Comparison of Risk Factors of Falls Between Older Community Dwellers With and Without Fear of Falling

Abstract

Background: Given that both falls and fear of falling (FOF) share many risk factors among older adults, differences in risk factors of falls between older adults with and without FOF have been reportedly limited.

Objective: This study aimed to compare the risk factors of falls between older adults with and without FOF using a two-wave cohort dataset.

Method: The study sample included 600 people aged 70.8–96.1 years (mean 76.6, standard deviation 4.6 in 1996)who finished two waves of community survey through a face-to-face questionnaire interview. The outcome of interest was overall fall experience in two surveys. The chi-square test was used to examine differences in the prevalence of falls across explanatory variables, including sociodemographic factors, postural stability (balance and gait maneuverability), sensory factors (Parkinson’s disease, vision, and hearing), number of comorbidities, number of medications, geriatric syndromes (urinary incontinence, depressive symptoms, cognitive impairment, and FOF), lifestyle (alcohol drinking and physical activity level), and environmental home hazards. The potential risk predictors significantly associated with the risk of falls in the chi-square test were examined with univariate logistic regression modeling and included in a multivariate logistic regression model with backward stepwise selection after forcibly including FOF into the model.All analyses were further stratified by FOF to compare the risk factors of falls among older adults with and without FOF.

Results: The overall prevalence of falls and FOF were 20.8% and 48.5%, respectively.

Bivariate analyses revealed that explanatory variables significantly associated with the risk of falls included sex, marital status, number of instrumental activities of daily living difficulty, use of a walking aid, self-rated health, balance/gait maneuverability, vision, number of comorbidities, depressive symptoms, FOF, and PASE score. Multivariate logistic regression models revealed that older adults with FOF shared several predictors of risk for falls, including impaired gait maneuverability, depressive symptoms, and the number of comorbidities.

Conclusion: The study reveals that most modifiable risk factors for falls were more concentrated in older adults with FOF than those without FOF. Study results support the risk stratification of current clinical fall prevention guidelines. FOF experiences combined with other relevant risk factors might be used to classify the target population for fall prevention interventions.

Background

The number of older adults with prolonged life expectancy is continuously increasing and presents a propensity to fall and/or fear of falling (FOF). More than one-fourth of older adults reported falling at least once in the past year based on the data analysis from the 2014 Behavioral Risk Factor Surveillance System survey [1]. In Taiwan, the prevalence of falls over the previous year among community-dwelling older adults gradually dropped from 21.3–17.5% finally to 16.5% across three waves of the national health interview survey (2005, 2009, and 2013); however, the overall, sex-specific, and age-specific fall-related hospitalization rates increased between 2003 and 2009, especially among older women and individuals aged ≥ 75 years [2].

Compared with falls, FOF is more prevalent among injured fallers and has recently gained more attention for its association with activity restriction. Given that the prevalence of falls was only 13.8% (10.8% and 17.6% for males and females, respectively) among non-institutionalized older adults surveyed from mid-1999 to the end of 2002 in Beitou, the prevalence of FOF was 53.4% and increased to 75.1% among the falling injury group [3]. Data analysis from the first wave of the Malaysian Elders Longitudinal Research showed that more than three-quarters (76.4%) of older participants experienced FOF [4]. A Korean study revealed that the prevalence during the last 12 months was 21.2%, 43.3%, and 32.4% for falls, mild FOF, and severe FOF, respectively, in the 2011 national survey of non-institutionalized older adults [5]. In a cohort of community-dwelling older adults in Brazil, the FOF incidence was 33.5%, whereas FOF persistence was 71.3% [6]. The prevalence of restricting outdoor activities due to FOF was 41.2% among community-dwelling older adults receiving home care services in Canada [7]. Approximately 65% of older adults with FOF also reported fear-induced activity restriction in community-dwelling older adults in Chianti countryside, Italy [8].

