Comparison of balance, functional capacity, and quality of life between older adults with lower and higher levels of physical activity

Abstract

Objective

The primary objective was to compare health outcomes (balance, functional capacity, lower extremity strength, fear of falling, sleep quality, and quality of life) between older adults with higher and lower levels of physical activity. A secondary objective was to explore whether these health-related factors mediated the association between physical activity level and quality of life.

Methods

A comparative cross-sectional study involved 88 older adults with higher levels of physical activity (higher-PA group) and 88 with lower levels of physical activity (lower-PA group), who underwent mobility tests including the Timed Up and Go Test (TUGT), 2-Minute Walk Test (2MWT), and 10-Meter Walk Test (10MWT); balance and strength assessments such as the Modified Clinical Test of Sensory Interaction in Balance (mCTSIB) and Five-Time Sit-to-Stand Test (5xSTST); and completed self-reported questionnaires including the Fall Efficacy Scale-International (FES-I), Pittsburgh Sleep Quality Index (PSQI), and World Health Organization Quality of Life - Brief (WHOQOL-BREF) Primary analyses compared outcomes between groups with lower and higher levels of physical activity. Mediation analysis was performed to explore indirect effects of balance, fear of falling, lower extremity strength, and sleep quality on the relationship between PA level and Quality of Life (QoL).

Results

A total of 176 participants with a mean ± SD age of 65.90 ± 3.84 years and a mean BMI of 23.08 ± 1.43 kg/m² were included. Higher-PA group demonstrated significantly better performance on TUGT, 2MWT, and 10MWT than the lower-PA group, with the younger higher-PA group showing greater 2MWT scores. They demonstrated significantly better mCTSIB, FES-I, and PSQI scores than the lower-PA group. Participants with higher levels of physical activity showed significantly lower FES-I and 10MWT scores compared to those with lower levels. Additionally, mCTSIB scores were significantly influenced by age, income, and occupation, with higher physical activity levels associated with higher mCTSIB scores. Higher-PA group reported significantly better physical and psychological health and environmental and social relationships in QoL domains. Furthermore, adequate sleep duration was significantly associated with improved sleep quality. Parallel mediation analyses revealed that in the physical health domain, all mediators showed significant partial mediation, with the strongest indirect effect via 10MWT (β = 1.116, p = .003). In the psychological domain, PSQI emerged as the most prominent mediator (β = 1.061, p = .001). For the social relationship domain, 2MWT demonstrated the strongest mediation effect (β = 0.546, p = .006). In the environmental health domain, only PSQI (β = 0.475, p = .002) and FES-I (β = 0.239, p = .047) showed significant partial mediation. While controlling for sociodemographic characteristics in the models, all domains showed significant total effects, with partial mediation observed through psychosocial and physical performance variables; however, none of the indirect effects reached statistical significance.

Conclusion

Older adults engaging in higher levels of physical activity demonstrated markedly better mobility, balance, and overall QoL compared to their less active counterpart. Physical activity positively influenced all domains of QoL through multiple functional pathways, with significant partial mediation observed via mobility, balance, sleep quality, and fear of falling, most notably within the physical and psychological domains. However, after adjusting for key sociodemographic covariates, the indirect effects were no longer statistically significant. These results highlight the critical role of functional capacities in translating physical activity into improved well-being among older adults.

Introduction

Aging, an inevitable biological process of declining physical and cognitive functions [1], is increasingly significant as the global geriatric population continues to rise, with estimates indicating that by 2030, one in six persons worldwide will be 60 or older [2]. As of 2020 in Pakistan, approximately 7.3 million were 60 years or older. By 2050, this demographic is expected to double, reaching 12.4% of the total population and growing significantly faster than other age groups [3]. A large proportion of Pakistan’s elderly population remains sedentary, exacerbating age-related physical and cognitive declines [4]. Physical inactivity is a considerable contributor to progressive physical impairment in this demographic [5]. The World Health Organization’s (WHO) active aging model emphasizes a holistic approach to well-being, advocating for physical, social, and mental health along with active societal participation for older adults, which is in accordance with the United Nations Sustainable Development Goals (SDGs), particularly Goal 3, by providing insights into factors that promote healthy aging [6, 7]. Evidence from studies in diverse settings, including Thailand, China, Iran, and India, has consistently demonstrated a strong relation between physical activity (PA) and improvements in balance, fear of falling (FoF), functional capacity, lower extremity strength (LES), and overall quality of life (QoL) in the elderly [8–13].

In Pakistan, national health policies largely prioritize the younger population while overlooking the needs of the elderly. Unique cultural and socioeconomic factors in the country may influence PA levels and related health outcomes among older adults. However, these influences remain under-researched. Although comprehensive, nationally representative data on fall-related injuries among older adults in Pakistan are lacking, existing local and hospital-based studies suggest an elevated risk compared to younger populations. For example, one study reports 42.6% of older adults experiencing falls, comprising 39.0% in the high-risk group and 61.0% in the low-risk category [14], while another hospital-based source recorded 3,335 fall-related injuries [15], a figure that likely underestimates the true burden at the national level. Identified contributors to fall risk include increasing age, female gender, lower education, cognitive decline, reduced walking speed, physical inactivity, visual impairment, and prior history of falls [16]. Additionally, 62.6% of older adults report a fear of falling (FoF), which is associated with factors such as age, BMI, and previous fall history [17]. Physical inactivity is a key contributor to fall risk among Pakistani elderly [18], although some studies suggest that lifestyle factors alone may not fully predict fall risk [19].

Although the physiological processes of aging are universal, local health system limitations and sociocultural barriers can shape how these changes manifest in specific contexts. While age-related declines in strength, balance, and quality of life are universal, Pakistan’s limited healthcare access, cultural norms, and low rates of physical activity participation may influence these outcomes, warranting a local study. Despite the well-documented global benefits of physical activity for older adults, research specific to Pakistan’s elderly population remains limited. This study is guided by a conceptual model in which physical activity is hypothesized to improve lower extremity strength, which in turn supports balance and functional mobility, reduces fear of falling, improves sleep quality, and ultimately enhances quality of life. This model recognizes the interrelated pathways through which PA can benefit older adults’ health. No previous study has holistically assessed key health outcomes such as balance, functional capacity, lower extremity strength, sleep quality, fear of falling, and quality of life in relation to physical activity levels among older adults in Pakistan. This study aimed to compare outcomes between older adults with higher and lower levels of physical activity and to examine whether balance, lower extremity strength, fear of falling, and sleep quality mediate the relationship between physical activity and quality of life, in order to inform targeted interventions for promoting active aging. While the study is grounded in the Pakistani context, its findings also have broader relevance for other low- and middle-income countries facing similar systemic and cultural barriers to physical activity. Ultimately, this research contributes to a deeper understanding of elderly health, informing evidence-based policies and public health strategies aligned with the WHO’s active aging framework and UN Sustainable Development Goal 3, clarifying that while this goal promotes health and well-being broadly, healthy aging is a cross-cutting concern rather than a direct target.

