Factors Associated With Falls Among Community-Dwelling Older Adults With Osteoarthritis: A Sex Subgroup Analysis

Abstract

Osteoarthritis elevates the risk of falling among older adults due to joint pain and stiffness, especially among women, yet sex influences in these associations remain understudied. This study investigated factors associated with falls by sex. A sample of community-dwelling older adults with osteoarthritis from the 2016 Health and Retirement Study (2624 females; 1271 males) was analyzed using survey-weighted logistic regression, controlling for sociodemographic characteristics such as geographic residence and health-related issues. For women with osteoarthritis, higher risk of falling was associated with being White compared to Black, living in rural areas compared to urban areas, in addition to opioid use. Among men with osteoarthritis, having heart problems and better distal vision increased the risk of falling. Sex-specific fall prevention strategies, such as rural programs, opioid education for women, and increasing awareness and fall education for men with heart problems, are encouraged to promote active living among older adults with osteoarthritis.

Keywords

falls sex difference frailty osteoarthritis population-based study

What this paper adds

• This study fills a research gap by examining sex-specific fall risks and their related factors among older men and women with osteoarthritis.

• This study highlights environmental (i.e., rural residence), behavioral (i.e., opioid use), and health (i.e., comorbidity) factors influencing fall risks among older adults with osteoarthritis.

Applications of study findings

• Fall prevention programs and services should be further tailored for at-risk older women and men with osteoarthritis, such as rural communities.

• More education on opioid use and pain management is critical for older women with osteoarthritis.

• Further research should investigate the mechanism of fall risk among older adults with osteoarthritis and other chronic conditions to address the impact on their functional capacity and quality of life.

Introduction

Falls among older adults with osteoarthritis are a pressing public health concern, causing fatal injuries, such as hip fractures, traumatic brain injuries, and death (Centers for Disease Control and Prevention [CDC], 2024a). More than 33 million adults in the United States with osteoarthritis (CDC, 2024b) face the risk of functional limitations and falls due to joint stiffness and chronic pain (Wojcieszek et al., 2022). Compared to those without osteoarthritis, older adults with hip osteoarthritis (Knox et al., 2021) and knee osteoarthritis (Harris et al., 2022) are more likely to fall multiple times by 41% per year and 22% per five years, respectively. Research has identified a wide range of risk factors for falls among individuals with osteoarthritis, including female sex, White/Caucasian race, higher levels of education (Ofori-Asenso et al., 2021), impaired balance, muscle weakness (Manlapaz et al., 2019; Ofori-Asenso et al., 2021), more comorbidities (Manlapaz et al., 2019; Ofori-Asenso et al., 2021), and opioid use (Lo-Ciganic et al., 2017; Ofori-Asenso et al., 2021; van Schoor et al., 2020).

Previous research also suggested osteoarthritis-related symptoms such as pain (Manlapaz et al., 2019; Munch et al., 2015; Xiong et al., 2023), depressive symptoms (Ofori-Asenso et al., 2021), sleep problems (De Baets et al., 2023; Pickering et al., 2016), and fatigue (Hackney et al., 2019) increasing the risk of fall. For example, knee (Manlapaz et al., 2019; Munch et al., 2015; Xiong et al., 2023) and hip pain (Munch et al., 2015) have been identified as a risk factor for falls among adults with osteoarthritis. A cross-sectional study using the Osteoarthritis Initiative (OAI) data (n = 4796) reported that those who had higher levels of depression were more likely to report a fall in the past 12 months (Soh et al., 2020). Despite the limited fall studies examining sleep problems and fatigue among adults with osteoarthritis, a population-based study in the United States (Min et al., 2016) reported that community-dwelling older adults who had sleep problems were 16% more likely to fall than those without sleep problems. The risk of falling further increased to 19% for those who took sleep medication (Min et al., 2016). Similarly, a systematic review has documented that fatigued older adults were 4% to 53% more likely to fall (Pana et al., 2021). Although older adults with osteoarthritis were 25% more likely to have sleep problems (Allen et al., 2008) and 35%–41% more likely to report fatigue (Overman et al., 2016; Wolfe et al., 1996) compared to those without osteoarthritis, the influence of those symptoms on fall risks among older adults with osteoarthritis has not been considered.

Strong evidence supports sex differences in sleep (Ehlers & Kupfer, 1997; Fukuda et al., 1999; Hume et al., 1998; Meers et al., 2019; Redline et al., 2004; Walsleben et al., 2004), fatigue (Laghousi et al., 2016; Valko et al., 2014), and depressive symptoms (Eid et al., 2019) in non-osteoarthritis conditions. Women were less satisfied with their sleep than men (Campbell et al., 1989). Similar patterns in different sex have been reported for older adults with osteoarthritis. Compared to men with osteoarthritis, women with osteoarthritis are more likely to have pain (Tschon et al., 2021), depression (Sharma et al., 2016; Wang & Ni, 2022), and fatigue (Hackney et al., 2019). Despite known sex differences in sleep, fatigue, and depression, differential risk factors for falls between men and women is currently unknown. Segregating analyses by sex can provide a clearer picture of risk factors to facilitate the development of targeted preventive measures and policies. Thus, the goal of this study was to investigate sex-dependent risk factors associated with falls among older adults with osteoarthritis by including osteoarthritis-associated symptoms (i.e., pain, depression, sleep problems, and fatigue) and sociodemographic and other health characteristics.

Methods

Study Design and Sample

This cross-sectional study used data from the 2016 Health and Retirement Study (HRS). HRS has collected interview-based survey data on health, employment, and family structure from a nationally representative sample of adults aged 50 years or older in the United States every 2 years since 1992 (HRS, 2008). The sample consisted of the 2016 cohort participants (n = 3895) who met the following criteria: 1) aged 65 or older; 2) resided in non-nursing home settings; and 3) responded to a question about whether they had experienced a fall in the previous two years. This study that used a de-identified public dataset did not qualify as human subject research and was found exempt by the Institutional Review Board of Texas Woman’s University.