Furthermore, falls and FOF are also independent risk predictors of each other [9, 10]. A broader range of independent risk factors for FOF among older adults was identified in a literature review of previous studies [5–8, 11–15]. These risk factors overlap those of falls [16, 17] and could be categorized into eight main groups: (1) demographic and psychosocial factors (old age, female gender, history of falls, low educational level, marital status, current driving status, living arrangement, being alone during day time, no support from spouse/partner, increased informal support, activities of daily living (ADL) and/or instrumental activities of daily living (IADL) limitations, poor self-rated health, self-perceived well-being, personal mastery, and use of a walking aid), (2) postural stability (chair standing performance, gait disturbance, walking speed, reporting worse activity level, and functional mobility), (3) medications (polypharmacy and use of antipsychotics), (4) sensory and neuromuscular factors (poor vision impairment, visual contrast sensitivity, hearing impairment, and Parkinson’s disease), (5) comorbidities (common chronic conditions: hypertension, diabetes, heart diseases, stroke, gout, cataract/glaucoma, arthritis/rheumatoid, and respiratory diseases), (6) geriatric syndromes (urinary incontinence, frailty, lower limb strength, depressive symptoms, anxiety, and cognitive impairment), (7) lifestyles (exercise), and (8) environmental factors (discomfort with the neighborhood environment, high access to neighborhood facilities, and high social support and residence in a small city or rural area).

Although total FOF was strongly independently associated with activity restriction, the presence of depression possibly modulates the associations between psychological and physical factors with fear-induced activity restriction [8]. Furthermore, older adults with FOF exclusively for activities at home, compared with those reported FOF in community environment, were older and had significantly less psychological and social resources, lower global functional capacities, and more difficulty with physical performance [18].

However, the aforementioned findings cannot completely explain the reason why the proportion of older adults without a history of falls but still have FOF (65.63%) is still high in robust community-dwelling older adults [13] and a regression tree analysis identified as risk factors of FOF age, sex, self-rated health, functional impairment measured by Short Physical Performance Battery, and mobility disability, instead of falling history and recurrent falls [15]. This condition is possibly caused by the correlation between individual and environmental factors with FOF varied based on the fall history, which was not a potential moderator with these factors [14].

Hereby, FOF does not necessarily follow a fall. We hypothesized that older adults with FOF are supposed to have some risk factors precipitating a fall, which do not apply to those older adults without FOF. This study aimed to examine differences in risk factors of falls between those older adults with and without FOF.

Methods And Measures

Study subjects and data collection

Data used were collected from a cohort completing two waves of cross-sectional survey conducted at Hunei, Kaohsiung, in southern Taiwan. The Hunei community is located in northern Kaohsiung and people mainly relied on agriculture and fishery for a livelihood. The eligibility criteria were as follows: community-dwellers aged 70 and older and had lived for more than 6 months with local household registration. Meanwhile, the exclusion criteria included individuals who were hospitalized, those who had moved, and those who lived in institutions. From September 1996 to April 1997, 1,092 older adults finished the first-wave survey for face-to-face questionnaire interviews related to fall experience in the previous year. Among them, only 600 persons (54.9%) completed a follow-up with a modified ShihPai Medical Services Questionnaire [19] from November 1999 to May 2000. However, 492 persons were lost to follow-up due to death (n = 304) and unknown causes (n = 188). Orientation training was carried out to improve inter-interviewer consistency. All questionnaires had been pretested before administered to the subjects. Informed consent was obtained before questionnaire administration to each participant. The rationale and detailed methodology of the second-wave survey has been described elsewhere [19].

Definitions of Falls and FOF

A fall was defined as “an event of falling to a lower surface occurring while one stands up, sits, gets into bed, or walks, regardless of its underlying causes.”

The number of falls in the past year reported in two surveys was summed as fall frequency, which was further grouped based on three fall categories: without falls (NF) and with falls, including single fall (SF) and recurrent falls (RF) (two or more falls). The outcome of interest was overall fall experience in two surveys.

