Physical Frailty and Its Associated Factors Among Elderly Nursing Home Residents in China

doi:10.21203/rs.3.rs-36416/v1

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Physical Frailty and Its Associated Factors Among Elderly Nursing Home Residents in China

https://doi.org/10.21203/rs.3.rs-36416/v1

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published 17 Aug, 2020

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Background: Evidence is scarce on the trend in prevalence of physical frailty in China; the primary purpose of this study was to identify the prevalence and correlates of physical frailty among older nursing home residents in China.

Methods: Cross-sectional study in 20 nursing homes in Changsha, China. Physical frailty was defined based on the frailty phenotype including weight loss, low grip strength, exhaustion, slow gait speed, and low physical activity. Participants with at least three affected criteria were defined as being frail. Participants with one or two affected criteria were considered as pre-frail, and those with no affected criteria were considered as robust. A total of 1004 nursing home residents aged 60 and over were included in this study. A multinomial logistic regression model was used to analyze the associations of physical frailty with its potential risk factors, including age, sex, education levels, marital status, type of institution, living status, current drinking, current smoking, regular exercise, and self-reported health.

Results: The overall prevalence of physical frailty and prefrailty was 55.6%, and 38.5%, respectively. The rate of physical frailty substantially increased with age, and was higher in women than in men (69.5% vs. 30.5%). The multinomial logistic regression analysis showed that older age, being women, living in a private institution, living alone or with unknown person, having no regular exercise (≤ 2 times/week), and poor self-reported health were significantly associated with increased odds of being physically frail.

Conclusion: We demonstrated physical frailty is highly prevalent among older residents in nursing homes in China, especially in women. The potential role of those associated factors of physical frailty warrant further investigations to explore their clinical application among elderly nursing home residents.

Geriatrics & Gerontology

Frailty

nursing home

older adults

China

aging

There is a growing research interest in frailty worldwide, especially in countries where the population is rapidly aging including China [1, 2]. Frailty refers to a state of increased vulnerability to poor resolution of homeostasis after a stressor event [3, 4]. Although there is no universal consensus in the operational criteria used in different practice settings and epidemiological investigations [5], two main operational approaches have been widely used to measure frailty: the frailty phenotype [4] and the Frailty Index [6].The Frailty Index is composed of at least 30 Items and can be obtained through a comprehensive geriatric assessment [3]. The frailty phenotype can be calculated by having older adults complete of simple tasks without a preliminary clinical evaluation, the frailty phenotype is more easily for identifying objectively older residents living in nursing homes who were at increased risk for negative events [7]. However, due to the differences (i.e., physiological and psychological dimension) in different populations, there are no reference criteria for frailty phenotype in the elderly living in nursing homes in China.

According to various previous studies among the community-dwelling older adults in western countries, the prevalence of frailty varied enormously (range 4.0–59.1%) which is likely due to different measurement tools and frailty definitions used [8]. The prevalence of frailty has been reported to vary between 5.9–17.4% in China [9]. For residents living in nursing homes, physical frailty is highly prevalent (range 19.0–75.6%) in western populations [10]. Numerous studies on frailty in China, to date, have been conducted in community-dwelling older adults [9], but epidemiological data is scant among older nursing home residents [11]. Compared to community-dwelling older adults, individuals living in nursing homes might be more vulnerable and tend to simultaneously have multiple risk factors of frailty (i.e., self-reported health status, activity of daily living disability) [12–14]; consequently, the prevention and management of frailty could be more challenging in nursing homes. Therefore, it is of substantial clinical interest to identify the prevalence of physical frailty and its modifiable risk factors among elderly nursing home residents. Such epidemiological evidence could help to develop effective interventions on the prevention and management of physical frailty in the setting of nursing homes to delay the onset of frailty and maintain independence in daily activities [15–17]. Thus, in this present study, we aimed to investigate the prevalence of physical frailty and its associated factors among older people living in nursing homes in China.

