10.1177/1056789514562152

Vollständiger Abstract

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INTRODUCTION: Accurate evaluation of upper limb recovery after stroke is essential for guiding rehabilitation targeting specific impairments and activity limitations. To achieve this, outcome measures must clearly reflect what they assess, whether neuromuscular impairments or activity limitations. However, classification of measures is not always clear-cut, which can lead to misinterpretation of recovery progress and reduce rehabilitation planning effectiveness. PROBLEM: Current classification practices often prioritize item content over the scoring criteria that define what is being quantified. This becomes particularly problematic when task-based items are scored using impairment-oriented criteria (e.g., range of motion), blurring whether change reflects impairment recovery or activity-level improvement. This ambiguity can mislead clinical decision-making and the interpretation of research findings. SOLUTION: We propose a construct-driven application of the International Classification of Functioning, Disability and Health (ICF) in which component-level attribution is determined by the construct operationalized through scoring rather than inferred from item content. As scoring structures differ, three strategies are suggested based on item-level scoring attribution where feasible: (1) proportional classification for measures in which one ICF component predominates (with a pragmatic, provisional ≥60% dominance guideline for near-boundary cases), otherwise (2) dual-component reporting for mixed measures; and (3) non-proportional interpretation for measures exhibiting construct shifting across score levels or using dominant-construct scoring with embedded grading criteria. CONCLUSION: Grounding classification in the construct operationalized by scoring criteria provides a more defensible basis for outcome selection and interpretation. This supports clearer interpretation of intervention effects, more targeted rehabilitation, and stronger comparability across trials and evidence synthesis.

Abstract: PubMed · Datensatz