Methods

Study design and setting

A total of 176 healthy older adults participated in this comparative cross-sectional study (mean age, 65.90 ± 3.84 years), recruited through convenience sampling and stratified into a lower-PA group (n = 88) and a higher-PA group (n = 88). This sample size was calculated (“G*Power 3.1 statistical software; Faul, Erdfelder, Lang, and Buchner, Germany”) [20, 21]. The calculation was based on our pilot study, which provided an estimated standardized effect size (Cohen’s d) of 0.522 for differences in Modified Clinical Test of Sensory Interaction on Balance (mCTSIB) scores between groups. This effect size was used as the input for the power analysis, along with a significance level (α) of 0.05 and desired statistical power (1 − β) of 0.90. Participants were recruited from the community in Khyber Pakhtunkhwa Province, Pakistan, between March 1 and June 30, 2024. All respondents were initially informed about the study’s purpose, with screening and assessments conducted in Physiotherapy Laboratory 2 at Abasyn University, Peshawar. The outcome assessor was blinded to group assignments (higher-PA group vs. lower-PA group) to minimize measurement bias.

Sample selection

Inclusion and exclusion criteria

Participants aged 60 and older, capable of following verbal instructions and walking independently without assistance, were eligible to take part in the study. The inclusion criteria stipulated that participants must not have any underlying diseases affecting the musculoskeletal or cardiovascular systems, as screened by the Physical Activity Readiness Questionnaire (PAR-Q) [22, 23] having no history of hospitalization within the past three months, and be able to comprehend either English or Urdu. The higher-PA group participated in a minimum of 150 min of moderate-intensity exercise or 75 min of vigorous-intensity exercise weekly, as measured by the Rapid Assessment of Physical Activity (RAPA). In contrast, the lower-PA group comprised individuals reporting less than 30 min of moderate PA three times per week, as determined by the RAPA [24], and no participation in structured exercise programs in the preceding six months.

Potential participants were excluded if they had acute musculoskeletal injuries or pain affecting mobility, or if they displayed cognitive impairments, as evidenced by a Mini-Mental State Examination score below 23. Similarly, those with visual or auditory impairments, severe chronic conditions, cognitive disorders, or neurological diseases, significant health issues such as the presence of a pacemaker, uncontrolled hypertension, obesity (with a BMI of 25 or higher according to the WHO Asian classification), or those who were unable to give informed consent for study participation were excluded.

Variables and measurement

The Timed Up and Go test (TUGT) assessed dynamic standing balance and gait. Participants completed three trials, and the average time recorded with a digital stopwatch was utilized for subsequent data analysis. The Five-Time Sit-to-Stand test (5xSTST) evaluated LES in older adults [25]. For the 5xSTST, a stopwatch was used to measure the time to complete five sit-to-stand-to-sit cycles, with an average of two trials recorded. The 2-Minute Walk test (2MWT) assessed self-paced walking ability and functional capacity [26], wherein participants were instructed to walk as fast as possible without using assistive devices for two minutes; the distance covered in meters was subsequently recorded to evaluate performance. Before the 2MWT, participants completed two practice walks to familiarize themselves with the protocol [27]. Functional mobility and gait were evaluated using the 10-Meter Walk Test (10MWT), where individuals walked 20 m without an assistive device, with the time measured for the intermediate 10 m, allowing 5 m for acceleration and deceleration. The average time for two trials was recorded to the nearest hundredth of a second. A rest period was permitted between trials to minimize fatigue, as necessary [28].

The mCTSIB evaluated the individuals’ ability to maintain static standing balance under different sensory conditions. They stood with their feet together for 30 s across four scenarios: (i) eyes open on a firm surface, (ii) eyes closed on a firm surface, (iii) eyes open on a foam surface, and (iv) eyes closed on a foam surface. The foam was medium density, measuring 24 × 24 × 4 inches (“SunMate; Dynamic System Inc., Leicester, NC, USA”). Each condition was repeated three times, with the average duration of the three trials representing each condition. The total mCTSIB score (mCTSIB-Tol) was calculated by summing the average times across all conditions [29].

FoF was assessed using the Falls Efficacy Scale-International (FES-I) questionnaire, with higher scores indicating a greater FoF [30]. Quality of Sleep was evaluated using the Pittsburgh Sleep Quality Index (PSQI). A global PSQI score of ≤ 5 denotes good sleep quality, while scores ≥ 6 denote poor sleep [31]. To ensure consistency and minimize inter-rater variability, all outcome questionnaires (FES-I, PSQI, WHOQOL-BREF) were administered by the same assessor, either in person or via self-administration.

QoL was measured using the World Health Organization Quality of Life Brief Version (WHOQOL-BREF) [32]. It assesses four domains: physical health, psychological well-being, social connections, and environmental factors. Responses were scored on a five-point ordinal scale for each domain, with higher scores indicating better quality of life (QoL) [33].

Physical activity levels served as the independent (predictor) variable, while the outcome variables were the four domains of the WHOQOL-BREF (Physical Health, Psychological, Social Relationships, and Environmental Health). Multiple functional, psychological, and sensory measures, including the TUGT, 5xSTST, 2MWT, 10MWT, mCTSIB, FES-I, PSQI, and mCTSIB Total Score, were examined as mediators.

Statistical analysis

Data were summarized using descriptive statistics. Normality of continuous variables was assessed using the Shapiro–Wilk test. The primary aim of this study was to compare balance, functional capacity, and lower extremity strength, fear of falling, sleep quality, and quality of life between older adults with higher and lower levels of physical activity. For the primary analyses, independent t-tests were used for normally distributed continuous variables, while the Mann–Whitney U test was applied for non-normally distributed variables. One-way ANOVA or Kruskal–Wallis tests were also used, as appropriate, based on data distribution.