FOF was measured with a single question in the first wave survey “Did you worry about falling and getting injured?” (Never/Seldom/Sometimes/Often). Moreover, in the second-wave survey, “Are you afraid of falling?” (Yes/No). A score was assigned based on the answer: 0 for (Never; No) and 1 for (Seldom/Sometimes/Often; Yes). A FOF score was the total score obtained from both surveys and was categorized as follows: 0, without FOF, and 1 or 2, with FOF (including 1, temporary FOF, and 2, persistent FOF). The overall FOF experience in two surveys was used as a stratification variable of for a between-group comparison of risk factors for falls among older adults with and without FOF.

Covariates

Explanatory variables included sociodemographic factors (age, sex, marital status, number of ADL/IADL difficulty [20, 21], use of a walking aid, and self-rated health), postural stability (balance and gait maneuverability [22]), sensory factors (Parkinson’s disease, vision, and hearing), number of comorbidities (including seven common chronic conditions: hypertension, diabetes, heart diseases, stroke, gouty arthritis, cataract, and respiratory diseases), number of medications (the total number of medications taken in the past month was counted and categorized among 15 kinds of selected medications, with polypharmacy ≥ 5 medications [23]), geriatric syndromes (urinary incontinence, depressive symptoms [24], cognitive impairment [25], and FOF), lifestyle (alcohol drinking and physical activity level by the Physical Activity Scale in the Elderly [PASE][26]) (Additional file 1: Table S1), and environmental home hazards (Additional file 2: Table S2). Almost all explanatory variables were derived from the first-wave survey, except that comorbidities, depressive symptoms, cognitive impairment, and physical activities, among others were derived from the second-wave survey.

Statistical Analyses

All statistical analyses were conducted using the STATA/SE 17 for Windows (College Station, TX: StataCorp LLC.). The statistical significance level (α) was set at 0.05. In the population completing two surveys, the overall prevalence of falls was defined according to the proportion of participants who experienced at least a fall, whereas the overall prevalence of FOF was defined according to the proportion of participants who reported temporary or persistent fear of falls. The distribution of selected explanatory variables was presented. The chi-square test was used to examine differences in the prevalence of falls among all participant characteristics. The potential risk predictors significantly associated with falls in the aforementioned chi-square test were examined with univariate logistic regression modeling and selected into a multivariate logistic regression model with backward stepwise selection, removing variables with p ≥ 0.2 and adding those with p < 0.1, after forcibly including FOF into the model. All analyses were further stratified by FOF to compare the risk factors of falls among older adults with and without FOF.

Results

A total of 600 participants finished two surveys, with a mean baseline age of 76.6 years (standard deviation of 4.6). Of the respondents, 44% were aged 70–74 years, 49.2% were males, 57.5% were married or living with a companion, 2.8% had difficulty in performing at least one ADL, and approximately one-third had difficulty performing at least one IADL. Among them, 12.5% perceived a fair-to-poor health status, 9.5% needed to use a walking aid, and 30.8% and 20.0% had a certain degree of impaired balance and gait maneuverability, respectively. They also reported having proportions of medical problems: unclear vision (20.5%), unclear hearing (11.3%), Parkinson’s disease (0.8%), three or more comorbidities (15.0%), depressive symptoms (35.2%), polypharmacy (4.5%), urinary incontinence (1.2%), and cognitive impairment (41.7%). Regarding the lifestyles and living environment, only 5.3% reported drinking liquor at least once a week, 35% had a low physical activity level, and 45% lived at home with a high environmental home hazards score. Participants’ characteristics significantly associated with the risk of falls included sex, marital status, number of IADL difficulty, use of a walking aid, self-rated health, balance/gait maneuverability, vision, number of comorbidities, depressive symptoms, FOF, and PASE score (Table 1).

[Insert Table 1 here]

The overall prevalence of falls (and 95% confidence interval [CI]) was 20.8% (95% CI, 17.7–24.3%), and the corresponding rates were 9.5% (95% CI, 7.3–12.1%) and 13.8% (95% CI, 11.2–16.9%) for the first- and second-wave surveys and 12.2% and 8.7% for SF and RF, respectively. Furthermore, the overall prevalence of FOF was 48.5% (95% CI, 44.6–52.6%), and the corresponding rates were 17.2% (95% CI, 14.2–20.4%) and 40.7% (95% CI, 36.7–44.7%) in the first- and second-wave surveys and 39.2% and 9.3% for temporary and persistent FOF, respectively (Fig. 1).

