Arash Alavi

All Publications

• Smartwatch-based detection of moderate-to-severe and high-risk obstructive sleep apnea. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Alavi, A., Costa, E., Matsumoto, M. M., Odenwald, N., Elkarra, N., Ma, Y., Taweesedt, P. T., Kawai, M., Kushida, C., Capasso, R. 2026; 22 (1)

  Abstract

  Obstructive sleep apnea (OSA) is a prevalent condition associated with long-term consequences, such as cardiovascular and neurocognitive diseases, yet many cases remain undiagnosed and therefore untreated due to the cost and limited access to traditional in-lab overnight polysomnography (PSG). The ubiquity of consumer wearable technology offers a practical way to mitigate these limitations, enabling more accessible, large-scale screening. However, the clinical validity of these devices requires further testing.In this novel prospective study, we rigorously evaluated the diagnostic performance of the Samsung Galaxy Watch for detecting moderate-to-severe OSA (apnea-hypopnea index, AHI ≥ 15 events/h) compared to in-lab PSG using the AHI, and "high-risk" OSA subjects using the PSG-derived hypoxic burden (HB) criteria. We enrolled 152 adults aged 22 years or older with a prior diagnosis of moderate-to-severe OSA or a high pre-test likelihood of having it (STOP-Bang score ≥ 3), who wore the smartwatch during two in-lab PSG nights (147 completed both nights; total 1,850 h of in-lab PSG sleep): an initial in-lab PSG night, followed by ≥ 3 watch-only at-home nights, and then a second in-lab PSG night. In addition to AHI, we evaluated Galaxy Watch performance across PSG-derived HB-defined risk strata (low- vs. high-risk OSA) as HB integrates the depth, duration, and frequency of oxygen desaturations and has been shown to better reflect cardiometabolic risk and disease severity than AHI.In our cohort, the Galaxy Watch achieved an area under the receiver operating characteristic (AUROC) curve of 0.94 (95% CI 0.889-0.980) for detecting moderate-to-severe OSA. With the default estimated AHI (eAHI) threshold of 15, the device yielded a sensitivity of 94.1% (95% CI 84.1-98%) and a specificity of 66.7% (95% CI 51-79.4%), while with the cohort-optimized eAHI threshold of 25.95, sensitivity was 82.4% (95% CI 69.7-90.4%) and specificity was 94.9% (95% CI 83.1-98.6%). Notably, within the HB defined high-risk OSA group, the Galaxy Watch achieved 100% sensitivity and 100% specificity using the default threshold, indicating robust discrimination between HB-defined low- and high-risk OSA categories.Overall, these findings highlight the substantial potential of consumer-grade wearables to accurately detect moderate-to-severe sleep apnea using both AHI- and HB-defined risk strata across controlled and real-world settings.ClinicalTrials.gov ID: NCT06603441; First posted: 2024-09-19.Obstructive sleep apnea (OSA) remains substantially underdiagnosed because definitive testing with polysomnography (PSG) is resource-intensive and not readily accessible to all patients. Although consumer smartwatches offer a promising approach for large-scale OSA screening, prospective validation against PSG and evaluation using physiologically meaningful measures such as hypoxic burden have been limited.In this prospective study of 152 adults undergoing repeated PSG and smartwatch assessment, the Galaxy Watch demonstrated high accuracy for detecting moderate-to-severe OSA and showed excellent performance for identifying individuals with high hypoxic burden, a subgroup at elevated cardiometabolic risk. These findings support the use of consumer-grade wearable technology as a scalable, accessible screening tool to improve OSA risk identification, triage, and referral for definitive diagnostic evaluation.

  View details for DOI 10.1007/s44470-026-00159-8

  View details for PubMedID 42661136

  View details for PubMedCentralID PMC13522277

• Comprehensive Evaluation of Night-to-Night Variability in PSG Metrics and AHI-Based Diagnostic Reclassification. Chest Alavi, A., Costa, E., Matsumoto, M. M., Odenwald, N., Kushida, C., Bahmani, A., Capasso, R. 2026

