Assessing Muscle Quality as a Key Predictor of Fall Risk in Older Adults

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Evaluating muscle performance is important when assessing the risk of falling. The aim of this study was to identify factors (namely muscle functionality and spatio-temporal gait attributes) that best discriminate between fallers and non-fallers in older adults. The main hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk. Methods 184 patients were included, 81% (n = 150) were women and the mean age was 73.6 ± 6.83 years. We compared the body composition, mean handgrip strength, spatio-temporal parameters and muscle function (strength, quality and power) of fallers and non-fallers. Muscle quality was calculated as the ratio of maximum strength to fat-free mass. Mean handgrip strength and power were also weighted by fat-free mass. Results The falling patients had lower muscle quality, weighted power and mean weighted handgrip strength than the non-falling patients. The univariate analysis, logistic regression and ROC curves enabled us to highlight the importance of muscle quality rather than quantity. The ROC curves have shown that muscle quality is the most predictive factor of falling. Conclusion This study of older adults showed that muscle quality is the best predictor of fall risk, more than muscle mass and spatial and temporal gait parameters. Our results confirm that muscle quality is a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the ageing population. muscle quality older adults fall ageing risk fall Figures Figure 1 Figure 2 1. INTRODUCTION Falls and their consequences (e.g., physical trauma and restriction of activity) are among the principal causes of morbidity in older adults [ 1 ]. Falling is a common geriatric syndrome due to its frequency and multifactorial causes [ 2 ]. Risk factors for falls are divided in two groups: i) intrinsic risk factors linked to the person's state of health, and ii) environmental risk factors linked to the characteristics of the place of fall. Intrinsic risk factors are considered to be the main causes of falls in older adults [ 2 ]. Ageing of the neuromuscular system is an important intrinsic risk factor for falls among seniors [ 3 ]. The major components currently known to link ageing and falls are a decline in muscle strength and in muscle mass [ 4 ]. Age-related loss of muscle function involves quantitative and qualitative changes in skeletal muscle structure, function [ 5 ], or both (sarcopenia). As individuals age, they undergo various physiological changes that could lead to sarcopenia and thus to an increased risk of falls [ 6 ] Sarcopenia is a progressive and generalised skeletal muscle disorder associated with increased likelihood of adverse outcomes including falls [ 6 ]. In 2018, EWGSOP2 improved the definition of sarcopenia and uses low muscle strength as the primary endpoint of sarcopenia; thus, muscle strength is currently the most reliable measure of muscle function [ 6 ]. Specifically, a sarcopenia diagnosis is confirmed by the presence of low muscle quantity or quality. By definition, this loss of muscle strength is the result of two main factors: i) a reduction in muscle mass; ii) a loss of muscle quality (Muscle Quality = Muscular Strength /Muscle Quantity, therefore, Muscular Strength = Muscle Quality * Muscle Quantity). Recent years have seen a significant increase in literature on the subject. While initial research suggested that the deterioration of strength, and consequently functional ability, was attributed to muscle mass reduction, a number of contemporary studies challenge this idea [ 7 , 8 ]. They suggest instead that muscle quality is the primary determinant. The term "muscle quality" has been originally introduced to refer to the relationship between muscle strength and muscle volume [ 9 , 10 ]. Muscle quality defined as force produced per unit of muscle mass (ratio of force to muscle mass) is most commonly used. Previous studies have emphasised the importance of muscle quality over muscle strength or muscle mass alone when assessing muscle performance among older people [ 11 , 12 ]. Abe et al. showed that the relationship between handgrip strength and muscle thickness was a significant predictor of physical performance [ 12 ]. A recent cross-sectional study has shown that muscle quality is negatively associated with dynamic balance, fear of falling and history of recurrent falls in older women [ 13 ]. Gadelha et al. also showed that low muscle quality was associated with a higher risk of falls [ 10 ]. Finally, Goodpaster et al showed that the loss of muscle strength is more rapid than the loss of muscle mass, suggesting a decline in muscle quality [ 14 ]. However, the above-mentioned studies only evaluated the muscle quality in relation to the risk of falling and did not compare other parameters identified as other fall predictor factors, such as muscle power, handgrip strength, or spatio-temporal gait parameters. The aim of this study was to identify the factors (muscle functionality and spatio-temporal gait attributes) that best discriminate between fallers and non-fallers in older adults. Our methodology stands out due to its comprehensive assessment of various identified risk elements including muscle quality, power, spatio-temporal parameters, handgrip strength, as well as the particular demographic evaluated (older adults individuals of both genders). The main hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk. Indeed, a decrease in muscle quality may precede the loss of muscle mass, enabling an earlier assessment of muscle impairment and thus preventive management of muscle mass loss. 2. METHODS 2.1. Design and setting This study was a descriptive, retrospective, observational and single-center transversal case-control study, carried out within the day hospital facility in the Nice University Hospital Center between 1 September 2019 and 13 March 2020. Ethics Committee issued a favourable opinion (reference number 15089). 2.2. Sample size calculation According to Gadelha et al., worse muscle quality was associated with a higher risk of falling in older women (OR = 3.56) [ 13 ]. For this case-control study, we calculated the number of participants needed by applying the Case-Control Chi-square method with Yates continuity correction. Using an Alpha risk of 5%, a Power (1 - beta) of 0.9, and 3 controls per case, the number of patients required for this study was 180 (45 fallers (cases) and 135 non-fallers (controls)). 2.3. Participants Patients aged over 65 years old and able to walk without walking aids assistance were included in the study. Patients suffering from a neurological disease, not affiliated to a Social Insurance, or under legal protection, were not included. All participants signed an informed consent. 2.4. Protocol The screening was conducted on a voluntary basis following a comprehensive geriatric assessment. The falls history was obtained. Patients with at least one recorded instance of falling in the previous year were categorized as fallers, while patients with no history of falls were classified as non-fallers. A fall was defined as an unintentional landing on the ground. Participants completed the assessment in the following order: an impedancemetry, a measurement of handgrip strength, a quantified gait analysis and an isokinetic evaluation. 2.4.1. Impedancemetry This measurement was performed with an impedance meter (Quadscan 4000, Bodystat Ltd., Isle of Man, British Isles). The weight in kilograms (kg) and height in meters (m) of each of the participants were collected. Then we calculated the body mass index (BMI) in kilograms per square meter according to the Quetelet formula: \(BMI (kg/m²)=\frac{weight \left(kg\right)}{\left[height \left(m\right)\right]²}\) . The impedance measurement allowed us to collect the lean body mass, the fat-free massand the body fat mass in kilograms (kg). These values were expressed as a percentage of the total mass. 2.4.2. Handgrip strength The handgrip strength was measured using a manual dynamometer (MicroFET handgrip). The patient was sitting on a chair with the feet firmly on the floor and the back supported by the chair. The shoulder was held in adduction (elbows to the body), with no extension or flexion, and in neutral rotation. The elbow was kept at 90° of flexion and in neutral pronosupination. The wrist was also maintained in a neutral position. The assistant held slightly the elbow and the base of the dynamometer in order to avoid any position change. Each handgrip force measurement consisted of three readings of each limb alternated with a resting position. The best of the three maximum strength tests (from each hand) was selected. Data collected measured mean handgrip strength in Newton (N) and mean weighted handgrip strength calculated according to the handgrip to fat-free mass ratio (N/kg). 2.4.3. Gait analysis Patients underwent a gait test with Optogait® (Optogait, Microgate, Bolzano, Italy). Spatio-temporal gait parameters during a gait cycle were recorded. This test was conducted over a distance of 10 metres at comfort and maximal velocity. Five successive measurements were recorded for each velocity and were averaged. Data collected were gait speed (meters per second), cadence (steps per minute), and step lengths in meters. Single stance duration and the oscillation phase were calculated as a percentage of the gait cycle. 