Introduction Low back pain is a significant burden to society and the lack of reliable outcome measures, combined with a prevailing inability to quantify the biopsychosocial elements implicated in the disease, impedes clinical decision-making and distorts treatment efficacy. This paper aims to validate the utility of a biopsychosocial spine platform to provide standardized wearable sensor-derived functional motion assessments to assess spine function and differentiate between healthy controls and patients. Secondarily, we explored the correlation between these motion features and subjective biopsychosocial measures.
Methods An observational study was conducted on healthy controls (n=50) and patients with low back pain (n=50) to validate platform utility. The platform was used to conduct functional assessments along with patient-reported outcome assessments to holistically document cohort differences. Our primary outcomes were motion features; and our secondary outcomes were biopsychosocial measures (pain, function, etc).
Results Our results demonstrated statistically significant differences in motion features between healthy and patient cohorts across anatomical planes. Importantly, we found velocity and acceleration in the axial plane showed the largest difference, with healthy controls having 49.7% and 55.7% higher values, respectively, than patients. In addition, we found significant correlations between motion features and biopsychosocial measures for pain, physical function and social role only.
Conclusions Our study validated the use of wearable sensor-derived functional motion metrics in differentiating healthy controls and patients. Collectively, this technology has the potential to facilitate holistic biopsychosocial evaluations to enhance spine care and improve patient outcomes.
Trial registration number NCT05776771.
- Back Pain
- CHRONIC PAIN
- Pain Management
Data availability statement
Data are available on reasonable request.
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Contributors All authors made significant contributions to this research, have read and agreed to the published version of the manuscript. The following are specific contributions by each author: Conceptualization, PM, JD, GK, AA and WSM; methodology, PM, JD, GK, AA, DMB, JV, MT, NH, SK, TW, and WSM; software, JD, and AA; validation, JD, AA, and WSM; formal analysis, PM, JD, GK, AA, and WSM; investigation, PM, JD, GK, AA and WSM; resources, DMB, JV, NH, SK, TW, and WSM; data curation, DMB, JV, NH, SK, and TW; writing—original draft preparation, PM, JD,GK, and WSM; writing—review and editing, PM, JD, GK, DB, MT, and WSM; visualization, DB, SK, NH, MT, and JV; supervision, WM, and TW; project administration, PM, JD and WSM; funding acquisition, TW and WSM. PM is the guarantor of this article.
Funding This research was funded in part by National Institutes of Health (NIH) through the NIH HEAL Initiative under award numbers 1UH2AR076729-01, 4UH3AR076729-02, 1U24AR076730-01, 3UH3AR076729-02S1 and 3UH3AR076729-02S2. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or its NIH HEAL Initiative. Additionally, this research was also supported by a variety of funds from Defense Health Agency (DHA) under contract numbers - W81XWH-20-C-0045, and W81XWH21C0047.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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