Development and Validation of a Multidimensional Physical Pre-Frailty Index for Early Detection of Functional Decline in Community-Dwelling Older Adults

Development and Validation of a Multidimensional Physical Pre-Frailty Index for Early Detection of Functional Decline in Community-Dwelling Older Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and Validation of a Multidimensional Physical Pre-Frailty Index for Early Detection of Functional Decline in Community-Dwelling Older Adults Myeounggon Lee, Hwayoung Park, Jae-Young Lim, Jaewon Beom, Changhong Youm This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9550110/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Frailty assessment is essential for evaluating functional decline in older adults and informing prevention and intervention strategies; however, existing approaches are often limited to physical domains or rely on clinical and laboratory-based assessments, restricting accessibility and scalability. Given that frailty is a multidimensional condition encompassing physical, cognitive, and psychosocial domains, a comprehensive and quantifiable index is needed to detect early functional decline in community-dwelling populations. This study aimed to develop and validate a Physical Pre-Frailty Index (P-FI) in 729 adults aged ≥ 65 years. The P-FI was constructed using demographics, comorbidities, patient-reported outcomes, and gait performance, with outcome variables defined as the composite z-score of grip strength, preferred gait speed, and physical activity. Regression modeling identified key contributors, including gait parameters across multiple speed conditions, cognitive function, and quality of life, generating a normalized index ranging from 0 to 1. The P-FI demonstrated strong discriminative validity, with higher scores in individuals with a history of falls (OR = 4.80, p = 0.006). Quartile stratification revealed progressive functional decline, with the highest-risk group showing impairments in sleep quality, psychological stress, and lower-extremity strength (AUC = 0.86). The P-FI may support early detection and targeted interventions. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Frailty Pre-frail Functional Decline Multidimensional Assessment Older Adults Digital Health Figures Figure 1 Figure 2 Figure 3 Introduction Population aging is accelerating globally, with the World Health Organization (WHO) projecting that by 2030, one in six people will be aged 60 years or older 1 , 2 . This trend is particularly pronounced in South Korea, which is rapidly transitioning to a super-aged society 3 . As populations age, maintaining functional capacity and independence has become a major public health priority 2 . Frailty, characterized by reduced physiological reserve and increased vulnerability to adverse outcomes, is strongly associated with functional decline, disability, hospitalization, and mortality 4 , 5 . In South Korea, the prevalence of frailty and pre-frailty has been reported to be as high as 56.0% 6 . Key physical impairments—including reduced muscle strength, impaired gait, and low physical activity—are closely linked to increased risks of falls and functional decline 7 – 10 . The Fried frailty phenotype is a widely used approach for assessing physical frailty, classifying individuals as non-frail, pre-frail, or frail based on five criteria, including weakness, slowness, and low physical activity 4 , 11 – 14 . However, it has several limitations, such as its categorical nature and limited ability to capture the multidimensional aspects of frailty 15 – 18 . To overcome these limitations, continuous frameworks such as the frailty index (FI) have been developed. The FI quantifies frailty based on a deficit accumulation approach, incorporating a wide range of health-related variables 17 , 18 . Although widely used, many FI models rely heavily on clinical and laboratory data, which may limit their applicability in non-clinical settings and may not directly reflect physical functional capacity 15 – 22 . Recent studies have proposed FI models derived from biomechanical variables obtained through simple physical tasks 23 , 24 . These models have been shown to reflect frailty status and related clinical characteristics 21 , 23 – 29 . However, frailty is inherently multidimensional and requires assessment across multiple domains, including gait, muscle strength, cognitive function, and quality of life 15 . In particular, gait tasks performed under varying speed conditions allow for a more comprehensive evaluation of locomotor function than single-speed assessments 30 – 33 . Furthermore, individuals with physical pre-frailty exhibit reduced functional performance and impaired resilience, underscoring the need for indices capable of identifying early functional decline 34 – 36 . Therefore, this study aimed to develop a refined, multidimensional quantitative index that more precisely captures functional decline, based on key frailty-related domains (e.g., weakness, slowness, and physical inactivity), and provides a visual representation of such deterioration. The validity of the proposed physical pre-frailty index (P-FI) was evaluated using a known-groups validation approach. Previous studies have reported that frailty is associated with both fear of falling and fall experience 13 , 15 , 16 ,37 38–42 , highlighting the need to examine whether the functional decline underlying frailty is linked to fall-related outcomes. Accordingly, we hypothesized that, among older adults aged ≥ 65 years, individuals with a history of falls would exhibit higher P-FI values than non-fallers. Furthermore, participants were stratified into quartiles based on the P-FI, and we also hypothesized a progressive decline in functional outcomes from the lowest to the highest quartile, with the highest group exhibiting worse functional characteristics compared with lowest group (Fig. 1 ). Methods Participants Community-dwelling older adults aged 65–89 years were recruited from Busan and Gwangju, South Korea, between 2018 and 2019. Individuals were excluded if they were unable to walk independently, regardless of whether assistive devices were used. Additional exclusion criteria included a history of musculoskeletal or neurological disorders that could influence gait or physical fitness assessments within the previous six months, as well as severe cognitive impairment or dementia that would limit the ability to provide informed consent. The study protocol was reviewed and approved by the Institutional Review Board of Dong-A University (IRB No. 2–104709–AB–N–01–201808–HR–023–04). The study was registered with the Clinical Research Information Service (CRIS; KCT0004529, registered on September 13, 2018), a primary registry participating in the WHO International Clinical Trials Registry Platform. Written informed consent was obtained from all participants prior to participation. Gait task: over ground walking test Participants completed an overground walking task along a 20-m walkway under three speed conditions: 80% (slow), preferred, and 120% (fast) of their self-selected walking speed. Gait data were collected using a shoe-type inertial measurement unit (IMU) system (DynaStab™, JEIOS, Busan, Republic of Korea; 100 Hz), comprising wearable data loggers (Smart Balance® SB-1) with embedded IMU sensors (IMU-3000™, InvenSense, USA). The sensors, mounted in both outsoles, recorded tri-axial acceleration (± 6 g) and angular velocity (± 500°/s), and transmitted data wirelessly via Bluetooth®. Shoe sizes were adjusted individually (225–280 mm) 43 . The initial and final 2 m were excluded to account for acceleration and deceleration. Target speeds were determined based on preferred walking speed and guided by a metronome during familiarization trials, but not during actual testing 30 . For each condition, spatiotemporal gait parameters—including stride length, stride time, double-support phase, stance phase, and gait variability—were derived based on established methods 30 , 44 , 45 and incorporated as multidimensional components of the P-FI. Demographic and clinical characteristics Basic demographic variables collected for this study included age, sex, body mass index (BMI), waist-to-hip ratio, and educational attainment. Clinical conditions were recorded as binary variables indicating the presence or absence of each condition (0 = no, 1 = yes), allowing the prevalence of these conditions to be summarized within the sample. Patient-reported outcomes (PROs) Self-reported questionnaires were administered to evaluate several health-related domains, including cognitive status, sleep quality, stress response, and health-related quality of life. Global cognition was assessed using the Mini-Mental State Examination (MMSE) 46 . Sleep disturbance was measured using the Insomnia Severity Index (ISI) 47 , and stress responses were evaluated using the Modified Stress Response Inventory (SRI-MF) 48 . Health-related quality of life was assessed using the Short Form-36 (SF-36) 49 . Participants were additionally asked to report fall-related information, including the occurrence of falls within the past six months, the frequency of such events, and their level of fear associated with falling. Physical fitness test A battery of physical fitness assessments was administered to all participants. The tests included handgrip strength, the 30-s bicep curl test, the five-times sit-to-stand test, standing time from a long sitting position (ST-LSP), single-leg balance, the 3-m timed up and go (TUG) test, and the 6-min walk test (6MWT) 44 . Construction of the Physical Pre-Frailty Index The P-FI was developed to quantify early-stage physical vulnerability using three frailty-related variables: handgrip strength (weakness), walking speed (slowness), and physical activity level (inactivity). Handgrip strength was measured using a digital dynamometer 4 , 44 , walking speed was calculated from the preferred-speed overground walking task 30 , 50 , and physical activity was assessed using the International Physical Activity Questionnaire–Short Form (IPAQ-SF) 12 , 51 . Participants were classified according to frailty phenotype criteria as non-frail, pre-frail, or frail 4 . To focus on early functional decline, frail individuals were excluded. The three variables were standardized (z-scores) and summed to construct the P-FI, which was used as the dependent variable in subsequent analyses 52 . To construct the P-FI models while minimizing circularity, variables directly related to its components (handgrip strength, gait speed, and physical activity) or highly correlated with them (r ≥ 0.70), exhibiting multicollinearity (variance inflation factor ≥ 3), or conceptually overlapping were excluded. The remaining variables—including gait parameters, demographics, comorbidities, and PROs—were standardized (z-scores). Multiple regression analyses were performed to develop domain-specific models predicting the composite of the three P-FI components. Four models were constructed: comorbidity-, PRO-, gait-, and combined models, with the combined model adjusted for age, sex, and BMI. Based on the regression equations derived from each model, the predicted P-FI values were subsequently transformed into an index ranging from 0 to 1 using min–max normalization (Eq. 1). The index was constructed so that values closer to 1 indicated poorer physical status and a higher likelihood of frailty, following methodological approaches used in previous studies 23 , 24 . \(\:Physical\:pre-frail\:index(P-FI)=1-\frac{\widehat{y}-{\widehat{y}}_{min}}{{\widehat{y}}_{max}-{\widehat{y}}_{min}}\:\) (Eq. 1) Validation of the Physical Pre-Frailty Index To evaluate the validity of the constructed P-FI, two validation approaches were performed. First, known-groups validity was examined by comparing P-FI scores between participants with and without a history of falls. Previous studies have reported that older adults who have experienced falls tend to exhibit reduced gait performance as well as increased depressive symptoms and psychological withdrawal compared with age-matched controls 38,40–42,53−57 . Therefore, participants were classified into non-faller and faller groups, and differences in P-FI scores between the two groups were examined. Second, participants were categorized into quartiles based on their P-FI scores (Q1: lowest 25%; Q2: 25th–50th percentile; Q3: 50th–75th percentile; Q4: highest 25%). Demographic characteristics, PROs, and physical function variables—excluding those used to construct the P-FI—were then compared across the quartile groups. Particular attention was given to differences between the lowest and highest quartiles (Q1 vs. Q4) to examine whether the P-FI effectively discriminated between individuals with relatively low and high levels of physical vulnerability. Statistical analysis All statistical analyses were performed using IBM SPSS Statistics for Windows (version 29.0; IBM Corp., Armonk, NY, USA). The Shapiro–Wilk test was used to assess the normality of continuous variables. Categorical variables were compared using the chi-square test. For continuous variables, either the independent t-test or the Mann–Whitney U test was applied depending on the normality of the data distribution. These tests were used to compare differences between participants with and without a history of falls (non-faller vs. faller). To examine the association between P-FI and fall history, binomial logistic regression analysis was conducted. Odds ratios (ORs) were calculated for the P-FI derived from each model — (1) comorbidity-based model, (2) PRO-based model, (3) gait-factor model, and (4) combined model—to