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Objective: To assess the prevalence of psychoactive substance use disorders (PSUDs) among a large, unselected group of seriously injured trauma center patients, using a standardized diagnostic interview and criteria.Design: Prevalence study.Setting: A level I regional trauma center.Patients: Trauma center patients fulfilling the following criteria were eligible subjects: aged 18 years or older, admission from injury scene, length of stay of 2 days or longer, and intact cognition.Outcome Measures: The PSUDs were diagnosed using the Structured Clinical Interview (SCID) for the Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition (DSM-III-R) and were categorized as abuse or dependence and past or current (within past 6 months). The SCID results were analyzed with respect to demographic factors, injury type, and blood alcohol concentration and urine toxicology results, using chi2 and logistic regression techniques.Results: Of the 1220 patients approached for study, 1118 (91.6%) consented. More than half (54.2%) had a diagnosis of a PSUD in their lifetime. Approximately 90% of alcohol and other drug use diagnoses were for dependence and more than 62% were current. Overall, 24.1% of patients were currently alcohol dependent (men, 27.7%; women, 14.7%; P<.001), and 17.7% were currently dependent on other drugs (men, 20.2%; women, 11.2%; P<.001). Current alcohol dependence rates were not associated with race; rates of dependence on other drugs were higher among nonwhites and victims classified with intentional injuries. While 54.3% of blood alcohol-positive patients were currently alcohol dependent and 38.7% of patients with positive urine screening test results for drugs other than alcohol and nicotine were currently drug dependent, 11.7% of blood alcohol-negative and 3.9% of drug-negative patients, respectively, had current diagnoses of dependence on psychoactive substances.Conclusions: A high percentage of seriously injured trauma center patients are at risk of having current PSUDs. Patients with positive toxicology screening test results and/or positive screening questionnaire responses should be referred for formal evaluation and treatment. [ABSTRACT FROM AUTHOR]

Alcohol withdrawal is a serious complication of heavy alcohol use and a condition requiring patient stabilization before initiating surgery or implementing lifesaving procedures for injury. Intravenous ethanol (IVE) is used to prevent withdrawal during these maneuvers. This report explores the use and potential problems of this practice in an academic urban medical center. This study was undertaken to improve the treatment of IVE recipients in an urban, academic health system providing trauma, surgery, and general inpatient services. All 68 patients, identified by a review of the pharmacy database for the period August 1993 through January 1994, received IVE during their stay. A priori outcome measures related to the course of therapy in the selected cases. Of all patients studied, 67.6% were admitted for alcohol-related trauma; 61.8% of IVE recipients had no documented risk factors for delirium tremens (59.5% of these were oriented); 17.6% were discharged on the same day the drip was discontinued; only 17.6% were referred to the alcohol consult team; and, throughout the course of therapy in all cases, no blood alcohol level (BAL) determinations were recorded in patients' records. The use of IVE is associated with potentially serious clinical concerns. We found a high prevalence of alcohol-related admissions, inconsistent IVE administration, and a low rate of alcohol consult requests. Guidelines to improve the selection, management, and disposition of IVE recipients are suggested. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest is a time-sensitive emergency that requires prompt identification and intervention: sudden, unwitnessed cardiac arrest is nearly unsurvivable 1-3 . A cardinal sign of cardiac arrest is sudden loss of pulse 4 . Automated biosensor detection of unwitnessed cardiac arrest, and dispatch of medical assistance, may improve survivability given the significant prognostic role of time 3,5 , but only if the false positive burden on public emergency medical systems is minimized 5-7 . Here we show that a multimodal, machine learning-based algorithm on a smartwatch can reach performance thresholds making it deployable at societal scale. First, using photoplethysmography (PPG), we show that wearable PPG measurements of peripheral pulselessness (induced via an arterial occlusion model) manifest similarly to pulselessness caused by a common cardiac arrest arrhythmia, ventricular fibrillation (VF). Based on the similarity of the PPG signal (from VF or arterial occlusion), we developed and validated a loss of pulse detection algorithm using data from peripheral pulselessness and free-living conditions. Once developed, we evaluated the end-to-end algorithm prospectively: there was 1 unintentional emergency call per 21.67 user-years across two prospective studies; the sensitivity was 67.23% (95% confidence interval, 64.32%-70.05%) in a prospective arterial occlusion cardiac arrest simulation model. These results suggest a new opportunity, deployable at scale, for wearable-based detection of sudden loss of pulse while minimizing societal costs of excess false detections 7 ., (© 2025. The Author(s), under exclusive licence to Springer Nature Limited.)

