Post-concussion symptom burden and dynamics - RESEARCH ARTICLE - Insights from a digital health intervention and machine learning

Aug 5, 2026

Individuals sustaining a concussion may experience symptoms that meaningfully affect daily functioning and recovery. This study characterizes post-concussion symptom nature and recovery trajectories using an unsupervised machine learning approach on data from the HeadOn digital health intervention. Over a 35-day program, patients completed daily diaries rating eight symptoms; 758 diaries from 84 patients yielded 6064 symptom ratings. K-means clustering identified three symptom-severity profiles and supported distinct recovery patterns, including early physical and emotional improvement.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Abstract

Study design and data collection

Machine learning clustering approach

Symptom prevalence and recovery trajectories

Identified patient clusters and follow-up associations

Edinburgh Research Explorer Post-concussion symptom burden and dynamics Insights from a digital health intervention and machine learning Citation for published version: Blundell, R, d’Offay, C, Hand, C, Tadmor, D, Carson, A, Gillespie, D, Reed, M & Jamjoom, AAB 2025, 'Postconcussion symptom burden and dynamics: Insights from a digital health intervention and machine learning', PLOS Digital Health, vol. 4, no. 1, e0000697 . [https://doi.org/10.1371/journal.pdig.0000697](https://doi.org/10.1371/journal.pdig.0000697) Digital Object Identifier (DOI): 10.1371/journal.pdig.0000697 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: PLOS Digital Health General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim. Download date: 29. Nov. 2025 PLOS DIGITAL HEALTH OPEN ACCESS Citation: Blundell R, d’Offay C, Hand C, Tadmor D, Carson A, Gillespie D, et al. (2025) Postconcussion symptom burden and dynamics: Insights from a digital health intervention and machine learning. PLOS Digit Health 4(1): e0000697 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pdig.0000697 Editor: Syed Sibte Raza Abidi, Dalhousie University, CANADA Received: May 2, 2024 Accepted: November 10, 2024 Published: January 7, 2025 Peer 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: [https://doi.org/10.1371/journal.pdig.0000697](https://doi.org/10.1371/journal.pdig.0000697) [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. Data 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)) . RESEARCH ARTICLE Post-concussion symptom burden and dynamics: Insights from a digital health intervention and machine learning Rebecca Blundell1, Christine d’Offay2, Charles Hand2, Daniel Tadmor3, Alan Carson4, David Gillespie4, Matthew Reed5,6, Aimun A. B. Jamjoom2,7,8 * 1 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 * [v1ajamjo@ed.ac.uk](v1ajamjo@ed.ac.uk) Abstract

FAQs

What were the main symptom patterns and cluster results?

Fatigue, sleep disturbance, and difficulty concentrating were the most prevalent symptoms. Three clusters were identified: low symptom burden (Cluster 0), moderate burden with pronounced fatigue (Cluster 1), and high burden across symptoms (Cluster 2).

Aug 5, 2026

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