Happiness at Your Fingertips: Assessing Mental Health with Smartphone Photoplethysmogram-Based Heart Rate Variability Analysis

Simultaneous Determination of Tobacco Smoke Exposure and Stress Biomarkers in Saliva Using In-Tube SPME and LC-MS/MS for the Analysis of the Association between Passive Smoking and Stress

Passive smoking from environmental tobacco smoke not only increases the risk of lung cancer and cardiovascular disease but may also be a stressor triggering neuropsychiatric and other disorders. To prevent these diseases, understanding the relationship between passive smoking and stress is vital. In this study, we developed a simple and sensitive method to simultaneously measure nicotine (Nic) and cotinine (Cot) as tobacco smoke exposure biomarkers, and cortisol (CRT), serotonin (5-HT), melatonin (MEL), dopamine (DA), and oxytocin (OXT) as stress-related biomarkers. These were extracted and concentrated from saliva by in-tube solid-phase microextraction (IT-SPME) using a Supel-Q PLOT capillary as the extraction device, then separated and detected within 6 min by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a Kinetex Biphenyl column (Phenomenex Inc., Torrance, CA, USA). Limits of detection (S/N = 3) for Nic, Cot, CRT, 5-HT, MEL, DA, and OXT were 0.22, 0.12, 0.78, 0.39, 0.45, 1.4, and 3.7 pg mL-1, respectively, with linearity of calibration curves in the range of 0.01-25 ng mL-1 using stable isotope-labeled internal standards. Intra- and inter-day reproducibilities were under 7.9% and 14.6% (n = 5) relative standard deviations, and compound recoveries in spiked saliva samples ranged from 82.1 to 106.6%. In thirty nonsmokers, Nic contents positively correlated with CRT contents (R2 = 0.5264, n = 30), while no significant correlation was found with other biomarkers. The standard deviation of intervals between normal beats as the standard measure of heart rate variability analysis negatively correlated with CRT contents (R2 = 0.5041, n = 30). After passive smoke exposure, Nic levels transiently increased, Cot and CRT levels rose over time, and 5-HT, DA, and OXT levels decreased. These results indicate tobacco smoke exposure acts as a stressor in nonsmokers.

Alleviating premenstrual symptoms with smartphone-based heart rate variability biofeedback training: a pilot study

Introduction

Heart rate variability biofeedback (HRVB) is a well-studied intervention known for its positive effects on emotional, cognitive, and physiological well-being, including relief from depressive symptoms. However, its practical use is hampered by high costs and a lack of trained professionals. Smartphone-based HRVB, which eliminates the need for external devices, offers a promising alternative, albeit with limited research. Additionally, premenstrual symptoms are highly prevalent among menstruating individuals, and there is a need for low-cost, accessible interventions with minimal side effects. With this pilot study, we aim to test, for the first time, the influence of smartphone-based HRVB on depressive and premenstrual symptoms, as well as anxiety/stress symptoms and attentional control.

Methods

Twenty-seven participants with above-average premenstrual or depressive symptoms underwent a 4-week photoplethysmography smartphone-based HRVB intervention using a waitlist-control design. Laboratory sessions were conducted before and after the intervention, spaced exactly 4 weeks apart. Assessments included resting vagally mediated heart rate variability (vmHRV), attentional control via the revised attention network test (ANT-R), depressive symptoms assessed with the BDI-II questionnaire, and stress/anxiety symptoms measured using the DASS questionnaire. Premenstrual symptomatology was recorded through the PAF questionnaire if applicable. Data analysis employed linear mixed models.

Results

We observed improvements in premenstrual, depressive, and anxiety/stress symptoms, as well as the Executive Functioning Score of the ANT-R during the intervention period but not during the waitlist phase. However, we did not find significant changes in vmHRV or the Orienting Score of the ANT-R.

Discussion

These findings are promising, both in terms of the effectiveness of smartphone-based HRVB and its potential to alleviate premenstrual symptoms. Nevertheless, to provide a solid recommendation regarding the use of HRVB for improving premenstrual symptoms, further research with a larger sample size is needed to replicate these effects.

Your blush gives you away: detecting hidden mental states with remote photoplethysmography and thermal imaging

Multimodal emotion recognition techniques are increasingly essential for assessing mental states. Image-based methods, however, tend to focus predominantly on overt visual cues and often overlook subtler mental state changes. Psychophysiological research has demonstrated that heart rate (HR) and skin temperature are effective in detecting autonomic nervous system (ANS) activities, thereby revealing these subtle changes. However, traditional HR tools are generally more costly and less portable, while skin temperature analysis usually necessitates extensive manual processing. Advances in remote photoplethysmography (r-PPG) and automatic thermal region of interest (ROI) detection algorithms have been developed to address these issues, yet their accuracy in practical applications remains limited. This study aims to bridge this gap by integrating r-PPG with thermal imaging to enhance prediction performance. Ninety participants completed a 20-min questionnaire to induce cognitive stress, followed by watching a film aimed at eliciting moral elevation. The results demonstrate that the combination of r-PPG and thermal imaging effectively detects emotional shifts. Using r-PPG alone, the prediction accuracy was 77% for cognitive stress and 61% for moral elevation, as determined by a support vector machine (SVM). Thermal imaging alone achieved 79% accuracy for cognitive stress and 78% for moral elevation, utilizing a random forest (RF) algorithm. An early fusion strategy of these modalities significantly improved accuracies, achieving 87% for cognitive stress and 83% for moral elevation using RF. Further analysis, which utilized statistical metrics and explainable machine learning methods including SHapley Additive exPlanations (SHAP), highlighted key features and clarified the relationship between cardiac responses and facial temperature variations. Notably, it was observed that cardiovascular features derived from r-PPG models had a more pronounced influence in data fusion, despite thermal imaging's higher predictive accuracy in unimodal analysis.

Perceived Parenting Stress Is Related to Cardiac Flexibility in Mothers: Data from the NorBaby Study

Heart rate variability (HRV) is an indicator of autonomic nervous system activity, and high levels of stress and/or depressive symptoms may reduce HRV. Here, we assessed whether (a) parental stress affected HRV in mothers during the perinatal period and whether this is mediated by bonding and (b) whether antenatal maternal mental states, specifically repetitive negative thinking, depressive symptoms, and pregnancy-related anxiety, have an impact on infant HRV, and lastly, we investigated (c) the relationship between maternal HRV and infant HRV. Data are from the Northern Babies Longitudinal Study (NorBaby). In 111 parent-infant pairs, cardiac data were collected 6 months after birth. In the antenatal period, we used the Pregnancy-Related Anxiety Questionnaire-Revised, the Edinburgh Postnatal Depression Scale, and the Perseverative Thinking Questionnaire; in the postnatal period, we used the Parenting Stress Index and the Maternal Postnatal Attachment Scale. Higher levels of perceived parenting stress but not depressive symptoms were associated with lower HRV in mothers (τ = -0.146), and this relationship was not mediated by maternal bonding. Antenatal maternal mental states were not associated with infant HRV. There was no significant correlation between maternal HRV and infant HRV. Our observational data suggest that perceived stress reduces cardiac flexibility. Future studies should measure HRV and parenting stress repeatedly during the perinatal period.

A calibration method for smartphone camera photophlethysmography

Smartphone camera photoplethysmography (cPPG) enables non-invasive pulse oximetry and hemoglobin concentration measurements. However, the aesthetic-driven non-linearity in default image capture and preprocessing pipelines poses challenges for consistency and transferability of cPPG across devices. This work identifies two key parameters-tone mapping and sensor threshold-that significantly impact cPPG measurements. We propose a novel calibration method to linearize camera measurements, thus enhancing consistency and transferability of cPPG across devices. A benchtop calibration system is also presented, leveraging a microcontroller and LED setup to characterize these parameters for each phone model. Our validation studies demonstrate that, with appropriate calibration and camera settings, cPPG applications can achieve 74% higher accuracy than with default settings. Moreover, our calibration method proves effective across different smartphone models (N = 4 ), and calibrations performed on one phone can be applied to other smartphones of the same model (N = 6 ), enhancing consistency and scalability of cPPG applications.

Association and interaction between clinician-rated measures of depression and anxiety with heart rate variability in elderly patients with psychiatric disorders

Background

Older adults are vulnerable to comorbid depression and anxiety symptoms; however, these conditions are widely underrecognized and often untreated. Understanding their combined manifestation using objective measurements, such as clinician-rated scales and heart rate variability (HRV), can help refine the diagnosis and select a treatment strategy for geriatric patients.

Methods

This study included patients over 65 years who were mainly diagnosed with either category of depressive or anxiety disorders from the psychiatric outpatient clinic in a university hospital. A total of 114 patients met eligibility with a completed collection of electrocardiograms, the Hamilton Depression Rating Scale (HDRS; clinician-rated depression), and the Hamilton Anxiety Scale (HAS; clinician-rated anxiety) to assess the severity of symptoms. Both main and interaction effects between HDRS and HAS on HRV parameters were examined.

Results

Significant interaction effects between clinician-rated depression and anxiety (HDRS × HAS) on HRV reduction in frequency parameters (i.e., nuLF, nuHF, LF/HF ratio) were found, which consistently indicated autonomic nervous system dysregulation. Findings imply that HRV could reflect synergistic effects of comorbid depressive and anxiety symptoms, perhaps due to the amplification of individual symptoms in geriatric patients.

Conclusions

The results imply that using objective measurements can improve diagnostic accuracy, particularly in geriatric patients with comorbid status, and the normalization of the autonomic nervous system might be a candidate target for prevention and treatment.

Pre-partum HRV as a predictor of postpartum depression: The potential use of a smartphone application for physiological recordings

BACKGROUND

This study aimed to investigate the role of a time-domain Heart Rate Variability index (the root mean square of successive difference between NN intervals, rMSSD) as a predictor of the onset of postpartum depression. HRV has been related to an increased risk of depression in the general population. However, its role in pregnant women is not clear, and the potential use of smartphone applications to evaluate HRV in this population has not been investigated.

METHODS

In study 1, simultaneous electrocardiogram and smartphone photoplethysmography were collected. The rMSSD was determined from each recording to evaluate the accuracy of a smartphone application in the measurement of HRV. In study 2, 135 pregnant women provided rMSSD values measured through a smartphone application in the prepartum (second or third trimester) and filled in the Edinburgh Postnatal Depression Scale in the postpartum (one month after the childbirth).

RESULTS

Study 1 showed the excellent accuracy of the smartphone application in the measurement of rMSSD. Study 2 indicated that lower prepartum rMSSD predicted higher depressive symptoms in the postpartum (β = -0.217, p = 0.010) after controlling for prepartum depressive symptoms and other potential covariates.

LIMITATIONS

Artefacts (e.g., hand movements) might have corrupted the physiological signal registered.

CONCLUSION

This study showed that a reduced vagal tone, indexed by lower rMSSD, during pregnancy was a predictor of depressive symptoms one month after childbirth. The prepartum period may offer an important timeframe to implement preventive intervention on vagal modulation in order to prevent depressive symptoms in the postpartum.

Psychophysiological and Psychosocial Profile of Patients Attending Drug Addiction Centers

Drug treatment centres provide the highest level of rehab services for patients diagnosed with drug addictions. Most inpatient drug rehab programs focus on medical detox and mental health interventions. However, how to optimize the later remains a challenge. The aim of this study was to examine the psychophysiological and psychosocial profile of patients attending drug addiction centres in comparation with the general population. A total of 105 inpatient drug rehab patients and 50 participants from the general population were compared based on standardized psychophysiological and psychosocial measures. Results of this study suggest that patients attending drug addiction centers differ from general population in several different psychophysiological and psychosocial factors. Patients reported significantly lower levels of physical activity and increased sympathetic responsiveness, and significantly higher levels in loneliness, psychologically inflexibility and neuroticism. The results of this study highlight the importance of address healthy lifestyle behaviors such as sport practice and psychological variables such as loneliness, psychological (in)flexibility and neuroticism to improve current programs aim to prevent or reduce problematic drug consumptions.

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review

The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.

Effects of Meditation on Mental Health and Cardiovascular Balance in Caregivers

Background: Caring for a loved one can be rewarding but is also associated with substantial caregiver burden, developing mental outcomes and affecting happiness. The aim of this study was to determine the effects of a four-week, 16-h presential meditation program on physiological and psychological parameters and vagal nerve activity in high-burden caregivers, as compared to a control group. Methods: A non-randomized repeated-measures controlled clinical trial was conducted. Results: According to the ANCOVA results, the global happiness score (F = 297.42, p < 0.001) and the scores for all subscales were significantly higher in the experimental group than in the control group at 5 weeks. Anxiety levels were also significantly reduced in the experimental group (F = 24.92, p < 0.001), systolic (F = 16.23, p < 0.001) and diastolic blood (F = 34.39, p < 0.001) pressures, and the resting heart rate (F = 17.90, p < 0.05). HRV results revealed significant between-group differences in the HRV Index (F = 8.40, p < 0.05), SDNN (F = 13.59, p < 0.05), and RMSSD (F = 10.72, p < 0.05) in the time domain, and HF (F = 4.82 p < 0.05)) in the frequency domain, which were all improved in the experimental group after the meditation program. Conclusions: Meditation can be a useful therapy to enhance the mental health and autonomic nervous system balance of informal caregivers, improving symptoms of physical and mental overload.

Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach

Heart rate variability (HRV) provides essential health information such as the risks of heart attacks and mental disorders. However, inconvenience related to the accurate detection of HRV limits its potential applications. The ubiquitous use of smartphones makes them an excellent choice for regular and portable health monitoring. Following this trend, smartphone photoplethysmography (PPG) has recently garnered prominence; however, the lack of robustness has prevented both researchers and practitioners from embracing this technology. This study aimed to bridge the gap in the literature by developing a novel smartphone PPG quality index (SPQI) that can filter corrupted data. A total of 226 participants joined the study, and results from 1343 samples were used to validate the proposed sinusoidal function-based model. In both the correlation coefficient and Bland–Altman analyses, the agreement between HRV measurements generated by both the smartphone PPG and the reference electrocardiogram improved when data were filtered through the SPQI. Our results support not only the proposed approach but also the general value of using smartphone PPG in HRV analysis.