Trends in frailty and its associated factors in the community dwelling elderly Indian population during the COVID-19 pandemic: A prospective analytical study

List of abbreviations

BMR: Basal metabolic rate

EFIP: Evaluative Frailty Index for Physical Activity

MNA^®-SF: Mini Nutritional Assessment Short Form

MOCA: Montreal Cognitive Assessment

PASE: Physical Activity Scale for the Elderly

TUG: Timed Up and Go

WHO: World Health Organization

Introduction

Frailty derived from the Latin word ‘fradilita’ meaning brittleness, is an important and emerging term in geriatric medicine.¹ There is no definition that is internationally recognized, but it is usually associated with adverse outcomes developed as a consequence of increased vulnerability. It refers to a decline in physiological systems with increasing age triggered by any minor stressor, which collectively leads to sudden changes in state of health.² Frailty is a geriatric syndrome, which is multidimensional in nature. In 2017, the World Health Organization (WHO) defined frailty as “a clinically recognizable state in which the ability of older people to cope with every day or acute stressors is compromised by an increased vulnerability brought by age-associated declines in physiological reserve and function across multiple organ systems”.³

According to the WHO, 900 million people around the world are classified as being in an elderly age group, out of which there are 104 million elderly (>60 years of age) in India. It is also estimated that India will hold the largest geriatric population around the globe by the year 2050.⁴ With the advancement in medical sciences there is a decrease in mortality rates, life expectancy is increased and so is frailty among the elderly.⁵ It is estimated that 4% to 10% of the elderly population dwelling in the United States are frail, also 8.1% of the elderly are observed to be frail in the United Kingdom, and 6.5% and 7% in Italy and France respectively.⁶

A large compressive study by the WHO showed that among middle- and low-income countries (South Africa, China, Russia, Ghana, India and Mexico) India has the highest prevalence of frailty (i.e., 56.9%) and a greater number of women are frail compared to men (47% of elderly men and 67% of elderly women).^7,8

The evaluation of frailty is difficult because of a lack of any standardized tool. There are 67 tools for quantifying frailty, out of which only nine of these screening tools are highly cited (more than 200 citations).⁹ Phenotype of frailty¹⁰ and Frailty index¹¹ are the validated and most widely used screening tools.⁹ The phenotype of frailty model defines a person as frail when three or more physiological deficits out of five are present,¹⁰ whereas the frailty index model expresses frailty as a “ratio of existing deficits to the total probable deficits there could be”. These deficits are defined as a wide range of diseases, disabilities, signs and symptoms.¹¹

Ageing leads to numerous changes in the physiological systems of the body, which are fundamental to the development of frailty, specifically the immunological system, the neuromuscular system and neuroendocrine system.¹² These changes in the body interact progressively and adversely, leading to loss of physiological function and reserve (state of compromised homeostasis).¹² The risk factors for frailty are varied and have been found to have multiple linear and non-linear interactions. For example, a consequence of normal ageing leads to loss of muscle mass and strength.¹³ Muscle loss can also be accelerated due to chronic illness, poor nutrition, decrease in growth hormone production, and reduced physical activity.¹³ All these factors are inter-related to each other through complex interactions and ultimately lead to frailty. Socio economic and demographic variables like availability of disposable income/finances, level of education, nutritional status, and general living conditions have been found to be confounders of frailty.⁷

Several studies have identified factors like sarcopenia, loss of muscle strength, functional mobility and gait velocity changes, loss of weight, reduced physical activity and easy exhaustibility to be strong independent confounders of frailty. The most closely associated biological parameters of frailty have been identified as inflammatory markers, dyslipidemic markers, endocrinological markers, insulin resistance and state of glycemia.¹²

Available literature on the feasibility of predicting frailty state that changes in functional as well as biological parameters could be used as well qualified candidates to estimate and quantify frailty. Out of these risk factors, it is purported that functionality could be the strongest predictor or measurer of frailty. Sarcopenia, connective tissue remodeling, and inflammatory marker mediated physiological and functional changes and their interaction as well as other confounders of frailty need to be studied to develop a predictive model of frailty.¹⁴

A recent large-scale review on the state of frailty related research in India stated that there is a rather alarming lack of conceptualization or epidemiological data regarding frailty in an Indian scenario, and there is a dire need to identify the key confounders for frailty syndrome among Indian elderly.⁷ There is a scarcity of quality literature on the prevalence of frailty among community dwelling elderly in India. This study was originally planned to analyze the longitudinal trends in frailty status of community dwelling elderly in an Indian population as well to identify factors associated with frailty in the Indian context. However, the recruitment phase of this study coincided with one of the largest lockdowns in history, mandated to minimize the spread of COVID-19 in India. Restrictions were put in place to minimize the outdoor movement of the population in general and specifically the elderly, who were recognized to be the most vulnerable group with respect to the pandemic. This gave us a unique opportunity to study the effects this pandemic enforced, as a result of the necessary restrictions, on frailty status as well as the factors affecting frailty in the elderly.

