Amiotrophic Lateral Sclerosis Rehabilitation Adaptation Model for Gait Recovery

Thesis Abstract

Amitotrophic Lateral Sclerosis (ALS) presents progressive motor decline that compromises gait and mobility, imposing substantial functional limitation and reduced quality of life; current rehabilitation approaches are heterogeneous and lack an integrative framework to optimize gait recovery trajectories across disease stages. This study develops the ALS Rehabilitation Adaptation Model for Gait Recovery (ARAM-GR), a theory-driven framework that synthesizes biomechanics, neuroplasticity, and patient-centered rehabilitation to predict and enhance gait outcomes in ALS. The aims are (1) to delineate the core components and mechanisms linking neuromuscular decline, compensatory strategies, and gait adaptability; (2) to construct a parsimonious, testable model that integrates motor learning principles, energy cost optimization, and multisystem assessment; and (3) to evaluate the model’s predictive validity for gait performance under targeted rehabilitation interventions. The specific objectives are to (a) identify modifiable determinants of gait recovery in ALS through longitudinal observation; (b) develop a measurement framework comprising quantitative gait metrics, muscle strength, spatiotemporal parameters, and qualitative patient-reported outcomes; (c) specify intervention protocols aligned with the model’s constructs, including task-specific locomotor training, assistive device customization, and aerobic conditioning; and (d) test the model’s explanatory and predictive power using structural equation modeling and repeated measures analyses. The study adopts a mixed-methods design within a convergent parallel framework. A longitudinal cohort of 120 adults diagnosed with probable or definite ALS, recruited from three tertiary neurological rehabilitation centers, will be followed over 12 months with assessments at baseline, 3, 6, 9, and 12 months. Quantitative data will include instrumented gait analysis (GAITRite or equivalent pressure-sensitive mat), three-dimensional motion capture for spatiotemporal and kinematic profiles, isokinetic muscle testing of major lower-limb groups, the ALS Functional Rating Scale-Revised (ALSFRS-R), the nine-hole peg test-based dexterity proxy, and accelerometer-based activity monitoring. Qualitative insights will be gathered from semi-structured interviews with a purposive subsample of 30 participants and their caregivers to capture perceived barriers, facilitators, and adaptive strategies. Intervention exposure will be recorded, including frequency and intensity of task-specific gait training, assistive device adjustments, and endurance activities. Analyses will proceed in three steps. First, descriptive statistics will characterize the sample and trajectory of gait-related measures. Second, a structural equation modeling (SEM) approach will test the ARAM-GR, incorporating latent constructs for neuromuscular integrity, motor learning efficacy, energy optimization, compensatory strategy use, and environmental/assistive support, with gait outcome variables as endogenous indicators. Third, hierarchical linear modeling (HLM) will examine within-person change over time and the moderating effects of disease duration, baseline function, and intervention exposure. Thematic analysis will be applied to qualitative transcripts to identify recurring patterns related to adaptation processes and model congruence, with triangulation against quantitative findings to refine ARAM-GR. Expected findings include (i) identification of core determinants most strongly associated with meaningful gait improvement, such as preserved hip extensor strength, high-quality motor learning signals, and effective energy management; (ii) confirmation that integrated task-specific locomotor training augmented by optimized assistive devices yields superior gait outcomes compared with isolated interventions; (iii) delineation of a staged adaptation pathway in ALS that aligns rehabilitation intensity with neurodegenerative progression; and (iv) a validated predictive model that can forecast gait trajectories and guide individualized rehabilitation planning. The study contributes to knowledge by introducing ARAM-GR, a unified, theory-informed framework for gait rehabilitation in ALS that integrates biomechanics, motor learning, energy efficiency, and patient-centered care. It offers a validated analytical model and actionable guidelines for clinicians to tailor interventions across disease stages, enhancing functional mobility and quality of life. Limitations include potential attrition and variability in disease progression; strategies such as multiple imputation for missing data and sensitivity analyses will be employed. Practical recommendations include adopting ARAM-GR-informed protocols in multidisciplinary clinics, standardizing outcome measures for gait, and prioritizing early, progressive, and adaptable locomotor training paired with precise device optimization. Future research should extend ARAM-GR to multisystem rehabilitation contexts and explore digital health tools for remote monitoring and coaching.