The current depression assessment tools are limited by subjectivity and potential bias. This study investigated the relationship between heart rate variability (HRV), body composition, and self-reported depressive symptoms to develop an objective depression screening model for Chinese university students. Data from 2,094 students, including demographics, body composition, Self-Rating Depression Scale (SDS) scores, and HRV indicators, were analyzed using SPSS 26.0 to construct a predictive regression model, with accuracy validated in GraphPad Prism 9.4.1. Subsequently, a subgroup of 359 students with depressive symptoms was screened using the model. The results showed no significant differences between the predicted and actual SDS scores (p > .05), with over 91% of the predicted scores falling within the 95% confidence interval of the actual scores. The strong correlation between HRV and SDS scores supports the use of HRV as a reliable indicator for depression screening. Overall, the model demonstrated a prediction accuracy of 92.61%, highlighting its potential for objective mental health assessment among university populations.
AmekranY.El HangoucheA. J. (2024). Effects of exercise training on heart rate variability in healthy adults: A systematic review and meta-analysis of randomized controlled trials. Cureus, 16(6), e62465. https://doi.org/10.7759/cureus.62465
2.
BeauchaineT. P.ThayerJ. F. (2015). Heart rate variability as a transdiagnostic biomarker of psychopathology. International Journal of Psychophysiology, 98, 338–350. https://doi.org/10.1016/j.ijpsycho.2015.08.004
3.
BerntsonG. G.LozanoD. L.ChenY. J. (2005). Filter properties of root mean square successive difference (RMSSD) for heart rate. Psychophysiology, 42(2), 246–252.
4.
CameronI. M.CardyA.CrawfordJ. R.du ToitS. W.HayS.LawtonK.MitchellK.SharmaS.ShivaprasadS.WinningS.ReidI. C. (2011). Measuring depression severity in general practice: Discriminatory performance of the PHQ-9, HADS-D, and BDI-II. The British Journal of General Practice, 61(588), e419–e426. https://doi.org/10.3399/bjgp11X583209
5.
CostantiniL.PasquarellaC.OdoneA.ColucciM. E.CostanzaA.SerafiniG.AgugliaA.Belvederi MurriM.BrakouliasV.AmoreM.GhaemiS. N.AmerioA. (2021). Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review. Journal of Affective Disorders, 279, 473–483. https://doi.org/10.1016/j.jad.2020.09.131
6.
CoulehanJ. L.SchulbergH. C.BlockM. R. (1989). The efficiency of depression questionnaires for case finding in primary medical care. Journal of General Internal Medicine, 4(6), 541–547. https://doi.org/10.1007/BF02599556
7.
DunstanD. A.ScottN. (2019). Clarification of the cut-off score for Zung’s self-rating depression scale. BMC Psychiatry, 19(1), 177. https://doi.org/10.1186/s12888-019-2161-0
8.
DunstanD. A.ScottN.ToddA. K. (2017). Screening for anxiety and depression: Reassessing the utility of the Zung scales. BMC Psychiatry, 17(1), 329. https://doi.org/10.1186/s12888-017-1489-6
9.
GaoC.SunY.ZhangF.ZhouF.DongC.KeZ.WangQ.YangY.SunH. (2021). Prevalence and correlates of lifestyle behavior, anxiety, and depression in Chinese college freshmen: A cross-sectional survey. International Journal of Nursing Sciences, 8(3), 347–353.
10.
GaoL.XieY.JiaC.WangW. (2020). Prevalence of depression among Chinese university students: A systematic review and meta-analysis. Scientific Reports, 10(1), 15897. https://doi.org/10.1038/s41598-020-72998-1
11.
GaoW.PingS.LiuX. (2020). Gender differences in depression, anxiety, and stress among college students: A longitudinal study from China. Journal of Affective Disorders, 263, 292–300.
12.
GarciaC.BuschD. W.RussellN. G. (2024). Best practices for depression screening among college students: A literature review. Journal of Psychosocial Nursing and Mental Health Services, 62(6), 13–17. https://doi.org/10.3928/02793695-20231024-02
13.
GhazisaeediM.MahmoodiH.ArpaciI.MehrdarS.BarzegariS. (2022). Validity, reliability, and optimal cut-off scores of the WHO-5, PHQ-9, and PHQ-2 to screen depression among university students in Iran. International Journal of Mental Health and Addiction, 20(3), 1824–1833. https://doi.org/10.1007/s11469-021-00483-5
14.
GoldenS. A.ChristoffelD. J.HeshmatiM.HodesG. E.MagidaJ.DavisK.RussoS. J. (2013). Epigenetic regulation of RAC1 induces synaptic remodeling in stress disorders and depression. Nature Medicine, 19, 337–344. https://doi.org/10.1038/nm.3090
15.