[Insert Fig. 1 here]

Table 2 shows the associations between comorbidities and falls. Older adults with gouty arthritis (p = 0.049) or respiratory diseases (p = 0.044) tended to have a higher risk of falling than their counterparts. However, no significant association was found between falls and hypertension, diabetes, heart diseases, stroke, or cataract, whether stratified with or without FOF. As for the medications, older adults who took anti-arthritic (p = 0.002) or other specified medications (p = 0.030) were at about twice the risk of falling as those who did not (Additional file 3: Table S3).

[Insert Table 2 here]

Table 3 reveals the results of univariate logistic regression models for falls. The risk for falls of females tended to be twice as high as that of males. Older adults using a walking aid tended to be twice highly at risk for falls as their counterparts. Furthermore, those with impaired balance or gait maneuverability had twice or two and three-quarters higher risk for falls than those who were normal. Older adults with depressive symptoms were twice more likely to fall than those without. Moreover, potential risk factors showing a gradient risk for falls in the test for trend (p < 0.05) included the number of IADL difficulties, self-rated health, vision, number of comorbidities, and FOF. Conversely, older adults who were married or lived with a companion, or at a high physical activity level, seemed to be more likely protected from falls when compared with their counterparts. However, after stratifying the model based on experiences of FOF, the significant risk predictors of falls among older adults with FOF included marital status, number of IADL difficulty, self-rated health, gait maneuverability, vision, number of comorbidities, and depressive symptoms, whereas the significant risk predictors of falls in those without FOF were sex, number of IADL difficulty, self-rated health, and gait maneuverability.

[Insert Table 3 here]

Table 4 demonstrates that only some covariates were identified as independent risk predictors of falls in multivariate logistic regression modeling, including female gender (OR 1.67, 95% CI 1.07–2.63), impaired gait maneuverability (OR 1.94, 95% CI 1.16–3.23), number of comorbidities (OR 1.88, 95% CI 1.04–3.40 for one or two comorbidities; OR 2.20, 95% CI 1.05–4.60 for three or more comorbidities), and depressive symptoms (OR 1.93, 95% CI 1.22–3.08). Moreover, a gradient risk for falls was observed for self-rated health (test for trend, p = 0.039) and the number of comorbidities (test for trend, p = 0.031). With either worse self-rated health or more comorbidities, older adults tended to have a higher risk of falling than their reference groups. Older adults with FOF shared almost all risk predictors of falls, including impaired gait maneuverability (OR 2.17, 95% CI 1.14–4.15), having three or more comorbidities (OR 3.02, 95% CI 1.01–8.96), and depressive symptoms (OR 1.90, 95% CI 1.06–3.40), compared with those without FOF. Conversely, among the older adults without FOF, females only had two and three-tenths times higher risk for falls than males (OR 2.29, 95% CI 1.16–4.53). There was no significant interaction between FOF and other covariates in the adjusted models, such as sex, self-rated health, gait maneuverability, number of comorbidities, and depressive symptoms.

[Insert Table 4 here]

Discussion

Our study reveals that compared with older adults without FOF, those with FOF shared several risk predictors of falls in multivariate logistic regression models, such as impaired gait maneuverability, number of comorbidities, and depressive symptoms, implying that experience of FOF might be adopted, in combination with other relevant risk factors, for segmentation of the target population for fall prevention interventions.

The prevalence of falls in the second-wave survey, although similar to that of Beitou study [3], was still lower than reported in Korea [5], posing two possible explanations: First, Chinese older adults had more vigilant attitudes and behaviors in walking stick usage, higher levels of planned activities, and lower levels of incidental activities [27], even though some were reluctant to report fall experiences for a health belief leaning toward fatalism [28]. Second, it might be attributed to the healthy-participant effect. Most of the older adults in Hunei used to make a livelihood in agriculture or fishery, and these blue-collar occupations, involved in more physical activities, might protect them from falls [29]. Moreover, the prevalence of FOF in the second-wave survey, even higher than that in the first wave survey, was also lower than in those preceding studies [3–6]. Further studies should be conducted to elucidate whether it reflected not only cultural differences in reporting FOF but also variations in the definition and measurement of FOF across countries/studies.

