Key result
Machine learning combining gait and balance identifies moderate-to-high depression with ~85% accuracy.
Why the study?
Depressive mood states in healthy populations are prevalent but often under-reported due to self-reporting biases, and objective markers like gait and balance control could help identify affected individuals.
Can gait and balance control variables measured via IMU sensors accurately detect current depressive mood states in healthy young adults?
Cross-Sectional (n=133)
Single-blind
No
Can gait and balance control variables measured via IMU sensors accurately detect current depressive mood states in healthy young adults?
Effect estimate: 84.91% accuracy
Objective gait and balance parameters measured via IMU sensors can accurately detect transient feelings of mild to moderate-high depression in otherwise healthy young adults.
Gait-balance IMU models may detect depressive states in young adults; leaves open prospective validation before clinical use.
Depressive mood states in healthy populations are prevalent but often under-reported. Biases exist in self-reporting of depression in otherwise healthy individuals. Gait and balance control can serve as objective markers for identifying those individuals, particularly in real-world settings. We utilized inertial measurement units (IMU) to measure gait and balance control. An exploratory, cross-sectional design was used to compare individuals who reported feeling depressed at the moment (n = 49) with those who did not (n = 84). The Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was employed to ensure internal validity. We recruited 133 participants aged between 18–36 years from the university community. Various instruments were used to evaluate participants’ present depressive symptoms, sleep, gait, and balance. Gait and balance variables were used to detect depression, and participants were categorized into three groups: not depressed, mild depression, and moderate–high depression. Participant characteristics were analyzed using ANOVA and Kruskal–Wallis tests, and no significant differences were found in age, height, weight, BMI, and prior night’s sleep between the three groups. Classification models were utilized for depression detection. The most accurate model incorporated both gait and balance variables, yielding an accuracy rate of 84.91% for identifying individuals with moderate–high depression compared to non-depressed individuals.
No takes yet. Share an insight, caveat, or question.
Boolani et al. (2023) conducted a cross-sectional in Healthy young adults (assessed for current feelings of depression) (n=133). Current depressive mood states (mild to moderate-high) vs. No current depressive mood states was evaluated on Accuracy of identifying individuals with moderate-high depression vs non-depressed using combined gait and balance variables (84.91% accuracy). Machine learning models incorporating both gait and balance variables accurately identified individuals with moderate-to-high depression compared to non-depressed individuals with an 84.91% accuracy rate.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: