Relationship Between Lower Limb Muscle Strength and Future Falls Among Community-Dwelling Older Adults With No History of Falls: A Prospective 1-Year Study

Abstract

Objective: To investigate whether lower limb muscle strength could be a risk factor for the first fall among nonfaller community-dwelling older adults. Method: Hip, knee, and ankle peak torque (PT) was measured with an isokinetic dynamometer in 101 older adults with no history of falls in the previous year. Next, the authors followed up the participants on a monthly basis by telephone contact to determine the occurrence of fall episodes over a period of 1 year. Multivariate logistic regression adjusted for confounding variables was applied to assess the relationship between falls and lower limb PT. Results: there was no association between lower limb PT and future falls (p > .05). Conclusion: Based on these results, it is important to identify other factors that predispose older adults with no history of falls to falling for the first time, so that early and effective preventive strategies may be elaborated.

Keywords

falls prevention risk factors nonfallers first fall muscle weakness

Introduction

Falls are considered to be a public health problem as they represent one of the major causes of morbidity and mortality among older adults (Orr, 2010; Sherrington et al., 2019). Among the consequences of falls are physical (fracture, loss of mobility), psychological (fear of falling, depression), social (isolation, dependence, early institutionalization), and economic (hospitalization, medication and rehabilitation costs) losses (Orr, 2010; Salvà et al., 2004; Sherrington et al., 2019). The estimate is that one third of independent community-dwelling older adults will fall within a period of 1 year (Orr, 2010; Salvà et al., 2004; Sherrington et al., 2019; Tinetti et al., 1988).

As fall is a multifactorial phenomenon, several factors appear to contribute to an increased risk of falling such as age, balance and gait problems, muscle weakness, sensory losses (visual, somatosensory, and vestibular), cognitive and psychological disorders, and environmental factors (e.g., inadequate lighting, absence of grab bars; Rubenstein & Josephson, 2006). However, one of the factors more strongly associated with an increased risk of falling is a history of previous falls (Ambrose et al., 2013; Gerdhem et al., 2005; Rogers et al., 2003; Rubenstein & Josephson, 2006) because about 50% of older adults who fell will fall again (Mignardot et al., 2014; Pijnappels et al., 2008). Thus, avoiding or delaying the first fall may be a way to prevent new falls among older adults (Mignardot et al., 2014). On this basis, it is important to identify specific markers that are significantly associated with an increased risk of the first fall, which would permit the prescription of an objective and effective intervention to prevent the first fall (Mignardot et al., 2014).

Muscle strength decreases with the aging process, and this decline can negatively affect the functionality and gait of older adults (Rogers et al., 2003). However, there are conflicting results in the literature regarding the direct and independent relationship of lower limb muscle strength with falls (Orr, 2010). Thus, the objective of the present study was to investigate whether lower limb muscle strength can be a risk factor for the first fall among community-dwelling nonfaller older adults. The hypothesis of the present study was that the muscle strength of hip, knee, and ankle may be negatively associated with the occurrence of a first fall among older adults with no history of falls in the previous year.

Method

Study Design and Sample

This was an observational, longitudinal prospective study conducted in 101 community-dwelling older adults aged 60 to 80 years, of both sexes, with no history of falls during the 12 months preceding the initial evaluation. The Research Ethics Committee involving Human Beings approved the study (CAAE: 62209916.5.0000.5440) and all participants gave written informed consent to participate. The study was carried out at the University of São Paulo (Ribeirão Preto). The participants were recruited by direct contact with the community (direct invitation followed by the delivery of explanatory folders by researchers in public squares, churches, and community centers for the older adults) and with events offered to older adults by the University of São Paulo—Ribeirão Preto (convenience sample). Eligibility criteria were older adults aged 60 or above, of both sexes, independent and autonomous, living in the community. Ineligibility criteria were history of falls in the previous year; presence of musculoskeletal or neurological conditions that might interfere with lower limb muscle function or increase the risk of falls such as a report of daily pain, prostheses, recent fractures, symptomatic orthopedic disorders of the lower limbs or spine (such as osteoarthritis or tendinitis), Parkinson’s disease, motor sequelae of a stroke; complaints of dizziness; visual complaints that impair daily activities; deficit of protective foot sensitivity (Feng et al., 2009); cardiovascular or metabolic conditions that would contraindicate physical activity; and low score on the 10-point Cognitive Screener (10-CS) according to educational level (Apolinario et al., 2016), as the participants should understand and correctly execute the commands for the physical tests and report monthly episodes of falls. After inclusion in the study, the participants could be excluded if they did not complete the proposed tests or did not respond to monthly monitoring by telephone contact over a period of 12 months after the initial evaluation.