2.4.4. Isokinetic dynamometer Isokinetic dynamometer measurements were carried out on the dominant lower limb using the Biodex System 4 dynamometer (Biodex Medical Systems, Inc, Shirley, NY). Each patient was placed on the device with the lever arm adjusted to their height. Participants were seated in an adjustable chair with the axis of rotation of the dynamometer aligned with the centre of the knee joint. (i.e., lateral femoral condyle). The knee and thigh were fixed to the seat and to the tip of the of lever arm of the dynamometer. To restrict body movements, participants were strapped to the chair using broad straps across the pelvis and upper body. Finally, the arms were kept crossed over the torso in order to avoid any movements. Any tests that did not meet the required performance conditions were excluded and were repeated after a rest period of four minutes. To familiarize participants with isokinetic extension movements of the lower limbs, and to perform warm-ups with specific movements, participants did several sub-maximal practice repetitions in order to get familiar with the isokinetic device before starting the evaluation. Participants were then asked to perform three maximal contractions at six predefined speeds (180, 150, 120, 90, 60 and 30°/s). Only the best result of the three trials was used for statistical analysis. All participants were verbally encouraged in a standard manner during each test and a 4-min recovery period was set between repetitions. Data were recorded and stored on a computer and were sampled at 100 Hz using an electronic interface card (Biodex Medical Systems Inc., X2151, Shirley, NY, USA). The maximal torque was identified as the highest value reached during the movement at each constant speed. The maximum instantaneous power was the product of torque and speed. The linear moment-speed relationship was calculated from the maximum torque value obtained at each speed. The power (P)-velocity (V) relation was based on a second order polynomial: $$\varvec{P}=\varvec{a}\bullet \varvec{V}²+\varvec{b}\bullet \varvec{V}+\varvec{c}$$ where a, b and c are the regression coefficients of the polynomial. From this equation, the maximum power (P MAX ), and the corresponding optimum speed (V OPT ) were determined as follows: $${\varvec{V}}_{\varvec{O}\varvec{P}\varvec{T}}=-\frac{\varvec{b}}{2\varvec{a}} \text{e}\text{t} {\varvec{P}}_{\varvec{M}\varvec{A}\varvec{X}}=-\frac{\varvec{b}²}{2\varvec{a}}+\varvec{c}$$ The theoretical maximum moment (M MAX ) and the theoretical maximum velocity (V MAX ) were obtained by extrapolating the linear relation where it meets, respectively, the abscissa axis at M = 0 and the ordinate axis at V = 0. All these relationships and parameters were processed using a Matlab script (R2008b, The Mathworks, Natick, Massachusetts, USA). Data collected were maximum muscular strength (Newton meter), optimum muscular strength (Newton), maximal power (Watt), maximum velocity (degrees per second), optimum speed (degrees per second). The maximal power was weighted by fat-free mass according to the formula: $$\varvec{w}\varvec{e}\varvec{i}\varvec{g}\varvec{h}\varvec{t}\varvec{e}\varvec{d} \varvec{p}\varvec{o}\varvec{w}\varvec{e}\varvec{r} (\varvec{W}\varvec{a}\varvec{t}\varvec{t}/\varvec{k}\varvec{g})= \frac{maximal power \left(Watt\right)}{\text{f}\text{a}\text{t}-\text{f}\text{r}\text{e}\text{e} \text{m}\text{a}\text{s}\text{s} \left(\text{k}\text{g}\right)}$$ The muscular quality was calculated according to the formula: $$\varvec{M}\varvec{u}\varvec{s}\varvec{c}\varvec{l}\varvec{e} \varvec{q}\varvec{u}\varvec{a}\varvec{l}\varvec{i}\varvec{t}\varvec{y}=\frac{\varvec{m}\varvec{a}\varvec{x}\varvec{i}\varvec{m}\varvec{u}\varvec{m} \varvec{m}\varvec{u}\varvec{s}\varvec{c}\varvec{l}\varvec{e} \varvec{s}\varvec{t}\varvec{r}\varvec{e}\varvec{n}\varvec{g}\varvec{t}\varvec{h} \left(\varvec{N}\varvec{e}\varvec{w}\varvec{t}\varvec{o}\varvec{n} \varvec{m}\varvec{e}\varvec{t}\varvec{e}\varvec{r}\right)}{\text{f}\text{a}\text{t}-\text{f}\text{r}\text{e}\text{e} \text{m}\text{a}\text{s}\text{s}{ \left(\varvec{k}\varvec{g}\right)}^{} }$$ . 2.4.5. Statistical Analysis All statistical analyses were performed using XLSTAT® (version 2021.1.1.1089). For comparative analyses, two different groups were defined: non-fallers (no falls) and fallers. Descriptive statistics were calculated for both groups. The categorical variables are reported as absolute frequencies. Quantitative variables are described by the number of participants, mean and standard deviation. The Kolmogorov-Smirnov test was used to assess the normality of the data. For continuous variables, parametric analyses were performed using the Student's t-test. For the independent samples t-test, the effect size was determined by calculating Cohen's d, that is, the mean difference between our two groups divided by the pooled standard deviation. We consider Cohen’s d of 0.2 to be a "small" effect, 0.5 to be "medium" and 0.8 to be "large". For all statistical analyses the significance threshold was set at p < 0.002, in accordance with a Bonferroni correction (0.05/22 = 0.002) due to the number of variables. Logistic regression was performed to understand and predict the effect of one or more explicative variables on a binary response variable (fall or not). The most common functions used to link probability p to the explanatory variables are the logistic function (we refer to the Logit model). Variables that showed a significant trend in the univariate analysis, after applying the Bonferroni correction (p < 0.2/22 = 0.009), were included in the binary logistic regression model (BMI, fat mass, mean weighted handgrip, double stance, muscle quality and muscle power). The response variable was a binary qualitative variable: the presence of a fall. The explanatory variables are quantitative variables. We also performed a Receiver operating characteristic (ROC) curve to determine if the value of a quantitative parameter was able to accurately discriminate fallers and non-fallers. ROC curves were constructed for the significant variables in univariate analysis. Area under the curve (AUC) was used to compare the different tests with each other. 3. RESULTS 184 patients were included, 81% (n = 150) were women and the mean age was 73.6 ± 6.83 years. 3.1. Univariate analysis (Table 1 ) Table 1 Univariate analysis Total (n = 184) Fallers (n = 46) Non-fallers (n = 138) test result t, IC Effect size p value Socio-demographic Age (years ± SD) 73,64 ± 6,83 73.72 ± 7.49 73.62 ± 6.63 t = -0.09; [-2.40;2.20] 0.01 0,931 Gender (Female. %) 150 (81.5) 41 (89.1) 109 (78.9) χ² = 2.36 0,125 Weight (Kg ± SD)* 64.58 ± 11.06 69.82 ± 10.66 62.84 ± 10.67 t = -3.85; [-10.57; -3.40] 0.655 < 0.001* BMI (Kg/m² ± SD)* 24.53 ± 4.04 26.22 ± 3.77 23.97 ± 3.98 t = -3.35; [-3.56; -0.92] 0.578 0.001* Handgrip Mean Handgrip strength (N ± SD) 225.16 ± 80.96 210.73 ± 66.07 229.97 ± 85.02 t = 1.39; [-7.89;46.36] 0.253 0.164 Mean weighted Handgrip strength (N/Kg ± SD)* 39.89 ± 18.45 31.91 ± 18.21 42.56 ± 17.81 t = 3.49; [4.63;16.67] 0.591 0.001* Impedance Fat mass (% ± SD)* 36.78 ± 7.78 39.44 ± 5.55 35.90 ± 8.23 t = -2.71; [-6.11; -0.96] 0.498 0.007 Lean mass (% ± SD) 63.29 ± 9.31 61.07 ± 6.55 63.58 ± 10.00 t = 1.593; [0.01;0.06] 0.297 0.113 fat-free mass (% ± SD)* 9.81 ± 3.38 10.71 ± 2.96 9.50 ± 3.45 t = -2.12; [-2.33; -0.08] 0.374 0.035 Spatio-temporal parameters Walking speed (m/s ± SD)* 1.19 ± 0.22 1.14 ± 0.19 1.22 ± 0.22 t = 2.18; [0.01;0.15] 0.380 0.031 Cadence (step/min ± SD) 113.82 ± 10.76 111.94 ± 9.81 114.45 ± 11.02 t = 1.38; [-1.09;6.12] 0.241 0.171 Step length (cm ± SD) 62.75 ± 8.44 60.68 ± 7.27 63.44 ± 8.72 t = 1.94; [-0.05;5.58] 0.345 0.054 Double stance (% ± SD)* 24.77 ± 4.88 26.55 ± 5.28 24.18 ± 4.61 t = -2.90; [-3.98;-0.76] 0.466 0.004 Simple Support (% ± SD)* 37.58 ± 2.49 36.69 ± 2.62 37.88 ± 2.38 t = 2.85; [0.36;2.00] 0.473 0.005 Oscillating (% ± SD)* 37.51 ± 2.49 36.60 ± 2.74 37.81 ± 2.33 t = 2.92; [0.39;2.03] 0.477 0.004 Isokinetic dynamometer Muscle strength (Nm ± SD)* 117.56 ± 38.14 107.42 ± 24.78 120.94 ± 41.17 t = 2.10; [0.83;26.22] 0.398 0.037 Muscle quality (Nm/Kg ± SD)* 20.29 ± 7.17 15.84 ± 6.54 21.78 ± 6.77 t = 5.19; [3.68;8.19] 0.891 < 0.0001* Muscle power (W ± SD) 170.57 ± 58.09 157.31 ± 53.18 174.99 ± 59.17 t = 1.79;[-1.71;37.08] 0.314 0.074 Weighted power (W/Kg ± SD)* 29.02 ± 11.87 22.04 ± 9.36 31.35 ± 11.73 t = 4.88; [5.55;13.06] 0.877 < 0.0001* Optimal strength (Nm ± SD)* 53.26 ± 18.08 46.87 ± 15.75 55.39 ± 18.35 t = 2.82; [2.56;14.48] 0.498 0.005 Maximal speed (°/sec) 322.69 ± 62.09 316.25 ± 71.67 324.84 ± 58.69 t = 0.81; [-12.29;29.47] 0.131 0.418 Optimal speed (°/sec) 173.84 ± 31.94 174.06 ± 43.37 173.76 ± 27.29 t = -0.05; [-11.06;10,46] 0.009 0.957 Note : IC: confidence interval; SD: standard deviation; Kg: kilograms; m: meter; s: second; cm: centimetre; N: Newton; Nm: Newton meter; W: Watt; *: significate result p < 0.003 with bonferonni correction The weight (p < .001) and BMI (p = .001), were significantly higher among fallers, with medium effect size for weight and BMI. Analysis of spatio-temporal gait parameters revealed a longer double stance time (p = .004), and a shorter simple stance (p = .005) and oscillating phase (p = .004) among fallers but a small effect size. The weighted mean handgrip strength was significantly lower among fallers (p = .001; effect size = .591). Finally, the muscle quality (p < .001) and weighted power (p < .001) were lower among fallers, with large effect size. 3.2. ROC Curves (Fig. 1 ) The variable muscle quality has the highest area under the curve (0.794), with a threshold value of 13.35 Newton/Kg. The comparison test was significant (p < .0001). Weighted power has a calculated AUC 0.788 with a threshold value of 23.41 Newton meter/Kg. The comparison test was significant (p < 0.0001). The threshold value of the mean weighted handgrip strength was 29.11 Newton meter/Kg. The area under the curve was 0.765. The comparison test was significant (p < 0.0001). Finally, the following variables had a significant comparison test: mean weighted handgrip strength (AUC = 0.765), weight (AUC = 0.689) and BMI (AUC = 0.682). All ROC curves are shown in Fig. 1 . 3.3. Logistic regression (Table 2 and Fig. 2 ) Table 2 Logistic regression Variable Value P value OR IC BMI -0.017 0.805 0.984 [0.862;1.123] Fat mass 0.108 0.003* 1.115 [1.037;1.198] Mean weighted Handgrip strength 0.085 0.003* 1.089 [1.030;1.151] Double stance 0.036 0.470 1.037 [0.940;1.144] Muscle quality* -0.269 0.007* 0.764 [0.629;0.928] Weighted power -0.096 0.087 0.909 [0.814;1.014] Note : OR: odds ratio; IC: confidence interval; BMI: body mass index; *: significant result p < 0.05 The equation of the model was: Pr(Fall) =: \(\frac{1}{(1 + exp(-(-1.3-0.01*BMI+0.09*Mean wheigthed handgrip+0.1*Fat Mass+0.04*Double stance-0.3*Muscle quality-0.1*Wheighted power)\left)\right)}\) Results of the logistic regression were significant (p < .0001). The ROC curve established from the logistic regression model is presented in Fig. 2 (AUC = 0.837). Muscle quality has a negative impact on the risk of falling (-0.269) (p = .007*, OR = 0,76, IC [0.63;0.93]). Mean weighted handgrip strength also has a significant (p = .003*, OR = 1,09, IC [1.03;1.15]) as a Fat mass (p = 0.003*, OR = 1.115, IC [1.03;1.19]. The other variables were not significant (BMI, double stance and weighted power). 