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Introduction: Early screening and identification are crucial for fall prevention, and developing a new method to predict fall risk in the elderly can address the current lack of objectivity in assessment tools., Methods: A total of 132 elderly individuals over 80 years old residing in some nursing homes in Shanghai were selected using a convenient sampling method. Fall history information was collected, and gait data during a 10-meter walk were recorded. Logistic regression was employed to establish the prediction model, and a nomogram was used to assess the importance of the indicators. The Bootstrap method was utilized for internal validation of the model, while the verification set was used for external validation. The predictive performance of the model was evaluated using the area under the ROC curve, calibration curve, and decision curve analysis (DCA) to assess clinical benefits., Results: The incidence of falls in the sample population was 36.4%. The Tinetti Gait and Balance Test (TGBT) score (OR = 0.832, 95% CI: 0.734,0.944), stride length (OR = 0.007, 95% CI: 0.000,0.104), difference in standing time (OR = 0.001, 95% CI: 0.000,0.742), and mean stride time (OR = 0.992, 95% CI:0.984,1.000) were identified as significant factors. The area under the ROC curve was 0.878 (95% CI: 0.805, 0.952), with a sensitivity of 0.935 and specificity of 0.726. The Brier score was 0.135, and the Hosmer-Lemeshow test (χ 2  = 10.650, P = 0.222) indicated a good fit and calibration of the model., Conclusion: The TGBT score, stride length, difference in standing time, and stride time are all protective factors associated with fall risk among the elderly. The developed risk prediction model demonstrates good discrimination and calibration, providing valuable insights for early screening and intervention in fall risk among older adults., (© 2024. The Author(s).)

Objective: This systematic review aims to assess the feasibility, acceptability, and efficacy of exercise-based interventions in reducing craving levels among individuals with drug dependency., Methods: This study included randomized controlled trials that investigated the effects of exercise on craving levels in individuals with drug dependence. We searched for relevant literature in PubMed, Web of Science, EMbase, The Cochrane Library, CNKI, China Biomedicine, Wanfang, and VIP databases from their inception until July 2024. Two researchers independently reviewed the literature. The quality of the studies was assessed using the PEDro scale, and the GRADE profiler software was utilized to evaluate the strength of the evidence. A qualitative synthesis was performed to describe the findings., Results: We included 26 studies involving a total of 1381 participants, with 787 in the experimental group and 594 in the control group. These studies were mainly conducted in China, the United States, and the United Kingdom, and were published mostly after 2018. The participants had typically been dependent on drugs for more than 5 years. The review found that exercise interventions were feasible and well-accepted, and effectively reduced drug cravings. Among the 26 studies, 22 showed positive outcomes in reducing cravings. The type of exercise appears to be a crucial factor. Aerobic exercises were more effective than resistance exercises. Out of 18 studies that included aerobic exercises, 17 reported significant reductions in cravings. In contrast, among the 4 studies that included resistance exercises, 3 did not find a significant impact on cravings., Conclusion: Exercise is highly feasible and acceptable, significantly contributing to the reduction of drug cravings among individuals with drug dependency. The specific type of exercise appears to be a key determinant of the intervention's effectiveness. Aerobic exercises were more effective than resistance exercises. The evidence supporting these findings is of high quality, with an average score of 6.92 on the PEDro scale., Others: The research was supported by the Shanghai Key Laboratory of Human Performance, with the project number 11DZ2261100. Registration details can be found on PROSPEO under the number CRD42024525700 at www.crd.york.ac.uk., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)