Consistent with our previous study [2], female gender, number of comorbidities, and depressive symptoms were identified as significant independent risk factors for falls. In addition to age-related differences in the knee muscle strength in women [30], the study findings reflected the additive effects of chronic diseases on the risk for falling [31] and the substantial influence of depression on fall risk driven by biologically based mechanisms [32, 33]. As these mechanisms contribute to poor concentration and low-energy levels [33], older adults with depression are less capable to perform the dual task and pay less attention to fall-related environmental hazards and participation in their usual ADLs. Hereby, inactivity results in reducing strength, range of motion, and endurance [34] and leads to increased risk for falling [9, 16]. However, our study did not identify whether the depressive symptoms–fall relationship carries more weight than the opposite pathway [35] because it is beyond the scope of the present study to explore an intricate bidirectional and self-perpetuating interaction between falls and depression [32, 33]. Moreover, the fact that gait maneuverability was also an important risk predictor of falls is consistent with several previous study findings. Older adults at risk for falls had a greater gait variability when compared with both older adults who are non-fallers [36]. Some quantitative gait markers are independent predictors of falls in older adults [37], especially for slow gait velocity [38], stride time variability [39], and stride-to-stride variability [40].

Several other risk factors, whether established in univariate analyses or not, did not withstand the multivariate adjustment. For instance, although taking drugs from the central nervous system therapeutic group increases the risk for falls by seven times for patients [41] and by twofold for old home-dwellers [42], our study did not proclaim polypharmacy or use of sleeping pills or sedatives as an independent risk factor. Interestingly, our study reported that both having gouty arthritis and taking anti-arthritic or other specified medications were significantly associated with an increased risk of falling in bivariate analysis. Further study is needed to investigate whether it was due to arthritis-related sarcopenia in patients with rheumatoid arthritis [43] or knee osteoarthritis [44]. In contrast to a previous study [42], self-rated health and FOF, although selected into the multivariate logistic regression models, were not significantly associated with the risk for falls, increasing two possible explanations: First, it might be due to the limited power for a small sample size, and second, the over-presentation of some selected characteristics among non-participants makes it difficult to tell their relative differences in the risk for falls.

This study has two strengths. First, different questionnaires were administered during each survey to collect data with the following validated scales: balance/gait maneuverability, GDS, MMSE, and PASE, among others. Second, the overall fall experience in two surveys provided broader outcome base for identification of correlated risk factors of falls. The overall FOF experience in two surveys as a stratification variable made it possible to obtain an almost equivalent number of older adults with and without FOF for a between-group comparison of risk factors for falls. Our study findings inform twofold policies. On one hand, it implies a likely clustering of risk factors for falls among older adults with FOF. As reported, having more risk factors for falls predisposes to not only a higher risk of falls for older adults living at home or in sheltered housing [42] but also a higher risk of RF for residents of intermediate care facilities [45]. Remarkably, regardless of a fall history, older adults might still have other risk factors of falls, such as depressive symptoms, to become an anxious group with low physiologic fall risk but a high perceived risk for falls [46]. This might also elucidate the phenomenon of a high proportion of older adults without a history of falls but still having FOF in robust community-dwelling older adults [13]. Noteworthily, even though usually precipitated by body and mind incompetence, FOF in older adults can be quantified and interpreted in terms of psychological and physical characteristics, social support, and global functional capabilities [18], and probably reduced to a limited extent immediately after exercise interventions [47]. Hereby, geriatric rehabilitation aims to increase muscle strength, trunk stability, balance and coordination, together with better gait performance. While doing these, those older people with FOF may have the opportunity to rebuild their physical capabilities and self-confidence to FOF. On the other hand, it not only supports the common recommendation for risk stratification of clinical fall prevention guidelines that fall history, FOF, and gait and balance difficulties should be incorporated into “case-finding” self-reported questions [48], but also expands the conceptualization of risk factors for falls from functional outcomes to the psychosocial context of the individual [33]. Accordingly, further study might consider a large sample size whether a simple question of FOF experience, combined with questions related to fall history, and assessment of postural stability, comorbidities, and depressive symptoms, could successfully identify the potential target population of preventability from the risk for falls with multifactorial interventions [49].