There is an acute need for solutions to treat stress and trauma-related sequelae, and there are well-documented shortages of qualified human professionals. Artificial intelligence (AI) presents an opportunity to create advanced screening, diagnosis, and treatment solutions that relieve the burden on people and can provide just-in-time interventions. Large language models (LLMs), in particular, are promising given the role language plays in understanding and treating traumatic stress and other mental health conditions. In this article, we provide an overview of the state-of-the-art LLMs applications in diagnostic assessments, clinical note generation, and therapeutic support. We discuss the open research direction and challenges that need to be overcome to realize the full potential of deploying language models for use in clinical contexts. We highlight the need for increased representation in AI systems to ensure there are no disparities in access. Public datasets and models will help lead progress toward better models; however, privacy-preserving model training will be necessary for protecting patient data., (© 2024 International Society for Traumatic Stress Studies.)

The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis., (© 2024. The Author(s).)

Background: The impact of digital device use on health and well-being is a pressing question. However, the scientific literature on this topic, to date, is marred by small and unrepresentative samples, poor measurement of core constructs, and a limited ability to address the psychological and behavioral mechanisms that may underlie the relationships between device use and well-being. Recent authoritative reviews have made urgent calls for future research projects to address these limitations. The critical role of research is to identify which patterns of use are associated with benefits versus risks and who is more vulnerable to harmful versus beneficial outcomes, so that we can pursue evidence-based product design, education, and regulation aimed at maximizing benefits and minimizing the risks of smartphones and other digital devices., Objective: The objective of this study is to provide normative data on objective patterns of smartphone use. We aim to (1) identify how patterns of smartphone use impact well-being and identify groups of individuals who show similar patterns of covariation between smartphone use and well-being measures across time; (2) examine sociodemographic and personality or mental health predictors and which patterns of smartphone use and well-being are associated with pre-post changes in mental health and functioning; (3) discover which nondevice behavior patterns mediate the association between device use and well-being; (4) identify and explore recruitment strategies to increase and improve the representation of traditionally underrepresented populations; and (5) provide a real-world baseline of observed stress, mood, insomnia, physical activity, and sleep across a representative population., Methods: This is a prospective, nonrandomized study to investigate the patterns and relationships among digital device use, sensor-based measures (including both behavioral and physiological signals), and self-reported measures of mental health and well-being. The study duration is 4 weeks per participant and includes passive sensing based on smartphone sensors, and optionally a wearable (Fitbit), for the complete study period. The smartphone device will provide activity, location, phone unlocks and app usage, and battery status information., Results: At the time of submission, the study infrastructure and app have been designed and built, the institutional review board of the University of Oregon has approved the study protocol, and data collection is underway. Data from 4182 enrolled and consented participants have been collected as of March 27, 2023. We have made many efforts to sample a study population that matches the general population, and the demographic breakdown we have been able to achieve, to date, is not a perfect match., Conclusions: The impact of digital devices on mental health and well-being raises important questions. The Digital Well-Being Study is designed to help answer questions about the association between patterns of smartphone use and well-being., International Registered Report Identifier (irrid): DERR1-10.2196/49189., (©Daniel McDuff, Andrew Barakat, Ari Winbush, Allen Jiang, Felicia Cordeiro, Ryann Crowley, Lauren E Kahn, John Hernandez, Nicholas B Allen. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 14.05.2024.)

