Evaluating Recovery following Closed Head Injuries: The Role of Wearable Monitors in Tracking Severity and Recovery Kinetics

Evaluating Recovery following Closed Head Injuries: The Role of Wearable Monitors in Tracking Severity and Recovery Kinetics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Recovery following Closed Head Injuries: The Role of Wearable Monitors in Tracking Severity and Recovery Kinetics Ralph J. Mobbs, Lianne Koinis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6105208/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Closed head injuries, including mild to moderate traumatic brain injuries (TBIs), present challenges in assessment due to subjective symptom reporting and individual variability in recovery. Wearable technology offers an objective approach to tracking physiological metrics, providing insights into post-injury recovery trajectories. Methods This case report examines a 26-year-old female psychologist who sustained a closed head injury after a vehicular collision while riding a scooter. A Garmin Forerunner 955 smartwatch, which she had been using prior to the injury, provided continuous tracking of heart rate variability (HRV), sleep duration, step count, and gait speed. Results Following the injury, step count dropped from 10,000 + to 1,000–2,000 per day, HRV remained suppressed, sleep duration increased from 6 to over 14 hours per night, and gait speed declined from 1.3 m/s to 0.47 m/s. While improvements were observed over three months, key activity and mobility metrics remained below baseline. Conclusion Wearable devices provided continuous, objective data that complemented traditional clinical assessments. These metrics informed rehabilitation strategies and demonstrated potential in tracking autonomic dysfunction and mobility recovery. Further research is necessary to validate wearable-derived data for clinical applications and establish standardized integration protocols. Closed Head Injury Concussion Wearables Objective Health Metrics Recovery Kinetics Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Mild to moderate closed head injuries, commonly referred to as traumatic brain injuries (TBIs), pose diagnostic and management challenges due to the subjective nature of current assessment tools and the variability in recovery. Post-traumatic symptoms such as cognitive fatigue, sleep disturbance, and autonomic dysfunction are inherently complex to quantify, frequently leading to underdiagnosis and delays in intervention ( 1 ). Recovery trajectories following TBI are highly individual, influenced by factors such as the severity of the injury, pre-existing health conditions, and the quality of rehabilitation efforts. Recent advancements in wearable technology provide a promising solution by providing continuous, non-invasive monitoring of key physiological metrics ( 2 ) such as heart rate variability (HRV), sleep patterns ( 3 ), and activity levels. These devices facilitate objective data collection, enabling more precise tracking of recovery. For instance, HRV serves as a potential biomarker of autonomic nervous system function ( 4 ), providing valuable insights into the autonomic disturbances often observed in TBIs. By integrating these metrics, wearable devices allow for a more personalized and dynamic approach to patient care, enhancing recovery monitoring and prognostication. However, significant barriers remain in incorporating wearable technology into TBI management. Consumer-grade wearables require validation against medical-grade standards to ensure data accuracy and reliability. The absence of standardized algorithms for interpreting wearable-derived data complicates clinical decision-making and the development of consistent care strategies. Furthermore, seamless incorporation of wearable data into clinical workflows requires clinician training and systemic adjustments to manage the continuous influx of physiological data ( 5 ). This case report explores the use of wearable technology in the recovery of a female psychologist following a closed head injury. By leveraging a wrist-based wearable (Garmin Forerunner 955 smartwatch), key recovery metrics—including HRV, sleep patterns, and activity levels—were objectively tracked, complementing traditional clinical assessments. These insights informed rehabilitation strategies, improved prognostic evaluations, and highlighted the potential of wearables in advancing TBI management ( 6 ). While this technology holds significant promise further research is needed to validate devices, standardize data interpretation, and seamlessly integrate wearable-derived insights into routine clinical practice. CASE REPORT A 26-year-old female psychologist, who was previously healthy and physically well with no prior medical conditions or injuries, sustained a closed head injury after being struck by a car while riding a scooter. She experienced several hours of unconsciousness, and an initial head CT scan revealed multiple skull fractures and occipital and contrecoup frontal contusions. The patient was hospitalized for two weeks for monitoring and stabilization but did not require surgery. During the initial period, she experienced symptoms consistent with post-concussion syndrome, including persistent headache, vomiting, dizziness, nausea, and photophobia, which were managed through supportive care. The patient had been using a wrist-based wearable (Garmin Forerunner 955, Garmin USA) for general health monitoring before the injury. Her healthcare team maintained its use throughout her recovery, providing a unique dataset of pre- and post-injury metrics. The validity of such consumer-grade devices for tracking physiological metrics has been previously established ( 9 ). Key