Manganelli, S.; Giancamilli, F.; Galli, F.; Diotaiuti, P.; et al. The Psychometric Properties of the Behavioural Regulation in Exercise Questionnaire (BREQ-3): Factorial Structure, Invariance and Validity in the Italian Context. Int. J. Environ. Res. Public Health 2022, 19, 1937. [Google Scholar] [CrossRef]
  17. González-Cutre, D.; Sicilia, Á.; Fernández, A. Hacia una mayor comprensión de la motivación en el ejercicio físico: Medición de la regulación integrada en el contexto español. Psicothema 2010, 22, 841–847. [Google Scholar]
  18. Deci, E.L.; Ryan, R.M. Intrinsic Motivation and Self-Determination in Human Behavior; Plenum Press: New York, NY, USA, 1985. [Google Scholar]
  19. Ryan, R.M.; Deci, E.L. Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness; The Guilford Press: New York, NY, USA, 2017. [Google Scholar]
  20. Alivernini, F.; Cavicchiolo, E.; Girelli, L.; Lucidi, F.; Biasi, V.; Leone, L.; Cozzolino, M.; Manganelli, S. Relationships between sociocultural factors (gender, immigrant and socioeconomic background), peer relatedness and positive affect in adolescents. J. Adolesc. 2019, 76, 99–108. [Google Scholar] [CrossRef]
  21. Alivernini, F.; Lucidi, F.; Manganelli, S. Assessment of academic motivation: A mixed methods study. Int. J. Mult. Res. Approaches 2008, 2, 71–82. [Google Scholar] [CrossRef]
  22. Jang, H.; Kim, E.J.; Reeve, J. Longitudinal test of self-determination theory’s motivation mediation model in a naturally occurring classroom context. J. Educ. Psychol. 2012, 104, 1175–1188. [Google Scholar] [CrossRef]
  23. Manganelli, S.; Cavicchiolo, E.; Mallia, L.; Biasi, V.; Lucidi, F.; Alivernini, F. The interplay between self-determined motivation, self-regulated cognitive strategies, and prior achievement in predicting academic performance. Educ. Psychol. 2019, 39, 470–488. [Google Scholar] [CrossRef]
  24. Reeve, J.; Cheon, S.H. Autonomy-supportive teaching: Its malleability, benefits, and potential to improve educational practice. Educ. Psychol. 2021, 56, 54–77. [Google Scholar] [CrossRef]
  25. Ryan, R.M.; Deci, E.L. Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemp. Educ. Psychol. 2020, 61, 101860. [Google Scholar] [CrossRef]
  26. Lucidi, F.; Zelli, A.; Mallia, L.; Nicolais, G.; Lazuras, L.; Hagger, M.S. Moral attitudes predict cheating and gamesmanship behaviors among competitive tennis players. Front. Psychol. 2017, 8, 571. [Google Scholar] [CrossRef]
  27. Ntoumanis, N. A Self-Determination Theory Perspective on Motivation in Sport and Physical Education: Current Trends and Possible Future Research Directions. In Advances in Motivation in Sport and Exercise; Human Kinetics: Champaign, IL, USA, 2020. [Google Scholar] [CrossRef]
  28. Standage, M.; Curran, T.; Rouse, P.C. Self-Determination- Based Theories of Sport, Exercise, and Physical Activity Motivation. In Advances in Sport and Exercise Psychology; Human Kinetics: Champaign, IL, USA, 2020. [Google Scholar] [CrossRef]
  29. Alivernini, F.; Manganelli, S.; Girelli, L.; Cozzolino, M.; Lucidi, F.; Cavicchiolo, E. Physical Distancing Behavior: The Role of Emotions, Personality, Motivations, and Moral Decision-Making. J. Pediatr. Psychol. 2021, 46, 15–26. [Google Scholar] [CrossRef] [PubMed]