Procedures

The study covered the period from March 2018 to July 2019, consisting of an initial evaluation and monthly monitoring by telephone contact over a period of 12 months to determine the occurrence of falls.

The initial evaluation occurred on 2 days separated by an interval of 2 to 7 days. Sample characterization data were collected on the first day and the participants familiarized themselves with isometric contractions on the isokinetic dynamometer (Biodex System 4 Pro, New York, USA). The variables of interest for sample characterization were age, sex, number of comorbidities (self-report), number of medications being used, level of physical activity (International Physical Activity Questionnaire—IPAQ; Matsudo et al., 2001), weight, height, and body mass index (BMI). In addition, as studies have indicated that impairment of both functionality and balance performances may be related to the risk of future falls (Alexandre et al., 2012; Michikawa et al., 2009), the single-leg stance (SS) and Timed Up and Go test (TUG) were performed to characterize the sample regarding static balance (SS) and mobility (TUG). The participants performed the SS 3 times with the dominant lower limb for 30 s as described by Porto, Freire Júnior, et al. (2019) to obtain the mean time test during the one-leg support. For the TUG, the participants were instructed to start from a sitting position with feet placed on the floor and back resting on the chair. After the verbal command to begin, the participants were supposed to stand up, to walk 3 m at their habitual gait speed, to turn 180°, to walk back to the chair and sit down (Alexandre et al., 2012). The participants repeated the test 3 times and the mean time spend to complete the test was calculated.

On the second day of the initial evaluation, the participants performed maximum voluntary isometric contractions on an isokinetic dynamometer (Biodex System 4 Pro) to obtain the peak torque (PT) of hip flexors, extensors, abductors and adductors, knee flexors and extensors, ankle dorsiflexors, and plantar flexors.

Before the PT evaluations, the equipment was duly calibrated according to manufacturer recommendations and the participants performed 5-min warm-up on a bicycle ergometer. The authors evaluated only the dominant lower limb based on its preferred use for kicking a ball. The protocol of evaluation of each muscle group consisted of three maximum voluntary isometric contractions of 5 s duration and with 30 s of rest between them. The participants were instructed to perform the contractions as strong as possible, being constantly stimulated with verbal encouragement. To optimize the evaluation, the order of execution of the contractions was hip-knee-ankle for half the sample and the inverse order for the other half. The isometric PT of each muscle group was normalized by body mass to reflect the functional performance of the muscles according to the body mass of the individual (LaRoche et al., 2010). Isometric contractions were chosen because they are safer and more reproducible among older adults who may have limitations of range of motion, the presence of pain, and greater compensations in concentric contractions (Arnold et al., 2010). The isokinetic dynamometer was positioned as described by Porto, Nakaishi, et al. (2019) for the assessment of each muscle group. We determined the test–retest reliability of the assessment of each muscle group considered in the present study by calculating the intraclass correlation coefficient (ICC) using the two-way mixed effects model, considering the mean PT on the first and second day of evaluation in part of the sample (n = 30, six men and 24 women). Excellent reliability was obtained for all muscle groups assessed: ICC = 0.91, 95% confidence interval (CI) = [0.78, 0.96] for hip flexors; ICC = 0.87; 95% CI = [0.70, 0.94] for hip extensors; ICC = 0.96, 95% CI = [0.93, 0.98] for hip abductors; ICC = 0.88, 95% CI = [0.75, 0.94] for hip adductors; ICC = 0.96, 95% CI = [0.92, 0.98] for knee flexors; ICC = 0.96, 95% CI = [0.92, 0.98] for knee extensors; ICC = 0.77, 95% CI = [0.53, 0.89] for plantar flexors; and ICC = 0.94, 95% CI = [0.88, 0.97] for dorsiflexors.

After the initial evaluation, we monitored the participants monthly by telephone contact over a period of 12 months to determine the occurrence of fall episodes. For the present study, the definition of a fall was any unintentional event that would result in a change of position of an individual toward a lower level than his initial position (Buchner et al., 1997). This concept was duly explained to the participants on the first day of evaluation so that falls could be properly reported along the study.

Statistical Analysis

All statistical analyses were performed with the aid of SPSS software (Version 17.0—SPSS Inc.; IBM, Chicago, IL, USA), with the level of significance set at 5% (p ≤ .05). Data were reported as mean, standard deviation, and frequency. The characteristics of older participants who did not fall (CG) and who fell (FG) over a prospective period of 1 year were compared by the t test for continuous variables, by the Mann–Whitney test for ordinal variables, and by the chi-square test for nominal variables. We determined the association of the dependent variable (fall episodes during the 12 months of the study) with the independent variables (PT of the lower limb) by multivariate binary logistic regression. To include in the model only covariates that showed a significant relationship with future falls, we performed univariate regression analysis and included in the model only those variables showing p < .20. Thus, the confounding variables included in the multivariate logistic regression model were age, sex, number of comorbidities, number of medications, height, and TUG. The statistical power of 98% was calculated considering R² = .16, Error Type I of 0.05 and the n size used (n = 101), using the G*Power software, Version 3.1.92 (Universitat Kiel, Kiel, Germany).

Results

The flowchart in Figure 1 illustrates the process of sample selection.

Figure 1.

Flowchart showing the sample selection process in the present study.

The sample was predominantly female (77.2%), relatively young (mean age 67.6 ± 5), and with a moderate level of physical activity (66.3%). Regarding the comparison between the FG and CG, FG was older (p = .003) and had a higher number of comorbidities (p = .030) than CG (Table 1).

Table 1.

Sample Characterization.

Variables	Total sample (n = 101)	CG (n = 72)	FG (n = 29)	p value
Age (years)	67.6 (5.0)	66.7 (4.5)	70.0 (5.7)	.003 *
Males, n (%)	23 (22.8)	19 (26.3)	4 (13.7)	.201
Number of comorbidities	2.1 (1.5)	1.9 (1.4)	2.6 (1.6)	.030 *
Number of medications	3.1 (2.5)	2.8 (2.5)	3.8 (2.5)	.081
Level of physical activity, n (%)				.377
Low	25 (24.8)	17 (23.6)	8 (27.5)
Moderate	67 (66.3)	47 (65.2)	20 (68.9)
High	9 (8.9)	8 (11.1)	1 (3.4)
Height (m)	1.57 (0.08)	1.58 (0.08)	1.55 (0.07)	.119
Weight (kg)	70.2 (14.8)	70.4 (16.1)	69.7 (11.4)	.822
BMI (kg m⁻²)	28.0 (4.3)	27.8 (4.5)	28.7 (4.1)	.342
Single-leg stance test (s)	20.7 (9.7)	21.4 (9.5)	18.8 (10.1)	.244
Timed Up and Go test (s)	8.9 (1.9)	8.7 (1.7)	9.4 (2.3)	.122
PT (Nm kg ⁻¹)
Knee flexors	0.80 (0.29)	0.84 (0.29)	0.72 (0.27)	.065
Knee extensors	1.57 (0.47)	1.61 (0.48)	1.48 (0.44)	.236
Plantar flexors	0.88 (0.40)	0.93 (0.42)	0.77 (0.33)	.083
Dorsiflexors	0.37 (0.11)	0.37 (0.11)	0.38 (0.11)	.823
Hip flexors	0.53 (0.20)	0.55 (0.21)	0.48 (0.17)	.157
Hip extensors	1.08 (0.40)	1.10 (0.39)	1.03 (0.43)	.433
Hip abductors	0.76 (0.23)	0.78 (0.24)	0.72 (0.20)	.289
Hip adductors	0.72 (0.26)	0.73 (0.27)	0.69 (0.25)	.504

Note. Data are reported as mean (standard deviation) and frequency. CG = nonfaller older adults; FG = faller older adults; BMI = body mass index; PT = peak torque.

p < .05 according to the t test.

Table 2 presents the characterization of the falls occurring in part of the sample (n = 29) over a prospective period of 12 months. The proportion of older adults who fell during a follow-up of 1 year (28.71%) agreed with that previously reported in the literature (Orr, 2010; Salvà et al., 2004; Sherrington et al., 2019; Tinetti et al., 1988). In addition, 31.03% of these fallers became recurrent fallers over a period of 1 year.

Table 2.

Characterization of the Falls That Occurred Over the 12 Prospective Months (n = 29).

Total number of falls	41
Single fallers, n (%)	20 (68.96%)
Recurrent fallers (≥2 falls), n (%)	09 (31.03%)
Causes of falls
Stumbling, n (%)	10 (24.39%)
Slipping, n (%)	09 (21.95%)
Loss of balance while walking, n (%)	08 (19.51%)
Climbing or descending stairs/steps, n (%)	05 (12.19%)
Transfers, n (%)	04 (9.75%)
Squatting, n (%)	02 (4.87%)
Others, n (%)	03 (7.31%)
Site of falls
At home, n (%)	24 (58.53%)
Outside the home, n (%)	17 (41.46%)

Note. Data are reported as absolute values (frequency).