Measures

The dependent variable in this study, the incidence of falls, was assessed by asking whether participants fell in the past two years (yes; no). The primary predictor variables of interest were bothersome pain, depressive symptoms, sleep problems, and severe fatigue or exhaustion. Troubling with pain and no pain were coded 1 and 0, respectively. Depressive status was determined using the adopted short version of the Center for Epidemiologic Studies Depression Scale (CESD-8) that asks if participants experienced the following symptoms in the past week (yes = 1; no = 0): (1) feeling depressed, (2) feeling that everything one did was an effort, (3) having restless sleep, (4) feeling happy, (5) feeling lonely; (6) enjoying life, (7) feeling sad, and (8) could not get going. The item scores on positive feelings (i.e., items 4 and 6) were reverse-coded to represent higher levels of depressive symptoms for analysis. We created a total score that counts the number of depressive symptoms from 0 to 8 (Steffick, 2000) and a dichotomous variable of the CESD-8 scores, using cut-points of 3 or more to indicate a major depressive episode (Turvey et al., 1999), validated with high sensitivity and specificity (Glymour et al., 2012; Soh et al., 2022; Steffick, 2000). Sleep problems were assessed using the adapted Brief Insomnia Questionnaire (BIQ), which consists of four questions asking how often participants had: (1) trouble falling asleep, (2) trouble waking up during the night, (3) trouble with waking up too early and not being able to return to sleep, and (4) felt really rested on waking up in the morning. Responses to the first three items were coded as follows: 2 = most of the time, 1 = sometimes, and 0 = rarely or never. With the last item (i.e., item 4) reverse-coded, a total sleep problems score ranging from 0 to 8 was geenrated, with higher values indicating more severe sleep problems. Participants also responded to the question about whether they had severe fatigue or exhaustion (yes; no).

For covariates, this study included sociodemographic characteristics: age by year; sex (male; female); race (White; Black; other races); and education (less than high school; high school graduate; some college or beyond). Following the terminology used in the HRS interviewer instructions and codebook (i.e., “Verify the spelling of first and last name and sex.”), the term “sex” rather than “gender” was used in this study. HRS defined the geographic context of participants’ residence by three categories: urban (metropolitan area with population >1,000,000), suburban (metropolitan area with a population of 250,000–1,000,000), and ex-urban or hereinafter referred to as rural (nonmetropolitan counties with population <250,000), based on the United States Department of Agriculture (USDA)’s 2013 Beale Rural-Urban Continuum Code system (U.S. Department of Agriculture Economic Research Service, 2026).

The health-related characteristics included functional limitation, body mass index (BMI), balance impairment, vision problems, seven chronic diseases, and medication use. The restriction on physical function was assessed by counting the number of activities of daily living (ADL) limitations (i.e., having difficulty with walking across a room, bathing, eating, dressing, getting in or out of bed, and using the toilet), which ranged from 0 to 6 (no limitation; 1–2 limitations; 3 or more limitations). BMI was determined based on self-reported weight and height. The presence of balance problems was coded as 1 = having difficulty with balance often, sometimes, rarely, and 0 = never. Two vision problems with vision aids (e.g., glasses, contacts) were included: seeing things at a distance, like recognizing someone across the street (1 = fair or poor; 0 = excellent, very good, or good), and seeing things up close, like reading ordinary newspaper print (1 = fair or poor; 0 = excellent, very good, or good). Participants responded whether a doctor diagnosed the following seven chronic diseases by yes or no: high blood pressure, diabetes, cancer, lung disease, heart problems, stroke, and dementia. The use of opioid pain medication in the past three months was coded as 1 = yes or 0 = no, and the regular use of prescription medications to help sleep was coded as 1 = yes or 0 = no.

Statistical Analyses

The weighted proportion or the mean and standard deviation within categories of sociodemographic and health characteristics was calculated to describe the sample. We conducted bivariate analyses to examine differences in the prevalence of sociodemographic and health characteristics between fallers and non-fallers by sex, using chi-square tests for categorical data or t-tests for numerical data. No multicollinearity was identified among the proposed variables with all variance inflation factor (VIF) values less than 10.0 (Kutner et al., 2005). Then, associations between falls and four primary predictor variables (i.e., pain, depressive symptoms, sleep problems, and severe fatigue or exhaustion) were assessed using weight logistic regression models. Model 1 (unadjusted) included only the four primary predictor variables. Model 2 added sociodemographic characteristics. Built on Model 2, Model 3 added other health-related characteristics. To highlight differential risk factors for falling by sex, separate analyses for the total sample, female and male participants were conducted. The Hosmer–Lemeshow test was performed to assess model fits, where a larger p-value indicates a better fit. All analyses were conducted using STATA 17.0 (StataCorp, College Station, TX), applying the analytic weight of HRS’ stratified and multistage sampling design. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported with statistical significance set at p < .05.

Results

Sample Characteristics

Most participants were female (64.5%), in their seventies (mean age = 73.7, standard deviation = 6.4), White (89.0%), had education beyond high school (89.3%), and lived in urban or suburban areas (70.9%) (Table 1). About 41.2% of participants reported a fall in the past two years, and nearly 55% of the participants reported pain.

Table 1.

Sociodemographic and Health Characteristics of Community-dwelling Older Adults With Osteoarthritis.

	Total(n = 3895)%
Age, mean (standard deviation: SD)	73.74 (6.4)
Female sex	64.5
Race
White/Caucasian	89.0
Black/African-American	7.3
Other^a	3.7
Education
Less than high school	10.7
High school graduate	53.1
Some college or beyond	36.2
Geographic residence
Urban	45.2
Suburban	25.7
Rural^b	29.1
ADL limitation (range: 0–6)
No limitation	67.4
1–2 limitations	27.4
3 or more limitations	5.2
Body Mass index, mean (SD)	28.85 (5.5)
Falls	41.2
Balance problems	39.1
Depressive symptoms score ≥3 (range: 0–8)^c	19.4
Vision problems
Distal vision problems^d	12.0
Near vision problems^e	16.8
High blood pressure	66.5
Diabetes	26.4
Cancer^f	21.6
Lung disease	13.0
Heart problems^g	33.6
Stroke	8.5
Dementia	2.2
pain	54.9
Opioid pain medication use	20.6
Overall sleep problem score (0–8), mean (SD)	2.94 (2.0)
Sleep medicine	18.2
Sever fatigue or exhaustion	21.8

^aIncludes American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander.

^bReferred to as ex-urban in the Health and Retirement Study Urban-Rural Code 2016 (BEALE, 2013) (i.e., population fewer than 250,000).

^cThree or more symptoms indicate depression “caseness” (Turvey et al., 1999).

^dEyesight for seeing things at a distance (0 = excellent, very good, or good; 1 = fair, or poor).

^eEyesight for seeing things up close (0 = excellent, very good, or good; 1 = fair, or poor).

^fExcluding skin cancer.

^gIncluding heart attack, coronary heart, disease, angina, and congestive heart failure.