  30. Ntoumanis, N.; Ng, J.Y.; Prestwich, A.; Quested, E.; Hancox, J.E.; Thøgersen-Ntoumani, C.; Deci, E.L.; Ryan, R.M.; Lonsdale, C.; Williams, G.C. A meta-analysis of self-determination theory-informed intervention studies in the health domain: Effects on motivation, health behavior, physical, and psychological health. Health Psychol. Rev. 2021, 15, 214–244. [Google Scholar] [CrossRef]
  31. Gillison, F.B.; Rouse, P.; Standage, M.; Sebire, S.J.; Ryan, R.M. A meta-analysis of techniques to promote motivation for health behaviour change from a self-determination theory perspective. Health Psychol. Rev. 2019, 13, 110–130. [Google Scholar] [CrossRef] [PubMed]
  32. Barkoukis, V.; Chatzisarantis, N.; Hagger, M.S. Effects of a School-Based Intervention on Motivation for Out-of-School Physical Activity Participation. Res. Q. Exerc. Sport 2020, 92, 477–491. [Google Scholar] [CrossRef] [PubMed]
  33. Hagger, M.; Chatzisarantis, N. Self-determination Theory and the psychology of exercise. Int. Rev. Sport Exerc. Psychol. 2008, 1, 79–103. [Google Scholar] [CrossRef]
  34. Hancox, J.E.; Quested, E.; Ntoumanis, N.; Thøgersen-Ntoumani, C. Putting self-determination theory into practice: Application of adaptive motivational principles in the exercise domain. Qual. Res. Sport Exerc. Health 2018, 10, 75–91. [Google Scholar] [CrossRef]
  35. Quested, E.; Kritz, M.; Hancox, J.; Ntoumanis, N.; Thøgersen-Ntoumani, C. Promoting self-determined motivation for physical activity: From theory to intervention work. In Essentials of Exercise and Sport Psychology: An Open Access Textbook; Society for Transparency, Openness, and Replication in Kinesiology: Hayward, CA, USA, 2021. [Google Scholar] [CrossRef]
  36. Deci, E.L.; Ryan, R.M. The “What” and “Why” of Goal Pursuits: Human Needs. Psychol. Inq. 2000, 11, 227–268. [Google Scholar] [CrossRef]
  37. Cantarero, K.; van Tilburg, W.A.P.; Smoktunowicz, E. Affirming Basic Psychological Needs Promotes Mental Well-Being During the COVID-19 Outbreak. Soc. Psychol. Pers. Sci. 2021, 12, 821–828. [Google Scholar] [CrossRef]
  38. Diotaiuti, P.; Corrado, S.; Mancone, S.; Falese, L. Resilience in the Endurance Runner: The Role of Self-Regulatory Modes and Basic Psychological Needs. Front. Psychol. 2021, 11, 558287. [Google Scholar] [CrossRef] [PubMed]
  39. Martela, F.; Ryan, R.M. In selecting measures for a comprehensive assessment of well-being, it is essential to include indicators of psychological need satisfaction. Prev. Med. Rep. 2021, 23, 101474. [Google Scholar] [CrossRef]
  40. Ng, J.Y.Y.; Ntoumanis, N.; Thøgersen-Ntoumani, C.; Stott, K.; Hindle, L. Predicting psychological needs and well-being of individuals engaging in weight management: The role of important others. Appl. Psychol. Health Well Being 2013, 5, 291–310. [Google Scholar] [CrossRef] [PubMed]
  41. Alivernini, F.; Cavicchiolo, E.; Manganelli, S.; Chirico, A.; Lucidi, F. Students’ psychological well-being and its multilevel relationship with immigrant background, gender, socioeconomic status, achievement, and class size. Sch. Eff. Sch. Improv. 2020, 31, 172–191. [Google Scholar] [CrossRef]
  42. Deci, E.L.; Ryan, R.M.; Vallerand, R.J.; Pelletier, L.G. Motivation and Education: The Self-Determination Perspective. Educ. Psychol. 1991, 26, 325–346. [Google Scholar] [CrossRef]
  43. Ryan, R.M.; Frederick, C.M.; Lepes, D.; Rubio, N.; Sheldon, K.M. Intrinsic motivation and exercise adherence. Int. J. Sport Psychol. 1997, 28, 335–354. [Google Scholar]
  44. Ryan, R.M.; Deci, E.L. Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. Am. Psychol. 2000, 55, 68–78. [Google Scholar] [CrossRef]