4. DISCUSSION The aim of this study was to identify the parameters (muscle performance or spatio-temporal walking parameters) that best discriminate fallers and non-fallers in older adults. As hypothesized, muscle quality appears to be a determining factor in the risk of falling, with our results showing that lower muscle quality is associated with a higher risk of falling (effect size = 0.891). Muscle quality is also significant with logistic regression (p = .007, OR = 0,76, IC [0.63;0.93]). Muscle quality is the most predictive risk factor for falls, as demonstrated by various statistical analyses. These results confirm those of the literature showing that muscle quality is an important factor on physical function in frail, obese, older adults [ 15 , 16 ] and that muscle quality is strongly related to an increased risk of falling in older women (68 ± 6.2 years) [ 10 ]. This study is the first to compare muscle quality with other risk factors such as muscle power, handgrip strength, or spatio-temporal parameters of walking. This is noteworthy because, although muscular strength has been associated with better performance on functional tasks [ 17 ], an assessment of muscle quality, rather than a muscle strength evaluation, may be more appropriate. Additionally, we found differences in patients' weights, leading to variations in impedance measurements. Fallers often had a higher BMI and fat mass. While a higher body mass index can be associated with thicker muscles [ 13 ], it's evident that muscle strength is a more crucial health indicator than muscle size in older individuals [ 18 , 19 ]. Notably, just having more muscle doesn't mean it's stronger, especially since strength and size don't always correlate linearly [ 14 ]. The significant decline in strength, even without a matching decrease in muscle mass, highlights the importance of muscle quality [ 16 ]. Also, individuals who fall more frequently tend to have a higher BMI, pointing to the issue of sarcopenic obesity [ 20 ]. This mix of weaker muscles and increased weight can lead to further functional decline [ 21 ]. In older individuals, muscle quality tends to deteriorate for various reasons. First and foremost, the increasing infiltration of fat into skeletal muscles, combined with a decrease in the proportion of Type II fibers [ 22 ], undermines muscle quality. Furthermore, as aging progresses, there's a noticeable reduction in the number of motor units [ 23 ] and incomplete re-innervation [ 24 ]. These phenomena, together with the reorganization of the remaining motoneurons toward the abandoned muscle fibers [ 25 ], lead to a qualitative impairment in muscle functionality [ 26 ]. Finally, a deficit in the activation of motor units is commonly observed in the older adults [ 27 ]. Thus, muscular quality, which encompasses various aspects of muscle function and performance, including strength, muscle composition, functional performance, and other neuromuscular factors, should be considered a more relevant indicator of fall risk than parameters such as muscle strength or power. Indeed, muscular quality is defined as the ability of a muscle to generate force per unit of muscle mass, and it is influenced not only by a muscle's strength or power but also by other factors such as muscle composition, neuromuscular coordination, and the presence of fibrosis or fat infiltration. By integrating these elements, muscular quality provides a more comprehensive and nuanced view of an older individual's motor capabilities. Several studies have suggested that superior muscular quality may compensate for lower muscle quantity [ 28 , 29 ], meaning that muscles of "good quality" can effectively and precisely respond to functional demands, which are essential for maintaining balance and preventing falls. The assessment of muscle quality, requiring a certain number of resources, should be used to understand why an individual is at risk of falling in order to best tailor their care. Indeed, a meta-analysis has revealed that resistance training can significantly improve strength and muscle quality in elderly patients with sarcopenia [ 30 ]. In the same manner, the mean weighted handgrip strength was associated with a risk of falling whereas the simple handgrip strength was not. Indeed, this parameter is significant in the univariate analysis (p = .001). Furthermore, logistic regression confirmed these results (p = 0.003). It is known that weak handgrip strength is a strong predictor of an increase in the duration of hospitalisation, functional limitations, poor quality of life, and death [ 31 , 32 ]. Of note, handgrip strength and lower extremity muscle strength have shown moderate to strong correlations in older adults [ 33 ]. However, studies suggest that handgrip strength should be used with caution to assess overall strength [ 34 ]. Finally, Ostolin et al. showed that the evolution of handgrip strength over time does not seem to predict the evolution of lower limb strength involved in the risk of falling [ 28 ]. Our findings are in line with these and also highlight the importance of muscle quality rather than muscle quantity. Although muscular quality is a key factor, other aspects, particularly spatio-temporel parameters, should not be overlooked when assessing fall risk. Finally, there are significant trend in most of the spatio-temporal gait parameters between the two groups. In the non-faller group, the single stance time was longer (p = .005, effect size 0.473) and double stance was shorter (p = .004, effect size = 0.466). This is consistent with the literature. Many studies have already shown the relationship between gait disorders and the risk of falling in older people, supporting the idea that decrease in gait speed and modification of gait parameters are strong predictors of falls [ 35 , 36 ]. Thus, more than gait speed, it is the gait quality and single and double stance durations that is important. Variability in stride length and double stance duration are important predictors of gait among older adults [ 37 ]. However, gait parameters obtained with the Optogait are based on a number of limited and repeated stages and do not take into account other important variables such as joint angle [ 38 ]. Moreover, the walking speed and the time of double support did not appear significant in the logistic regression model. Thus, a simple walking speed or spatio-temporal parameters are not sufficient to detect fallers. Muscle quality seems to be much more effective. Limits and biases There could be several sources of bias in our study. Indeed, many tests that can accurately discriminate using retrospective data do not demonstrate prospective predictive ability for falls in longitudinal studies. Therefore, it is necessary to conduct a prospective study with the various known variables of fall risk including muscle quality. Study was retrospective and a prospective study is necessary to establish temporal relationships between muscle quality and falls, as we did not have information on the implementation of preventive measures after previous falls. As with any observational study, it is impossible to assess risk factors and events preceding falls. While there is a significant link between muscle function and the risk of falling (with a clinical impact, in terms of prevention through adapted physical activity programs), no causal link between the actual observed fall parameters and their prior occurrence at the first fall can be established. Finally, the appropriate definition of muscle quality is a subject of ongoing debate. In our current study, muscle quality was defined as muscle strength expressed in relation to fat-free mass. Thus, a minor variation in fat-free mass results in a large variation in quality. This implies caution is needed in the interpretation of the results. CONCLUSIONS In conclusion, this retrospective, observational study of older adults showed that muscle quality is the best predictor of fall risk, more than muscle mass and spatial and temporal gait parameters. Our results confirm that muscle quality is a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the ageing population. Further studies are needed to establish whether an increase in muscle quality could improve gait parameters and decrease fall risk. These results would be useful in recommending an appropriate physical activity program for fall prevention. Declarations Conflicts of Interest and Source of Funding: none declared ACKNOWLEDGMENTS The authors would like to thank patients and their caregivers. We would make a special acknowledgment to all the teams of the frailty platform unit for their everyday work with the patients. This study was made possible thanks to the Aging well project (Malakoff collaboration). References Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med. 29 déc 1988;319(26):1701‑7. Tinetti ME. Clinical practice. Preventing falls in elderly persons. N Engl J Med. 2 janv 2003;348(1):42‑9. Bloch F, Thibaud M, Tournoux-Facon C, Brèque C, Rigaud AS, Dugué B, et al. Estimation of the risk factors for falls in the elderly: can meta-analysis provide a valid answer? Geriatr Gerontol Int. avr 2013;13(2):250‑63. Newman AB, Kupelian V, Visser M, Simonsick EM, Goodpaster BH, Kritchevsky SB, et al. Strength, But Not Muscle Mass, Is Associated With Mortality in the Health, Aging and Body Composition Study Cohort. J Gerontol Ser A. 1 janv 2006;61(1):72‑7. Korhonen MT, Cristea A, Alén M, Häkkinen K, Sipilä S, Mero A, et al. Aging, muscle fiber type, and contractile function in sprint-trained athletes. J Appl Physiol Bethesda Md 1985. sept 2006;101(3):906‑17. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. janv 2019;48(1):16‑31. Clark BC, Manini TM. Functional consequences of sarcopenia and dynapenia in the elderly. Curr Opin Clin Nutr Metab Care. mai 2010;13(3):271‑6. Janssen I. Evolution of sarcopenia research. Appl Physiol Nutr Metab Physiol Appl Nutr Metab. oct 2010;35(5):707‑12. Francis P, McCormack W, Toomey C, Lyons M, Jakeman P. Muscle strength can better differentiate between gradations of functional performance than muscle quality in healthy 50–70y women. Braz J Phys Ther. 1 nov 2017;21(6):457‑64. Gadelha AB, Neri SGR, Bottaro M, Lima RM. The relationship between muscle quality and incidence of falls in older community-dwelling women: An 18-month follow-up study. Exp Gerontol. 1 sept 2018;110:241‑6. Pinto RS, Correa CS, Radaelli R, Cadore EL, Brown LE, Bottaro M. Short-term strength training improves muscle quality and functional capacity of elderly women. AGE. 1 févr 2014;36(1):365‑72. Abe T, Thiebaud RS, Loenneke JP. Forearm muscle quality as a better indicator of physical performance than handgrip strength in older male ground golf players aged 70 to 89. J Musculoskelet Neuronal Interact. déc 2016;16(4):296‑301. Gadelha A, Neri S, Nóbrega O, Pereira J, Bottaro M, Fonsêca A, et al. Muscle quality is associated with dynamic balance, fear of falling, and falls in older women. Exp Gerontol. avr 2018;104:1‑6. Goodpaster BH, Park SW, Harris TB, Kritchevsky SB, Nevitt M, Schwartz AV, et al. The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J Gerontol A Biol Sci Med Sci. oct 2006;61(10):1059‑64. Maffiuletti NA, Jubeau M, Munzinger U, Bizzini M, Agosti F, De Col A, et al. Differences in quadriceps muscle strength and fatigue between lean and obese subjects. Eur J Appl Physiol. sept 2007;101(1):51‑9. Villareal DT, Banks M, Siener C, Sinacore DR, Klein S. Physical Frailty and Body Composition in Obese Elderly Men and Women. Obes Res. 2004;12(6):913‑20. Puthoff ML, Nielsen DH. Relationships among impairments in lower-extremity strength and power, functional limitations, and disability in older adults. Phys Ther. oct 2007;87(10):1334‑47. Menant JC, Weber F, Lo J, Sturnieks DL, Close JC, Sachdev PS, et al. Strength measures are better than muscle mass measures in predicting health-related outcomes in older people: time to abandon the term sarcopenia? Osteoporos Int. 1 janv 2017;28(1):59‑70. Schaap LA, van Schoor NM, Lips P, Visser M. Associations of Sarcopenia Definitions, and Their Components, With the Incidence of Recurrent Falling and Fractures: The Longitudinal Aging Study Amsterdam. J Gerontol Ser A. 10 août 2018;73(9):1199‑204. Yang M, Jiang J, Hao Q, Luo L, Dong B. Dynapenic obesity