The primary objective of the study was to identify the changes in frailty status occurring in a group of community dwelling elderly over a period of three months, during a phase of reduced social mobility due to the COVID-19 imposed lockdown. The study also aimed to identify the changes, if any, that occurred in certain recognized predictors of frailty such as muscle strength, body composition, flexibility, physical activity, cognitive function, and nutritional status.

Methods

A prospective observational study was designed and after obtaining the necessary permission to recruit subjects, the study recruitment commenced in March 2020. The study was approved by the Scientific and Institutional Ethics committee of KMC Mangalore (IEC KMC MLR 11-19/590). All stages of the study were conducted in strict adherence to the principles of the “Helsinki Declaration” for research on human subjects.

Results

A total of 28 subjects were screened of which 22 were recruited after fulfilling the inclusion and exclusion criteria. Of the 22 recruited subjects, only 19 returned for follow-up evaluation and hence the data of only these participants were analysed. The demographic data of all participants are represented in Table 1. The characteristics of the participants such as gender, marital status, height, education level are presented in Table 2.

We found there was neither a statistically nor a clinically significant change in the frailty status of the studied elderly, over the three-month period. However a statistically significant difference was observed in MOCA (Mean difference = 0.7368, p < 0.05), TUG (Mean difference = -0.64, p < 0.05), Body fat percentage (Mean difference = -0.3632, p < 0.05), Visceral fat (Mean difference = -0.4211, p < 0.05), 10 meter walk test (Mean difference = -1.097, p < 0.029), Muscle mass (Mean difference = -0.55, p < 0.05), and PASE scores (Mean difference = -43, p < 0.05) (Table 3).

Comparison of muscle strength of bilateral major muscle groups over the three-month period, revealed that there was a statistically significant reduction in the strength of the left shoulder extensor, adductor, and abductor, elbow flexors on both sides, and elbow extension on the right side. In the lower extremities there was a statistically significant reduction in the strength of all major muscle groups (Table 4).

On analysis of the relationship between the change in frailty status as measured by EFIP, and the other outcome variables, we found that none of the variables among cognitive function, body composition, functional mobility, muscle strength, or gait speed showed any statistically significant correlation with EFIP (Table 5). The entirety of data collected for the study is available in an anonymized form at OSF, as a registered project.¹⁹

Discussion

The present study was undertaken primarily to analyze the trends in frailty status of a cohort of community dwelling elderly, residing in the Dakshina Kannada district of Karnataka state in India over a period of three months. At the same time the strength of association between the change in frailty score and cognition, nutritional status, gait velocity, functional mobility, body mass, and strength were also analyzed. However, of the 22 subjects, the follow up evaluation could only be done for a total of 19 subjects and hence the goals of the study were realigned to investigate the influence of the pandemic induced lockdown and the associated reduction in physical activity on the outcome variables. The sample size at the outset seems inadequate to bring out any meaningful observations, however we decided to proceed with follow-up of subjects in light of the arguments put forth by Indrayan A et al. (2021).²⁰ As per the arguments a big sample may be required in cases where the variability is high or the event under the study is rare and a precise estimate is required. We assumed that the age related changes would present with minimal variability, and none of the outcomes studied were rare in the studied population as well as none of them required extreme precision of estimation. Under these circumstances it was deemed prudent to persist with the follow-up evaluation of recruited subjects. In the current study we found that there was an observable change in frailty status over a period of three months, but it was not statistically significant.

Frailty is an umbrella term and there are many tools to measure frailty. The EFIP scale was used in the current study because it covers all domains of frailty (physical, psychological, social functioning and general health), and has been proven to have good reliability and validity.²¹ The data collection involved administering four questionnaires (EPIF, MOCA, MNA^®-SF and PASE) which on an average took 45 minutes to one hour to complete. Objective measures of strength, functional mobility, gait velocity, and body composition analysis would take an additional hour to complete. This made the entire data collection process a time consuming one thereby adversely affecting the number of subjects recruited in a day. However, other than the three subjects who chose to forgo the follow-up evaluation because of the pandemic situation, there were no additional dropouts in the span of the study and no reported discomfort or adverse event pertaining to data collection.

The primary objective of the study was to detect any association between changes in frailty status and other outcome variables. It must be noted that there was only a very minimal difference (over a period of three months) in frailty score (mean difference 0.625) and findings were not statistically significant. We could not find any statistically significant relationship between changes in frailty score and the changes in strength, muscle mass, cognition, nutritional status, gait velocity, or functional mobility.

