Introduction

Ageing is a natural life stage that affects all individuals and involves various physical, psychological, and social changes1. Globally, the population aged 60 years and older was estimated at approximately 600 million in 2000, with projections demonstrating an increase to 1.2 billion by 2025 and 2 billion by 20502. In Iran, life expectancy has increased steadily over the past five decades, resulting in a growing proportion of older adults. According to the 2016 census, individuals aged 60 years and above account for approximately 9.3% of the total population3,4. Given the rapid demographic shift toward an aging population, ensuring health and well-being has become an increasingly critical public health priority5.

Healthy ageing extends beyond the prevention of disease and encompasses multiple dimensions, including quality of life, emotional well-being, social participation, and physical health6,7. Together, these domains determine whether older adults are able to maintain independence, engage meaningfully in their communities, and experience life satisfaction in later years. Physical activity plays a central role in supporting all these dimensions8,9.

Physical activity is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. It encompasses a broad spectrum of activities, including vigorous exercise (e.g., heavy lifting, aerobics, fast cycling), moderate-intensity activities (e.g., carrying light loads, recreational volleyball), and walking (e.g., for transportation, leisure, or occupational purposes). Conversely, sedentary behaviors include prolonged sitting, such as at a desk, while watching television, or during social interactions10.

Physical activity preserves functional capacity, reduces psychological distress and the risk of chronic diseases, and fosters opportunities for social interaction and well-being in older adults11,12. Furthermore, even minimal physical activity has been shown to yield significant benefits13.

Evidence has showed that engaging in physical activity among older adults improves balance, muscular strength, and cardiorespiratory function while reducing risk factors for chronic diseases, pain, and osteoporosis and improving flexibility, ultimately increasing overall quality of life14,15. In a study by Alhambra-Borrás et al., balance and strength training were recognized as particularly effective programs for healthy aging, as they not only prevent frailty and falls but also improve quality of life by maintaining balance, muscle strength, and functional autonomy16. Furthermore, physical exercise is broadly recommended as a nonpharmacological strategy to improve quality of life and promote active and healthy aging17. In alignment with these studies, the World Health Organization’s active aging framework recommends maintaining physical activity to preserve functional independence, enhance quality of life, and promote social engagement and emotional well-being among older adults18,19.

While physical limitations increase with age, psychosocial resources such as self-efficacy, the belief in one’s ability to overcome obstacles, and social support, the encouragement and companionship provided by others, emerge as crucial determinants of remaining physically active. These factors not only have the potential to buffer the impact of functional decline but also enhance motivation, persistence, and adherence to physical activity, thereby compensating for age-related barriers that cannot be addressed through physical capacity alone. Furthermore, despite extensive evidence on the benefits of physical activity for older adults, the psychosocial mechanisms that sustain this behaviour remain underexamined.

Psychosocial factors are recognized as important determinants of physical activity because they represent modifiable and actionable targets for interventions in ageing populations20,21. Understanding these psychosocial determinants is essential for designing culturally tailored interventions that can effectively promote sustained physical activity and healthy ageing in rapidly ageing societies such as Iran.

According to Bandura’s social cognitive theory, self-efficacy reflects an individual’s confidence in their ability to successfully engage in specific behaviours, which in turn shapes their choices22. Older adults with higher self-efficacy are more likely to initiate and sustain physical activity, cope with challenges, and overcome barriers23,24. Moreover, self-efficacy is a key facilitator in the adoption and long-term maintenance of health-promoting behaviours25.

Social support refers to the perception and reality that one is cared for, has assistance available from a network of people, and is part of a supportive social group. They are often categorized into different types, each serving a unique function. Social support encompasses emotional (provision of empathy, affection, love, trust, and a sense of belonging), instrumental (provision of concrete aid and services), informational (provision of concrete aid and services), and appraisal (provision of feedback and affirmation that is useful for self-evaluation) resources received from one’s social network26.

