Seasonal association between spatiotemporal gait variables and falls among community-dwelling older adults living in snowy areas: a cross-sectional study

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Methods This cross-sectional study included 287 community-dwelling older adults (mean age, 77.6 ± 5.7 years; sex, 69.0% female) living in Hokkaido, Japan. The fall history in winter and non-winter seasons was assessed through face-to-face interviews. Spatiotemporal gait variables, including gait speed, cadence, stride length, stride length variability, double support time, and double support time variability, were measured using an electronic gait analysis system. Results The prevalence of falls was 19.5% (n = 56) during winter and 18.1% (n = 52) during non-winter months. Logistic regression analyses showed no significant associations between gait variables and a history of falls during winter. However, during non-winter months, a shorter stride length (odds ratio, 0.98; 95% confidence interval, 0.96–0.99; p = 0.027) was significantly associated with a history of falls, even after controlling for age, sex, body mass index, living alone, polypharmacy, fear of falling, cognitive function, and depressive symptoms. Conclusion Spatiotemporal gait variables played a lesser role in identifying risk factors for fall history during winter compared to non-winter months in community-dwelling older adults living in snowy regions. aging fall mobility season Key Summary Points Aim: This aimed to examine whether gait kinematic variables are associated with fall history during winter and non-winter among community-dwelling older adults living in snowy regions. Findings: We found seasonal variations in the relationship between falls and gait variables. A decreased stride length was significantly associated with a history of falls in non-winter months; however, the fall risk in winter was not linked to gait variables. Message: Fall prevention strategies should consider function and environmental conditions. Introduction Falls are a major cause of disability and the leading cause of injury-related mortality and long-term care [ 1 ]. One in three older adults experience at least one fall per year [ 2 ], approximately 10% of which result in major injuries, such as fractures [ 3 ]. Experiencing a fall can also trigger a fear of falling [ 4 ], which can lead to activity restriction, loss of functional capacity, and a decreased quality of life [ 5 ]. Therefore, fall prevention is crucial for maintaining the health status of community-dwelling older adults [ 6 ]. Age-related deterioration of gait function has been identified as a significant risk factor for falls [ 7 ]. Although gait speed is a standard measure for assessing mobility and fall risk in older adults [ 8 ], quantitative assessments have further clarified the role of gait in overall physical capability, serving as a clinical tool for identifying older adults at increased risk of falls [ 9 ]. Previous reviews have reported that falls are associated with a slower gait speed and cadence, longer stride time, prolonged double support duration, shorter stride and step lengths, and a wider step width [ 10 ]. Additionally, increased gait variability in older adults contributes to unstable gait and is associated with an increased risk of falls [ 11 ]. These spatiotemporal gait variables have been reported to objectively characterize gait performance and potentially improve the prediction capability of the fall risk [ 12 – 14 ]. The risk of falls is strongly influenced by external factors, particularly seasonal environmental changes, such as icy and snowy road surfaces in winter [ 15 ]. These conditions increase the likelihood of slips or trips while walking or performing daily activities. Enhanced gait stability may be necessary to ensure safe mobility and prevent falls during outdoor activities in winter. Although a previous study reported that gait impairments, including slower walking speed and poorer performance on the Timed Up and Go (TUG) test, were not related to falls in winter [ 16 ], the relationships between spatiotemporal gait variables and winter falls in community-dwelling older adults remain insufficiently understood. To improve the accuracy of seasonal fall risk screening in older adults, we aimed to investigate whether gait kinematic variables are associated with fall history during winter and non-winter periods among community-dwelling older adults residing in snowy regions. The hypothesis was that a history of falls in winter would be associated with gait variables, particularly spatiotemporal parameters and their variability, distinguishing individuals with a history of falls from those without, similar to non-winter falls. Materials and methods Participants Participants were recruited from the Widely Hokkaido Individual Training for Elderly (WHITE) study, a community-based health check-up initiative conducted by Sapporo Medical University in September 2017, October 2018, October 2022, September 2023, and September 2024. Study recruitment employed convenience sampling, with responses collected from postcards mailed to Sapporo City residents aged ≥ 65 years at the time of the study. The WHITE study included face-to-face interviews and assessments of physical and cognitive function. Among the 368 individuals who participated in this study, the following exclusion criteria were applied: (1) receiving support under the long-term care insurance system (n = 8); (2) history of stroke (n = 11); (3) diagnosis of Alzheimer’s disease (n = 1) or Parkinson’s disease (n = 1); (4) presence of cognitive impairment (Mini-Mental State Examination score of ≤ 23 points [ 17 ]) (n = 20); and (5) missing data (n = 40). A total of 287 older adults provided data available for analysis. Ethical approval was obtained from the Ethics Committee of Sapporo Medical University, and written informed consent was obtained from all participants following an explanation of the study procedures. Gait variables Participants were instructed to walk at their usual pace along a 9-m straight pathway, which included 2-m acceleration and deceleration sections. Gait variables were measured using an electronic measurement device (WalkWay MW-1000; Anima Co., Tokyo, Japan) mounted in the middle of the pathway. The WalkWay was 800 mm wide, 2400 mm long, and 5 mm thick, with strain gauges placed 10 mm apart. The following spatiotemporal gait variables were measured: gait speed, cadence, stride length, stride length variability, double support time, and double support time variability. Cadence was calculated as the number of steps per minute. Stride length and double support time were calculated using the mean values from the trials. Variability in stride length and double support time was determined using the coefficient of variation (CV): CV (%) = (standard deviation/mean) × 100 (%). When fewer than five stride data points were acquired over six trials, the data were regarded as missing values. Fall history Participants were asked whether they had experienced a fall in the past 12 months. Falls were defined as "falling to the ground or a lower level against one’s will" [ 18 ]. Those who reported falls were further questioned concerning the number, season, and location of the falls. In Sapporo City, snowfall typically occurs between December and March, with average temperatures from 1991 to 2020 of − 0.9°C in December, − 3.2°C in January, − 2.7°C in February, and 1.1°C in March. The average snow accumulation during this period was 113 cm in December, 137 cm in January, 116 cm in February, and 74 cm in March [ 19 ]. Based on this climate data, the non-winter period was defined as April to November, while the winter period was defined as December to March. Confounding factors Demographic data, including age, sex, body mass index (BMI), living situation (living alone or with others), polypharmacy (defined as the use of five or more medications), and fear of falling, were collected through a questionnaire. Face-to-face interviews were conducted by well-trained physiotherapists, operators, and staff. Cognitive function and depressive symptoms were assessed using the Mini-Mental State Examination (MMSE) and the 15-item Geriatric Depression Scale (GDS-15) [ 20 ]. Statistical analysis Participants were categorized as fallers or non-fallers for both winter and non-winter periods based on whether they had experienced a fall during each respective season. Demographic data and gait variables were compared between fallers and non-fallers in both winter and non-winter seasons using the unpaired t-test, Mann–Whitney U test, or chi-square test, as appropriate. Logistic regression analysis was used to examine the association between gait variables and fall history in both winter and non-winter seasons. Univariate logistic regressions were conducted for each gait variable, including gait speed, cadence, stride length, stride length variability, double support time, and double support time variability. Multivariate logistic regression analyses were conducted for gait variables that were significantly related to fall history, adjusting for age, sex, BMI, living alone, polypharmacy, fear of falling, MMSE, and GDS-15. All statistical analyses were conducted using IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA), with statistical significance set at p < 0.05. Results The mean participant age was 77.6 ± 5.7 years, and 198 (69.0%) were female. In total, 56 participants (19.5%) experienced falls during the winter, while 52 (18.1%) fell during the non-winter period. Seventeen participants (5.9%) reported falls in both winter and non-winter. The characteristics of fallers and non-fallers in each season are summarized in Table 1 . In winter, there were no significant differences in demographic data and gait variables between fallers and non-fallers. Fallers in non-winter had a significantly higher prevalence of fear of falling ( p = 0.042), slower gait speed ( p = 0.009), and shorter stride length ( p = 0.001) compared with non-fallers in non-winter. The details of fall characteristics during winter and non-winter are presented in Table 2 . Among participants who fell in winter, a total of 84 falls were reported, with 60 (71.4%) occurring outdoors. In contrast, participants who fell in non-winter reported a total of 74 falls, of which 42 (56.8%) occurred outdoors. Table 1 Characteristics of fallers and non-fallers during winter and non-winter seasons Overall (n = 287) Winter Non-winter Faller (n = 56) Non-faller (n = 231) p Faller (n = 52) Non-faller (n = 235) p Age (years) 77.6 ± 5.7 77.5 ± 5.6 77.7 ± 5.7 0.811 78.8 ± 6.5 77.4 ± 5.4 0.142 Sex (male/female, n) 89 / 198 18 / 38 71 / 160 0.873 14 / 38 75 / 160 0.513 BMI (kg/m 2 ) 22.7 ± 3.4 22.6 ± 3.5 22.8 ± 3.3 0.722 23.0 ± 3.8 22.7 ± 3.2 0.568 Living alone (yes/no, n) 98 / 189 23 / 33 75 / 156 0.271 20 / 32 78 / 157 0.519 Polypharmacy (yes/no, n) 93 / 194 21 / 35 72 / 159 0.426 23 / 29 70 / 165 0.050 Fear of falling (yes/no, n) 117 / 170 29 / 27 88 / 143 0.070 28 / 24 89 / 146 0.042* MMSE (score) 28.3 ± 1.8 28.2 ± 1.9 28.3 ± 1.8 0.664 28.2 ± 1.9 28.3 ± 1.7 0.603 GDS15 (score) 3.2 ± 1.8 3.1 ± 2.7 3.2 ± 2.8 0.851 4.0 ± 3.5 3.0 ± 2.6 0.050 Gait speed (m/s) 1.39 ± 0.22 1.36 ± 0.21 1.40 ± 0.23 0.304 1.32 ± 0.24 1.41 ± 0.22 0.009* Cadence (steps/min) 132.2 ± 12.8 132.4 ± 12.6 132.1 ± 12.9 0.880 133.3 ± 12.7 131.9 ± 12.9 0.481 Stride length (cm) 125.0 ± 16.7 123.0 ± 16.9 125.5 ± 16.6 0.313 118.0 ± 19.1 126.6 ± 15.7 0.001* Stride length variability (%) 14.1 ± 3.2 3.4 ± 2.6 4.8 ± 12.8 0.404 3.8 ± 3.6 4.7 ± 12.7 0.638 Double support time (ms) 4.5 ± 11.6 14.1 ± 3.3 14.3 ± 3.0 0.594 14.1 ± 3.2 14.5 ± 3.4 0.446 Double support time variability (%) 16.8 ± 9.7 16.8 ± 10.1 17.0 ± 7.7 0.880 16.8 ± 10.2 17.0 ± 7.2 0.901 Values are expressed as means ± standard deviations or number. BMI, Body Mass Index; MMSE, Mini-Mental State Examination; GDS15,15-item Geriatric Depression Scale. (*) indicates statistical significance at p 3 4 4 Location (number of falls) Total 84 74 Bedroom 2 2 Living room 1 7 Bathroom 2 4 Entrance 0 5 Outdoor 60 42 Data not available 19 14 The results of the logistic regression analysis are presented in Table 3 . No gait variables were significantly associated with a history of falls during winter. However, univariate logistic regression analysis revealed that slower gait speed (OR, 