000168300 001__ 168300
000168300 005__ 20260202151222.0
000168300 0247_ $$2doi$$a10.1109/ACCESS.2025.3643644
000168300 0248_ $$2sideral$$a147801
000168300 037__ $$aART-2025-147801
000168300 041__ $$adeu
000168300 100__ $$aCajal, Diego
000168300 245__ $$aEvaluation of Stress Response Using Smartphone PPG for Anxiety and Depression Monitoring
000168300 260__ $$c2025
000168300 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168300 5203_ $$aDiminished stress reactivity is frequently reported in individuals with depression and anxiety. Smartphone-camera photoplethysmography (SCPPG) could offer an innovative, objective, and ambulatory metric for monitoring these conditions. This study aims to evaluate the use of SCPPG to monitor anxiety and depression by analyzing stress responses. Specifically, it examines the autonomic nervous system through heart rate variability using pulse rate variability (PRV) metrics derived from SCPPG. The study involved 79 participants, including patients diagnosed with generalized anxiety disorder and major depressive disorder (n = 22), as well as a control group (n = 57). SCPPG signals were compared with those from a validated device during a stress-inducing protocol, consisting of baseline, stress tests (Trail Making Test and Stroop Test), and recovery phases. Pearson's correlation and Bland-Altman analysis were used to assess the agreement. The results indicate a high correlation (r >= 0.96, p < 0.001) between PRV metrics derived from SCPPG and those from reference devices. Additionally, exhibited minimal bias (1 <= 2%) with the exception of RMSSD (1 = 12%). Notably, SCPPG reliably detects stress reactivity differences between patient and control groups across all PRV metrics (p < 0.05). The study highlights the significance of SCPPG in understanding and personalizing mental health treatments, considering factors such as stress reactivity and recovery. Future research directions include longitudinal studies and improving SCPPG accuracy, particularly for patients with tremors or during dynamic tasks.
000168300 536__ $$9info:eu-repo/grantAgreement/ES/AEI/AEI PID2024-160041OB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T27-17R$$9info:eu-repo/grantAgreement/ES/DGA/T39-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PDC2021-120775$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-138585OA-C32$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131106B-I00$$9info:eu-repo/grantAgreement/ES/UZ/UZ2022-IAR-06
000168300 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000168300 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168300 700__ $$aDe La Cámara, Concepción
000168300 700__ $$aPosadas-De Miguel, Mar
000168300 700__ $$aTorrijos, Noel
000168300 700__ $$aNadal, Óscar
000168300 700__ $$aBlanco, Teresa
000168300 700__ $$aSiddi, Sara
000168300 700__ $$0(orcid)0000-0001-5918-1043$$aArmañac, Pablo$$uUniversidad de Zaragoza
000168300 700__ $$0(orcid)0000-0001-7285-0715$$aGil, Eduardo$$uUniversidad de Zaragoza
000168300 700__ $$0(orcid)0000-0001-8742-0072$$aLázaro, Jesús$$uUniversidad de Zaragoza
000168300 700__ $$0(orcid)0000-0003-1272-0550$$aBailón, Raquel$$uUniversidad de Zaragoza
000168300 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000168300 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000168300 773__ $$g13 (2025), 212689-212703$$pIEEE Access$$tIEEE Access$$x2169-3536
000168300 8564_ $$s5750528$$uhttps://zaguan.unizar.es/record/168300/files/texto_completo.pdf$$yVersión publicada
000168300 8564_ $$s2723132$$uhttps://zaguan.unizar.es/record/168300/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168300 909CO $$ooai:zaguan.unizar.es:168300$$particulos$$pdriver
000168300 951__ $$a2026-02-02-14:40:17
000168300 980__ $$aARTICLE