Does the time of day differently impact the effects of an exercise program on postural control in older subjects? A pilot study

Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach

Background: Over one billion people worldwide suffer from neurological conditions that cause mobility impairments, often persisting despite rehabilitation. Chronic neurological disease (CND) patients who lack access to continuous rehabilitation face gradual functional decline. The International Classification of Functioning, Disability, and Health (ICF) provides a comprehensive framework for assessing these patients. Objective: This study aims to evaluate the outcomes of a non-hospitalized neuromotor rehabilitation project for CND patients in Italy using the Barthel Index (BI) as the primary outcome measure. The rehabilitation was administered through an Individual Rehabilitation Plan (IRP), tailored by a multidisciplinary team and coordinated by a physiatrist. The IRP involved an initial comprehensive assessment, individualized therapy administered five days a week, and continuous adjustments based on patient progress. The secondary objectives include assessing mental status and sensory and communication functions, and identifying predictive factors for BI improvement using an artificial neural network (ANN). Methods: A retrospective observational study of 128 CND patients undergoing a rehabilitation program between 2018 and 2023 was conducted. Variables included demographic data, clinical assessments (BI, SPMSQ, and SVaMAsc), and ICF codes. Data were analyzed using descriptive statistics, linear regressions, and ANN to identify predictors of BI improvement. Results: Significant improvements in the mean BI score were observed from admission (40.28 ± 29.08) to discharge (42.53 ± 30.02, p < 0.001). Patients with severe mobility issues showed the most difficulty in transfers and walking, as indicated by the ICF E codes. Females, especially older women, experienced more cognitive decline, affecting rehabilitation outcomes. ANN achieved 86.4% accuracy in predicting BI improvement, with key factors including ICF mobility codes and the number of past rehabilitation projects. Conclusions: The ICF mobility codes are strong predictors of BI improvement in CND patients. More rehabilitation sessions and targeted support, especially for elderly women and patients with lower initial BI scores, can enhance outcomes and reduce complications. Continuous rehabilitation is essential for maintaining progress in CND patients.

The impact of exercise on Alzheimer's disease progression

INTRODUCTION

The preventive effects of chronic physical exercise (CPE) on Alzheimer's disease (AD) are now admitted by the scientific community. Curative effects of CPE are more disputed, but they deserve to be investigated, since CPE is a natural non-pharmacological alternative for the treatment of AD.

AREAS COVERED

In this perspective, the authors discuss the impact of CPE on AD based on an exhaustive literature search using the electronic databases PubMed, ScienceDirect and Google Scholar.

EXPERT OPINION

Aerobic exercise alone is probably not the unique solution and needs to be complemented by other exercises (physical activities) to optimize the slowing down of AD. Anaerobic, muscle strength and power, balance/coordination and meditative exercises may also help to slow down the AD progression. However, the scientific evidence does not allow a precise description of the best training program for patients with AD. Influential environmental conditions (e.g. social relations, outdoor or indoor exercise) should also be studied to optimize training programs aimed at slowing down the AD progression.