metrics monitored included heart rate variability (HRV), sleep patterns, activity levels, and gait speed. Pre-injury data served as a baseline for assessing recovery kinetics. Pre / Post-Injury Findings: Activity Metrics . A significant reduction in daily step count was observed following the injury, reflecting post-injury fatigue, reduced physical endurance, and activity limitations. Pre-injury step count averaged over 10,000 steps per day but dropped to 1,000–2,000 steps per day in the immediate post-injury phase. While gradual recovery was observed by three months post-injury, activity levels remained substantially below baseline, indicating ongoing physical limitations (Fig. 1 ). HRV. HRV analysis revealed marked autonomic dysregulation immediately post-injury, with unbalanced and low HRV readings persisting throughout the first month. Gradual stabilization occurred by the second month, and HRV values returned to baseline by the third month, indicating recovery of autonomic nervous system function. HRV emerged as a sensitive biomarker for assessing physiological resilience during the recovery process (Fig. 2 ). Sleep Metrics. Post-injury sleep patterns demonstrated a substantial increase in duration, reflecting heightened physiological demands for neural repair. Pre-injury, the patient averaged six hours of sleep per night; however, this increased to approximately 14 hours per night during the first month post-injury, representing a 230% rise. Although sleep duration gradually decreased by the third month, it remained elevated compared to pre-injury levels, emphasizing the critical role of sleep in recovery and its value as an objective indicator of head injury severity (Fig. 3 ). Gait Speed . Gait speed, a reliable measure of physical functionality, also showed significant impairment. The patient’s pre-injury average walking speed of 1.3 meters per second (m/s) was within the normal range for her age ( 7 ) and sex. However, this dropped to 0.47 m/s during the first month post-injury, consistent with marked disability ( 8 ). While gradual improvement was observed over three months, gait speed did not return to baseline levels, indicating residual functional impairment (Fig. 4 ). These findings highlight the value of wearable devices, such as a consumer grade smart watch, in providing continuous objective data to monitor physiological and functional recovery following traumatic brain injuries. Metrics such as HRV, activity levels, and sleep patterns emerged as sensitive and actionable biomarkers for tracking recovery dynamics. These insights supported therapeutic interventions and emphasized the potential of wearable technology in guiding personalized rehabilitation strategies. DISCUSSION This case highlights the potential of wearable devices in providing continuous, objective, and longitudinal data during recovery from mild to moderate closed head injuries. By enabling real-time tracking of key metrics such as heart rate variability (HRV), sleep patterns, gait speed, and step count, consumer grade deliver valuable insights for both clinicians and patients. These metrics quantify recovery progress and detect subtle physiological changes ( 10 , 11 ) that traditional monitoring methods or subjective assessment tools may overlook. Wearable devices bridge the gap between subjective symptom reporting - often influenced by recall bias - and objective data collection ( 12 ). Continuous data streams offer granular insights into recovery trajectories ( 13 ), allowing clinicians to develop a nuanced understanding of post-injury dynamics. For instance, the observed drop in HRV immediately post-injury, followed by gradual normalisation, reflects the resolution of autonomic dysfunction. Such data emphasizes the utility of wearables in autonomic monitoring and recovery assessment. The personalised insights gained from wearable metrics support tailored rehabilitation strategies. For example, extended sleep duration post-injury highlighted the heightened physiological demands associated with neural repair, while reduced activity levels signalled the need for cautious, phased reintroduction of physical activity. Individualized recovery plans, informed by these metrics, not only enhance outcomes but also improve adherence to treatment plans. Furthermore, wearables provide early prognostic indicators of recovery, such as HRV normalization ( 16 ) and stabilization of sleep patterns, enabling timely adjustments to interventions. ( 14 ). Data-driven insights further reassure patients and caregivers, reducing anxiety and fostering active engagement in the recovery process. A review of related studies on TBI and wearables supports these findings ( 2 – 5 ). Recent research has demonstrated that wearable devices accurately monitor physiological changes in TBI patients, such as sleep disturbances, HRV variability, and activity limitations ( 3 , 4 ). Studies have highlighted that continuous monitoring through wearables enhances clinical assessments and rehabilitation outcomes ( 2 ), aligning with the present case report’s observations. Incorporating findings from existing literature enhances the generalizability of this study, demonstrating that wearable metrics can serve as reliable indicators across diverse TBI cases ( 3 , 4 ). While consumer-grade wearables demonstrate utility, their accuracy could be enhanced by integrating features of medical-grade devices. The combination of wearable technology's convenience with clinical-grade precision would enhance reliability and strengthen their role as a complement to routine assessments ( 15 ). This integration could also pave the way for advanced diagnostic and therapeutic monitoring platforms. However, several barriers must be addressed to