Exploring the Factors Influencing Walking Speed: A Cross-Sectional Study of Elderly Physical Examinations

Background Walking speed, as a simple measurement indicator, is valuable in assessing the motor function of the elderly and predicting hospitalization and mortality rates. This study utilizes physical examination data from community health service centers for individuals aged 65 and above, focusing on walking speed to explore the factors influencing substandard walking speed. Methods From January 2022 to December 2022, elderly individuals who underwent physical examinations at Qingling Community Health Service Center were selected. Based on inclusion and exclusion criteria, a total of 4865 participants were eligible. According to walking speed results, participants were divided into two groups: the qualified group (≤ 8 seconds) with 3889 people (73.8%) and the unqualified group (> 8 seconds) with 1276 people (26.2%). Additional information on the participants was obtained through the electronic information system. A comparison of relevant information between the two groups was conducted to summarize the influencing factors of walking speed, with P < 0.05 indicating statistical significance. Results Age, marital status, caregiving situation, weekly exercise routine, drinking habits, height, weight, BMI, blood urea nitrogen, low-density lipoprotein, Chinese visceral adiposity index, cardiometabolic index, and systemic immune-inflammation index are all risk factors for decreased walking speed. ROC curve analysis showed that the systemic immune-inflammation index had the best predictive efficiency. Conclusion Marital status, caregiving situation, weekly exercise routine, and drinking habits are all correlated with decreased walking speed. Age, weight, waist circumference, and the systemic immune-inflammation index (SII) are positively correlated with decreased walking speed. In contrast, height, BMI, blood urea nitrogen, LDL-C, Chinese visceral adiposity index (CVAI), and cardiometabolic index (CMI) are negatively correlated with decreased walking speed. Among these factors, SII has the strongest predictive efficiency for decreased walking speed.