ABSTRACT
Context:
The locomotor capacity of elderly individuals enables them to perform their daily activities without any assistance, but when it gets limited, it curbs their independence, and in resource-limited settings of rural India, it contributes heavily to their wellbeing.
Aims:
This study aimed to assess their locomotor capacity and various factors associated with it.
Settings and Design:
This community-based cross-sectional study was done among 195 elderly individuals residing in a rural area of the state of West Bengal, India.
Methods and Materials:
Study participants were selected using a cluster sampling method. Sociodemographic, nutritional, and behavioral factors and comorbidities were assessed, followed by gait testing using short-performance physical battery testing.
Statistical Analysis Used:
Binary logistic regression was used to identify the associated factors via SPSS version 16 software.
Results:
By applying short-performance physical battery testing among them, it was found that 57.4% had limited mobility. Individuals with higher age [aOR: 1.08; 95% CI: 1.01–1.15], female gender [aOR: 3.85; 95% CI: 1.78–8.34], higher nutritional vulnerability [aOR: 1.74; 95% CI: 1.36–2.23], lower physical activity [aOR: 0.98; 95% CI: 0.97–0.99], and the presence of diabetes mellitus [aOR: 4.21; 95% CI: 1.21–14.64] were reported as major factors to be associated with limited mobility.
Conclusions:
Locomotor limitation in the elderly emerged as a significant public health problem. Along with regular physical activity and a healthy-diverse diet, a resilient community support system and social security measures were suggested, to promote and preserve the locomotor capacity of the elderly.
Keywords: India, locomotor capacity, mobility limitation, physical activity, rural area
Introduction
Entering into the golden years of life, elderly individuals are a valuable sociocultural resource for the community and nation in terms of their knowledge, wisdom, and life experience, which enable them to guide the younger generations and preserve the cultural traditions of the community. This ladder of age is also accompanied by the process of aging and senescence, which make their health needs unique from the rest, requiring specific care. World Health Organization promoted the concept of ‘Healthy Aging’ in the elderly, which can be described as ‘a process of developing and maintaining the functional ability of an individual, who is growing older, enabling him or her well-being’. This functional ability of an individual is determined by the interaction between their intrinsic capacity with their surroundings, and ‘Locomotor Capacity’ is one of the determinants of their intrinsic capacity.[1,2]
Locomotor capacity is described as “a state of the musculoskeletal system of the body which encompasses the endurance, balance, muscle strength, muscle power, muscle function and joint function of the body”.[2] In day-to-day life, it actualizes when interacting with the environment such as the ability to move from one place to another.
In a nationwide survey in 2020, for example, Longitudinal Aging Study of India, Wave I, it was seen that though a majority of elderly individuals were suffering from noncommunicable diseases like hypertension and diabetes, almost 19% of them reported musculoskeletal diseases, marking it as a significant burden.[3] Musculoskeletal diseases in the elderly were responsible for a total of 43.3 million DALYs with 60% of this burden occurring in lower and lower-middle-income regions.[4] This burden of musculoskeletal diseases poses a significant threat to the locomotor capacity of elderly individuals as a decline in locomotor capacity makes them partially or fully dependent on their caregivers for daily activities of living and other routine activities like seeking healthcare on their own. This in turn compromised their independence and dignity.[5]
Amid a demographic transition, as the global fertility rate is falling and life expectancy is increasing, by 2050, it was estimated that 21.3% of the world population would be aged 65 years and above. It was also projected that this growth in the elderly population would occur mostly in developing countries.[6] India as a developing nation is currently in the late expanding phase of the demographic cycle and will experience similar growth in the elderly population such that 19.5% of the total population will be aged 65 years and above in 2050.[6] This increasing elderly population as mentioned before needs specific care for the preservation of their locomotor capacity, and among the elderly individuals residing in a rural area of the state of West Bengal in India, it became much more pertinent because of their vulnerable socioeconomic condition and limited access to specialized healthcare. Thus, the objective of this study was to assess the status of locomotor capacity and to find out the factors associated with it, among the elderly population residing in the rural areas of the Singur block of the state of West Bengal, India, and the findings of the study can provide valuable insights to the primary care physicians regarding diagnosis and management of mobility-related issues in elderly in the community setting.