Characteristics of Fallers and Non-fallers

For both female and male participants with osteoarthritis (N = 3895) (Table 2), those who reported falls were older (p < .05), had limited ADL (p < .001), impaired balance (p < .001), pain (p < .005), depressive symptoms (p < .001), sleep problems (p < .005), near vision problems (p < .05), severe fatigue or exhaustion (p < .005), and used opioids (p < .05) or sleep medicines (p < .01). Among the seven chronic diseases, diabetes (p ≤ .05), heart problems (p < .001), strokes (p < .01), and dementia (p < .005) were associated with the incidence of falls. Fall incidents were more common among female (66.8%) participants than male (62.9%) participants (p = .048). Among female participants, fallers were more likely to have a higher BMI (p < .001), high blood pressure (p < .005), and cancer (p < .01) than non-fallers. Male participants did not exhibit these patterns.

Table 2.

Sociodemographic and Health Characteristics of Fallers Versus Non-fallers by Sex.

	Female (n = 2624)			Male (n = 1271)
	Fallers (n = 1138) %	Non-fallers (n = 1486) %	Group Test p	Fallers (n = 537) %	Non-fallers (n = 734) %	Group Test p
Age, mean (standard deviation: SD)	74.59 (7.24)	73.67 (6.74)	<0.05	74.40 (6.91)	72.61 (5.43)	<0.005
Race			0.214			0.298
White/Caucasian	89.4	87.6		91.6	89.1
Black/African-American	6.9	9.1		4.9	6.4
Other^a	3.6	3.3		3.5	4.6
Education			0.157			0.357
Less than high school	13.1	10.6		10.3	8.5
High school graduate	52.2	56.4		52.4	49.9
Some college or beyond	34.7	33.0		37.3	41.6
Geographic residence			0.102			0.731
Urban	40.7	46.0		46.5	48.6
Suburban	29.3	25.7		22.3	23.4
Rural^b	30.0	28.3		31.2	28.0
ADL limitation (range: 0–6)			<0.001			<0.001
No limitation	60.0	74.5		55.3	78.1
1-2 limitations	31.4	23.2		36.0	20.4
3 or more limitations	8.7	2.4		8.7	1.5
Body Mass index, mean (SD)	29.64 (6.71)	28.17 (5.69)	<0.001	29.05 (5.46)	28.84 (3.99)	0.727
Balance problems	56.6	31.7	<0.001	56.0	17.8	<0.001
Depressive symptoms score ≥3 (range: 0–8)^c	27.1	17.2	<0.001	23.0	11.1	<0.001
Vision problems
Distal vision problems^d	15.1	12.0	0.078	12.3	8.0	0.108
Near vision problems^e	21.6	14.0	<0.001	20.6	13.2	<0.05
High blood pressure	70.9	63.3	<0.005	64.1	67.7	0.375
Diabetes	28.9	21.9	<0.005	33.0	26.9	0.05
Cancer^f	23.2	17.7	<0.01	25.2	24.4	0.831
Lung disease	14.7	11.7	0.064	14.6	12.1	0.301
Heart problems^g	36.3	25.5	<0.001	48.2	35.1	<0.001
Stroke	10.8	6.0	<0.005	12.7	7.4	<0.01
Dementia	4.0	1.2	<0.005	3.8	1.0	<0.005
pain	61.3	50.5	<0.005	60.9	50.6	<0.005
Opioid pain medication use	25.0	13.1	<0.001	29.7	21.7	<0.05
Overall sleep problem score (0–8), mean (SD)	3.30 (2.14)	2.84 (2.02)	<0.001	3.14 (2.11)	2.53 (1.83)	<0.005
Sleep medicine	24.9	16.1	<0.001	20.1	11.6	<0.01
Sever fatigue or exhaustion	31.9	20.1	<0.001	23.9	10.7	<0.001

^aIncludes American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander.

^bReferred to as ex-urban in the Health and Retirement Study Urban-Rural Code 2016 (BEALE, 2013) (i.e., population fewer than 250,000).

^cThree or more symptoms indicate depression “caseness” (Turvey et al., 1999).

^dEyesight for seeing things at a distance (0 = excellent, very good, or good; 1 = fair, or poor).

^eEyesight for seeing things up close (0 = excellent, very good, or good; 1 = fair, or poor).

^fExcluding skin cancer.

^gIncluding heart attack, coronary heart, disease, angina, and congestive heart failure.

Factors Associated With Falls

Collectively, among all participants with osteoarthritis (N = 3895) (Table 3), having pain, sleep problems, depressive symptoms, and severe fatigue or exhaustion increased the risk of falling in the unadjusted model (Model 1). Those symptom factors remained significant after adjusting for the sociodemographic characteristics (Model 2). In addition, individuals with older age (odds ratio (OR) = 1.02, 95% confidence interval (CI): 1.01, 1.04) and being White (OR = 0.74, 95% CI: 0.55, 0.98 compared to Black race) were more likely to fall. In the fully adjusted model (Model 3), pain, sleep problems, depressive symptoms, and severe fatigue or exhaustion no longer have a significant impact on falls. Instead, the risk of falling increased when participants had one or more ADL limitations (1–2 limitations OR = 1.58, 95% CI = 1.10, 2.28 compared to no ADL limitations; 3 or more limitations OR = 3.24, 95% CI = 1.41, 7.43 compared to no ADL limitations), balance problems (OR = 3.17, 95% CI = 1.99, 5.04), near vision problems (OR = 1.82, 95% CI = 1.21, 2.73), cancer (OR = 1.95, 95% CI = 1.25, 3.04), heart problems (OR = 1.84, 95% CI = 1.24, 2.72), or strokes (OR = 1.79, 95% CI = 1.02, 3.15).

Table 3.

Factors Associated With Falls Among Community-dwelling Older Adults With Osteoarthritis (n = 3895).

