Evaluating the association of physical frailty with cognitive impairment: a clinical perspective in older adults of Bangladesh

Abstract

Background

Although physical frailty has long been recognized as a clinical condition, it has only recently gained attention in Bangladesh. This growing interest reflects the country’s increasing focus on the health challenges faced by its aging population. This study examined the prevalence of physical frailty and its association with cognitive impairment among older adults.

Methods

The study used a cross-sectional design conducted among 540 older adults aged 65 and above using a multistage sampling technique. Physical frailty status was assessed using the Fried frailty phenotype, and cognitive function was measured with the 30-item Mini-Mental State Examination.

Results

Across two districts, 60% of participants were classified as frail, and 33% were classified as pre-frail. Significant demographic differences were observed across frailty categories, including age, gender, marital status, net monthly income level, and smoking history (p < 0.001). Multinomial logistic regression analyses indicated that several demographic and clinical factors were associated with cognitive impairment, including gender, education, household income, and multimorbidity. Higher household income (> 60,000 Bangladeshi Taka [BDT] per month) was associated with lower odds of severe cognitive impairment (OR = 0.14, 95% CI = 0.04, 0.45). Regular visits from family or friends showed a suggestive association with lower odds of cognitive impairment (OR = 0.59, 95% CI = 0.03, 1.54), although this association did not reach statistical significance at the 0.05 level. Cognitive function tended to be lower among frail participants aged 81 and above, with the lowest observed scores.

Conclusion

The findings demonstrate a significant association between physical frailty and cognitive impairment among older adults in Bangladesh, emphasizing the role of social determinants in shaping these outcomes. The results highlight the need for targeted interventions and policy strategies addressing these determinants to promote healthy aging and mitigate cognitive decline in this population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-026-06971-4.

Background

Physical frailty, characterized by reduced physiological resilience and loss of muscle mass and strength, with consequent impairments in balance and coordination, increases vulnerability to a range of adverse health outcomes [1]. Given the anticipated demographic shift in Bangladesh, where the proportion of citizens aged 60 and older is projected to rise to one in five by 2050 [2], a trend driven by advances in healthcare and increased life expectancy, it is imperative to mitigate the risk of physical frailty in older adults. Physical frailty often precipitates a cascade of consequences, including functional decline [3], increased fall risk [4], nutritional challenges [5], dependence on caregivers [6], higher rates of hospitalization [7], potential cognitive impairment [8], and elevated risk of mortality. Although direct estimates of the economic burden of physical frailty in Bangladesh are limited, national data show that older adults experience substantial financial strain due to high out-of-pocket healthcare expenditures. On average, these expenditures amount to 7,714 Bangladeshi Taka (BDT) per illness episode, and overall healthcare costs increase with age, particularly in the presence of chronic illness, disability, and limited social protection [9, 10]. Global evidence suggests that similar patterns exist elsewhere on a much larger scale; studies from the United States report that a frail individual incurs an annual healthcare cost of approximately USD 23,207 (~ 2.7 million BDT), nearly three times the cost of a healthy peer [11]. Similarly, in Japan, national projections estimate that the annual economic burden associated with frailty-related medical and long-term care is expected to reach USD 97 billion (~ 11.4 trillion BDT) by 2043 [12]. Together, these figures underscore the urgent need for targeted interventions to address frailty within Bangladesh before its aging population faces a similar economic crisis.

A study of community-dwelling older adults in Bangladesh reported that 36% were moderately frail and 25% were severely frail [13]. Beyond individual healthcare costs, frailty imposes broader societal costs through reduced productivity and increased pressure on formal and informal support systems. Rapid urbanization and migration of younger family members have eroded traditional family-based caregiving structures, reducing informal support for older adults [14, 15]. These changes, combined with gendered caregiving expectations and evolving household structures, increase the caregiving burden on remaining family members [16], further highlighting the societal implications of physical frailty in Bangladesh.

Addressing physical frailty and, separately, cognitive impairment in older adults is essential, as each condition independently affects quality of life and functional independence [17]. These interconnected conditions limit daily functioning and social engagement, thereby increasing the risk of disability and loss of autonomy. Research shows that physical frailty correlates with cognitive performance; prefrail individuals often score lower on cognitive assessments than non-frail individuals [18]. Early detection and intervention are therefore essential. Timely treatment can reduce complications and preserve independence while improving the management of common comorbidities. Supporting caregivers is essential because they face emotional and financial burdens; providing them with adequate resources improves their well-being and care quality. The relationship between physical frailty and cognitive impairment is complex and potentially bidirectional, with each condition possibly exacerbating the other; however, the present study examines them as distinct variables [19]. Biological mechanisms (i.e., oxidative stress and hormonal dysregulation) may negatively impact muscle and brain function, creating a vicious cycle that increases the risk of falls and hospitalizations and contributes to dependency and psychological distress [20]. Tackling these interrelated issues is vital for optimizing healthcare resource allocation, improving patient outcomes, and fostering a compassionate healthcare system that meets the needs of the aging population.

Physical frailty constitutes a pressing public health challenge among older adults in Bangladesh and is closely linked to cognitive impairment, including vascular and other non-Alzheimer’s dementias [21]. Although advanced age is a major risk factor, cognitive decline may also arise from potentially reversible causes (i.e., medication effects, metabolic disorders, delirium, and depression). Despite the urgency of this issue, substantial gaps persist in the Bangladeshi context. Priority research areas include elucidation of the biological mechanisms underlying frailty, improving early detection methods, and exploring the relationship between physical and cognitive health. Evidence on the effectiveness of interventions (i.e., exercise, nutrition, and cognitive training) remains limited; comprehensive, context-specific clinical studies are therefore needed to validate these approaches for frailty and cognitive impairment in Bangladesh. Addressing these gaps will enable healthcare providers to recommend evidence-based interventions and inform policy initiatives aimed at preserving health and independence among the country’s aging population. Therefore, this study aims to examine the prevalence of physical frailty and its association with cognitive impairment among older adults in Bangladesh.

