Una Pizca de Felicidad: Un Estudio Piloto para Evaluar la Salud Mental Mediante el Análisis de la Forma de Onda del...

Pulse characteristics are well-established biomarkers of physical health; however, their relevance to psychological well-being remains insufficiently explored. A key barrier is the difficulty of acquiring pulse recordings of adequate quality outside clinical or laboratory settings using accessible measurement approaches.

This study aimed to examine the feasibility of using smartphone photoplethysmography (PPG) to extract fingertip pulse-waveform features and to evaluate their associations with psychological measures. It further aimed to systematically compare time-, curvature-, and frequency-domain pulse-waveform features in relation to psychological variables.

A total of 127 students and university employees in Shenzhen, China, were recruited. Participants recorded repeated 4-minute fingertip videos using a custom smartphone application while an FDA-cleared fingertip oximeter simultaneously acquired reference pulse signals. Smartphone videos were converted into PPG signals, segmented into beat-to-beat intervals, and summarized into time-, curvature-, and frequency-domain features using median values, with normalization for heart rate and stature. Psychological well-being and mental health were assessed using the Satisfaction With Life Scale (SWLS), Subjective Vitality Scale (SVS), Positive and Negative Affect Schedule (PANAS), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and the Self-Assessment Manikin (SAM). Associations between pulse-waveform features and psychological measures were examined using univariate regression with participant-level aggregation and cluster-robust standard errors. Random forest models evaluated multivariate predictive performance using participant-level cross-validation. Agreement between smartphone-derived and oximeter-derived waveform features was assessed using Bland-Altman analysis.

Correlation analyses revealed strong within-domain associations among time-, curvature-, and frequency-domain pulse-waveform features, with comparatively weaker cross-domain correlations. A correlation-based feature-selection procedure reduced multicollinearity and yielded a final set of seven features (ERI, CT, F/A, H/A, rPSD1, V0, and SBP). Univariate regression analyses indicated that negative psychological states were primarily associated with time- and curvature-domain features. Depressive symptoms were significantly related to F/A and V0, with ERI remaining significant after Bonferroni correction. Generalized anxiety showed a Bonferroni-corrected association with F/A, and negative affect was associated with CT and F/A. In contrast, positive affect measures showed fewer and weaker associations. Momentary valence was related to F/A and H/A, whereas arousal was associated with CT and H/A. Random forest models demonstrated statistically significant but modest predictive performance for negative mental health outcomes, with weaker performance for positive affect. Bland-Altman analyses indicated minimal systematic bias for outcomes with significant predictive correlations. Comparisons with an FDA-cleared fingertip oximeter showed significant correlations and acceptable agreement, with time-domain features demonstrating greater robustness than reflection-based metrics.

Smartphone-based PPG can capture pulse-waveform features associated with psychological measures, particularly negative psychological states. However, predictive performance remains limited, and variability in signal quality from user-operated recordings poses a practical challenge. Future studies incorporating more diverse samples, improved signal acquisition, and longitudinal designs are needed to further evaluate the utility of this approach for mental health monitoring.