Support from family and friends, in particular, fosters motivation and adherence to healthy behaviour by providing encouragement, companionship, and emotional reinforcement27. The evidence shows that older adults embedded in supportive social environments are more likely to engage in and sustain physical activity than those lacking such support28. The primary sources of social support for older adults typically include family members and friends29. Those with robust social networks often exhibit greater self-efficacy30. This support, whether from family, peers, or community structures, facilitates goal attainment in physical activity31. Cultural and economic factors further shape the perception and utilization of social support.

A recent systematic review and meta-analysis revealed that both self-efficacy and social support are predictors of physical activity in older adults28,32,33,34. While a number of studies have recognized family-based support as a determinant of physical activity35,36, evidence regarding peer-based social support remains limited and inconsistent, particularly in non-Western cultural settings, where family-oriented norms overshadow peer relationships37. In Iran’s predominantly family-oriented society, the role of friends as alternative or complementary sources of social support remains understudied, warranting further investigation.

The evidence regarding the influence of social support remains inconsistent, with variations observed across cultural contexts and individual characteristics38. Although self-efficacy has consistently been identified as a predictor of health behaviours39,40, its interaction with peer-based social support in shaping physical activity is poorly understood. By delimiting the problem with these two psychosocial constructs, our study seeks to clarify whether peer-based social support contributes independently to physical activity behaviours or whether its effect operates indirectly through self-efficacy. A better understanding of these relationships is essential for designing culturally sensitive, evidence-based interventions that effectively address the unique factors faced by older adults in sustaining physical activity. Furthermore, whether this relationship persists across different cultures is ambiguous and remains unclear.

Given the inconsistent findings across cultural contexts and the limited research conducted in Middle Eastern populations, this study seeks to address these gaps by examining the role of self-efficacy and peer-based social support in physical activity engagement among Iranian older adults. The present study aims to investigate the associations between self-efficacy, social support from friends, and physical activity levels among Iranian older adults. We hypothesize that higher levels of self-efficacy and greater perceived social support from friends are independently associated with greater engagement in physical activity.

Materials and methods

Study design

This cross-sectional study investigated the associations between physical activity and psychosocial determinants (self-efficacy and social support) among community-dwelling older adults aged 65–75 years in Rasht, Iran. The study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The protocol was approved by the Research Board of Tehran University of Medical Sciences (grant no. 72651).

Study setting and population

The study was conducted in Rasht city, the capital of Guilan Province in northern Iran. Rasht has a humid subtropical climate with an annual average precipitation of 1200 mm, which is 1.5 times the global average and 4.5 times the national average. These climatic conditions may restrict outdoor physical activity and encourage reliance on indoor exercise modalities among older adults.

Sampling and recruitment

Sampling was conducted at all Comprehensive Health Service Centres (CHSCSs) of Rasht city (n = 16). Since the entire urban CHSC network was included, no additional selection criteria were applied at the center level, ensuring comprehensive coverage of the urban older adult population. The participants were systematically selected from these centers. The sample size was calculated via the following formula, assuming a correlation coefficient of 0.2, 95% confidence interval (CI), and 90% power.

$$\omega =\frac{1}{2}{\text{~}} \times ln\left( {\frac{{1+r}}{{1 - r}}} \right){\text{~}}n=\frac{{{{\left( {{Z_{1 - \frac{\alpha }{2}}}+{Z_{1 - \beta }}} \right)}^2}}}{{{\omega ^2}}}+3$$
$$n=\frac{{{{\left( {1.96+1.28} \right)}^2}}}{{{{0.203}^2}}}+3=260$$

To address potential clustering effects within CHSCs, the sample size was multiplied by a design effect of 2. This conservative assumption is consistent with a previous similar community-based study41 where intracluster correlation occur42,43. Additionally, a 10% attrition rate was incorporated into the calculation.

$$n=\frac{{520}}{{1 - 0.1}}=578$$

The eligibility criteria for participation were as follows:1 voluntary participation;2 aged 65–75 years;3 absence of diagnosed cognitive impairment (dementia or Alzheimer’s disease);4 no medical diagnosis of severe osteoporosis; and5 sufficient literacy to read questionnaires.

Participants were recruited between June 4 and September 28, 2024. In total, 650 eligible individuals were contacted, 550 of whom agreed to participate (n = 550), yielding a response rate of 84.6%. The most common reasons for nonparticipation were lack of interest (n = 54), health-related limitations (n = 32), and relocation outside Rasht during the study period (n = 14).