Methods

Participants and procedures

All procedures were approved by the university’s Institutional Review Board. Data come from a cross-sectional study conducted in two districts in Bangladesh. From March to October 2023, older adults aged 65 and above participated in this study. As part of this research, participants who were capable of independent ambulation and able to understand simple commands were included. Participants who were severely physically ill and failed to give consent, with a diagnosis of neurodegenerative or uncontrolled chronic diseases, with any diagnosed psychiatric disorder (past or present), and with any problems of speech, hearing, or vision, which would impede the conduct of the examination, were excluded. The screening criteria included observations of participants during interactions and specific questioning to assess medical history, psychiatric status, and sensory capabilities by data collectors, ensuring adherence to ethical guidelines and eligibility requirements for the study. A multistage sampling technique was used. Two districts were purposively selected to represent contrasting settings (one predominantly rural, characterized by agricultural livelihoods and limited healthcare access, and one predominantly urban, with higher population density, greater access to healthcare facilities, and more diversified economic activities). In each district, six villages (rural) or municipalities (urban) were randomly selected; households within those clusters were then sampled using a systematic random sampling technique. All individuals aged ≥ 65 years living in the household were included in this study; the survey was collected in paper and pencil form, administered via in-home assessments. The first household was randomly selected, and if a participant from one household did not respond, data collectors proceeded to the next one. The selection criterion was to get an equal number of samples (n = 270) from both districts. Considering 80% power, 1.96 with 95% CI (alpha set to 0.05), and assuming 30% attrition, the required sample size was 540.

Data were collected using a structured questionnaire; the questionnaire was first constructed in English, then translated into Bangla, and again back-translated to English by a third person to see the accuracy of the original Bangla translation. The interview was conducted using the Bangla questionnaire. After each interview, it was checked for completeness and accuracy by a senior researcher. Six trained physician-researchers with postgraduate education and public health training were involved in data collection. Two days of practical training were conducted before data collection, covering questionnaire approaches and ethical considerations. Following the training, the questionnaire was administered to 20 people anonymously in a separate sample to check the feasibility of this research; the questionnaire was modified accordingly afterward. The face validity was obtained by consulting with experts in the field. Interviewers acquired written informed consent from the participants. For participants without formal education, interviewers informed and explained to them as per ethical guidelines, and then they obtained informed consent signed by the participant’s legally authorized representative. The study included baseline socio-demographic variables and physical health indicators, which were as follows: age, gender, marital status, area of residence, educational attainment, net monthly household income in BDT, body mass index (BMI) (kg/m^2), presence of any previous chronic diseases, and smoking history. Participants were also asked about their lifestyle status, categorized as ‘Healthy’, ‘Not healthy/not unhealthy’, or ‘Unhealthy’. In addition, sleep patterns were assessed with participants reporting ‘Normal’, ‘Short period’, or ‘Long period’ sleep times. The study also examined social engagement with participants in activities including ‘Sports club/gym’, ‘Social club’, ‘Adult education class’, ‘Another group activity’, and ‘None of the above’. Lastly, participants reported the frequency of visits to relatives’ or friends’ homes, ranging from ‘Almost daily’ to ‘Never or almost never.’

Measures

Physical frailty assessment

To evaluate physical frailty, the Fried frailty phenotype indicators [22] were used, which include five criteria (i.e., shrinking/unintentional weight loss, physical endurance/exhaustion, low physical activity, slow walking speed, and weakness/poor hand grip strength). In this study, some parts of the questions and measurement scale were gathered from the Cardiovascular Health Study (CHS) on which the phenotype is based, and others were adapted based on previously validated studies [23, 24], considering the cultural context of the Bangladeshi population (Table 1 [23]; also see Supplementary material A). Participants were classified as robust/not frail (met none of the frailty criteria), intermediate/pre-frail (met one or two criteria), or frail (met three or more criteria) according to cutoffs described by Fried and colleagues, to ensure consistency with the existing literature. Sample-specific reliability estimates (e.g., internal consistency or test–retest reliability) were not calculated for the present dataset.

Table 1.

Frailty criteria

	Cardiovascular Health Study*	Bangla Adaptation of Frailty
Shrinking	Self-reported: “In the last year, have you lost ≥ 10 pounds unintentionally?” (Response: Yes = 1, No = 0)	Self-reported: “Compared with last year, has your weight changed?” (Response: Lost weight = 1, Other = 0)
Grip strength	Grip strength in the lowest 20% at baseline, adjusted for gender and body mass index. (Dominant hand)	Grip strength gender and body mass index adjusted cutoffs taken from Fried and colleagues. (Dominant hand)
Physical endurance/energy	Self-reported exhaustion, (identified by two questions from the CES–D scale (36): “How often in the last week – (a) did you feel that everything was an effort, or (b) could you not get going?” (Response: moderate amount of the time (3–4 days) or most of the time = 1, other = 0)	Self-reported: “Over the past two weeks, how often have you felt tired or had little energy?” (Response: every week or nearly every day = 1, other = 0)
Slow walking speed	Walking time in seconds (usual pace) over 15 feet, the slowest 20% of the population was defined at baseline adjusting for gender and standing height	Self-reported: “How would you describe your usual walking pace?” (Response: Slow = 1, Other = 0)
Low physical activity	Self-reported: Minnesota Leisure Time Activity Questionnaire (18 items). Kcal of activity per week estimated, and the lowest quintile of physical activity was identified for each gender	Self-reported: Over the last 4 weeks, how often have you were physically active? And classified as – Hardly ever/never, 1–3 times per month, 1–2 times per week, and ≥ 3 times per week) (Response: Hardly ever/never, 1–3 times per month = 1, 1–2 times per week, and ≥ 3 times per week = 0)