evaluate the extent to which higher P-FI values were associated with increased odds of belonging to the faller group. Effect sizes were calculated using Cohen’s d and interpreted as trivial (< 0.20), small (0.20–0.49), medium (0.50–0.79), and large (≥ 0.80) 58 . In addition, participants were categorized into quartiles based on the P-FI score. One-way ANOVA was used to assess overall differences across P-FI quartiles, with emphasis on general patterns rather than multiple pairwise comparisons. Interpretation focused on effect sizes, prioritizing variables with at least moderate effects, and contrasts between the lowest (Q1) and highest (Q4) quartiles. To further compare the lowest and highest P-FI quartiles (Q1 vs. Q4), binomial logistic regression analysis was conducted. Demographic characteristics, PROs, and physical function variables—excluding those used to construct the P-FI—were examined to identify factors that distinguished the two groups. First, univariate logistic regression analyses were performed for variables that showed significant differences in the main effect analysis. Subsequently, significant variables were entered into a stepwise logistic regression analysis to identify the most relevant predictors. Age, sex, and BMI were adjusted as covariates. Based on the final classification model, receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative ability of the identified variables, and the area under the curve (AUC) was calculated. Statistical significance was set at p < 0.05. Results Data Preprocessing A total of 765 participants were initially screened for inclusion in this study. Among them, 20 individuals were excluded because they did not meet the eligibility criteria, including severe cognitive impairment (MMSE < 20; n = 19) or missing cognitive function data (n = 1). As a result, 745 participants were deemed eligible for further evaluation. Subsequently, 16 additional participants were excluded due to incomplete physical fitness assessments (n = 3) or classification as frail rather than pre-frail (n = 13). Consequently, 729 participants completed all required assessments and were included in the P-FI analysis. The final sample had a mean age of 73.1 ± 5.1 years and a mean body mass index (BMI) of 24.8 ± 3.1 kg/m², with females accounting for 66.9% of the participants. The prevalence of pre-frailty in the study population was 42.1%. Physical Pre-Frailty Index models Regression models were developed using variables from three domains—comorbidities, PROs, and gait parameters—to construct the P-FI. A combined model including variables from all domains was also developed. The composite score was calculated by summing the z-standardized values of handgrip strength, gait speed, and physical activity level, with higher values indicating better functional status; however, this directionality was reversed when the score was transformed into the P-FI, such that higher P-FI values indicate worse functional status. In the disease-based model, the total number of comorbidities was significant predictors of the composite P-FI score derived from the z-score–standardized values of handgrip strength, gait speed, and physical activity level (β = -0.20, SE = 0.07, p = 0.003, adjusted R 2 = 0.02, F = 8.610, p = 0.003). In the PRO-based model, quality of life (β = 0.61, SE = 0.08, p < 0.001), fear of falling (β = -0.42, SE = 0.08, p < 0.001), and cognitive function (β = 0.36, SE = 0.07, p < 0.001) were significant predictors of the composite score (adjusted R 2 = 0.20, F = 62.515, p < 0.001). In the gait-factor model, double support phase (β = -0.80, SE = 0.08, p < 0.001), the CV of double support phase (β = -0.27, SE = 0.07, p < 0.001), and stride time (β = -0.27, SE = 0.09, p = 0.002) during the slow-speed walking task; the CV of stride time during the preferred-speed walking task (β = -0.23, SE = 0.07, p = 0.001); and stride time (β = -0.46, SE = 0.08, p < 0.001) and the CV of stride length (β = -0.25, SE = 0.07, p < 0.001) during the fast-speed walking task were significant predictors (adjusted R 2 = 0.26, F = 43.884, p < 0.001). In the combined model, double support phase during the slow-speed walking task (β = -0.56, SE = 0.07, p < 0.001), the CV of stride length during the preferred-speed walking task (β = -0.27, SE = 0.06, p < 0.001), stride time during the fast-speed walking task (β = -0.35, SE = 0.07, p < 0.001), quality of life (β = 0.54, SE = 0.07, p < 0.001), and cognitive function (β = 0.28, SE = 0.06, p < 0.001) were significant predictors of the composite P-FI score (adjusted R 2 = 0.38, F = 55.566, p < 0.001) (Eq. 2 and Fig. 2 ). \(\:{\varvec{P}\varvec{h}\varvec{y}\varvec{s}\varvec{i}\varvec{c}\varvec{a}\varvec{l}\:\varvec{p}\varvec{r}\varvec{e}-\varvec{f}\varvec{r}\varvec{a}\varvec{i}\varvec{l}\:\varvec{i}\varvec{n}\varvec{d}\varvec{e}\varvec{x}}_{\varvec{C}\varvec{o}\varvec{m}\varvec{b}\varvec{i}\varvec{n}\varvec{e}\varvec{d}}=\:\) 5.851 – (0.051*Age) – (0.839*Sex) – (0.023*BMI) – (0.564* double support phase at SWS) – (0.269* CV of stride length at PWS) – (0.353* stride time at FWS) + (0.540*Quality of life) + (0.278 * Cognitive function) (Eq. 2) Validation of the Physical Pre-Frailty Index: Non-faller vs. Faller Among the 729 participants, 140 individuals reported experiencing at least one fall within the past 12 months (prevalence = 19.2%). Group comparisons revealed significant differences in the demographic domain, including body fat (p = 0.021, d = 0.218) and sex (p = 0.040, d = 0.153). In the comorbidy domain, the faller group showed a higher prevalence of diabetes mellitus (p = 0.021, d = 0.171) and osteoporosis (p = 0.006, d = 0.204), as well as a greater number of comorbidities (p = 0.019, d = 0.222), compared with the non-faller group. Among the frailty phenotypes, slowness was significantly different between the two groups (p = 0.005, d = 0.211). Within the PRO domain, the faller group demonstrated poorer cognitive function, sleep, quality of life, and greater fear of falling compared with the non-faller group (all p < 0.05; d range = 0.213–0.609). In the physical function domain, the faller group exhibited significantly lower physical performance than the non-faller group, including habitual walking speed, grip strength, 30-second bicep curls, the 3-m timed up-and-go test, single-leg stance, and the 6-min walking test (all p < 0.05; d range = 0.222–0.337) (Table 1 ). Table 1 Baseline demographic, clinical, mobility, sleep, and physiological characteristics of study participants Variables Non-faller (n = 589) Faller (n = 140) p value Effect-size: Cohen's d Demographics Age (years) 73.0 ± 5.0 73.6 ± 5.3 0.173 0.128 BMI (kg/m 2 ) 24.7 ± 3.2 24.9 ± 2.9 0.609 0.048 Body fat (%) 31.0 ± 7.1 32.6 ± 2.9 0.021 0.218 Waist to hip ratio (n.u.) 0.93 ± 0.07 0.93 ± 0.06 0.761 0.029 Sex (female, %) 65.2 74.3 0.040 0.153 Highest level of education attained (%) Less than elementary school 3.9 3.6 0.343 0.177 Elementary school 35.0 35.7 Middle school 25.6 20.8 High school 24.3 32.1 Bachelor's degree 9.3 5.7 Master’s degree or above 1.9 2.1 Comorbidities Hypertension 44.7 41.4 0.490 0.051 Diabetes mellitus 16.6 25.0 0.021 0.171 Glaucoma/cataract 1.5 0.7 0.457 0.055 Cardiovascular disease 11.0 15.7 0.125 0.114 Osteoporosis 9.2 17.1 0.006 0.204 Low back pain 6.1 9.3 0.178 0.100 Number of comorbidities (n) 1.3 ± 1.1 1.6 ± 1.3 0.019 0.222 Frailty phenotypes Weakness (%) 28.7 34.3 0.193 0.097 Slowness (%) 9.5 17.9 0.005 0.211 Physical inactivity (%) 8.8 10.0 0.664 0.032 Patient-reported outcomes (PROs) Cognition (MMSE, score) 26.9 ± 2.4 26.4 ± 2.6 0.024 0.213 Physical activity (IPAQ, METs/week) 2853.1 ± 2385.7 2577.5 ± 2517.8 0.225 0.090 Sleep (ISI, score) 3.7 ± 4.6 5.5 ± 5.9 < 0.001 0.359 Stress (SRI-MF, score) 4.8 ± 8.7 8.6 ± 12.7 < 0.001 0.396 Quality of life (SF-36, score) 78.0 ± 12.8 72.9 ± 15.3 < 0.001 0.382 Fear of falling (score) 0.65 ± 1.00 1.31 ± 1.35 < 0.001 0.609 Physical function Habitual walking speed (m/s) 1.21 ± 0.20 1.15 ± 0.18 0.002 0.298 Grip strength (kg) 26.7 ± 7.3 24.3 ± 6.7 < 0.001 0.337 30-second bicep curls 26.4 ± 7.6 24.2 ± 6.8 0.002 0.285 3-meter timed up and go test (sec) 7.2 ± 1.9 7.6 ± 1.7 0.010 0.244 Single-leg stance (sec) 19.7 ± 21.8 14.4 ± 16.7 0.007 0.254 5-times sit-to-stand (sec) 9.7 ± 3.6 10.1 ± 3.6 0.200 0.121 ST-LSP (sec) 3.5 ± 2.0 3.8 ± 1.9 0.089 0.160 6-min walking test (m) 469.7 ± 104.7 447.0 ± 90.4 0.018 0.222 Components of Physical Pre-Frail Index (n.u.) Comorbidities 0.19 ± 0.16 0.22 ± 0.18 0.019 0.260 PROs 0.28 ± 0.14 0.34 ± 0.17 < 0.001 0.430 Gait parameters 0.28 ± 0.11 0.29 ± 0.12 0.075 0.167 Combined model 0.44 ± 0.16 0.48 ± 0.17 0.006 0.260 Data are presented as mean and SD for demographics. P values were derived from independent t-test or Mann-Whitney U test, and Chi-square tests for categorical variables. IPAQ: International physical activity questionnaire. ISI: Insomnia severity index. MMSE: Mini-mental state examination. SRI-MF: The modified stress response inventory. ST-LSP: Standing time from a long sitting position. Among the four P-FI models, significant differences between the two groups were observed in the comorbidity-based model (p < 0.001, d = 0.430), the PRO-based model (p = 0.005, d = 0.211), and the combined model (p = 0.006, d = 0.261), with the faller group exhibiting significantly higher P-FI scores than the non-faller group. However, no significant difference was observed in the gait parameter–based model (p = 0.075, d = 0.167) (Table 2 ). In addition, binomial logistic regression analysis revealed that higher P-FI scores were associated with an increased likelihood of being classified as a faller. Significant associations were observed in the comorbidity-based model (OR = 3.63, 95% CI = 1.23–10.71, p = 0.020), the PRO-based model (OR = 13.40, 95% CI = 4.23–42.45, p < 0.001), and the combined model (OR = 4.80, 95% CI = 1.56–14.76, p = 0.006), whereas the gait parameter–based model was not significantly associated with fall status (OR = 4.22, 95% CI = 0.86–20.71, p = 0.077) (Table 2 ). Table 2 Associations between the physical pre-frail index and fall-related outcomes across domains Variables Beta SE ORs (95% CI) p value Comorbidities Physical pre-frail index (n.u.) 1.289 0.552 3.63 (1.23–10.71) 0.020 PROs Physical pre-frail index (n.u.) 2.595 0.588 13.40 (4.23–42.45) < 0.001 Gait parameters Physical pre-frail index (n.u.) 1.439 0.812 4.22 (0.86–20.71) 0.077 Combined model Physical pre-frail index (n.u.) 1.568 0.573 4.80 (1.56–14.76) 0.006 Odds ratios (ORs) were estimated using binomial logistic regression with the Non-faller group as the reference category. Validation of the Physical Pre-Frailty Index: Lowest (Q1) vs. Highest (Q4) groups The P-FI based on the combined model was categorized into quartiles, and the characteristics of each domain were examined. Overall, participants in higher P-FI quartiles showed progressively worse conditions across most domains (Table 3 ). Among the variables, those showing moderate-to-large effect sizes included body fat (p < 0.001, d = 0.527), sex (p < 0.001, d = 0.728), highest level of education (p < 0.001, d = 0.580), stress (p < 0.001, d = 0.614), and the 6-min walking test (p < 0.001, d = 0.514) (Table 3 ). Table 3 Baseline demographic, clinical, mobility, sleep, and physiological characteristics of study participants Variables Q1: lowest 25% (n = 182) Q2: 25th-50th percentile (n = 181) Q3: 50th-75th percentile (n = 183) Q4: highest 25% (n = 183) p value for group Effect-size: Cohen's d Demographics Age (years) 71.6 ± 5.0 73.2 ± 5.1 72.9 ± 4.4 74.7 ± 5.4 < 0.001 0.439 BMI (kg/m 2 ) 24.4 ± 2.8 24.8 ± 2.7 24.9 ± 3.4 25.0 ± 3.5 0.213 0.155 Body fat (%) 28.8 ± 7.2 30.5 ± 7.0 32.5 ± 6.7 33.6 ± 7.2 < 0.001 0.527 Waist to hip ratio (n.u.) 0.94 ± 0.07 0.93 ± 0.07 0.93 ± 0.07 0.94 ± 0.08 0.652 0.090 Sex (female, %) 46.2 56.9 78.1 86.3 < 0.001 0.728 Highest level of education attained (%) Less than elementary school 2.7 3.9 1.1 7.7 < 0.001 0.580 Elementary school 25.8 32.6 37.7 44.3 Middle school 22.5 23.2 29.5 23.5 High school 28.6 31.5 21.9 21.3 Bachelor's degree 15.9 6.6 8.7 3.3 Master’s degree or above 4.4 2.2 1.1 0.0 Comorbidities Hypertension 46.7 44.8 41.0 43.7 0.738 0.085 Diabetes mellitus 18.7 17.7 17.5 19.1 0.973 0.033 Glaucoma/cataract 0.5 0.6 1.6 2.7 0.221 0.156 Cardiovascular disease 6.6 11.0 12.0 18.0 0.009 0.254 Osteoporosis 9.9 7.2 11.5 14.2 0.176 0.165 Low back pain 5.5 5.5 7.1 8.7 0.555 0.108 Fall history in the past 12 months 15.9 16.0 21.3 23.5 0.162 0.168 Number of comorbidities (n) 1.3 ± 1.1 1.3 ± 1.1 1.2 ± 1.1 1.6 ± 1.3 0.006 0.010 Frailty phenotypes Weakness (%) 42.3 31.5 18.0 27.3 < 0.001 0.388 Slowness (%) 7.7 7.2 12.0 17.5 0.005 0.266 Physical inactivity (%) 5.5 6.6 7.7 16.4 0.001 0.304 Patient-reported outcomes Cognition (MMSE, score)* 27.7 ± 2.0 26.8 ± 2.4 26.8 ± 2.3 25.8 ± 2.7 < 0.001 0.578 Physical activity (IPAQ, METs/week)* 3211.6 ± 2507.6 2882.4 ± 2488.6 2736.7 ± 2232.6 2373.0 ± 2355.9 0.010 0.255 Sleep (ISI, score) 3.2 ± 4.2 3.4 ± 4.3 4.1 ± 5.3 5.5 ± 5.6 < 0.001 0.364 Stress (SRI-MF, score) 2.4 ± 4.0 4.0 ± 7.2 5.7 ± 8.6 10.0 ± 14.2 < 0.001 0.614 Quality of life (SF-36, score)* 84.2 ± 6.8 81.2 ± 9.0 77.8 ± 10.5 64.9 ± 16.5 < 0.001 1.303 Fear of falling 0.54 ± 0.93 0.71 ± 1.05 0.81 ± 1.18 1.04 ± 1.21 < 0.001 0.333 Physical function Physical Pre-Frail Index (n.u.)* 0.25 ± 0.07 0.38 ± 0.03 0.49 ± 0.03 0.66 ± 0.09 < 0.001 4.837 Habitual walking speed (m/s)* 1.24 ± 0.19 1.24 ± 0.19 1.18 ± 0.19 1.14 ± 0.19 < 0.001 0.454 Grip strength (kg)* 26.4 ± 7.0 26.6 ± 7.1 27.0 ± 7.6 24.9 ± 7.1 0.023 0.23 30-second bicep curls 27.9 ± 7.9 26.2 ± 6.8 26.1 ± 7.6 23.6 ± 7.2 < 0.001 0.419 3-meter timed up and go test (sec) 6.8 ± 1.8 7.1 ± 1.6 7.2 ± 1.9 8.0 ± 2.0 < 0.001 0.487 Single-leg stance (sec) 21.6 ± 23.6 17.5 ± 19.0 19.3 ± 20.4 16.4 ± 20.3 0.086 0.191 5-times sit-to-stand (sec) 8.8 ± 3.1 9.4 ± 3.0 9.7 ± 3.4 11.0 ± 4.5 < 0.001 0.464 ST-LSP (sec) 3.3 ± 2.1 3.2 ± 1.4 3.5 ± 1.7 4.1 ± 2.4 < 0.001 0.381 6-min walking test (m) 493.3 ± 91.8 470.9 ± 97.4 473.6 ± 91.2 423.8 ± 115.0 < 0.001 0.514 Data are presented as mean and SD for demographics. P values were derived from One-way ANOVA or Kruskal-Wallis test, and Chi-square tests for categorical variables. IPAQ: International physical activity questionnaire. ISI: Insomnia severity index. MMSE: Mini-mental state examination. SRI-MF: The modified stress response inventory. ST-LSP: Standing time from a long sitting position. *: indicates variables directly associated with the Physical Pre-Frailty Index (P-FI). In addition, logistic regression analysis identified several significant factors that distinguished the Q1 and Q4 groups, including sleep (OR = 1.08, 95% CI = 1.02–1.15, p = 0.015), stress (OR = 1.11, 95% CI = 1.05–1.17, p < 0.001), and the 5-times sit-to-stand (OR = 1.11, 95% CI = 1.02–1.20, p = 0.011) (Table 4 ). Based on the final classification model, ROC curve analysis demonstrated an acceptable discriminative ability, with an AUC of 0.86 (sensitivity = 79.1%; specificity = 76.0%) (Fig. 3 ). Table 4 Logistic regression comparing lowest (Q1) vs. highest (Q4) quartile of the physical pre-frail index Variables Beta SE ORs (95% CI) p value Highest (Q4) Age (years) 0.136 0.028 1.15 (1.09–1.21) < 0.001 BMI (kg/m 2 ) 0.104 0.044 1.11 (1.02–1.21) 0.019 Sex -2.336 0.325 0.10 (0.05–0.18) < 0.001 Sleep (ISI, score) 0.076 0.031 1.08 (1.02–1.15) 0.015 Stress (SRI-MF, score) 0.101 0.026 1.11 (1.05–1.17) < 0.001 5-times sit-to-stand (sec) 0.103 0.040 1.11 (1.02–1.20) 0.011 Odds ratios (ORs) were estimated using binomial logistic regression with the Lowest (Q1) group as the reference category. Discussion This study aimed to develop and validate a P-FI for quantitatively assessing functional decline in community-dwelling older adults aged 65 years and older. We successfully constructed both domain-specific indices and an integrated (combined) model based on demographic characteristics, comorbidities, PROs, and gait parameters. Notably, the combined model was designed to comprehensively reflect the characteristics of older adults by incorporating functional performance across diverse gait tasks, along with measures of quality of life and cognitive function. To evaluate the validity of the proposed index, we compared participants according to fall history and observed that individuals with a history of falls exhibited significantly higher P-FI values than non-fallers, suggesting that the index effectively captures functional vulnerability. Furthermore, when participants were stratified into quartiles based on the combined model, marked differences were observed between the highest and lowest groups in sleep quality, stress levels, and 5-times sit-to-stand performance. These findings indicate that the proposed P-FI is capable of sensitively capturing the continuum of functional status and subtle changes in early decline, highlighting its potential utility as a screening tool for detecting early functional deterioration in older adults. Frailty in older adults is associated with an increased risk of adverse health outcomes, including unmet care needs, falls and fractures, hospitalization, reduced quality of life, iatrogenic complications, and early mortality 4 , 15 , 16 . Importantly, these risks may arise independently of comorbid conditions 15 , 16 , underscoring the need for effective strategies to prevent and manage frailty at both individual and healthcare system levels 16 . As described by Clegg et al., frailty is inherently multidimensional and should therefore be assessed through the integration of multiple domains 15 . In this context, the P-FI developed in the present study extends beyond traditional physical performance measures by integrating variables related to cognitive function and quality of life, which are closely associated with frailty. This integrative approach enables a more comprehensive assessment of an individual’s vulnerability. Notably, while previous studies have primarily relied on habitual walking speed as a measure of physical function, the present study incorporates gait characteristics obtained under multiple conditions, including slow and fast walking speeds. These more challenging conditions have been shown to more sensitively capture impairments in dynamic stability 30 – 33 . In particular, double support time (slow-speed condition), stride length variability (preferred-speed condition), and stride time (fast-speed condition) are indicators of dynamic gait stability, with higher values consistently reflecting reduced stability 44 , 59 , 60 . Taken together, the P-FI proposed in this study is based on functional performance-based measures and provides a practical and efficient tool for assessing functional decline and frailty risk in older adults. Its multidimensional nature suggests strong potential for use in both clinical and community-based settings. In the present study, we developed the P-FI based on a combined model integrating comorbidities, PROs, gait parameters, and selected significant variables across these domains, and examined its association with fall status. Overall, the faller group demonstrated consistently worse profiles across demographics, comorbidities, PROs, and physical function compared with the non-faller group. Notably, the multidomain P-FI score was significantly higher in fallers, suggesting that it effectively captures the overall functional decline associated with falls. Interestingly, the P-FI derived from PROs showed the strongest statistical significance and effect size (p < 0.001, d = 0.430). However, this finding should be interpreted with caution, as individuals with a history of falls tend to exhibit elevated levels of fear of falling due to its psychological impact, with reported increases ranging from approximately 20% to 40% 38,53 . As fear of falling is conceptually closely related to fall experience, the observed association may have been structurally amplified. This suggests that the PRO-based index may partially share underlying constructs with fall status, potentially leading to an overestimation of its association. In contrast, the P-FI constructed solely from gait parameters did not reach statistical significance (p = 0.075, d = 0.167), although a modest trend was observed. This finding indicates that gait parameters alone may have limited discriminative ability for distinguishing fall status. Falls are widely recognized as multifactorial events arising from complex interactions among physical impairments, cognitive decline, psychological factors, and environmental hazards 38 , 54 – 57 . From this perspective, an index that integrates multiple domains may provide a more sensitive representation of functional deficits related to fall risk. The combined model-based P-FI in this study was designed to capture multidimensional functional decline by incorporating spatiotemporal gait parameters—such as stride variability and double support time across different walking speeds—alongside cognitive function and quality of life. Previous studies have demonstrated that physical frailty is associated with impaired gait performance, particularly reduced walking speed and diminished dynamic stability 13 , 15 , 16 , 37 . In addition, frailty has been linked to activity limitations and functional decline, which are in turn associated with cognitive impairment. Importantly, older adults with a history of falls have been reported to exhibit not only impaired gait function but also reduced quality of life and cognitive decline 38 – 42 . Therefore, higher P-FI values in individuals with a history of falls may indicate an increased likelihood of transitioning to pre-frailty, supporting the utility of the P-FI as both a measure of current functional status and a potential marker of vulnerability to future functional decline. As described earlier, the combined model-based P-FI developed in this study incorporates multidimensional components, including gait performance, cognitive function, and quality of life, all of which are closely associated with frailty. Consistent with this conceptual framework, a progressive worsening across multiple domains was observed toward the highest quartile (Q4) (Table 3 ). In particular, the comparison between extreme groups (Q1 vs. Q4) revealed pronounced functional decline in the lowest-functioning group. Among the contributing factors, insomnia risk (+ 8%), stress levels (+ 11%), and 5-times sit-to-stand performance (+ 11%) were significantly elevated, which is generally consistent with findings from previous studies. Frail older adults typically exhibit reduced gait performance and cognitive decline, which ultimately contribute to a lower quality of life 13 , 15 , 16 , 61 . These impairments are often accompanied by decreased muscle strength, increased psychological stress, and poor sleep quality, creating a self-reinforcing cycle that accelerates functional deterioration 15 , 16 , 62 – 66 . Importantly, the ability of these variables to discriminate between Q1 and Q4 was supported by a ROC analysis, yielding an AUC of 0.83 and an overall classification accuracy of 78%, indicating good discriminative performance. These findings are particularly noteworthy, as the observed associations were derived from independent variables while minimizing potential circularity with components included in the P-FI. Taken together, this supports the robustness of the proposed index and highlights its potential utility as a multidimensional tool for identifying individuals at risk of physical pre-frailty. Conventional frailty assessment tools, such as Fried’s frailty, are often limited by their primary focus on physical frailty or their inability to adequately capture its continuous nature 15 – 18 . In addition, although some frailty index-based approaches provide continuous measures, they typically require the collection of numerous variables and rely heavily on hospital- or laboratory-based assessments 15 – 22 , thereby limiting their applicability in community-dwelling older adults. In contrast, the gait parameters used in this study, although measured in controlled environments, are functionally based and less resource-intensive, supporting their potential use in community-dwelling older adults. Frailty is inherently a multidimensional construct reflecting declines across multiple functional domains, including gait performance, muscle strength, cognitive function, and quality of life, all of which are closely related to daily functioning 15 . In this context, the P-FI index proposed in the present study offers a distinct advantage by incorporating challenging gait tasks under various conditions, rather than relying solely on preferred walking speed, enabling a more comprehensive assessment of dynamic stability 30 – 33 . Furthermore, the P-FI integrates key frailty-related domains, including cognitive function and quality of life, thereby capturing the multidimensional nature of frailty beyond conventional approaches that primarily focus on physical components. Its functional performance–based and time-efficient measurement protocol enhances feasibility, enabling practical and scalable assessment of frailty risk in community settings. Importantly, the P-FI may facilitate early identification of individuals at risk of frailty and enable detection of subtle functional decline in older adults, supporting timely and targeted interventions. In this context, recent initiatives by the WHO and ARPA-H have emphasized the Intrinsic Capacity (IC) framework as a standardized approach for capturing functional changes across aging trajectories, highlighting physical performance–based domains such as locomotion and muscle strength as core components strongly linked to functional decline 2 , 67 . Reflecting this paradigm shift, recent studies have increasingly adopted data-driven approaches grounded in standardized frameworks 2 , 68 . IC comprises five domains—locomotion, vitality, cognition, psychological, and sensory functions—and enables classification of individuals into physiological aging states (robust, pre-frail to frail, and dependent), underscoring the importance of integrated assessment across physical and mental capacities 69 . Although IC was not directly quantified in the present study, the selected features largely represent domains aligned with core IC constructs, particularly locomotion, vitality (partially reflected by BMI), cognition, and psychological well-being (e.g., quality of life), thereby providing indirect yet meaningful insight into IC-related functional decline. It may also serve as a foundational tool for developing personalized intervention programs aimed at improving functional capacity. Moreover, as the P-FI is derived from quantifiable functional measures, it may be adaptable to digital healthcare platforms, particularly those incorporating wearable sensors and mobile health applications to facilitate remote monitoring and user engagement. Such integration could enhance its applicability in supporting functional independence and quality of life in older populations. Although the present study successfully developed the P-FI index and demonstrated its validity through a known-groups validation approach, several limitations should be acknowledged. First, in constructing the frailty index, outcome variables such as handgrip strength, walking speed, and physical activity were included; however, key phenotype components such