For sampling, a list of older adults (aged 65–75 years) was obtained from the Ministry of Health’s electronic record SIB system for each of the 16 CHSCs (N = 45000). Using systematic random sampling, every nth eligible individual was selected on the basis of the sampling interval calculated for each CHSC (every 82nd). The selected individuals were contacted by telephone and invited to participate until the required sample size was achieved.

Data collection procedures

Following approval from Tehran University of Medical Sciences and Guilan University of Medical Sciences, the research team conducted site visits to the CHSCs. The interviewers participated in a two-hour training session conducted by the principal investigator (SRS) on the study protocol, ethical principles and confidentiality, correct administration of questionnaires, and strategies for addressing participants’ questions and concerns. The questionnaire type is reported in the data gathering section as interviewer-led.

To minimize comprehension difficulties and ensure completeness, all questionnaires were interviewer-administered. Data were collected after written informed consent was obtained.

Instruments

Four validated instruments were used:

Sociodemographic characteristics form

This 10-item instrument collects data on the following: chronological age (in years); sex (male/female); marital status (single/married/divorced/widowed); educational attainment (literate/lower than high school/high school/diploma/university degree); employment status (laborer/employee/self-employed/retired/housewife/student/unemployed); economic status (insufficient/somewhat sufficient/fully sufficient household income); health insurance coverage (yes/no); history of activity-limiting injury (yes/no); family composition (alone/with spouse/with family/with relatives); and number of children (none/one/two/more than two).

Social support scale for physical activity

The Persian version of the Sallis Social Support Scale for Physical Activity Behaviour (validated by Norouzi et al., 2010)44 was employed. This 20-item instrument uses a 5-point Likert scale (1 = never to 5 = very often). An example item reads “Has anyone encouraged you to stick to your physical activity program?” For the current study, the 5-item friend support subscale was utilized, yielding scores ranging from 5 to 25, with higher scores indicating greater perceived social support. The psychometric evaluation demonstrated optimal internal consistency (Cronbach’s α = 0.86 for friend support). Content validity was confirmed, with a content validity ratio (CVR) ≥ 0.8 for all the items and an overall CVR of 0.9444, and has been validated in more recent studies (Wingood M et al., 2021; Roth SE et al., 201945,46.

Self-efficacy for physical activity questionnaire

The validated Persian version of the Self-Efficacy for Physical Activity Questionnaire, developed by Shamsalinia et al. (2019)47, was explicitly used in this study. This 21-item instrument assesses older adults’ confidence in their ability to engage in physical activity across various conditions. For example, an item reads, “How confident are you in your ability to engage in physical activity when you are tired?”

The responses are scored on a 4-point Likert scale ranging from “Completely Confident” to “Not at all Confident”, with total scores ranging from 21 to 84; higher scores indicate greater self-efficacy.

The scale comprises three domains—cognition, situational adaptation, and self-regulation. Exploratory factor analysis confirmed the expected three-factor structure, supporting the construct validity of the instrument so that these three factors explained 78.75%, 85.82%, and 90.18% of the total variance, respectively.

International physical activity questionnaire–short form (IPAQ-SF)

The validated Persian version of the IPAQ-SF (Gholami Fesharaki et al., 2011)48 was used in this study. This 7-item instrument assesses participation in vigorous, moderate, and walking and sitting activities during the preceding seven days.

Metabolic equivalent (MET) values were assigned according to WHO recommendations, with specific values typically used for walking (3.3 METs), moderate-intensity activities (4.0 METs), and vigorous-intensity activities (8.0 METs) to calculate energy expenditure49. Total physical activity was calculated by multiplying the duration (minutes/day), frequency (days/week), and MET values for each activity type. On this basis, participants were divided into three physical activity levels:

  • Low: <600 MET-minutes/week.

  • Moderate: 600–3000 MET-min/week.

  • High: >3000 MET-minutes/week.

The Persian version has demonstrated acceptable psychometric properties in Iranian populations (Cronbach’s α = 0.70; CVR = 0.70) and has been validated in more recent studies by Cheshmeh et al., 2022; Zare Javid et al., 202050,51.