  Abstract

  Night-to-night variability (NtNV) in polysomnography (PSG) contributes to diagnostic uncertainty in obstructive sleep apnea (OSA), yet multi-metric evaluations using closely spaced PSG nights-particularly in moderate-to-severe disease-remain limited. The comparative stability of apnea-hypopnea index (AHI) definitions, hypoxic burden (HB), and threshold calibration remains unclear.What is the NtNV across PSG-derived metrics, and how do AHI scoring definitions and threshold calibration influence diagnostic stability in OSA?We performed a retrospective analysis of a prospective study including 147 participants with prior diagnosis or high pretest likelihood of moderate-to-severe OSA who underwent two PSGs within 10 days. NtNV was quantified across 20 PSG-derived metrics. A normalized NtNV matrix was analyzed using PCA followed by unsupervised k-means clustering to identify data-driven variability-pattern groups. Diagnostic stability was compared across AHI definitions (3%/arousal vs. 4%) and HB risk categories. Statistical calibration models derived AHI 4% thresholds aligned with AHI 3%/arousal severity cutpoints.NtNV demonstrated heterogeneity. Maximum heart rate, positional fractions, and sleep latency were most variable, whereas average SpO2, average heart rate, minimum SpO2, and HB were most stable. In the PCA followed by k-means analysis, respiratory event frequency metrics contributed most strongly and separated participants into lower- and higher-respiratory-variability pattern groups. AHI 4% showed higher classification disagreement than AHI 3%/arousal in short-interval (29.9% vs. 21.2% overall; 14.3% vs. 5.4% at the moderate-to-severe threshold) and longitudinal comparisons (45.9% vs. 31.1%; 20.9% vs. 8.2%). HB showed low inter-night disagreement (11.8%). Calibration models aligned AHI 4% thresholds of 6.1-6.9 and 18.4-22.3 events/h with AHI 3%/arousal cutpoints of 15 and 30 events/h.Positional, autonomic, and sleep architecture metrics showed the highest NtNV; respiratory event frequency metrics were intermediate and oxygenation most stable. Greater classification disagreement with AHI 4% was threshold-driven, with implications for hypopnea scoring, and payer policy in OSA diagnosis.

  View details for DOI 10.1016/j.chest.2026.06.004

  View details for PubMedID 42302984

• Achieving inclusive healthcare through integrating education and research with AI and personalized curricula. Communications medicine Bahmani, A., Cha, K., Alavi, A., Dixit, A., Ross, A., Park, R., Goncalves, F., Ma, S., Saxman, P., Nair, R., Akhavan-Sarraf, R., Zhou, X., Wang, M., Contrepois, K., Li-Pook-Than, J., Monte, E., Rodriguez, D. J., Lai, J., Babu, M., Tondar, A., Schüssler-Fiorenza Rose, S. M., Akbari, I., Zhang, X., Yegnashankaran, K., Yracheta, J., Dale, K., Miller, A. D., Edmiston, S., McGhee, E. M., Nebeker, C., Wu, J. C., Kundaje, A., Snyder, M. 2025; 5 (1): 356

  Abstract

  Precision medicine promises significant health benefits but faces challenges such as complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) such as Generative Pretrained Transformer (GPT) and Claude highlights the importance of making complex data accessible to non-specialists.We evaluated the Stanford Data Ocean (SDO) precision medicine training program's learning outcomes, AI Tutor performance, and learner satisfaction by assessing self-rated competency on key learning objectives through pre- and post-learning surveys, along with formative and summative assessment completion rates. We also analyzed AI Tutor accuracy and learners' self-reported satisfaction, and post-program academic and career impacts. Additionally, we demonstrated the capabilities of the AI Data Visualization tool.SDO demonstrates the ability to improve learning outcomes for learners from broad educational and socioeconomic backgrounds with the support of the AI Tutor. The AI Data Visualization tool enables learners to interpret multi-omics and wearable data and replicate research findings.SDO strives to mitigate challenges in precision medicine through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning. SDO provides AI Tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible to users from broad educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.

  View details for DOI 10.1038/s43856-025-01034-y

  View details for PubMedID 40819118

  View details for PubMedCentralID 9108683

• AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond NATURE METABOLISM Baxter, S. L., de Sa, V. R., Ferryman, K., Jain, P., Lee, C. S., Li-Pook-Than, J., Liu, T., Owen, J. P., Patel, B., Yu, Q., Zangwill, L. M., Bahmani, A., Chute, C. G., Edberg, J. C., Hurst, S., Ishikawa, H., Lee, A. Y., McGwin, G., McWeeney, S., Nebeker, C., Owsley, C., Singer, S. J., Adib, R., Adibuzzaman, M., Alavi, A., Ashley, C., Baer, A., Benton, E., Blazes, M., Cohen, A., Cordier, B., Crist, K., Cuddy, C., Gasimova, A., Gim, N., Hong, S., Kim, T., Lin, W., Mitchell, J., Ngadisastra, C., Patronilo, V., Shaffer, J., Soundarajan, S., Zhao, K., Drolet, C., Lucero, A., Matthies, D., Pittock, H., Watkins, K., York, B., Amankwa, C. E., Bangudi, M., Haboudal, N., Hallaj, S., Heinke, A., Huang, L., Kalaw, F. P., Karsolia, A., Khazaei, H., Mohammed, M., Simpkins, K., Wang, X. 2024

  View details for DOI 10.1038/s42255-024-01165-x

  View details for Web of Science ID 001350162600001

  View details for PubMedID 39516364

  View details for PubMedCentralID 4792175

• California Stress, Trauma, and Resilience Study (CalSTARS) protocol: A multiomics-based cross-sectional investigation and randomized controlled trial to elucidate the biology of ACEs and test a precision intervention for reducing stress and enhancing resilience. Stress (Amsterdam, Netherlands) Kim, L. Y., Schüssler-Fiorenza Rose, S. M., Mengelkoch, S., Moriarity, D. P., Gassen, J., Alley, J. C., Roos, L. G., Jiang, T., Alavi, A., Thota, D. D., Zhang, X., Perelman, D., Kodish, T., Krupnick, J. L., May, M., Bowman, K., Hua, J., Liao, Y. J., Lieberman, A. F., Butte, A. J., Lester, P., Thyne, S. M., Hilton, J. F., Snyder, M. P., Slavich, G. M. 2024; 27 (1): 2401788

