Comparative study of demographic information, clinical scales and questionnaires, and mobility tests for fall risk assessment in older adults

Abstract

Background: Falls are the leading cause of injury and mortality in older adults; however, the relative contributions of different fall risk factor domains remain unclear. A past fall history strongly predicts future falls, making fall history classification critical for prospective risk assessment.
Objective: This study compared three domains for classifying fall history status in older adults as the basis for fall risk assessment.
Study design: Cross-sectional observational study.
Methods: We analyzed the G-STRIDE dataset (163 older adults; mean age [standard deviation] = 82.6 [6.2] years; 72.4% female; 52.8% fallers). Three domains were examined: demographic information (DGI), clinical scales and questionnaires (CSQ), and mobility tests and contexts (MTC). Four classifiers (logistic regression, support vector machine, random forest, and artificial neural network) were evaluated using 10-fold cross-validation, leave-one-out, and hold-out validation. Bootstrap 95% confidence intervals (CIs) and paired t-tests were used for area under the receiver operating characteristic curve (AUC) comparisons.
Results: MTC alone achieved AUC = 0.89 (95% CI: 0.83-0.94), significantly outperforming DGI (AUC = 0.76, P < 0.001). DGI plus MTC showed a marginal advantage over DGI plus CSQ (P = 0.064). The evolutionary optimization identified a seven-variable subset dominated by mobility measures that matched the full-model performance (AUC = 0.90). A multi-method feature importance analysis identified the examination location, frailty index, and short Falls Efficacy Scale-International as the top predictors. The external validation of GAIT2CARE (N = 127) achieved an AUC of 0.802 for DGI plus MTC.
Conclusions: Objective mobility tests combined with demographic data provided efficient fall risk assessment without extensive questionnaire-based assessments, supporting streamlined clinical screening.

Keywords

Fall risk assessment / Demographic information / Clinical scales and questionnaires / Mobility tests and contexts / Machine learning

CRediT authorship contribution statement

Peng Wu: Conceptualisation, Methodology, Software, Formal analysis, Writing - Original Draft; Jianlei Fang: Software, Validation, Data Curation; Ziyun Ding: Conceptualisation, Formal analysis, Investigation, Project administration, Validation; Zeyang Guan: Methodology, Software, Validation; Jiachen Wang: Investigation, Resources; Yikai He: Investigation, Resources; Yihao Zhang: Software, Visualisation; Huanghe Zhang: Conceptualisation, Supervision, Writing - Review & Editing, Project administration, Funding acquisition. All authors reviewed and approved the final manuscript.

Ethical approval

This study used the publicly available G-STRIDE dataset, which was collected with appropriate ethics approval from the participating institutions in Spain. The original data collection was approved by the relevant ethics committees, and all participants provided written informed consent. As this secondary analysis used de-identified publicly available data, no additional ethics approval was required.

Funding

This work was supported in part by the Young Scientists Fund of the National Natural Science Foundation of China under Grant 62403281, in part by the Taishan Scholars Project (Young Expert Program) under Grant NO. tsqn202408040, in part by the Shandong Excellent Young Scientists Fund Program (Overseas) under Grant 2024HWYQ-019, and in part by the Open Project Fund of International Joint Research Center for Perception and Control of Intelligent Rehabilitation Systems of Sichuan Province under Grant No. 25-H-01.

Data availability

The G-STRIDE dataset is publicly available on Zenodo: https://doi.org/10.5281/zenodo.8003441.

Declaration of Competing Interest

The authors declare no conflicts of interest.

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this manuscript, the authors used generative AI tools including GPT-5.2 solely for language polishing and format modification. The AI tools were not involved in data analysis, result interpretation or conclusion formulation. All AI-generated content was rigorously reviewed and revised by the authors, who take full responsibility for the content of the published article.

Acknowledgements

The authors thank the G-STRIDE research team for making their dataset publicly available.

Appendix A. Supplementary material

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.hcr.2026.100069

References

[1] Montero-Odasso M, van der Velde N, Martin FC, et al. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. 2022; 51(9):afac205. https://doi.org/10.1093/ageing/afac205

[2] Salari N, Darvishi N, Ahmadipanah M, Shohaimi S, Mohammadi M. Global prevalence of falls in the older adults: a comprehensive systematic review and meta-analysis. J Orthop Surg Res. 2022; 17(1):334. https://doi.org/10.1186/s13018-022-03222-1

[3] Chen Y, Dai F, Huang S, et al. Global, regional, and national burden of falls among older adults: findings from the Global Burden of Disease Study 2021 and Projections to 2040. npj Aging. 2025; 11(1):85. https://doi.org/10.1038/s41514-025-00275-4

[4] Centers for Disease Control and Prevention. Older adult falls data. Accessed January 15, 2025. 〈https://www.cdc.gov/falls/〉.