as exhaustion and unintentional weight loss were not considered. Future studies need to consider to involve those rest of the phenotypes. Second, further validation is required to determine whether the P-FI yields consistent and clinically meaningful results across populations with varying health conditions. In particular, it would be important to examine its applicability in older adults with a high risk of frailty, such as those with peripheral neuropathy, diabetes, or sarcopenia, in comparison with age-matched healthy individuals. Additionally, longitudinal follow-up and interventional studies are warranted to evaluate changes in function based on the P-FI; furthermore, as this study conducted only internal validation without external validation in an independent cohort, the generalizability of the findings is limited. Finally, the current study did not include frail individuals, limiting the generalizability of the findings across the full frailty spectrum. Future studies should incorporate frail populations to further refine and extend the P-FI into a more comprehensive and robust index. Conclusion This study developed a novel P-FI to quantitatively assess pre-frailty status in community-dwelling older adults aged 65 years and above. The P-FI was designed as a multidimensional index that not only reflects physical function but also incorporates key frailty-related domains, including cognitive function and quality of life. The validity of the P-FI was supported by its ability to discriminate functional differences by fall status. Additionally, when stratified into quartiles, the lowest-functioning group (Q4) consistently showed the greatest decline across domains. The use of a functional performance-based and efficient measurement protocol enhances the feasibility of the P-FI, supporting its application in community and clinical settings. The P-FI may facilitate early identification of individuals at risk of frailty and enable the detection of subtle functional decline. Ultimately, the P-FI has the potential to serve as a foundational tool for developing personalized intervention strategies aimed at improving functional capacity. Its integration into digital healthcare systems such as wearable devices and mobile health application may further expand its applicability, contributing to the promotion of functional independence and the enhancement of quality of life in older populations. Declarations Ethic approval The studies involving human participants were reviewed and approved by the Institutional Review Board of Dong-A University (IRB No. 2–104709–AB–N–01–201808–HR–023–04) and registered with the Clinical Research Information Service (CRIS; KCT0004529; registered on September 13, 2018), a primary registry participating in the WHO International Clinical Trials Registry Platform. Written informed consent was obtained from all participants and/or their legal guardians prior to participation. This study was conducted in accordance with the Declaration of Helsinki. Competing interests The authors declare no competing interests. Funding This research was supported by a grant of Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2025-25454559; Jaewon Beom). In addition, this research was funded by the Sports Promotion Fund of Seoul Olympic Sports Promotion Foundation from the Ministry of Culture, Sports and Tourism (grant number: B0080605000494; Changhong Youm). This work was also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2025S1A5B5A16007605; Myeounggon Lee). The funders had no role in the study design, data collection, analysis, or interpretation of the data, or in the preparation of the manuscript. Author Contribution Concept and study design: M.L., J.B., and C.Y.; Data acquisition: M.L., and H.P.; Data analysis: M.L., and J.B.; Preparing tables and figures: M.L.; Interpretation of the data: M.L., J.B., H.P.; Drafting the manuscript: M.L.; Critical revision of the manuscript: M.L., H.P., J.L., J.B., and C.Y. All authors contributed to the article and approved the submitted version. Acknowledgement The authors thank all participants who took part in this study, as well as the research staff of the biomechanics laboratories at Dong-A University and Chonnam National University for their assistance with data collection. Data Availability The data that support the findings of this study are not publicly available but are available from the corresponding author C.Y., [email protected] upon reasonable request. References Rudnicka, E. et al. The World Health Organization (WHO) approach to healthy ageing. Maturitas 139 , 6–11 (2020). Cheng, Y., Li, W., Xiao, S., Chen, Y. & Qi, X. 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Relationship between depression and frailty in older adults: a systematic review and meta-analysis. Ageing Res. Rev. 36 , 78–87 (2017). Kim, J. Y., Lee, T. & Koh, S. The relationship between frailty status and psychosocial indices in older Korean adults. GeroScience , 1–15 (2025). Freitag, S. & Schmidt, S. Psychosocial correlates of frailty in older adults. Geriatrics 1 , 26 (2016). Sales, W. B., de Souza Silva, P. V., Vital, B. S. B. & Câmara, M. Sarcopenia and intrinsic capacity in older adults: a systematic review. Archives Gerontol. geriatrics , 105875 (2025). Sánchez-Sánchez, J. L. et al. Association of intrinsic capacity with functional decline and mortality in older adults: a systematic review and meta-analysis of longitudinal studies. Lancet Healthy Longev. 5 , e480–e492 (2024). Kemoun, P. et al. A gerophysiology perspective on healthy ageing. Ageing Res. Rev. 73 , 101537 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 05 May, 2026 Editor invited by journal 04 May, 2026 Editor assigned by journal 29 Apr, 2026 Submission checks completed at journal 29 Apr, 2026 First submitted to journal 28 Apr, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9550110","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":638528555,"identity":"b15b7925-0960-4cb1-801a-91b13629f950","order_by":0,"name":"Myeounggon Lee","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Myeounggon","middleName":"","lastName":"Lee","suffix":""},{"id":638528556,"identity":"188205bc-d72f-47b9-8d63-caa7aa14cf87","order_by":1,"name":"Hwayoung Park","email":"","orcid":"","institution":"Dong-A University","correspondingAuthor":false,"prefix":"","firstName":"Hwayoung","middleName":"","lastName":"Park","suffix":""},{"id":638528557,"identity":"3b92d412-5d91-472b-b06b-309691d12b50","order_by":2,"name":"Jae-Young Lim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Jae-Young","middleName":"","lastName":"Lim","suffix":""},{"id":638528558,"identity":"009130d7-e26a-48ee-8a93-3f8085ac43aa","order_by":3,"name":"Jaewon Beom","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Jaewon","middleName":"","lastName":"Beom","suffix":""},{"id":638528560,"identity":"5b7a95f1-31bf-46de-b581-de09fc353a3c","order_by":4,"name":"Changhong Youm","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDACCTBpw9gA4SYQrSWNdC2HSdDCP7vH8DFvznnZ/hkJjB9+MKTlE7bkzhljY95tt41n3EhgluxhyLFsIKTFQCLHTBqoJbHhRgKDNANDhQFBW6BaziXOB9rymxQtBxI33EhgA9qSQ1iLxI20YsO525KNN5552GbZY5BGWAv/jOSND95us5Oddzz58I0fFcmEtTAwcMAUgaKGGA0MDOwPiFI2CkbBKBgFIxgAAMSZOXu0+WMMAAAAAElFTkSuQmCC","orcid":"","institution":"Dong-A University","correspondingAuthor":true,"prefix":"","firstName":"Changhong","middleName":"","lastName":"Youm","suffix":""}],"badges":[],"createdAt":"2026-04-28 07:25:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9550110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9550110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109249111,"identity":"ae00fa45-e707-4d3f-ab35-b21f1cdd914b","added_by":"auto","created_at":"2026-05-14 08:42:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3340984,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework illustrating the development and validation of a multidimensional Physical Pre-Frailty Index (P-FI) in community-dwelling older adults aged ≥65 years. The P-FI ranges from 0 to 1, with higher values indicating poorer overall functional status. The framework hypothesizes that individuals with a history of falls will exhibit higher P-FI scores. Furthermore, quartile-based stratification of the P-FI is expected to demonstrate progressive functional decline, with lower quartiles reflecting better function and higher quartiles indicating greater impairment.\u003c/p\u003e","description":"","filename":"Figure1V1.png","url":"https://assets-eu.researchsquare.com/files/rs-9550110/v1/e3cb67b5d766abbaa215da1d.png"},{"id":109249150,"identity":"f07b22e4-1631-411e-8be6-59f3e0bcb7c6","added_by":"auto","created_at":"2026-05-14 08:42:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291197,"visible":true,"origin":"","legend":"\u003cp\u003eNetworking plot. Edge color indicates the direction of the correlation (positive in blue and negative in red), while both edge thickness and color intensity reflect the strength of the relationships.\u003c/p\u003e","description":"","filename":"Figure2V1.png","url":"https://assets-eu.researchsquare.com/files/rs-9550110/v1/1b14178f01eb57f16f705b6b.png"},{"id":109215940,"identity":"96b2bfc0-f606-48c1-aab3-4d047251c0ce","added_by":"auto","created_at":"2026-05-13 17:57:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":484866,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the classification model distinguishing the lowest (Q1) and highest (Q4) Physical Pre-Frailty Index groups\u003c/p\u003e","description":"","filename":"Figure3V1.png","url":"https://assets-eu.researchsquare.com/files/rs-9550110/v1/07b068c0288662aec9132c87.png"},{"id":109299185,"identity":"9a6e72e6-e9ef-4dda-a094-ed0dd19feda4","added_by":"auto","created_at":"2026-05-15 09:17:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4646610,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9550110/v1/6e221ba7-9ffe-40d0-96d3-7d022a5ec054.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Multidimensional Physical Pre-Frailty Index for Early Detection of Functional Decline in Community-Dwelling Older Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation aging is accelerating globally, with the World Health Organization (WHO) projecting that by 2030, one in six people will be aged 60 years or older \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This trend is particularly pronounced in South Korea, which is rapidly transitioning to a super-aged society \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. As populations age, maintaining functional capacity and independence has become a major public health priority \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Frailty, characterized by reduced physiological reserve and increased vulnerability to adverse outcomes, is strongly associated with functional decline, disability, hospitalization, and mortality \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In South Korea, the prevalence of frailty and pre-frailty has been reported to be as high as 56.0% \u003csup\u003e6\u003c/sup\u003e. Key physical impairments\u0026mdash;including reduced muscle strength, impaired gait, and low physical activity\u0026mdash;are closely linked to increased risks of falls and functional decline \u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Fried frailty phenotype is a widely used approach for assessing physical frailty, classifying individuals as non-frail, pre-frail, or frail based on five criteria, including weakness, slowness, and low physical activity \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, it has several limitations, such as its categorical nature and limited ability to capture the multidimensional aspects of frailty \u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. To overcome these limitations, continuous frameworks such as the frailty index (FI) have been developed. The FI quantifies frailty based on a deficit accumulation approach, incorporating a wide range of health-related variables \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Although widely used, many FI models rely heavily on clinical and laboratory data, which may limit their applicability in non-clinical settings and may not directly reflect physical functional capacity \u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies have proposed FI models derived from biomechanical variables obtained through simple physical tasks \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These models have been shown to reflect frailty status and related clinical characteristics \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, frailty is inherently multidimensional and requires assessment across multiple domains, including gait, muscle strength, cognitive function, and quality of life \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In particular, gait tasks performed under varying speed conditions allow for a more comprehensive evaluation of locomotor function than single-speed assessments \u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Furthermore, individuals with physical pre-frailty exhibit reduced functional performance and impaired resilience, underscoring the need for indices capable of identifying early functional decline \u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to develop a refined, multidimensional quantitative index that more precisely captures functional decline, based on key frailty-related domains (e.g., weakness, slowness, and physical inactivity), and provides a visual representation of such deterioration. The validity of the proposed physical pre-frailty index (P-FI) was evaluated using a known-groups validation approach. Previous studies have reported that frailty is associated with both fear of falling and fall experience \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,37 38\u0026ndash;42\u003c/sup\u003e, highlighting the need to examine whether the functional decline underlying frailty is linked to fall-related outcomes. Accordingly, we hypothesized that, among older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, individuals with a history of falls would exhibit higher P-FI values than non-fallers. Furthermore, participants were stratified into quartiles based on the P-FI, and we also hypothesized a progressive decline in functional outcomes from the lowest to the highest quartile, with the highest group exhibiting worse functional characteristics compared with lowest group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eCommunity-dwelling older adults aged 65\u0026ndash;89 years were recruited from Busan and Gwangju, South Korea, between 2018 and 2019. Individuals were excluded if they were unable to walk independently, regardless of whether assistive devices were used. Additional exclusion criteria included a history of musculoskeletal or neurological disorders that could influence gait or physical fitness assessments within the previous six months, as well as severe cognitive impairment or dementia that would limit the ability to provide informed consent.