0.17; 95% CI, 0.04–0.65; p = 0.010) and shorter stride length (OR, 0.97; 95% CI, 0.95–0.99; p = 0.001) were significantly associated with an increased OR for fall history in non-winter. Furthermore, after adjusting for age, sex, BMI, living alone, polypharmacy, fear of falling, MMSE, and GDS-15, we found that a shorter stride length (OR, 0.98; 95% CI, 0.96–0.99; p = 0.027) remained significantly associated with a history of falls in non-winter months. Table 3 Logistic regression analysis of factors associated with falls during winter and non-winter seasons Winter Non-winter OR 95% CI p OR 95% CI p Gait speed (m/s) Crude 0.51 0.14–1.85 0.303 0.17 0.04–0.65 0.010* Adjusted 0.44 0.10–1.93 0.278 0.25 0.06–1.17 0.079 Cadence (steps/min) Crude 1.00 0.98–1.03 0.880 1.01 0.99–1.03 0.479 Adjusted 1.00 0.98–1.03 0.845 1.01 0.99–1.04 0.433 Stride length (cm) Crude 0.99 0.97–1.01 0.312 0.97 0.95–0.99 0.001* Adjusted 0.99 0.97–1.01 0.241 0.98 0.96–0.99 0.027* Stride length variability (%) Crude 0.97 0.90–1.04 0.408 0.99 0.95–1.03 0.650 Adjusted 0.97 0.90–1.04 0.355 0.99 0.94–1.04 0.616 Double support time (ms) Crude 1.00 0.99–1.01 0.593 1.00 0.99–1.01 0.445 Adjusted 1.00 0.99–1.01 0.979 1.00 0.99–1.01 0.927 Double support time variability (%) Crude 1.00 0.97–1.03 0.897 1.00 0.97–1.03 0.900 Adjusted 0.99 0.96–1.03 0.614 1.00 0.96–1.03 0.825 (*) indicates statistical significance at p < 0.05. CI, confidence interval; OR, odds ratio. Crude: not adjusted; adjusted: adjusted for age sex, body mass index, living alone, polypharmacy, fear of falling, Mini-Mental State Examination score, and the 15-item Geriatric Depression Scale. Each odds ratio was analyzed for its association with winter and non-winter falls in separate logistic regression models. Discussion This study examined the relationship between gait kinematic parameters and fall history in community-dwelling older adults during winter and non-winter seasons. The main finding was that fall history in winter was not associated with spatiotemporal gait parameters, which were significant in the non-winter season. These results suggest that the quantitative assessment of gait function played a smaller role in identifying fall risk factors in winter than in non-winter among community-dwelling older adults living in snowy regions. Numerous studies have explored the relationship between kinematic gait variables and falls in non-winter. Stride length, a spatial gait parameter, has been reported to be sensitive in detecting fall risk in older adults [ 10 , 21 ]. Fallers reportedly exhibit reduced ankle plantar flexor torque while walking compared to non-fallers [ 22 ], which may lead to weaker push-off and shorter stride lengths. Therefore, kinematic gait variables have been used to assess overall physical capability and have been associated with the risk of disability [ 23 ] and mortality [ 24 ]. These findings align with those of previous studies reporting a relationship between gait kinematic variables and fall history in non-winter. Contrary to our hypothesis, this study found that spatiotemporal gait parameters were not associated with fall history in winter among community-dwelling older adults living in snowy areas, despite an observed relationship in non-winter months. This finding is consistent with that of a previous study reporting no significant differences in physical performance measures—including gait speed, TUG test results, and knee extensor strength—between older adults who experienced falls in winter and those who did not [ 16 ]. Fall risk factors in older adults include internal factors, such as physical and cognitive function, as well as external factors. Seasonal changes in external (environmental) factors are particularly significant in cold, snowy regions, where slips or trips commonly occur during walking. Previous studies have reported that higher physical activity levels are associated with an increased risk of falls in winter among older adults [ 16 , 25 ]. In this study, falls most frequently occurred in outdoor locations. This finding may explain why individuals with higher physical activity levels had greater exposure to frozen roads and other high-risk environments. To mitigate fall risk during outdoor activities in winter, addressing external factors should be prioritized over restricting physical activity. A previous review found that using gait-stabilizing devices reduced the risk of outdoor slips and falls compared to usual winter footwear in older adults [ 26 ]. Collectively, these results suggest that environmental conditions have a significant influence on falls that occur outdoors in winter. Furthermore, environmental-induced threats have been reported to modify gait control, leading to reduced speed, shorter steps, decreased cadence, and prolonged double support time [ 27 ]. Thus, predictive and conscious gait control may be necessary for walking on snowy, frozen roads to prevent falls. Future studies investigating the relationship between gait kinematic variables and falls in community-dwelling older adults during winter should focus not only on quantitative gait parameters but also on adaptive gait strategies in response to environmental conditions. Despite the important findings of this study regarding seasonal differences in the relationship between fall occurrence and gait variables in community-dwelling older adults in snowy areas, some limitations should be noted. First, as this was a cross-sectional study, it was not possible to establish a causal relationship between seasonal falls and gait variables. Second, fall history over the past 12 months was assessed using a recall-based questionnaire, which may have introduced recall bias. Third, the study sample may have included individuals with relatively higher physical function and activity levels, potentially leading to selection bias. Further research with a more diverse participant pool is necessary to clarify the causal relationship between seasonal falls and gait variables. In conclusion, this study identified specific seasonal variations in the relationship between falls and gait variables in community-dwelling older adults. A decreased stride length was significantly associated