ArticleBMC psychiatry2025
Psychometric evaluation of the Nursing Homes Short Depression Inventory (NH-SDI) across long-term care facilities in Lebanon.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Risk factors associated with depressive symptoms among institutionalized elderly in Lebanon.Scientific reports · 2026Article
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4 authors.
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Abstract
backgroundThe Nursing Homes Short Depression Inventory (NH-SDI) is an observer-reported measure used to identify depressive symptoms in older adults living in nursing homes. This study aimed to test the psychometric properties of the NH-SDI (Arabic version) in long-term care facilities (LTCF).
methodsThe NH-SDI was translated to Arabic, assessed by a panel of experts, and pilot-tested. Data were collected from 21 nursing homes and assisted-living centers over a period of 9 months in 2022. The psychometric properties of the 16-item scale were examined via principal component analysis (PCA), confirmatory factor analysis (CFA), receiver operating characteristic (ROC), Cronbach’s α, and intraclass correlation coefficient (ICC).
resultsA total of 245 individuals, ranging in age from 65 to 100 years, were included in the study. The mean age of participants was 77.8 ± 8.4 years and the prevalence of depression was 31.8%, based on physicians’ diagnosis. The PCA produced a four-factor model, accounting for 63.3% of the overall variance. A good model fit in CFA was achieved for a 13-item model (χ2/df = 1.95, RMSEA = 0.07, GFI = 0.91, AGFI = 0.89, CFI = 0.90, NFI = 0.86, and TLI = 0.89). A positive correlation was found between NH-SDI and WHO-5 Well-being Index (r = 0.46, p < 0.001). The cut-off point was 3.5 (area under the curve = 0.83, sensitivity = 88.5%, specificity = 69.5%). The Cronbach’s α and ICC achieved good values (0.83 and 0.93, respectively).
conclusionThe Arabic version of the NH-SDI (13 items) is valid and reliable for detecting depressive symptoms among elderly residents in Lebanon.
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