\u003c/p\u003e \u003cp\u003eThe study protocol was reviewed and approved by the Institutional Review Board of Dong-A University (IRB No. 2\u0026ndash;104709\u0026ndash;AB\u0026ndash;N\u0026ndash;01\u0026ndash;201808\u0026ndash;HR\u0026ndash;023\u0026ndash;04). The study was registered with the Clinical Research Information Service (CRIS; KCT0004529, registered on September 13, 2018), a primary registry participating in the WHO International Clinical Trials Registry Platform. Written informed consent was obtained from all participants prior to participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGait task: over ground walking test\u003c/h3\u003e\n\u003cp\u003eParticipants completed an overground walking task along a 20-m walkway under three speed conditions: 80% (slow), preferred, and 120% (fast) of their self-selected walking speed. Gait data were collected using a shoe-type inertial measurement unit (IMU) system (DynaStab\u0026trade;, JEIOS, Busan, Republic of Korea; 100 Hz), comprising wearable data loggers (Smart Balance\u0026reg; SB-1) with embedded IMU sensors (IMU-3000\u0026trade;, InvenSense, USA). The sensors, mounted in both outsoles, recorded tri-axial acceleration (\u0026plusmn;\u0026thinsp;6 g) and angular velocity (\u0026plusmn;\u0026thinsp;500\u0026deg;/s), and transmitted data wirelessly via Bluetooth\u0026reg;. Shoe sizes were adjusted individually (225\u0026ndash;280 mm) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The initial and final 2 m were excluded to account for acceleration and deceleration. Target speeds were determined based on preferred walking speed and guided by a metronome during familiarization trials, but not during actual testing \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. For each condition, spatiotemporal gait parameters\u0026mdash;including stride length, stride time, double-support phase, stance phase, and gait variability\u0026mdash;were derived based on established methods \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and incorporated as multidimensional components of the P-FI.\u003c/p\u003e\n\u003ch3\u003eDemographic and clinical characteristics\u003c/h3\u003e\n\u003cp\u003eBasic demographic variables collected for this study included age, sex, body mass index (BMI), waist-to-hip ratio, and educational attainment. Clinical conditions were recorded as binary variables indicating the presence or absence of each condition (0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes), allowing the prevalence of these conditions to be summarized within the sample.\u003c/p\u003e\n\u003ch3\u003ePatient-reported outcomes (PROs)\u003c/h3\u003e\n\u003cp\u003eSelf-reported questionnaires were administered to evaluate several health-related domains, including cognitive status, sleep quality, stress response, and health-related quality of life. Global cognition was assessed using the Mini-Mental State Examination (MMSE) \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Sleep disturbance was measured using the Insomnia Severity Index (ISI) \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and stress responses were evaluated using the Modified Stress Response Inventory (SRI-MF) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Health-related quality of life was assessed using the Short Form-36 (SF-36) \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Participants were additionally asked to report fall-related information, including the occurrence of falls within the past six months, the frequency of such events, and their level of fear associated with falling.\u003c/p\u003e\n\u003ch3\u003ePhysical fitness test\u003c/h3\u003e\n\u003cp\u003eA battery of physical fitness assessments was administered to all participants. The tests included handgrip strength, the 30-s bicep curl test, the five-times sit-to-stand test, standing time from a long sitting position (ST-LSP), single-leg balance, the 3-m timed up and go (TUG) test, and the 6-min walk test (6MWT) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the Physical Pre-Frailty Index\u003c/h2\u003e \u003cp\u003eThe P-FI was developed to quantify early-stage physical vulnerability using three frailty-related variables: handgrip strength (weakness), walking speed (slowness), and physical activity level (inactivity). Handgrip strength was measured using a digital dynamometer \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, walking speed was calculated from the preferred-speed overground walking task \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and physical activity was assessed using the International Physical Activity Questionnaire\u0026ndash;Short Form (IPAQ-SF) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Participants were classified according to frailty phenotype criteria as non-frail, pre-frail, or frail \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. To focus on early functional decline, frail individuals were excluded. The three variables were standardized (z-scores) and summed to construct the P-FI, which was used as the dependent variable in subsequent analyses \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo construct the P-FI models while minimizing circularity, variables directly related to its components (handgrip strength, gait speed, and physical activity) or highly correlated with them (r\u0026thinsp;\u0026ge;\u0026thinsp;0.70), exhibiting multicollinearity (variance inflation factor\u0026thinsp;\u0026ge;\u0026thinsp;3), or conceptually overlapping were excluded. The remaining variables\u0026mdash;including gait parameters, demographics, comorbidities, and PROs\u0026mdash;were standardized (z-scores). Multiple regression analyses were performed to develop domain-specific models predicting the composite of the three P-FI components. Four models were constructed: comorbidity-, PRO-, gait-, and combined models, with the combined model adjusted for age, sex, and BMI.\u003c/p\u003e \u003cp\u003eBased on the regression equations derived from each model, the predicted P-FI values were subsequently transformed into an index ranging from 0 to 1 using min\u0026ndash;max normalization (Eq.\u0026nbsp;1). The index was constructed so that values closer to 1 indicated poorer physical status and a higher likelihood of frailty, following methodological approaches used in previous studies \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Physical\\:pre-frail\\:index(P-FI)=1-\\frac{\\widehat{y}-{\\widehat{y}}_{min}}{{\\widehat{y}}_{max}-{\\widehat{y}}_{min}}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Eq.\u0026nbsp;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidation of the Physical Pre-Frailty Index\u003c/h3\u003e\n\u003cp\u003eTo evaluate the validity of the constructed P-FI, two validation approaches were performed. First, known-groups validity was examined by comparing P-FI scores between participants with and without a history of falls. Previous studies have reported that older adults who have experienced falls tend to exhibit reduced gait performance as well as increased depressive symptoms and psychological withdrawal compared with age-matched controls \u003csup\u003e38,40\u0026ndash;42,53\u0026minus;57\u003c/sup\u003e. Therefore, participants were classified into non-faller and faller groups, and differences in P-FI scores between the two groups were examined.\u003c/p\u003e \u003cp\u003eSecond, participants were categorized into quartiles based on their P-FI scores (Q1: lowest 25%; Q2: 25th\u0026ndash;50th percentile; Q3: 50th\u0026ndash;75th percentile; Q4: highest 25%). Demographic characteristics, PROs, and physical function variables\u0026mdash;excluding those used to construct the P-FI\u0026mdash;were then compared across the quartile groups. Particular attention was given to differences between the lowest and highest quartiles (Q1 vs. Q4) to examine whether the P-FI effectively discriminated between individuals with relatively low and high levels of physical vulnerability.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics for Windows (version 29.0; IBM Corp., Armonk, NY, USA). The Shapiro\u0026ndash;Wilk test was used to assess the normality of continuous variables. Categorical variables were compared using the chi-square test. For continuous variables, either the independent t-test or the Mann\u0026ndash;Whitney U test was applied depending on the normality of the data distribution. These tests were used to compare differences between participants with and without a history of falls (non-faller vs. faller).\u003c/p\u003e \u003cp\u003eTo examine the association between P-FI and fall history, binomial logistic regression analysis was conducted. Odds ratios (ORs) were calculated for the P-FI derived from each model \u0026mdash; (1) comorbidity-based model, (2) PRO-based model, (3) gait-factor model, and (4) combined model\u0026mdash;to evaluate the extent to which higher P-FI values were associated with increased odds of belonging to the faller group. Effect sizes were calculated using Cohen\u0026rsquo;s d and interpreted as trivial (\u0026lt;\u0026thinsp;0.20), small (0.20\u0026ndash;0.49), medium (0.50\u0026ndash;0.79), and large (\u0026ge;\u0026thinsp;0.80) \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, participants were categorized into quartiles based on the P-FI score. One-way ANOVA was used to assess overall differences across P-FI quartiles, with emphasis on general patterns rather than multiple pairwise comparisons. Interpretation focused on effect sizes, prioritizing variables with at least moderate effects, and contrasts between the lowest (Q1) and highest (Q4) quartiles. To further compare the lowest and highest P-FI quartiles (Q1 vs. Q4), binomial logistic regression analysis was conducted. Demographic characteristics, PROs, and physical function variables\u0026mdash;excluding those used to construct the P-FI\u0026mdash;were examined to identify factors that distinguished the two groups. First, univariate logistic regression analyses were performed for variables that showed significant differences in the main effect analysis. Subsequently, significant variables were entered into a stepwise logistic regression analysis to identify the most relevant predictors. Age, sex, and BMI were adjusted as covariates. Based on the final classification model, receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative ability of the identified variables, and the area under the curve (AUC) was calculated. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Preprocessing\u003c/h2\u003e \u003cp\u003eA total of 765 participants were initially screened for inclusion in this study. Among them, 20 individuals were excluded because they did not meet the eligibility criteria, including severe cognitive impairment (MMSE\u0026thinsp;\u0026lt;\u0026thinsp;20; n\u0026thinsp;=\u0026thinsp;19) or missing cognitive function data (n\u0026thinsp;=\u0026thinsp;1). As a result, 745 participants were deemed eligible for further evaluation. Subsequently, 16 additional participants were excluded due to incomplete physical fitness assessments (n\u0026thinsp;=\u0026thinsp;3) or classification as frail rather than pre-frail (n\u0026thinsp;=\u0026thinsp;13). Consequently, 729 participants completed all required assessments and were included in the P-FI analysis. The final sample had a mean age of 73.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 years and a mean body mass index (BMI) of 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 kg/m\u0026sup2;, with females accounting for 66.9% of the participants. The prevalence of pre-frailty in the study population was 42.1%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePhysical Pre-Frailty Index models\u003c/h2\u003e \u003cp\u003eRegression models were developed using variables from three domains\u0026mdash;comorbidities, PROs, and gait parameters\u0026mdash;to construct the P-FI. A combined model including variables from all domains was also developed. The composite score was calculated by summing the z-standardized values of handgrip strength, gait speed, and physical activity level, with higher values indicating better functional status; however, this directionality was reversed when the score was transformed into the P-FI, such that higher P-FI values indicate worse functional status.