with a fall history in non-winter months; however, fall risk in winter was not linked to gait variables. These findings highlight the need for fall prevention strategies in older adults to consider a broad range of factors, including internal factors, such as physical and cognitive function and physical activity levels, in addition to external factors, such as environmental conditions. Declarations Authors’ contributions: Hideyuki Tashiro: conceptualization, methodology, software, formal analysis, data curation, investigation, writing–original draft preparation, writing–review, and editing. Hikaru Ihira and Kazuki Yokoyama: data curation, funding acquisition, and project administration. Yuriko Matsuzaki-Kihara, Atsushi Mizumoto, Hidekazu Saito, Suguru Shimokihara, Keitaro Makino, Kida Takuto, Rin Takahashi, and Takeshi Sasaki: investigation. Nozomu Ikeda: supervision. Acknowledgments We would like to thank all the study participants and the research staff for their contributions to the study. Funding This research was funded by the Japan Agency for Medical Research and Development (Research Project No. 20dk0207027h0005) and academic grant of Sapporo Medical University (Grant Number 2200265, 2300235). The sponsor had no contributions in study design; in the collection, analyses and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The authors declare no conflicts of interest. Conflict of interest disclosure The authors declare no conflict of interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6800337","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472158015,"identity":"aceb98a3-5173-4d0c-be33-bcd5d9a139b8","order_by":0,"name":"Hideyuki 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Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Hikaru","middleName":"","lastName":"Ihira","suffix":""},{"id":472158017,"identity":"12625b29-f93b-4b10-b956-85e393132963","order_by":2,"name":"Kazuki Yokoyama","email":"","orcid":"","institution":"Sapporo Medical University: Sapporo Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Yokoyama","suffix":""},{"id":472158018,"identity":"39cff3c2-4562-417e-9e41-9c06406f8e78","order_by":3,"name":"Yuriko Matsuzaki-Kihara","email":"","orcid":"","institution":"Japan Health Care College: Nihon Iryo Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yuriko","middleName":"","lastName":"Matsuzaki-Kihara","suffix":""},{"id":472158019,"identity":"b67edd4e-3e6e-4ea0-9523-c546137e4f2b","order_by":4,"name":"Atsushi Mizumoto","email":"","orcid":"","institution":"Hokkaido Bunkyo University: Hokkaido Bunkyo Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Mizumoto","suffix":""},{"id":472158020,"identity":"f73ea4b6-fcd3-4433-b054-35e9c49cb595","order_by":5,"name":"Hidekazu Saito","email":"","orcid":"","institution":"Sapporo Medical University: Sapporo Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Hidekazu","middleName":"","lastName":"Saito","suffix":""},{"id":472158021,"identity":"bcebaf88-144c-4ac8-bc4d-7be600f3ffdb","order_by":6,"name":"Suguru Shimokihara","email":"","orcid":"","institution":"Nagasaki University: Nagasaki Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Suguru","middleName":"","lastName":"Shimokihara","suffix":""},{"id":472158022,"identity":"10d8392d-99c7-42c6-a70a-ecca90919d45","order_by":7,"name":"Keitaro Makino","email":"","orcid":"","institution":"Hokkaido University: Hokkaido Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Keitaro","middleName":"","lastName":"Makino","suffix":""},{"id":472158023,"identity":"031a1989-d02c-4105-a03c-c070cfcf6a4e","order_by":8,"name":"Takuto Kida","email":"","orcid":"","institution":"National Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Takuto","middleName":"","lastName":"Kida","suffix":""},{"id":472158024,"identity":"166d82c1-9906-45a1-8760-d89830a59e25","order_by":9,"name":"Lin Takahashi","email":"","orcid":"","institution":"Sapporo Medical University: Sapporo Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Takahashi","suffix":""},{"id":472158025,"identity":"7e8f1bc6-c8c8-4664-a7b9-d3099e272aa3","order_by":10,"name":"Nozomu Ikeda","email":"","orcid":"","institution":"Sapporo Medical University: Sapporo Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Nozomu","middleName":"","lastName":"Ikeda","suffix":""}],"badges":[],"createdAt":"2025-06-02 08:48:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6800337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6800337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90814973,"identity":"ad6c246d-6c31-4f0b-8f8d-58b2a22b6f43","added_by":"auto","created_at":"2025-09-08 12:56:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":781323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6800337/v1/367ccbdb-24e7-4384-8f85-4051c9e08e2a.pdf"}],"financialInterests":"","formattedTitle":"Seasonal association between spatiotemporal gait variables and falls among community-dwelling older adults living in snowy areas: a cross-sectional study","fulltext":[{"header":"Key Summary Points ","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAim:\u003c/em\u003e\u003c/strong\u003e This aimed to examine whether gait kinematic variables are associated with fall history during winter and non-winter among community-dwelling older adults living in snowy regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFindings:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eWe found seasonal variations in the relationship between falls and gait variables. A decreased stride length was significantly associated with a history of falls in non-winter months; however, the fall risk in winter was not linked to gait variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMessage:\u003c/em\u003e\u003c/strong\u003e Fall prevention strategies should consider function and environmental conditions.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eFalls are a major cause of disability and the leading cause of injury-related mortality and long-term care [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. One in three older adults experience at least one fall per year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], approximately 10% of which result in major injuries, such as fractures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Experiencing a fall can also trigger a fear of falling [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which can lead to activity restriction, loss of functional capacity, and a decreased quality of life [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, fall prevention is crucial for maintaining the health status of community-dwelling older adults [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge-related deterioration of gait function has been identified as a significant risk factor for falls [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although gait speed is a standard measure for assessing mobility and fall risk in older adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], quantitative assessments have further clarified the role of gait in overall physical capability, serving as a clinical tool for identifying older adults at increased risk of falls [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous reviews have reported that falls are associated with a slower gait speed and cadence, longer stride time, prolonged double support duration, shorter stride and step lengths, and a wider step width [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, increased gait variability in older adults contributes to unstable gait and is associated with an increased risk of falls [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These spatiotemporal gait variables have been reported to objectively characterize gait performance and potentially improve the prediction capability of the fall risk [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe risk of falls is strongly influenced by external factors, particularly seasonal environmental changes, such as icy and snowy road surfaces in winter [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These conditions increase the likelihood of slips or trips while walking or performing daily activities. Enhanced gait stability may be necessary to ensure safe mobility and prevent falls during outdoor activities in winter. Although a previous study reported that gait impairments, including slower walking speed and poorer performance on the Timed Up and Go (TUG) test, were not related to falls in winter [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the relationships between spatiotemporal gait variables and winter falls in community-dwelling older adults remain insufficiently understood.\u003c/p\u003e \u003cp\u003eTo improve the accuracy of seasonal fall risk screening in older adults, we aimed to investigate whether gait kinematic variables are associated with fall history during winter and non-winter periods among community-dwelling older adults residing in snowy regions. The hypothesis was that a history of falls in winter would be associated with gait variables, particularly spatiotemporal parameters and their variability, distinguishing individuals with a history of falls from those without, similar to non-winter falls.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants were recruited from the Widely Hokkaido Individual Training for Elderly (WHITE) study, a community-based health check-up initiative conducted by Sapporo Medical University in September 2017, October 2018, October 2022, September 2023, and September 2024. Study recruitment employed convenience sampling, with responses collected from postcards mailed to Sapporo City residents aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years at the time of the study. The WHITE study included face-to-face interviews and assessments of physical and cognitive function. Among the 368 individuals who participated in this study, the following exclusion criteria were applied: (1) receiving support under the long-term care insurance system (n\u0026thinsp;=\u0026thinsp;8); (2) history of stroke (n\u0026thinsp;=\u0026thinsp;11); (3) diagnosis of Alzheimer\u0026rsquo;s disease (n\u0026thinsp;=\u0026thinsp;1) or Parkinson\u0026rsquo;s disease (n\u0026thinsp;=\u0026thinsp;1); (4) presence of cognitive impairment (Mini-Mental State Examination score of \u0026le;\u0026thinsp;23 points [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]) (n\u0026thinsp;=\u0026thinsp;20); and (5) missing data (n\u0026thinsp;=\u0026thinsp;40). A total of 287 older adults provided data available for analysis. Ethical approval was obtained from the Ethics Committee of Sapporo Medical University, and written informed consent was obtained from all participants following an explanation of the study procedures.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGait variables\u003c/h3\u003e\n\u003cp\u003eParticipants were instructed to walk at their usual pace along a 9-m straight pathway, which included 2-m acceleration and deceleration sections. Gait variables were measured using an electronic measurement device (WalkWay MW-1000; Anima Co., Tokyo, Japan) mounted in the middle of the pathway. The WalkWay was 800 mm wide, 2400 mm long, and 5 mm thick, with strain gauges placed 10 mm apart. The following spatiotemporal gait variables were measured: gait speed, cadence, stride length, stride length variability, double support time, and double support time variability. Cadence was calculated as the number of steps per minute. Stride length and double support time were calculated using the mean values from the trials. Variability in stride length and double support time was determined using the coefficient of variation (CV): CV (%) = (standard deviation/mean) \u0026times; 100 (%). When fewer than five stride data points were acquired over six trials, the data were regarded as missing values.\u003c/p\u003e\n\u003ch3\u003eFall history\u003c/h3\u003e\n\u003cp\u003eParticipants were asked whether they had experienced a fall in the past 12 months. Falls were defined as \"falling to the ground or a lower level against one\u0026rsquo;s will\" [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Those who reported falls were further questioned concerning the number, season, and location of the falls. In Sapporo City, snowfall typically occurs between December and March, with average temperatures from 1991 to 2020 of \u0026minus;\u0026thinsp;0.9\u0026deg;C in December, \u0026minus;\u0026thinsp;3.2\u0026deg;C in January, \u0026minus;\u0026thinsp;2.7\u0026deg;C in February, and 1.1\u0026deg;C in March. The average snow accumulation during this period was 113 cm in December, 137 cm in January, 116 cm in February, and 74 cm in March [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Based on this climate data, the non-winter period was defined as April to November, while the winter period was defined as December to March.\u003c/p\u003e\n\u003ch3\u003eConfounding factors\u003c/h3\u003e\n\u003cp\u003eDemographic data, including age, sex, body mass index (BMI), living situation (living alone or with others), polypharmacy (defined as the use of five or more medications), and fear of falling, were collected through a questionnaire. Face-to-face interviews were conducted by well-trained physiotherapists, operators, and staff. Cognitive function and depressive symptoms were assessed using the Mini-Mental State Examination (MMSE) and the 15-item Geriatric Depression Scale (GDS-15) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eParticipants were categorized as fallers or non-fallers for both winter and non-winter periods based on whether they had experienced a fall during each respective season. Demographic data and gait variables were compared between fallers and non-fallers in both winter and non-winter seasons using the unpaired t-test, Mann\u0026ndash;Whitney U test, or chi-square test, as appropriate. Logistic regression analysis was used to examine the association between gait variables and fall history in both winter and non-winter seasons. Univariate logistic regressions were conducted for each gait variable, including gait speed, cadence, stride length, stride length variability, double support time, and double support time variability. Multivariate logistic regression analyses were conducted for gait variables that were significantly related to fall history, adjusting for age, sex, BMI, living alone, polypharmacy, fear of falling, MMSE, and GDS-15. All statistical analyses were conducted using IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA), with statistical significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean participant age was 77.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 years, and 198 (69.0%) were female. In total, 56 participants (19.5%) experienced falls during the winter, while 52 (18.1%) fell during the non-winter period. Seventeen participants (5.9%) reported falls in both winter and non-winter. The characteristics of fallers and non-fallers in each season are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In winter, there were no significant differences in demographic data and gait variables between fallers and non-fallers. Fallers in non-winter had a significantly higher prevalence of fear of falling (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), slower gait speed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and shorter stride length (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) compared with non-fallers in non-winter. The details of fall characteristics during winter and non-winter are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among participants who fell in winter, a total of 84 falls were reported, with 60 (71.4%) occurring outdoors. In contrast, participants who fell in non-winter reported a total of 74 falls, of which 42 (56.8%) occurred outdoors.\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\u003eCharacteristics of fallers and non-fallers during winter and non-winter seasons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c4\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;287)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c10\" namest=\"c5\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c17\" namest=\"c12\"\u003e \u003cp\u003eNon-winter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFaller (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eNon-faller (n\u0026thinsp;=\u0026thinsp;231)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eFaller (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c17\" namest=\"c15\"\u003e \u003cp\u003eNon-faller (n\u0026thinsp;=\u0026thinsp;235)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e77.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male/female, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.513\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\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone (yes/no, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypharmacy (yes/no, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of falling (yes/no, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE (score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDS15 (score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait speed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence (steps/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e132.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e133.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e131.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e118.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e126.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length variability (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support time variability (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eValues are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or number. BMI, Body Mass Index; MMSE, Mini-Mental State Examination; GDS15,15-item Geriatric Depression Scale.