fully incorporate wearable technology into traumatic brain injury (TBI) management. The lack of standardized algorithms for interpreting wearable-derived data complicates clinical decision-making, and the absence of uniform benchmarks limits the generalizability of results. Establishing evidence-based guidelines for metrics such as HRV, sleep patterns, and activity levels could improve consistency in care delivery. Additionally, larger, longitudinal are needed to validate the role of wearable metrics in predicting recovery and functional outcomes. Research should focus on defining normative recovery trajectories across varying TBI severities and correlating wearable data with clinical endpoints. Exploring the psychological benefits of wearable data, such as enhanced patient engagement and self-management, could further support their integration into routine care. Despite their promise, wearable technologies face limitations. Consumer-grade devices may lack the precision necessary to detect subtle but significant physiological changes. In addition, the metrics discussed in this case are highly sensitive to change but may lack specificity, potentially complicating their interpretation. Effective use of wearable data requires clinicians to develop familiarity with the technology and its associated metrics, adding a layer of complexity to its adoption. Patient adherence to wearing the device consistently and correctly also influences data reliability and completeness. Conclusion This case report emphasizes the potential of wearable monitors to transform the management of TBI recovery, offering a novel paradigm for objective data capture and personalized care delivery. By providing continuous, non-invasive insights into key physiological and functional metrics, wearable devices enable more precise monitoring, tailored rehabilitation strategies, and improved prognostication. The seamless integration of wearable health data into clinical workflows could revolutionize recovery management by enhancing decision-making, optimizing intervention strategies, and fostering patient engagement. As research in this field expands, wearables are set to play an increasingly pivotal role in advancing healthcare, bridging the gap between technology and personalized medicine to improve outcomes for TBI patients and beyond. Declarations Ethics approval and consent to participate Ethical approval was not required for this case report. Written informed consent was obtained from the patient for the use of their data and participation in this report. Consent for publication The patient provided full written informed consent for the publication of this case report, including all accompanying data and images. A copy of the consent form is available from the corresponding author upon request. Availability of data and materials The materials used during the current study are available from the corresponding author on request. Competing interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors' contributions Ralph J. Mobbs contributed to the study concept, clinical assessment, and manuscript writing. Lianne Koinis contributed to data collection, data analysis, and manuscript drafting. Both authors read and approved the final manuscript. COI Statement There are no conflicts of interest from any author with this report. References Subbarao B, Hayani Z, Clemmens Z. Complementary and Integrative Medicine in Treating Headaches, Cognitive Dysfunction, Mental Fatigue, Insomnia, and Mood Disorders Following Traumatic Brain Injury: A Comprehensive Review. Phys Med Rehabil Clin N Am. 2024;35(3):651–664. doi: 10.1016/j.pmr.2024.02.013. Epub 2024 Mar 23. PMID: 38945657. Melinosky, C., Yang, S., Hu, P., Li, H. C. T., & Miller, C. H. T. (2018). Continuous vital sign analysis to predict secondary neurological decline after traumatic brain injury. Frontiers in Neurology. Link Fedele B, Williams G, McKenzie D, Giles R, McKay A, Olver J. Sleep Disturbance During Post-Traumatic Amnesia and Early Recovery After Traumatic Brain Injury. J Neurotrauma. 2024;41(15–16):e1961-e1975. doi: 10.1089/neu.2023.0656 . Epub 2024 May 6. PMID: 38553904. Paniccia M, Taha T, Keightley M, Thomas S, Verweel L, Murphy J, Wilson K, Reed N. Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology. J Vis Exp. 2018;(139):58203. doi: 10.3791/58203 . PMID: 30295657; PMCID: PMC6235273. Mobbs, R. J., Ho, D., Choy, W. J., Betteridge, C., & Lin, H. (2020). COVID-19 is shifting the adoption of wearable monitoring and telemedicine (WearTel) in the delivery of healthcare: Opinion piece. Annals of Translational Medicine, 8(20), 1285. DOI: 10.21037/atm-20-3678 Powell D, Stuart S, Godfrey A. Exploring Inertial-Based Wearable Technologies for Objective Monitoring in Sports-Related Concussion: A Single-Participant Report. Phys Ther. 2022;102(5):pzac016. doi: 10.1093/ptj/pzac016 . PMID: 35196371; PMCID: PMC9155164. Koinis, L., Fernando, V., Fonseka, R. D., Natarajan, P., Maharaj, M., & Mobbs, R. J. (2025). Normative Database of Spatiotemporal Gait Metrics Across Age Groups: An Observational Case–Control Study. Sensors, 25(2), 581. https://doi.org/10.3390/s25020581 Braun T, Thiel C, Peter RS, Bahns C, Büchele G, Rapp K, Becker C, Grüneberg C. Association of clinical outcome assessments of mobility capacity and incident disability in community-dwelling older adults - a systematic review and meta-analysis. Ageing Res Rev. 2022;81:101704. doi: 10.1016/j.arr.2022.101704 . Epub 2022 Aug 3. PMID: 35931411. Wahl Y, Düking P, Droszez A, Wahl P, Mester J. Criterion-Validity of Commercially Available Physical Activity Tracker to Estimate Step Count, Covered Distance and Energy Expenditure during Sports Conditions. Front Physiol. 