The Intergenerational Transfer of Mental Disorders: A Population-Based Multigenerational Linkage Study: Le transfert intergénérationnel des troubles mentaux : une étude sur les liens multigénérationnels basée sur la population.

OBJECTIVES: The aetiology of mental disorders involves genetic and environmental factors, both reflected in family health history. We examined the intergenerational transmission of multiple mental disorders from parents and grandparents using population-based, objectively measured family histories. METHODS: This population-based retrospective cohort study used administrative healthcare databases in Manitoba, Canada and included adults living in Manitoba from 1977 to 2020 with linkages to at least one parent and one grandparent. Index date was when individuals turned 18 or 1 April 1977, whichever occurred later. Mental disorder diagnoses (mood and anxiety, substance use and psychotic disorders) were identified in individuals, parents and grandparents from hospitalization and outpatient records. Cox proportional hazards regression models included sociodemographic characteristics, individual's comorbidity and mental disorder history in a grandparent, mother and father. RESULTS: Of 109,359 individuals with no mental disorder prior to index date, 47.1% were female, 36.3% had a mental disorder during follow-up, and 90.9% had a parent or grandparent with a history of a mental disorder prior to the index date. Both paternal and maternal history of a mental disorder increased the risk of the disorder in individuals. Psychotic disorders had the strongest association with parental history and were mostly influenced by paternal (hazards ratio [HR] 3.73, 95% confidence interval [CI] 2.99 to 4.64) compared to maternal history (HR 2.23, 95% CI, 1.89 to 2.64). Grandparent history was independently associated with the risk of all mental disorders but had the strongest influence on substance use disorders (HR 1.42, 95% CI, 1.34 to 1.50). CONCLUSIONS: Parental history of mental disorders was associated with an increased risk of all mental disorders. Grandparent history of mental disorders was associated with a small risk increase of the disorders above and beyond parental history influence. This three-generation study further highlights the need for family-based interventional programs in families affected by mental disorders. PLAIN LANGUAGE SUMMARY TITLE: The Intergenerational Transfer of Mental Illnesses.

ObjectivesBoth genetics and environmental factors, such as poverty, maltreatment and parental education, have a role in the development of mental illnesses. Some genetic and environmental risk factors for mental illnesses are shared within families. We conducted a large study to test the extent to which mental illnesses are passed down through generations.MethodsThis study used healthcare data from Manitoba, Canada captured during the delivery of healthcare services for administrative purposes. These data included all adults from 1977 to 2020 who had at least one parent and one grandparent with linked data. Mental illnesses were diagnosed in individuals, parents and grandparents by doctors during hospitalizations or physician visits. The illnesses included mood and anxiety, substance use, and psychotic illnesses. We estimated the likelihood of developing a mental illness when parents and/or grandparents had a mental illness as well.ResultsThe study included 109,359 individuals; a third developed a mental illness during the study period. The majority had a history of a mental illness in a parent or grandparent. We found that a history of mental illness in a mother and father increased the chance of developing the illness. Psychotic illnesses had the strongest relation with parental history. In particular, having a father with a psychotic illness increased the chance of developing the illness by four times. The likelihood of developing a mental illness was higher if a grandparent had a mental illness, above and beyond parental history influence, particularly for substance use disorders.ConclusionsHaving a parent or grandparent with a mental illness increases an individual's chance of developing a mental illness. Family-based intervention programs are needed to support families affected by mental illnesses in coping with their heavy burden.

Innovative Approaches to Menstruation and Fertility Tracking Using Wearable Reproductive Health Technology: Systematic Review.

BACKGROUND: Emerging digital health technology has moved into the reproductive health market for female individuals. In the past, mobile health apps have been used to monitor the menstrual cycle using manual entry. New technological trends involve the use of wearable devices to track fertility by assessing physiological changes such as temperature, heart rate, and respiratory rate. OBJECTIVE: The primary aims of this study are to review the types of wearables that have been developed and evaluated for menstrual cycle tracking and to examine whether they may detect changes in the menstrual cycle in female individuals. Another aim is to review whether these devices are effective for tracking various stages in the menstrual cycle including ovulation and menstruation. Finally, the secondary aim is to assess whether the studies have validated their findings by reporting accuracy and sensitivity. METHODS: A review of PubMed or MEDLINE was undertaken to evaluate wearable devices for their effectiveness in predicting fertility and differentiating between the different stages of the menstrual cycle. RESULTS: Fertility cycle-tracking wearables include devices that can be worn on the wrists, on the fingers, intravaginally, and inside the ear. Wearable devices hold promise for predicting different stages of the menstrual cycle including the fertile window and may be used by female individuals as part of their reproductive health. Most devices had high accuracy for detecting fertility and were able to differentiate between the luteal phase (early and late), fertile window, and menstruation by assessing changes in heart rate, heart rate variability, temperature, and respiratory rate. CONCLUSIONS: More research is needed to evaluate consumer perspectives on reproductive technology for monitoring fertility, and ethical issues around the privacy of digital data need to be addressed. Additionally, there is also a need for more studies to validate and confirm this research, given its scarcity, especially in relation to changes in respiratory rate as a proxy for reproductive cycle staging.