Subjects and Methods
This was a community-based cross-sectional study conducted in the villages of Singur block of the state of West Bengal, India. It was under the rural field practice area of a premier public health institute in Kolkata, West Bengal. This study was conducted for two and a half years from July 2022 to June 2024.
All elderly people (aged 60 years and above) permanently residing in the study area for 6 months or more were considered as the study population (which was approximately 10,206). Those who gave informed consent were included in the study, but the individuals who were critically ill and unable to respond were excluded from the study on ethical grounds.
The minimum sample size for this study was calculated as 195, based on Cochran’s formula at a 95% confidence level (Zα value 1.96), an absolute error of 10%, a design effect of two, and taking the prevalence of limited mobility as 52.1%.[7] For the selection of study participants, a two-stage cluster sampling was performed. The field practice area was divided into 64 villages, where each village was considered a cluster or primary sampling unit (PSU). Among these 64 clusters, 15 clusters were chosen using the probability proportionate to population size (PPS) method [Figure 1]. In each selected cluster, 13 study participants were selected. At first, in the cluster, a road was chosen randomly and the households on that road were visited sequentially until 13 eligible study participants were selected. If the road ended before all 13 participants could be selected, a new road was chosen randomly.
Figure 1.
Villages (Clusters) selected by the PPS method in the study area
Data collection
At first, rapport was built with the study participants and they were briefed about the purpose and relevance of the study in the local language, that is, Bengali. They were also assured of individual anonymity and confidentiality. A participant information sheet in Bengali, containing all necessary information regarding the study, was given to them, and if required, their caregivers were also briefed about the same. Only after that, if they agreed to sign the informed consent form, they were taken as study participants. Initially, a face-to-face interview was conducted using a predesigned, pretested, semistructured, interviewer-administered questionnaire enquiring about sociodemographic information including their financial independence, behavioral information like physical activity and substance use, and nutritional information like food security, dietary diversity, and nutritional status. Physical activity was assessed by the PASE tool.[8] Substance usage risk was assessed by the WHO-modified ASSIST tool version 3.1.[9] The risk of malnutrition was assessed by the SCREEN-3 extend tool.[10] Food security was assessed by the household food insecurity assessment scale.[11] Dietary diversity was assessed using the household dietary diversity scale.[12] All the tools used in this study were validated and free to use. They were also asked about any comorbidities they were currently suffering from, and it was cross-verified by reviewing their medical documents like prescriptions and investigation.
Next, a short Performance Physical Battery (SPPB) test was performed, following a standard operating procedure (SOP), in which participants were asked to make and hold a series of postures like a side-by-side stand, semi-tandem stand, full tandem stand, and repeated rising from a chair [Figure 2]. They were also asked to walk a length of four meters path. Based on the time taken by a participant to hold a position or complete the movements, their locomotor capacity was assessed.[1,13,14] Then their height and weight were measured following an SOP. During the study process, if any participants were found to have severe limitations of mobility, they were referred to the nearby public health facility for further management.
Figure 2.
Posture and Movements under Short Performance Physical Battery (SPPB) testing
This study was approved by the institutional scientific committee and institutional ethics committee (IEC) of All India Institute of Hygiene and Public Health, Kolkata. All ethical principles as per the declarations of Helsinki were strictly followed during the study procedure.