[5] Deandrea S, Lucenteforte E, Bravi F, Foschi R, La Vecchia C, Negri E. Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis. Epidemiology. 2010; 21(5):658-668. https://doi.org/10.1097/EDE.0b013e3181e89905

[6] Tromp AM, Pluijm SME, Smit JH, Deeg DJH, Bouter LM, Lips P. Fall-risk screening test: a prospective study on predictors for falls in community-dwelling elderly. J Clin Epidemiol. 2001; 54(8):837-844. https://doi.org/10.1016/S0895-4356(01)00349-3

[7] Beck Jepsen D, Robinson K, Ogliari G, et al. Predicting falls in older adults: an umbrella review of instruments assessing gait, balance, and functional mobility. BMC Geriatr. 2022; 22(1):615. https://doi.org/10.1186/s12877-022-03271-5

[8] Lauretani F, Ticinesi A, Gionti L, et al. Short-Physical Performance Battery (SPPB) score is associated with falls in older outpatients. Aging Clin Exp Res. 2019; 31(10):1435-1442. https://doi.org/10.1007/s40520-018-1082-y

[9] Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001; 56(3):M146-M157. https://doi.org/10.1093/gerona/56.3.M146

[10] Leghissa M, Carrera Á, Iglesias CA. Machine learning approaches for frailty detection, prediction and classification in elderly people: a systematic review. Int J Med Inf. 2023; 178:105172. https://doi.org/10.1016/j.ijmedinf.2023.105172

[11] González-Castro A, Benítez-Andrades JA, González-González R, Prada-García C, Leirós-Rodríguez R. Predicting fall risk in older adults: a machine learning comparison of accelerometric and non-accelerometric factors. Digit Health. 2025; 11:20552076251331752. https://doi.org/10.1177/20552076251331752

[12] Jia S, Si Y, Guo C, et al. The prediction model of fall risk for the elderly based on gait analysis. BMC Public Health. 2024; 24(1):2206. https://doi.org/10.1186/s12889-024-19760-8

[13] Barry E, Galvin R, Keogh C, Horgan F, Fahey T. Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta-analysis. BMC Geriatr. 2014; 14:14. https://doi.org/10.1186/1471-2318-14-14

[14] Herman T, Giladi N, Hausdorff JM. Properties of the ‘timed up and go’ test: more than meets the eye. Gerontology. 2011; 57(3):203-210. https://doi.org/10.1159/000314963

[15] Zhang H, Guo Y, Zanotto D. Accurate ambulatory gait analysis in walking and running using machine learning models. IEEE Trans Neural Syst Rehabil Eng. 2020; 28(1):191-202. https://doi.org/10.1109/TNSRE.2019.2958679

[16] Zhang H, Wu C, Huang Y, et al. Two-dimensional deep convolutional neural networks for estimating stride length and velocity in institutionalized older adults. IEEE Sens J. 2024; 24(17):28267-28275. https://doi.org/10.1109/JSEN.2024.3408900

[17] Wang J, Guan Z, Liang T, et al. Multi-task learning for gait phase and gait cycle percentage prediction with wearable sensors in frail older adults. IEEE J Biomed Health Inf. 2025. https://doi.org/10.1109/JBHI.2025.3643724

[18] Montesinos L, Castaldo R, Pecchia L. Wearable inertial sensors for fall risk assessment and prediction in older adults: a systematic review and meta-analysis. IEEE Trans Neural Syst Rehabil Eng. 2018; 26(3):573-582. https://doi.org/10.1109/TNSRE.2017.2771383

[19] Chen M, Wang H, Yu L, et al. A systematic review of wearable sensor-based technologies for fall risk assessment in older adults. Sensors (Basel). 2022; 22(18):6752. https://doi.org/10.3390/s22186752

[20] Yu X, Cai Y, Yang R, Ma F, Kim W. Revisiting sensor-based intelligent fall risk assessment for older people: a systematic review. Eng Appl Artif Intell. 2025; 144:110176. https://doi.org/10.1016/j.engappai.2025.110176