\u003c/p\u003e \u003cp\u003eIn the disease-based model, the total number of comorbidities was significant predictors of the composite P-FI score derived from the z-score\u0026ndash;standardized values of handgrip strength, gait speed, and physical activity level (β = -0.20, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;=\u0026thinsp;0.003, adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02, F\u0026thinsp;=\u0026thinsp;8.610, p\u0026thinsp;=\u0026thinsp;0.003). In the PRO-based model, quality of life (β\u0026thinsp;=\u0026thinsp;0.61, SE\u0026thinsp;=\u0026thinsp;0.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fear of falling (β = -0.42, SE\u0026thinsp;=\u0026thinsp;0.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and cognitive function (β\u0026thinsp;=\u0026thinsp;0.36, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant predictors of the composite score (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.20, F\u0026thinsp;=\u0026thinsp;62.515, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the gait-factor model, double support phase (β = -0.80, SE\u0026thinsp;=\u0026thinsp;0.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the CV of double support phase (β = -0.27, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and stride time (β = -0.27, SE\u0026thinsp;=\u0026thinsp;0.09, p\u0026thinsp;=\u0026thinsp;0.002) during the slow-speed walking task; the CV of stride time during the preferred-speed walking task (β = -0.23, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;=\u0026thinsp;0.001); and stride time (β = -0.46, SE\u0026thinsp;=\u0026thinsp;0.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the CV of stride length (β = -0.25, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) during the fast-speed walking task were significant predictors (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.26, F\u0026thinsp;=\u0026thinsp;43.884, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn the combined model, double support phase during the slow-speed walking task (β = -0.56, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the CV of stride length during the preferred-speed walking task (β = -0.27, SE\u0026thinsp;=\u0026thinsp;0.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), stride time during the fast-speed walking task (β = -0.35, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), quality of life (β\u0026thinsp;=\u0026thinsp;0.54, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and cognitive function (β\u0026thinsp;=\u0026thinsp;0.28, SE\u0026thinsp;=\u0026thinsp;0.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant predictors of the composite P-FI score (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.38, F\u0026thinsp;=\u0026thinsp;55.566, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Eq.\u0026nbsp;2 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{P}\\varvec{h}\\varvec{y}\\varvec{s}\\varvec{i}\\varvec{c}\\varvec{a}\\varvec{l}\\:\\varvec{p}\\varvec{r}\\varvec{e}-\\varvec{f}\\varvec{r}\\varvec{a}\\varvec{i}\\varvec{l}\\:\\varvec{i}\\varvec{n}\\varvec{d}\\varvec{e}\\varvec{x}}_{\\varvec{C}\\varvec{o}\\varvec{m}\\varvec{b}\\varvec{i}\\varvec{n}\\varvec{e}\\varvec{d}}=\\:\\)\u003c/span\u003e\u003c/span\u003e5.851 \u0026ndash; (0.051*Age) \u0026ndash; (0.839*Sex) \u0026ndash; (0.023*BMI) \u0026ndash; (0.564* double support phase at SWS) \u0026ndash; (0.269* CV of stride length at PWS) \u0026ndash; (0.353* stride time at FWS) + (0.540*Quality of life) + (0.278 * Cognitive function)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Eq.\u0026nbsp;2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the Physical Pre-Frailty Index: Non-faller vs. Faller\u003c/h2\u003e \u003cp\u003eAmong the 729 participants, 140 individuals reported experiencing at least one fall within the past 12 months (prevalence\u0026thinsp;=\u0026thinsp;19.2%). Group comparisons revealed significant differences in the demographic domain, including body fat (p\u0026thinsp;=\u0026thinsp;0.021, d\u0026thinsp;=\u0026thinsp;0.218) and sex (p\u0026thinsp;=\u0026thinsp;0.040, d\u0026thinsp;=\u0026thinsp;0.153). In the comorbidy domain, the faller group showed a higher prevalence of diabetes mellitus (p\u0026thinsp;=\u0026thinsp;0.021, d\u0026thinsp;=\u0026thinsp;0.171) and osteoporosis (p\u0026thinsp;=\u0026thinsp;0.006, d\u0026thinsp;=\u0026thinsp;0.204), as well as a greater number of comorbidities (p\u0026thinsp;=\u0026thinsp;0.019, d\u0026thinsp;=\u0026thinsp;0.222), compared with the non-faller group. Among the frailty phenotypes, slowness was significantly different between the two groups (p\u0026thinsp;=\u0026thinsp;0.005, d\u0026thinsp;=\u0026thinsp;0.211). Within the PRO domain, the faller group demonstrated poorer cognitive function, sleep, quality of life, and greater fear of falling compared with the non-faller group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; d range\u0026thinsp;=\u0026thinsp;0.213\u0026ndash;0.609). In the physical function domain, the faller group exhibited significantly lower physical performance than the non-faller group, including habitual walking speed, grip strength, 30-second bicep curls, the 3-m timed up-and-go test, single-leg stance, and the 6-min walking test (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; d range\u0026thinsp;=\u0026thinsp;0.222\u0026ndash;0.337) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\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\u003eBaseline demographic, clinical, mobility, sleep, and physiological characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-faller\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;589)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFaller\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect-size: Cohen's d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody fat (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaist to hip ratio (n.u.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex (female, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHighest level of education attained (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than elementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlaucoma/cataract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow back pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of comorbidities (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrailty phenotypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeakness (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlowness (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical inactivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003ePatient-reported outcomes (PROs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognition (MMSE, score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical activity (IPAQ, METs/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2853.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2385.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2577.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2517.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep (ISI, score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress (SRI-MF, score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuality of life (SF-36, score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFear of falling (score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003ePhysical function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHabitual walking speed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrip strength (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30-second bicep curls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-meter timed up and go test (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle-leg stance (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-times sit-to-stand (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST-LSP (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-min walking test (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469.7\u0026thinsp;\u0026plusmn;\u0026thinsp;104.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447.0\u0026thinsp;\u0026plusmn;\u0026thinsp;90.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eComponents of Physical Pre-Frail Index (n.u.)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePROs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGait parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombined model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are presented as mean and SD for demographics. P values were derived from independent t-test or Mann-Whitney U test, and Chi-square tests for categorical variables. IPAQ: International physical activity questionnaire. ISI: Insomnia severity index. MMSE: Mini-mental state examination. SRI-MF: The modified stress response inventory. ST-LSP: Standing time from a long sitting position.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the four P-FI models, significant differences between the two groups were observed in the comorbidity-based model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.430), the PRO-based model (p\u0026thinsp;=\u0026thinsp;0.005, d\u0026thinsp;=\u0026thinsp;0.211), and the combined model (p\u0026thinsp;=\u0026thinsp;0.006, d\u0026thinsp;=\u0026thinsp;0.261), with the faller group exhibiting significantly higher P-FI scores than the non-faller group. However, no significant difference was observed in the gait parameter\u0026ndash;based model (p\u0026thinsp;=\u0026thinsp;0.075, d\u0026thinsp;=\u0026thinsp;0.167) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, binomial logistic regression analysis revealed that higher P-FI scores were associated with an increased likelihood of being classified as a faller. Significant associations were observed in the comorbidity-based model (OR\u0026thinsp;=\u0026thinsp;3.63, 95% CI\u0026thinsp;=\u0026thinsp;1.23\u0026ndash;10.71, p\u0026thinsp;=\u0026thinsp;0.020), the PRO-based model (OR\u0026thinsp;=\u0026thinsp;13.40, 95% CI\u0026thinsp;=\u0026thinsp;4.23\u0026ndash;42.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the combined model (OR\u0026thinsp;=\u0026thinsp;4.80, 95% CI\u0026thinsp;=\u0026thinsp;1.56\u0026ndash;14.76, p\u0026thinsp;=\u0026thinsp;0.006), whereas the gait parameter\u0026ndash;based model was not significantly associated with fall status (OR\u0026thinsp;=\u0026thinsp;4.22, 95% CI\u0026thinsp;=\u0026thinsp;0.86\u0026ndash;20.71, p\u0026thinsp;=\u0026thinsp;0.077) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\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\u003eAssociations between the physical pre-frail index and fall-related outcomes across domains\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eORs (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical pre-frail index (n.u.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.63 (1.23\u0026ndash;10.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePROs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical pre-frail index (n.u.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.40 (4.23\u0026ndash;42.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGait parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical pre-frail index (n.u.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.22 (0.86\u0026ndash;20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCombined model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical pre-frail index (n.u.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.80 (1.56\u0026ndash;14.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eOdds ratios (ORs) were estimated using binomial logistic regression with the Non-faller group as the reference category.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the Physical Pre-Frailty Index: Lowest (Q1) vs. Highest (Q4) groups\u003c/h2\u003e \u003cp\u003eThe P-FI based on the combined model was categorized into quartiles, and the characteristics of each domain were examined. Overall, participants in higher P-FI quartiles showed progressively worse conditions across most domains (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the variables, those showing moderate-to-large effect sizes included body fat (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.527), sex (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.728), highest level of education (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.580), stress (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.614), and the 6-min walking test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.514) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic, clinical, mobility, sleep, and physiological characteristics of study participants\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1: lowest 25%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;182)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2: 25th-50th percentile\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3: 50th-75th percentile\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;183)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4: highest 25%\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;183)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003cp\u003efor group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEffect-size: Cohen's d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody fat (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaist to hip ratio (n.u.