  45. Vallerand, R.J.; Pelletier, L.G.; Blais, M.R.; Briere, N.M.; Senecal, C.; Vallieres, E.F. The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educ. Psychol. Meas. 1992, 52, 1003–1017. [Google Scholar] [CrossRef]
  46. Mullan, E.; Markland, D.; Ingledew, D.K. A graded conceptualisation of self-determination in the regulation of exercise behaviour: Development of a measure using confirmatory factor analytic procedures. Pers. Individ. Differ. 1997, 23, 745–752. [Google Scholar] [CrossRef]
  47. Edmunds, J.; Ntoumanis, N.; Duda, J.L. A test of self-determination theory in the exercise domain. J. Appl. Soc. Psychol. 2006, 36, 2240–2265. [Google Scholar] [CrossRef]
  48. Hagger, M.S.; Barkoukis, V.; Chatzisarantis, N.L.D.; Wang, C.K.J.; Baranowski, J. Perceived autonomy support in physical education and leisure-time physical activity: A cross-cultural evaluation of the trans-contextual model. J. Educ. Psychol. 2005, 97, 376–390. [Google Scholar] [CrossRef]
  49. Thøgersen-Ntoumani, C.; Ntoumanis, N. The role of self-determined motivation in the understanding of exercise-related behaviours, cognitions and physical self-evaluations. J. Sports Sci. 2006, 24, 393–404. [Google Scholar] [CrossRef] [PubMed]
  50. Wilson, P.M.; Rodgers, W.M.; Fraser, S.N. Examining the psychometric properties of the behavioral regulation in exercise questionnaire. Meas. Phys. Educ. Exerc. Sci. 2002, 6, 1–21. [Google Scholar] [CrossRef]
  51. Markland, D.; Tobin, V. A modification to the behavioural regulation in exercise questionnaire to include an assessment of amotivation. J. Sport Exerc. Psychol. 2004, 26, 191–196. [Google Scholar] [CrossRef]
  52. Chen, H.; Dai, J.; Liu, J. An integrative perspective of validating a simplified Chinese version behavioral regulation in exercise questionnaire-2. Meas. Phys. Educ. Exerc. Sci. 2018, 22, 213–223. [Google Scholar] [CrossRef]
  53. Cid, L.; Moutão, J.; Leitão, J.; Alves, J. Behavioral Regulation Assessment in Exercise: Exploring an Autonomous and Controlled Motivation Index. Span. J. Psychol. 2012, 15, 1520–1528. [Google Scholar] [CrossRef]
  54. Costa, S.; Oliva, P.; Cuzzocrea, F.; Larcan, R. Assessing sport motivation in non-English speaking exercisers: Validation of an Italian version of the Behavioural Regulation in Exercise Questionnaire-2 (BREQ-2). Gazz. Med. Ital. Arch. Sci. Med. 2013, 172, 869–876. [Google Scholar]
  55. Liu, J.D.; Chung, P.K.; Zhang, C.Q.; Si, G. Chinese-translated Behavioral Regulation in Exercise Questionnaire-2: Evidence from university students in the Mainland and Hong Kong of China. J. Sport Health Sci. 2015, 4, 228–234. [Google Scholar] [CrossRef]
  56. No-in, K.; Tuicomepee, A.; Jiamjarasrangsi, W.; Sithisarankul, P. Validation of Behavioral Regulation in Exercise Questionnaire-2 (Breq-2) and Dietary Self-Regulation (Dsr) in overweight high school students in Thailand. J. Health Res. 2015, 29, 269–276. [Google Scholar]
  57. Dias, I.; Abreu, M.O.; Figueiredo, C. Contributos para o Estudo das Propriedades Psicométricas do BREQ-3. Rev. Iberoam. Diagn. Eval.-e Aval. Psicol. 2020, 57, 53–63. [Google Scholar] [CrossRef]
  58. Mullins, L.C.; Lopez, M.A. Death anxiety among nursing home residents: A comparison of the young-old and the old-old. Death Educ. 1982, 6, 75–86. [Google Scholar] [CrossRef] [PubMed]
  59. Zanjani, F.A.K.; Schaie, W.K.; Willis, S.L. Age Group and Health Status Effects on Health Behavior Change. Behav. Med. 2006, 32, 36–46. [Google Scholar] [CrossRef] [PubMed]
