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Matching Perceived Physical Capacity and Work Demands: A New Classification of the Modified Spinal Function Sort (M-SFS).

David BühneTorsten AllesChristian HetzelMarco StreibeltMaurizio Trippolini
Published in: Journal of occupational rehabilitation (2021)
Purpose The aims of this study were (1) to develop a new classification for the scores of the Modified Spinal Function Sort (M-SFS) which is related to the level of physical work demands and (2) to test the predictive value of the M-SFS classification. Methods The classification was carried out in 194 subjects with musculoskeletal disorders (MSD) attending a work-related medical rehabilitation from four rehabilitation centers. External criterion was a Functional Capacity Evaluation (FCE)-based work capacity estimation according to the classification used in Germany ("REFA") which differentiates between light, light to medium, medium and heavy work. The optimal cut-offs for the M-SFS were allocated using the Youden index. Logistic regression models were calculated based on 147 subjects who participated in the follow-up survey to evaluate the predictive validity of the M-SFS classification with regard to sustainable return to work (RTW; employment at the 3-month follow-up combined with a low level of sick leave). Results Cut-offs for M-SFS scores were 44 (light work), 54 (light to medium work), 62 (medium work) and 73 (heavy work). A match between the M-SFS category and the level of physical work demands was associated with a more than threefold higher RTW chance compared to subjects with a negative discrepancy. In case the M-SFS category was above the physical demand level the RTW-chance was more than 13-fold higher. Conclusions M-SFS scores can be classified into four levels of physical work demands. If the perceived work capacity matches or exceeds the level of physical work demands patients with MSD have a substantially higher probability to return to work after rehabilitation. More studies are needed to confirm or reject our findings and overcome some of the weaknesses of this study.
Keyphrases
  • physical activity
  • mental health
  • deep learning
  • machine learning
  • depressive symptoms
  • social support
  • healthcare