Study design and participants

Between January 2018 and April 2019, a cross-sectional study was conducted in 20 nursing homes in the metropolitan area of Changsha, China. A total of 2630 adults who were aged ≥ 60 years and invited to participate in this study. Changsha is a middle-size provincial city with a population of 8 million in the central region of China, which has the characteristics of low population mobility and a traditional Chinese lifestyle [18]. The survey comprised questionnaires, anthropometric measurements, and physical fitness tests, which were conducted according to the standard protocols of the study assessment tools. The questionnaire survey was completed via face-to-face interviews by well-trained nurses. In this study, inclusion criteria were as follows: having clear consciousness identified by registered nurses in nursing homes and the ability to communicate independently, including people who were visually or hearing impaired but could communicate through family members as identified by nursing home staff in each of the homes. We contacted all eligible participants by sending brochures explaining the study and inviting them to participate (n = 2550), and a total of 2204 residents agreed to participate in the survey, with a response rate of 86.4%. Of those, 1607 participants who completed the baseline survey by sending brochures were included in this study (597 died or moved out of those nursing homes during the survey period). We excluded participants who had a history of dementia (n = 66), Parkinson’s disease (n = 52), stroke (n = 208), or a Mini-Mental State Examination (MMSE) score < 18 (n = 69). Also, participants with missing data on any components of physical frailty were excluded (n = 208). Therefore, the final sample included 1004 participants (339 men and 665 women) (Fig. 1).

Instrumentations

Physical frailty

Frailty was defined according to the phenotype of physical frailty [4], which consists of weakness, slowness, low level of physical activity, shrinking and exhaustion [4]. The operational definitions of each component are shown in Table 1. Weakness was measured in kilograms by a handgrip dynamometer (KD - WLJ; KonDak, China). Participants were required to perform the test for twice for each hand in a standing position. The maximum value among the four measurements was used for the analyses. The cut-off points [19] were stratified by sex and body mass index (BMI). Slowness was defined as the average walk speed in a 5-meter walking test. Starting from a motionless position, participants were instructed to take this test twice, and the time (sec) of gait speed was recorded with a digital stopwatch between the 3 and 8 meters in each trial. We measured the time taken (in seconds) to pass 8 meters to calculate gait speed (m/s). The cut-off points [19] were considered slow gait speed stratified by sex and standing height. We used the cut-off points [19] in weakness and slowness referring to the general older adults in the CHARLS cohort, which was previously conducted and validated by the population-based lowest quintile [4]. Low level of physical activity was measured with the Chinese version of the Physical Activity Scale for the Elderly (PASE) questionnaire [20]. The cut-off points were stratified by sex groups to collect physical activity scale data [21]. Shrinking was measured by unintentional weight loss > 5 kg during the previous year, except for dieting or exercise. Exhaustion was determined to base on a positive answer to either of the following two self-reported questions of the Center for Epidemiologic Studies-Depression (CES-D) Scale: “I felt that everything I did was an effort” (in the past month) and “ I could not get going.” According to the frailty phenotype [4], participants with three or more affected components were considered frail, those with one or two affected components were considered prefrail, and those without any component were considered robust.

Table 1

Operational definition of physical frailty phenotype
Frailty Phenotype	Measurement	Men	Women
Weakness	Grip strength ^[16] (stratified by sex and BMI [kg/m²], maximum value of either hand)	BMI ≤ 20.6: grip strength ≤ 25.2 kg BMI 20.6–23.2: grip strength ≤ 28.5 kg BMI 23.2–25.9: grip strength ≤ 30.0 kg BMI > 25.9: grip strength ≤ 30.0 kg	BMI ≤ 20.0: grip strength ≤ 15 kg BMI 20.0-22.1: grip strength ≤ 17.5 kg BMI 22.1–24.8: grip strength ≤ 17.8 kg BMI > 24.8: grip strength ≤ 20.0 kg
Slowness	Walking speed (5 meters) ^[16] (stratified by sex, averaged value of two repeated measurements)	Height ≤ 163cm: ≥ 0.90 m/s Height > 163cm: ≥ 0.96 m/s	Height ≤ 151 cm: ≥ 0.72 m/s Height > 151 cm: ≥ 0.86 m/s
Low level of physical activity	Self-reported: Physical Activity Scale for the Elderly (9 items)	Score of physical activity per week < 56.4 points ^[18]	Score of physical activity per week < 58.8 points ^[18]
Shrinking	Unintentional weight loss	In the last year, self-report of losing more than 5 kg unintentionally (i.e., not due to dieting or exercise) or unintentional weight loss of at least 5% of body weight
Exhaustion	Two items of the Center for Epidemiologic Studies-Depression Scale	a) I felt that everything I did was an effort. b) I could not get going. The question asked, “how often in the last week did you feel this way?” 0 = rarely or none of the time (< 1 day), 1 = some or a little of the time (1–2 days), 2 = a moderate amount of the time (3–4 days), or 3 = most of the time. Subjects answering “2” or “3” to either of these two questions were categorized as being exhausted.
Overall frailty status	Non-frail: 0 affected criteria; pre-frail: 1–2 affected criterion; frail: ≥ 3 affected criterion.
Note. BMI = body mass index.