Table 3 shows that there was no association between lower limb PT (hip, knee, and ankle) and future falls. There was also no association between future falls and the clinical tests (SS and TUG). The only variables showing a positive relationship with future falls by univariate analysis were age (p = .005) and number of comorbidities (p = .034).

Table 3.

Binary Logistic Regression of the Relationship of Lower Limb Peak Torque and Confounding Variables With the Occurrence of Future Falls (n = 101).

Independent variables (PT)	Main effects
	Unadjusted model			Adjusted model^a
	β	OR (95% CI)	p value	β	OR (95% CI)	p value
Knee flexors (Nm kg ⁻¹)	−1.41	0.24 [0.04, 1.21]	.080	−0.01	0.98 [0.10, 8.81]	.986
Knee extensors (Nm kg ⁻¹)	−0.56	0.56 [0.22, 1.45]	.233	0.60	1.83 [0.49, 6.80]	.365
Plantar flexors (Nm kg ⁻¹)	−1.02	0.36 [0.10, 1.19]	.095	−0.39	0.67 [0.15, 2.88]	.594
Dorsiflexors (Nm kg ⁻¹)	0.21	1.24 [0.02, 58.66]	.912	2.55	12.81 [0.11, 1,381.86]	.285
Hip flexors (Nm kg ⁻¹)	−1.88	0.15 [0.01, 1.72]	.128	−0.61	0.54 [0.03, 9.69]	.677
Hip extensors (Nm kg ⁻¹)	−0.40	0.66 [0.21, 2.06]	.480	0.05	1.05 [0.29, 3.70]	.936
Hip abductors (Nm kg ⁻¹)	−1.22	0.29 [0.03, 2.22]	.235	−0.29	0.74 [0.07, 7.24]	.798
Hip adductors (Nm kg ⁻¹)	−0.82	0.44 [0.08, 2.42]	.346	−0.14	0.86 [0.13, 5.77]	.884
Covariables
Age (years)	0.13	1.14 [1.04, 1.24]	.005 *
Sex	−0.80	0.44 [0.13, 1.45]	.180
Number of comorbidities	0.31	1.36 [1.02, 1.81]	.034 *
Number of medications	0.14	1.15 [0.97, 1.36]	.086
Level of physical activity	−0.37	0.68 [0.31, 1.49]	.345
Height (m)	−4.51	0.01 [0.00, 3.31]	.121
Weight (kg)	−0.00	0.99 [0.96, 1.02]	.819
BMI (kg m⁻²)	0.04	1.04 [0.95, 1.15]	.340
Single-leg stance test (s)	−0.02	0.97 [0.93, 1.01]	.227
Timed Up and Go test (s)	0.19	1.21 [0.97, 1.50]	.081

Note. PT = peak torque; OR = odds ratio; CI = confidence interval; BMI = body mass index.

Model adjusted for age, sex, number of comorbidities, number of medication, height, and Timed Up and Go test.

p < .05 according to the unadjusted logistic regression analysis.

Discussion

The present study demonstrated that lower limb muscle strength (hip, knee, and ankle) was not directly related to the occurrence of the first fall among community-dwelling nonfaller older adults. Also, there were no differences in lower limb muscle strength between FG and CG. The influence of age and number of comorbidities on the incidence of falls observed by univariate analysis agreed with previous studies (Heesch et al., 2008; Paliwal et al., 2017; Rubenstein & Josephson, 2006). Although we did not detect prospective studies that suggested a cut-off score for lower limb muscle strength to prevent falls, the PT obtained in the present study was close to the isometric PT of cross-sectional studies that compared fallers (FG) and nonfallers (CG), with a variation of 0.01 to 0.17 Nm kg⁻¹ between the studies. In the study by LaRoche et al. (2010), the PT (Nm kg^-1) of CG and FG, respectively, was 0.72 (±0.25) and 0.59 (±0.53) for knee flexors; 1.72 (±0.56) and 1.49 (±0.46) for knee extensors; 1.04 (±0.27) and 0.76 (±0.14) for plantar flexors; 0.38 (±0.09) and 0.32 (±0.06) for dorsiflexors. In the study by Morcelli et al. (2016), the PT (Nm kg⁻¹) of CG and FG, respectively, was 0.82 (±0.15) and 0.72 (±0.22) for hip abductors; 0.57 (±0.14) and 0.52 (±0.19) for hip adductors. We did not find any studies for comparison of the isometric strength of hip extensors and flexors between FG and CG.