and lower extremity function in elderly adults. J Am Med Dir Assoc. janv 2015;16(1):31‑6. Ochi M, Tabara Y, Kido T, Uetani E, Ochi N, Igase M, et al. Quadriceps sarcopenia and visceral obesity are risk factors for postural instability in the middle-aged to elderly population. Geriatr Gerontol Int. juill 2010;10(3):233‑43. Visser M, Goodpaster BH, Kritchevsky SB, Newman AB, Nevitt M, Rubin SM, et al. Muscle Mass, Muscle Strength, and Muscle Fat Infiltration as Predictors of Incident Mobility Limitations in Well-Functioning Older Persons. J Gerontol Ser A. 1 mars 2005;60(3):324‑33. Faulkner JA, Larkin LM, Claflin DR, Brooks SV. Age-related changes in the structure and function of skeletal muscles. Clin Exp Pharmacol Physiol. nov 2007;34(11):1091‑6. McNeil CJ, Doherty TJ, Stashuk DW, Rice CL. Motor unit number estimates in the tibialis anterior muscle of young, old, and very old men. Muscle Nerve. avr 2005;31(4):461‑7. Midrio M. The denervated muscle: facts and hypotheses. A historical review. Eur J Appl Physiol. sept 2006;98(1):1‑21. Kaya RD, Nakazawa M, Hoffman RL, Clark BC. Interrelationship between muscle strength, motor units, and aging. Exp Gerontol. sept 2013;48(9):920‑5. Harridge SD, Kryger A, Stensgaard A. Knee extensor strength, activation, and size in very elderly people following strength training. Muscle Nerve. juill 1999;22(7):831‑9. Ostolin TLVDP, Gonze B de B, de Oliveira Vieira W, de Oliveira ALS, Nascimento MB, Arantes RL, et al. Association between the handgrip strength and the isokinetic muscle function of the elbow and the knee in asymptomatic adults. SAGE Open Med. 27 févr 2021;9:2050312121993294. Scott D, Johansson J, McMillan LB, Ebeling PR, Nordstrom A, Nordstrom P. Mid-calf skeletal muscle density and its associations with physical activity, bone health and incident 12-month falls in older adults: The Healthy Ageing Initiative. Bone. 1 mars 2019;120:446‑51. Zhao H, Cheng R, Song G, Teng J, Shen S, Fu X, et al. The Effect of Resistance Training on the Rehabilitation of Elderly Patients with Sarcopenia: A Meta-Analysis. Int J Environ Res Public Health. 22 nov 2022;19(23):15491. Ibrahim K, May C, Patel HP, Baxter M, Sayer AA, Roberts H. A feasibility study of implementing grip strength measurement into routine hospital practice (GRImP): study protocol. Pilot Feasibility Stud. 2016;2:27. Leong DP, Teo KK, Rangarajan S, Lopez-Jaramillo P, Avezum A, Orlandini A, et al. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet Lond Engl. 18 juill 2015;386(9990):266‑73. Fragala MS, Alley DE, Shardell MD, Harris TB, McLean RR, Kiel DP, et al. Comparison of Handgrip and Leg Extension Strength in Predicting Slow Gait Speed in Older Adults. J Am Geriatr Soc. janv 2016;64(1):144‑50. Felicio DC, Pereira DS, Assumpção AM, de Jesus-Moraleida FR, de Queiroz BZ, da Silva JP, et al. Poor correlation between handgrip strength and isokinetic performance of knee flexor and extensor muscles in community-dwelling elderly women. Geriatr Gerontol Int. janv 2014;14(1):185‑9. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, et al. Gait Speed and Survival in Older Adults. JAMA. 5 janv 2011;305(1):50‑8. Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative Gait Markers and Incident Fall Risk in Older Adults. J Gerontol Ser A. 1 août 2009;64A(8):896‑901. Mbourou GA, Lajoie Y, Teasdale N. Step length variability at gait initiation in elderly fallers and non-fallers, and young adults. Gerontology. févr 2003;49(1):21‑6. Kang HG, Dingwell JB. Effects of walking speed, strength and range of motion on gait stability in healthy older adults. J Biomech. 20 oct 2008;41(14):2899‑905. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 02 Apr, 2024 Reviewers agreed at journal 19 Mar, 2024 Reviewers invited by journal 28 Feb, 2024 Editor invited by journal 20 Feb, 2024 Editor assigned by journal 14 Feb, 2024 First submitted to journal 13 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3956550","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275603665,"identity":"07f049b3-2ba8-4876-938d-6679a26ad5ea","order_by":0,"name":"Emeline MICHEL","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACZgY2BoYCBgZ+9gY2EJ8HRHwgrMWAgUGy5wBcC+MMAvZAtBjcSGCDieDXotvO/OzBBwMGOYabj5895mG4I8Pf3sDYXIFHi9lhNnPDGQYMxoyz08yNeRie8UicOcDYeAavFh42aR4DhsRm6Rw2ad5/h3kMJBLYHzYQ0vLHgKG+TfIMUC8DUIv8A8ZGglqA3k/gkeCBapFgIKSFzUyyx0DCcAZPmpnkHKAWiTOJjfi1nD/8TOJHhY28/XEg4w3DYXv+9sMH8WqBAglkDiMRGkbBKBgFo2AU4AUAv1g/8VqrzwYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5082-1817","institution":"CHU Nice: Centre Hospitalier Universitaire de Nice","correspondingAuthor":true,"prefix":"","firstName":"Emeline","middleName":"","lastName":"MICHEL","suffix":""},{"id":275603666,"identity":"62e4eb4b-7296-4276-bfca-f17adda054f6","order_by":1,"name":"Raphael ZORY","email":"","orcid":"","institution":"Universite Cote d'Azur","correspondingAuthor":false,"prefix":"","firstName":"Raphael","middleName":"","lastName":"ZORY","suffix":""},{"id":275603667,"identity":"2d109635-24b6-4891-86ee-561b86199ab7","order_by":2,"name":"Olivier GUERIN","email":"","orcid":"","institution":"CHU Nice: Centre Hospitalier Universitaire de Nice","correspondingAuthor":false,"prefix":"","firstName":"Olivier","middleName":"","lastName":"GUERIN","suffix":""},{"id":275603668,"identity":"6dd77c92-b48e-449f-90bf-8ef4bed6c811","order_by":3,"name":"Frederic PRATE","email":"","orcid":"","institution":"CHU Nice: Centre Hospitalier Universitaire de Nice","correspondingAuthor":false,"prefix":"","firstName":"Frederic","middleName":"","lastName":"PRATE","suffix":""},{"id":275603669,"identity":"7c2eec2c-b59d-40c8-9644-c3bcf7d78489","order_by":4,"name":"Guillaume SACCO","email":"","orcid":"","institution":"CHU Nice: Centre Hospitalier Universitaire de Nice","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"SACCO","suffix":""},{"id":275603670,"identity":"90575c1c-d503-4a12-8ddf-c45b98554809","order_by":5,"name":"Fréderic CHORIN","email":"","orcid":"","institution":"CHU Nice: Centre Hospitalier Universitaire de Nice","correspondingAuthor":false,"prefix":"","firstName":"Fréderic","middleName":"","lastName":"CHORIN","suffix":""}],"badges":[],"createdAt":"2024-02-14 16:13:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3956550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3956550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52026796,"identity":"40c33203-f784-47b5-a389-ba202055614e","added_by":"auto","created_at":"2024-03-05 15:54:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eNote\u003c/u\u003e: *: significant result\u003cem\u003e p \u0026lt; \u0026nbsp;\u0026nbsp;0.002 with a Bonferonni correction;\u003c/em\u003e AUC: area under the curve\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3956550/v1/b4c9d23b3a8929b341d1a06c.png"},{"id":52026797,"identity":"59784c6d-00d5-4d5e-82ab-dd3d1f96156e","added_by":"auto","created_at":"2024-03-05 15:54:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves: logistic regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eNote\u003c/u\u003e: AUC: area under the curve\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3956550/v1/e0e2e9ba8d93e83807942599.png"},{"id":52027450,"identity":"6eb8b6a8-93c3-47cb-a64c-d3944757e358","added_by":"auto","created_at":"2024-03-05 16:02:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":390770,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3956550/v1/47648eb9-d283-440f-b6c4-67b15a2a1da4.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAssessing Muscle Quality as a Key Predictor of Fall Risk in Older Adults\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eFalls and their consequences (e.g., physical trauma and restriction of activity) are among the principal causes of morbidity in older adults [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Falling is a common geriatric syndrome due to its frequency and multifactorial causes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Risk factors for falls are divided in two groups: i) intrinsic risk factors linked to the person's state of health, and ii) environmental risk factors linked to the characteristics of the place of fall. Intrinsic risk factors are considered to be the main causes of falls in older adults [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Ageing of the neuromuscular system is an important intrinsic risk factor for falls among seniors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The major components currently known to link ageing and falls are a decline in muscle strength and in muscle mass [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Age-related loss of muscle function involves quantitative and qualitative changes in skeletal muscle structure, function [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], or both (sarcopenia). As individuals age, they undergo various physiological changes that could lead to sarcopenia and thus to an increased risk of falls [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSarcopenia is a progressive and generalised skeletal muscle disorder associated with increased likelihood of adverse outcomes including falls [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In 2018, EWGSOP2 improved the definition of sarcopenia and uses low muscle strength as the primary endpoint of sarcopenia; thus, muscle strength is currently the most reliable measure of muscle function [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Specifically, a sarcopenia diagnosis is confirmed by the presence of low muscle quantity or quality. By definition, this loss of muscle strength is the result of two main factors: i) a reduction in muscle mass; ii) a loss of muscle quality (Muscle Quality\u0026thinsp;=\u0026thinsp;Muscular Strength /Muscle Quantity, therefore, Muscular Strength\u0026thinsp;=\u0026thinsp;Muscle Quality * Muscle Quantity).\u003c/p\u003e \u003cp\u003eRecent years have seen a significant increase in literature on the subject. While initial research suggested that the deterioration of strength, and consequently functional ability, was attributed to muscle mass reduction, a number of contemporary studies challenge this idea [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. They suggest instead that muscle quality is the primary determinant. The term \"muscle quality\" has been originally introduced to refer to the relationship between muscle strength and muscle volume [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Muscle quality defined as force produced per unit of muscle mass (ratio of force to muscle mass) is most commonly used. Previous studies have emphasised the importance of muscle quality over muscle strength or muscle mass alone when assessing muscle performance among older people [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Abe et al. showed that the relationship between handgrip strength and muscle thickness was a significant predictor of physical performance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A recent cross-sectional study has shown that muscle quality is negatively associated with dynamic balance, fear of falling and history of recurrent falls in older women [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Gadelha et al. also showed that low muscle quality was associated with a higher risk of falls [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Finally, Goodpaster et al showed that the loss of muscle strength is more rapid than the loss of muscle mass, suggesting a decline in muscle quality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the above-mentioned studies only evaluated the muscle quality in relation to the risk of falling and did not compare other parameters identified as other fall predictor factors, such as muscle power, handgrip strength, or spatio-temporal gait parameters.