[21] Lockhart TE, Soangra R, Yoon H, et al. Prediction of fall risk among community-dwelling older adults using a wearable system. Sci Rep. 2021; 11(1):20976. https://doi.org/10.1038/s41598-021-00458-5

[22] González-Castro A, Leirós-Rodríguez R, Prada-García C, Benítez-Andrades JA. The applications of artificial intelligence for assessing fall risk: systematic review. J Med Internet Res. 2024; 26:e54934. https://doi.org/10.2196/54934

[23] Zhang H., Chen Z., Zanotto D., Guo Y.Robot-assisted and wearable sensor-mediated autonomous gait analysis. In:Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020:6795-6802. https://doi.org/10.1109/ICRA40945.2020.9196757

[24] Buisseret F, Catinus L, Grenard R, et al. Timed up and go and six-minute walking tests with wearable inertial sensor: one step further for the prediction of the risk of fall in elderly nursing home people. Sensors (Basel). 2020; 20(11):3207. https://doi.org/10.3390/s20113207

[25] Noh B, Youm C, Goh E, et al. XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes. Sci Rep. 2021; 11(1):12183. https://doi.org/10.1038/s41598-021-91797-w

[26] Speiser JL, Callahan KE, Houston DK, et al. Machine learning in aging: an example of developing prediction models for serious fall injury in older adults. J Gerontol A Biol Sci Med Sci. 2021; 76(4):647-654. https://doi.org/10.1093/gerona/glaa138

[27] García-de-Villa S, Neira GG, Álvarez MN, et al. A database with frailty, functional and inertial gait metrics for the research of fall causes in older adults. Sci Data. 2023; 10(1):566. https://doi.org/10.1038/s41597-023-02428-0

[28] Neira-Álvarez M, Rodríguez-Sánchez C, Huertas-Hoyas E, et al. Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study. BMC Geriatr. 2023; 23(1):737. https://doi.org/10.1186/s12877-023-04379-y

[29] Fang J., Guan Z., Wang J., et al. Machine learning models for fall risk assessment using wearable-derived gait features on the GSTRIDE dataset. In:Proceedings of the International Conference on Deep Learning and Computer Vision (DLCV). 2025. In press.

[30] Guan Z, Cai J, Wang J, et al. Accuracy and precision of wearable-derived gait parameters: how these affect the performance of models for fall prediction in the elderly. IEEE Trans Neural Syst Rehabil Eng. 2025; 33:4255-4266. https://doi.org/10.1109/TNSRE.2025.3623129

[31] Cai J, Guan Z, Wang J, et al. Impact of gait parameters and their variability on fall risk assessment accuracy using wearable sensor. IEEE Trans Neural Syst Rehabil Eng. 2025; 33:1996-2003. https://doi.org/10.1109/TNSRE.2025.3572109

[32] Zhang H, Duong TTH, Rao AK, et al. Transductive learning models for accurate ambulatory gait analysis in elderly residents of assisted living facilities. IEEE Trans Neural Syst Rehabil Eng. 2022; 30:124-134. https://doi.org/10.1109/TNSRE.2022.3143094

[33] Mohan D, Chong PHJ, Gutierrez J. A novel cooperative AI-based fall risk prediction model for older adults. Sensors (Basel). 2025; 25(13):3991. https://doi.org/10.3390/s25133991

[34] Álvarez MN, Ruiz ARJ, Neira GG, et al. Assessing falls in the elderly population using G-STRIDE foot-mounted inertial sensor. Sci Rep. 2023; 13(1):9208. https://doi.org/10.1038/s41598-023-36241-x

[35] Neira-Álvarez M, Huertas-Hoyas E, Novak R, et al. Stratification of older adults according to frailty status and falls using gait parameters explored using an inertial system. Appl Sci (Basel). 2024; 14(15):6704. https://doi.org/10.3390/app14156704

[36] Tang YT, Romero-Ortuno R. Using explainable artificial intelligence for the prediction of falls in older adults. Algorithms. 2022; 15(10):353. https://doi.org/10.3390/a15100353

[37] Zhang H, Wu C, Huang Y, Song R, Zanotto D, Agrawal SK. Fall risk prediction using instrumented footwear in institutionalized older adults. IEEE Trans Neural Syst Rehabil Eng. 2024; 32:4260-4269. https://doi.org/10.1109/TNSRE.2024.3510300