Table 2

Characteristics of participants according to physical frailty status
	Overall	Frailty status
	Overall	Robust (n = 59)	Prefrail (n = 387)	Frail (n = 558)	P value for trend
Age, mean (SD), years	80.8 (8.9)	70.9 (7.5)	79.6 (8.6)	82.7 (8.4)	< 0.001
Sex					0.01
Men, %	33.8	42.4	37.2	30.5
Women, %	66.2	57.6	62.8	69.5
Education levels					0.469
Elementary and below, %	51.1	57.6	50.9	50.5
Junior middle school and above, %	48.9	42.4	49.1	49.5
Marital status					< 0.001
Married, %	23.9	59.3	26.9	18.1
Others (widowed, divorced, never married), %	76.1	40.7	73.1	81.9
Type of institution					< 0.001
Public, %	55.5	78.0	63.0	47.8
Private, %	44.5	22.0	37.0	52.2
Living status					< 0.001
Living with husband/wife, %	23.8	61.0	27.1	17.6
Living with alone or others (unknown person), %	76.2	39.0	72.9	82.4
Current drinking (yes), %	24.8	30.5	20.4	27.2	0.217
Current smoking (yes), %	11.6	16.9	11.9	10.8	0.213
Regular exercise					< 0.001
≤ 2 times/week, %	28.4	11.9	22.5	34.2
≥ 3 times/week, %	71.6	88.1	77.5	65.8
Self-reported health					< 0.001
Very good or good, %	19.2	33.9	22.5	15.4
Fair or poor, %	80.8	66.1	77.5	84.6
Continuous variables are expressed as mean ± standard deviation; categorical variables are expressed as percentages.

Correlates Of Physical Frailty

Information on the following covariates was collected through the questionnaire: age, sex, education level (elementary and below, or junior middle school and above), marital status (married, others [widowed, divorced, never married]), type of institution (public or private), living status (living with husband/wife, living with alone or others [unknown person], current drinking (yes or no), current smoking (yes or no), regular exercise (≤ 2 times/week, ≥ 3 times/week), and self-reported health (very good or good, fair or poor). The basic Activity of Daily Living (ADL) as the correlate of frailty was measured by the Katz scale, which comprises including the following 6 items: bathing, dressing, toileting, transferring, continence, and feeding [22]. Disability in ADL ability was defined as having difficulty and/or requiring assistance in time ≥ 1 activities. Information on the medical history of diseases was collected from the medical records by physicians. Comorbidity was defined as having 2 or more of the following 9 diseases, including hypertension, diabetes, cancer, chronic heart disease, stroke, chronic digestive disease, arthritis/rheumatism, chronic lung disease, and chronic kidney disease.

Statistical analysis

The characteristics of the sample were summarized by physical frailty groups. Categorical variables were presented as percentages. We tested the trends in the characteristics across physical frailty status by using the Cochran-Mantel-Haenszel test. Odds ratios (OR) with 95% confidence interval (CI) of physical frailty for the potential associated factors were estimated by using multinomial logistic regression models. The logistic regression model included age (continuous), women (reference men), low education level (reference junior middle school and above), being widowed or divorced or never married (reference being married,), living in a private institution (reference public), living alone or with unknown person (reference living with husband/wife), current drinking (reference no), current smoking (reference no), regular exercise ≤ 2 times/week (reference regular exercise ≥ 3 times/week), and poor self-reported health (reference very good/good self-reported health). A Venn diagram was used to illustrate the overlap of ADL disability and comorbidity with physical frailty.

All statistical analyses were performed using IBM SPSS Statistics Version 24.0 (IBM; Armonk, NY, USA). All p-values were two-tailed, and p ≤ .05 was considered statistically significant.

Participants were 60 to 107 years of age, with a mean age (SD) of 80.8 (8.9) years and 33.8% were men. The prevalence of physical frailty and prefrailty were 55.6% and 38.5% respectively. In men, 37.5% were pre-frail and 30.5% were frail; in women, 62.8% were pre-frail and 69.5% were frail. Table 1 shows characteristics of participants according to physical frailty status. A trend test of all factors across physical frailty status revealed that those participants who were more frail compared with participants who were less frail, tended to be older, more likely to be women, to be widowed or divorced or never married, to be living in private institution, to be living with alone or others (unknown person), and to report poor health status. They were less likely to have regular exercise.