Nevertheless, this study has several limitations. First, data collection based on questionnaire interviews is liable to recall bias, and a higher missing rate (5.7%) was found based on balance/gait maneuverability. Second, the study explored the association, instead of causal linkage, between selected covariates and falls. Despite the overall prevalence of falls/FOF standing for summary experiences during the two-wave surveys, almost all explanatory variables were taken from just one wave. The lack of repeated measurements of selected covariates makes it difficult to detect their time-dependent changes associated with falls/FOF and to examine a cascade effect between previous and future falls [42]. Third, in contrast to the likely underestimation of the incidence rate reported in an 8-year follow-up study [33], prevalence odds ratios overestimate the prevalence rate when the proportion of outcome is not rare (≥ 10%) [50]. Fourth, FOF was measured with a “yes/no” question and could not detect variability in FOF degrees [11]. Fifth, more falls/FOF could have been missed because ascertainment of falls/FOF was not performed between the first- and second-wave surveys. Effects of RF or persistent FOF might be underestimated. Sixth, environmental home hazards were not adopted into further analysis in the study due to a lack of discrimination validity between fallers and non-fallers. Notwithstanding, older adults with a higher level of FOF were less likely to have environmental home hazards but were more likely to have functional home hazards compared with those without FOF [4]. Finally, the overall number of medications does not reflect the real added impact of each medication because not all medications affect falls/FOF with a similar magnitude [41].

Conclusions

This study is so far the first to illustrate how data collected from a cohort completing two waves of cross-sectional survey were used to identify multiple risk factors for falls, including impaired gait maneuverability, depressive symptoms, and number of comorbidities, among community-dwelling older adults with FOF, compared with those without FOF. Study results support the risk stratification of the current clinical fall prevention guidelines. Experience of FOF, combined with other relevant risk factors of falls, might apply to the segmentation of the target population for fall prevention interventions.

Abbreviations

Declarations

Ethical approval and consent to participate

The study protocols were approved as medical ethics concern was addressed appropriately. Informed consent was obtained, and all methods followed the declaration of Helsinki. Ethics approval by an institutional review board was deemed unnecessary according to national regulations (DOH-MA 0920202507). 

Consent for publication

Not applicable. 

Availability of data and materials

The Hunei study datasets are available from the corresponding author on reasonable request.

Competing interest

The authors declare no competing interests.

Funding

This study was supported by the Taiwan Provincial Government (Technical Contract No. 007843) and National Science Council, Taiwan (NSC 89-2320-B-065-001-M56).

Authors’ contributions

YJT had full access to the Hunei study datasets and took responsibility for the integrity of data analysis. YJT, WJS, and YCY were responsible for the study design. WJS was in charge of data collection and curation. YJT drafted the manuscript and was responsible for data interpretation. WJS, YCY, and MYW helped with the study methodology and participated in the manuscript revision, writing review, and editing. All authors contributed to the critical revision of the manuscript for important intellectual content, and all approved the article submitted for publication.

Acknowledgments

The authors are grateful to the interviewers and staff of Taiwan Provincial Institute of Family Planning (currently Surveillance, Research and Health Education Division, Health Promotion Administration, Ministry of Health and Social Welfare) and the Hunei Health Station for data collection. The authors are also indebted to Professor Pesus Chou (Institute of Public Health, National Yang Ming Chiao Tung University, Taipei, Taiwan) and Honorary Chair Professor Jung-Der Wang (Department of Public Health College of Medicine, National Cheng Kung University, Tainan, Taiwan) for help with questionnaire design.

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