Data analysis
Data were analyzed using SPSS software version 16. Findings of all four nutritional variables, that is, nutritional status, dietary diversity, food security, and BMI (classified as per WHO criteria), were merged to form a newly derived variable ‘Nutritional Vulnerability’ with a score ranging from zero to seven, where zero means no vulnerability and seven means highest vulnerability. Initial descriptive statistics were followed by the appropriate tests of significance to check if there were any significant differences in the independent variables based on locomotor capacity. Pearson’s Chi-square test or Fisher’s exact test was performed for categorical variables as applicable. For continuous data, an unpaired t-test was performed in the case of normally distributed data; otherwise, Mann–Whitney U test was used. A P value < 0.05 was considered significant. To determine the strength of association, bivariable logistic was performed separately with each independent variable. All independent variables with a significant P value in bivariable logistic regression were put together in a multivariable logistic regression model. The multicollinearity of the independent variable in the multivariable model was assessed using the Variance Inflation Factor (VIF), with VIF less than 10 indicating no multicollinearity. Model fitness was assessed by the Hosemer–Lemeshow test statistic.
Results
The mean age of the study participants was 67.3 ± 7.3 years. More than half (56.4%) of them were identified as female. Most of them (59%) were living in a joint family with 33.8% of them being widowed. As for socioeconomic status, most (45.1%) of them belong to the lower-middle socioeconomic class as per the modified B.G. Prasad Scale, 2023.[15] More than half (54.4%) of the study participants were financially fully dependent on their family members [Table 1].
Table 1.
The Socio-demographic characteristics of the study participants (n=195)
| Characteristics | Number (%) |
|---|---|
| Age | Mean±SD (67.3±7.3) years |
| Young old (60–69 years) | 135 (69.2) |
| Middle old (70–79 years) | 42 (21.5) |
| Oldest old (80 years and more) | 18 (9.3) |
| Gender | |
| Male | 85 (43.6) |
| Female | 110 (56.4) |
| Religion | |
| Hindu | 186 (95.3) |
| Muslim | 9 (4.7) |
| Socioeconomic status | |
| Upper class | 2 (1) |
| Upper-middle class | 16 (8.2) |
| Middle class | 74 (37.9) |
| Lower-middle class | 88 (45.1) |
| Lower class | 15 (7.7) |
| Education | |
| Illiterate | 60 (30.8) |
| Primary | 44 (22.5) |
| Middle | 50 (25.6) |
| Secondary | 20 (10.3) |
| Higher Secondary | 10 (5.1) |
| Graduate and above | 11 (5.6) |
| Occupation | |
| Retired | 25 (12.8) |
| Homemaker | 95 (48.7) |
| Business | 22 (11.3) |
| Farmer | 50 (25.6) |
| Driver | 2 (1) |
| Domestic helper | 1 (0.5) |
| Family Type | |
| Joint Family | 115 (59) |
| Nuclear Family | 80 (41) |
| Marital Status | |
| Never married | 3 (1.5) |
| Currently married | 122 (62.6) |
| Widowed | 66 (33.8) |
| Divorced and separated | 4 (2.1) |
| Financial dependence | |
| Fully dependent | 106 (54.4) |
| Partially dependent | 44 (22.6) |
| Independent | 45 (23.1) |
| Total | 195 (100) |
Nutritional factors
Dietary diversity was present in 61% of the study participants. More than half (57.4%) of them had food security. Based on BMI, it was seen that 10.8% of them were underweight and 19.5% were overweight. On assessment of nutritional status, it was found that 62.1% of them were at higher risk of malnutrition. Combining these four factors into nutritional vulnerability, it was found that only 87.7% of the study participants were nutritionally vulnerable [Table 2].
Table 2.