Prevalence, causes, and impact of non-visualized pregnancy losses in a recurrent pregnancy loss population.

STUDY QUESTION: Does the occurrence of non-visualized pregnancy loss (NVPL) affect future reproductive outcomes in patients with recurrent pregnancy loss (RPL)? SUMMARY ANSWER: The number of previous NVPLs is a significant predictor of subsequent live birth in patients with RPL. WHAT IS KNOWN ALREADY: The number of preceding miscarriages is a strong indicator for future reproductive outcomes. However, NVPL particularly has been sparsely addressed in previous literature. STUDY DESIGN, SIZE, DURATION: We performed a retrospective cohort study of 1981 patients attending a specialized recurrent pregnancy loss clinic (RPL) from January 2012 to March 2021. A total of 1859 patients met the inclusion criteria of the study and were included in the analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients with a history of RPL, defined as ≥2 pregnancy losses before 20 weeks gestation, who attended a specialized RPL clinic in a tertiary care center were included. Patients' evaluation included parental karyotyping, antiphospholipid antibodies screening, uterine cavity assessment with hysterosalpingography (HSG) or hysteroscopy, maternal thyroid stimulating hormone (TSH) testing, and serum hemoglobin A1C testing. Other investigations were performed only when indicated such as testing for inherited thrombophilias, serum prolactin, oral glucose tolerance test, and endometrial biopsy. Patients were divided into three groups; patients who experienced NVPLs only (pure NVPLs group), patients with only visualized pregnancy losses (pure VPLs group), and patients with history of both NVPLs and VPLs (mixed group). Statistical analysis was performed using Wilcoxon rank-sum tests for continuous variables and Fisher's exact tests for categorical variables. Significance was detected when P values <0.05. A logistic regression model was used to determine the impact of NVPLs and VPLs numbers on any live birth subsequent to the initial RPL clinic visit. MAIN RESULTS AND THE ROLE OF CHANCE: The prevalence of patients with pure NVPLs, pure VPLs, and mixed losses was 14.7% (274/1859), 31.8% (591/1859), and 53.5% (994/1859), respectively. The prevalence of acquired and congenital uterine anomalies diagnosed by HSG or hysteroscopy was significantly different between pure NVPLs, pure VPLs, and mixed groups (16.8% versus 23.7% versus. 20.7%, respectively P = 0.05). There were no significant differences in the results of other RPL investigations or baseline demographics between the three groups. A logistic regression model controlling for maternal age at the initial RPL clinic visit and the follow-up duration showed that the numbers of NVPLs (odds ratio (OR): 0.77, CI: 0.68-0.88) and VPLs (OR: 0.75, CI: 0.64-0.86) are strong predictors for subsequent live births after the initial RPL clinic visit (P < 0.001). The odds of having a live birth decreased by 23% and 25% with each additional NVPL and VPL, respectively. LIMITATIONS, REASONS FOR CAUTION: This study may be limited by its retrospective design. Some of our data, including home pregnancy tests and obstetric history, are based on patient self-reporting, which could have overstated the true prevalence of NVPLs. Another limitation is the lack of available live birth data for all patients at the time of the analysis. WIDER IMPLICATIONS OF THE FINDINGS: To our knowledge, this is the first study to examine and analyze the reproductive outcomes of patients with pure NVPLs in a substantial cohort of patients with RPL. NVPLs seem to affect future live births the same way as clinical miscarriages, which supports their inclusion in RPL definitions. STUDY FUNDING/COMPETING INTEREST(S): This study was supported in part by Canadian Institute Heath Grant (CIHR): Reference Number/W11-179912 and Women's Health Research Institute (WHRI), Vancouver, BC, Canada. M.A.B: Research grants from Canadian Institute for Health Research (CIHR) and Ferring Pharmaceutical. M.A.B. is on the advisory board for AbbVie and Baxter. TRIAL REGISTRATION NUMBER: N/A.