GuW.BaoK.LiX.XiangS.HeJ.HeJ.YeL.HuangZ. (2025). Association between body fat percentage and depression: A cross-sectional study of NHANES. Journal of Affective Disorders, 371, 305–314. https://doi.org/10.1016/j.jad.2024.11.066
16.
HanS. S.ZhangY. S.ZhuW.YeY. P.LiY. X.MengS. Q.FengS.LiH.CuiZ. L.ZhangY.ZhangQ.WangG. X.LouH.LiB.XuC. Y. (2025). Status and epidemiological characteristics of depression and anxiety among Chinese university students in 2023. BMC Public Health, 25(1), 1189. https://doi.org/10.1186/s12889-025-22443-7
17.
HartmannR.SchmidtF. M.SanderC.HegerlU. (2019). Heart rate variability as an indicator of clinical state in depression. Frontiers in Psychiatry, 9, 735. https://doi.org/10.3389/fpsyt.2018.00735
18.
HeC.LevisB.RiehmK. E.SaadatN.LevisA. W.AzarM.RiceD. B.KrishnanA.WuY.SunY.ImranM.BoruffJ.CuijpersP.GilbodyS.IoannidisJ. P. A.KlodaL. A.McMillanD.PattenS. B.ShrierI.. . .BenedettiA. (2020). The Accuracy of the Patient Health Questionnaire-9 algorithm for screening to detect major depression: An individual participant data meta-analysis. Psychotherapy and Psychosomatics, 89(1), 25–37. https://doi.org/10.1159/000502294
19.
HillR. M.YaroslavskyI.PettitJ. W. (2015). Enhancing depression screening to identify college students at risk for persistent depressive symptoms. Journal of Affective Disorders, 174, 1–6. https://doi.org/10.1016/j.jad.2014.11.025
20.
HongS.ParkD. H.RyuS. H.HaJ. H.JeonH. J. (2022). Association between heart rate variability indices and depressed mood in patients with panic disorder. Clinical Psychopharmacology and Neuroscience, 20(4), 737–746. https://doi.org/10.9758/cpn.2022.20.4.737
21.
HsuC. C.ChenT. Y.OuH.KuoT. B. J.YangC. C. H. (2025). Interactions between depression, autonomic dysfunction, inhibitory control, and reaction time: Insights from heart rate variability during continuous performance test. Psychiatry Investigation, 22(8), 921–929. https://doi.org/10.30773/pi.2025.0043
22.
JangpangiD.MondalS.BandhuR.KatariaD.GandhiA. (2016). Alteration of heart rate variability in patients with depression. Journal of Clinical and Diagnostic Research, 10(12), CM04–CM06. https://doi.org/10.7860/JCDR/2016/22882.9063
23.
JokelainenJ.TimonenM.Keinänen-KiukaanniemiS.HärkönenP.JurvelinH.SuijaK. (2019). Validation of the Zung self-rating depression scale (SDS) in older adults. Scandinavian Journal of Primary Health Care, 37(3), 353–357. https://doi.org/10.1080/02813432.2019.1639923
24.
JungS. J.WooH. T.ChoS.ParkK.JeongS.LeeY. J.KangD.ShinA. (2017). Association between body size, weight change, and depression: Systematic review and meta-analysis. The British Journal of Psychiatry, 211(1), 14–21. https://doi.org/10.1192/bjp.bp.116.186726
25.
KeumB. T.MillerM. J.InkelasK. K. (2018). Testing the factor structure and measurement invariance of the PHQ-9 across racially diverse U.S. college students. Psychological Assessment, 30(8), 1096–1106. https://doi.org/10.1037/pas0000550
26.
KochC.WilhelmM.SalzmannS.RiefW.EuteneuerF. (2019). A meta-analysis of heart rate variability in major depression. Psychological Medicine, 49(12), 1948–1957.
27.
LeiX. Y.XiaoL. M.LiuY. N.LiuY. M. (2016). Prevalence of depression among Chinese university students: A meta-analysis. PLOS ONE, 11(4), e0153454.
28.
LevisB.BenedettiA.ThombsB. D. (2019). DEPRESsion Screening Data (DEPRESSD) Collaboration. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: Individual participant data meta-analysis. BMJ, 365, l1476. https://doi.org/10.1136/bmj.l1476
29.
LiX.ZouL.LiH. (2024). Multilayer perceptron-based wearable exercise-related heart rate variability predicts anxiety and depression in college students. Sensors (Basel), 24(13), 4203. https://doi.org/10.3390/s24134203
30.
