A nomogram combining age, PHQ-9, GAD-7, and four physical fitness measures showed moderate internal performance (optimism-corrected AUC 0.751) for identifying current poor sleep quality in community-dwelling adults aged ≥45 with abdominal obesity and outperformed a routine-information model, though external validation is required and PHQ-9/PSQI content overlap may have inflated estimates.
More than half of the community-dwelling middle-aged and older adults with abdominal obesity in the sample had poor sleep quality.
The final multivariable model retained seven predictors: age, PHQ-9, GAD-7, grip strength, 30-s chair stand repetitions, chair sit-and-reach distance, and 2-min step count.
The final model showed moderate discrimination, with an apparent AUC of 0.779.
After bootstrap optimism correction, model performance declined modestly but remained moderate.
The final model outperformed a model using only routinely available demographic and health information.
Decision curve analysis showed greater theoretical net benefit for the final model across a wide range of threshold probabilities in the development sample.
The authors caution that the estimates may be inflated and that external validation is needed before use in public health practice.
This study looked at 212 adults aged 45 and older with abdominal obesity (excess belly fat) who visited a community health center in Beijing. More than half (55%) had poor sleep quality based on a standard sleep questionnaire. The researchers built a scoring tool, called a nomogram, that uses age, scores on depression and anxiety questionnaires, and four simple fitness tests (grip strength, chair stands, a sit-and-reach flexibility test, and a 2-minute step test) to estimate whether someone currently has poor sleep. The tool distinguished people with and without poor sleep moderately well (an AUC of about 0.75 after correcting for overfitting, where 0.5 is chance and 1.0 is perfect). It did noticeably better than a simpler model using only routine demographic and health information (AUC about 0.65). This research suggests that mental health and physical fitness measures, which many community programs already collect, may help flag people who could benefit from a closer look at their sleep. There are important limits. The data came from one center and were tested only internally, so the tool has not been shown to work in other populations. The depression questionnaire includes a sleep-related question that overlaps with the sleep questionnaire, which may make the results look better than they truly are. The tool identifies current poor sleep rather than predicting future sleep problems, and the authors say it needs external validation before being used in public health practice.
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Upload Your LabsQiao C, Yang S, Wang Y, Yang C, He Y, Zeng S. (2026). Community-based identification of poor sleep quality in middle-aged and older adults with abdominal obesity using psychological and physical fitness indicators.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1964356