We conducted individual (simple) mediation analyses to evaluate whether health-related outcomes mediated the relationship between physical activity level (higher vs. lower) and quality of life (WHOQOL-BREF domains). Each potential mediator (balance, functional capacity, lower extremity strength, fear of falling, and sleep quality) was tested in a separate model. Subsequently, we fit a parallel multiple mediation model including all mediators simultaneously to estimate their unique indirect effects.

We used bootstrapped confidence intervals (5,000 samples) to estimate indirect effects, following contemporary recommendations that do not require significant total effects for mediation. Analyses were performed using SPSS PROCESS macro (Model 4) [34]. The significance of mediation was determined by whether the bias-corrected 95% confidence interval for the indirect effect excluded zero. All parallel models adjusted for potential confounders selected by theoretical or empirical evidence.

Physical activity level was labelled nominal, and all mediators and outcome variables were treated as continuous measures. A two-tailed p-value < 0.05 was considered statistically significant. All analyses were conducted using SPSS (version 27) and R (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria) statistical software.

Results

During data collection, 203 individuals consented to participate in the study. Following screening based on inclusion and exclusion criteria, 17 participants were excluded because they did not meet the age requirement, had uncontrolled medical conditions, or declined to continue, leaving 186 eligible participants. However, 4 individuals were further excluded from higher-PA group and 6 from lower-PA group due to incomplete filling of questionnaires or unable to complete the objective assessments or decline to participate. Consequently, the final analysis included 176 participants, evenly distributed between the lower- and higher-PA groups (n = 88 per group; Fig. 1).

Fig. 1.

Flow of participants through the study. PA; Physical Activity

Table 1 describes the baseline sociodemographic characteristics of the participants. Almost two-thirds were male and married (n = 136, 77.3%). Most participants were aged 60–64 years (n = 78, 44.3%), had completed high school education (n = 92, 52.3%), and reported an average monthly income (n = 94, 53.4%). Thirty-four (19.3%) participants reported falls in the past year, with injuries occurring in 26 (14.8%) cases.

Table 1.

Sociodemographic characteristics and clinical features of the participants (N = 176)

Characteristics		n (%)	Mean ± SD
Gender	Male	116 (65.91)	-
	Female	60 (34.09)	-
Age groups	60–64 years	78 (44.30)	-
	65–69 years	61 (34.70)	-
	70–74 years	37 (21.00)	-
Marital Status	Married	136 (77.27)	-
	Widowed	40 (22.73)	-
Educational level	Middle School	16 (9.10)	-
	High School	92 (52.30)	-
	Bachelor	36 (20.50)	-
	Masters or higher	32 (18.20)	-
Income status	Very Rich (> 1 lack PKR)	12 (6.82)	-
	Rich (50,000–1 lack PKR)	45 (25.57)	-
	Average (20,000–49,999 PKR)	94 (53.41)	-
	Poor (< 19,999 PKR)	25 (14.21)	-
Occupation	Teaching	13 (7.39)	-
	Retired	63 (35.80)	-
	Business	33 (18.75)	-
	Housewife	50 (28.41)	-
	Farmers/Others	17 (9.66)	-
Falls (Previous 1 Year)	Yes	34 (19.32)	-
	No	142 (80.68)	-
Fall-related injury	Yes	26 (14.77)	-
	No	150 (85.23)	-
Nightly sleep hours (past 1 month)	< 7	106 (60.20)	-
	≥ 7	70 (39.80)	-
Age(years)	-	-	65.90 ± 3.84
BMI (kg/m2)	-	-	23.08 ± 1.43

SD Standard deviation, n Number of participants, PKR Pakistani rupees, BMI Body mass index (kg/m²)

Table 2 compares all outcomes between the higher-PA group and the lower-PA group. The higher-PA group demonstrated significantly lower TUGT, 5xSTST, 10MWT, and FES-I scores than the lower-PA group. The 2MWT, mCTSIG-Tol, and all domains of the WHOOL-BREF were significantly greater for the higher-PA group than the lower-PA group. These results indicated that the higher-PA group had better physical performance, less FoF, and better QoL in all domains than the lower-PA group.

Table 2.

Between-group comparison of physical performance outcome measures, fear of falling, sleep, and quality of life (N = 176)

Outcome Measure	Physical Activity Level		Effect size(d)	P value
	Higher-PA group (n = 88)	Lower-PA group (n = 88)
TUGT (s)	12.20 ± 1.50	13.76 ± 2.06	0.864	< 0.001
5xSTST (s)	15.20 ± 2.58	15.53 ± 1.87	0.148	0.035
2MWT (m)	99.45 ± 8.29	95.59 ± 6.90	0.51	0.003
10MWT (s)¶	12.18 ± 0.92	12.64 ± 0.87	−0.51	0.001*
mCTSIB (Total score)	108.59 ± 12.64	101.68 ± 18.25	−0.44	0.033
FES-I (score)	26.44 ± 7.16	31.77 ± 8.36	−0.69	< 0.001
PSQI (global score)	6.50 ± 2.99	7.19 ± 2.92	−0.23	0.116
WHOQOL-BREF
Domain 1: PH	25.42 ± 4.40	22.97 ± 3.98	−0.59	< 0.001
Domain 2: PS	23.65 ± 3.95	21.56 ± 4.31	−0.51	0.001
Domain 3: SR	12.66 ± 2.41	11.56 ± 2.29	−0.47	< 0.001
Domain 4: EH	29.91 ± 4.27	27.98 ± 4.99	−0.42	0.008

Data is presented using mean ± SD while nonparametric probability statistics are used to report p-values

SD Standard deviation, TUGT Timed Up and Go test, 5xSTST Five times sit to stand, 2MWT Two-minute Walk test, mCTSIB modified Clinical Test of Sensory Interaction in Balance, FES-I Fall Efficacy Scale-International, PSQI Pittsburgh Sleep Quality Index, WHOQOL-BREF WHO Quality of Life Brief Questionnaire, PH Physical Health, PS Psychological Health, SR Social Relationships, EH Environmental Health

*p <.05

¶Ten-meter walk test (10MWT) was assessed using parametric statistics 

Significant differences in multiple physical performance outcome measures were observed when compared between different sociodemographic groups (Table 3). The TUGT revealed significantly slower performance among females than males, with younger participants (60–64 years) significantly outperforming older groups. In the 2MWT, males covered a significantly greater distance than females, with younger participants (60–64 years) achieving the highest distances compared to older groups. Likewise, 10MWT showed males were significantly faster than females, and younger participants significantly outperformed older groups. Active individuals had significantly higher mCTSIB scores than the lower-PA group, while females and older participants exhibited poorer balance.

Table 3.

Differences in physical performance outcome measures between sociodemographic groups

Characteristic		Functional measures
		TUGT (s)	5xSTST (s)	2MWT (m)	10MWT (s) ¶	mCTSIB Total score
Gender	Male	12.84 ± 2.08	15.24 ± 2.34	98.84 ± 8.25*	12.30 ± 0.94	107.87 ± 13.09*
	Female	13.26 ± 1.67*	15.61 ± 2.06	94.98 ± 6.34	12.64 ± 0.85*	99.84 ± 19.63
Age group	60–64 years	12.22 ± 1.47	15.68 ± 2.48	98.99 ± 8.13*	12.27 ± 0.91	109.53 ± 15.38*
	65–69 years	13.14 ± 1.46	15.13 ± 1.95	98.10 ± 7.24	12.35 ± 0.84	107.29 ± 13.39
	70–74 years	14.33 ± 2.69*	15.10 ± 2.19	93.49 ± 7.00	12.81 ± 0.97*	92.31 ± 15.03
Marital status	Married	12.91 ± 1.99	15.36 ± 2.37	98.36 ± 7.80*	12.31 ± 0.89	107.72 ± 14.96*
	Widowed	13.22 ± 1.86	15.37 ± 1.83	94.68 ± 7.42	12.75 ± 0.95*	96.34 ± 16.63
Educational level	Middle School	12.78 ± 1.51	15.85 ± 1.69	95.00 ± 6.34	12.69 ± 0.80	97.43 ± 18.03
	High School	13.21 ± 2.09	15.37 ± 2.48	98.83 ± 8.07	12.21 ± 0.87	106.35 ± 16.49
	Bachelor	12.85 ± 2.05	14.87 ± 1.57	96.94 ± 8.04	12.58 ± 0.98	104.53 ± 14.33
	Masters or higher	12.57 ± 1.61	15.66 ± 2.43	95.69 ± 7.23	12.66 ± 0.94*	106.17 ± 15.08
Income status	Very Rich	12.89 ± 1.36	15.98 ± 3.10	101.42 ± 8.58	12.03 ± 1.02	113.31 ± 7.21*
	Rich	12.68 ± 2.16	15.25 ± 2.04	98.18 ± 8.40	12.39 ± 1.03	100.68 ± 16.10
	Average	12.89 ± 1.78	15.45 ± 2.34	96.98 ± 8.08	12.49 ± 0.93	106.26 ± 17.25
	Poor	13.93 ± 2.27	14.95 ± 1.82	96.52 ± 4.74	12.33 ± 0.51	104.98 ± 12.13
Occupation	Teaching	11.25 ± 1.53	15.11 ± 1.65	99.69 ± 8.27*	12.25 ± 0.97	114.87 ± 9.61*
	Retired	12.79 ± 1.87	15.09 ± 2.08	98.84 ± 8.25	12.49 ± 0.90*	105.60 ± 14.21
	Business	12.53 ± 1.75	15.39 ± 2.87	94.98 ± 6.34	12.03 ± 0.90	110.53 ± 12.41
	Housewife	13.42 ± 1.55	15.77 ± 2.14	98.99 ± 8.13	12.67 ± 0.83	98.30 ± 19.33
	Worker or Farmer	14.58 ± 2.6*	15.33 ± 2.31	98.10 ± 7.24	12.23 ± 1.05	105.57 ± 14.93
Falls in previous year	Yes	13.12 ± 2.27	15.24 ± 2.05	93.49 ± 7.00	12.36 ± 0.86	105.69 ± 16.08
	No	12.95 ± 1.88	15.40 ± 2.30	98.36 ± 7.80	12.42 ± 0.94	105.00 ± 16.08
Fall-related injury	Yes	13.05 ± 1.55	15.01 ± 2.04	94.68 ± 7.42	12.37 ± 0.89	104.37 ± 17.48
	No	12.97 ± 2.02	15.43 ± 2.29	95.00 ± 6.34	12.42 ± 0.93	105.27 ± 15.83
Nightly sleep hours (past 1 month)	< 7	13.00 ± 2.22	15.28 ± 2.33	98.83 ± 8.07	12.37 ± 0.93	103.80 ± 16.89
	≥ 7	12.94 ± 1.48	15.50 ± 2.13	96.94 ± 8.04	12.48 ± 0.90	107.15 ± 14.53

Data are presented using mean ±SD, while nonparametric probability statistics are used to report p-values

TUGT Timed Up and Go test, 5xSTST Five times sit to stand, 2MWT Two-minute Walk test, mCTSIB modified Clinical Test of Sensory Interaction in Balance

*p<0.05

¶Ten-meter walk test (10MWT) was assessed using parametric statistics

Table 4 compares the FoF, sleep, and QoL between different sociodemographic groups. FES-I scores were notably higher in the 70–74 age group and among housewives compared to their counterparts. Those sleeping < 7 h/night had higher FES-I scores than those sleeping ≥ 7 h. Participants with a history of falling had significantly higher FES-I score than those without. Individuals aged 70–74 years had significantly higher PSQI scores among age groups. Participants with a middle school education had significantly the highest PSQI scores among their group. Workers or farmers had notably higher scores than other professions. Individuals who experienced falls or fall-related injuries in the past year demonstrated significantly higher PSQI scores than their counterparts. Nightly sleep duration below 7 h also corresponded to poor sleep quality. Figure 2 illustrates the proposed mediation model examining the relationship between physical activity level and quality of life (QoL). In this framework, balance (mCTSIB), functional capacity (2MWT), lower extremity strength (5xSTS), fear of falling (FES-I), and sleep quality (PSQI) act as potential mediators. The model hypothesizes that physical activity enhances these health-related factors, which in turn contribute to improved QoL. Pathways a and b represent the indirect effects through mediators, while path c′ denotes the direct effect of physical activity on QoL after accounting for mediation.

Table 4.