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003e(*) indicates statistical significance at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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\u003eFall characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-winter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of falls (number of participants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation (number of falls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBedroom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiving room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBathroom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutdoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\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\u003eThe results of the logistic regression analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. No gait variables were significantly associated with a history of falls during winter. However, univariate logistic regression analysis revealed that slower gait speed (OR, 0.17; 95% CI, 0.04\u0026ndash;0.65; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) and shorter stride length (OR, 0.97; 95% CI, 0.95\u0026ndash;0.99; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were significantly associated with an increased OR for fall history in non-winter. Furthermore, after adjusting for age, sex, BMI, living alone, polypharmacy, fear of falling, MMSE, and GDS-15, we found that a shorter stride length (OR, 0.98; 95% CI, 0.96\u0026ndash;0.99; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) remained significantly associated with a history of falls in non-winter months.\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\u003eLogistic regression analysis of factors associated with falls during winter and non-winter seasons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eNon-winter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait speed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026ndash;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u0026ndash;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u0026ndash;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence (steps/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length variability (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support time variability (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e(*) indicates statistical significance at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCI, confidence interval; OR, odds ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCrude: not adjusted; adjusted: adjusted for age sex, body mass index, living alone, polypharmacy, fear of falling, Mini-Mental State Examination score, and the 15-item Geriatric Depression Scale.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eEach odds ratio was analyzed for its association with winter and non-winter falls in separate logistic regression models.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the relationship between gait kinematic parameters and fall history in community-dwelling older adults during winter and non-winter seasons. The main finding was that fall history in winter was not associated with spatiotemporal gait parameters, which were significant in the non-winter season. These results suggest that the quantitative assessment of gait function played a smaller role in identifying fall risk factors in winter than in non-winter among community-dwelling older adults living in snowy regions.\u003c/p\u003e \u003cp\u003eNumerous studies have explored the relationship between kinematic gait variables and falls in non-winter. Stride length, a spatial gait parameter, has been reported to be sensitive in detecting fall risk in older adults [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Fallers reportedly exhibit reduced ankle plantar flexor torque while walking compared to non-fallers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which may lead to weaker push-off and shorter stride lengths. Therefore, kinematic gait variables have been used to assess overall physical capability and have been associated with the risk of disability [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and mortality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These findings align with those of previous studies reporting a relationship between gait kinematic variables and fall history in non-winter.\u003c/p\u003e \u003cp\u003eContrary to our hypothesis, this study found that spatiotemporal gait parameters were not associated with fall history in winter among community-dwelling older adults living in snowy areas, despite an observed relationship in non-winter months. This finding is consistent with that of a previous study reporting no significant differences in physical performance measures\u0026mdash;including gait speed, TUG test results, and knee extensor strength\u0026mdash;between older adults who experienced falls in winter and those who did not [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Fall risk factors in older adults include internal factors, such as physical and cognitive function, as well as external factors. Seasonal changes in external (environmental) factors are particularly significant in cold, snowy regions, where slips or trips commonly occur during walking. Previous studies have reported that higher physical activity levels are associated with an increased risk of falls in winter among older adults [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, falls most frequently occurred in outdoor locations. This finding may explain why individuals with higher physical activity levels had greater exposure to frozen roads and other high-risk environments. To mitigate fall risk during outdoor activities in winter, addressing external factors should be prioritized over restricting physical activity. A previous review found that using gait-stabilizing devices reduced the risk of outdoor slips and falls compared to usual winter footwear in older adults [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Collectively, these results suggest that environmental conditions have a significant influence on falls that occur outdoors in winter.\u003c/p\u003e \u003cp\u003eFurthermore, environmental-induced threats have been reported to modify gait control, leading to reduced speed, shorter steps, decreased cadence, and prolonged double support time [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, predictive and conscious gait control may be necessary for walking on snowy, frozen roads to prevent falls. Future studies investigating the relationship between gait kinematic variables and falls in community-dwelling older adults during winter should focus not only on quantitative gait parameters but also on adaptive gait strategies in response to environmental conditions.