{"doi":"10.1093/geront/gnaf241","title":"Predicting psychological resilience in older adults during the COVID-19 pandemic: a machine learning approach","abstract":"BACKGROUND AND OBJECTIVES: This study predicted psychological resilience among older adults during the COVID-19 pandemic based on a comprehensive, theory-informed set of factors at the individual, interpersonal, and community levels. RESEARCH DESIGN AND METHODS: The study sample consisted of 3,364 individuals who completed the 2016 and 2020 Leave-Behind Questionnaire from the Health and Retirement Study. A longitudinal design was used, with pre-pandemic predictors measured in 2016 and resilience measured in 2020. Three machine learning algorithms (LASSO, Ridge, and Random Forest) were trained with five-fold cross-validation. SHAP values were used to interpret feature importance. RESULTS: LASSO had the best model fit (RMSE = 0.873; R2 = 0.195). Twenty-four features emerged as important predictors. Psychological dispositions and resources, including four Big Five personality traits, optimism, purpose in life, life satisfaction, and religiosity, were strong predictors of resilience. Pre-pandemic social participation, social support, and neighborhood cohesion were also positively associated with resilience. Several indicators of technology adaptation, particularly learning a new device, and socio-behavioral adaptation during the pandemic were additional positive predictors of resilience. In contrast, older subjective age was linked to lower resilience. Several non-linear and interaction effects were identified. DISCUSSION AND IMPLICATIONS: Study findings underscore the complex, multifactorial nature of resilience and demonstrate the value of theory-informed data science approach in advancing our understanding of resilience. Addressing digital inequities and fostering supportive social relationships and community participation are potential targets for population-based strategies as we face increasing threats from disasters.","journal":"The Gerontologist","year":2025,"id":529256,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.906,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":885871,"name":"Xuan Lü","orcid":"0000-0003-4886-0918","position":1,"is_corresponding":false},{"id":1408037,"name":"Xianmin Guan","orcid":"0009-0003-2282-8730","position":2,"is_corresponding":false},{"id":331927,"name":"Xiaoling Xiang","orcid":"0000-0002-4926-4707","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T02:50:56.971987Z","pmid":"41191741","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}