^*Criteria were adapted from Fried et al. Definition used in original description by Fried and colleagues; CES-D scale = the Center for Epidemiologic Studies Depression scale

Cognitive assessment

Cognitive function was assessed using a 30-item mini-mental state examination (MMSE). The MMSE is a widely used instrument with established reliability and validity for assessing cognitive decline in older populations. The MMSE assesses seven cognitive domains (i.e., temporal orientation, spatial orientation, word encoding, attention and calculation, word recall, language, and visual constructive capacity) [25]. The maximum score on the MMSE is 30 points. The Bangla-adapted version of the MMSE (BAMSE; see Supplementary material A) has been previously validated for use in Bangladesh across varying literacy levels was administered [26]. Although the BAMSE validation reduced educational bias, it did not propose education-specific diagnostic cutoffs [26]; therefore, standard MMSE severity bands were used to classify cognitive status. MMSE scores were categorized as follows: a score of 25 or above indicates normal; a score of 21–24 indicates mild impairment; a score of 10–20 indicates moderate impairment; and a score of less than 10 indicates severe impairment [27]. These standard categories were applied uniformly across participants. Years of schooling were recorded and included as a categorical variable in multinomial analyses to adjust for residual confounding by education, with the following categories: none or primary education [reference], up to class 8, class 9–12, and graduation or post-graduation. Sample-specific reliability estimates (e.g., internal consistency or test–retest reliability) were not calculated for the present dataset.

Statistical analysis

Data were analyzed using R version 4.5.1 [28]. Descriptive statistics were computed as means and standard deviations (SD) for continuous variables and numbers (n) and percentages (%) for categorical variables. Pearson’s Chi-square test was used to compare categorical variables. Continuous variables such as age were grouped into meaningful categories (i.e., 65–70, 71–75, 75–80 and 81 +) prior to analysis, allowing their comparison between groups using the Chi-square test. In addition, plots were generated as part of the analysis to visualize relationships between variables of interest, including age groups vs. cognitive function scores and frailty status vs. cognitive function scores. These visualizations were used to support the interpretation of group differences and trends in the data.

Multinomial logistic regression was used to examine associations between cognitive impairment (dependent variable; categories: Normal [reference], Mild, Moderate, Severe) and frailty status, adjusting for demographic and clinical covariates. Reference categories were specified for all categorical variables (also see Table 3). Multimorbidity was included as a covariate to assess its potential confounding or mediating role, and sensitivity analyses compared models with and without multimorbidity. Because multinomial logistic regression does not assume proportional odds, a Test of Parallel Lines is not applicable. Model fit was assessed using the likelihood-ratio χ² test (model vs intercept), pseudo-R² (Nagelkerke), and the Pearson and deviance goodness-of-fit statistics where applicable. Because the Hosmer–Lemeshow test applies to binary logistic models, it was only used for any secondary binary logistic models; it is not reported for the multinomial model. Logistic regression coefficients from the model were exponentiated and presented as adjusted odds ratios (AOR) with a corresponding 95% confidence interval (CI), with a two-sided p-value < 0.05 statistically significant. Multicollinearity was assessed with variance inflation factors (VIF) and tolerances; all VIF values were < 3 and all tolerances > 0.33, indicating no problematic multicollinearity.

Table 3.

Association between total cognitive function with demographics variables by multinomial regression (N = 540)