  Abstract

  Adverse Childhood Experiences (ACEs) are very common and presently implicated in 9 out of 10 leading causes of death in the United States. Despite this fact, our mechanistic understanding of how ACEs impact health is limited. Moreover, interventions for reducing stress presently use a one-size-fits-all approach that involves no treatment tailoring or precision. To address these issues, we developed a combined cross-sectional study and randomized controlled trial, called the California Stress, Trauma, and Resilience Study (CalSTARS), to (a) characterize how ACEs influence multisystem biological functioning in adults with all levels of ACE burden and current perceived stress, using multiomics and other complementary approaches, and (b) test the efficacy of our new California Precision Intervention for Stress and Resilience (PRECISE) in adults with elevated perceived stress levels who have experienced the full range of ACEs. The primary trial outcome is perceived stress, and the secondary outcomes span a variety of psychological, emotional, biological, and behavioral variables, as assessed using self-report measures, wearable technologies, and extensive biospecimens (i.e. DNA, saliva, blood, urine, & stool) that will be subjected to genomic, transcriptomic, proteomic, metabolomic, lipidomic, immunomic, and metagenomic/microbiome analysis. In this protocol paper, we describe the scientific gaps motivating this study as well as the sample, study design, procedures, measures, and planned analyses. Ultimately, our goal is to leverage the power of cutting-edge tools from psychology, multiomics, precision medicine, and translational bioinformatics to identify social, molecular, and immunological processes that can be targeted to reduce stress-related disease risk and enhance biopsychosocial resilience in individuals and communities worldwide.

  View details for DOI 10.1080/10253890.2024.2401788

  View details for PubMedID 39620249

• A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19. NPJ digital medicine Shandhi, M. M., Cho, P. J., Roghanizad, A. R., Singh, K., Wang, W., Enache, O. M., Stern, A., Sbahi, R., Tatar, B., Fiscus, S., Khoo, Q. X., Kuo, Y., Lu, X., Hsieh, J., Kalodzitsa, A., Bahmani, A., Alavi, A., Ray, U., Snyder, M. P., Ginsburg, G. S., Pasquale, D. K., Woods, C. W., Shaw, R. J., Dunn, J. P. 2022; 5 (1): 130

  Abstract

  Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73±0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55±0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.

  View details for DOI 10.1038/s41746-022-00672-z

  View details for PubMedID 36050372

• Real-time alerting system for COVID-19 and other stress events using wearable data. Nature medicine Alavi, A., Bogu, G. K., Wang, M., Rangan, E. S., Brooks, A. W., Wang, Q., Higgs, E., Celli, A., Mishra, T., Metwally, A. A., Cha, K., Knowles, P., Alavi, A. A., Bhasin, R., Panchamukhi, S., Celis, D., Aditya, T., Honkala, A., Rolnik, B., Hunting, E., Dagan-Rosenfeld, O., Chauhan, A., Li, J. W., Bejikian, C., Krishnan, V., McGuire, L., Li, X., Bahmani, A., Snyder, M. P. 2021

  Abstract

  Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

  View details for DOI 10.1038/s41591-021-01593-2

  View details for PubMedID 34845389

• A scalable, secure, and interoperable platform for deep data-driven health management. Nature communications Bahmani, A., Alavi, A., Buergel, T., Upadhyayula, S., Wang, Q., Ananthakrishnan, S. K., Alavi, A., Celis, D., Gillespie, D., Young, G., Xing, Z., Nguyen, M. H., Haque, A., Mathur, A., Payne, J., Mazaheri, G., Li, J. K., Kotipalli, P., Liao, L., Bhasin, R., Cha, K., Rolnik, B., Celli, A., Dagan-Rosenfeld, O., Higgs, E., Zhou, W., Berry, C. L., Van Winkle, K. G., Contrepois, K., Ray, U., Bettinger, K., Datta, S., Li, X., Snyder, M. P. 2021; 12 (1): 5757

  Abstract

  The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

  View details for DOI 10.1038/s41467-021-26040-1

  View details for PubMedID 34599181

• Pre-symptomatic detection of COVID-19 from smartwatch data. Nature biomedical engineering Mishra, T., Wang, M., Metwally, A. A., Bogu, G. K., Brooks, A. W., Bahmani, A., Alavi, A., Celli, A., Higgs, E., Dagan-Rosenfeld, O., Fay, B., Kirkpatrick, S., Kellogg, R., Gibson, M., Wang, T., Hunting, E. M., Mamic, P., Ganz, A. B., Rolnik, B., Li, X., Snyder, M. P. 2020

  Abstract

  Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.

  View details for DOI 10.1038/s41551-020-00640-6

  View details for PubMedID 33208926