	Model 1 Unadjusted	Model 2Model 1 + Sociodemographics	Model 3Model 2 + Health-related issues
Pain	1.30 (1.06, 1.60)*	1.30 (1.07, 1.59)*	1.05 (0.69, 1.62)
Sleep problem score	1.05 (1.00, 1.09)*	1.05 (1.00, 1.10)*	1.03 (0.93, 1.14)
Depressive symptoms score ≥3 (range: 0–8)	1.49 (1.15, 1.93)*	1.50 (1.45, 1.97)*	1.07 (0.70, 1.61)
Sever fatigue or exhaustion	1.64 (1.31, 2.05)*	1.68 (1.32, 2.13)*	0.97 (0.64, 1.46)
Sociodemographics
Age		1.02 (1.01, 1.04)*	1.00 (0.96, 1.03)
Female sex		1.08 (0.90, 1.29)	1.19 (0.80, 1.78)
Race
White (Reference)
Black		0.74 (0.55, 0.98)*	0.49 (0.27, 0.89)*
Other^a		0.89 (0.58, 1.35)	1.56 (0.47, 5.15)
Education
Less than high school (Reference)
High school graduate		0.94 (0.66 , 1.34)	1.34 (0.67, 2.70)
Some college or beyond		1.05 (0.73, 1.50)	1.35 (0.63, 2.91)
Geographic residence
Urban (Reference)
Suburban		1.08 (0.84, 1.38)	1.01 (0.59, 1.70)
Rural		1.15 (0.90, 1.46)	1.27 (0.81, 1.99)
Health-related issues
ADL limitation (range: 0–6)
No limitation (Reference)
1–2 limitations			1.58 (1.10, 2.28)*
3 or more limitations			3.24 (1.41, 7.43)*
Body Mass index			1.01 (0.98, 1.04)
Balance problems			3.17 (1.99, 5.04)*
Vision problems
Distal vision problems^b			0.63 (0.40, 1.00)
Near vision problems^c			1.82 (1.21, 2.73)*
High blood pressure			0.98 (0.63, 1.51)
Diabetes			1.16 (0.80, 1.70)
Cancer^d			1.95 (1.25, 3.04)*
Lung disease			0.86 (0.54, 1.36)
Heart problems^e			1.84 (1.24, 2.72)*
Stroke			1.79 (1.02, 3.15)*
Dementia			1.49 (0.50, 4.49)
Opioid pain medication			1.30 (0.89, 1.90)
Sleep medicine			1.08 (0.69, 1.68)
Model fit^h	p < 0.001	p < 0.001	p < 0.037

^aThree or more symptoms indicate depression “caseness” (Turvey et al., 1999).

^bIncludes American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander.

^cReferred to as ex-urban in the Health and Retirement Study Urban-Rural Code 2016 (BEALE, 2013) (i.e., population fewer than 250,000).

^dEyesight for seeing things at a distance (0 = excellent, very good, or good; 1 = fair, or poor).

^eEyesight for seeing things up close (0 = excellent, very good, or good; 1 = fair, or poor).

^fExcluding skin cancer.

^g. Including heart attack, coronary heart, disease, angina, and congestive heart failure.

^hHosmer–Lemeshow Goodness of Fit test (p-value).

While adding health-related variables improved model fit, none of the three regression models fit the data well (Hosmer–Lemeshow Goodness of Fit p < .001 Models 1 and 2; p = .037 Model 3).

Factors Associated With Falls by Sex

When the analysis was separated by sex (n = 2624 for females; n = 1271 for males), sex differences in factors associated with falls were confirmed (Table 4). In the unadjusted model (Model 1), depressive symptoms (female OR = 1.40, 95% CI: 1.05, 1.86; male OR = 1.73, 95% CI: 1.07, 2.82), and severe fatigue or exhaustion (female OR = 1.49, 95% CI: 1.18, 1.88; male OR = 2.05, 95% CI: 1.30, 3.22) were significant predictors (or risk factors) for falls in both women and men. However, pain was significant only for female participants (OR = 1.37, 95% CI: 1.08, 1.72). In the adjusted model including sociodemographic factors (Model 2), depressive symptoms (OR = 1.41, 95% CI: 1.04, 1.91), severe fatigue or exhaustion (OR = 1.54, 95% CI: 1.19, 1.98), and pain (OR = 1.40, 95% CI: 1.11, 1.77) remained significant for females; and, depressive symptoms (OR = 1.75, 95% CI: 1.05, 2.93) and severe fatigue or exhaustion (OR = 1.04, 95% CI: 1.01, 1.07) remained significant for males. Among all the sociodemographic factors, older age was the only predictor that significantly increased the risk of falling in both females and males (female OR = 1.02, 95% CI: 1.00, 1.98; male OR = 1.04, 95% CI: 1.01, 1.07). When limited to women only, the fully adjusted regression model (i.e., the model including independent variables, sociodemographic and health-related factors) fit the data well (Hosmer–Lemeshow Goodness of Fit p = 0.626 Model 3). Adding sociodemographic and health-related factors did not improve model fit for men (p ≤ .005).

Table 4.

Factors Associated With Falls Among Community-dwelling Older Adults With Osteoarthritis by Sex (n = 3895).