Variables	Total Cognitive Function1
	Severe Impairment	Moderate Impairment	Mild Impairment
	Adjusted OR
Age groups, in years
65—70	Reference	Reference	Reference
71—75	5.70 (1.04–31.39)a	3.22 (1.76–5.89)c	2.53 (1.32–4.86)c
76—80	23.32 (4.42–122.95)c	8.79 (4.19–18.44)c	3.02 (1.30–7.00)b
81 +	24.17 (44.04–124.21)c	8.54 (3.84–18.99)c	1.23 (0.42–3.59)
Gender
Female	Reference	Reference	Reference
Male	0.91 (0.51–1.63)	0.52 (0.32–0.82)b	0.56 (0.32–0.96)a
Marital status
Married	Reference	Reference	Reference
Unmarried	0.67 (0.07–6.66)	0.42 (0.08–2.13)	0.67 (0.11–4.13)
Widow/Widower	9.35 (4.02–21.74)c	1.69 (0.75–3.79)	1.52 (0.59–3.89)
Area of residence
Urban	Reference	Reference	Reference
Rural	1.04 (0.58–1.86)	1.29 (0.81–2.04)	1.12 (0.65–1.94)
Highest level of education
None or primary education	Reference	Reference	Reference
Up to Class 8	1.61 (0.19–13.13)	2.61 (0.81–8.43)	0.76 (0.24–2.47)
Class 9–12	1.95 (0.23–16.39)	2.73 (0.81–9.19)	1.14 (0.34–3.87)
Graduation or post graduation	0.33 (0.14–0.76)b	0.41 (0.22–0.74)b	0.98 (0.19–0.79)b
Net monthly household income
< 20,000 BDT	Reference	Reference	Reference
20,000–40000 BDT	0.54 (0.27–1.07)	0.56 (0.31–0.99)a	0.47 (0.24–0.93)a
40,000–60000 BDT	0.48 (0.19–1.19)	0.58 (0.28–1.19)	0.57 (0.25–1.32)
> 60,000 BDT	0.14 (0.04–0.45)c	0.12 (0.05–0.28)c	0.31 (0.13–0.77)b
Lifestyle status
Healthy	Reference	Reference	Reference
Not healthy/not unhealthy	1.09 (0.53–2.25)	0.87 (0.49–1.55)	0.61 (0.29–1.28)
Unhealthy	1.37 (0.68–2.73)	1.56 (0.90–2.69)	1.59 (0.85–3.01)
Multimorbidity
No long-term conditions	Reference	Reference	Reference
One long-term condition	0.69 (0.36–1.36)	0.94 (0.55–1.63)	0.82 (0.43–1.56)
Two long-term conditions	1.23 (0.55–3.02)	2.21 (1.09–4.49)b	1.36 (0.59–3.13)
Three long-term conditions	0.53 (0.15–1.93)	2.00 (0.83–4.79)	1.25 (0.44–3.54)
At least four long-term conditions	-	2.00 (2.15–18.57)	4.00 (0.43–37.61)
BMI (kg/m^2)
Underweight	Reference	Reference	Reference
Healthy weight	0.26 (0.05–1.34)	0.49 (0.10–2.28)	0.51 (0.09–2.92)
Overweight	0.23 (0.04–1.21)	0.39 (0.08–1.82)	0.48 (0.08–2.75)
Obese	0.36 (0.06–2.15)	0.41 (0.08–2.14)	0.54 (0.08–3.51)
Smoking history
Current smoker	Reference	Reference	Reference
Former smoker	4.23 (0.46–38.99)	1.46 (0.38–5.56)	0.39 (0.09–1.51)
Never smoker	4.23 (0.48–37.17)	2.93 (0.82–10.42)	1.01 (0.29–3.45)
Sleep time
Long period	Reference	Reference	Reference
Normal	0.98 (0.38–2.51)	1.04 (0.48–2.23)	0.59 (0.25–1.38)
Short period	0.94 (0.38–2.31)	1.17 (0.57–2.42)	0.69 (0.31–1.55)
Social activities
Social evenings organized for officers	Reference	Reference	Reference
Field trips/road trips with spouse or children	7.00 (1.14–42.96)a	4.81 (0.91–25.53)	2.33 (0.36–15.05)
Religious club	4.00 (0.31–52.07)	9.63 (1.10–84.23)a	1.17 (0.07–20.72)
Community gardening	1.00 (0.15–6.85)	1.53 (0.37–6.35)	2.04 (0.48–8.71)
Any voluntary activities	0.67 (0.14–3.27)	1.46 (0.49–4.36)	-
None of the above	1.72 (0.65–4.54)	2.32 (1.08–4.99)	1.29 (0.56–2.99)
Frequency of friend or family visits
Almost daily	Reference	Reference	Reference
2–4 times a week	0.84 (0.62–1.89)	0.59 (0.03–1.54)a	0.38 (0.71–2.16)
About once a week	0.81 (0.20–3.27)	0.19 (0.39–3.58)	0.38 (0.44–2.69)
About once a month	1.50 (0.38–5.97)	1.74 (0.57–5.30)	3.67 (0.68–19.70)
Once every few months	1.63 (0.43–6.19)	2.09 (0.71–6.13)	4.35 (0.84–22.53)
Never or almost never	1.56 (0.33–7.36)	3.19 (0.94–10.91)	5.83 (0.98–34.44)a

Dependent variable:¹Total Cognitive Function; Adjusted OR = adjusted odds ratio; BDT = Bangladeshi taka; BMI-body mass index

^ap < 0.05

^bp < 0.01

^cp < 0.001

Results

Of the 540 participants, 27% were older than 81 years, and 53% were female. Most participants were married (82%) and resided in rural areas (56%). Regarding education, 31% had no formal education or only primary education, and 38% reported a monthly income below 20,000 BDT. About one-third of participants (34%) reported following a healthy lifestyle, while 36% had at least one long-term medical condition. Nearly half of the participants (48%) had a body mass index within the healthy range. Concerning smoking status, 18% were former smokers, and 79% had never smoked. More than half (54%) reported shorter sleep duration. Only 9% participated in social evenings organized for officers, and 32% reported visiting with family or friends once every few months (Table 2).

Table 2.

Demographic characteristics by frailty category (N = 540)