Figure 2 shows the prevalence of physical frailty by sex and age group. The prevalence of physical frailty increased with each successive 5-year age group (p for trend < 0.001). The increasing trend of the curve was similar in both men and women. The percentage of physical frailty dramatically increased from the 75–79 age group, especially in women.

Figure 3 shows the results of multinomial logistic regression model on the associations of physical frailty with its potential risk factors. The multivariable-adjusted OR with each five-year increment in age was 2.20 (95% CI 1.79–2.70) for being frail and 1.73 (95% CI 1.41–2.11) for being prefrail compared to the robust group. Women were approximately 5 times more likely to be frail (multivariable-adjusted OR 4.98, 95% CI 2.41–10.28), and 3.5 times more likely to be prefrail (multivariable-adjusted OR 3.48, 95% CI 1.69–7.15) than men. Living alone or others (unknown person) was associated with significantly increased odds of frailty (multivariable-adjusted OR 5.49, 95% CI 3.00-10.05) and prefrailty (multivariable-adjusted OR 3.12, 95% CI 1.41–2.11). Compared with participants with regular exercise, those who were not engaged in regular exercise had a fourfold higher risk of being frail (multivariable-adjusted OR 4.46, 95% CI 1.84–10.84) and a twofold higher risk of being prefrail (multivariable-adjusted OR 2.49, 95% CI 1.03–6.01). Those who reported poorer self-reported health were approximately 4 times more likely to be frail (multivariable-adjusted OR 3.77, 95% CI 1.82–7.80) and 2 times more likely to be pre-frail (multivariable-adjusted OR 2.07, 95% CI 1.03–4.16). Living in private (vs. public) institutions was associated with an increased odd of frailty (multivariable-adjusted OR 2.96, 95% CI 1.42–6.19), but not with prefrailty. No statistically significant associations were found for education levels, marital status, current drinking, and current smoking with frailty status.

Figure 4 shows the overlap of ADL disability and comorbidity with frailty. Among the overall 1004 participants, each participant had at least one of those three conditions. Of these who were frail, 12.7% had comorbid diseases, 8.0% had ADL disability, 32.2% had both comorbid disease and ADL disability, and 2.7% had neither ADL disability nor comorbidity.

In this present study, our results showed that physical frailty was highly prevalent among older adults living in nursing homes in China, especially in women. We observed older age, being women, living in a private institution, living alone or others unknown person, having no regular exercise, and poor self-reported health was significantly associated with physical frailty. Also, we found that although physical frailty, to some extent, overlapped with disability and comorbidity, many frail participants did not have disability or comorbidity, suggesting that physical frailty did not equate with comorbidity or disability in the population of elderly nursing home residents. At present, there is no the gold standard for comprehensive geriatric assessment which is the standard for admission in the resident assessment instrument among the elderly living in nursing homes in China, therefore, China does not use RAI as that is what is in use in North America and Europe in most nursing homes as the standard and that data can be automatically converted in the frailty index [23].

Our preliminary study underlined the fact that as high as about one-half of the older adults living in nursing homes were frail, and 38.5% were still prefrail; moreover, women were frailer than men. Many studies [24–26] from different countries (i.e., Japan, South Italy) are consistent with our results, which demonstrated that frailty was highly prevalent, and the prevalence of frailty in women was higher than in men. A greater understanding of the reasons and implications of this physical phenotype across sex is required. An important reason suggested is that older women with frailty have more abdominal fat than older men[27]. The abdominal adiposity was associated with systemic inflammation by mediating its link with metabolic syndromes, which was higher markers of oxidative stress resulting in skeletal muscle damage and low grip strength [27]. This factor might be a core mechanism leading to sex-associated frailty. However, in nursing homes in China, women were also older, and women more often than men had better learned to take care of themselves and others and to arrange care. Hence, it would be interesting to look at who is admitted and why to live in a nursing home, men may be admitted with lower frailty levels when they live alone because they are less able to support themselves or organize support for themselves [28]. Therefore, we can make effective interventions to prevent or delay the onset of frailty according to the result of the sex differences in frail older adults.