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex (female, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHighest level of education attained (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than elementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlaucoma/cataract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow back pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFall history in the past 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of comorbidities (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFrailty phenotypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeakness (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlowness (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical inactivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.4\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\" colname=\"c8\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003ePatient-reported outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognition (MMSE, score)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical activity (IPAQ, METs/week)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3211.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2507.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2882.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2488.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2736.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2232.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2373.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2355.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep (ISI, score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress (SRI-MF, score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.614\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuality of life (SF-36, score)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFear of falling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cb\u003ePhysical function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Pre-Frail Index (n.u.)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHabitual walking speed (m/s)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrip strength (kg)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30-second bicep curls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.419\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-meter timed up and go test (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.487\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle-leg stance (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-times sit-to-stand (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.464\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST-LSP (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-min walking test (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e493.3\u0026thinsp;\u0026plusmn;\u0026thinsp;91.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e470.9\u0026thinsp;\u0026plusmn;\u0026thinsp;97.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e473.6\u0026thinsp;\u0026plusmn;\u0026thinsp;91.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e423.8\u0026thinsp;\u0026plusmn;\u0026thinsp;115.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.514\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eData are presented as mean and SD for demographics. P values were derived from One-way ANOVA or Kruskal-Wallis test, and Chi-square tests for categorical variables. IPAQ: International physical activity questionnaire. ISI: Insomnia severity index. MMSE: Mini-mental state examination. SRI-MF: The modified stress response inventory. ST-LSP: Standing time from a long sitting position. *: indicates variables directly associated with the Physical Pre-Frailty Index (P-FI).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, logistic regression analysis identified several significant factors that distinguished the Q1 and Q4 groups, including sleep (OR\u0026thinsp;=\u0026thinsp;1.08, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.15, p\u0026thinsp;=\u0026thinsp;0.015), stress (OR\u0026thinsp;=\u0026thinsp;1.11, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;1.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the 5-times sit-to-stand (OR\u0026thinsp;=\u0026thinsp;1.11, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.20, p\u0026thinsp;=\u0026thinsp;0.011) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on the final classification model, ROC curve analysis demonstrated an acceptable discriminative ability, with an AUC of 0.86 (sensitivity\u0026thinsp;=\u0026thinsp;79.1%; specificity\u0026thinsp;=\u0026thinsp;76.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression comparing lowest (Q1) vs. highest (Q4) quartile of the physical pre-frail index\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eORs (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest (Q4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (1.09\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.02\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10 (0.05\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep (ISI, score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1.02\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress (SRI-MF, score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.05\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-times sit-to-stand (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.02\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eOdds ratios (ORs) were estimated using binomial logistic regression with the Lowest (Q1) group as the reference category.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to develop and validate a P-FI for quantitatively assessing functional decline in community-dwelling older adults aged 65 years and older. We successfully constructed both domain-specific indices and an integrated (combined) model based on demographic characteristics, comorbidities, PROs, and gait parameters. Notably, the combined model was designed to comprehensively reflect the characteristics of older adults by incorporating functional performance across diverse gait tasks, along with measures of quality of life and cognitive function. To evaluate the validity of the proposed index, we compared participants according to fall history and observed that individuals with a history of falls exhibited significantly higher P-FI values than non-fallers, suggesting that the index effectively captures functional vulnerability. Furthermore, when participants were stratified into quartiles based on the combined model, marked differences were observed between the highest and lowest groups in sleep quality, stress levels, and 5-times sit-to-stand performance. These findings indicate that the proposed P-FI is capable of sensitively capturing the continuum of functional status and subtle changes in early decline, highlighting its potential utility as a screening tool for detecting early functional deterioration in older adults.\u003c/p\u003e \u003cp\u003eFrailty in older adults is associated with an increased risk of adverse health outcomes, including unmet care needs, falls and fractures, hospitalization, reduced quality of life, iatrogenic complications, and early mortality \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Importantly, these risks may arise independently of comorbid conditions \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, underscoring the need for effective strategies to prevent and manage frailty at both individual and healthcare system levels \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. As described by Clegg et al., frailty is inherently multidimensional and should therefore be assessed through the integration of multiple domains \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In this context, the P-FI developed in the present study extends beyond traditional physical performance measures by integrating variables related to cognitive function and quality of life, which are closely associated with frailty. This integrative approach enables a more comprehensive assessment of an individual\u0026rsquo;s vulnerability. Notably, while previous studies have primarily relied on habitual walking speed as a measure of physical function, the present study incorporates gait characteristics obtained under multiple conditions, including slow and fast walking speeds. These more challenging conditions have been shown to more sensitively capture impairments in dynamic stability \u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In particular, double support time (slow-speed condition), stride length variability (preferred-speed condition), and stride time (fast-speed condition) are indicators of dynamic gait stability, with higher values consistently reflecting reduced stability \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Taken together, the P-FI proposed in this study is based on functional performance-based measures and provides a practical and efficient tool for assessing functional decline and frailty risk in older adults. Its multidimensional nature suggests strong potential for use in both clinical and community-based settings.\u003c/p\u003e \u003cp\u003eIn the present study, we developed the P-FI based on a combined model integrating comorbidities, PROs, gait parameters, and selected significant variables across these domains, and examined its association with fall status. Overall, the faller group demonstrated consistently worse profiles across demographics, comorbidities, PROs, and physical function compared with the non-faller group. Notably, the multidomain P-FI score was significantly higher in fallers, suggesting that it effectively captures the overall functional decline associated with falls. Interestingly, the P-FI derived from PROs showed the strongest statistical significance and effect size (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;0.430). However, this finding should be interpreted with caution, as individuals with a history of falls tend to exhibit elevated levels of fear of falling due to its psychological impact, with reported increases ranging from approximately 20% to 40% \u003csup\u003e38,53\u003c/sup\u003e. As fear of falling is conceptually closely related to fall experience, the observed association may have been structurally amplified. This suggests that the PRO-based index may partially share underlying constructs with fall status, potentially leading to an overestimation of its association. In contrast, the P-FI constructed solely from gait parameters did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.075, d\u0026thinsp;=\u0026thinsp;0.167), although a modest trend was observed. This finding indicates that gait parameters alone may have limited discriminative ability for distinguishing fall status. Falls are widely recognized as multifactorial events arising from complex interactions among physical impairments, cognitive decline, psychological factors, and environmental hazards \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFrom this perspective, an index that integrates multiple domains may provide a more sensitive representation of functional deficits related to fall risk. The combined model-based P-FI in this study was designed to capture multidimensional functional decline by incorporating spatiotemporal gait parameters\u0026mdash;such as stride variability and double support time across different walking speeds\u0026mdash;alongside cognitive function and quality of life. Previous studies have demonstrated that physical frailty is associated with impaired gait performance, particularly reduced walking speed and diminished dynamic stability \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In addition, frailty has been linked to activity limitations and functional decline, which are in turn associated with cognitive impairment. Importantly, older adults with a history of falls have been reported to exhibit not only impaired gait function but also reduced quality of life and cognitive decline \u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Therefore, higher P-FI values in individuals with a history of falls may indicate an increased likelihood of transitioning to pre-frailty, supporting the utility of the P-FI as both a measure of current functional status and a potential marker of vulnerability to future functional decline.\u003c/p\u003e \u003cp\u003eAs described earlier, the combined model-based P-FI developed in this study incorporates multidimensional components, including gait performance, cognitive function, and quality of life, all of which are closely associated with frailty. Consistent with this conceptual framework, a progressive worsening across multiple domains was observed toward the highest quartile (Q4) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In particular, the comparison between extreme groups (Q1 vs. Q4) revealed pronounced functional decline in the lowest-functioning group. Among the contributing factors, insomnia risk (+\u0026thinsp;8%), stress levels (+\u0026thinsp;11%), and 5-times sit-to-stand performance (+\u0026thinsp;11%) were significantly elevated, which is generally consistent with findings from previous studies. Frail older adults typically exhibit reduced gait performance and cognitive decline, which ultimately contribute to a lower quality of life \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. These impairments are often accompanied by decreased muscle strength, increased psychological stress, and poor sleep quality, creating a self-reinforcing cycle that accelerates functional deterioration \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR63 CR64 CR65\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Importantly, the ability of these variables to discriminate between Q1 and Q4 was supported by a ROC analysis, yielding an AUC of 0.83 and an overall classification accuracy of 78%, indicating good discriminative performance. These findings are particularly noteworthy, as the observed associations were derived from independent variables while minimizing potential circularity with components included in the P-FI. Taken together, this supports the robustness of the proposed index and highlights its potential utility as a multidimensional tool for identifying individuals at risk of physical pre-frailty.