2017;8:725. doi: 10.3389/fphys.2017.00725 . PMID: 29018355; PMCID: PMC5615304. Lui, G. Y., Loughnane, D., Polley, C., & Jayarathna, T. (2022). The Apple Watch for monitoring mental health–related physiological symptoms: Literature review. JMIR Mental Health. Link Parkinson, M. E., Dani, M., Fertleman, M., & Soreq, E. (2023). Protocol: Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: A feasibility study. BMJ Open. Link Witt, D. R., Kellogg, R. A., Snyder, M. P., & Dunn, J. (2019). Windows into human health through wearables data analytics. Current Opinion in Biomedical Engineering. Link Huber, D. L., Thomas, D. G., & Danduran, M. (2019). Quantifying activity levels after sport-related concussion using mHealth technologies. Journal of Athletic Training. Link Koinis, L., Mobbs, R. J., Fonseka, R. D., & Natarajan, P. (2022). A commentary on the potential of smartphones and other wearable devices to be used in the identification and monitoring of mental illness. Annals of Translational Medicine, 10(24), 1420. DOI: 10.21037/atm-21-6016 Maharaj M, Natarajan P, Fonseka RD, Khanna S, Choy WJ, Rooke K, Phan K, Mobbs RJ. The concept of recovery kinetics: an observational study of continuous post-operative monitoring in spine surgery. J Spine Surg. 2022;8(2):196–203. doi: 10.21037/jss-22-5 . PMID: 35875621; PMCID: PMC9263729. Mercier LJ, Batycky J, Campbell C, Schneider K, Smirl J, Debert CT. Autonomic dysfunction in adults following mild traumatic brain injury: A systematic review. NeuroRehabilitation. 2022;50(1):3–32. doi: 10.3233/NRE-210243 . PMID: 35068421. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6105208","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":422160364,"identity":"28d07e86-0fa9-4d34-aad0-1f5c5f436126","order_by":0,"name":"Ralph J. Mobbs","email":"","orcid":"","institution":"Neurospine clinic","correspondingAuthor":false,"prefix":"","firstName":"Ralph","middleName":"J.","lastName":"Mobbs","suffix":""},{"id":422160365,"identity":"80979923-fa5f-4efe-9f90-bfd32df6fc0d","order_by":1,"name":"Lianne Koinis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACPmYwZWPAAGQcIEoLG0RLGilaINRhA+IdxsbO/OxxQcV5Y3523oMHfzDYyek2MD+TwO8wNnPjGWdum0k28yUc5mFINjY7wGZGQAuDmTRv220bg8M8BoeB3kncdoCBkBb2b9K8/86BtQAdBtLC/o2AFh6gLQ0HzEBaDvCAtfAQsoWnTJrnWLKxZDMPSBfQL4d5ii3waeHnP75NmqfGzrCf/4zxxx8VdnJmx9s33sCnBQ2AooeZBPWjYBSMglEwCrADAHsMOpk4Wn/oAAAAAElFTkSuQmCC","orcid":"","institution":"WAGAR Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Lianne","middleName":"","lastName":"Koinis","suffix":""}],"badges":[],"createdAt":"2025-02-25 12:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6105208/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6105208/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77682454,"identity":"504683d6-d28d-473b-9001-93f158bc7be0","added_by":"auto","created_at":"2025-03-04 08:48:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":197756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage Daily Step Count (DSC).\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eAverage DSC is an accurate measure of general activity levels and health status. For the 3 months preceding the injury (Aug/Sept/Oct), DSC would average approximately 13,000 steps per day. Following the head injury, this metric reduced significantly for the first month (less than 2,000 steps per day), then slowly returned towards the baseline.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6105208/v1/c04df1ec7695005287b69a58.jpeg"},{"id":77684193,"identity":"aaa14009-5297-4769-9cc6-ea49b88aaaf0","added_by":"auto","created_at":"2025-03-04 08:56:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSleep duration\u003c/em\u003e. Prior to the injury, average hours sleep per night was approximately 6 hours. Following head injury, this increased to over 16 hours per night for the initial month, then reduced slightly, however overall sleep duration was significantly higher than preinjury.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6105208/v1/81c8fcfc33eda122577e8e24.jpeg"},{"id":77682455,"identity":"0b99ba8c-1ed7-41f7-9004-8c8dcab84553","added_by":"auto","created_at":"2025-03-04 08:48:45","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHeart Rate Variability (HRV)\u003c/em\u003e. Prior to the injury, HRV averaged between 50 – 80ms. Following head injury, a significant drop is detected that remains significantly low for a month (less than 40 ms), then slowly trends upwards consistent with physical and cognitive recovery.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6105208/v1/31eef42fcf6f4806bbeb1353.jpeg"},{"id":77682459,"identity":"5949d314-a051-4263-8e9a-06e0fcc1f2c3","added_by":"auto","created_at":"2025-03-04 08:48:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":222390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean Gait Speed\u003c/em\u003e. Walking speed is an accurate measure of general health, with a significant drop in walking speed following the head injury (red line). For the initial month following head injury, walking speed dropped to less than 0.5 m/s, consistent with severe disability.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6105208/v1/de02c53ed45d6a102419c523.jpeg"},{"id":77902634,"identity":"1173471e-6838-4ada-bf89-5f5455e7e61e","added_by":"auto","created_at":"2025-03-06 15:54:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1216310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6105208/v1/05a37523-a49c-4c7f-af20-325feae07a50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Recovery following Closed Head Injuries: The Role of Wearable Monitors in Tracking Severity and Recovery Kinetics","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMild to moderate closed head injuries, commonly referred to as traumatic brain injuries (TBIs), pose diagnostic and management challenges due to the subjective nature of current assessment tools and the variability in recovery. Post-traumatic symptoms such as cognitive fatigue, sleep disturbance, and autonomic dysfunction are inherently complex to quantify, frequently leading to underdiagnosis and delays in intervention (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Recovery trajectories following TBI are highly individual, influenced by factors such as the severity of the injury, pre-existing health conditions, and the quality of rehabilitation efforts.\u003c/p\u003e \u003cp\u003eRecent advancements in wearable technology provide a promising solution by providing continuous, non-invasive monitoring of key physiological metrics (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) such as heart rate variability (HRV), sleep patterns (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and activity levels. These devices facilitate objective data collection, enabling more precise tracking of recovery. For instance, HRV serves as a potential biomarker of autonomic nervous system function (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), providing valuable insights into the autonomic disturbances often observed in TBIs. By integrating these metrics, wearable devices allow for a more personalized and dynamic approach to patient care, enhancing recovery monitoring and prognostication.\u003c/p\u003e \u003cp\u003eHowever, significant barriers remain in incorporating wearable technology into TBI management. Consumer-grade wearables require validation against medical-grade standards to ensure data accuracy and reliability. The absence of standardized algorithms for interpreting wearable-derived data complicates clinical decision-making and the development of consistent care strategies. Furthermore, seamless incorporation of wearable data into clinical workflows requires clinician training and systemic adjustments to manage the continuous influx of physiological data (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis case report explores the use of wearable technology in the recovery of a female psychologist following a closed head injury. By leveraging a wrist-based wearable (Garmin Forerunner 955 smartwatch), key recovery metrics\u0026mdash;including HRV, sleep patterns, and activity levels\u0026mdash;were objectively tracked, complementing traditional clinical assessments. These insights informed rehabilitation strategies, improved prognostic evaluations, and highlighted the potential of wearables in advancing TBI management (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While this technology holds significant promise further research is needed to validate devices, standardize data interpretation, and seamlessly integrate wearable-derived insights into routine clinical practice.\u003c/p\u003e"},{"header":"CASE REPORT","content":"\u003cp\u003eA 26-year-old female psychologist, who was previously healthy and physically well with no prior medical conditions or injuries, sustained a closed head injury after being struck by a car while riding a scooter. She experienced several hours of unconsciousness, and an initial head CT scan revealed multiple skull fractures and occipital and contrecoup frontal contusions. The patient was hospitalized for two weeks for monitoring and stabilization but did not require surgery. During the initial period, she experienced symptoms consistent with post-concussion syndrome, including persistent headache, vomiting, dizziness, nausea, and photophobia, which were managed through supportive care.\u003c/p\u003e \u003cp\u003eThe patient had been using a wrist-based wearable (Garmin Forerunner 955, Garmin USA) for general health monitoring before the injury. Her healthcare team maintained its use throughout her recovery, providing a unique dataset of pre- and post-injury metrics. The validity of such consumer-grade devices for tracking physiological metrics has been previously established (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Key metrics monitored included heart rate variability (HRV), sleep patterns, activity levels, and gait speed. Pre-injury data served as a baseline for assessing recovery kinetics.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePre / Post-Injury Findings:\u003c/h2\u003e \u003cp\u003e \u003cb\u003eActivity Metrics\u003c/b\u003e. A significant reduction in daily step count was observed following the injury, reflecting post-injury fatigue, reduced physical endurance, and activity limitations. Pre-injury step count averaged over 10,000 steps per day but dropped to 1,000\u0026ndash;2,000 steps per day in the immediate post-injury phase. While gradual recovery was observed by three months post-injury, activity levels remained substantially below baseline, indicating ongoing physical limitations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHRV.\u003c/b\u003e HRV analysis revealed marked autonomic dysregulation immediately post-injury, with unbalanced and low HRV readings persisting throughout the first month. Gradual stabilization occurred by the second month, and HRV values returned to baseline by the third month, indicating recovery of autonomic nervous system function. HRV emerged as a sensitive biomarker for assessing physiological resilience during the recovery process (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSleep Metrics.