It must be emphasized that, when the independent variables were compared at baseline and three months follow-up there was a statistically significant difference found in the scores of MOCA, TUG, visceral fat, PASE and muscle mass. The muscle mass and gait velocity showed a marginal but statistically significant reduction, whereas total body fat as well as visceral fat content showed an increment. Cognitive functions as measured by MOCA and gait velocity (implied by an increase in time taken to complete a 10-meter walk test) showed a decline in the above-mentioned period, whereas the time taken to complete TUG had marginally increased. The observed differences in MOCA scores though were never sufficient to imply a cognitive decline. It can be inferred from these findings that a short span of three months has brought about measurable differences in variables which have been previously associated with frailty.

Previous research corroborated our findings in that there is a definitive decline in muscle mass ranging from 2 to 4% annually in older men and women of all ethnicities. There is also a concurrent increase in body fat content averaging about 0.8% within the same time span.²²

Factors that influence body composition, especially muscle mass include genetic variables, metabolic variables, endocrinological variables, co-morbidities, diet, alcoholism, smoking, as well as gender and ethnicity. It must be emphasized however that physical activity as an independent variable is a strong predictor for loss of muscle mass and changes in body composition in the elderly.²² The data collection of the present study coincided with the period of pandemic enforced restriction and all of the recruited subjects had reported a considerable decline in the amount of physical activity they indulged in the same period. For measuring physical activity, we used PASE and we found a highly significant reduction in physical activity (Mean difference = 43, p < 0.05) over the three-month period. For the study population, the major source of physical activity used to be walking in public places like parks or attending organized social gatherings like yoga and group exercise sessions. Since most of these activities were deemed to be unsafe, especially in the elderly population, there was virtually a complete absence of these activities in the lockdown period.

Our data analysis shows there is a statistically significant decline in functional mobility as measured by TUG with ageing, but it must be emphasized that this decline was barely consequential, and it is safe to assume there was no decline in functional mobility of the studied cohort. Gait velocity showed a statistically significant difference when compared over the three-month period.

In all major muscle groups of lower extremity, there was a significant difference noted in strength, which ranged from a difference of 0.7 kg to 1.5 kg. One of the key associated finding was that the decline in strength of bilateral hip and knee musculature (hip abductors, hip adductors, knee extension of right side and knee extensors, hip flexors, hip extensors and hip adductors on the left side) showed a statistically significant moderate correlation with decline in muscle mass. Previous studies have shown that there is insufficient evidence of a linear relationship between the loss of muscle strength and muscle mass in ageing, though both have been individually established as definitive outcomes of ageing.^22–24 Other factors affecting muscle strength have been identified as impaired reciprocal inhibition, alteration in rate coding of motor unit activation, as well as changes in metabolic characteristic of muscle fibers.²⁵ These changes can happen independent of the changes in muscle mass.²⁴ The changes in muscle strength could then be attributed to the definitive decline in physical activity levels as previously stated, which would have precipitated a deconditioning/reversal effect on muscle strength. In our study cohort, we observed neither a statistically significant nor any amount of change in the nutritional status of the study population as measured by MNA^®-SF.

A major limitation in generalization of the findings of present study is that we studied relatively a small sample from one geographical location. Follow up over a longer time period, with repeated outcome evaluation would have given greater clarity on the changes in frailty as well the contributing factors, which could not be done because of the prevailing restrictions. Since the sample size of the current study was small, it was not feasible to statistically analyse the influence of co-morbidities like diabetes and hypertension on frailty in the population studied, which is a major limitation.

Future researchers must emphasis on recruiting larger samples with greater representation from different geographical, socio-economic and vocational strata. Future studies should also have longer follow-up duration with outcome evaluation at multiple time point to identify and analyze the trends in age related changes.

The findings of the current study suggest that, even in the absence of a major change in frailty status in elderly, the individual factors like muscle strength which contribute towards frailty syndrome show a deterioration or decline, over relative shorter time periods, with major shifts in lifestyle and outdoor/social mobility such as in the case of Pandemic induced restrictions. There is a need to explore ways in which these deteriorations can be contained or reversed before they cross a probable critical limit.

Conclusions

Two key findings of this study are that 1) There was a definitive decline in physical activity of the elderly participants within the lockdown period, and 2) There was absolutely no significant change in the frailty status of community dwelling elderly, even in a time period characterized by physical activity restrictions due to the COVID-19 induced lockdown, although some of the independent determinants of frailty showed a decline in the same period. The present study failed to establish any association between frailty and changes in cognitive, functional mobility, body composition, strength, or nutritional factors, during a relatively short span of three months.

Data availability

Consent

Written informed consent for publication of the participants’ details was obtained from the participants.