The Use of Audio Signals for Detecting COVID-19: A Systematic Review.

A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%.

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals.

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

Hyperoxia Reduces Oxygen Consumption in Children with Pulmonary Hypertension.

High inspired oxygen concentration (FiO2 > 0.85) is administered to test pulmonary vascular reactivity in children with pulmonary hypertension (PH). It is difficult to measure oxygen consumption (VO2) if the subject is breathing a hyperoxic gas mixture so the assumption is made that baseline VO2 does not change. We hypothesized that hyperoxia changes VO2. We sought to compare the VO2 measured by a thermodilution catheter in room air and hyperoxia. A retrospective review of the hemodynamic data obtained in children with PH who underwent cardiac catheterization was conducted between 2009 and 2014. Cardiac index (CI) was measured by a thermodilution catheter in room air and hyperoxia. VO2 was calculated using the equation CI = VO2/arterial-venous oxygen content difference. Data were available in 24 subjects (males = 10), with median age 8.3 years (0.8-17.6 years), weight 23.3 kg (7.5-95 kg), and body surface area 0.9 m2 (0.4-2.0 m2). In hyperoxia compared with room air, we measured decreased VO2 (154 ± 38 to 136 ± 34 ml/min/m2, p = 0.007), heart rate (91 [Formula: see text] 20 to 83 [Formula: see text] 21 beats/minute, p=0.005), mean pulmonary artery pressure (41 [Formula: see text] 16 to 35 [Formula: see text] 14 mmHg, p=0.024), CI (3.6 [Formula: see text] 0.8 to 3.3 [Formula: see text] 0.9 L/min/m2, p = 0.03), pulmonary vascular resistance (9 [Formula: see text] 6 to 7 [Formula: see text] 3 WU m2, p = 0.029), increased mean aortic (61 [Formula: see text] 11 to 67 [Formula: see text] 11 mmHg, p = 0.005), pulmonary artery wedge pressures (11 [Formula: see text] 8 to 13 [Formula: see text] 9 mmHg, p = 0.006), and systemic vascular resistance (12 [Formula: see text] 6 to 20 [Formula: see text] 7 WU m2, p=0.001). Hyperoxia decreased VO2 and CI and caused pulmonary vasodilation and systemic vasoconstriction in children with PH. The assumption that VO2 remains unchanged in hyperoxia may be incorrect and, if the Fick equation is used, may lead to an overestimation of pulmonary blood flow and underestimation of PVRI.

Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves.

BACKGROUND: There are limited studies on the automatic detection of T waves in arrhythmic electrocardiogram (ECG) signals. This is perhaps because there is no available arrhythmia dataset with annotated T waves. There is a growing need to develop numerically-efficient algorithms that can accommodate the new trend of battery-driven ECG devices. Moreover, there is also a need to analyze long-term recorded signals in a reliable and time-efficient manner, therefore improving the diagnostic ability of mobile devices and point-of-care technologies. METHODS: Here, the T wave annotation of the well-known MIT-BIH arrhythmia database is discussed and provided. Moreover, a simple fast method for detecting T waves is introduced. A typical T wave detection method has been reduced to a basic approach consisting of two moving averages and dynamic thresholds. The dynamic thresholds were calibrated using four clinically known types of sinus node response to atrial premature depolarization (compensation, reset, interpolation, and reentry). RESULTS: The determination of T wave peaks is performed and the proposed algorithm is evaluated on two well-known databases, the QT and MIT-BIH Arrhythmia databases. The detector obtained a sensitivity of 97.14% and a positive predictivity of 99.29% over the first lead of the validation databases (total of 221,186 beats). CONCLUSIONS: We present a simple yet very reliable T wave detection algorithm that can be potentially implemented on mobile battery-driven devices. In contrast to complex methods, it can be easily implemented in a digital filter design.

On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress.

There are a limited number of studies on heat stress dynamics during exercise using the photoplethysmogram (PPG) and its second derivative (APG). However, we investigate the most suitable index from short PPG signal recordings for heat stress assessment. The APG waveform consists of a, b, c and d waves in systole and an e wave in diastole. Our preliminary results indicate that the use of the energy of aa area, derived from PPG signals measured from emergency responders in tropical conditions, is promising in determining the heat stress level using 20-s recordings. After examining 14 time domain features using leave-one-out cross-validation, we found that the aa energy extracted from PPG signals is the most informative feature for classifying heat-stressed subjects, with an overall accuracy of 79%. Moreover, the combination of the aa energy with the traditional Sensors 2015, 15 24717 heart rate variability index of heat stress (i.e., the square root of the mean of the squares of the successive aa intervals) improved the heat stress detection to an overall accuracy of 83%.