\u003c/b\u003e Post-injury sleep patterns demonstrated a substantial increase in duration, reflecting heightened physiological demands for neural repair. Pre-injury, the patient averaged six hours of sleep per night; however, this increased to approximately 14 hours per night during the first month post-injury, representing a 230% rise. Although sleep duration gradually decreased by the third month, it remained elevated compared to pre-injury levels, emphasizing the critical role of sleep in recovery and its value as an objective indicator of head injury severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGait Speed\u003c/b\u003e. Gait speed, a reliable measure of physical functionality, also showed significant impairment. The patient\u0026rsquo;s pre-injury average walking speed of 1.3 meters per second (m/s) was within the normal range for her age (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and sex. However, this dropped to 0.47 m/s during the first month post-injury, consistent with marked disability (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). While gradual improvement was observed over three months, gait speed did not return to baseline levels, indicating residual functional impairment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings highlight the value of wearable devices, such as a consumer grade smart watch, in providing continuous objective data to monitor physiological and functional recovery following traumatic brain injuries. Metrics such as HRV, activity levels, and sleep patterns emerged as sensitive and actionable biomarkers for tracking recovery dynamics. These insights supported therapeutic interventions and emphasized the potential of wearable technology in guiding personalized rehabilitation strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis case highlights the potential of wearable devices in providing continuous, objective, and longitudinal data during recovery from mild to moderate closed head injuries. By enabling real-time tracking of key metrics such as heart rate variability (HRV), sleep patterns, gait speed, and step count, consumer grade deliver valuable insights for both clinicians and patients. These metrics quantify recovery progress and detect subtle physiological changes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) that traditional monitoring methods or subjective assessment tools may overlook.\u003c/p\u003e \u003cp\u003eWearable devices bridge the gap between subjective symptom reporting - often influenced by recall bias - and objective data collection (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Continuous data streams offer granular insights into recovery trajectories (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), allowing clinicians to develop a nuanced understanding of post-injury dynamics. For instance, the observed drop in HRV immediately post-injury, followed by gradual normalisation, reflects the resolution of autonomic dysfunction. Such data emphasizes the utility of wearables in autonomic monitoring and recovery assessment.\u003c/p\u003e \u003cp\u003eThe personalised insights gained from wearable metrics support tailored rehabilitation strategies. For example, extended sleep duration post-injury highlighted the heightened physiological demands associated with neural repair, while reduced activity levels signalled the need for cautious, phased reintroduction of physical activity. Individualized recovery plans, informed by these metrics, not only enhance outcomes but also improve adherence to treatment plans. Furthermore, wearables provide early prognostic indicators of recovery, such as HRV normalization (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and stabilization of sleep patterns, enabling timely adjustments to interventions. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Data-driven insights further reassure patients and caregivers, reducing anxiety and fostering active engagement in the recovery process.\u003c/p\u003e \u003cp\u003eA review of related studies on TBI and wearables supports these findings (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Recent research has demonstrated that wearable devices accurately monitor physiological changes in TBI patients, such as sleep disturbances, HRV variability, and activity limitations (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Studies have highlighted that continuous monitoring through wearables enhances clinical assessments and rehabilitation outcomes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), aligning with the present case report\u0026rsquo;s observations. Incorporating findings from existing literature enhances the generalizability of this study, demonstrating that wearable metrics can serve as reliable indicators across diverse TBI cases (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile consumer-grade wearables demonstrate utility, their accuracy could be enhanced by integrating features of medical-grade devices. The combination of wearable technology's convenience with clinical-grade precision would enhance reliability and strengthen their role as a complement to routine assessments (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This integration could also pave the way for advanced diagnostic and therapeutic monitoring platforms.\u003c/p\u003e \u003cp\u003eHowever, several barriers must be addressed to fully incorporate wearable technology into traumatic brain injury (TBI) management. The lack of standardized algorithms for interpreting wearable-derived data complicates clinical decision-making, and the absence of uniform benchmarks limits the generalizability of results. Establishing evidence-based guidelines for metrics such as HRV, sleep patterns, and activity levels could improve consistency in care delivery. Additionally, larger, longitudinal are needed to validate the role of wearable metrics in predicting recovery and functional outcomes. Research should focus on defining normative recovery trajectories across varying TBI severities and correlating wearable data with clinical endpoints. Exploring the psychological benefits of wearable data, such as enhanced patient engagement and self-management, could further support their integration into routine care.