Variables	Whole population	Frailty status			p-value
		Frail	Pre-frail	Robust
		n (%)	n (%)	n (%)
Age groups (in years)
65—70	141 (26.1%)	6 (2.1%)	64 (43.0%)	71 (26.1%)	< 0.001b
71—75	135 (25.0%)	43 (15.1%)	57 (38.3%)	35 (33.0%)
76—80	119 (22.0%)	91 (31.9%)	28 (18.8%)	0 (0.0%)
81 +	145 (26.9%)	145 (50.9%)	0 (0.0%)	0 (0.0%)
Gender
Female	286 (53.0%)	156 (54.7%)	95 (63.8%)	35 (33.0%)	< 0.001b
Male	254 (47.0%)	129 (45.3%)	54 (36.2%)	71 (67.0%)
Marital status
Married	442 (81.9%)	13 (74.7%)	131 (87.9%)	98 (92.5%)	< 0.001b
Unmarried	9 (1.7%)	2 (0.7%)	5 (3.4%)	2 (1.9%)
Widow/Widower	89 (16.5%)	70 (24.6%)	13 (8.7%)	6 (5.7%)
Area of residence
Urban	240 (44.4%)	119 (41.8%)	71 (47.7%)	50 (47.2%)	0.412
Rural	300 (55.6%)	166 (58.2%)	78 (52.3%)	56 (52.8%)
Highest level of education
None or primary education	166 (30.7%)	78 (27.4%)	55 (36.9%)	33 (31.1%)	0.056
Up to Class 8	98 (18.1%)	60 (21.1%)	20 (13.4%)	18 (17.0%)
Class 9–12	139 (25.7%)	82 (28.8%)	37 (24.8%)	20 (18.9%)
Graduation or post graduation	137 (25.4%)	65 (22.8%)	37 (24.8%)	35 (33.0%)
Net monthly income
< 20,000 BDT	203 (37.6%)	113 (39.6%)	54 (36.2%)	36 (34.0%)	0.010a
20,000–40000 BDT	204 (37.8%)	107 (37.5%)	55 (36.9%)	4 (39.6%)
40,000–60000 BDT	88 (16.3%)	52 (18.2%)	25 (16.8%)	11 (10.4%)
> 60,000 BDT	45 (8.3%)	13 (4.6%)	15 (10.1%)	17 (16.0%)
Lifestyle status
Healthy	183 (33.9%)	97 (34.0%)	57 (38.3%)	29 (27.4%)	0.429
Not healthy/not unhealthy	131 (24.3%)	70 (24.6%)	31 (20.8%)	30 (28.3%)
Unhealthy	226 (41.9%)	118 (41.4%)	61 (40.9%)	47 (44.3%)
Multimorbidity
No long-term conditions	173 (32.0%)	88 (30.9%)	45 (30.2%)	40 (37.7%)	0.537
One long-term condition	192 (35.6%)	1.5 (36.8%)	49 (32.9%)	38 (35.8%)
Two long-term conditions	112 (20.7%)	60 (21.1%)	37 (24.8%)	15 (14.2%)
Three long-term conditions	54 (10.0%)	26 (9.1%)	17 (11.4%)	11 (10.4%)
At least four long-term conditions	9 (1.7%)	6 (2.1%)	1 (0.7%)	2 (1.9%)
BMI (kg/m^2)
Underweight	23 (4.3%)	14 (4.9%)	5 (3.4%)	4 (3.8%)	0.355
Healthy weight	257 (47.6%)	133 (46.7%)	81 (54.4%)	43 (40.6%)
Overweight	195 (36.1%)	107 (37.5%)	46 (30.9%)	42 (39.6%)
Obese	65 (12.0%)	31 (10.9%)	17 (11.4%)	17 (16.0%)
Smoking history
Current smoker	17 (3.1%)	3 (1.1%)	8 (5.4%)	6 (5.7%)	0.001b
Former smoker	98 (18.1%)	59 (20.7%)	14 (9.4%)	25 (23.6%)
Never smoker	425 (78.7%)	223 (78.2%)	127 (85.2%)	75 (70.8%)
Sleep time
Long period	72 (13.3%)	39 (13.7%)	21 (14.1%)	12 (11.3%)	0.505
Normal	175 (32.4%)	84 (29.5%)	50 (33.6%)	41 (38.7%)
Short period	293 (54.3%)	162 (56.8%)	78 (52.3%)	53 (50.0%)
Social activities
Social evenings organized for officers	49 (9.1%)	18 (6.3%)	16 (10.7%)	15 (14.2%)	0.024
Field trips/road trips with spouse or children	24 (4.4%)	16 (5.6%)	3 (2.0%)	5 (4.7%)
Religious club	15 (2.8%)	13 (4.6%)	0 (0.0%)	2 (1.9%)
Community gardening	20 (3.7%)	8 (2.8%)	5 (3.4%)	7 (6.6%)
Any voluntary activities	27 (5.0%)	14 (4.9%)	8 (5.4%)	5 (4.7%)
None of the above	405 (75.0%)	216 (75.8%)	117 (78.5%)	72 (67.9%)
Frequency of family and friends visit
Almost daily	22 (4.1%)	10 (3.5%)	8 (5.4%)	4 (3.8%)	0.272
2–4 times a week	68 (12.6%)	46 (16.1%)	12 (8.1%)	10 (9.4%)
About once a week	103 (19.1%)	58 (20.4%)	24 (16.1%)	21 (19.8%)
About once a month	108 (20.0%)	54 (18.9%)	31 (20.8%)	23 (21.7%)
Once every few months	170 (31.5%)	87 (30.5%)	48 (32.2%)	35 (33.0%)
Never or almost never	69 (12.8%)	30 (10.5%)	26 (17.4%)	13 (12.3%)

Data are presented as n (%)

BDT Bangladeshi taka, BMI Body-mass index

^ap < 0.01

^bp < 0.001

Building on the baseline characteristics, participants who exhibited one or more components indicative of frailty were subject to classification; one-third of this subgroup (33%) were categorized as pre-frail, while a substantial majority (60%) fell within the frail classification. Out of all five components, only 7% were in the normal range and were classified as robust or non-frail. Significant differences were observed across frailty categories for age groups, gender, marital status, net monthly income level, and smoking history (p < 0.001); presented in Table 2. Compared with robust/non-frail participants, those with frailty were more likely to be older, with 51% of frail participants aged 81 or above. Female participants were more prevalent in the frail (55%) and pre-frail (64%) groups, while male participants represented a higher percentage of the robust group (67%). Widowhood was more common among the frail (25%). Higher-income (> 60,000 BDT) was more prevalent in the robust group (16%) compared to the frail (5%). Never smokers were most common across all groups, but former smokers were more prevalent in the frail (21%) and robust (24%) categories compared to pre-frail (9%). Social activities showed some variation (p = 0.024), with the robust group having higher participation in social evenings (14%) compared to the frail (6%) and pre-frail (11%) groups. No significant differences were found for area of residence, education level, lifestyle status, multimorbidity, BMI, sleep time, or frequency of family and friends visits.