Arm movement speed assessment via a Kinect camera: a preliminary study in healthy subjects.

BACKGROUND: Many clinical studies have shown that the arm movement of patients with neurological injury is often slow. In this paper, the speed of arm movements in healthy subjects is evaluated in order to validate the efficacy of using a Kinect camera for automated analysis. The consideration of arm movement appears trivial at first glance, but in reality it is a very complex neural and biomechanical process that can potentially be used for detecting neurological disorders. METHODS: We recorded hand movements using a Kinect camera from 27 healthy subjects (21 males) with a mean age of 29 years undergoing three different arbitrary arm movement speeds: fast, medium, and slow. RESULTS: Our developed algorithm is able to classify the three arbitrary speed classes with an overall error of 5.43% for interclass speed classification and 0.49% for intraclass classification. CONCLUSIONS: This is the first step toward laying the foundation for future studies that investigate abnormality in arm movement via use of a Kinect camera.

Detection of a and b waves in the acceleration photoplethysmogram.

BACKGROUND: Analyzing acceleration photoplethysmogram (APG) signals measured after exercise is challenging. In this paper, a novel algorithm that can detect a waves and consequently b waves under these conditions is proposed. Accurate a and b wave detection is an important first step for the assessment of arterial stiffness and other cardiovascular parameters. METHODS: Nine algorithms based on fixed thresholding are compared, and a new algorithm is introduced to improve the detection rate using a testing set of heat stressed APG signals containing a total of 1,540 heart beats. RESULTS: The new a detection algorithm demonstrates the highest overall detection accuracy--99.78% sensitivity, 100% positive predictivity--over signals that suffer from 1) non-stationary effects, 2) irregular heartbeats, and 3) low amplitude waves. In addition, the proposed b detection algorithm achieved an overall sensitivity of 99.78% and a positive predictivity of 99.95%. CONCLUSIONS: The proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination.

Identifying unique spectral fingerprints in cough sounds for diagnosing respiratory ailments.

Coughing, a prevalent symptom of many illnesses, including COVID-19, has led researchers to explore the potential of cough sound signals for cost-effective disease diagnosis. Traditional diagnostic methods, which can be expensive and require specialized personnel, contrast with the more accessible smartphone analysis of coughs. Typically, coughs are classified as wet or dry based on their phase duration. However, the utilization of acoustic analysis for diagnostic purposes is not widespread. Our study examined cough sounds from 1183 COVID-19-positive patients and compared them with 341 non-COVID-19 cough samples, as well as analyzing distinctions between pneumonia and asthma-related coughs. After rigorous optimization across frequency ranges, specific frequency bands were found to correlate with each respiratory ailment. Statistical separability tests validated these findings, and machine learning algorithms, including linear discriminant analysis and k-nearest neighbors classifiers, were employed to confirm the presence of distinct frequency bands in the cough signal power spectrum associated with particular diseases. The identification of these acoustic signatures in cough sounds holds the potential to transform the classification and diagnosis of respiratory diseases, offering an affordable and widely accessible healthcare tool.

Evaluating reliability in wearable devices for sleep staging.

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

Users' Acceptability and Perceived Efficacy of mHealth for Opioid Use Disorder: Scoping Review.

BACKGROUND: The opioid crisis continues to pose significant challenges to global public health, necessitating the development of novel interventions to support individuals in managing their substance use and preventing overdose-related deaths. Mobile health (mHealth), as a promising platform for addressing opioid use disorder, requires a comprehensive understanding of user perspectives to minimize barriers to care and optimize the benefits of mHealth interventions. OBJECTIVE: This study aims to synthesize qualitative insights into opioid users' acceptability and perceived efficacy of mHealth and wearable technologies for opioid use disorder. METHODS: A scoping review of PubMed (MEDLINE) and Google Scholar databases was conducted to identify research on opioid user perspectives concerning mHealth-assisted interventions, including wearable sensors, SMS text messaging, and app-based technology. RESULTS: Overall, users demonstrate a high willingness to engage with mHealth interventions to prevent overdose-related deaths and manage opioid use. Users perceive mHealth as an opportunity to access care and desire the involvement of trusted health care professionals in these technologies. User comfort with wearing opioid sensors emerged as a significant factor. Personally tailored content, social support, and encouragement are preferred by users. Privacy concerns and limited access to technology pose barriers to care. CONCLUSIONS: To maximize benefits and minimize risks for users, it is crucial to implement robust privacy measures, provide comprehensive user training, integrate behavior change techniques, offer professional and peer support, deliver tailored messages, incorporate behavior change theories, assess readiness for change, design stigma-reducing apps, use visual elements, and conduct user-focused research for effective opioid management in mHealth interventions. mHealth demonstrates considerable potential as a tool for addressing opioid use disorder and preventing overdose-related deaths, given the high acceptability and perceived benefits reported by users.

