IntroductionChronic low back pain (CLBP) is a major public health problem. Reliably measuring the effects of chronic pain on movement and activity, and any changes due to treatment, is a healthcare challenge. A recently published paper demonstrated that a novel digital therapeutic (DTxP) was efficacious in reducing fear of movement and increasing the quality of life of adult patients with moderate to severe CLBP. In this paper, we report a study of how data from wearable devices collected in this study could be used as a digital measure for use in studies of chronic low back pain.MethodsMovement, electrodermal recording, general activity and clinical assessment data were collected in a clinical trial of a novel digital therapeutic intervention (DTxP) by using the sensors in commercial Garmin Vivosmart 4, Empatica Embrace2 and Oculus Quest wearables. Wearable data were collected during and between the study interventions (frequent treatment sessions of DTxP). Data were analyzed using exploratory statistical analysis.ResultsA pattern of increased longitudinal velocity in the movement data collected with right-hand, left-hand, and head sensors was observed in the study population. Correlations were observed with the changes in clinical scales (Tampa Scale of Kinesiophobia, EQ5D Overall health VAS, and EQ5D QoL score). The strongest correlation was observed with the increased velocity of head and right-hand sensors (Spearman correlation with increasing head sensor velocity and Tampa Scale of Kinesiophobia −0.45, Overall health VAS +0.67 and EQ5D QoL score −0.66). The sample size limited interpretation of electrodermal and general activity data.Discussion/ConclusionWe found a novel digital signal for use in monitoring the efficacy of a digital therapeutics (DTxP) in adults with CLBP. We discuss the potential use of such movement based digital markers as surrogate or additional endpoints in studies of chronic musculoskeletal pain.Clinical Trial Registrationhttps://clinicaltrials.gov/ct2/show/NCT04225884?cond=NCT04225884&draw=2&rank=1, identifier: NCT04225884.
Chronic low back pain (CLBP) is a globally common musculoskeletal problem. Measuring the sensation of pain and the effect of a treatment has always been a challenge for healthcare. Here, we study how the movement data, collected while using a virtual reality (VR) program, could be used as an objective measurement in patients with CLBP. A specific data collection method based on VR was developed and used with CLBP patients and healthy volunteers. We demonstrate that the movement data in VR can be used to classify individuals in these two groups with a high accuracy by using logistic regression. The most discriminative features are the duration of the movements and the total variation of movement velocity. Furthermore, we show that hidden Markov models can divide movement data into meaningful segments, which creates possibilities for defining even more detailed features, with potential to improve accuracy, when larger datasets become available in the future.
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