	Female (n = 2624)			Male (n = 1271)
	Model 1 Unadjusted	Model 2Model 1 + Sociodemographics	Model 3Model 2 + Other health-related issues	Model 1 Unadjusted	Model 2Model 1 + Sociodemographics	Model 3Model 2 + Other health-related issues
Pain	1.37 (1.08, 1.72)*	1.40 (1.11, 1.77)*	1.19 (0.72, 1.97)	1.20 (0.87, 1.65)	1.16 (0.84, 1.59)	0.83 (0.40, 1.70)
Sleep problem score	1.04 (0.98, 1.10)	1.04 (0.99, 1.11)	1.02 (0.91, 1.91)	1.06 (0.97, 1.16)	1.06 (0.97, 1.17)	1.08 (0.91, 1.30)
Depressive symptoms score ≥3^a (range: 0–8)	1.40 (1.05, 1.86)*	1.41 (1.04, 1.91)*	1.19 (0.91, 1.51)	1.73 (1.07, 2.82)*	1.75 (1.05, 2.93)*	0.86 (0.26, 2.82)
Sever fatigue or exhaustion	1.49 (1.18, 1.88)*	1.54 (1.19, 1.98)*	0.92 (0.62, 1.38)	2.05 (1.30, 3.22)*	1.04(1.01, 1.07)*	0.93 (0.31, 2.78)
Sociodemographics
Age		1.02 (1.00, 1.03)*	0.99 (0.95, 1.03)		1.04 (1.01, 1.07)*	1.00 (0.94, 1.08)
Race
White (Reference)
Black		0.79 (0.55, 1.13)	0.39 (0.16, 0.95)*		0.62 (0.38, 1.02)	0.92 (0.22, 3.90)
Other^b		1.05 (0.58, 1.88)	1.84 (0.45, 7.65)		0.64 (0.33, 1.23)	0.91 (0.13, 1.67)
Education
Less than high school (Reference)
High school graduate		0.92 (0.65 , 1.30)	1.63 (0.91, 2.94)		0.97 (0.53, 1.78)	0.46 (0.10, 2.16)
Some college or beyond		1.17 (0.79, 1.75)	1.65 (0.74, 3.68)		0.82 (0.45, 1.51)	0.46 (0.13, 1.67)
Geographic residence
Urban (Reference)
Suburban		1.21 (0.92, 1.58)	0.97 (0.53, 1.80)		0.85 (0.50, 1.42)	0.99 (0.35, 2.77)
Rural^c		1.18 (0.91, 1.53)	1.59 (1.01, 2.50)*		1.08 (0.71, 1.63)	0.85 (0.26, 2.79)
Health-related issues
ADL limitation (range: 0–6)
No limitation (Reference)
1–2 limitations			1.50 (0.94, 2.39)			2.57 (1.09, 6.07)*
3 or more limitations			3.51 (1.45, 8.51)*			4.42 (0.75, 26.08)
Body Mass index			1.01 (0.98, 1.04)			0.97 (0.90, 1.04)
Balance problems			2.71 (1.60, 4.60)*			5.52 (2.54, 12.03)*
Vision problems
Distal vision problems^d			0.77 (0.40, 1.49)			0.32 (0.10, 0.99)*
Near vision problems^e			1.65 (0.94, 2.91)			1.87 (0.76, 4.61)
High blood pressure			1.07 (0.72, 1.61)			0.87 (0.29, 2.36)
Diabetes			1.05 (0.68, 1.61)			1.25 (0.59, 2.66)
Cancer^f			1.84 (1.11, 3.05)*			2.37 (1.56, 5.31)*
Lung disease			0.84 (0.52, 1.35)			1.10 (0.48, 2.53)
Heart problems^g			1.30 (0.86, 1.98)			3.87 (1.86, 8.05)*
Stroke			1.78 (0.87, 3.65)			2.26 (0.88, 5.79)
Dementia			1.80 (0.46, 7.06)			1.81 (0.16, 20.14)
Opioid pain medication			1.60 (1.00, 2.54)*			0.73 (0.34. 1.59)
Sleep medicine			1.18 (0.67, 2.10)			1.01 (0.39, 2.65)
Model fit^h	p < 0.001	p < 0.001	p = 0.626	p = 0.005	p < 0.001	p < 0.001

^aThree or more symptoms indicate depression “caseness” (Turvey et al., 1999).

^bIncludes American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander.

^cReferred to as ex-urban in the Health and Retirement Study Urban-Rural Code 2016 (BEALE, 2013) (i.e., population fewer than 250,000).

^dEyesight for seeing things at a distance (0 = excellent, very good, or good; 1 = fair, or poor).

^eEyesight for seeing things up close (0 = excellent, very good, or good; 1 = fair, or poor).

^fExcluding skin cancer.

^gIncluding heart attack, coronary heart, disease, angina, and congestive heart failure.

^hHosmer–Lemeshow Goodness of Fit test (p-value).

In the final models (Model 3) adjusting for sociodemographic and health-related issues, having balance problems (female OR = 2.71, 95% CI: 1.60, 4.60; male OR = 5.52, 95% CI: 2.54, 12.03) and cancer (female OR = 1.84, 95% CI: 1.11, 3.05; male OR = 2.37, 95% CI: 1.56, 5.31) significantly increased the risk of falling for both males and females. Sex differences were notable in the final models (Model 3). Women with osteoarthritis had a higher risk of falling if they were White, compared to Black (OR = 0.39, 95% CI: 0.16–0.95), lived in a rural area (OR = 1.59, 95% CI: 1.01–2.50 compared to an urban area), had three or more ADL limitations (OR = 3.51, 95% CI: 1.45–8.51 compared to no limitations), or used opioid pain medications (OR = 1.60, 95% CI: 1.00, 2.54). While among male participants, having one to two ADL limitations (OR = 2.57, 95% CI: 1.09, 6.07 compared to no limitations) and having heart problems (OR = 3.87, 95% CI: 1.86, 8.05) significantly increased their risk of falling. On the other hand, among males, having distal vision problems lower the risk of falling (OR = 0.32, 95% CI: 0.10, 0.99).

Discussion

This study, using a nationally representative sample, highlights sex differences in risk factors of falling among community-dwelling older adults with osteoarthritis. First, four primary predictor variables (i.e., symptom variables), including pain, sleep problems, depression, and fatigue or exhaustion, did not appear as major contributors to the risk of falling among older women and men with osteoarthritis after controlling for sociodemographic and other health-related characteristics. Second, the risk of falling increased when older women and men with osteoarthritis had physical limitations (i.e., ADL limitations and balance problems) and chronic conditions (i.e., cancer, heart problems, stroke, and near vision problems), after controlling for the four primary predictor variables, sociodemographic and other health-related characteristics. Finally, sex differences in factors associated with falls were found. Among older women with osteoarthritis, race (i.e., White participants compared to Black participants), geographic residence (rural residence compared to urban residence), and use of opioid medication appeared to increase the risk of falling, while, among older men with osteoarthritis, having heart problems and no distal vision problems were associated with increased risk of falling. The fully adjusted model including the primary predictor variables as well as sociodemographic and health-related covariates, adequately explain fall risk in women with osteoarthritis. The findings highlight distinct sex-specific risk factors for falls among older adults with osteoarthritis.

Our findings on physical capacity and chronic conditions as risk factors for falls are consistent with the previous studies of adults with osteoarthritis. Older adults with osteoarthritis were more likely to report functional limitations (Manlapaz et al., 2019), balance impairment (Manlapaz et al., 2019), and multiple chronic conditions (Manlapaz et al., 2019; Ofori-Asenso et al., 2021). This study confirmed that balance impairment is a strong predictor of falls, with the largest odds of falling regardless of sex. Balance impairment as a risk factor for falls has been addressed in previous research, including population-based studies using data from the Osteoarthritis Initiative (OAI) (Ofori-Asenso et al., 2021) and the National Health and Aging Trends Study (Yoshikawa and Fortinsky, 2024) as well as a systematic review of adults with knee osteoarthritis (Manlapaz et al., 2019). While this study identified functional limitations as another predictor of falling among men and women, the degrees of functional limitations slightly differed by sex. Higher levels of physical limitations (i.e., 3 or more limitations) were associated with increased fall risks in women, while lower levels of physical limitations (i.e., 1–2 limitations) were associated with increased fall risks in men. Previous studies have addressed a higher number of comorbidities as a risk factor for falls, but this study expanded the scope by identifying specific comorbid conditions. Our findings showed an increased risk of falls among older adults with osteoarthritis who also had cancer. Future studies should further investigate the magnitude of fall risk and its mechanism among older adults with cancer and osteoarthritis, as these chronic conditions are increasingly common due to aging, impacting functional capacity and the quality of life (Pilleron et al., 2019; Steinmetz et al., 2023). When segregating analysis by sex, heart problems appeared to significantly increase the risk of falling only among men with osteoarthritis. Due to limited physical functioning, individuals with osteoarthritis are at risk of cardiovascular disease (Corsi et al., 2018). Having both osteoarthritis and cardiovascular disease was likely to magnify the risk of falling.