Regarding the cause–effect relationship between lower limb muscle strength and future falls, there are still conflicting results in the literature (Orr, 2010). Several factors contribute to these conflicting results, impairing the identification of the risk factors for the occurrence of the first fall among nonfaller older adults. First of all, many studies assess lower limb muscle strength in an indirect manner by means of grip strength or functional tests, which may lead to divergent conclusions. Schwartz et al. (1999), for example, reported in a prospective study that community-dwelling older adults with lower grip strength or who performed the sit-to-stand test at very low or very high speed had a greater risk of falling, although the authors did not observe a direct relationship between future falls and lower limb muscle strength assessed by leg press.

Second, one of the greatest problems in the attempt to identify risk factors for the first fall among older adults is that many studies with prospective monitoring of falls do not guarantee that the older adults included in the cohort had not fallen before the initial evaluation (Mignardot et al., 2014). Thus, even though these studies usually adjust the regression models according to previous falls, they do not permit the identification of factors specifically related to the first fall among nonfaller older adults.

Third, many studies that objectively assess muscle strength to identify the association with falls, in general, include few muscle groups or apply a single score to a combination of tests (Horlings et al., 2008; Moreland et al., 2004). Wolfson et al. (1995) observed that institutionalized older adults with a history of falls had significantly lower muscle strength of ankle and knee than older adults with no history of falls. Indeed, a systematic review and meta-analysis conducted by Moreland et al. (2004) indicated that lower limb muscle weakness represents a risk factor for falls, especially among institutionalized older persons. In a study of a cohort of Japanese older women, Davis et al. (1999) observed that the knee extensor muscle strength assessed with an extensor chair and with the 5 times sit-to-stand test was associated with time to the first fall. Scott et al. (2014) assessed with a dynamometer the ability of the quadriceps muscle strength to predict falls over a mean period of 3.7 years among community-dwelling older women considered to be at high risk for hip fracture. The authors observed that quadriceps muscle strength was able to predict multiple falls and the time to the occurrence of the first fall among these women (Scott et al., 2014). Menant et al. (2017) observed over a period of 1 year that the definition of sarcopenia based on knee extensor muscle strength (determined with an electronic tension gauge) was able to discriminate the older adults with a higher risk of falls, with participants considered to be weak having a 43% higher risk of falls at home than their stronger peers. Gadelha et al. (2018) observed that impairment of knee extensor muscle quality (strength/muscle mass) significantly increased the risk of falls among community-dwelling older women over a follow-up of 18 months. Buchner et al. (1997) demonstrated that muscle strengthening and/or endurance exercises over a period of 6 months (3 times a week) had a protective effect regarding time to the first fall among older adults with moderate impairment of muscle strength (low knee extensor muscle strength) and/or balance (inability to perform tandem gait without mistakes). On the contrary, in a prospective longitudinal study, Gerdhem et al. (2005) observed that, among other factors, isometric PT of knee flexors and extensors was unable to predict the occurrence of falls within 1 year among community-dwelling older women. It is worth mentioning that none of the studies cited included a sample of nonfaller older people, thus failing to identify whether the muscle strength of lower limbs is a risk factor for the first fall.

Even the studies that investigate the influence of muscle strength on the balance of older people show divergent results. In a study of community-dwelling older adults, Wolfson et al. (1995) demonstrated that, the greater the PT of the ankle, the lower the chance of losing balance during the test of sensory integration. Ding and Yang (2016) investigated the relationship between the isometric muscle strength of knee flexors and extensors and slip-related falls and observed that a reduction of 1 Nm kg⁻¹ in the knee extensors increases the risk of falling after slipping by 3.23 times and a reduction of 1 Nm kg⁻¹ in the knee flexors increases this risk by 2.30 times. Similarly, Carty et al. (2012) reported that older adults with weakness of hip flexors and knee extensors are more likely to use reactive strategies with multiple steps to recover from anterior loss of balance. Furthermore, in a systematic review and meta-analysis aiming to quantitate the association between balance variables and muscle strength/power of the lower limbs in healthy participants along life, Muehlbauer et al. (2015) observed that, regardless of age, the correlations between balance and muscle function were of low magnitude, suggesting that these components are independent from one another (Muehlbauer et al., 2015).