The participants received instructions from trained interviewers and were assisted in minimizing recall difficulties and ensuring accurate answers.

Ethical considerations

The study protocol was approved by the Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.SPH.REC.1403.017). Informed electronic consent was obtained. Before data collection, electronic informed consent was obtained from all participants via the Porsline platform.

The participants were fully informed about the study objectives, procedures, and confidentiality measures. All the data were anonymized, and confidentiality was strictly maintained by storing the datasets on password-protected computers with restricted access.

Data management

Survey responses were collected via a Porsline (digital survey platform). Daily and weekly quality control checks were performed throughout the data collection period. Data collectors underwent standardized training to ensure the consistent administration of questionnaires. Data quality was ensured through automated consistency checks within the online system. In addition, the platform was programmed with automatic range and consistency checks to minimize entry errors, and duplicate responses were prevented. Any discrepancies were discussed with the study supervisors and corrected immediately. These procedures ensured the accuracy, completeness, and reliability of the dataset.

Statistical analysis

Descriptive statistics (means, standard deviations, frequencies, and percentages) were used to summarize and describe the main characteristics of the dataset. Generalized estimating equation (GEE) models were employed to account for potential intraclass correlations because the participants were nested within CHSCs. The multivariable models included the following covariates: sex, age, marital status, employment status, education attainment, economic status, insurance status, history of activity-limiting injury, family composition, and number of children on the basis of prior evidence of their association with physical activity in older adults. All covariates were entered simultaneously into the models.

The normality of continuous variables (e.g., age) was assessed via the Shapiro–Wilk test to compare the use of parametric and nonparametric tests. All the statistical analyses were conducted via SPSS version 25 (IBM Corp, Armonk, NY) via the GENLIN procedure for the GEE model. Statistical significance was considered at p < 0.05.

Results

Sociodemographic characteristics of the older adults (n = 550)

Table 1 presents the sociodemographic profile of the participants. The study sample comprised mainly younger elderly individuals (78.2% aged 65–70 years vs. 21.8% aged 70–75 years), with a balanced sex distribution (52.9% female, 47.1% male). Most participants were married (81.6%), while 2.7% were single and 14.3% were widowed. With respect to educational attainment, 40.2% had completed high school, and 26.5% had only basic literacy. In terms of employment status, 49.5% were retired, 38.7% were housewives, and 7.6% were self-employed. Economic status was reported as sufficient by 25.1% of participants, whereas 27.8% described financial insufficiency. Healthcare coverage was high, reaching 90.1%. In terms of family composition, 53.1% lived with a spouse, and 36% lived with extended family; 60.4% reported having more than two children.

Table 1 Sociodemographic characteristics of older adults (n = 550).
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Mean scores of self-efficacy, social support, and physical activity in the elderly population

Table 2 presents the descriptive statistics for self-efficacy, its subscale components, social support, and physical activity levels. The overall mean self-efficacy score was 58.24 (SD = 12.23), with subscale scores as follows: self-regulation, 11.13 ± 2.76; situational adaptation, 26.85 ± 6.75; and cognition, 20.26 ± 3.77. The mean social support score was 12.47 (SD = 4.85).

The physical activity levels exhibited substantial variability, ranging from 0 to 6780 MET-minutes/week (mean = 894.91, SD = 980.08), indicating considerable heterogeneity in activity patterns among the participants.

Table 2 Mean scores of self-efficacy, social support, and physical activity in the elderly population (n = 550).
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Physical activity distribution among the participants

Among 550 older adults, 51.3% (n = 282) reported low physical activity, 44.2% (n = 243) reported moderate activity, and 4.6% (n = 25) reported high activity levels.

Associations between physical activity and psychosocial and sociodemographic factors

Table 3 presents the comprehensive regression analysis examining determinants of physical activity, incorporating both univariate and multivariate models. The analysis revealed the following results:

  1. 1.

    Psychological constructs: Self-efficacy (including adaptability, cognition, and self-regulation subscales) and social support.

  2. 2.