A multinomial logistic regression examined associations between demographic and social factors and levels of cognitive impairment (Table 3). Age showed a significant relationship with higher odds of experiencing cognitive impairment across all categories. Male participants were 48% less likely to have moderate (OR = 0.52, 95% CI = 0.32, 0.82) and 44% less likely to have mild cognitive impairment (OR = 0.56, 95% CI = 0.32, 0.96). Widowed/widower participants had significantly higher odds of severe cognitive impairment compared to married participants (OR = 9.35, 95% CI = 4.02, 21.74, n = 89), although the wide confidence interval suggests this estimate is imprecise due to the relatively small number of widowed participants. Participants with a graduate/post-graduate education showed a significant association; with 67% less likely to have severe (OR = 0.33, 95% CI = 0.14, 0.76), and 59% less likely to have moderate cognitive impairment (OR = 0.41, 95% CI = 0.22, 0.74) compared to others. Participants with a net monthly household income > 60,000 BDT had significantly lower odds of cognitive impairment across all severity levels compared with those earning < 20,000 BDT, including severe (OR = 0.14, 95% CI = 0.04, 0.45), moderate (OR = 0.12, 95% CI = 0.05, 0.28), and mild impairment (OR = 0.31, 95% CI = 0.13, 0.77). Multimorbidity was associated with higher odds, participants with two long-term conditions being 2.2 times more likely to have moderate cognitive impairment (OR = 2.21, 95% CI = 1.09, 4.49) compared to others. Participation in field trips/road trips with spouse or children was associated with higher odds of severe cognitive impairment (OR = 7.00, 95% CI = 1.14, 42.96, n = 24), and involvement in religious clubs was associated with higher odds of moderate impairment (OR = 9.63, 95% CI = 1.10, 84.23, n = 15); both estimates are imprecise due to small subgroup sizes. Frequency of friend/family visits showed mixed findings, receiving visits from their family/friends 2–4 times per week was associated with lower odds of moderate cognitive impairment (OR = 0.59, 95% CI = 0.03, 1.54, n = 68), though this did not reach statistical significance at α = 0.05; participants who never or almost never received visits had higher odds of mild cognitive impairment (OR = 5.83, 95% CI = 0.98, 34.44, n = 69), a borderline association that should be interpreted cautiously.

The relationship of total cognitive function scores across different subgroups among older adults is shown in Fig. 1. The first plot (age groups vs. cognitive function score) showed a general trend of declining cognitive function scores as age increased, particularly noticeable in the 81 + group exhibited a more pronounced shift (median = 2.0, IQR = 1.0–2.0), towards lower cognitive scores, where a larger proportion of participants exhibited moderate to severe cognitive impairment. The 71–75 and 76–80 age groups (median = 2.0, IQR = 2.0–3.0), reflecting a gradual shift towards lower cognitive scores compared to the 65–70 group (median = 3.0, IQR = 2.0–4.0), indicating relatively preserved cognitive function.

Fig. 1.

Correlation of age and frailty with cognitive impairment

The second plot (frailty vs. cognitive function score) showed a negative association between frailty and cognitive function. As frailty status progressed from robust to frail, cognitive function scores tended to decrease. Robust participants clustered predominantly in the normal and mild cognitive function categories, with fewer in moderate and very few in severe. Pre-frail participants showed a greater dispersion in cognitive scores (median = 2.0, IQR = 2.0–3.0) compared with robust participants (median = 3.0, IQR = 2.0–4.0), indicating increased heterogeneity in cognitive performance. Frail participants exhibited the lowest overall cognitive function scores, with a higher concentration in the moderate and severe categories and a more restricted score distribution (median = 2.0, IQR = 1.0–2.0).

Discussion

In this study, the association between physical frailty and cognitive impairment was investigated, summarizing data involving community-dwelling older adults in Bangladesh. The findings indicated that frailty was associated with older age, being female, widowhood, lower income, and past smoking. Cognitive impairment was linked to increasing age, being female, widowhood, lower education, lower income, and multimorbidity. Social factors (i.e., participation in religious clubs or infrequent family visits) showed a suggestive association with cognitive impairment. A significant correlation was found between higher frailty scores and lower cognitive function scores.

The rise in average life expectancy since 2000 has led to a surge in frailty-related research due to the aging global population [29]. However, frailty, a multifactorial condition associated with negative health outcomes, remains under-researched from a global health perspective [30]. This area of research is still in its early stages in Bangladesh, with limited exploration and awareness. Two district-level studies in Bangladesh reported a 60% frailty prevalence among older adults, whereas a cross-sectional survey covering all eight divisions found a 25% prevalence [21]. Globally, frailty prevalence among older adults in developing countries ranges from 5 to 44% [31], and prefrailty from 13 to 72% [32].

The findings showed frailty prevalence of 51% among older adults aged 81 or above and 58% among female participants. Previous studies support these findings, showing that frailty is more prevalent among older adults, particularly women and those with low education levels [33–35]. Frailty prevalence differs by age group, with 10% of adults aged 50–64, 44% of those 65 and older, and 62% of individuals aged 90 and above [36], indicating that research findings can be influenced by the age range of recruited participants. A population-level cohort study highlighted a higher incidence of frailty among female participants of Asian ethnicity [37]. Marital status also plays a crucial role in the relationship between social engagement and frailty in older adults. Research indicates that married individuals have better health outcomes than their unmarried counterparts [38, 39]. However, widowed individuals face an even greater risk of frailty compared to both married and unmarried groups [40, 41].