Thus, based on a synthesis of the present results with literature evidence, we may assume that muscle strength itself seems to be more important among subjects with some problems of mobility, who are institutionalized or who are recurrent fallers (Moreland et al., 2004; Scott et al., 2014; Wolfson et al., 1995), with such older adults being more eligible for preventive strategies based on intrinsic risk factors for falls (Graafmans et al., 1996). Indeed, some studies have suggested that physiological performance seems to be similar among nonfaller older adults and fallers with a single fall (Bento et al., 2010; Kamo et al., 2019; Scott et al., 2014). This is important within the context of public health to select appropriate interventions that would prevent the occurrence of the first fall among nonfaller older adults.

Community fall prevention programs may include a single-component or multifactorial ones (Malik et al., 2020). Programs with multifactorial components include the combination of several interventions (e.g., medication reviews, home adaptations, dietary supplementation, and physical exercises) and seem to be more effective in preventing falls in older adults, although they are more expensive (Malik et al., 2020). Thus, single-component programs appear to be more cost-effective in the community setting and more effective when they include physical exercise (balance and functional exercises associated with strengthening exercise; Malik et al., 2020; Sherrington et al., 2019).

However, considering (a) the association between the number of comorbidities and the occurrence of a future fall found in the present study and (b) that several literature reviews have reported that strength training alone is not sufficient to improve balance (Pizzigalli et al., 2011) or to reduce the risk of falls (Ambrose et al., 2013; Horlings et al., 2008; Moreland et al., 2004; Sherrington et al., 2019), the findings of the present study highlight the importance of multifactorial interventions including a multidisciplinary health team for the proper management of comorbidities and not only focused on muscle strength gain proper to prevent the occurrence of the first fall among community-dwelling nonfaller older adults. The lack of association between clinical tests (SS and TUG) and future falls and the fact that about 60% of the falls occurred at home reinforce the hypothesis that extrinsic environmental factors may be more important for the first fall in nonfaller older adults than intrinsic factors. However, as the present study did not assess the environmental risk factors for falls, further studies are needed to confirm this hypothesis. Once the major influence of environmental risk factors for the first fall is confirmed, fall prevention programs for nonfallers community-dwelling older adults should implement and reinforce environmental adaptations and educational strategies to avoid risky behaviors, recommendations that, so far, seem to have low adherence by community-dwelling older adults (Taylor et al., 2017).

Among the limitations of the present study, we may mention the lack of inclusion of other variables of lower limb muscle function that might influence the occurrence of the first fall among nonfaller older adults, such as muscle power (Pizzigalli et al., 2011), as well as the fact that we evaluated only the dominant lower limb. Although complaints of dizziness and deficit in protective sensitivity of the feet were two ineligibility criteria, we did not evaluate other sensory factors related to falls in older adults, such as vision acuity and joint proprioception impairments (Rubenstein & Josephson, 2006). In addition, we also did not evaluate whether the older adults live alone, have limitations for instrumental activities of daily living (Ek et al., 2019), or if there are environmental risk factors for falls which may be more associated with the first fall among nonfaller older people. Nonfaller older adults were defined as individuals who did not report falls during the year preceding their initial evaluation, although it was not possible to guarantee that such individuals had not fallen before this period. In addition, even though we monitored the occurrence of falls on a monthly basis by means of telephone contact, the quantity and characteristics of falls continued to be reported in a subjective manner, fully depending on the participant’s response. Participants older than 80 years, who usually show generalized muscle weakness and balance problems, were not included in the study. If they had been, their data would have complemented those obtained in the present study, confirming or not the hypothesis that muscle weakness may be a risk factor for the first fall among persons who already show some degree of physical impairment.

Conclusion

The present study is clinically relevant within the public health context as it demonstrated that lower limb muscle strength (hip, knee, and ankle) is not directly and independently related to the occurrence of the first fall in nonfaller community-dwelling older adults monitored over a period of 1 year. Based on these results, it is important to identify other factors that predispose nonfaller older adults to falls, so that early and effective preventive strategies may be elaborated.

Footnotes

Acknowledgements

The authors thank Professor Eduardo Ferriolli for the use of the isokinetic dynamometer equipment (Biodex System 4 Pro).

Authors’ Note

The study was approved by the Research Ethics Committee involving Human Beings of Ribeirão Preto Medical School, University of São Paulo (CAAE: 62209916.5.0000.5440), and all participants gave written informed consent to participate.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) (scholarship—Finance Code 001).

ORCID iD

Jaqueline Mello Porto

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