    Sociodemographic variables: Age, sex, marital status, educational attainment, economic status, household income, and insurance coverage.

Health and family factors: History of activity-limiting injury or disease, family composition, and number of children. “The univariate analysis indicated that psychosocial factors—including cognition (cOR = 1.18, P < 0.001), adaptability (cOR = 1.12, P < 0.001), self-regulation (cOR = 1.36, P < 0.001), and social support (cOR = 1.06, P < 0.001)—were significantly associated with greater physical activity. Among the sociodemographic factors, economic status, history of activity-limiting injury, and number of children were significant. For example, insufficient economic status was inversely associated with physical activity (cOR = 0.56, P = 0.012), and older adults with a history of activity-limiting injury reported substantially lower activity levels (cOR = 0.48, P < 0.001).

In the multivariate analysis, self-regulation remained the strongest predictor (aOR = 1.26, P < 0.001). Educational attainment was also significant (aOR = 2.10, P = 0.035), whereas a history of activity-limiting injury or disease reduced the likelihood of being physically active (aOR = 0.58, P = 0.003).

Table 3 Multivariate and univariate GEE analyses of factors associated with physical activity in older adults.
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Discussion

This study examined the relationships between self-efficacy, peer-based social support, sociodemographic factors, and physical activity levels among older adults in Rasht, Iran, identifying key modifiable psychosocial determinants. In interpreting the findings of this study, emphasis is placed on multivariate analyses, as they account for potential confounding factors and identify independent predictors of physical activity among older adults.

The main finding is that self-regulation, a subdomain of self-efficacy, emerged as the strongest predictor of physical activity engagement and that peer-based social support was not significantly associated with levels of physical activity. These results underscore the central role of self-regulatory processes described in Banduras’ social cognitive theory52. Self-regulation operates through mechanisms such as goal setting and planning, self-monitoring (tracking progress), and self-reactive influences (strategies to maintain effort), which together help translate motivation into sustained physical activity behaviour49.

Influence of self-efficacy

Consistent with Bandura’s social cognitive theory and previous studies53,54, our results indicate that greater self-efficacy, especially within the self-regulation domain, is significantly associated with higher physical activity levels among older adults. Self-regulation provides the agentic capacity to maintain behavior in the face of barriers, making it a particularly actionable target for intervention. This construct, which serves as the mechanism through which intention changes to sustained behavior, is particularly important55. Accordingly, interventions that target self-regulatory skills, goal setting, action planning, self-monitoring, problem solving, and coping planning are likely to promote long-term adherence among older adults. Nonetheless, situational adaptation remain a barrier in contexts where socioeconomic or environmental constraints limit opportunities to adjust and maintain activity patterns56,57.

Influence of social support

Contrary to some existing evidence, this study did not identify a statistically significant association between peer-based social support and physical activity.

This finding may reflect the study’s measurement focus, as we assessed only friendship-based support, which may have underestimated the broader influence of family or community support networks. While previous research has consistently demonstrated that various forms of social support facilitate engagement in physical activity31,58, the quality and type of support appear more influential than its mere presence. In certain sociocultural contexts, excessive or overprotective support reduce autonomy and discourage activity59. Findings about overdependence on support, which discourage autonomous activity, help explain our results, especially in contexts where support is perceived as excessive caretaking rather than promoting autonomy60. The current findings emphasize the importance of examining supportive behaviors that enhance the independence of older adults, as opposed to those that may unintentionally limit independence.

Future research should incorporate multiple sources of support—including family, community, and organizational networks—as well as different types of support, such as emotional, informational, and instrumental support. It is also important to distinguish between autonomy-supportive behaviors, which encourage independence, and overprotective behaviors, which may inadvertently discourage physical activity.

Influence of sociodemographic factors

Marital status

No significant relationship emerged between marital status and physical activity level, which contrasts with the findings of some studies representing that spousal support enhances activity61,62.

This discrepancy may reflect contextual variations in how marital dynamics influence activity patterns, with potential caregiving responsibilities offsetting any positive effects in our sample. Another possible explanation is the limited diversity of marital roles among our participants, as most were married and shared relatively similar role expectations, reducing variability in the observed associations.

