The global aging population faces significant health challenges, including an increasing vulnerability to disability due to natural aging processes. Wearable lower limb exoskeletons (LLEs) have emerged as a promising solution to enhance physical function in older individuals. This systematic review synthesizes the use of LLEs in alignment with the WHO’s healthy aging vision, examining their impact on intrinsic capacities and functional abilities. We conducted a comprehensive literature search in six databases, yielding 36 relevant articles covering older adults (65+) with various health conditions, including sarcopenia, stroke, Parkinson’s Disease, osteoarthritis, and more. The interventions, spanning one to forty sessions, utilized a range of LLE technologies such as Ekso^®, HAL^®, Stride Management Assist^®, Honda Walking Assist^®, Lokomat^®, Walkbot^®, Healbot^®, Keeogo Rehab^®, EX1^®, overground wearable exoskeletons, Eksoband^®, powered ankle–foot orthoses, HAL^® lumbar type, Human Body Posturizer^®, Gait Enhancing and Motivation System^®, soft robotic suits, and active pelvis orthoses. The findings revealed substantial positive outcomes across diverse health conditions. LLE training led to improvements in key performance indicators, such as the 10 Meter Walk Test, Five Times Sit-to-Stand test, Timed Up and Go test, and more. Additionally, enhancements were observed in gait quality, joint mobility, muscle strength, and balance. These improvements were accompanied by reductions in sedentary behavior, pain perception, muscle exertion, and metabolic cost while walking. While longer intervention durations can aid in the rehabilitation of intrinsic capacities, even the instantaneous augmentation of functional abilities can be observed in a single session. In summary, this review demonstrates consistent and significant enhancements in critical parameters across a broad spectrum of health conditions following LLE interventions in older adults. These findings underscore the potential of LLE in promoting healthy aging and enhancing the well-being of older adults.

To explore pre-treatment risk factors for cognitive decline in patients with breast cancer using a machine learning approach applied to a comprehensive multimodal clinical, biological, and neuroimaging dataset. Sixty-seven women with early breast cancer were assessed at diagnosis (T0), 8 months (T1), and 17 months after diagnosis (T2). Cognitive decline was defined using the Reliable Change Index. Patients were classified as showing no decline or decline at either follow-up. Five feature sets were evaluated: (1) patient characteristics, treatment, and psychosocial measures; (2) inflammatory and neural health markers, (3) structural brain volumes, (4) resting-state functional MRI connectivity, and (5) diffusion MRI measures. Random forest models were first trained on feature set 1, then sequentially combined with sets 2-5 to explore their additive predictive value. Each model underwent standardized preprocessing, recursive feature selection of the top 6 predictors, and tuning before random forest classification. A final composite model was constructed by pooling the six top predictors from each feature set to assess potential complementary multimodal information. Feature contributions were examined using SHAP values. Of 67 patients, 33 (49%) experienced cognitive decline following treatment. Models achieved prediction accuracies of 76%, improving up to 81% when MRI measures and/or serum markers were included. Key baseline predictors of cognitive decline included more aggressive subtypes, planned systemic therapy, perceived stress, and limited cognitive and brain reserve. Machine learning explored potential pre-treatment risk factors for cancer-related cognitive decline in patients with breast cancer. These findings highlight potential risk factors that could support risk-stratification.