\u003c/p\u003e \u003cp\u003eDespite the important findings of this study regarding seasonal differences in the relationship between fall occurrence and gait variables in community-dwelling older adults in snowy areas, some limitations should be noted. First, as this was a cross-sectional study, it was not possible to establish a causal relationship between seasonal falls and gait variables. Second, fall history over the past 12 months was assessed using a recall-based questionnaire, which may have introduced recall bias. Third, the study sample may have included individuals with relatively higher physical function and activity levels, potentially leading to selection bias. Further research with a more diverse participant pool is necessary to clarify the causal relationship between seasonal falls and gait variables.\u003c/p\u003e \u003cp\u003eIn conclusion, this study identified specific seasonal variations in the relationship between falls and gait variables in community-dwelling older adults. A decreased stride length was significantly associated with a fall history in non-winter months; however, fall risk in winter was not linked to gait variables. These findings highlight the need for fall prevention strategies in older adults to consider a broad range of factors, including internal factors, such as physical and cognitive function and physical activity levels, in addition to external factors, such as environmental conditions.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e Hideyuki Tashiro: conceptualization, methodology, software, formal analysis, data curation, investigation, writing\u0026ndash;original draft preparation, writing\u0026ndash;review, and editing. Hikaru Ihira and Kazuki Yokoyama: data curation, funding acquisition, and project administration. Yuriko Matsuzaki-Kihara, Atsushi Mizumoto, Hidekazu Saito, Suguru Shimokihara, Keitaro Makino, Kida Takuto, Rin Takahashi, and Takeshi Sasaki: investigation. Nozomu Ikeda: supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the study participants and the research staff for their contributions to the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Japan Agency for Medical Research and Development (Research Project No. 20dk0207027h0005) and academic grant of Sapporo Medical University (Grant Number 2200265, 2300235). The sponsor had no contributions in study design; in the collection, analyses and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and patient consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Ethics Committee of Sapporo Medical University, and written informed consent was obtained from all participants following an explanation of the procedures.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMinistry of Health, Government of Japan (2016) Summary report of the situation of aging and implementation status of aging public policy in 2016 (in Japanese); \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www8.cao.go.jp/kourei/whitepaper/w-2017/zenbun/pdf/1s1s_01.pdf\u003c/span\u003e\u003cspan address=\"http://www8.cao.go.jp/kourei/whitepaper/w-2017/zenbun/pdf/1s1s_01.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, [accessed May 15, 2025].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergen G, Stevens MR, Burns ER (2016) Falls and fall injuries among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years \u0026ndash; United States, 2014. 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Age Ageing 52:afad093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ageing/afad093\u003c/span\u003e\u003cspan address=\"10.1093/ageing/afad093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"aging, fall, mobility, season","lastPublishedDoi":"10.21203/rs.3.rs-6800337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6800337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo examine whether kinematic gait variables are related to fall history during the winter and non-winter seasons in community-dwelling older adults living in snowy regions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included 287 community-dwelling older adults (mean age, 77.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 years; sex, 69.0% female) living in Hokkaido, Japan. The fall history in winter and non-winter seasons was assessed through face-to-face interviews. Spatiotemporal gait variables, including gait speed, cadence, stride length, stride length variability, double support time, and double support time variability, were measured using an electronic gait analysis system.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of falls was 19.5% (n\u0026thinsp;=\u0026thinsp;56) during winter and 18.1% (n\u0026thinsp;=\u0026thinsp;52) during non-winter months. Logistic regression analyses showed no significant associations between gait variables and a history of falls during winter. However, during non-winter months, a shorter stride length (odds ratio, 0.98; 95% confidence interval, 0.96\u0026ndash;0.99; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) was significantly associated with a history of falls, even after controlling for age, sex, body mass index, living alone, polypharmacy, fear of falling, cognitive function, and depressive symptoms.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSpatiotemporal gait variables played a lesser role in identifying risk factors for fall history during winter compared to non-winter months in community-dwelling older adults living in snowy regions.\u003c/p\u003e","manuscriptTitle":"Seasonal association between spatiotemporal gait variables and falls among community-dwelling older adults living in snowy areas: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 06:36:23","doi":"10.21203/rs.3.rs-6800337/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1627bfb-dc94-46d6-82f9-b95b6a1d46b3","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T12:47:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 06:36:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6800337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6800337","identity":"rs-6800337","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}