{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T20:21:25Z","timestamp":1790713285045,"version":"4.1.0"},"reference-count":116,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NCMIC Foundation (Iowa, USA)"},{"name":"French Government"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Non-specific low back pain (NSLBP) is a significant and pervasive public health issue in contemporary society. Despite the widespread prevalence of NSLBP, our understanding of its underlying causes, as well as our capacity to provide effective treatments, remains limited due to the high diversity in the population that does not respond to generic treatments. Clustering the NSLBP population based on shared characteristics offers a potential solution for developing personalized interventions. However, the complexity of NSLBP and the reliance on subjective categorical data in previous attempts present challenges in achieving reliable and clinically meaningful clusters. This study aims to explore the influence and importance of objective, continuous variables related to NSLBP and how to use these variables effectively to facilitate the clustering of NSLBP patients into meaningful subgroups. Data were acquired from 46 subjects who performed six simple movement tasks (back extension, back flexion, lateral trunk flexion right, lateral trunk flexion left, trunk rotation right, and trunk rotation left) at two different speeds (maximum and preferred). High-density electromyography (HD EMG) data from the lower back region were acquired, jointly with motion capture data, using passive reflective markers on the subject\u2019s body and clusters of markers on the subject\u2019s spine. An exploratory analysis was conducted using a deep neural network and factor analysis. Based on selected variables, various models were trained to classify individuals as healthy or having NSLBP in order to assess the importance of different variables. The models were trained using different subsets of data, including all variables, only anthropometric data (e.g., age, BMI, height, weight, and sex), only biomechanical data (e.g., shoulder and lower back movement), only neuromuscular data (e.g., HD EMG activity), or only balance-related data. The models achieved high accuracy in categorizing individuals as healthy or having NSLBP (full model: 93.30%, anthropometric model: 94.40%, biomechanical model: 84.47%, neuromuscular model: 88.07%, and balance model: 74.73%). Factor analysis revealed that individuals with NSLBP exhibited different movement patterns to healthy individuals, characterized by slower and more rigid movements. Anthropometric variables (age, sex, and BMI) were significantly correlated with NSLBP components. In conclusion, different data types, such as body measurements, movement patterns, and neuromuscular activity, can provide valuable information for identifying individuals with NSLBP. To gain a comprehensive understanding of NSLBP, it is crucial to investigate the main domains influencing its prognosis as a cohesive unit rather than studying them in isolation. Simplifying the conditions for acquiring dynamic data is recommended to reduce data complexity, and using back flexion and trunk rotation as effective options should be further explored.<\/jats:p>","DOI":"10.3390\/brainsci13060946","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:26:56Z","timestamp":1686709616000},"page":"946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Non-Specific Low Back Pain: An Inductive Exploratory Analysis through Factor Analysis and Deep Learning for Better Clustering"],"prefix":"10.3390","volume":"13","author":[{"given":"Lucien","family":"Robinault","sequence":"first","affiliation":[{"name":"Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8752-7224","authenticated-orcid":false,"given":"Imran Khan","family":"Niazi","sequence":"additional","affiliation":[{"name":"Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand"},{"name":"Faculty of Health and Environmental Sciences, Health and Rehabilitation Research Institute, AUT University, Auckland 1010, New Zealand"},{"name":"Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5135-1682","authenticated-orcid":false,"given":"Nitika","family":"Kumari","sequence":"additional","affiliation":[{"name":"Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2824-0079","authenticated-orcid":false,"given":"Imran","family":"Amjad","sequence":"additional","affiliation":[{"name":"Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand"},{"name":"Faculty of Rehabilitation and Allied Health Sciences and Department of Biomedical Engineering, Riphah International University, Islamabad 46000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Menard","sequence":"additional","affiliation":[{"name":"M2S Laboratory, ENS Rennes, University of Rennes 2, 35065 Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7182-2085","authenticated-orcid":false,"given":"Heidi","family":"Haavik","sequence":"additional","affiliation":[{"name":"Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/S0140-6736(16)30970-9","article-title":"Non-specific low back pain","volume":"389","author":"Maher","year":"2017","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1097\/BRS.0b013e31815e7f94","article-title":"A consensus approach toward the standardization of back pain definitions for use in prevalence studies","volume":"33","author":"Dionne","year":"2008","journal-title":"Spine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.1016\/S0140-6736(18)30480-X","article-title":"What low back pain is and why we need to pay attention","volume":"391","author":"Hartvigsen","year":"2018","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1097\/AJP.0b013e3181817a8d","article-title":"Characteristics of patients with acute low back pain presenting to primary care in Australia","volume":"25","author":"Henschke","year":"2009","journal-title":"Clin. 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