The nutritional characteristics of the study participants (n=195)
| Nutritional characteristics | Number (%) |
|---|---|
| Risk of malnutrition | |
| No risk | 47 (24.1) |
| Potential risk | 27 (13.8) |
| High risk | 13 (86.7) |
| Dietary diversity | |
| Present | 119 (61) |
| Absent | 76 (39) |
| Food security | |
| Present | 112 (57.4) |
| Absent | 83 (42.6) |
| BMI status | |
| Underweight | 21 (10.8) |
| Normal weight | 121 (62.1) |
| Overweight | 38 (19.5) |
| Obese | 15 (7.7) |
| Nutritional vulnerability | |
| Nonvulnerable | 24 (12.3) |
| Vulnerable | 171 (87.7) |
| Total | 195 (100) |
Behavioral factors and comorbidities
The majority (67.7%) of them currently do not use any substances; thus, most of them were at lower usage risk of tobacco (66.7%) and alcohol (96.9%) [Table 3]. The median physical activity score was found to be statistically significantly different based on mobility status (Mann–Whitney U test statistic –3.917, P value < 0.001) [Table 4]. Among the study participants, almost 3/4th of them (73.8%) were reported to have at least one comorbid condition, the most common being hypertension (54.9%), followed by diabetes mellitus (28.2%). Arthritis was reported by 15.6% of the study participants.
Table 3.
The Behavioral characteristics and comorbidties of the study participants (n=195)
| Behavioral characteristics and comorbidities | Number (%) |
|---|---|
| Current substance use | |
| No substance use | 132 (67.7) |
| Only smokes tobacco | 20 (10.3) |
| Only use smokeless tobacco (SLT) | 9 (4.7) |
| Both tobacco smoking and SLT use | 31 (15.8) |
| Alcohol use and tobacco smoking | 3 (1.5) |
| Tobacco usage risk | |
| Low risk | 130 (66.70 |
| Moderate risk | 48 (24.6) |
| High risk | 17 (8.7) |
| Alcohol usage risk | |
| Low risk | 189 (96.9) |
| Moderate risk | 1 (0.5) |
| High risk | 5 (2.6) |
| Comorbidities | |
| Present | 144 (73.8) |
| Absent | 51 (26.2) |
| Total | 195 (100) |
Table 4.
Factors associated with locomotor capacity in the study population: Binary logistic regression analysis (n=195)
| Parameters | Normal mobility* (n=83) | Limited mobility* (n=112) | P | uOR (95% CI) | P | aOR (95% CI) | P | VIF |
|---|---|---|---|---|---|---|---|---|
| Age (Years)** | 65.57±5.5$ | 68.62±8.7$ | 0.004 | 1.05 (1.01–1.1) | 0.009 | 1.08 (1.01–1.15) | 0.01 | 1.123 |
| Gender | ||||||||
| Male | 50 (58.8) | 35 (41.2) | <0.001 | 1 (Reference) | – | 1 (Reference) | – | 1.083 |
| Female | 33 (30) | 77 (70) | 3.33 (1.84–6.03) | <0.001 | 3.85 (1.78–8.34) | 0.001 | ||
| Nutritional vulnerability score** | 2 (1, 3)# | 3 (2, 4)# | <0.001 | 1.84 (1.47–2.3) | <0.001 | 1.74 (1.36–2.23) | <0.001 | 1.116 |
| Physical activity score** | 84.11# (56.05, 110.8) | 58.6# (31.9, 82.9) | <0.001 | 0.93 (0.12–1.42) | <0.001 | 0.98 (0.97–0.99) | 0.013 | 1.076 |
| Tobacco usage risk | ||||||||
| Higher risk | 43 (66.1) | 90 (69.2) | <0.001 | 1.22 (0.12–1.42) | 0.076 | – | – | – |
| Low risk | 40 (30.8) | 22 (33.9) | 1 (Reference) | – | – | |||
| Alcohol usage Risk | ||||||||
| Higher risk | 2 (33.3) | 4 (66.7) | 0.704+ | – | – | – | – | – |
| Low risk | 81 (42.8) | 108 (57.2) | – | – | ||||
| Hypertension and diabetes | ||||||||
| Both absent | 37 (61.7) | 23 (38.3) | <0.001 | 1 (Reference) | – | 1 (Reference) | – | 1.133 |
| Only Hypertension | 34 (42.5) | 46 (57.5) | 2.17 (1.09–4.31) | 0.026 | 1.031 (0.44–2.41) | 0.943 | ||
| Only diabetes | 5 (17.9) | 23 (82.1) | 7.4 (2.46–22.19) | <0.001 | 4.21 (1.21–14.64) | 0.024 | ||
| Both present | 7 (25.9) | 20 (74.1) | 4.59 (1.68–12.56) | 0.003 | 1.913 (0.597–6.13) | 0.275 |
*Number (%) with Chi-square test, $Mean ± SD with Unpaired t-test, #Median (IQR) with Mann-Whitney U test +Fisher’s Exact test. uOR: Unadjusted Odds Ratio, aOR: Adjusted Odds Ratio, 95% CI: 95% Confidence Interval, **Continuous variable in ascending order Hosmer-Lemeshow 0.185 (Model is fit) & Cox-Snell Pseudo R2 0.322, Nagelkarke Pseudo R2 0.433 VIF (Variance Inflation Factor) < 10 indicates the absence of multicollinearity between the independent variables
Locomotor capacity and associated factors
On application of SPPB testing, it was found that 57.4% of the study participants had limited mobility [Figure 3]. On regression analysis, it was revealed that higher age [aOR: 1.08; 95% CI: 1.01–1.15], female gender [aOR: 3.85; 95% CI: 1.78–8.34], higher level of nutritional vulnerability [aOR: 1.74; 95% CI: 1.36–2.23], and presence of diabetes mellitus were statistically significantly associated with higher odds [aOR: 4.21; 95% CI: 1.21–14.64] of having limited mobility among the study participants. On the contrary, higher levels of physical activity came as protective [aOR: 0.98; 95% CI: 0.97–0.99] against limitation of mobility [Table 4].
Figure 3.
Pie chart showing the distribution of study participants as per locomotor capacity status (N = 195)
Discussion
We conducted a community-based cross-sectional study involving 195 elderly individuals in a rural area of the state of West Bengal, India, for assessment of their locomotor capacity, which was found to be 57.4%, similar to other studies.[7,16] It was found that most of the participants were female and though they lived in a joint family, they had to rely on their family members for financial assistance, which was also consistent with other studies.[17,18,19,20] Among sociodemographic factors, individuals with higher age and female gender were found to be at higher odds of having limited mobility.[16,21,22,23] The underlying cause of these findings might be due to the cumulative effect of physical wear and tear in the musculoskeletal system over time such as osteoarthritic changes in joints and on the other hand females being more vulnerable to osteoporotic changes due to postmenopausal loss of bone mineral density. These factors might be responsible for higher odds of limited mobility in more elderly and female participants.
Nutritional vulnerability was found in 87.7% of the study participants and higher levels of vulnerability corroborated with higher odds of limited mobility because of malnutrition, food insecurity, nondiverse diet, and underweight/obesity together creating a cascade, resulting in lower availability of protein, and micronutrients in the body, essential to maintain muscle mass and joint function.[24] This vulnerability was more pronounced by lower socioeconomic status and financial dependency. In our study, higher levels of physical activity were associated with lower odds of limited mobility, similar to other studies.[25,26] The causes behind these findings may be that regular physical activity enhances the circulatory system, strengthens the muscles, and maintains joint mobility. Participants with diabetes were found to have higher odds of limited mobility which can be attributed to the multisystem nature of diabetes.[22] Thus, this close inter-relationship between sociodemographic, nutritional, and behavioral factors and comorbidities in the elderly was responsible for the limitation in their locomotor capacity.
Strengths and limitations
This study was the first of its kind on the locomotor capacity of the elderly considering the study area along with the use of robust statistical analysis methods, including an adjusted regression model. However, a certain amount of recall bias and subjectivity on behalf of the participants could not be ruled out. For the measurement of nutritional status, rather than employing various questionnaires, more objective measures like skin-fold thickness and body fat percentage estimation might be more useful but could not be done due to logistical constraints.