Objective: We explore the relationships between objective communication patterns displayed during virtual team meetings and established, qualitative measures of team member effectiveness., Background: A key component of teamwork is communication. Automated measures of objective communication patterns are becoming more feasible and offer the ability to measure and monitor communication in a scalable, consistent and continuous manner. However, their validity in reflecting meaningful measures of teamwork processes are not well established, especially in real-world settings., Method: We studied real-world virtual student teams working on semester-long projects. We captured virtual team meetings using the Zoom video conferencing platform throughout the semester and periodic surveys comprising peer ratings of team member effectiveness. Leveraging audio transcripts, we examined relationships between objective measures of speaking time, silence gap duration and vocal turn-taking and peer ratings of team member effectiveness., Results: Speaking time, speaking turn count, degree centrality and (marginally) speaking turn duration, but not silence gap duration, were positively related to individual-level team member effectiveness. Time in dyadic interactions and interaction count, but not interaction length, were positively related to dyad-level team member effectiveness., Conclusion: Our study highlights the relevance of objective measures of speaking time and vocal turn-taking to team member effectiveness in virtual project-based teams, supporting the validity of these objective measures and their use in future research., Application: Our approach offers a scalable, easy-to-use method for measuring communication patterns and team member effectiveness in virtual teams and opens the opportunity to study these patterns in a more continuous and dynamic manner.

Aims: Utilizing Ruthven's (2022) transition model, we explored how identity, voluntariness, and reason for retirement are related to subjective wellbeing throughout an athlete's retirement., Methods: 541 participants completed an anonymous, online survey and estimated their wellbeing starting before retirement and up to the present. A set of linear mixed models regressed the seven wellbeing outcomes on the three measures, with education, gender, and time since retirement as covariates., Results: Identity: Athletic Identity wellbeing was significantly and consistently lower than Diversified Identity. Voluntariness: Surprise retirement was significantly more difficult and contributed to a sharper decline on the day of transition. Reason for retirement: Pursue Something Else and Right Time experienced no significant wellbeing changes, while Injury had a significant decline on their transition day. Motivation Loss experienced significantly lower wellbeing prior to retirement, and a significant, gradual rise on the day of and throughout their transition., Conclusions: There are wellbeing benefits for a diverse identity, voluntary retirement, and retiring due to pursuing something else, or feeling ready to retire. At risk groups include retiring due to injury, low motivation, and loss of eligibility/graduation. Findings support the benefit of utilizing a theoretical model to explain elite athlete outcomes.

Electrodermal activity (EDA) is considered a standard marker of sympathetic activity. However, traditional EDA measurement requires electrodes in steady contact with the skin. Can sympathetic arousal be measured using only an optical sensor, such as an RGB camera? This paper presents a novel approach to infer sympathetic arousal by measuring the peripheral blood flow on the face or hand optically. We contribute a self-recorded dataset of 21 participants, comprising synchronized videos of participants' faces and palms and gold-standard EDA and photoplethysmography (PPG) signals. Our results show that we can measure peripheral sympathetic responses that closely correlate with the ground truth EDA. We obtain median correlations of 0.57 to 0.63 between our inferred signals and the ground truth EDA using only videos of the participants' palms or foreheads or PPG signals from the foreheads or fingers. We also show that sympathetic arousal is best inferred from the forehead, finger, or palm., Competing Interests: The authors declare no conflicts of interest., (© 2023 Optica Publishing Group.)