Differences in fear of falling, and sleep between different sociodemographic groups

Characteristic		Psychosocial measures
		FES-I (score)	PSQI (global score)
Gender	Male	28.10 ± 7.92	6.76 ± 2.94
	Female	31.05 ± 8.47*	7.02 ± 3.04
Age groups	60–64 years	26.68 ± 6.80	6.14 ± 2.60
	65–69 years	29.79 ± 8.92	7.13 ± 3.08
	70–74 years	33.11 ± 8.12*	7.86 ± 3.22*
Marital status	Married	28.19 ± 7.69	6.70 ± 2.95
	Widowed	32.23 ± 9.20*	7.35 ± 3.03
Educational level	Middle School	30.44 ± 6.72	8.56 ± 1.97*
	High School	28.47 ± 7.70	7.15 ± 3.20
	Bachelor	29.39 ± 8.96	6.22 ± 2.53
	Masters or higher	29.97 ± 9.54	5.81 ± 2.71
Income status	Very Rich	26.58 ± 8.39	6.58 ± 2.97
	Rich	29.47 ± 8.86	6.24 ± 2.74
	Average	28.69 ± 7.80	7.22 ± 3.01
	Poor	31.24 ± 8.40	6.64 ± 3.17
Occupation	Teaching	27.15 ± 7.19	6.23 ± 2.65
	Retired	27.92 ± 7.72	6.14 ± 2.75
	Business	26.58 ± 7.37	6.85 ± 2.72
	Housewife	31.42 ± 8.92	7.14 ± 3.15
	Worker or Farmer	33.12 ± 7.70*	9.06 ± 3.03*
Falls in the previous year	Yes	29.24 ± 8.14	7.76 ± 2.64*
	No	29.08 ± 8.25	6.63 ± 3.01
Fall-related injury	Yes	30.04 ± 8.51	7.92 ± 2.64*
	No	28.95 ± 8.17	6.66 ± 2.99
Nightly sleep hours (past 1 month)	< 7	29.62 ± 8.62	7.69 ± 2.82*
	≥ 7	28.33 ± 7.54	5.57 ± 2.74

Data are presented using mean ±SD, while nonparametric probability statistics are used to report p-values

FES-I Fall Efficacy Scale-International, PSQI Pittsburgh Sleep Quality Index

*p<0.05

Fig. 2 .

Conceptual mediation model showing how physical activity level may influence quality of life (QoL) via simple health-related mediators: balance (mCTSIB), functional capacity (2MWT), lower extremity strength (5xSTS), fear of falling (FES-I), and sleep quality (PSQI). Paths a and b indicate indirect effects, while path c′ represents the direct effect controlling for mediators

The simple mediation analyses (Model 4) assessing whether functional mobility tests and mCTSIB total score, as well as psychological measures [FES-I and PSQI global score] mediate the association between physical activity levels and four domains of QoL as measured by the WHOQOL-BREF. Across the domains, significant total effects of physical activity levels were observed on WHOQOL-PH (β = 2.375, p =.001), WHOQOL-PS (β = 2.263, p =.003), WHOQOL-SR (β = 1.812, p =.014), and WHOQOL-EH (β = 1.932, p =.006).

The direct effects of PA levels remained statistically significant for all domains when controlling for each mediator individually, although the magnitude was reduced compared to total effects, indicating partial mediation in most cases.

Among the mediators, the 2MWT consistently demonstrated statistically significant indirect effects across WHOQOL-PH (β = 0.612, p =.002), WHOQOL-PS (β = 0.537, p =.005), WHOQOL-SR (β = 0.438, p =.010), and WHOQOL-EH (β = 0.422, p =.012), suggesting that walking endurance partially mediates the relationship between physical activity levels and overall QoL.

Similarly, TUGT was a significant mediator for WHOQOL-PH (β = 0.576, p =.003), WHOQOL-PS (β = 0.493, p =.006), WHOQOL-SR (β = 0.407, p =.014), and WHOQOL-EH (β = 0.401, p =.017), indicating the relevance of dynamic balance in this association.

The FES-I mediated the effects of physical activity levels on WHOQOL-PH (β = 0.520, p =.007), WHOQOL-PS (β = 0.486, p =.009), and WHOQOL-SR (β = 0.375, p =.020), while the PSQI mediated the relationships with WHOQOL-PS (β = 0.432, p =.011), WHOQOL-SR (β = 0.387, p =.015), and WHOQOL-EH (β = 0.475, p =.009).

The mCTSIB total score was a significant mediator for WHOQOL-PH (β = 0.439, p =.011) and WHOQOL-PS (β = 0.391, p =.017).

The mediation type was classified as partial for all significant paths due to the persistence of a significant direct effect in each case. Full details, including unstandardized coefficients, 95% confidence intervals, and exact p-values for each total, direct, and indirect effect, are provided in Table 5.

Table 5.

Effects of physical activity levels on WHOQOL domains mediated by functional and psychosocial measures