LuoW.ZhongB. L.ChiuH. F. (2021). Prevalence of depressive symptoms among Chinese university students amid the COVID-19 pandemic: A systematic review and meta-analysis. Epidemiology and Psychiatric Sciences, 30, e31. https://doi.org/10.1017/S2045796021000202
31.
ManeaL.GilbodyS.McMillanD. (2015). A diagnostic meta-analysis of the Patient Health Questionnaire-9 (PHQ-9) algorithm scoring method as a screen for depression. General Hospital Psychiatry, 37(1), 67–75. https://doi.org/10.1016/j.genhosppsych.2014.09.009
32.
Marek MalikJ.Thomas BiggerA.CammJ.KleigerR. E.MallianiA.MossA. J.SchwartzP. J. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal, 17(3), 354–381. https://doi.org/10.1093/oxfordjournals.eurheartj.a014868
33.
MillerJ. G.ChahalR.KirshenbaumJ. S.HoT. C.GifuniA. J.GotlibI. H. (2021). Heart rate variability moderates the effects of COVID-19-related stress and family adversity on emotional problems in adolescents: Testing models of differential susceptibility and diathesis stress. Development and Psychopathology, 34(5), 1974–1985. https://doi.org/10.1017/S095457942100033X
34.
MitchellA. J.YadegarfarM.GillJ.StubbsB. (2016). Case finding and screening clinical utility of the Patient Health Questionnaire (PHQ-9 and PHQ-2) for depression in primary care: A diagnostic meta-analysis of 40 studies. BJPsych Open, 2(2), 127–138. https://doi.org/10.1192/bjpo.bp.115.001685
35.
MoriartyA. S.GilbodyS.McMillanD.ManeaL. (2015). Screening and case finding for major depressive disorder using the Patient Health Questionnaire (PHQ-9): A meta-analysis. General Hospital Psychiatry, 37(6), 567–576. https://doi.org/10.1016/j.genhosppsych
36.
MorrisonK. M.ShinS.TarnopolskyM.TaylorV. H. (2015). Association of depression & health-related quality of life with body composition in children and youth with obesity. Journal of Affective Disorders, 172, 18–23. https://doi.org/10.1016/j.jad.2014.09.014
37.
NegeriZ. F.LevisB.SunY.HeC.KrishnanA.WuY.BhandariP. M.NeupaneD.BrehautE.BenedettiA.ThombsB. D. (2021). Depression Screening Data (DEPRESSD) PHQ Group. Accuracy of the Patient Health Questionnaire-9 for screening to detect major depression: Updated systematic review and individual participant data meta-analysis. BMJ, 375, n2183. https://doi.org/10.1136/bmj.n2183
38.
PaansN. P. G.BotM.BrouwerI. A.VisserM.PenninxB. W. J. H. (2018). Contributions of depression and body mass index to body image. Journal of Psychiatric Research, 103, 18–25. https://doi.org/10.1016/j.jpsychires.2018.05.003
39.
PatelJ. S.OhY.RandK. L.WuW.CydersM. A.KroenkeK.StewartJ. C. (2019). Measurement invariance of the patient health questionnaire-9 (PHQ-9) depression screener in U.S. adults across sex, race/ethnicity, and education level: NHANES 2005-2016. Depression and Anxiety, 36(9), 813–823. https://doi.org/10.1002/da.22940
40.
SayarK.GüleçH.GökçeM.AkI. (2002). Heart rate variability in depressed patients. Klinik Psikofarmakoloji Bulteni-Bulletin of Clinical Psychopharmacology, 12, 130–133.
41.
SchiweckC.PietteD.BerckmansD.ClaesS.VriezeE. (2019). Heart rate and high-frequency heart rate variability during stress as a biomarker for clinical depression: A systematic review. Psychological Medicine, 49(2), 200–211. https://doi.org/10.1017/S0033291718001988
42.
SchneiderM.SchwerdtfegerA. (2020). Autonomic dysfunction in posttraumatic stress disorder indexed by heart rate variability: A meta-analysis. Psychological Medicine, 50(12), 1937–1948. https://doi.org/10.1017/S003329172000207X
43.
SgoifoA.CarnevaliL.AlfonsoM. L.AmoreM. (2015). Autonomic dysfunction and heart rate variability in depression. Stress, 18(3), 343–352. https://doi.org/10.3109/10253890.2015.1045868
44.
ShafferF.GinsbergJ. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 58. https://doi.org/10.3389/fpubh.2017.00258
45.