Lower-income and racial/ethnic minority groups show higher frailty rates, which is concerning given frailty’s association with adverse health outcomes. These groups often have limited resources to mitigate vulnerability, increasing the risk of widening health disparities [42–44]. Financial constraints are particularly significant in developing countries, where limited resources hinder effective management of frailty and the maintenance of quality of life [31]. Despite improvements in education and income equity, rising frailty rates remain linked to mortality, highlighting the necessity for strategic approaches to support the aging population [45]. This study demonstrates that smoking is detrimental to aging-related frailty, with 21% of past smokers exhibiting frailty. Consistent with previous research, lifetime smoking has been associated with a substantially increased risk of frailty, suggesting that long-term behavioral and biological pathways may influence frailty risk in later life [46, 47].

Importantly, income in this context should be interpreted as an indicator of broader socioeconomic position rather than as an isolated exposure. According to the theory of fundamental causes, socioeconomic status (SES) embodies multiple resources (e.g., money, knowledge, power) that shape health through diverse pathways, including access to healthcare, information, and material resources [48]. Evidence from studies of health inequalities shows that income and related SES indicators capture overlapping dimensions of social advantage and disadvantage that are linked to health outcomes in older adults [49]. In frailty research, lower socioeconomic position, often measured by income, education, and wealth, is strongly associated with increased frailty prevalence, suggesting that income reflects cumulative material and social circumstances [50]. Moreover, broader social determinants, including education and access to resources, have been shown to influence cognitive functioning and overall health among older adults [51].

Age-related changes in cognitive function reflect structural and functional brain alterations that contribute to neuronal dysfunction [52] and to age-associated diseases, accelerating cognitive decline and impairing daily activities [53]. As individuals age, measurable declines occur across various cognitive domains, particularly in sensory perception and processing speed. While some aspects of memory remain stable with aging, significant differences in new learning abilities emerge as age increases. Research indicates that cognitive functions (i.e., decision-making, problem-solving, planning, sequencing responses, and multitasking) also decline with advancing age [53]. When examining clinical progression, studies consistently show that female participants tend to progress more rapidly than males toward a clinical diagnosis of mild cognitive impairment (MCI) and dementia [54, 55]. Mixed evidence suggests that although females may possess certain metabolic and functional advantages over males, they are significantly influenced by protective and risk factors that affect cognitive decline and related pathologies [56].

There is a substantial and independent association between marital status and cognitive function among older adults. Those without a partner are twice as likely to develop cognitive impairment and Alzheimer’s disease (AD) [57], with an even higher risk for those who are single in both mid-life and later life [58]. Studies have found that older adults with less education, irrespective of their occupation, exhibited signs of cognitive decline [59]. Although education promotes the development of more synapses, boosting cognitive reserve and helping prevent MCI [60], research suggests this decline can stem from other factors (i.e., socioeconomic challenges, limited access to healthcare [61], and limited social interactions [62, 63]), all of which can negatively affect cognitive health. Individuals with less education often face health disparities, including higher rates of chronic health issues, which are linked to an increased risk of cognitive decline [64]. The coexistence of these chronic conditions leading to multimorbidity accelerates cognitive decline among older adults [65, 66].

Social factors play a significant role in cognitive function. Engagement in social activities can provide mental stimulation, emotional support, and a sense of belonging, all of which are decisive for maintaining cognitive health as we age [67]. Evidence from an observational study shows that increased social engagement during midlife and late life is associated with a reduced risk of developing dementia [68]. Enhanced social support and regular interactions may be linked to lower odds of cognitive decline and dementia, potentially delaying its onset or reducing overall risk [69]. The findings of this study suggest that engaging in overwhelming or stressful activities (e.g., family road trips) can lead to higher odds of severe cognitive impairment in older adults. Participation in religious clubs showed a suggestive association with moderate cognitive impairment. Religious clubs, particularly when attendance is infrequent or interactions are not intellectually stimulating, indicate that not all social activities confer the same cognitive benefit. Balancing social activities with cognitive demands may be important for maintaining cognitive function.

Marked individual variability within subgroups indicates that frailty status and age, although important, are not the sole determinants of cognitive function. In Bangladesh, distinct factors (i.e., limited access to healthcare, poor nutrition, and restricted cognitive stimulation) in older age may exacerbate cognitive decline. Cognitive impairment has been linked to frailty, with the risk of dementia potentially more associated with the aging population than with frailty status itself [70]. Previous studies have also shown a correlation between frailty and an increased risk of developing dementia [71]. Specific frailty phenotypes, including low grip strength, slow gait speed, and balance impairments, have been associated with poorer cognitive outcomes in older adults [72]. Notably, gait speed and grip strength have emerged as independent early markers of dementia, demonstrating strong associations with cognitive impairment linked to frailty [73]. The clustering of robust individuals in the normal and mild cognitive function categories suggests that early interventions to maintain physical health could play a critical role in preserving cognitive function among Bangladeshi older adults. The wider distribution of cognitive scores, particularly in the pre-frail group, indicates the transitional nature of frailty and the potential benefits of early intervention.

The findings of this study should be interpreted in light of several limitations. Firstly, the cross-sectional design of the study poses challenges in establishing causality for the observed associations. Secondly, this study’s external validity is limited, as data were collected from only two districts; while these districts encompassed urban and rural areas and varied socioeconomic profiles, the results may not reflect national patterns. Thirdly, the use of self-reported measures of the questionnaire assessing frailty may introduce the potential for recall bias. Future research should consider implementing a longitudinal study design to follow individuals over time, which will reduce the reliance on retrospective self-reporting and can provide more accurate results. Fourthly, several risk factors (i.e., depression, loneliness, activities of daily living) [74] were not included in this study. These inclusions could enhance prevention strategies and clinical practices by promoting healthy lifestyles and addressing issues related to aging. Participants were randomly selected from community settings. This selection reduces concerns about response bias. Another strength of the study involves the use of validated scales to measure frailty phenotypes and cognitive domains, ensuring a reliable assessment.

The study’s findings support integration of frailty and cognitive screening into routine primary-care and community-health services. Routine screening at Community Clinics and during community health-worker (CHW) outreach should employ validated brief tools with predetermined referral criteria for complex cases [75]. District-level pilots of the World Health Organization (WHO) Integrated care for older people approach (ICOPE) pathway are recommended to operationalize person-centred assessment and targeted multimodal interventions, including strength-and-balance physical-activity programmes, nutritional supplementation for undernourished older adults, medication review, and cognitive-stimulation activities, and to establish linkages with social services and secondary care [76, 77]. Implementation can be achieved within existing health and social-protection architectures through incremental reallocation. CHWs and community clinics can absorb screening and referral tasks with short competency-based training modules. Non-Governmental Organizations (NGOs) with extensive community networks (e.g., BRAC) can deliver group-based exercise and social-engagement activities, and targeted financing may be drawn from the old age allowance/pension mechanisms to subsidize group activities, nutritional supplements, or home-based support for eligible low-income older adults [78]. A simple pilot costing template (staff time, CHW hours per participant, training, venue/transport, materials, and supplements) can help estimate the extra cost per participant and inform decisions about scaling up [79]. In addition to these service-level actions, broader community and systems responses are needed. Development of community-based support networks and promotion of age-friendly urban planning can strengthen informal care and help older people remain connected and mobile. Targeted health-education programmes for older adults and their families can improve care at home. Incentives for training healthcare providers in frailty management and support for technology solutions for elder care can reduce pressure on the health system and improve the quality of life for frail people. Finally, frailty and cognitive indicators should be incorporated into routine noncommunicable diseases (NCDs) and older-person monitoring systems, and implementation-research partnerships with national institutions (e.g., icddr,b) should evaluate effectiveness, equity, and cost-effectiveness prior to national scale-up. Given the limited accessibility of preventive healthcare in the country, these findings underscore the importance of early screening for frailty and cognitive decline as strategies to reduce dementia risk. These findings align with and can directly inform implementation of the Bangladesh National Policy on Older Persons (2013) [80], which emphasizes preventive care, community-based services, and social protection for older adults. In particular, the results support prioritizing routine frailty and cognitive screening, clear referral pathways, and targeted community-level interventions within the policy’s primary-care and social-support frameworks to operationalize its preventive and community-based objectives.

Conclusions

The rapid aging of the population in lower-income countries, such as Bangladesh, presents significant challenges for healthcare systems and the economies. Promoting good health in later life is crucial to achieving global healthy aging goals, particularly by preventing frailty, a condition closely associated with physical decline and cognitive impairment. In Bangladesh, addressing frailty requires attention to the social determinants of health that contribute to its onset and progression. A healthy aging framework that accounts for local factors (i.e., socioeconomic conditions, access to healthcare, and genetic predispositions) is essential for developing effective strategies. Such strategies should aim both to manage chronic diseases and to reduce the risk of cognitive decline. By prioritizing frailty prevention, health outcomes for older adults in Bangladesh can be significantly improved, including their cognitive health. These considerations underscore the need for comprehensive research and policy initiatives tailored to the country’s specific context and epidemiological profile. Priority actions should include integrated frailty and cognitive screening, targeted prevention programs, and research to inform evidence-based policy development.

Abbreviations

MCI

Mild cognitive impairment

AD

Alzheimer’s disease

MMSE

Mini-mental state examination

CHS

Cardiovascular health study

BAMSE

Bangla-adapted version of the MMSE

BDT

Bangladeshi taka

BMI

Body mass index

SD

Standard deviations

AOR

Adjusted odds ratios

VIF

Variance inflation factors

SES

Socioeconomic status

WHO

World health organization

ICOPE

Integrated care for older people approach

NGOs

Non-governmental organizations

CHW

Community health worker

NCDs

Noncommunicable diseases

Authors’ contributions

Conceptualization, N.E.M.; Methodology, N.E.M., I.H.M.; Investigation, N.E.M., I.H.M., S.S. and S.B.A.; Data collection procedure, N.E.M.; Field implementation, I.H.M., S.S. and S.B.A.; Data entry, N.E.M., I.H.M., S.S. and S.B.A.; Formal analysis, N.E.M; Writing-original draft preparation, N.E.M.; Writing-review and editing, N.E.M., I.H.M., S.S. and S.B.A.; Project administration, N.E.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study did not receive any funds from the public or any donor agency.

Data availability

The data underlying the results presented in this study are available in the following link (https://doi.org/10.6084/m9.figshare.27242532.v1).

Declarations

Ethics approval and consent to participate

Ethical approval of this study was obtained from the Institutional Review Board (IRB)/Ethical Review Committee (ERC) (2022/OR-NSU/IRB/0206). All methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from each respondent. For participants without formal education, interviewers informed and explained to them as per ethical guidelines, and then they obtained informed consent signed by the participant’s legally authorized representative.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.