Assessing Mental Health and Emotional States by Using Smartphone Photoplethysmography-Based Digital Pulse Waveform Analysis: Cross-Sectional Observational Study.

BACKGROUND: 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 and blood pressure measurements of adequate quality outside clinical or laboratory settings by using accessible measurement approaches.

OBJECTIVE: This study aimed to examine the feasibility of using smartphone photoplethysmography 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.

METHODS: A total of 127 students and university employees in Shenzhen, China, were recruited. Participants recorded repeated 4-minute fingertip videos by using a custom smartphone app while a fingertip oximeter simultaneously acquired reference pulse signals. Smartphone videos were converted into photoplethysmography signals, segmented into beat-to-beat intervals, and summarized into time-, curvature-, and frequency-domain features, with normalization for heart rate and stature. Psychological well-being and mental health were assessed using the Satisfaction With Life Scale, Subjective Vitality Scale, Positive and Negative Affect Schedule, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and the Self-Assessment Manikin. 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 by using participant-level cross-validation. Agreement between smartphone-derived and oximeter-derived waveform features was assessed using Bland-Altman analysis.

RESULTS: 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 7 features: estimated reflection index, crest time (CT), the third curvature minimum (F/A), the fourth curvature minimum (H/A), the first power spectrum density component, the baseline of Fourier decomposition (V0), and systolic blood pressure. 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, V0, and the estimated reflection index. Anxiety showed an association with F/A, and negative affect was associated with CT and F/A. In contrast, positive affect measures showed fewer and weaker associations. 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 oximeter showed significant correlations and acceptable agreement, with time-domain features demonstrating greater robustness than reflection-based metrics.

CONCLUSIONS: Smartphone-based photoplethysmography 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.