\u003c/p\u003e \u003cp\u003eThe aim of this study was to identify the factors (muscle functionality and spatio-temporal gait attributes) that best discriminate between fallers and non-fallers in older adults. Our methodology stands out due to its comprehensive assessment of various identified risk elements including muscle quality, power, spatio-temporal parameters, handgrip strength, as well as the particular demographic evaluated (older adults individuals of both genders). The main hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk. Indeed, a decrease in muscle quality may precede the loss of muscle mass, enabling an earlier assessment of muscle impairment and thus preventive management of muscle mass loss.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Design and setting\u003c/h2\u003e \u003cp\u003eThis study was a descriptive, retrospective, observational and single-center transversal case-control study, carried out within the day hospital facility in the Nice University Hospital Center between 1 September 2019 and 13 March 2020. Ethics Committee issued a favourable opinion (reference number 15089).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample size calculation\u003c/h2\u003e \u003cp\u003eAccording to Gadelha et al., worse muscle quality was associated with a higher risk of falling in older women (OR\u0026thinsp;=\u0026thinsp;3.56) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For this case-control study, we calculated the number of participants needed by applying the Case-Control Chi-square method with Yates continuity correction. Using an Alpha risk of 5%, a Power (1 - beta) of 0.9, and 3 controls per case, the number of patients required for this study was 180 (45 fallers (cases) and 135 non-fallers (controls)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Participants\u003c/h2\u003e \u003cp\u003ePatients aged over 65 years old and able to walk without walking aids assistance were included in the study. Patients suffering from a neurological disease, not affiliated to a Social Insurance, or under legal protection, were not included. All participants signed an informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Protocol\u003c/h2\u003e \u003cp\u003eThe screening was conducted on a voluntary basis following a comprehensive geriatric assessment. The falls history was obtained. Patients with at least one recorded instance of falling in the previous year were categorized as fallers, while patients with no history of falls were classified as non-fallers. A fall was defined as an unintentional landing on the ground. Participants completed the assessment in the following order: an impedancemetry, a measurement of handgrip strength, a quantified gait analysis and an isokinetic evaluation.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Impedancemetry\u003c/h2\u003e \u003cp\u003eThis measurement was performed with an impedance meter (Quadscan 4000, Bodystat Ltd., Isle of Man, British Isles). The weight in kilograms (kg) and height in meters (m) of each of the participants were collected. Then we calculated the body mass index (BMI) in kilograms per square meter according to the Quetelet formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(BMI (kg/m\u0026sup2;)=\\frac{weight \\left(kg\\right)}{\\left[height \\left(m\\right)\\right]\u0026sup2;}\\)\u003c/span\u003e\u003c/span\u003e. The impedance measurement allowed us to collect the lean body mass, the fat-free massand the body fat mass in kilograms (kg). These values were expressed as a percentage of the total mass.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Handgrip strength\u003c/h2\u003e \u003cp\u003eThe handgrip strength was measured using a manual dynamometer (MicroFET handgrip). The patient was sitting on a chair with the feet firmly on the floor and the back supported by the chair. The shoulder was held in adduction (elbows to the body), with no extension or flexion, and in neutral rotation. The elbow was kept at 90\u0026deg; of flexion and in neutral pronosupination. The wrist was also maintained in a neutral position. The assistant held slightly the elbow and the base of the dynamometer in order to avoid any position change. Each handgrip force measurement consisted of three readings of each limb alternated with a resting position. The best of the three maximum strength tests (from each hand) was selected. Data collected measured mean handgrip strength in Newton (N) and mean weighted handgrip strength calculated according to the handgrip to fat-free mass ratio (N/kg).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Gait analysis\u003c/h2\u003e \u003cp\u003ePatients underwent a gait test with Optogait\u0026reg; (Optogait, Microgate, Bolzano, Italy). Spatio-temporal gait parameters during a gait cycle were recorded. This test was conducted over a distance of 10 metres at comfort and maximal velocity. Five successive measurements were recorded for each velocity and were averaged. Data collected were gait speed (meters per second), cadence (steps per minute), and step lengths in meters. Single stance duration and the oscillation phase were calculated as a percentage of the gait cycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Isokinetic dynamometer\u003c/h2\u003e \u003cp\u003eIsokinetic dynamometer measurements were carried out on the dominant lower limb using the Biodex System 4 dynamometer (Biodex Medical Systems, Inc, Shirley, NY). Each patient was placed on the device with the lever arm adjusted to their height. Participants were seated in an adjustable chair with the axis of rotation of the dynamometer aligned with the centre of the knee joint. (i.e., lateral femoral condyle). The knee and thigh were fixed to the seat and to the tip of the of lever arm of the dynamometer. To restrict body movements, participants were strapped to the chair using broad straps across the pelvis and upper body. Finally, the arms were kept crossed over the torso in order to avoid any movements. Any tests that did not meet the required performance conditions were excluded and were repeated after a rest period of four minutes. To familiarize participants with isokinetic extension movements of the lower limbs, and to perform warm-ups with specific movements, participants did several sub-maximal practice repetitions in order to get familiar with the isokinetic device before starting the evaluation. Participants were then asked to perform three maximal contractions at six predefined speeds (180, 150, 120, 90, 60 and 30\u0026deg;/s). Only the best result of the three trials was used for statistical analysis. All participants were verbally encouraged in a standard manner during each test and a 4-min recovery period was set between repetitions.\u003c/p\u003e \u003cp\u003eData were recorded and stored on a computer and were sampled at 100 Hz using an electronic interface card (Biodex Medical Systems Inc., X2151, Shirley, NY, USA). The maximal torque was identified as the highest value reached during the movement at each constant speed. The maximum instantaneous power was the product of torque and speed. The linear moment-speed relationship was calculated from the maximum torque value obtained at each speed. The power (P)-velocity (V) relation was based on a second order polynomial:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\varvec{P}=\\varvec{a}\\bullet \\varvec{V}\u0026sup2;+\\varvec{b}\\bullet \\varvec{V}+\\varvec{c}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere a, b and c are the regression coefficients of the polynomial. From this equation, the maximum power (P\u003csub\u003eMAX\u003c/sub\u003e), and the corresponding optimum speed (V\u003csub\u003eOPT\u003c/sub\u003e) were determined as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${\\varvec{V}}_{\\varvec{O}\\varvec{P}\\varvec{T}}=-\\frac{\\varvec{b}}{2\\varvec{a}} \\text{e}\\text{t} {\\varvec{P}}_{\\varvec{M}\\varvec{A}\\varvec{X}}=-\\frac{\\varvec{b}\u0026sup2;}{2\\varvec{a}}+\\varvec{c}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe theoretical maximum moment (M\u003csub\u003eMAX\u003c/sub\u003e) and the theoretical maximum velocity (V\u003csub\u003eMAX\u003c/sub\u003e) were obtained by extrapolating the linear relation where it meets, respectively, the abscissa axis at M\u0026thinsp;=\u0026thinsp;0 and the ordinate axis at V\u0026thinsp;=\u0026thinsp;0. All these relationships and parameters were processed using a Matlab script (R2008b, The Mathworks, Natick, Massachusetts, USA).\u003c/p\u003e \u003cp\u003eData collected were maximum muscular strength (Newton meter), optimum muscular strength (Newton), maximal power (Watt), maximum velocity (degrees per second), optimum speed (degrees per second). The maximal power was weighted by fat-free mass according to the formula:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\varvec{w}\\varvec{e}\\varvec{i}\\varvec{g}\\varvec{h}\\varvec{t}\\varvec{e}\\varvec{d} \\varvec{p}\\varvec{o}\\varvec{w}\\varvec{e}\\varvec{r} (\\varvec{W}\\varvec{a}\\varvec{t}\\varvec{t}/\\varvec{k}\\varvec{g})= \\frac{maximal power \\left(Watt\\right)}{\\text{f}\\text{a}\\text{t}-\\text{f}\\text{r}\\text{e}\\text{e} \\text{m}\\text{a}\\text{s}\\text{s} \\left(\\text{k}\\text{g}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe muscular quality was calculated according to the formula:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\varvec{M}\\varvec{u}\\varvec{s}\\varvec{c}\\varvec{l}\\varvec{e} \\varvec{q}\\varvec{u}\\varvec{a}\\varvec{l}\\varvec{i}\\varvec{t}\\varvec{y}=\\frac{\\varvec{m}\\varvec{a}\\varvec{x}\\varvec{i}\\varvec{m}\\varvec{u}\\varvec{m} \\varvec{m}\\varvec{u}\\varvec{s}\\varvec{c}\\varvec{l}\\varvec{e} \\varvec{s}\\varvec{t}\\varvec{r}\\varvec{e}\\varvec{n}\\varvec{g}\\varvec{t}\\varvec{h} \\left(\\varvec{N}\\varvec{e}\\varvec{w}\\varvec{t}\\varvec{o}\\varvec{n} \\varvec{m}\\varvec{e}\\varvec{t}\\varvec{e}\\varvec{r}\\right)}{\\text{f}\\text{a}\\text{t}-\\text{f}\\text{r}\\text{e}\\text{e} \\text{m}\\text{a}\\text{s}\\text{s}{ \\left(\\varvec{k}\\varvec{g}\\right)}^{} }$$\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using XLSTAT\u0026reg; (version 2021.1.1.1089). For comparative analyses, two different groups were defined: non-fallers (no falls) and fallers. Descriptive statistics were calculated for both groups. The categorical variables are reported as absolute frequencies. Quantitative variables are described by the number of participants, mean and standard deviation. The Kolmogorov-Smirnov test was used to assess the normality of the data. For continuous variables, parametric analyses were performed using the Student's t-test. For the independent samples t-test, the effect size was determined by calculating Cohen's d, that is, the mean difference between our two groups divided by the pooled standard deviation. We consider Cohen\u0026rsquo;s d of 0.2 to be a \"small\" effect, 0.5 to be \"medium\" and 0.8 to be \"large\". For all statistical analyses the significance threshold was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.002, in accordance with a Bonferroni correction (0.05/22\u0026thinsp;=\u0026thinsp;0.002) due to the number of variables.\u003c/p\u003e \u003cp\u003eLogistic regression was performed to understand and predict the effect of one or more explicative variables on a binary response variable (fall or not). The most common functions used to link probability p to the explanatory variables are the logistic function (we refer to the Logit model). Variables that showed a significant trend in the univariate analysis, after applying the Bonferroni correction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.2/22\u0026thinsp;=\u0026thinsp;0.009), were included in the binary logistic regression model (BMI, fat mass, mean weighted handgrip, double stance, muscle quality and muscle power). The response variable was a binary qualitative variable: the presence of a fall. The explanatory variables are quantitative variables. We also performed a Receiver operating characteristic (ROC) curve to determine if the value of a quantitative parameter was able to accurately discriminate fallers and non-fallers. ROC curves were constructed for the significant variables in univariate analysis. Area under the curve (AUC) was used to compare the different tests with each other.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e184 patients were included, 81% (n\u0026thinsp;=\u0026thinsp;150) were women and the mean age was 73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83 years.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Univariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFallers\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-fallers\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etest result\u003c/p\u003e \u003cp\u003et, IC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eSocio-demographic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73,64\u0026nbsp;\u0026plusmn;\u0026nbsp;6,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.72\u0026nbsp;\u0026plusmn;\u0026nbsp;7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.62\u0026nbsp;\u0026plusmn;\u0026nbsp;6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;-0.09; [-2.40;2.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Female. %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2; =\u0026nbsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (Kg\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.58\u0026nbsp;\u0026plusmn;\u0026nbsp;11.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.82\u0026nbsp;\u0026plusmn;\u0026nbsp;10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.84\u0026nbsp;\u0026plusmn;\u0026nbsp;10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;-3.85; [-10.57; -3.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg/m\u0026sup2;\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.53\u0026nbsp;\u0026plusmn;\u0026nbsp;4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.22\u0026nbsp;\u0026plusmn;\u0026nbsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.97\u0026nbsp;\u0026plusmn;\u0026nbsp;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;-3.35; [-3.56; -0.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eHandgrip\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Handgrip strength (N\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225.16\u0026nbsp;\u0026plusmn;\u0026nbsp;80.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210.73\u0026nbsp;\u0026plusmn;\u0026nbsp;66.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229.97\u0026nbsp;\u0026plusmn;\u0026nbsp;85.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;1.39; [-7.89;46.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean weighted Handgrip strength\u003c/p\u003e \u003cp\u003e(N/Kg\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.89\u0026nbsp;\u0026plusmn;\u0026nbsp;18.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.91\u0026nbsp;\u0026plusmn;\u0026nbsp;18.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.56\u0026nbsp;\u0026plusmn;\u0026nbsp;17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;3.49; [4.63;16.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eImpedance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat mass (%\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.78\u0026nbsp;\u0026plusmn;\u0026nbsp;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.44\u0026nbsp;\u0026plusmn;\u0026nbsp;5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.90\u0026nbsp;\u0026plusmn;\u0026nbsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;-2.71; [-6.11; -0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLean mass (%\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.29\u0026nbsp;\u0026plusmn;\u0026nbsp;9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.07\u0026nbsp;\u0026plusmn;\u0026nbsp;6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.58\u0026nbsp;\u0026plusmn;\u0026nbsp;10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;1.593; [0.01;0.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efat-free mass (%\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.81\u0026nbsp;\u0026plusmn;\u0026nbsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.71\u0026nbsp;\u0026plusmn;\u0026nbsp;2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.50\u0026nbsp;\u0026plusmn;\u0026nbsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;-2.12; [-2.33; -0.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eSpatio-temporal parameters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalking speed (m/s\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u0026nbsp;\u0026plusmn;\u0026nbsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026nbsp;\u0026plusmn;\u0026nbsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u0026nbsp;\u0026plusmn;\u0026nbsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;2.18; [0.01;0.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence (step/min \u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.82\u0026nbsp;\u0026plusmn;\u0026nbsp;10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.94\u0026nbsp;\u0026plusmn;\u0026nbsp;9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.45\u0026nbsp;\u0026plusmn;\u0026nbsp;11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;1.38; [-1.09;6.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep length (cm\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.75\u0026nbsp;\u0026plusmn;\u0026nbsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.68\u0026nbsp;\u0026plusmn;\u0026nbsp;7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.44\u0026nbsp;\u0026plusmn;\u0026nbsp;8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;1.94; [-0.05;5.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble stance (%\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.77\u0026nbsp;\u0026plusmn;\u0026nbsp;4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.55\u0026nbsp;\u0026plusmn;\u0026nbsp;5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.18\u0026nbsp;\u0026plusmn;\u0026nbsp;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;-2.90; [-3.98;-0.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimple Support (%\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.58\u0026nbsp;\u0026plusmn;\u0026nbsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.69\u0026nbsp;\u0026plusmn;\u0026nbsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.88\u0026nbsp;\u0026plusmn;\u0026nbsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;2.85; [0.36;2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOscillating (%\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.51\u0026nbsp;\u0026plusmn;\u0026nbsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.60\u0026nbsp;\u0026plusmn;\u0026nbsp;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.81\u0026nbsp;\u0026plusmn;\u0026nbsp;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;2.92; [0.39;2.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eIsokinetic dynamometer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle strength (Nm\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.56\u0026nbsp;\u0026plusmn;\u0026nbsp;38.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.42\u0026nbsp;\u0026plusmn;\u0026nbsp;24.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.94\u0026nbsp;\u0026plusmn;\u0026nbsp;41.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;2.10; [0.83;26.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle quality (Nm/Kg\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.29\u0026nbsp;\u0026plusmn;\u0026nbsp;7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.84\u0026nbsp;\u0026plusmn;\u0026nbsp;6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.78\u0026nbsp;\u0026plusmn;\u0026nbsp;6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;5.19; [3.68;8.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.0001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle power (W\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170.57\u0026nbsp;\u0026plusmn;\u0026nbsp;58.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.31\u0026nbsp;\u0026plusmn;\u0026nbsp;53.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.99\u0026nbsp;\u0026plusmn;\u0026nbsp;59.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;1.79;[-1.71;37.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted power (W/Kg\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.02\u0026nbsp;\u0026plusmn;\u0026nbsp;11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.04\u0026nbsp;\u0026plusmn;\u0026nbsp;9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.35\u0026nbsp;\u0026plusmn;\u0026nbsp;11.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;4.88; [5.55;13.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.0001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimal strength (Nm\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.26\u0026nbsp;\u0026plusmn;\u0026nbsp;18.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.87\u0026nbsp;\u0026plusmn;\u0026nbsp;15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.39\u0026nbsp;\u0026plusmn;\u0026nbsp;18.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;2.82; [2.56;14.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximal speed (\u0026deg;/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e322.69\u0026nbsp;\u0026plusmn;\u0026nbsp;62.