\u003c/p\u003e \u003cp\u003eConventional frailty assessment tools, such as Fried\u0026rsquo;s frailty, are often limited by their primary focus on physical frailty or their inability to adequately capture its continuous nature \u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In addition, although some frailty index-based approaches provide continuous measures, they typically require the collection of numerous variables and rely heavily on hospital- or laboratory-based assessments \u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, thereby limiting their applicability in community-dwelling older adults. In contrast, the gait parameters used in this study, although measured in controlled environments, are functionally based and less resource-intensive, supporting their potential use in community-dwelling older adults. Frailty is inherently a multidimensional construct reflecting declines across multiple functional domains, including gait performance, muscle strength, cognitive function, and quality of life, all of which are closely related to daily functioning \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In this context, the P-FI index proposed in the present study offers a distinct advantage by incorporating challenging gait tasks under various conditions, rather than relying solely on preferred walking speed, enabling a more comprehensive assessment of dynamic stability \u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Furthermore, the P-FI integrates key frailty-related domains, including cognitive function and quality of life, thereby capturing the multidimensional nature of frailty beyond conventional approaches that primarily focus on physical components. Its functional performance\u0026ndash;based and time-efficient measurement protocol enhances feasibility, enabling practical and scalable assessment of frailty risk in community settings. Importantly, the P-FI may facilitate early identification of individuals at risk of frailty and enable detection of subtle functional decline in older adults, supporting timely and targeted interventions.\u003c/p\u003e \u003cp\u003eIn this context, recent initiatives by the WHO and ARPA-H have emphasized the Intrinsic Capacity (IC) framework as a standardized approach for capturing functional changes across aging trajectories, highlighting physical performance\u0026ndash;based domains such as locomotion and muscle strength as core components strongly linked to functional decline \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Reflecting this paradigm shift, recent studies have increasingly adopted data-driven approaches grounded in standardized frameworks \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. IC comprises five domains\u0026mdash;locomotion, vitality, cognition, psychological, and sensory functions\u0026mdash;and enables classification of individuals into physiological aging states (robust, pre-frail to frail, and dependent), underscoring the importance of integrated assessment across physical and mental capacities \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Although IC was not directly quantified in the present study, the selected features largely represent domains aligned with core IC constructs, particularly locomotion, vitality (partially reflected by BMI), cognition, and psychological well-being (e.g., quality of life), thereby providing indirect yet meaningful insight into IC-related functional decline. It may also serve as a foundational tool for developing personalized intervention programs aimed at improving functional capacity. Moreover, as the P-FI is derived from quantifiable functional measures, it may be adaptable to digital healthcare platforms, particularly those incorporating wearable sensors and mobile health applications to facilitate remote monitoring and user engagement. Such integration could enhance its applicability in supporting functional independence and quality of life in older populations.\u003c/p\u003e \u003cp\u003eAlthough the present study successfully developed the P-FI index and demonstrated its validity through a known-groups validation approach, several limitations should be acknowledged. First, in constructing the frailty index, outcome variables such as handgrip strength, walking speed, and physical activity were included; however, key phenotype components such as exhaustion and unintentional weight loss were not considered. Future studies need to consider to involve those rest of the phenotypes. Second, further validation is required to determine whether the P-FI yields consistent and clinically meaningful results across populations with varying health conditions. In particular, it would be important to examine its applicability in older adults with a high risk of frailty, such as those with peripheral neuropathy, diabetes, or sarcopenia, in comparison with age-matched healthy individuals. Additionally, longitudinal follow-up and interventional studies are warranted to evaluate changes in function based on the P-FI; furthermore, as this study conducted only internal validation without external validation in an independent cohort, the generalizability of the findings is limited. Finally, the current study did not include frail individuals, limiting the generalizability of the findings across the full frailty spectrum. Future studies should incorporate frail populations to further refine and extend the P-FI into a more comprehensive and robust index.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed a novel P-FI to quantitatively assess pre-frailty status in community-dwelling older adults aged 65 years and above. The P-FI was designed as a multidimensional index that not only reflects physical function but also incorporates key frailty-related domains, including cognitive function and quality of life. The validity of the P-FI was supported by its ability to discriminate functional differences by fall status. Additionally, when stratified into quartiles, the lowest-functioning group (Q4) consistently showed the greatest decline across domains.\u003c/p\u003e \u003cp\u003eThe use of a functional performance-based and efficient measurement protocol enhances the feasibility of the P-FI, supporting its application in community and clinical settings. The P-FI may facilitate early identification of individuals at risk of frailty and enable the detection of subtle functional decline. Ultimately, the P-FI has the potential to serve as a foundational tool for developing personalized intervention strategies aimed at improving functional capacity. Its integration into digital healthcare systems such as wearable devices and mobile health application may further expand its applicability, contributing to the promotion of functional independence and the enhancement of quality of life in older populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthic approval\u003c/h2\u003e \u003cp\u003eThe studies involving human participants were reviewed and approved by the Institutional Review Board of Dong-A University (IRB No. 2\u0026ndash;104709\u0026ndash;AB\u0026ndash;N\u0026ndash;01\u0026ndash;201808\u0026ndash;HR\u0026ndash;023\u0026ndash;04) and registered with the Clinical Research Information Service (CRIS; KCT0004529; registered on September 13, 2018), a primary registry participating in the WHO International Clinical Trials Registry Platform. Written informed consent was obtained from all participants and/or their legal guardians prior to participation. This study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by a grant of Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health \u0026amp; Welfare, Republic of Korea (grant number: RS-2025-25454559; Jaewon Beom). In addition, this research was funded by the Sports Promotion Fund of Seoul Olympic Sports Promotion Foundation from the Ministry of Culture, Sports and Tourism (grant number: B0080605000494; Changhong Youm). This work was also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2025S1A5B5A16007605; Myeounggon Lee). The funders had no role in the study design, data collection, analysis, or interpretation of the data, or in the preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConcept and study design: M.L., J.B., and C.Y.; Data acquisition: M.L., and H.P.; Data analysis: M.L., and J.B.; Preparing tables and figures: M.L.; Interpretation of the data: M.L., J.B., H.P.; Drafting the manuscript: M.L.; Critical revision of the manuscript: M.L., H.P., J.L., J.B., and C.Y. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors thank all participants who took part in this study, as well as the research staff of the biomechanics laboratories at Dong-A University and Chonnam National University for their assistance with data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are not publicly available but are available from the corresponding author C.Y., [email protected] upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRudnicka, E. et al. 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Association of intrinsic capacity with functional decline and mortality in older adults: a systematic review and meta-analysis of longitudinal studies. \u003cem\u003eLancet Healthy Longev.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, e480\u0026ndash;e492 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKemoun, P. et al. A gerophysiology perspective on healthy ageing. \u003cem\u003eAgeing Res. Rev.\u003c/em\u003e \u003cb\u003e73\u003c/b\u003e, 101537 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Frailty, Pre-frail, Functional Decline, Multidimensional Assessment, Older Adults, Digital Health","lastPublishedDoi":"10.21203/rs.3.rs-9550110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9550110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFrailty assessment is essential for evaluating functional decline in older adults and informing prevention and intervention strategies; however, existing approaches are often limited to physical domains or rely on clinical and laboratory-based assessments, restricting accessibility and scalability. Given that frailty is a multidimensional condition encompassing physical, cognitive, and psychosocial domains, a comprehensive and quantifiable index is needed to detect early functional decline in community-dwelling populations. This study aimed to develop and validate a Physical Pre-Frailty Index (P-FI) in 729 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. The P-FI was constructed using demographics, comorbidities, patient-reported outcomes, and gait performance, with outcome variables defined as the composite z-score of grip strength, preferred gait speed, and physical activity. Regression modeling identified key contributors, including gait parameters across multiple speed conditions, cognitive function, and quality of life, generating a normalized index ranging from 0 to 1. The P-FI demonstrated strong discriminative validity, with higher scores in individuals with a history of falls (OR\u0026thinsp;=\u0026thinsp;4.80, p\u0026thinsp;=\u0026thinsp;0.006). Quartile stratification revealed progressive functional decline, with the highest-risk group showing impairments in sleep quality, psychological stress, and lower-extremity strength (AUC\u0026thinsp;=\u0026thinsp;0.86). The P-FI may support early detection and targeted interventions.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Multidimensional Physical Pre-Frailty Index for Early Detection of Functional Decline in Community-Dwelling Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 17:57:37","doi":"10.21203/rs.3.rs-9550110/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-09T15:09:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159164479907588018192696423060252184110","date":"2026-05-06T15:54:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T07:07:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T19:31:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T06:52:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T06:52:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-28T07:18:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38fa7293-76e0-424b-8ec8-49b4201e4631","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-09T15:09:47+00:00","index":24,"fulltext":""},{"type":"reviewerAgreed","content":"159164479907588018192696423060252184110","date":"2026-05-06T15:54:18+00:00","index":23,"fulltext":""},{"type":"reviewersInvited","content":"7","date":"2026-05-05T07:07:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T19:31:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T06:52:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T06:52:28+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67958460,"name":"Health sciences/Diseases"},{"id":67958461,"name":"Health sciences/Health care"},{"id":67958462,"name":"Health sciences/Medical research"},{"id":67958463,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-13T17:57:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 17:57:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9550110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9550110","identity":"rs-9550110","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}