A machine learning-based approach for constructing remote photoplethysmogram signals from video cameras.

BACKGROUND: Advancements in health monitoring technologies are increasingly relying on capturing heart signals from video, a method known as remote photoplethysmography (rPPG). This study aims to enhance the accuracy of rPPG signals using a novel computer technique. METHODS: We developed a machine-learning model to improve the clarity and accuracy of rPPG signals by comparing them with traditional photoplethysmogram (PPG) signals from sensors. The model was evaluated across various datasets and under different conditions, such as rest and movement. Evaluation metrics, including dynamic time warping (to assess timing alignment between rPPG and PPG) and correlation coefficients (to measure the linear association between rPPG and PPG), provided a robust framework for validating the effectiveness of our model in capturing and replicating physiological signals from videos accurately. RESULTS: Our method showed significant improvements in the accuracy of heart signals captured from video, as evidenced by dynamic time warping and correlation coefficients. The model performed exceptionally well, demonstrating its effectiveness in achieving accuracy comparable to direct-contact heart signal measurements. CONCLUSIONS: This study introduces a novel and effective machine-learning approach for improving the detection of heart signals from video. The results demonstrate the flexibility of our method across various scenarios and its potential to enhance the accuracy of health monitoring applications, making it a promising tool for remote healthcare.

This research explores a new way to monitor health using video, which is less invasive than traditional methods that require direct skin contact. We developed a computer program that improves the accuracy of heart signals captured from video. This is done by comparing these video-based signals with standard clinical signals from physical sensors on the skin. Our findings show that this new method can match the accuracy of conventional clinical methods, enhancing the reliability of non-contact health monitoring. This advancement could make health monitoring more accessible and comfortable, offering a potential for doctors to track patient health remotely, making everyday medical assessments easier and less intrusive.

Recommendations for evaluating photoplethysmography-based algorithms for blood pressure assessment.

Photoplethysmography (PPG) is a non-invasive optical technique that measures changes in blood volume in the microvascular tissue bed of the body. While it shows potential as a clinical tool for blood pressure (BP) assessment and hypertension management, several sources of error can affect its performance. One such source is the PPG-based algorithm, which can lead to measurement bias and inaccuracy. Here, we review seven widely used measures to assess PPG-based algorithm performance and recommend implementing standardized error evaluation steps in their development. This standardization can reduce bias and improve the reliability and accuracy of PPG-based BP estimation, leading to better health outcomes for patients managing hypertension.

GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation.

Remote photoplethysmography (rPPG) is a promising contactless technology that uses videos of faces to extract health parameters, such as heart rate. Several methods for transforming red, green, and blue (RGB) video signals into rPPG signals have been introduced in the existing literature. The RGB signals represent variations in the reflected luminance from the skin surface of an individual over a given period of time. These methods attempt to find the best combination of color channels to reconstruct an rPPG signal. Usually, rPPG methods use a combination of prepossessed color channels to convert the three RGB signals to one rPPG signal that is most influenced by blood volume changes. This study examined simple yet effective methods to convert the RGB to rPPG, relying only on RGB signals without applying complex mathematical models or machine learning algorithms. A new method, GRGB rPPG, was proposed that outperformed most machine-learning-based rPPG methods and was robust to indoor lighting and participant motion. Moreover, the proposed method estimated the heart rate better than well-established rPPG methods. This paper also discusses the results and provides recommendations for further research.

Conversational Agents and Avatars for Cardiometabolic Risk Factors and Lifestyle-Related Behaviors: Scoping Review.