  60. Mehta, M.; Whyte, E.; Lenze, E.; Hardy, S.; Roumani, Y.; Subashan, P.; Huang, W.; Studenski, S. Depressive symptoms in late life: Associations with apathy, resilience and disability vary between young-old and old-old. Int. J. Geriatr. Psychiatry 2008, 23, 238–243. [Google Scholar] [CrossRef]
  61. Alterovitz, S.S.R.; Mendelsohn, G.A. Relationship goals of middle-aged, young-old, and old-old internet daters: An analysis of online personal ads. J. Aging Stud. 2013, 27, 159–165. [Google Scholar] [CrossRef]
  62. Lee, S.B.; Oh, J.H.; Park, J.H.; Choi, S.P.; Wee, J.H. Differences in youngest-old, middle-old, and oldest-old patients who visit the emergency department. Clin. Exp. Emerg. Med. 2018, 5, 249–255. [Google Scholar] [CrossRef] [PubMed]
  63. Godin, G.; Shephard, R.J. A simple method to assess exercise behavior in the community. Can. J. Appl. Sport Sci. 1985, 10, 141–146. [Google Scholar]
  64. Galli, F.; Chirico, A.; Mallia, L.; Girelli, L.; De Laurentiis, M.; Lucidi, F.; Giordano, A.; Botti, G. Active lifestyles in older adults: An integrated predictive model of physical activity and exercise. Oncotarget 2018, 9, 25402–25413. [Google Scholar] [CrossRef]
  65. Owen, K.B.; Smith, J.; Lubans, D.R.; Ng, J.Y.Y.; Lonsdale, C. Self-determined motivation and physical activity in children and adolescents: A systematic review and meta-analysis. Prev. Med. 2014, 67, 270–279. [Google Scholar] [CrossRef]
  66. Wilson, P.M.; Rodgers, W.M.; Loitz, C.C.; Scime, G. “It’s Who I Am…Really!’ The Importance of Integrated Regulation in Exercise Contexts. J. Appl. Biobehav. Res. 2007, 11, 79–104. [Google Scholar] [CrossRef]
  67. Amireault, S.; Godin, G. The godin-shephard leisure-time physical activity questionnaire: Validity evidence supporting its use for classifying healthy adults into active and insufficiently active categories. Percept. Mot. Ski. 2015, 120, 604–622. [Google Scholar] [CrossRef] [PubMed]
  68. Muthén, B.O.; Muthén, L.K. Mplus: Statistical Analysis with Latent Variables: User’s Guide; Version 8; Muthén & Muthén: Los Angeles, CA, USA, 2017. [Google Scholar]
  69. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  70. Van de Schoot, R.; Lugtig, P.; Hox, J. A checklist for testing measurement invariance. Eur. J. Dev. Psychol. 2012, 9, 486–492. [Google Scholar] [CrossRef]
  71. Cheung, G.W.; Rensvold, R.B. Evaluating goodness-of-fit indexes for testing measurement invariance. Struct. Equ. Model. 2002, 9, 233–255. [Google Scholar] [CrossRef]
  72. Schreiber, J.B.; Stage, F.K.; King, J.; Nora, A.; Barlow, E.A. Reporting structural equation modeling and confirmatory factor analysis results: A review. J. Educ. Res. 2006, 99, 323–338. [Google Scholar] [CrossRef]
  73. Booth, F.W.; Roberts, C.K.; Laye, M.J. Lack of exercise is a major cause of chronic diseases. Compr. Physiol. 2012, 2, 1143–1211. [Google Scholar] [CrossRef]
  74. Powell, K.E.; King, A.C.; Buchner, D.M.; Campbell, W.W.; DiPietro, L.; Erickson, K.I.; Hillman, C.H.; Jakicic, J.M.; Janz, K.F.; Katzmarzyk, P.T.; et al. The scientific foundation for the physical activity guidelines for Americans, 2nd edition. J. Phys. Act. Health 2019, 16, 1–11. [Google Scholar] [CrossRef]
  75. Warburton, D.E.R.; Nicol, C.W.; Bredin, S.S.D. Health benefits of physical activity: The evidence. CMAJ 2006, 174, 801–809. [Google Scholar] [CrossRef]