\u003c/p\u003e \u003cp\u003eDespite their promise, wearable technologies face limitations. Consumer-grade devices may lack the precision necessary to detect subtle but significant physiological changes. In addition, the metrics discussed in this case are highly sensitive to change but may lack specificity, potentially complicating their interpretation. Effective use of wearable data requires clinicians to develop familiarity with the technology and its associated metrics, adding a layer of complexity to its adoption. Patient adherence to wearing the device consistently and correctly also influences data reliability and completeness.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis case report emphasizes the potential of wearable monitors to transform the management of TBI recovery, offering a novel paradigm for objective data capture and personalized care delivery. By providing continuous, non-invasive insights into key physiological and functional metrics, wearable devices enable more precise monitoring, tailored rehabilitation strategies, and improved prognostication.\u003c/p\u003e \u003cp\u003eThe seamless integration of wearable health data into clinical workflows could revolutionize recovery management by enhancing decision-making, optimizing intervention strategies, and fostering patient engagement. As research in this field expands, wearables are set to play an increasingly pivotal role in advancing healthcare, bridging the gap between technology and personalized medicine to improve outcomes for TBI patients and beyond.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this case report. Written informed consent was obtained from the patient for the use of their data and participation in this report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patient provided full written informed consent for the publication of this case report, including all accompanying data and images. A copy of the consent form is available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe materials used during the current study are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRalph J. Mobbs\u003c/strong\u003e contributed to the study concept, clinical assessment, and manuscript writing.\u003cbr\u003e\u003cstrong\u003eLianne Koinis\u003c/strong\u003e contributed to data collection, data analysis, and manuscript drafting.\u003cbr\u003e\u0026nbsp;Both authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOI Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest from any author with this report.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSubbarao B, Hayani Z, Clemmens Z. Complementary and Integrative Medicine in Treating Headaches, Cognitive Dysfunction, Mental Fatigue, Insomnia, and Mood Disorders Following Traumatic Brain Injury: A Comprehensive Review. Phys Med Rehabil Clin N Am. 2024;35(3):651\u0026ndash;664. doi: 10.1016/j.pmr.2024.02.013. Epub 2024 Mar 23. PMID: 38945657.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelinosky, C., Yang, S., Hu, P., Li, H. C. T., \u0026amp; Miller, C. H. T. (2018). Continuous vital sign analysis to predict secondary neurological decline after traumatic brain injury. Frontiers in Neurology. Link\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFedele B, Williams G, McKenzie D, Giles R, McKay A, Olver J. Sleep Disturbance During Post-Traumatic Amnesia and Early Recovery After Traumatic Brain Injury. J Neurotrauma. 2024;41(15\u0026ndash;16):e1961-e1975. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/neu.2023.0656\u003c/span\u003e\u003cspan address=\"10.1089/neu.2023.0656\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 May 6. PMID: 38553904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaniccia M, Taha T, Keightley M, Thomas S, Verweel L, Murphy J, Wilson K, Reed N. Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology. J Vis Exp. 2018;(139):58203. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3791/58203\u003c/span\u003e\u003cspan address=\"10.3791/58203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 30295657; PMCID: PMC6235273.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMobbs, R. J., Ho, D., Choy, W. J., Betteridge, C., \u0026amp; Lin, H. (2020). COVID-19 is shifting the adoption of wearable monitoring and telemedicine (WearTel) in the delivery of healthcare: Opinion piece. Annals of Translational Medicine, 8(20), 1285. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/atm-20-3678\u003c/span\u003e\u003cspan address=\"10.21037/atm-20-3678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowell D, Stuart S, Godfrey A. Exploring Inertial-Based Wearable Technologies for Objective Monitoring in Sports-Related Concussion: A Single-Participant Report. Phys Ther. 2022;102(5):pzac016. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ptj/pzac016\u003c/span\u003e\u003cspan address=\"10.1093/ptj/pzac016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 35196371; PMCID: PMC9155164.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoinis, L., Fernando, V., Fonseka, R. D., Natarajan, P., Maharaj, M., \u0026amp; Mobbs, R. J. (2025). Normative Database of Spatiotemporal Gait Metrics Across Age Groups: An Observational Case\u0026ndash;Control Study. Sensors, 25(2), 581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s25020581\u003c/span\u003e\u003cspan address=\"10.3390/s25020581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraun T, Thiel C, Peter RS, Bahns C, B\u0026uuml;chele G, Rapp K, Becker C, Gr\u0026uuml;neberg C. Association of clinical outcome assessments of mobility capacity and incident disability in community-dwelling older adults - a systematic review and meta-analysis. Ageing Res Rev. 2022;81:101704. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arr.2022.101704\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2022.101704\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Aug 3. PMID: 35931411.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahl Y, D\u0026uuml;king P, Droszez A, Wahl P, Mester J. Criterion-Validity of Commercially Available Physical Activity Tracker to Estimate Step Count, Covered Distance and Energy Expenditure during Sports Conditions. Front Physiol. 2017;8:725. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphys.2017.00725\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2017.00725\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 29018355; PMCID: PMC5615304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLui, G. Y., Loughnane, D., Polley, C., \u0026amp; Jayarathna, T. (2022). The Apple Watch for monitoring mental health\u0026ndash;related physiological symptoms: Literature review. JMIR Mental Health. Link\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParkinson, M. E., Dani, M., Fertleman, M., \u0026amp; Soreq, E. (2023). Protocol: Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: A feasibility study. BMJ Open. Link\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitt, D. R., Kellogg, R. A., Snyder, M. P., \u0026amp; Dunn, J. (2019). Windows into human health through wearables data analytics. Current Opinion in Biomedical Engineering. Link\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuber, D. L., Thomas, D. G., \u0026amp; Danduran, M. (2019). Quantifying activity levels after sport-related concussion using mHealth technologies. Journal of Athletic Training. Link\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoinis, L., Mobbs, R. J., Fonseka, R. D., \u0026amp; Natarajan, P. (2022). A commentary on the potential of smartphones and other wearable devices to be used in the identification and monitoring of mental illness. Annals of Translational Medicine, 10(24), 1420. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/atm-21-6016\u003c/span\u003e\u003cspan address=\"10.21037/atm-21-6016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaharaj M, Natarajan P, Fonseka RD, Khanna S, Choy WJ, Rooke K, Phan K, Mobbs RJ. The concept of recovery kinetics: an observational study of continuous post-operative monitoring in spine surgery. J Spine Surg. 2022;8(2):196\u0026ndash;203. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/jss-22-5\u003c/span\u003e\u003cspan address=\"10.21037/jss-22-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 35875621; PMCID: PMC9263729.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMercier LJ, Batycky J, Campbell C, Schneider K, Smirl J, Debert CT. Autonomic dysfunction in adults following mild traumatic brain injury: A systematic review. NeuroRehabilitation. 2022;50(1):3\u0026ndash;32. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3233/NRE-210243\u003c/span\u003e\u003cspan address=\"10.3233/NRE-210243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 35068421.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Closed Head Injury, Concussion, Wearables, Objective Health Metrics, Recovery Kinetics","lastPublishedDoi":"10.21203/rs.3.rs-6105208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6105208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eClosed head injuries, including mild to moderate traumatic brain injuries (TBIs), present challenges in assessment due to subjective symptom reporting and individual variability in recovery. Wearable technology offers an objective approach to tracking physiological metrics, providing insights into post-injury recovery trajectories.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis case report examines a 26-year-old female psychologist who sustained a closed head injury after a vehicular collision while riding a scooter. A Garmin Forerunner 955 smartwatch, which she had been using prior to the injury, provided continuous tracking of heart rate variability (HRV), sleep duration, step count, and gait speed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFollowing the injury, step count dropped from 10,000\u0026thinsp;+\u0026thinsp;to 1,000\u0026ndash;2,000 per day, HRV remained suppressed, sleep duration increased from 6 to over 14 hours per night, and gait speed declined from 1.3 m/s to 0.47 m/s. While improvements were observed over three months, key activity and mobility metrics remained below baseline.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWearable devices provided continuous, objective data that complemented traditional clinical assessments. These metrics informed rehabilitation strategies and demonstrated potential in tracking autonomic dysfunction and mobility recovery. Further research is necessary to validate wearable-derived data for clinical applications and establish standardized integration protocols.\u003c/p\u003e","manuscriptTitle":"Evaluating Recovery following Closed Head Injuries: The Role of Wearable Monitors in Tracking Severity and Recovery Kinetics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-04 08:48:40","doi":"10.21203/rs.3.rs-6105208/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07bcf360-fad7-499f-a14b-c92ced5430c5","owner":[],"postedDate":"March 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-06T15:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-04 08:48:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6105208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6105208","identity":"rs-6105208","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}