In 2019, the International Olympic Committee (IOC) published a consensus statement outlining the principles for recording and reporting injury and illness in elite sport. The authors encouraged sport federations to adapt the framework to their sport-specific context. Since this publication, several sports have published extensions to the IOC consensus statement.In response to a paucity of epidemiological data on athlete mental health, the IOC mental health working group adapted the IOC consensus statement on injury and illness surveillance to improve the capturing of athlete mental health data. In addition to the members of the working group, other experts and athlete representatives joined the project team to address gaps in expertise, and to add stakeholder perspective, respectively. Following an in-person meeting, the authors worked remotely, applying the scientific literature on athlete mental health to the IOC injury and illness surveillance framework. A virtual meeting was held to reach consensus on final recommendations.Practical outcomes based on the analysis of the scientific literature are provided with respect to surveillance design, data collection and storage, data analysis and reporting of athlete mental health data. Mental health-specific report forms for athlete and health professional utilisation are included for both longitudinal and event-specific surveillance.Ultimately, this publication should encourage the standardisation of surveillance methodology for mental health symptoms and disorders among athletes, which will improve consistency in study designs, thus facilitating the pooling of data and comparison across studies. The goal is to encourage systematic surveillance of athlete mental health., Competing Interests: Competing interests: MM is a Deputy Editor of the BJSM and a member of the BJSM IPHP Editorial Board. VG is Chair of the IOC Mental Health Working Group and MM, AB, CB, AC, DM, RP and MP are members. CLR and BH are co-Chairs of the IOC Consensus Group on Mental Health in Elite Athletes. RB is the IOC Medical and Scientific Director. LE is the IOC Head of Science Activities. TS is the IOC Scientific Manager. CB is an Associate Editor of the BJSM and a member of the BJSM IPHP Editorial Board. She is a member of the IOC Medical & Scientific Commission and the IPC Medical Committee. LE is an IPHP Editor of the BJSM., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Remote photoplethysmography (rPPG) is an attractive method for noninvasive, convenient and concomitant measurement of physiological vital signals. Public benchmark datasets have served a valuable role in the development of this technology and improvements in accuracy over recent years. However, there remain gaps in the public datasets. First, despite the ubiquity of cameras on mobile devices, there are few datasets recorded specifically with mobile phone cameras. Second, most datasets are relatively small and therefore are limited in diversity, both in appearance (e.g., skin tone), behaviors (e.g., motion) and environment (e.g., lighting conditions). In an effort to help the field advance, we present the Multi-domain Mobile Video Physiology Dataset (MMPD), comprising 11 hours of recordings from mobile phones of 33 subjects. The dataset is designed to capture videos with greater representation across skin tone, body motion, and lighting conditions. MMPD is comprehensive with eight descriptive labels and can be used in conjunction with the rPPG-toolbox [1]. The reliability of the dataset is verified by mainstream unsupervised methods and neural methods. The GitHub repository of our dataset: https://github.com/THU-CS-PI/MMPD&#95;rPPG&#95;dataset.

Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show strong performance on three large benchmark datasets and improved robustness to skin type and motion. These results highlight the promise of synthetic data for training camera-based pulse measurement; however, further research and validation is needed to establish whether synthetic data alone could be sufficient for training models.

Objective: We examined the prevalence of risky alcohol and cannabis use among Brazilian varsity college athletes and whether this group had a greater likelihood of risky use than non-athletes., Methods: In 2009, Brazilian college students (n=12,711) were recruited for a national stratified random survey. Their sociodemographic characteristics, mental health, substance use, and participation in varsity sports were assessed. Binary logistic regression models were used to examine the association between varsity athlete status and moderate to high-risk alcohol and cannabis use., Results: Among varsity athletes, 67.6 and 10.7% reported risky alcohol and cannabis use, respectively. Varsity athletes had greater odds of risky alcohol consumption than non-athletes (aOR = 2.02, 95%CI 1.08-3.78). Varsity athletes also had greater odds of risky cannabis use than non-athletes in unadjusted analyses (OR = 2.57, 95%CI 1.05-6.28), although this relationship was attenuated after covariate adjustment., Conclusions: Among college students in Brazil, varsity athletes had a higher prevalence of risky alcohol and cannabis use than non-athletes. The rates were considerably higher than those observed among samples of U.S. college athletes. Future research should examine the use of these substances among varsity college athletes in other middle-income countries since these findings will likely guide prevention and treatment efforts.

Synthetic data is a powerful tool in training data hungry deep learning algorithms. However, to date, camera-based physiological sensing has not taken full advantage of these techniques. In this work, we leverage a high-fidelity synthetics pipeline for generating videos of faces with faithful blood flow and breathing patterns. We present systematic experiments showing how physiologically-grounded synthetic data can be used in training camera-based multi-parameter cardiopulmonary sensing. We provide empirical evidence that heart and breathing rate measurement accuracy increases with the number of synthetic avatars in the training set. Furthermore, training with avatars with darker skin types leads to better overall performance than training with avatars with lighter skin types. Finally, we discuss the opportunities that synthetics present in the domain of camera-based physiological sensing and limitations that need to be overcome.