The results of this study highlight sex-specific risk factors for falls. Among women with osteoarthritis, the use of opioid pain medication and rural residence increased their risk of falling. Among men with osteoarthritis, lower levels of distal vision problems increased their risk of falling. Prior research (Lo-Ciganic et al., 2017; Taqi et al., 2021; van Schoor et al., 2020) reported opioid use as a risk factor for falls, yet it did not thoroughly investigate sex differences in the association. In addition, this study highlighted an increased risk of falling among rural women with osteoarthritis. Given that osteoarthritis, the most common type of arthritis (Barbour et al., 2017), is highly prevalent in rural areas, affecting approximately 1 in 3 adults and older women (Boring et al., 2017), there is a need to further explore the impact of geographic residence and healthcare access on fall risks. While vision problems are known risk factors for falls (CDC, 2024b), unexpectedly, the present study found that good distal vision (i.e., excellent, very good, or good) increased the risk of falling among men with osteoarthritis. With limited studies examining the impact of distal vision programs on falls among older adults with osteoarthritis (e.g., Quach & Burr, 2018), these findings are puzzling. Sex-specific behaviors associated with vision problems may contribute to the observed sex differences in fall risks. Future studies should further investigate the potential correlation between sex-specific health behaviors, such as physical activity and vision problems, as well as their risk of falling in older adults with osteoarthritis.

The major strength of this study lies in its focus on sex differences. The analysis allowed us to elucidate the risk of falling based on sex, thereby filling the research gaps in previous literature. Moreover, unlike most previous studies that assessed the fall risk associated with chronic conditions using a number/count of diseases without considering different impacts from different conditions (e.g., Lo-Ciganic et al., 2017; Ofori-Asenso et al., 2021), the present study addressed disease-specific impact on fall risks and provided additional insights into fall prevention. Furthermore, this study used a nationally representative sample, enhancing the investigation of trends and risks for increased generalizability. Nonetheless, this study has limitations. First, the scope of this secondary data analysis was restricted to available sociodemographic variables and health information gathered from the Health and Retirement Study (HRS). Therefore, the present study was unable to include other risk factors, such as fear of falling (Payette et al., 2016). Second, the selection of symptoms was limited to the availability of the data. There might be other symptoms associated with fall but were not considered in the study. In addition, most of the symptom measures were single-item or abbreviated and thus unable to capture multi-manifested nature of the symptoms. Third, participants self-reported their fall incidents over the previous two years. With such an extended timeframe for recalling fall incidents, there might be recall bias (Althubaiti, 2016). Last, this study used the 2013 Beale Rural-Urban Continuum Code system (U.S. Department of Agriculture Economic Research Service, 2026) to define urban, suburban, and rural (i.e., ex-urban or hereinafter) participants’ residency. Given the existence of multiple rural-urban classification schemes (Long et al., 2021), interpretation of the findings must be done with caution.

Despite these limitations, our study using a nationally representative sample contributes to understanding sex-related risk factors for falls in older adults with osteoarthritis. The findings suggest sex-specific fall prevention strategies, including the provision of rural programs and opioid education for women, as well as increased awareness and fall education for men with heart problems. Sex differences should be considered in fall prevention programs to help promote active living among older men and women with osteoarthritis. Furthermore, this study provides additional evidence about disease-specific influence on the risk of falling, such as cancer for both men and women and heart problems among older men with osteoarthritis. These findings underscore the necessity of taking into account individuals’ sex and chronic disease when providing counseling to address the risk of falling in older adults with osteoarthritis. More research should investigate sex- and chronic disease-related risk factors for falls. Such efforts would help inform and refine fall prevention strategies that better fit individual sociodemographic and health conditions.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The HRS (Health and Retirement Study) is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. This research was supported by the Texas Woman’s University Small Grant Program.

Ethical Statement

ORCID iD

Aya Yoshikawa

Data Availability Statement

This study utilized the 2016 RAND HRS Fat File, which is publicly accessible through the HRS data downloads website at via user registration.

References

Allen

K. D.

Renner

J. B.

Devellis

Helmick

C. G.

Jordan

J. M.

(2008). Osteoarthritis and sleep: The johnston county osteoarthritis project. The Journal of Rheumatology, 35(6), 1102–1107. https://www.jrheum.org/content/35/6/1102.abstract

Althubaiti

(2016). Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 9, 211–217. https://doi.org/10.2147/JMDH.S104807

Barbour

Helmick

Boring

Brady

(2017). Vital signs: Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation — United States, 2013–2015.

Boring

Hootman

Liu

Theis

Murphy

Barbour

Helmick

Brady

Croft

(2017). Prevalence of arthritis and arthritis-attributable activity limitation by urban-rural county classification — United States, 2015.

Campbell

S. S.

Gillin

J. C.

Kripke

D. F.

Erikson

Clopton

(1989). Gender differences in the circadian temperature rhythms of healthy elderly subjects: Relationships to sleep quality. Sleep, 12(6), 529–536. https://doi.org/10.1093/sleep/12.6.529

Centers for Disease Control and Prevention . (2024a). Facts about falls. https://www.cdc.gov/falls/data-research/facts-stats/index.html

Centers for Disease Control and Prevention . (2024b). Osteoarthritis. https://www.cdc.gov/arthritis/osteoarthritis/index.html

Corsi

Alvarez

Callahan

L. F.

Cleveland

R. J.

Golightly

Y. M.

Jordan

J. M.

Nelson

A. E.

Renner

Tsai

Allen

K. D.

(2018). Contributions of symptomatic osteoarthritis and physical function to incident cardiovascular disease. BMC Musculoskeletal Disorders, 19(1), 393. https://doi.org/10.1186/s12891-018-2311-4

De Baets

Runge

Labie

Mairesse

Malfliet

Verschueren

Van Assche

de Vlam

Luyten

F. P.

Coppieters

Herrero Babiloni

Martel

M. O.

Lavigne

G. J.

Nijs

(2023). The interplay between symptoms of insomnia and pain in people with osteoarthritis: A narrative review of the current evidence. Sleep Medicine Reviews, 70, Article 101793. https://doi.org/10.1016/j.smrv.2023.101793

10.