  76. Fortier, M.S.; Sweet, S.N.; O’Sullivan, T.L.; Williams, G.C. A self-determination process model of physical activity adoption in the context of a randomized controlled trial. Psychol. Sport Exerc. 2007, 8, 741–757. [Google Scholar] [CrossRef]
  77. Silva, M.N.; Vieira, P.N.; Coutinho, S.R.; Minderico, C.S.; Matos, M.G.; Sardinha, L.B.; Teixeira, P.J. Using self-determination theory to promote physical activity and weight control: A randomized controlled trial in women. J. Behav. Med. 2010, 33, 110–122. [Google Scholar] [CrossRef]
  78. Deci, E.L.; Ryan, R.M. Handbook of Self-Determination Research; University of Rochester Press: Rochester, NY, USA, 2002. [Google Scholar]
  79. Rodrigues, F.; Macedo, R.; Teixeira, D.; Cid, L.; Monteiro, D. Motivation in sport and exercise: A comparison between the BRSQ and BREQ. Qual. Quant. 2020, 54, 1335–1350. [Google Scholar] [CrossRef]
Healthcare 11 02707 g001
Figure 1. Confirmatory factor analysis. Note: All of the estimates are standardized. All of the estimates are statistically significant at p < 0.001. The covariances among latent factors are not reported in this figure.
Figure 1. Confirmatory factor analysis. Note: All of the estimates are standardized. All of the estimates are statistically significant at p < 0.001. The covariances among latent factors are not reported in this figure.
Healthcare 11 02707 g001
Table 1. SDT continuum.
Table 1. SDT continuum.
Non-Self-Determined Self-Determined
Motivation typeAmotivationExtrinsic motivationIntrinsic motivation
Regulatory styleNon-regulationExternal IntrojectedIdentifiedIntegratedIntrinsic
DescriptionLack of intentionality in performing exerciseExercise is performed in order to satisfy an external demand Exercise is regulated by internal rewards in the form of self-esteemPersonal importance and value is attributed to exerciseExercise is consistent with one’s life valuesPeople engage in exercise due to their inherent interest and joy
Table 2. Descriptive statistics for the BREQ-3 items and subscales.
Table 2. Descriptive statistics for the BREQ-3 items and subscales.
SkewnessKurtosis
NMeanSDMinimumMaximumSkewnessSEKurtosisSE
Amotivation3830.6370.813041.1840.1250.4750.249
Item23830.8091.110041.3160.1250.9280.249
Item123830.6160.993041.6990.1252.2470.249
Item173830.4860.909042.0810.1254.0150.249
External3830.7660.940041.2930.1251.1610.249
Item63830.8981.161041.1070.1250.1520.249
Item103830.8721.143041.0990.1250.0940.249
Item163830.5270.917041.8920.1253.1890.249
Introjected3831.6271.224040.1910.125−1.1310.249
Item43831.7151.447040.1670.125−1.3500.249
Item83831.7831.439040.0820.125−1.3670.249
Item143831.3811.371040.5010.125−1.0990.249
Identified3832.7550.97404−0.6700.125−0.2490.249
Item13832.6191.16304−0.5860.125−0.4210.249
Item73832.7211.32704−0.7650.125−0.6410.249
Item113832.9270.98904−0.8140.1250.2400.249
Integrated3831.8541.315040.0120.125−1.2830.249
Item53831.9901.44704−0.0800.125−1.3550.249
Item93831.7021.396040.1470.125−1.3010.249
Item153831.8691.42504−0.0030.125−1.3400.249
Intrinsic3832.1791.08404−0.3320.125−0.8130.249
Item33831.6871.297040.0760.125−1.1790.249
Item133832.4991.15504−0.6060.125−0.4050.249
Item183832.3521.26304−0.4520.125−0.8280.249
Note: SD = Standard Deviation; SE = Standard Error.
Table 3. Goodness-of-fit indices of the BREQ-3 model (including other existing versions).
Table 3. Goodness-of-fit indices of the BREQ-3 model (including other existing versions).