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316.25\u0026nbsp;\u0026plusmn;\u0026nbsp;71.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e324.84\u0026nbsp;\u0026plusmn;\u0026nbsp;58.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;0.81; [-12.29;29.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimal speed (\u0026deg;/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173.84\u0026nbsp;\u0026plusmn;\u0026nbsp;31.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174.06\u0026nbsp;\u0026plusmn;\u0026nbsp;43.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173.76\u0026nbsp;\u0026plusmn;\u0026nbsp;27.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026nbsp;=\u0026nbsp;-0.05; [-11.06;10,46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNote\u003c/span\u003e: IC: confidence interval; SD: standard deviation; Kg: kilograms; m: meter; s: second; cm: centimetre; N: Newton; Nm: Newton meter; W: Watt; *: significate result \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.003 with bonferonni correction\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe weight (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and BMI (p\u0026thinsp;=\u0026thinsp;.001), were significantly higher among fallers, with medium effect size for weight and BMI. Analysis of spatio-temporal gait parameters revealed a longer double stance time (p\u0026thinsp;=\u0026thinsp;.004), and a shorter simple stance (p\u0026thinsp;=\u0026thinsp;.005) and oscillating phase (p\u0026thinsp;=\u0026thinsp;.004) among fallers but a small effect size. The weighted mean handgrip strength was significantly lower among fallers (p\u0026thinsp;=\u0026thinsp;.001; effect size\u0026thinsp;=\u0026thinsp;.591). Finally, the muscle quality (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and weighted power (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were lower among fallers, with large effect size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. ROC Curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe variable muscle quality has the highest area under the curve (0.794), with a threshold value of 13.35 Newton/Kg. The comparison test was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Weighted power has a calculated AUC 0.788 with a threshold value of 23.41 Newton meter/Kg. The comparison test was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The threshold value of the mean weighted handgrip strength was 29.11 Newton meter/Kg. The area under the curve was 0.765. The comparison test was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Finally, the following variables had a significant comparison test: mean weighted handgrip strength (AUC\u0026thinsp;=\u0026thinsp;0.765), weight (AUC\u0026thinsp;=\u0026thinsp;0.689) and BMI (AUC\u0026thinsp;=\u0026thinsp;0.682). All ROC curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.862;1.123]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.115\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e[1.037;1.198]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean weighted Handgrip strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.089\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e[1.030;1.151]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble stance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.940;1.144]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle quality*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.007*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.764\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e[0.629;0.928]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.814;1.014]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNote\u003c/span\u003e: OR: odds ratio; IC: confidence interval; BMI: body mass index; *: significant result \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe equation of the model was: Pr(Fall) =:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{(1 + exp(-(-1.3-0.01*BMI+0.09*Mean wheigthed handgrip+0.1*Fat Mass+0.04*Double stance-0.3*Muscle quality-0.1*Wheighted power)\\left)\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResults of the logistic regression were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). The ROC curve established from the logistic regression model is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (AUC\u0026thinsp;=\u0026thinsp;0.837). Muscle quality has a negative impact on the risk of falling (-0.269) (p\u0026thinsp;=\u0026thinsp;.007*, OR\u0026thinsp;=\u0026thinsp;0,76, IC [0.63;0.93]). Mean weighted handgrip strength also has a significant (p\u0026thinsp;=\u0026thinsp;.003*, OR\u0026thinsp;=\u0026thinsp;1,09, IC [1.03;1.15]) as a Fat mass (p\u0026thinsp;=\u0026thinsp;0.003*, OR\u0026thinsp;=\u0026thinsp;1.115, IC [1.03;1.19]. The other variables were not significant (BMI, double stance and weighted power).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe aim of this study was to identify the parameters (muscle performance or spatio-temporal walking parameters) that best discriminate fallers and non-fallers in older adults. As hypothesized, muscle quality appears to be a determining factor in the risk of falling, with our results showing that lower muscle quality is associated with a higher risk of falling (effect size = 0.891). Muscle quality is also significant with logistic regression (p = .007, OR = 0,76, IC [0.63;0.93]).\u003c/p\u003e \u003cp\u003eMuscle quality is the most predictive risk factor for falls, as demonstrated by various statistical analyses. These results confirm those of the literature showing that muscle quality is an important factor on physical function in frail, obese, older adults [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and that muscle quality is strongly related to an increased risk of falling in older women (68 ± 6.2 years) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This study is the first to compare muscle quality with other risk factors such as muscle power, handgrip strength, or spatio-temporal parameters of walking. This is noteworthy because, although muscular strength has been associated with better performance on functional tasks [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], an assessment of muscle quality, rather than a muscle strength evaluation, may be more appropriate. Additionally, we found differences in patients' weights, leading to variations in impedance measurements. Fallers often had a higher BMI and fat mass. While a higher body mass index can be associated with thicker muscles [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], it's evident that muscle strength is a more crucial health indicator than muscle size in older individuals [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Notably, just having more muscle doesn't mean it's stronger, especially since strength and size don't always correlate linearly [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The significant decline in strength, even without a matching decrease in muscle mass, highlights the importance of muscle quality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Also, individuals who fall more frequently tend to have a higher BMI, pointing to the issue of sarcopenic obesity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This mix of weaker muscles and increased weight can lead to further functional decline [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn older individuals, muscle quality tends to deteriorate for various reasons. First and foremost, the increasing infiltration of fat into skeletal muscles, combined with a decrease in the proportion of Type II fibers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], undermines muscle quality. Furthermore, as aging progresses, there's a noticeable reduction in the number of motor units [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and incomplete re-innervation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These phenomena, together with the reorganization of the remaining motoneurons toward the abandoned muscle fibers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], lead to a qualitative impairment in muscle functionality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Finally, a deficit in the activation of motor units is commonly observed in the older adults [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, muscular quality, which encompasses various aspects of muscle function and performance, including strength, muscle composition, functional performance, and other neuromuscular factors, should be considered a more relevant indicator of fall risk than parameters such as muscle strength or power. Indeed, muscular quality is defined as the ability of a muscle to generate force per unit of muscle mass, and it is influenced not only by a muscle's strength or power but also by other factors such as muscle composition, neuromuscular coordination, and the presence of fibrosis or fat infiltration. By integrating these elements, muscular quality provides a more comprehensive and nuanced view of an older individual's motor capabilities. Several studies have suggested that superior muscular quality may compensate for lower muscle quantity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], meaning that muscles of \"good quality\" can effectively and precisely respond to functional demands, which are essential for maintaining balance and preventing falls. The assessment of muscle quality, requiring a certain number of resources, should be used to understand why an individual is at risk of falling in order to best tailor their care. Indeed, a meta-analysis has revealed that resistance training can significantly improve strength and muscle quality in elderly patients with sarcopenia [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the same manner, the mean weighted handgrip strength was associated with a risk of falling whereas the simple handgrip strength was not. Indeed, this parameter is significant in the univariate analysis (p = .001). Furthermore, logistic regression confirmed these results (p = 0.003). It is known that weak handgrip strength is a strong predictor of an increase in the duration of hospitalisation, functional limitations, poor quality of life, and death [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Of note, handgrip strength and lower extremity muscle strength have shown moderate to strong correlations in older adults [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, studies suggest that handgrip strength should be used with caution to assess overall strength [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Finally, Ostolin et al. showed that the evolution of handgrip strength over time does not seem to predict the evolution of lower limb strength involved in the risk of falling [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our findings are in line with these and also highlight the importance of muscle quality rather than muscle quantity. Although muscular quality is a key factor, other aspects, particularly spatio-temporel parameters, should not be overlooked when assessing fall risk.