Ehlers

Kupfer

(1997). Slow-wave sleep: Do young adult men and women age differently? Journal of Sleep Research, 6(3), 211–215. https://doi.org/10.1046/j.1365-2869.1997.00041.x

11.

Eid

R. S.

Gobinath

A. R.

Galea

L. A. M.

(2019). Sex differences in depression: Insights from clinical and preclinical studies. Progress in Neurobiology, 176, 86–102. https://doi.org/10.1016/j.pneurobio.2019.01.006

12.

Fukuda

Honma

Kohsaka

Kobayashi

Sakakibara

Kohsaka

Koyama

(1999). Gender difference of slow wave sleep in middle aged and elderly subjects. Psychiatry and Clinical Neurosciences, 53(2), 151–153. https://doi.org/10.1046/j.1440-1819.1999.00508.x

13.

Glymour

M. M.

Yen

J. J.

Kosheleva

Moon

J. R.

Capistrant

B. D.

Patton

K. K.

(2012). Elevated depressive symptoms and incident stroke in Hispanic, African-American, and White older Americans. Journal of Behavioral Medicine, 35(2), 211–220. https://doi.org/10.1007/s10865-011-9356-2

14.

Hackney

A. J.

Klinedinst

N. J.

Resnick

Renn

Fiskum

(2019). A review and synthesis of correlates of fatigue in osteoarthritis. International Journal of Orthopaedic and Trauma Nursing, 33, 4–10. https://doi.org/10.1016/j.ijotn.2019.01.003

15.

Harris

Strotmeyer

E. S.

Sharma

Kwoh

C. K.

Brach

J. S.

Boudreau

Cauley

J. A.

(2022). The association between severity of radiographic knee OA and recurrent falls in middle and older aged adults: The osteoarthritis initiative. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 78(1), 97–103. https://doi.org/10.1093/gerona/glac050

16.

Hume

VAan

Watson

(1998). A field study of age and gender differences in habitual adult sleep. Journal of Sleep Research, 7(2), 85–94. https://doi.org/10.1046/j.1365-2869.1998.00103.x

17.

Knox

P. J.

Coyle

P. C.

Pugliese

J. M.

Pohlig

R. T.

Sions

J. M.

Hicks

G. E.

(2021). Hip osteoarthritis signs and symptoms are associated with increased fall risk among community-dwelling older adults with chronic low back pain: A prospective study. Arthritis Research & Therapy, 23(1), 71. https://doi.org/10.1186/s13075-021-02455-5

18.

Kutner

Nachtsheim

Neter

(2005). Applied linear statistical models (5th ed.). McGraw-Hill/Irwin.

19.

Laghousi

Jafari

Nikbakht

Nasiri

Shamshirgaran

Aminisani

(2016). Gender differences in health-related quality of life among patients with colorectal cancer. Journal of Gastrointestinal Oncology, 10(3), 453–461. https://doi.org/10.21037/jgo.2019.02.04

20.

Lo-Ciganic

W. H.

Floden

Lee

J. K.

Ashbeck

E. L.

Zhou

Chinthammit

Purdy

A. W.

Kwoh

C. K.

(2017). Analgesic use and risk of recurrent falls in participants with or at risk of knee osteoarthritis: Data from the osteoarthritis initiative. Osteoarthritis and Cartilage, 25(9), 1390–1398. https://doi.org/10.1016/j.joca.2017.03.017

21.

Long

J. C.

Delamater

P. L.

Holmes

G. M.

(2021). Which definition of rurality should I use? The relative performance of 8 federal rural definitions in identifying rural-urban disparities. Medical Care, 59(Suppl 5), S413–S419. https://doi.org/10.1097/mlr.0000000000001612

22.

Manlapaz

D. G.

Sole

Jayakaran

Chapple

C. M.

(2019). Risk factors for falls in adults with knee osteoarthritis: A systematic review. PM & R: The Journal of Injury, Function, and Rehabilitation, 11(7), 745–757. https://doi.org/10.1002/pmrj.12066

23.

Meers

Stout-Aguilar

Nowakowski

(2019). Chapter 3 - sex differences in sleep health. In Grandner

M. A.

(Ed.), Sleep and health (pp. 21–29). Academic Press. https://doi.org/10.1016/B978-0-12-815373-4.00003-4

24.

Min

Nadpara

P. A.

Slattum

P. W.

(2016). The association between sleep problems, sleep medication use, and falls in community-dwelling older adults: Results from the health and retirement study 2010. Journal of Aging Research, 2016(1), Article 3685789. https://doi.org/10.1155/2016/3685789

25.

Munch

Harrison

S. L.

Barrett-Connor

Lane

N. E.

Nevitt

M. C.

Schousboe

J. T.

Stefanick

Cawthon

P. M.

(2015). Pain and falls and fractures in community-dwelling older men. Age and Ageing, 44(6), 973–979. https://doi.org/10.1093/ageing/afv125

26.

Ofori-Asenso

Ackerman

I. N.

Soh

S.-E.

(2021). Prevalence and correlates of falls in a middle-aged population with osteoarthritis: Data from the Osteoarthritis Initiative. Health & Social Care in the Community, 29(2), 436–444. https://doi.org/10.1111/hsc.13103

27.

Overman

C. L.

Kool

M. B.

Da Silva

J. A. P.

Geenen

(2016). The prevalence of severe fatigue in rheumatic diseases: An international study. Clinical Rheumatology, 35(2), 409–415. https://doi.org/10.1007/s10067-015-3035-6

28.

Pana

Sourtzi

Kalokairinou

Pastroudis

Chatzopoulos

S.-T.

Velonaki

V. S.

(2021). Association between self-reported or perceived fatigue and falls among older people: A systematic review. International Journal of Orthopaedic and Trauma Nursing, 43, Article 100867. https://doi.org/10.1016/j.ijotn.2021.100867

29.

Payette

Bélanger

Léveillé

Grenier

(2016). Fall-related psychological concerns and anxiety among community-dwelling older adults: Systematic review and meta-analysis. PLoS One, 11(4), Article e0152848. https://doi.org/10.1371/journal.pone.0152848

30.

Pickering

M. E.

Chapurlat

Kocher

Peter‐Derex

(2016). Sleep disturbances and osteoarthritis. Pain practice: The Official Journal of World Institute of Pain, 16(2), 237–244. https://doi.org/10.1111/papr.12271

31.