WHOQOL Domain	Mediator	Total effect	p-value	Direct effect	p-value	Indirect effect [95% CI]	Mediation type	P-value indirect?
Physical Health (PH)	TUGT	2.46	< 0.001**	2.02	0.004**	0.440 [−0.061, 1.000]	Partial	Non-significant
	5×STST	2.46	< 0.001**	2.46	< 0.001**	−0.001 [−0.120, 0.150]	No	Non-significant
	2MWT	2.46	< 0.001**	2.09	0.001**	0.360 [−0.073, 0.750]	Partial	Non-significant
	10MWT	2.46	< 0.001**	2.26	0.001**	0.200 [−0.110, 0.580]	No	Non-significant
	mCTSIB	2.46	< 0.001**	2.02	0.002**	0.440 [0.096, 0.860]	Partial	Significant
	FES-I	2.46	< 0.001**	1.20	0.048*	1.260 [0.623, 2.050]	Partial	Significant
	PSQI	2.46	< 0.001**	2.05	0.001**	0.410 [−0.120, 0.950]	No	Non-significant
Psychological Health (PS)	TUGT	2.09	0.001**	1.68	0.014*	0.420 [−0.190, 1.090]	Partial	Non-significant
	5×STST	2.09	0.001**	2.14	0.014*	0.420 [−0.190, 1.090]	No	Non-significant
	2MWT	2.09	0.001**	1.77	0.006**	0.320 [0.012, 0.730]	Partial	Significant
	10MWT	2.09	0.001**	1.86	0.004**	0.240 [−0.060, 0.630]	No	Non-significant
	mCTSIB	2.09	0.001**	1.90	0.003**	0.120 [−0.124, 0.510]	No	Non-significant
	FES-I	2.09	0.001**	0.96	0.114	1.280 [0.570, 1.780]	Full	Significant
	PSQI	2.09	0.001**	1.06	0.004**	0.410 [−0.110, 0.930]	No	Non-significant
Social Relationships (SR)	TUGT	1.10	0.002**	1.06	0.007**	0.040 [−0.100, 1.460]	No	Non-significant
	5×STST	1.10	0.002**	1.18	0.001**	−0.080 [−0.250, 0.090]	No	Non-significant
	2MWT	1.10	0.002**	1.07	0.442	0.040 [−0.140, 0.230]	No	Non-significant
	10MWT	1.10	0.002**	1.18	0.003**	−0.020 [−0.210, 0.170]	No	Non-significant
	mCTSIB	1.10	0.002**	1.05	0.005**	0.060 [−0.110, 0.220]	No	Non-significant
	FES-I	1.10	0.002**	0.75	0.042*	0.350 [0.120, 0.620]	Full	Significant
	PSQI	1.10	0.002**	0.91	0.007**	0.180 [−0.060, 0.450]	Partial	Non-significant
Environmental Health (EH)	TUGT	1.93	0.006**	1.93	0.013*	0.004 [−0.150, 0.130]	No	Non-significant
	5×STST	1.93	0.006**	1.95	0.006**	−0.020 [−0.170, 0.140]	No	Non-significant
	2MWT	1.93	0.006**	1.57	0.029*	0.360 [−0.030, 0.810]	No	Non-significant
	10MWT	1.93	0.006**	1.82	0.013*	0.110 [−0.300, 0.500]	No	Non-significant
	mCTSIB	1.93	0.006**	1.18	0.017*	0.220 [−0.120, 0.550]	No	Non-significant
	FES-I	1.93	0.006**	1.46	0.049*	0.480 [−0.130, 1.040]	No	Non-significant
	PSQI	1.93	0.006**	1.61	0.018*	0.320 [−0.080, 0.840]	No	Non-significant

TUGT Timed Up and Go test, 5xSTST Five times sit to stand, 2MWT Two-minute Walk test, 10MWT Ten-meter Walk test, mCTSIB modified Clinical Test of Sensory Interaction in Balance, FES-I Fall Efficacy Scale-International, PSQI Pittsburgh Sleep Quality Index, WHOQOL-Brief WHO Quality of Life Brief Questionnaire. Note: Bold values indicate statistically significant indirect effects at p < 0.05. * = p < 0.05; ** = p < 0.01

We initially examined each potential mediator separately, then fit a parallel mediation model to evaluate their unique contributions. A parallel mediation analysis was used to evaluate the indirect effects of physical activity levels on four domains of WHOQOL-BREF. This parallel framework allows for the estimation of each indirect path while controlling for all other mediators in the model.

In the WHOQOL-PH domain, significant indirect effects were observed through 2MWT (β = 1.032, p =.001), 10MWT (β = 1.116, p =.003), mCTSIB (β = 0.401, p =.006), and PSQI (β = 1.099, p =.001), suggesting partial mediation. For WHOQOL-PS, TUGT (β = 0.659, p =.006), 10MWT (β = 0.806, p =.005), mCTSIB (β = 0.514, p =.006), FES-I (β = 0.651, p =.002), and PSQI (β = 1.061, p =.001) showed significant parallel mediation effects.

In the WHOQOL- SR domain, 2MWT (β = 0.546, p =.006), mCTSIB (β = 0.357, p =.015), FES-I (β = 0.492, p =.001), and PSQI (β = 0.592, p =.001) demonstrated statistically significant indirect effects. For WHOQOL-EH, a significant indirect effect was observed only through FES-I (β = 0.475, p =.002), with other mediators showing non-significant effects.

A parallel mediation model evaluating the direct and indirect effects of physical activity levels on the QOL domains score, mediated through seven functional and psychosocial variables, while adjusting for age group, gender, marital status, education level, income, and occupation.

The total effect of physical activity levels on WHOQOL-PH was statistically significant, b = 1.55, SE = 0.61, t = 2.54, p =.01, with a standardized coefficient (β) of 0.36. Upon inclusion of mediators, the direct effect was attenuated and became non-significant (β = 0.97, SE = 0.60, p =.11), suggesting partial mediation.

Among the mediators, FES-I and PSQI were significant predictors of WHOQOL-PH. FES-I exhibited a negative and statistically significant effect on QoL (β = − 0.16, SE = 0.04, p <.001), with a standardized coefficient of –0.30. PSQI also showed a negative association (β = − 0.30, SE = 0.11, p =.00).

Bootstrapped indirect effects indicated that FES-I was the only statistically significant mediator of the relationship between physical activity levels and WHOQOL-PH, with a point estimate of 0.61, SE = 0.26, and a 95% Cl [0.18, 1.17], not crossing zero. The partially standardized indirect effect through FES-I was also significant (Effect = 0.14, SE = 0.06, 95% CI [0.04, 0.26]).

Other mediators, including TUGT, 5xSTST, 2MWT, 10MWT, mCTSIB, and PSQI, did not demonstrate statistically significant indirect effects, as their 95% CI included zero. The overall total indirect effect was non-significant (Effect = 0.58, SE = 0.41, 95% CI [–0.18, 1.46]).

Overall, the full model explained 42% of the variance in WHOQOL-PH (R² = 0.42, p <.001). Among covariates, gender (β = –0.15, p =.04) remained a significant predictor of the outcome in the adjusted model.

The total effect of physical activity levels on WHOQOL-PS was statistically significant, B = 1.49, SE = 0.64, p =.02, with a standardized effect size of 0.35. However, after controlling for the mediators, the direct effect was attenuated and no longer significant, B = 0.84, SE = 0.61, p =.17, indicating partial mediation.

Among the mediators, only FES-I and sleep quality significantly contributed to the model. Specifically, FES-I showed a significant negative effect on WHOQOL-PS (β = −0.13, p =.00) and a significant indirect effect from PA levels to WHOQOL-PS through FES-I (indirect effect = 0.48, SE = 0.21, 95% CI [0.11, 0.95]). PSQI also exerted a significant negative effect on WHOQOL-PS (β = −0.51, p =.00); however, the indirect effect of PA levels via PSQI was not statistically significant (indirect effect = 0.14, 95% CI [−0.32, 0.60]).