\u003c/p\u003e \u003cp\u003eFinally, there are significant trend in most of the spatio-temporal gait parameters between the two groups. In the non-faller group, the single stance time was longer (p = .005, effect size 0.473) and double stance was shorter (p = .004, effect size = 0.466). This is consistent with the literature. Many studies have already shown the relationship between gait disorders and the risk of falling in older people, supporting the idea that decrease in gait speed and modification of gait parameters are strong predictors of falls [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, more than gait speed, it is the gait quality and single and double stance durations that is important. Variability in stride length and double stance duration are important predictors of gait among older adults [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, gait parameters obtained with the Optogait are based on a number of limited and repeated stages and do not take into account other important variables such as joint angle [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, the walking speed and the time of double support did not appear significant in the logistic regression model. Thus, a simple walking speed or spatio-temporal parameters are not sufficient to detect fallers. Muscle quality seems to be much more effective.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimits and biases\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere could be several sources of bias in our study. Indeed, many tests that can accurately discriminate using retrospective data do not demonstrate prospective predictive ability for falls in longitudinal studies. Therefore, it is necessary to conduct a prospective study with the various known variables of fall risk including muscle quality. Study was retrospective and a prospective study is necessary to establish temporal relationships between muscle quality and falls, as we did not have information on the implementation of preventive measures after previous falls. As with any observational study, it is impossible to assess risk factors and events preceding falls. While there is a significant link between muscle function and the risk of falling (with a clinical impact, in terms of prevention through adapted physical activity programs), no causal link between the actual observed fall parameters and their prior occurrence at the first fall can be established.\u003c/p\u003e \u003cp\u003eFinally, the appropriate definition of muscle quality is a subject of ongoing debate. In our current study, muscle quality was defined as muscle strength expressed in relation to fat-free mass. Thus, a minor variation in fat-free mass results in a large variation in quality. This implies caution is needed in the interpretation of the results.\u003c/p\u003e "},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, this retrospective, observational study of older adults showed that muscle quality is the best predictor of fall risk, more than muscle mass and spatial and temporal gait parameters. Our results confirm that muscle quality is a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the ageing population. Further studies are needed to establish whether an increase in muscle quality could improve gait parameters and decrease fall risk. These results would be useful in recommending an appropriate physical activity program for fall prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest and Source of Funding:\u003c/strong\u003e none declared\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGMENTS\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank patients and their caregivers. We would make a special acknowledgment to all the teams of the frailty platform unit for their everyday work with the patients. This study was made possible thanks to the Aging well project (Malakoff collaboration).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med. 29 d\u0026eacute;c 1988;319(26):1701‑7. \u003c/li\u003e\n\u003cli\u003eTinetti ME. Clinical practice. Preventing falls in elderly persons. N Engl J Med. 2 janv 2003;348(1):42‑9. \u003c/li\u003e\n\u003cli\u003eBloch F, Thibaud M, Tournoux-Facon C, Br\u0026egrave;que C, Rigaud AS, Dugu\u0026eacute; B, et al. Estimation of the risk factors for falls in the elderly: can meta-analysis provide a valid answer? Geriatr Gerontol Int. avr 2013;13(2):250‑63. \u003c/li\u003e\n\u003cli\u003eNewman AB, Kupelian V, Visser M, Simonsick EM, Goodpaster BH, Kritchevsky SB, et al. Strength, But Not Muscle Mass, Is Associated With Mortality in the Health, Aging and Body Composition Study Cohort. 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Motor unit number estimates in the tibialis anterior muscle of young, old, and very old men. Muscle Nerve. avr 2005;31(4):461‑7. \u003c/li\u003e\n\u003cli\u003eMidrio M. The denervated muscle: facts and hypotheses. A historical review. Eur J Appl Physiol. sept 2006;98(1):1‑21. \u003c/li\u003e\n\u003cli\u003eKaya RD, Nakazawa M, Hoffman RL, Clark BC. Interrelationship between muscle strength, motor units, and aging. Exp Gerontol. sept 2013;48(9):920‑5. \u003c/li\u003e\n\u003cli\u003eHarridge SD, Kryger A, Stensgaard A. Knee extensor strength, activation, and size in very elderly people following strength training. Muscle Nerve. juill 1999;22(7):831‑9. \u003c/li\u003e\n\u003cli\u003eOstolin TLVDP, Gonze B de B, de Oliveira Vieira W, de Oliveira ALS, Nascimento MB, Arantes RL, et al. Association between the handgrip strength and the isokinetic muscle function of the elbow and the knee in asymptomatic adults. SAGE Open Med. 27 f\u0026eacute;vr 2021;9:2050312121993294. \u003c/li\u003e\n\u003cli\u003eScott D, Johansson J, McMillan LB, Ebeling PR, Nordstrom A, Nordstrom P. Mid-calf skeletal muscle density and its associations with physical activity, bone health and incident 12-month falls in older adults: The Healthy Ageing Initiative. Bone. 1 mars 2019;120:446‑51. \u003c/li\u003e\n\u003cli\u003eZhao H, Cheng R, Song G, Teng J, Shen S, Fu X, et al. The Effect of Resistance Training on the Rehabilitation of Elderly Patients with Sarcopenia: A Meta-Analysis. Int J Environ Res Public Health. 22 nov 2022;19(23):15491. \u003c/li\u003e\n\u003cli\u003eIbrahim K, May C, Patel HP, Baxter M, Sayer AA, Roberts H. A feasibility study of implementing grip strength measurement into routine hospital practice (GRImP): study protocol. Pilot Feasibility Stud. 2016;2:27. \u003c/li\u003e\n\u003cli\u003eLeong DP, Teo KK, Rangarajan S, Lopez-Jaramillo P, Avezum A, Orlandini A, et al. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet Lond Engl. 18 juill 2015;386(9990):266‑73. \u003c/li\u003e\n\u003cli\u003eFragala MS, Alley DE, Shardell MD, Harris TB, McLean RR, Kiel DP, et al. Comparison of Handgrip and Leg Extension Strength in Predicting Slow Gait Speed in Older Adults. J Am Geriatr Soc. janv 2016;64(1):144‑50. \u003c/li\u003e\n\u003cli\u003eFelicio DC, Pereira DS, Assump\u0026ccedil;\u0026atilde;o AM, de Jesus-Moraleida FR, de Queiroz BZ, da Silva JP, et al. Poor correlation between handgrip strength and isokinetic performance of knee flexor and extensor muscles in community-dwelling elderly women. Geriatr Gerontol Int. janv 2014;14(1):185‑9. \u003c/li\u003e\n\u003cli\u003eStudenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, et al. Gait Speed and Survival in Older Adults. JAMA. 5 janv 2011;305(1):50‑8. \u003c/li\u003e\n\u003cli\u003eVerghese J, Holtzer R, Lipton RB, Wang C. Quantitative Gait Markers and Incident Fall Risk in Older Adults. J Gerontol Ser A. 1 ao\u0026ucirc;t 2009;64A(8):896‑901. \u003c/li\u003e\n\u003cli\u003eMbourou GA, Lajoie Y, Teasdale N. Step length variability at gait initiation in elderly fallers and non-fallers, and young adults. Gerontology. f\u0026eacute;vr 2003;49(1):21‑6. \u003c/li\u003e\n\u003cli\u003eKang HG, Dingwell JB. Effects of walking speed, strength and range of motion on gait stability in healthy older adults. J Biomech. 20 oct 2008;41(14):2899‑905. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"muscle quality, older adults, fall, ageing, risk fall","lastPublishedDoi":"10.21203/rs.3.rs-3956550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3956550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFalling is an important public health issue because of its high frequency and severe consequences. Evaluating muscle performance is important when assessing the risk of falling. The aim of this study was to identify factors (namely muscle functionality and spatio-temporal gait attributes) that best discriminate between fallers and non-fallers in older adults. The main hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e184 patients were included, 81% (n\u0026thinsp;=\u0026thinsp;150) were women and the mean age was 73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83 years. We compared the body composition, mean handgrip strength, spatio-temporal parameters and muscle function (strength, quality and power) of fallers and non-fallers. Muscle quality was calculated as the ratio of maximum strength to fat-free mass. Mean handgrip strength and power were also weighted by fat-free mass.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe falling patients had lower muscle quality, weighted power and mean weighted handgrip strength than the non-falling patients. The univariate analysis, logistic regression and ROC curves enabled us to highlight the importance of muscle quality rather than quantity. The ROC curves have shown that muscle quality is the most predictive factor of falling.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study of older adults showed that muscle quality is the best predictor of fall risk, more than muscle mass and spatial and temporal gait parameters. Our results confirm that muscle quality is a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the ageing population.\u003c/p\u003e","manuscriptTitle":"Assessing Muscle Quality as a Key Predictor of Fall Risk in Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 15:54:46","doi":"10.21203/rs.3.rs-3956550/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-04-02T05:57:13+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-03-19T06:01:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-28T09:59:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Geriatric Medicine","date":"2024-02-20T11:17:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-14T12:39:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Geriatric Medicine","date":"2024-02-13T15:25:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"79ed6c4c-3a27-47ac-be94-651f74b73f0b","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-01T07:15:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 15:54:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3956550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3956550","identity":"rs-3956550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}