Objectives: To develop an assessment and recognition tool to identify elite athletes at risk for mental health symptoms and disorders., Methods: We conducted narrative and systematic reviews about mental health symptoms and disorders in active and former elite athletes. The views of active and former elite athletes (N=360) on mental health symptoms in elite sports were retrieved through an electronic questionnaire. Our group identified the objective(s), target group(s) and approach of the mental health tools. For the assessment tool, we undertook a modified Delphi consensus process and used existing validated screening instruments. Both tools were compiled during two 2-day meeting. We also explored the appropriateness and preliminary reliability and validity of the assessment tool. SPORT MENTAL HEALTH ASSESSMENT TOOL 1 AND SPORT MENTAL HEALTH RECOGNITION TOOL 1: The International Olympic Committee Sport Mental Health Assessment Tool 1 (SMHAT-1) was developed for sports medicine physicians and other licensed/registered health professionals to assess elite athletes (defined as professional, Olympic, Paralympic or collegiate level; aged 16 years and older) potentially at risk for or already experiencing mental health symptoms and disorders. The SMHAT-1 consists of: (i) triage with an athlete-specific screening tool, (ii) six subsequent disorder-specific screening tools and (iii) a clinical assessment (and related management) by a sports medicine physician or licensed/registered mental health professional (eg, psychiatrist and psychologist). The International Olympic Committee Sport Mental Health Recognition Tool 1 (SMHRT-1) was developed for athletes and their entourage (eg, friends, fellow athletes, family and coaches)., Conclusion: The SMHAT-1 and SMHRT-1 enable that mental health symptoms and disorders in elite athletes are recognised earlier than they otherwise would. These tools should facilitate the timely referral of those athletes in need for appropriate support and treatment., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Camera-based physiological measurement enables vital signs to be captured unobtrusively without contact with the body. Remote, or imaging, photoplethysmography involves recovering peripheral blood flow from subtle variations in video pixel intensities. While the pulse signal might be easy to obtain from high quality uncompressed videos, the signal-to-noise ratio drops dramatically with video bitrate. Uncompressed videos incur large file storage and data transfer costs, making analysis, manipulation and sharing challenging. To help address these challenges, we use compression specific supervised models to mitigate the effect of temporal video compression on heart rate estimates. We perform a systematic evaluation of the performance of state-of-the-art algorithms across different levels, and formats, of compression. We demonstrate that networks trained on compressed videos consistently outperform other benchmark methods, both on stationary videos and videos with significant rigid head motions. By training on videos with the same, or higher compression factor than test videos, we achieve improvements in signal-to-noise ratio (SNR) of up to 3 dB and mean absolute error (MAE) of up to 6 beats per minute (BPM)., Competing Interests: The authors declare no conflicts of interest., (© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

Primary objective : In this review, we detail the issues and disorders athletes present with, guidelines for making diagnostic formulations and treatment goals, and strategies for delivering integrated treatment attuned to the athlete and their team and/or sport. In addition, we highlight special issues associated with athletes and their families. Reasoning : Data suggests athletes experience comprable incidence and prevalence rates to the general population of psychiatric disorders. The mental and physical demands placed on athletes are unique and may increase their susceptibility to certain mental health problems. The current literature regarding integrative interventions in athletes is sparse and largely anecdotal. We provide a summary of the scientific evidence from a literature review conducted by the IOC Consensus Committee on Mental Health in Elite Athletes, reviews and expert clinical strategies recommended by the authors of four books on sports psychiatry written since 2012, and our own review of the literature related to psychotherapy with elite and non-elite- athletes. Main outcomes and results : At the moment there are no randomized controlled trials assessing the use of psychotherapy or medication for mental illness in athletes to guide treatment. Controlled studies examining psychological interventions in collegiate athletes have demonstrated improvements in mental health symptomology. Although these studies were limited to collegiate athletes they assist in providing a foundation for elite athletes as well. For the assessment and management of the mental health needs of athletes' to be on a par with their physical needs, more high-quality scientific epidemiological and treatment intervention studies are needed. Conclusions : Development of specific models of psychiatric intervention for athletes with significant psychopathology and impairment appears to be warranted. These interventions should include individual, family/marital, and group psychotherapy with or without medication using evidence-based treatments.