Other mediators, including TUGT, 5xSTST, 2MWT, 10MWT, mCTSIB, did not demonstrate significant indirect effects, with all respective 95% Cl crossing zero. The total indirect effect of physical activity on WHOQOL-PS across all mediators was non-significant (indirect effect = 0.65, 95% CI [−0.17, 1.57]).

Model fit indices for individual mediator equations varied. Physical activity levels significantly predicted better TUGT performance (β = −1.08, p <.001) and faster 10MWT (β = −0.34, p =.02), but not 5xSTST, 2MWT, or mCTSIB. Physical activity levels were significantly associated with lower FES-I score (β = −3.82, p =.00) but not with PSQI (B = −0.27, p =.54).

A statistically significant total effect of PA levels on WHOQOL-SR was observed (β = 0.90, p =.02), which remained significant in the direct effect model (β = 0.92, p =.02), indicating a robust positive relationship between physical activity and social relationships independent of the mediators.

None of the individual or total indirect effects reached statistical significance, as all bootstrap confidence intervals (CIs) included zero. The largest negative indirect effect was observed through 5xSTST (β = −0.13, Boot 95% CI: −0.32, 0.03), and the largest positive effect was via PSQI (β = 0.08, Boot 95% CI: −0.20, 0.36), though both effects were nonsignificant. The total indirect effect was also nonsignificant (β = −0.02, 95% CI: −0.49, 0.41), suggesting that the mediators collectively do not account for a statistically significant proportion of the association between physical activity level and QoL.

Among the mediators, physical activity level significantly predicted better performance in TUGT (β = −1.08, p <.001), 10MWT (β = −0.34, p =.02), 2MWT (β = 2.34, p =.05), and FES-I (β = −3.82, p <.001). No significant effects were found for 5xSTST, mCTSIB, or PSQI. These associations indicate that higher physical activity levels were related to faster mobility, improved endurance, and reduced fear of falling, though these functional improvements did not significantly mediate the effect on WHOQOL-SR.

Age group and occupation showed significant associations across several models. Age was a strong predictor in TUGT (β = 0.88, p <.001), 2MWT (β = −2.28, p <.01), mCTSIB (β = −7.41, p <.001), and FES-I (β = 2.38, p <.01). Occupation was associated with TUGT (β = 0.55, p <.001), FES-I (β = 1.47, p =.01), and PSQI (β = 0.53, p =.02). Education level was inversely related to PSQI (β = −0.55, p <.01), indicating better sleep quality with higher education.

The total effect of physical activity on WHOQOL-EH was statistically significant, B = 1.57, SE = 0.73, p =.03, 95% CI [0.12, 3.02], with a partially standardized coefficient of 0.33. When controlling for all mediators, the direct effect was attenuated and no longer statistically significant (β = 1.36, SE = 0.75, p =.07, 95% CI [− 0.13, 2.84]), suggesting the presence of partial mediation.

Among the mediators, only the 2MWT demonstrated a statistically significant indirect effect (β = 0.50, SE = 0.33, 95% CI [0.01, 1.28]; partially standardized effect = 0.11, 95% CI [0.00, 0.27]), indicating that increased physical activity was associated with better walking endurance, which in turn predicted better scores on the WHOQOL-EH.

All other indirect effects via TUGT, 5xSTST, 10MWT, mCTSIB, FES-I, and PSQI were not statistically significant, as the corresponding bootstrap confidence intervals included zero. The total indirect effect across all mediators combined was non-significant (β = 0.22, SE = 0.42, 95% CI [− 0.62, 1.08]), with a partially standardized total indirect effect of 0.05, 95% CI [− 0.13, 0.23]. The model explained 23% of the variance in the outcome variable (R² = 0.23, F (14,161) = 3.35, p <.001), supporting a moderate overall fit.

Table 6 presents the total, direct, and indirect effects of physical activity levels on each WHOQOL domain, controlling for sociodemographic characteristics.

Table 6.

Parallel mediation analysis between physical activity and WHOQOL domains, adjusted for sociodemographic characteristics

WHOQOL Domain	Total Effect (B [95% CI], p)	Direct Effect (B [95% CI], p)	Total Indirect Effect (B [95% CI], BootSE)	Mediation type
WHOQOL-PH	2.46 [1.02, 3.91], p <.001	2.02 [0.66, 3.38], p =.004	0.44 [–0.06, 1.00], SE = 0.27	Partial, non-significant
WHOQOL-PS	2.02 [0.65, 3.39], p =.004	1.67 [0.25, 3.09], p =.021	0.35 [–0.23, 1.04], SE = 0.33	Partial, non-significant
WHOQOL-SR	2.02 [0.77, 3.27], p =.002	1.80 [0.46, 3.15], p =.009	0.22 [–0.33, 0.87], SE = 0.30	Partial, non-significant
WHOQOL-EH	1.57 [0.12, 3.02], p =.034	1.36 [–0.13, 2.84], p = 0 0.073	0.22 [–0.62, 1.08], SE = 0.42	Partial, non-significant

WHOQOL-BREF WHO Quality of Life Brief Questionnaire, PH Physical Health, PS Psychological Health, SR Social Relationships, EH Environmental Health

Discussion

This study primarily compared key health outcomes, including balance, functional capacity, LES, FoF, sleep quality, and QoL, between older adults with higher and lower levels of physical activity. Secondarily, this study examined whether these health-related factors mediated the association between physical activity level and QoL, using simple and parallel mediation analyses to evaluate the mediating roles of physical performance, FoF, and sleep quality, while controlling for key sociodemographic covariates, including age, gender, and education. The results indicated that PA had both direct and indirect effects on QoL, mediated through various functional and psychological factors.

Parallel mediation analysis revealed that PA was a significant direct predictor of QoL across all WHOQOL-BREF domains: physical, psychological, social, and environmental. These findings are consistent with previous studies, which have shown that older adults who maintain higher physical activity levels experience broad health benefits, including improved physical function, mental health, and social engagement [35, 36]. Indirect effects were evident through improved functional mobility as indicated by TUGT, 10MWT, 5xSTST, improved balance (mCTSIB), reduced FoF (FES-I), and better sleep quality (PSQI). Similar effects have been identified in community-based studies, reinforcing the multifactorial benefits of physical activity in aging populations [37].

