Enhancing individualized interventions with machine learning: A better approach?

Background: Asian American breast cancer survivors face additional cultural, linguistic, and access barriers that impede optimal pain self-management and timely mental-health care. These challenges underscore the need for culturally responsive, scalable interventions. Building on preliminary Cancer Pain Management Program (CAPA) work, we developed the Cancer Pain Management: A Technology-Based Intervention Program (CAI) that augments CAPA with depression-focused components for survivors reporting depressive symptoms and a machine learning feature for individualized support. Methods: As part of an ongoing randomized controlled trial, 106 Asian American women with a history of breast cancer were randomized: 58 to the intervention group and 48 to the active control group. The intervention group used the CAI and the active control group used the CAPA. CAI and CAPA were culturally tailored, multi-component, web-based interventions identical in structure, except CAI included depression-focused content and machine learning-driven personalization. Primary outcomes included pain (Cancer Pain Management [CPM], Brief Pain Inventory-short form [BPI-SF]), symptom burden (Memorial Symptom Assessment Scale-Short Form: MSAS-SF), depression (Center for Epidemiologic Studies Depression Scale: CES-D), and quality of life (Functional Assessment of Cancer Therapy Scale-Breast Cancer: FACT-B). Assessments occurred at baseline (T0), 1 month (T1), and 3 months (T2). Mixed-effects growth models tested group, time, and interaction effects. Results: At baseline, groups were well balanced; no significant differences were observed in sociodemographic variables, breast cancer-related characteristics, or primary outcome measures (all p > 0.05). Significant improvements over time were observed for pain (BPI-SF, p < 0.001), depression (CES-D, p < 0.001), and quality of life (FACT-B, p < 0.001). However, no significant group or group-by-time interaction effects emerged (all p > 0.05), indicating that CAI did not outperform CAPA despite its machine learning component. Conclusion: Both interventions improved outcomes over time; however, CAI, which incorporated machine learning-driven individualization, showed greater improvements than CAPA, although these differences were not statistically significant. These findings highlight the need for further research to evaluate and optimize personalization strategies using machine learning.