NMean Ageχ2dfχ2/dfCFIRMSEA
English version (BREQ-3) *20719.5357.511422.510.920.09
English version (BREQ-3) *13247.5253.821421.790.930.09
Brazilian version (BREQ-3) **104118–60406.352151.890.930.07
Spanish version (BREQ-3) ***52429.59689.132153.210.910.06
Portugese version (BREQ-3) ****99623.44931.692154.330.980.05
Portugese version (BREQ-3) *****37440.51254.081202.220.950.06
Italian version (BREQ-3) ******222236.4833.991206.940.960.05
Older adults version (present study)38373.2255.811202.130.950.05
Note: χ2, chi-squared; df = degrees of freedom; χ2/df, normative chi-square; CFI = Comparative Fit Index; RMSEA = Root-Mean-Square Error of Approximation. * [66]; ** [15]; *** [17]; **** [57]; ***** [14]; ****** [16].
Table 4. Goodness-of-fit indices for invariance of the BREQ-3 across sex and age groups.
Table 4. Goodness-of-fit indices for invariance of the BREQ-3 across sex and age groups.
χ2dfχ2/dfCFIRMSEASRMRModels Compared∆CFI
Sex (female/male)
Configural model477.2442401.9880.9410.0720.048 -
Metric model502.1962521.9920.9380.0720.054metric vs. configural0.003
Scalar model521.2272641.9740.9360.0710.056scalar vs. metric0.002
Age groups (65 to 74; over 75)
Configural model492.2562402.0510.9370.0740.047 -
Metric model506.5972522.0100.9370.0730.050metric vs. configural0.000
Scalar model516.2622641.9550.9370.0710.051scalar vs. metric0.000
Note: χ2 = chi-squared; df = degrees of freedom; χ2/df = normative chi-square; CFI = Comparative Fit Index; RMSEA = Root-Mean-Square Error of Approximation; SRMR = Standardized Root-Mean-Square Residual; ∆CFI = difference in the value of the comparative fit index.
Table 5. Correlation matrix between different subscales of the BREQ-3 and GSLTPAQ.
Table 5. Correlation matrix between different subscales of the BREQ-3 and GSLTPAQ.
ωAmotivationExternalIntrojectedIdentifiedIntegratedIntrinsicGSLTPAQ
Amotivation0.74—
External0.840.181 ***—
Introjected0.82−0.252 ***0.122 *—
Identified0.78−0.563 ***0.0090.512 ***—
Integrated0.91−0.402 ***0.0600.572 ***0.735 ***—
Intrinsic0.85−0.441 ***0.0550.451 ***0.730 ***0.751 ***—
GSLTPAQ0.65−0.281 ***0.0930.234 ***0.427 ***0.539 ***0.428 ***—
Note: GSLTAPQ = Godin–Shepard Leisure-Time Physical Activity Questionnaire; ω = McDonald’s omega. * p < 0.05, *** p < 0.001.
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Palombi, T.; Lucidi, F.; Chirico, A.; Alessandri, G.; Filosa, L.; Tavolucci, S.; Borghi, A.M.; Fini, C.; Cavicchiolo, E.; Pistella, J.; et al. Is the Behavioral Regulation in Exercise Questionnaire a Valid Measure in Older People? Healthcare 2023, 11, 2707. https://doi.org/10.3390/healthcare11202707

AMA Style

Palombi T, Lucidi F, Chirico A, Alessandri G, Filosa L, Tavolucci S, Borghi AM, Fini C, Cavicchiolo E, Pistella J, et al. Is the Behavioral Regulation in Exercise Questionnaire a Valid Measure in Older People? Healthcare. 2023; 11(20):2707. https://doi.org/10.3390/healthcare11202707

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Palombi, Tommaso, Fabio Lucidi, Andrea Chirico, Guido Alessandri, Lorenzo Filosa, Simone Tavolucci, Anna M. Borghi, Chiara Fini, Elisa Cavicchiolo, Jessica Pistella, and et al. 2023. "Is the Behavioral Regulation in Exercise Questionnaire a Valid Measure in Older People?" Healthcare 11, no. 20: 2707. https://doi.org/10.3390/healthcare11202707

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