Elite athletes suffer many mental health symptoms and disorders at rates equivalent to or exceeding those of the general population. COVID-19 has created new strains on elite athletes, thus potentially increasing their vulnerability to mental health symptoms. This manuscript serves as a narrative review of the impact of the pandemic on management of those symptoms in elite athletes and ensuing recommendations to guide that management. It specifically addresses psychotherapy, pharmacotherapy and higher levels of care. Within the realm of psychotherapy, crisis counselling might be indicated. Individual, couple/family and group psychotherapy modalities all may be helpful during the pandemic, with novel content and means of delivery. Regarding pharmacotherapy for mental health symptoms and disorders, some important aspects of management have changed during the pandemic, particularly for certain classes of medication including stimulants, medications for bipolar and psychotic disorders, antidepressants and medications for substance use disorders. Providers must consider when in-person management (eg, for physical examination, laboratory testing) or higher levels of care (eg, for crisis stabilisation) is necessary, despite potential risk of viral exposure during the pandemic. Management ultimately should continue to follow general principles of quality health care with some flexibility. Finally, the current pandemic provides an important opportunity for research on new methods of providing mental health care for athletes, and consideration for whether these new methods should extend beyond the pandemic., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Peripheral hemodynamics, measured via the blood volume pulse and vasomotion, provide a valuable way of monitoring physiological state. Camera imaging-based systems can be used to measure these peripheral signals without contact with the body, at distances of multiple meters. While researchers have paid attention to non-contact imaging photoplethysmography, the study of peripheral hemodynamics and the effect of autonomic nervous system activity on these signals has received less attention. Using a method, based on a tissue-like model of the skin, we extract melanin [Formula: see text] and hemoglobin [Formula: see text] concentrations from videos of the hand and face and show that significant decreases in peripheral pulse signal power (by 36% ± 29%) and vasomotion signal power (by 50% ± 26%) occur during periods of cognitive and psychological stress. Via three experiments we show that similar results are achieved across different stimuli and regions of skin (face and hand). While changes in peripheral pulse and vasomotion power were significant the changes in pulse rate variability were less consistent across subjects and tasks.

Competing Interests: Competing interests: None declared.

We propose a simple and affordable imaging technique to evaluate transcutaneously multiple physiological parameters by using a digital red-green-blue camera. In this method, the RGB -values were converted into tristimulus values in the CIE (Commission Internationale de l'Eclairage) XYZ color space, which is compatible with the common color spaces. Monte Carlo simulation for light transport in biological tissue was then performed to specify the relationship among the XYZ -values and the concentrations of oxygenated hemoglobin, deoxygenated hemoglobin, bilirubin, and melanin. The concentration of total hemoglobin and tissue oxygen saturation were also calculated from the estimated concentrations of oxygenated and deoxygenated hemoglobin. In vivo experiments with bile duct ligation in rats demonstrated that the estimated bilirubin concentration increased after ligation of the bile duct and reached around 22 mg/dl at 116 h after the onset of ligation, which corresponds to the ground truth value of bilirubin measured by a commercially available transcutaneous bilirubinometer. Experiments with rats while varying the fraction of inspired oxygen demonstrated that oxygenated hemoglobin and deoxygenated hemoglobin decreased and increased, respectively, as the fraction of inspired oxygen decreased. Consequently, tissue oxygen saturation dramatically decreased. We further extended the method to a non-contact imaging photo-plethysmograph and estimation of the percutaneous oxygen saturation. An empirical formula to estimate percutaneous oxygen saturation was derived from the pulse wave amplitudes of oxygenated and deoxygenated hemoglobin. The estimated percutaneous oxygen saturation dropped remarkably when a faction of inspired oxygen was below 19%, indicating the onset of hypoxemia due to hypoxia, whereas the tissue oxygen saturation decreased gradually according to the reduction of the faction of inspired oxygen. The results in this study indicate the potential of this method for imaging of multiple physiological parameters in skin tissue and evaluating an optical biomedical imaging technique that enables cost-effective, easy-to-use, portable, remotely administered, and/or point-of-care solutions., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)