Background: Patients treated in hand surgery (HS) belong to different demographic groups and have varying impairments related to different pathologies. HS outcomes are measured to assess treatment results, complication risks and intervention reliability. A one-dimensional and linear measure would allow for unbiased comparisons of manual ability between patients and different treatment effects. Objective: To adapt the ABILHAND questionnaire through Rasch analysis for specific use in HS patients and to examine its validity. Methods: A preliminary 90-item questionnaire was presented to 216 patients representing the diagnoses most frequently encountered in HS, including distal radius fracture (n = 74), basal thumb arthritis (n = 66), carpal tunnel syndrome (n = 53), and heavy wrist surgery (n = 23). Patients were assessed during the early recovery and in the late follow-up period (0–3 months, 3–6 months and >6 months), leading to a total of 305 assessments. They rated their perceived difficulty with queried activities as impossible, difficult, or easy. Responses were analyzed using the RUMM2030 software. Items were refined based on item-patient targeting, fit statistics, differential item functioning, local independence and item redundancy. Patients also completed the QuickDASH, 12-item Short Form Survey (SF-12) and a numerical pain scale. Results: The rating scale Rasch model was used to select 23 mostly bimanual items on a 3-level scale, which constitute a unidimensional, linear measure of manual ability with good reliability across all included diagnostic groups (Person-Separation Index = 0.90). The resulting scale was found to be invariant across demographic and clinical subgroups and over time. ABILHAND-HS patient measures correlated significantly (p<0.001) with the QuickDASH (r = -0.77), SF-12 Physical Component Summary (r = 0.56), SF-12 Mental Component Summary (r = 0.31), and pain scale (r = -0.49). Conclusion: ABILHAND-HS is a robust person-centered measure of manual ability in HS patients.

The ability to monitor activities of daily living in the natural environments of patients could become a valuable tool for various clinical applications. In this paper, we show that a simple algorithm is capable of classifying manual activities of daily living (ADL) into categories using data from wrist- and finger-worn sensors. Six participants without pathology of the upper limb performed 14 ADL. Gyroscope signals were used to analyze the angular velocity pattern for each activity. The elaboration of the algorithm was based on the examination of the activity at the different levels (hand, fingers and wrist) and the relationship between them for the duration of the activity. A leave-one-out cross-validation was used to validate our algorithm. The algorithm allowed the classification of manual activities into five different categories through three consecutive steps, based on hands ratio (i.e., activity of one or both hands) and fingers-to-wrist ratio (i.e., finger movement independently of the wrist). On average, the algorithm made the correct classification in 87.4% of cases. The proposed algorithm has a high overall accuracy, yet its computational complexity is very low as it involves only averages and ratios.

Background: Patients treated in hand surgery (HS) belong to different demographic groups and have varying impairments related to different pathologies. HS outcomes are measured to assess treatment results, complication risks and intervention reliability. Quantitative analysis of functional recovery requires a unidimensional, linear scale. Objective: To adapt the ABILHAND questionnaire through Rasch analysis for specific use in HS patients and to examine its validity. Methods: A preliminary 90-item questionnaire was presented to 216 patients representing the diagnoses most frequently encountered in HS, including distal radius fracture (n=74), basal thumb arthritis (n=66), carpal tunnel syndrome (n=53), and heavy wrist surgery (n=23). Patients were assessed during the early recovery and in the late follow-up period (0-3 months, 3–6 months and >6 months), leading to a total of 305 assessments. They rated their perceived difficulty with queried activities as impossible, difficult, or easy. Responses were analyzed using the RUMM2030 software. Items were refined based on item-patient targeting, fit statistics, differential item functioning, local independence and item redundancy. Patients also completed the QuickDASH, 12-item Short Form Survey (SF-12) and a numerical pain scale. Results: The rating scale Rasch model was used to select 23 mostly bimanual items on a 3-level scale, which constitute a unidimensional, linear measure of manual ability with good reliability across all included diagnostic groups (Person-Separation Index = 0.90). The resulting scale was found to be invariant across demographic and clinical subgroups and over time. ABILHAND-HS patient measures correlated significantly (p<0.001) with the QuickDASH (r=-0.77), SF-12 Physical Component Summary (r=0.56), SF-12 Mental Component Summary (r=0.31), and pain scale (r=-0.49). Conclusion: ABILHAND-HS is a robust person-centered measure of manual ability in HS patients.