Nutrients, Vol. 18, Pages 1737: SCI NutriTool: Development and Validation of a Questionnaire to Assess Non-Adherence to the Healthy Food Pyramid in Individuals with Spinal Cord Injury in Switzerland
Nutrients doi: 10.3390/nu18111737
Authors:
Marija Glisic
Inge Eriks-Hoogland
Angeline Chatelan
Khadija Maham
Silvia Mattmann
Pedro Marques-Vidal
Sara Rubinelli
Claudio Perret
Background/Objective: Rapid, validated dietary screening tools are lacking for individuals with spinal cord injury (SCI), where routine clinical check-ups do not allow sufficient time for extensive dietary assessments typically required to evaluate adherence to dietary recommendations. We developed a 15-item dietary screener (SCI NutriTool) and evaluated its accuracy in classifying non-adherence to a healthy food pyramid compared with a validated food frequency questionnaire (FFQ). Methods: The SCI NutriTool was developed through literature review and expert consensus. In a validation study, 51 adults with SCI (mean age 57.0 years; 76.5% men; 68.8% traumatic injury) completed the SCI NutriTool twice and a validated 97-item FFQ, which served as the reference method. Results: The SCI NutriTool demonstrated substantial variability in performance across food groups, reflecting its domain-specific screening properties. Sensitivity was high for fruits and vegetables (91.7%), protein-rich foods (90.5%), and sweetened/alcoholic beverages and snacks (82.4%), with relatively high positive predictive values (PPV: 73.7–90.5%), supporting the tool’s ability to identify individuals who are likely non-adherent and may benefit from further nutritional assessment or counselling. In contrast, for starchy foods and nuts, oils, and fatty spreads/sauces, sensitivity was low (20.0% and 50.0%), while specificity was modest. This indicates that the tool performs better in correctly identifying adherent individuals in these domains, which is reflected in higher negative predictive values (NPV: up to 94.1%). However, the low sensitivity suggests that individuals with non-adherence may be missed, limiting the tool’s usefulness as an early screening trigger for these food groups. Conclusions: The SCI NutriTool’s performance varies across food groups, demonstrating a stronger ability to identify non-adherence in protein-rich foods, fruit and vegetables, sweetened and alcoholic beverages, and snacks, but limited discriminatory capacity for others. In particular, it is not suitable for screening non-adherence to starchy foods and fats. Accordingly, it is best used as a triage tool to guide further dietary assessment and targeted nutritional interventions rather than as a standalone diagnostic instrument.
Background/Objective: Rapid, validated dietary screening tools are lacking for individuals with spinal cord injury (SCI), where routine clinical check-ups do not allow sufficient time for extensive dietary assessments typically required to evaluate adherence to dietary recommendations. We developed a 15-item dietary screener (SCI NutriTool) and evaluated its accuracy in classifying non-adherence to a healthy food pyramid compared with a validated food frequency questionnaire (FFQ). Methods: The SCI NutriTool was developed through literature review and expert consensus. In a validation study, 51 adults with SCI (mean age 57.0 years; 76.5% men; 68.8% traumatic injury) completed the SCI NutriTool twice and a validated 97-item FFQ, which served as the reference method. Results: The SCI NutriTool demonstrated substantial variability in performance across food groups, reflecting its domain-specific screening properties. Sensitivity was high for fruits and vegetables (91.7%), protein-rich foods (90.5%), and sweetened/alcoholic beverages and snacks (82.4%), with relatively high positive predictive values (PPV: 73.7–90.5%), supporting the tool’s ability to identify individuals who are likely non-adherent and may benefit from further nutritional assessment or counselling. In contrast, for starchy foods and nuts, oils, and fatty spreads/sauces, sensitivity was low (20.0% and 50.0%), while specificity was modest. This indicates that the tool performs better in correctly identifying adherent individuals in these domains, which is reflected in higher negative predictive values (NPV: up to 94.1%). However, the low sensitivity suggests that individuals with non-adherence may be missed, limiting the tool’s usefulness as an early screening trigger for these food groups. Conclusions: The SCI NutriTool’s performance varies across food groups, demonstrating a stronger ability to identify non-adherence in protein-rich foods, fruit and vegetables, sweetened and alcoholic beverages, and snacks, but limited discriminatory capacity for others. In particular, it is not suitable for screening non-adherence to starchy foods and fats. Accordingly, it is best used as a triage tool to guide further dietary assessment and targeted nutritional interventions rather than as a standalone diagnostic instrument. Read More