Educational attainment

Higher educational attainment was positively associated with physical activity in the present study, which is consistent with previous research63,64,65. This finding suggests that individuals with higher education levels may have greater health literacy, which enhances their understanding of the benefits of regular exercise and facilitates engagement in physical activity. Additionally, education may promote access to health-related information, social networks that encourage activity, and the development of self-regulatory skills necessary for maintaining an active lifestyle. These mechanisms collectively may explain the observed relationship between educational level and physical activity among older adults.

Employment and economic status

While occupational physical demands theoretically influence physical activity patterns66,67, our results revealed no significant employment-related effects. This may be explained by the high proportion of retired individuals in our sample, which reduced variability across employment categories and limited the statistical power to detect differences.

Similarly, economic status was found to be significantly associated with physical activity, aligning with previous research that highlights the role of economic resources in enabling access to facilities, equipment, and supportive environments conducive to an active lifestyle68,69,70.

Insurance coverage

While insurance theoretically facilitates healthcare access and activity opportunities71, persistent structural barriers (e.g., transportation, program availability, and out-of-pocket costs) are not alleviated by coverage alone. This observation points to potential ceiling effects in this specific setting.

Health and family factors

A history of activity-limiting injury or disease was strongly negatively associated with physical activity, echoing previous findings72,73. These conditions can often lead to functional decline and discourage ongoing activity. To mitigate these effects, it is crucial to identify rehabilitation and adequate support as concrete intervention targets (e.g., referral to physiotherapy/rehabilitation, progressive functional training, pain and symptom management, and autonomy-supportive assistance from caregivers/peers that encourages safe independent activity)74,75,76.

Strengths and limitations

This study benefits from validated instruments and robust multivariate analyses, which together provide a comprehensive assessment of psychosocial and sociodemographic factors associated with physical activity in older adults. The use of GEE models further strengthens the validity of the findings by accounting for clustering effects.

However, the cross-sectional design precludes causal inferences, and reliance on self-reported data may have introduced recall or social desirability bias. Future longitudinal studies with objective physical activity measures and broader psychosocial variables (e.g., depression, environmental factors) are warranted.

In this study, only peer-based social support was measured, and there is a need to evaluate family and community support.

Future research should address these limitations by employing longitudinal designs, incorporating objective physical activity measures, and expanding the scope of psychosocial variables to include factors such as depression, access to recreational facilities, and environmental influences.

Additionally, although the psychometric properties of the self-efficacy questionnaire were generally strong, the intraclass correlation coefficient (ICC) for the self-regulation subscale (0.65) was relatively low compared with those of the other domains. This may indicate modest measurement error and should be considered when interpreting associations involving self-regulation.

Implications for practice and future research

The findings highlight the importance of strengthening self-regulation skills in interventions designed to increase physical activity among older adults. Health promotion programs should integrate self-regulation strategies, such as goal setting, self-monitoring, and coping with barriers, adapted to the sociocultural context of Iran. While peer-based support does not directly predict activity, it contributes to enhancing self-efficacy, suggesting that carefully structured social support interventions that promote autonomy can still be valuable.

Conclusion

The study demonstrated no significant associations between physical activity levels and certain sociodemographic characteristics, such as age and sex. However, it did reveal a significant relationship with educational attainment and economic status. Additionally, health-related factors, such as a history of activity-limiting injury or disease, also had notable associations. Importantly, the multivariate regression analysis identified self-regulation as the principal psychosocial determinant of physical activity engagement in this population. Older adults with greater self-regulation reported substantially greater participation in physical activities, underscoring the critical importance of integrating self-regulation enhancement strategies into geriatric health promotion initiatives. Additionally, while peer-based social support enhances self-efficacy, it does not directly predict physical activity behavior.

These findings underscore that self-efficacy, especially self-regulation, is central to physical activity engagement, whereas the influence of social support depends on its source and quality.

This study provides empirical evidence to inform public health policy and practices aimed at improving physical activity and quality of life in aging populations. These findings underscore the need for tailored interventions focusing on enhancing self-regulation skills among older adults and suggest exploring diverse sources of social support in future studies to optimize physical activity promotion strategies.