Objective features of sedentary time and light activity differentiate people with low back pain from healthy controls: a pilot study

Tomkins-Lane, Christy; Sun, Ruopeng; Muaremi, Amir; Zheng, Patricia; Mohan, Manoj; Ith, Ma Agnes; Smuck, Matthew

doi:10.1016/j.spinee.2021.11.005

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Recent advances in the development of wearable technologies enabling objective measurement of movement, physical activity, and function 23 – 29 , gait and posture 30 – 34 , neuromuscular and physiological data 10 , 30 – 33 , sleep 35 , 36 , and behavioral assesment 37 , 38 have resulted in the emergence of “digital biomarkers” which could be measured outside the physical confines of the clinics avoiding some of the bias introduced in clinic measurements 30 – 33 , 39 , 40 . Many of these biomarkers from wearables have shown a potential to objectively measure different aspects of an individual’s chronic pain and its effect on physical activity, sleep, psychological health, and social participation 27 , 28 , 31 , 35 , 41 .…”

Section: Introductionmentioning

confidence: 99%

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Patterson

Wilson²,

Fishman

et al. 2023

npj Digit. Med.

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Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS therapy may vary among people with chronic pain due to diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened to a patient since their last visit, and digital PROs require patients to visit an app or otherwise regularly engage with software. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain levels and categories of PRO measures. The model reached an accuracy of 0.768 ± 0.012 in predicting the pain intensity of mild, moderate, and severe. Feature importance analysis showed that digital biomarkers from the smartwatch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain, enabling continuous, real-time monitoring of patients during the use of implanted therapies.

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Section: Introductionmentioning

confidence: 99%

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Patterson

Wilson²,

Fishman

et al. 2023

npj Digit. Med.

View full text Add to dashboard Cite

show abstract

“…Despite recent research highlighting the importance of using machine learning techniques in pain research, previous works have focused on correlating and monitoring symptoms and side effects of pain with digital biomarkers, and not necessarily predicting the subject’s reported outcomes including response to SCS therapy (Smuck et al 2017 ; Avila et al 2021 ; Koenig et al 2016a ; Tomkins-Lane et al 2022 ; Costa et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain

et al. 2023

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Background Neurostimulation is an effective therapy for treating and management of refractory chronic pain. However, the complex nature of pain and infrequent in-clinic visits, determining subject’s long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to neurostimulation therapy. Method Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects implanted by Spinal Cord Stimulator (SCS) or Dorsal Root Ganglia (DRG) neurostimulators. The REALITY sub-study was designed for collecting additional wearables data on a subset of 20 participants implanted with SCS devices for up to six months post implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict therapy outcome based on the subject’s response to the numerical rating scale (NRS) or patient global impression of change (PGIC). Results Principal component analysis showed that psychological aspects of pain were associated with heart rate variability, while movement-related measures were strongly associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted PGIC and NRS outcomes with high accuracy without subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions reflect an overall change since the study onset and could be a better predictor of long-term neurostimulation therapy outcome. Conclusions The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient’s response to therapy and their general well-being.

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“…Despite recent research highlighting the importance of using machine learning techniques in pain research, previous works have focused on correlating and monitoring symptoms and side effects of pain with digital biomarkers, and not necessarily predicting the subject's reported outcomes including response to SCS therapy (27,30,(34)(35)(36).…”

mentioning

confidence: 99%

Objective Wearable Measures and Subjective Questionnaires for Predicting Response to Spinal Cord Stimulation Therapy in People with Chronic Pain

Heros

Patterson²,

Huygen

et al. 2023

Preprint

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Background: Spinal cord stimulation (SCS) is a highly effective therapy for treating and management of refractory chronic pain. However, complex nature of pain and infrequent in-clinic visits, determining subject’s long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to SCS therapy. Method: Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects and the sub-study designed for collecting additional wearables data on a subset of participants for up to six months after SCS implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict SCS therapy outcome based on the subject’s response to NRS or PGIC. Results: Principal component analysis results showed that psychological aspects of pain were closely associated with heart rate variability, while movement-related measures were closely associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted both PGIC and NRS outcomes with high accuracy in the absence of subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions, reflects an overall change since the study onset and could be a better predictor of long-term therapy outcome. Conclusions: The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient’s response to therapy and their general well-being.

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Objective features of sedentary time and light activity differentiate people with low back pain from healthy controls: a pilot study

Cited by 7 publications

References 18 publications

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain

Objective Wearable Measures and Subjective Questionnaires for Predicting Response to Spinal Cord Stimulation Therapy in People with Chronic Pain

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