Hindi Translation , Validation and test retest reliability testing of the Telematic Fugl Meyer assessment scale- upper extremity in Stroke Patients (TFMA-UE)
Description
This dataset contains data related to the Hindi translation, cultural adaptation, validation, and test–retest reliability testing of the Telematic Fugl Meyer Assessment Scale–Upper Extremity (TFMA-UE) in stroke patients. The purpose of the study was to develop a standardized Hindi version of the TFMA-UE and evaluate its psychometric properties for use among Hindi-speaking stroke survivors. The TFMA-UE is a widely used clinical outcome measure for assessing upper extremity motor recovery following stroke, particularly in tele-rehabilitation settings. The dataset includes demographic and clinical details of participants such as age, gender, type of stroke, duration since stroke onset, affected side, and severity of upper limb impairment. It also contains translated assessment forms, scoring records, expert review data, and statistical outputs generated during validation and reliability analysis. The Hindi translation was performed using a standardized forward–backward translation process to ensure semantic and cultural equivalence with the original English version. Content validity was evaluated by a panel of rehabilitation experts, including physiotherapists and neurologists, using item-level and scale-level validity indices. Construct validity was assessed through comparison with established upper extremity functional assessment measures. Reliability analysis included internal consistency and test–retest reliability. Cronbach’s alpha was used to determine internal consistency, while Intraclass Correlation Coefficients (ICC) were used to evaluate stability of scores across repeated assessments. Additional statistical measures such as standard error of measurement (SEM) and descriptive statistics are included in the dataset. All participant information was anonymized, and data collection was conducted according to institutional ethical guidelines. The dataset may be useful for researchers, clinicians, and rehabilitation professionals involved in stroke rehabilitation, tele-rehabilitation, psychometric testing, and cross-cultural adaptation of clinical assessment tools. The validated Hindi TFMA-UE can support reliable remote assessment of upper extremity motor function in Hindi-speaking stroke populations and may contribute to improved accessibility of rehabilitation services in diverse clinical settings.
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Steps to reproduce
To reproduce this study, permission should first be obtained to translate and validate the original Telematic Fugl Meyer Assessment Scale–Upper Extremity (TFMA-UE). The scale should then undergo a standardized forward–backward translation process. Initially, bilingual experts translate the English version into Hindi, followed by backward translation into English by independent translators unfamiliar with the original scale. Both versions are compared to ensure semantic, conceptual, and cultural equivalence. An expert committee consisting of physiotherapists, neurologists, rehabilitation specialists, and language experts reviews the translated version and finalizes the Hindi TFMA-UE. The finalized scale should be pilot tested on a small sample of stroke patients to evaluate clarity, comprehension, and cultural relevance. After pilot testing, eligible stroke patients meeting predefined inclusion and exclusion criteria should be recruited. Demographic and clinical information, including age, gender, type of stroke, duration since stroke onset, affected side, and level of upper extremity impairment, should be recorded. The Hindi TFMA-UE is then administered through telematic or clinical assessment procedures. Content validity should be evaluated using expert ratings, while construct validity can be assessed by comparing TFMA-UE scores with established upper extremity functional assessment tools. To determine reliability, the same participants should complete the Hindi TFMA-UE again after a fixed interval under similar testing conditions. Statistical analysis should include Cronbach’s alpha for internal consistency, Intraclass Correlation Coefficient (ICC) for test–retest reliability, and Standard Error of Measurement (SEM). Finally, participant data should be anonymized, and datasets, scoring sheets, and statistical outputs should be organized to ensure transparency and reproducibility of the study.