Conclusion
Thus, based on our study results, we recommend regular physical activity involving moderate aerobic exercise like 15 to 30 minutes of brisk walking, flexibility-related exercise like Yoga, and muscle strengthening and balance activities involving all major muscle groups like wall-push, leg-raise, and calf raise is beneficial for maintaining mobility.[27] A balanced and diverse diet rich in proteins, calcium, micronutrients, and dietary fibers is also deemed essential.[28] To facilitate these physical and nutritional interventions, we suggest the creation of a team of community volunteers and peer groups to increase awareness regarding locomotor capacity among the elderly and their family members and also to help the elderly individuals with limited mobility in times of need, especially those who are living alone. The provision of social security and food security schemes for the elderly with special emphasis given to marginalized and vulnerable ones like widows can help them to become financially independent and food secure, respectively. Last, the incorporation of the locomotor component in the current national health program for elderly individuals was suggested as a forward measure toward preserving and promoting the locomotor capacity of the elderly individuals in the study area and beyond.
Ethical Policy and Institutional Review Board statement
This study was approved by the institutional scientific committee and institutional Ethics Committee (IEC/IRB) of All India Institute of Hygiene and Public Health, Kolkata with IEC reference number ‘IEC/2022 (1)/5’. All ethical principles as per the declarations of Helsinki, were strictly followed during the study procedure. Any participants found to have a severe limitation of mobility were referred to the nearest public health facility.
Patient declaration of consent statement
At first, rapport was built with the study participants and they were briefed about the purpose and relevance of the study in the local language i.e. Bengali. They were also assured of individual anonymity and confidentiality. A participant information sheet in Bengali, containing all necessary information regarding the study was given to them and if required their caregivers were also briefed about the same. Only after that if they agreed to sign the informed consent form, they were taken as study participants. During the initial briefing the participants were also informed that the findings of the study, if came as useful, would be published in scientific forums like journals, conferences, etc., They were also assured of non-disclosure of their identity during this process. Followed by which consent was taken.
Data availability statement
The data set used in this study is available on request to corresponding author.
Key message
Limited mobility is a significant public health concern among the elderly in rural India. Key risk factors include advanced age, female gender, poor nutrition, low physical activity, and diabetes. Strengthening community support, promoting physical activity, and ensuring nutritional wellbeing are crucial to preserving their independence and quality of life.
Abbreviations
| Abbreviation | Definition |
|---|---|
| DALY: Disability Adjusted Life Years | The measure of the effect of morbidity |
| SPSS: Statistical Package for Social Sciences | Statistical data analysis software |
| PSU: Primary Sampling Unit | Sampling unit for cluster sampling |
| CI: Confidence Interval | Interval estimate for odds ratio |
| aOR: Adjusted Odds Ratio | Measure of strength of association |
| SSPB: Short Performance Physical Battery | Test to assess mobility in the elderly. |
| SCREEN: Seniors in Community Risk Assessment for Eating and Nutrition | Validated tool to measure nutritional status in elderly |
| ASSIST: Alcohol, Smoking, Substance Involvement Screening Test | Validate the tool to assess substance use. |
| IEC: Institutional Ethics Committee | Independent committees review and permit research studies involving humans. |
| BMI: Body Mass Index | Measure of Obesity (BMI=Body weight/Height2) |
| WHO: World Health Organization | UN organization |
| VIF: Variance Inflation Factor | Measure to assess collinearity. |
| SOP: Standard Operation Procedure | Standard measurement procedure |
Conflicts of interest
The author (s) declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article.
Acknowledgement
We authors are thankful to the health workers working in the study area for helping us during the data collection.
Funding Statement
The author (s) declared that this study did not receive any funding from any source, in any form.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data set used in this study is available on request to corresponding author.