In agreement with some previous studies in other countries [8, 19, 29], we also observed that the prevalence of frailty increased with older age. In term of the associations between physical frailty and style of the institution, the present study was the first, to our knowledge, that had observed such an association. The possible explanation could be that older people who had been living in a private institution (more expensive in the metropolitan areas than public institution) usually have higher social economic status; they were more likely to feel lonely [30] and loss of appetite [31] when staying away from family and changing their ways of previous life, resulting in higher likelihood of being frailer. Compared with people living with their partners in the same nursing home, those who live with alone or share the room with unknown person may become more frail because of poorer social ties [32] and mental disorders [33]. In line with previous studies [34, 35], we also found that regular exercise was associated with physical frailty. One explanation is that there is a vicious cycle: due to the fear of falling [36], as the level of frailty increases, so does the tendency to avoid taking regular exercise. Participants with frailty had poorer self-reported health in our study, in concordance with results from previous studies[12, 37, 38]. This might be explained that frail older people have a state of greater vulnerability [39], so they were more likely to rate their health poorly. In all, the potential role of those factors of physical frailty warrant further investigations to explore their clinical application among elderly nursing home residents.

Reduced physical function reserve (i.e., physical frailty) may occur without showing any difficulties in tasks of daily living or having multiple chronic diseases [4]. However, there are concerns that whether physical frailty is synonymous to disability or comorbidity among the elderly in nursing homes in China. This is because most older adults who moved to nursing homes are usually severely physically dependent and suffering from comorbidities [40–42] as most older people tend to living at home with their families in keeping with the traditional family value in China [43, 44]. In the present study, in contrast, we found that many frail participants did not have disability or comorbidity, indicating that physical frailty did not equate with comorbidity or disability in the population of elderly nursing home residents. Our findings suggest that, physical frailty, related but distinct from disability and comorbidity, can be integrated into nursing-home settings as part of risk stratification and may serve a useful target for preventive interventions.

Our study had several strengths. Firstly, weakness and slowness for defining physical frailty were recorded by objective measures. Secondly, this study comprehensively reporting examined substantial sociodemographic differences in physical frailty prevalence. However, there were limitations to our study. Firstly, the limitations of the present study could lie in the cross-sectional study, there was no follow up to observe the progression from pre-frailty to frailty, and to establish causality of frailty and adverse health outcomes. Therefore, further research will be verified by the temporality of the exposure-effect association. Besides, because of the relatively low response rate, selection bias might have existed in our study. We could speculate that older people in nursing homes not participating might be frailer. Moreover, because most older adults in nursing homes were more 80 years and over, we might have underestimated frailty status.

In conclusion, we demonstrated that, physical frailty is highly prevalent among older adults living in nursing homes in China, especially in women. Older age, being women, living in a private institution, living alone or others unknown person, having no regular exercise, and poor self-reported health are significantly associated with physical frailty among elderly nursing home residents. Hence, give the reversible progression of frailty phenotype from pre-frailty to frailty, our findings should encourage the integration of physical frailty into nursing-home settings as part of risk stratification and as a useful target for preventive interventions. The potential role of those factors of physical frailty warrant further investigations to explore their clinical application among elderly nursing home residents.

BMI: body mass index; OR: Odds ratio; 95% CI: 95% confidence interval; SD: standard deviation; ADL: activity of daily living; MMSE: Mini-Mental State Examination; PASE: Physical Activity Scale for the Elderly; CES-D: the Center for Epidemiologic Studies-Depression.

Acknowledgment

We would like to thank the people who participated in data collection and data entry, as well as the nursing home administrators and residents for their valuable support and participation.

Authors’ contributions

WWL was involved in the conception and design, acquisition of data, analysis, and interpretation of data, drafting and revising manuscript; FJ participated in the design, acquisition of data; CYZ was involved in the design, acquisition of data; SMC, SYT contributed to the design, planning, coordination, and revision of manuscript; MP participated in the design and revising manuscript. The final manuscript had been read and approved by all authors.

Funding sources

This study was supported by the Graduate Innovation Project, Central South

University (Grant 2018zzts245) and Hunan Women’s Research Association (18ZDB03).

Availability of data and materials

The datasets used for the current study are available from the corresponding author upon reasonable request.

Ethical standards

The study protocol was reviewed and approved by the Institutional Review Board of behavioral and nursing research in School of Nursing of Central South University (2019008). All participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Physical Frailty and Its Associated Factors Among Elderly Nursing Home Residents in China

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Abstract

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Introduction

Methods

Study design and participants

Instrumentations

Physical frailty

Correlates Of Physical Frailty

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

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References

Status:

Journal Publication

Version 1