ShinbaT.SuzukiH.UritaM.ShinbaS.ShinbaY.UmedaM.HirakuniJ.MatsuiT.OnodaR. (2024). Heart rate variability measurement can be a point-of-care sensing tool for screening postpartum depression: Differentiation from adjustment disorder. Sensors (Basel), 24(5), 1459. https://doi.org/10.3390/s24051459
46.
SpeedM. S.JefsenO. H.BørglumA. D.SpeedD.ØstergaardS. D. (2019). Investigating the association between body fat and depression via Mendelian randomization. Translational Psychiatry, 9(1), 184. https://doi.org/10.1038/s41398-019-0516-4
47.
ThayerJ. F.LaneR. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–216. https://doi.org/10.1016/s0165-0327(00)00338-4
48.
UrtasunM.DarayF. M.TetiG. L.CoppolilloF.HerlaxG.SabaG.RubinsteinA.ArayaR.IrazolaV. (2019). Validation and calibration of the patient health questionnaire (PHQ-9) in Argentina. BMC Psychiatry, 19(1), 291. https://doi.org/10.1186/s12888-019-2262-9
49.
Villarreal-ZegarraD.Barrera-BegazoJ.Otazú-AlfaroS.Mayo-PuchocN.Bazo-AlvarezJ. C.Huarcaya-VictoriaJ. (2023). Sensitivity and specificity of the Patient Health Questionnaire (PHQ-9, PHQ-8, PHQ-2) and General Anxiety Disorder scale (GAD-7, GAD-2) for depression and anxiety diagnosis: A cross-sectional study in a Peruvian hospital population. BMJ Open, 13(9), e076193. https://doi.org/10.1136/bmjopen-2023-076193
50.
von ZimmermannC.WinkelmannM.Richter-SchmidingerT.MühleC.KornhuberJ.LenzB. (2020). Physical activity and body composition are associated with severity and risk of depression, and serum lipids. Frontiers in Psychiatry, 11, 494. https://doi.org/10.3389/fpsyt.2020.00494
51.
WangB. C.WuF.ZhongX. L. (2024). Discussion on screening and early warning of depression in universities. Qingdao Medicine and Health, 56(01), 72–75.
52.
WangL.KroenkeK.StumpT. E.MonahanP. O. (2020). Screening for perinatal depression with the Patient Health Questionnaire depression scale (PHQ-9): A systematic review and meta-analysis. General Hospital Psychiatry, 68, 74–82. https://doi.org/10.1016/j.genhosppsych.2020.12.007
53.
WeobongB.WeissH. A.CameronI. M.KungS. (2018). Measuring depression severity in global mental health: Comparing the PHQ-9 and the BDI-II. Wellcome Open Research, 3, 165.
WuQ.MiaoX.CaoY.ChiA.XiaoT. (2023). Heart rate variability status at rest in adult depressed patients: A systematic review and meta-analysis. Frontiers in Public Health, 11, 1243213. https://doi.org/10.3389/fpubh.2023.1243213
57.
WuY.LinC.WangC.WangR.JinB.ZhangX.ChenB.YangY.CuiJ.XuW.SongL.YangH.HeW.ZhangY.LiX. (2025). Association of BMI with mortality and health-related quality of life among 4.4 million adults: Evidence from a nationwide, population-based, prospective cohort study. Diabetes, Obesity & Metabolism, 27(6), 3132–3144. https://doi.org/10.1111/dom.16325
58.
XieL. (2024). Review of diagnostic methods and related interventions for depression. Social Sciences Frontiers, 13(1), 1–7.
59.
XuW. Q.TanW. Y.LiX. L.HuangZ. H.ZhengH. R.HouC. L.JiaF. J.WangS. B. (2022). Prevalence and correlates of depressive and anxiety symptoms among adults in Guangdong Province of China: A population-based study. Journal of Affective Disorders, 308, 535–544. https://doi.org/10.1016/j.jad.2022.04.089
60.
ZhaoY. J.GuiP. P.XuJ. J.GuoT.LiJ.WangJ.WangG.MengL. (2025). Exploring the differences in psychometric properties of commonly used self-rating depression scales across various populations in China: A quantitative systematic review. Asian Journal of Psychiatry, 111, 104635. https://doi.org/10.1016/j.ajp.2025.104635
ZhouY.HuangZ.LiuY.LiuD. (2024). The effect of replacing sedentary behavior with different intensities of physical activity on depression and anxiety in Chinese university students: An isotemporal substitution model. BMC Public Health, 24, 1388. https://doi.org/10.1186/s12889-024-18914-y
ZungW. W. K. (1969). A cross-cultural survey of symptoms in depression. The American Journal of Psychiatry, 126(1), 116–121. https://doi.org/10.1176/ajp.126.1.116