Pilleron

Sarfati

Janssen-Heijnen

Vignat

Ferlay

Bray

Soerjomataram

(2019). Global cancer incidence in older adults, 2012 and 2035: A population-based study. International Journal of Cancer, 144(1), 49–58. https://doi.org/10.1002/ijc.31664

32.

Quach

L. T.

Burr

J. A.

(2018). Arthritis, depression, and falls among community-dwelling older adults: Evidence from the health and retirement study. Journal of applied gerontology: The Official Journal of the Southern Gerontological Society, 37(9), 1133–1149. https://doi.org/10.1177/0733464816646683

33.

Redline

Kirchner

H. L.

Quan

S. F.

Gottlieb

D. J.

Kapur

Newman

(2004). The effects of age, sex, ethnicity, and sleep-disordered breathing on sleep architecture. Archives of Internal Medicine, 164(4), 406–418. https://doi.org/10.1001/archinte.164.4.406

34.

Sharma

Kudesia

Shi

Gandhi

(2016). Anxiety and depression in patients with osteoarthritis: Impact and management challenges. Open Access Rheumatology: Research and Reviews, 8(null), 103–113. https://doi.org/10.2147/OARRR.S93516

35.

Soh

S.-E.

Barker

A. L.

Morello

R. T.

Ackerman

I. N.

(2020). Applying the international classification of functioning, disability and health framework to determine the predictors of falls and fractures in people with osteoarthritis or at high risk of developing osteoarthritis: Data from the osteoarthritis initiative. BMC Musculoskeletal Disorders, 21(1), 138. https://doi.org/10.1186/s12891-020-3160-5

36.

Soh

Tiemeier

Kawachi

Berkman

L. F.

Kubzansky

L. D.

(2022). Eight-year depressive symptom trajectories and incident stroke: A 10-year follow-up of the HRS (health and retirement study). Stroke, 53(8), 2569–2576. https://doi.org/10.1161/STROKEAHA.121.037768

37.

Steffick

(2000). Documentation of affective functioning measures in the Health and Retirement Study. https://hrs.isr.umich.edu/sites/default/files/biblio/dr-005_0.pdf.

38.

Steinmetz

J. D.

Culbreth

G. T.

Haile

L. M.

Rafferty

Fukutaki

K. G.

Cruz

J. A.

Smith

A. E.

Vollset

S. E.

Brooks

P. M.

Cross

Woolf

A. D.

Hagins

Abbasi-Kangevari

Abedi

Ackerman

I. N.

Amu

Antony

Arabloo

Kopec

J. A.

(2023). Global, regional, and national burden of osteoarthritis, 1990–2020 and projections to 2050: A systematic analysis for the global burden of disease study 2021. The Lancet Rheumatology, 5(9), e508–e522. https://doi.org/10.1016/S2665-9913(23)00163-7

39.

Taqi

Gran

Knaggs

R. D.

(2021). Current use of analgesics and the risk of falls in people with knee osteoarthritis: A population-based cohort study using primary care and hospital records. Osteoarthritis and Cartilage Open, 3(2), Article 100165. https://doi.org/10.1016/j.ocarto.2021.100165

40.

The Health and Retirement Study . (2008). Design history. https://hrs.isr.umich.edu/sites/default/files/biblio/DesignHistory.pdf

41.

Tschon

Contartese

Pagani

Borsari

Fini

(2021). Gender and sex are key determinants in osteoarthritis not only confounding variables. A systematic review of clinical data. Journal of Clinical Medicine, 10(14), 3178. https://doi.org/10.3390/jcm10143178. https://www.mdpi.com/2077-0383/10/14/3178

42.

Turvey

C. L.

Wallace

R. B.

Herzog

(1999). A revised CES-D measure of depressive symptoms and a DSM-based measure of major depressive episodes in the elderly. International Psychogeriatrics, 11(2), 139–148. https://doi.org/10.1017/s1041610299005694

43.

U.S. Department of Agriculture Economic Research Service . (2026). Rural–urban continuum codes. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/.

44.

Valko

P. O.

Siddique

Linsenmeier

Zaugg

Held

Hofer

(2014). Prevalence and predictors of fatigue in glioblastoma: A prospective study. Neuro-Oncology, 17(2), 274–281. https://doi.org/10.1093/neuonc/nou127

45.

van Schoor

N. M.

Dennison

Castell

M. V.

Cooper

Edwards

M. H.

Maggi

Pedersen

N. L.

van der Pas

Rijnhart

J. J. M.

Lips

Deeg

D. J. H.

EPOSA research group . (2020). Clinical osteoarthritis of the hip and knee and fall risk: The role of low physical functioning and pain medication. Seminars in Arthritis and Rheumatism, 50(3), 380–386. https://doi.org/10.1016/j.semarthrit.2020.02.006

46.

Walsleben

J. A.

Kapur

V. K.

Newman

A. B.

Shahar

Bootzin

R. R.

Rosenberg

C. E.

O'Connor

Nieto

F. J.

(2004). Sleep and reported daytime sleepiness in normal subjects: The sleep heart health study. Sleep, 27(2), 293–298. https://doi.org/10.1093/sleep/27.2.293

47.

Wang

(2022). Depression in osteoarthritis: Current understanding. Neuropsychiatric Disease and Treatment, 18, 375–389. https://doi.org/10.2147/NDT.S346183

48.

Wojcieszek

Kurowska

Majda

Liszka

Gądek

(2022). The impact of chronic pain, stiffness and difficulties in performing daily activities on the quality of life of older patients with knee osteoarthritis. International Journal of Environmental Research and Public Health, 19(24), Article 16815. https://doi.org/10.3390/ijerph192416815. https://www.mdpi.com/1660-4601/19/24/16815

49.

Wolfe

Hawley

D. J.

Wilson

(1996). The prevalence and meaning of fatigue in rheumatic disease. The Journal of Rheumatology, 23(8), 1407–1417. https://europepmc.org/abstract/MED/8856621

50.

Xiong

Chen

Liu

Deng

Cheng

Hao

(2023). Anterior knee pain as a potential risk factor for falls in older adults: Insights from the osteoarthritis initiative data. BMC Public Health, 23(1), 2288. https://doi.org/10.1186/s12889-023-17237-8

51.

Yoshikawa

Fortinsky

R. H.

(2024). Pain medication frequency and fall risk among community-dwelling older adults with arthritis. Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, 44(1), 35–43. https://doi.org/10.1177/07334648241261425