[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126009-en":3,"doc-seo-126009-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126009,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","Post-concussion symptom burden and dynamics - Insights from a digital health intervention and machine learning","Individuals sustaining concussion may experience multiple symptoms that affect quality of life and functional recovery. This study applies an unsupervised machine learning approach to data from a 35-day digital health program (HeadOn), where patients complete daily diaries rating eight postconcussion symptoms. Symptom patterns are grouped using K-means clustering across 758 diaries from 84 patients. Fatigue, sleep disturbance, and concentration difficulty are most prevalent, with symptom burden declining over 35 days. Clusters differ by severity and relate to Rivermead and PHQ-9 outcomes at 6-week follow-up.","PLOS DIGITAL HEALTH  \nOPEN ACCESS  \nCitation: Blundell R, d’Offay C, Hand C, Tadmor D, Carson A, Gillespie D, et al. (2025) Postconcussion symptom burden and dynamics:  \nInsights from a digital health intervention and machine learning. PLOS Digit Health 4(1):  \ne0000697 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pdig.0000697  \nEditor: Syed Sibte Raza Abidi, Dalhousie University, CANADA  \nReceived: May 2, 2024  \nAccepted: November 10, 2024  \nPublished: January 7, 2025  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pdig.0000697](https://doi.org/10.1371/journal.pdig.0000697)  \n[Copyright:](Copyright:) © [2025](2025) Blundell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: Data is available via Edinburgh DataShare ([https://doi.org/10.7488/ds/](https://doi.org/10.7488/ds/)[ ](https://doi.org/10.7488/ds/)[7849](7849)) .  \nRESEARCH ARTICLE  \nPost-concussion symptom burden and dynamics: Insights from a digital health intervention and machine learning  \nRebecca Blundell1, Christine d’Offay2, Charles Hand2, Daniel Tadmor3, Alan Carson4, David Gillespie4, Matthew Reed5,6, Aimun A. B. Jamjoom2,7,8 *  \n1 Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom, 2 HeadOn Health Ltd, Edinburgh, United Kingdom, 3 Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom, 4 Department of Clinical Neurosciences (DCN), Royal Infirmary of Edinburgh, Edinburgh, United Kingdom, 5 The Emergency Medicine Research Group Edinburgh (EMERGE), Royal Infirmary of Edinburgh, Edinburgh, United Kingdom, 6 Acute Care Edinburgh, Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom Department of Clinical Neuroscience, Edinburgh Royal Infirmary, Edinburgh, United Kingdom, 7 Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, United Kingdom, 8 Department of Neurosurgery, Queen’s Hospital, Romford, United Kingdom  \n* [v1ajamjo@ed.ac.uk](v1ajamjo@ed.ac.uk)  \nAbstract  \nIndividuals who sustain a concussion can experience a range of symptoms which can significantly impact their quality of life and functional outcome. This study aims to understand the nature and recovery trajectories of post-concussion symptomatology by applying an unsupervised machine learning approach to data captured from a digital health intervention (HeadOn) . As part of the 35-day program, patients complete a daily symptom diary which rates 8 postconcussion symptoms. Symptom data were analysed using K-means clustering to categorize patients based on their symptom profiles. During the study period, a total of 758 symptom diaries were completed by 84 patients, equating to 6064 individual symptom ratings. Fatigue, sleep disturbance and difficulty concentrating were the most prevalent symptoms reported. A decline in symptom burden was observed over the 35-day period, with physical and emotional symptoms showing early rates of recovery. In a correlation matrix, there were strong positive correlations between low mood and irritability (r = 0.84), and poor memory and difficulty concentrating (r = 0.83) . K-means cluster analysis identified three distinct patient clusters based on symptom severity. Cluster 0 (n = 24) had a low symptom burden profile across all the postconcussion symptoms. Cluster 1 (n = 35) had moderate symptom burden but with pronounced fatigue. Cluster 2 (n = 25) had a high symptom burden profile across all the post-concussion symptoms. Reflecting the severity of ","cbCaitOXatLxF1wy","https://ap.wps.com/l/cbCaitOXatLxF1wy","pdf",2152168,6,1,15,"English","en",105,"# Abstract\n## Study design and intervention\n## Symptom assessment and clustering\n## Key findings and follow-up associations","[{\"question\":\"What data source and duration does the digital health intervention use?\",\"answer\":\"The HeadOn program runs for 35 days. Patients complete a daily symptom diary rating eight postconcussion symptoms throughout the program.\"},{\"question\":\"How were patients categorized in the study?\",\"answer\":\"K-means clustering was used to categorize patients based on their postconcussion symptom profiles, producing three distinct patient clusters by severity.\"},{\"question\":\"Which symptoms were most prevalent and how did symptom burden change over time?\",\"answer\":\"Fatigue, sleep disturbance, and difficulty concentrating were among the most prevalent symptoms. Overall symptom burden declined over the 35-day period, with physical and emotional symptoms recovering earlier.\"}]","Post-concussion symptom burden and dynamics - Insights from a digital health intervention and machine learning | PDF",1785902536,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"post-concussion-symptom-burden-and-dynamics-insights-from-a-digital-health-intervention-and-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/post-concussion-symptom-burden-and-dynamics-insights-from-a-digital-health-intervention-and-machine-learning/126009/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What data source and duration does the digital health intervention use?","Question",{"text":77,"@type":78},"The HeadOn program runs for 35 days. Patients complete a daily symptom diary rating eight postconcussion symptoms throughout the program.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How were patients categorized in the study?",{"text":82,"@type":78},"K-means clustering was used to categorize patients based on their postconcussion symptom profiles, producing three distinct patient clusters by severity.",{"name":84,"@type":75,"acceptedAnswer":85},"Which symptoms were most prevalent and how did symptom burden change over time?",{"text":86,"@type":78},"Fatigue, sleep disturbance, and difficulty concentrating were among the most prevalent symptoms. 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