BACKGROUND: In recent years, there has been a rise in the use of conversational agents for lifestyle medicine, in particular for weight-related behaviors and cardiometabolic risk factors. Little is known about the effectiveness and acceptability of and engagement with conversational and virtual agents as well as the applicability of these agents for metabolic syndrome risk factors such as an unhealthy dietary intake, physical inactivity, diabetes, and hypertension. OBJECTIVE: This review aimed to get a greater understanding of the virtual agents that have been developed for cardiometabolic risk factors and to review their effectiveness. METHODS: A systematic review of PubMed and MEDLINE was conducted to review conversational agents for cardiometabolic risk factors, including chatbots and embodied avatars. RESULTS: A total of 50 studies were identified. Overall, chatbots and avatars appear to have the potential to improve weight-related behaviors such as dietary intake and physical activity. There were limited studies on hypertension and diabetes. Patients seemed interested in using chatbots and avatars for modifying cardiometabolic risk factors, and adherence was acceptable across the studies, except for studies of virtual agents for diabetes. However, there is a need for randomized controlled trials to confirm this finding. As there were only a few clinical trials, more research is needed to confirm whether conversational coaches may assist with cardiovascular disease and diabetes, and physical activity. CONCLUSIONS: Conversational coaches may regulate cardiometabolic risk factors; however, quality trials are needed to expand the evidence base. A future chatbot could be tailored to metabolic syndrome specifically, targeting all the areas covered in the literature, which would be novel.

The Use of Photoplethysmography in the Assessment of Mental Health: Scoping Review.

BACKGROUND: With the rise in mental health problems globally, mobile health provides opportunities for timely medical care and accessibility. One emerging area of mobile health involves the use of photoplethysmography (PPG) to assess and monitor mental health. OBJECTIVE: In recent years, there has been an increase in the use of PPG-based technology for mental health. Therefore, we conducted a review to understand how PPG has been evaluated to assess a range of mental health and psychological problems, including stress, depression, and anxiety. METHODS: A scoping review was performed using PubMed and Google Scholar databases. RESULTS: A total of 24 papers met the inclusion criteria and were included in this review. We identified studies that assessed mental health via PPG using finger- and face-based methods as well as smartphone-based methods. There was variation in study quality. PPG holds promise as a potential complementary technology for detecting changes in mental health, including depression and anxiety. However, rigorous validation is needed in diverse clinical populations to advance PPG technology in tackling mental health problems. CONCLUSIONS: PPG holds promise for assessing mental health problems; however, more research is required before it can be widely recommended for clinical use.

The use of deep learning for smartphone-based human activity recognition.

The emerging field of digital phenotyping leverages the numerous sensors embedded in a smartphone to better understand its user's current psychological state and behavior, enabling improved health support systems for patients. As part of this work, a common task is to use the smartphone accelerometer to automatically recognize or classify the behavior of the user, known as human activity recognition (HAR). In this article, we present a deep learning method using the Resnet architecture to implement HAR using the popular UniMiB-SHAR public dataset, containing 11,771 measurement segments from 30 users ranging in age between 18 and 60 years. We present a unified deep learning approach based on a Resnet architecture that consistently exceeds the state-of-the-art accuracy and F1-score across all classification tasks and evaluation methods mentioned in the literature. The most notable increase we disclose regards the leave-one-subject-out evaluation, known as the most rigorous evaluation method, where we push the state-of-the-art accuracy from 78.24 to 80.09% and the F1-score from 78.40 to 79.36%. For such results, we resorted to deep learning techniques, such as hyper-parameter tuning, label smoothing, and dropout, which helped regularize the Resnet training and reduced overfitting. We discuss how our approach could easily be adapted to perform HAR in real-time and discuss future research directions.

An Open-Source Graphical User Interface-Embedded Automated Electrocardiogram Quality Assessment: A Balanced Class Representation Approach.

The rise in cardiovascular diseases necessitates accurate electrocardiogram (ECG) diagnostics, making high-quality ECG recordings essential. Our CNN-LSTM model, embedded in an open-access GUI and trained on balanced datasets collected in clinical settings, excels in automating ECG quality assessment. When tested across three datasets featuring varying ratios of acceptable to unacceptable ECG signals, it achieved an F1 score ranging from 95.87% to 98.40%. Training the model on real noise sources significantly enhances its applicability in real-life scenarios, compared to simulations. Integrated into a user-friendly toolbox, the model offers practical utility in clinical environments. Furthermore, our study underscores the importance of balanced class representation during training and testing phases. We observed a notable F1 score change from 98.09% to 95.87% when the class ratio shifted from 85:15 to 50:50 in the same testing dataset with equal representation. This finding is crucial for future ECG quality assessment research, highlighting the impact of class distribution on the reliability of model training outcomes.