Participants aged 60–64 years and males showed superior performance in mobility and balance tests compared to older age groups and females, reflecting better physiological resilience and functional reserve. These differences persisted even after adjusting for confounders, supporting previous literature that links aging and gender to declining physical performance and mobility [38, 39]. Similarly, another study on community-dwelling older adults found that age was significantly correlated to balance and mobility measures, and gender differences were present in balance but not in mobility [40]. Collectively, these results highlighted the influence of biological and hormonal factors, as well as lifelong physical activity exposure, on functional capacity in later life.

Similarly, higher-PA participants consistently outperformed their lower-PA peers on balance and mobility tests. PA was positively associated with TUGT and 10MWT scores, confirming its protective role against age-related motor decline and fall risk. Faster walking speeds among higher-PA reinforce the concept of gait speed as a key biomarker of health in aging populations [41, 42]. Likewise, superior mCTSIB scores in higher-PA reflect better sensory integration, which is vital for postural stability and fall prevention [43]. These findings align with evidence that balance is a multifactorial quality that can be effectively enhanced through various exercise programs, underscoring the importance of promoting physical activity in aging adults [44].

Importantly, higher-PA participants tended to have lower FoF levels than their lower-PA peers. Similar findings were reported in a large longitudinal study of community-dwelling older adults, where FoF was significantly associated with muscle strength, balance, gait speed, age, and a history of fall [45]. Regression analysis confirmed FoF as a significant mediator in the PA–QoL relationship. Lower FoF among physically active individuals may reflect increased confidence and reduced anxiety regarding mobility, consistent with prior research showing that structured physical activity interventions can reduce FoF and improve functional independence in older adults [46].

In addition to mobility and FoF, sleep quality also served as a mediating factor. Although overall PSQI scores did not differ significantly between higher-PA and lower-PA, indirect associations suggest that insufficient sleep (especially < 7 h) was linked with increased FoF, which may adversely affect QoL perceptions. These findings resonate with evidence that sleep impacts subjective well-being, even when physical performance is unaffected [47]. Contrary to expectations, PA did not show consistent associations with global sleep quality scores, echoing a recent meta-analysis [48]. However, specific domains such as sleep duration and subjective sleep quality may still benefit from PA, as reported by other systematic reviews [49]. The subjective nature of sleep assessment and unmeasured confounders (e.g., comorbidities, stress) may account for these inconsistencies.

Multiple sociodemographic variables, including age, gender, and educational level, significantly influenced all mediators and QoL domains. Consistent with previous research, older adults, females, and those with lower educational attainment generally exhibited poorer outcomes across measures of physical performance, FoF, sleep quality, and QoL [45, 50, 51]. These findings support WHO reports emphasizing the compounded vulnerability of these subgroups and the need for targeted health promotion strategies [52]. The parallel mediation model further indicated that educational attainment was positively associated with physical performance and indirectly improved QoL through increased PA engagement and better functional capacity. These findings align with previous studies, which have shown that higher education is associated with improved mobility and balance, likely due to enhanced health literacy, increased self-efficacy, and increased participation in regular exercise [53]. Occupation also played a role in balance among older adults. Teaching professionals showed the highest mCTSIB scores. This finding is consistent with previous research suggesting that occupations involving cognitive engagement and social interaction are associated with superior postural control and neuromotor function in later life [54]. In contrast, our study also showed that farmers, laborers, and housewives had lower mCTSIB scores, aligning with evidence that older workers in more physically demanding jobs tend to have poorer cognitive function, leading to poor balance [55]. These findings underscore the importance of addressing educational and occupational disparities when designing PA-based interventions for older adults. Public health programs or initiatives may be more effective if tailored for groups with lower education levels or occupations with limited opportunities for balance and mobility exercises, emphasizing PA’s broad benefits and the need to overcome sociodemographic barriers to health promotion in older adults. aging interventions.

Limitations

This study has certain limitations that warrant consideration. First, the cross-sectional design precludes any causal inference regarding the relationships among PA, functional mobility, FoF, sleep quality, and QoL. While mediation analyses provide insights into potential relationships, the directionality of these associations remains speculative. Future research employing longitudinal or interventional designs would help clarify causality and determine whether targeted PA programs could directly influence these mediating mechanisms. Second, the self-reported PA levels and sleep quality may introduce recall and social desirability bias, potentially affecting the precision of the estimates, particularly the observed indirect effects via sleep quality. In future research, objective measures, such as actigraphy, wearable trackers, or polysomnography, is recommended to enhance measurement accuracy. Thirdly, the gender imbalance, with 66% of the sample being male, may restrict the generalizability of these findings. Considering the known gender differences in physical performance, FoF, and QoL perceptions, implementing balanced recruitment strategies and including underrepresented groups, such as rural residents and low-literacy populations, could expand the applicability. Finally, although the current mediation models were pre-specified and statistically adjusted, the possibility of a type I error due to multiple comparisons cannot be completely ruled out. These findings should be considered hypothesis-generating but still preliminary. Replication in larger, more diverse cohorts with longer follow-up periods is needed, and future research could also compare different intervention settings (e.g., community centers, clinical rehabilitation, home-based programs) to identify the most scalable and cost-effective methods.

Conclusion

Higher physical activity in older adults is associated with better mobility, balance, and quality of life. Functional improvements, including mobility, balance, sleep quality, and reduced fear of falling, partially mediate this relationship. These findings underscore the importance of promoting physical activity to enhance well-being and functional independence in aging populations.

Authors’ contributions

Conceptualization: Abdullah, Mathiyakom W, Tantisuwat A. Methodology: Abdullah, Mathiyakom W, Tantisuwat A. Data acquisition: Abdullah, Ali I, Ullah S. Formal analysis: Abdullah, Mathiyakom W, Tantisuwat A. Project administration: Tantisuwat A. Writing – original draft: Abdullah. Writing – review, and editing: Mathiyakom W, Asai T, Tantisuwat A. All authors reviewed the manuscript.

Funding

This work was funded by the Chulalongkorn University Thailand Graduate Scholarship Program for ASEAN or Non-ASEAN countries and supported by the Physiotherapy Department at Abasyn University, Pakistan.

Data availability

The datasets used or analysed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval for this study was obtained from the Institutional Ethics Committee of Khyber Medical University, Ref. No. KMU/IPHSS/Ethics/2024/DH/0196, and the study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study. The study involved elderly participants; therefore, the informed consent form was written in clear and simple language to ensure comprehensibility. Investigators explained the purpose, procedures, potential risks, and benefits of the study to all participants before obtaining consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.