Wearable Technology in Spine Surgery

Lee, Thomas J; Galetta, Matthew S.; Nicholson, Kristen; Cifuentes, Elizabeth; Goyal, Dhruv K.C.; Mangan, John J.; Fang, Taolin; Schroeder, Gregory D.; Kepler, Christopher K.; Vaccaro, Alexander R.

doi:10.1097/bsd.0000000000000905

Cited by 16 publications

(14 citation statements)

References 27 publications

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“…Medical imaging of the spine can be performed at different ages. With the continuous progress of society and the increasing pressure of human work and life, sedentary work or long-time study are gradually leading to the abnormality of human spine [ 3 ]. Spinal diseases often occur in all kinds of people and ages.…”

Section: Introductionmentioning

confidence: 99%

Spine Medical Image Segmentation Based on Deep Learning

Wei

Liu

et al. 2021

Journal of Healthcare Engineering

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The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image segmentation of 22 research objects. The application value of this algorithm in MRI image processing was comprehensively evaluated by accuracy (Acc), sensitivity (Sen), specificity (Spe), and area under curve (AUC). The results show that the image processing time of fully convolutional network (FCN) algorithm and U-Net algorithm is greater than 6 min, while the processing time of BN-U-Net algorithm is only 5–10 s, and the processing time is significantly shortened ( P < 0.05 ). The Acc, Sen, and Spe results of BN-U-Net segmentation algorithm were 94.54 ± 3.56%, 88.76 ± 2.67%, and 86.27 ± 6.23%, respectively, which were significantly improved compared with FCN algorithm and U-Net algorithm ( P < 0.05 ). In summary, the improved U-Net network algorithm used in this study significantly improves the quality of spinal MRI images by automatic segmentation of MRI images, which is worthy of further promotion in the field of spinal medical image segmentation.

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Section: Introductionmentioning

confidence: 99%

Spine Medical Image Segmentation Based on Deep Learning

Wei

Liu

et al. 2021

Journal of Healthcare Engineering

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“…However, published conference abstracts [ 45 ] have shown that a wearable-based intervention, which included calibrated pedometers, telephonic counseling from a research personnel, education on physical activity, and walking goals had an effect in improving patient-reported physical activity at 6 and 12 months after spine surgery compared to a control group [ 45 ]. Thus, though wearables are a potential tool for promoting physical activity [ 46 , 47 ], more evidence is needed on the benefits following spine surgery because evidence suggest that this surgical population demonstrate low physical activity levels that do not improve to a similar degree as other outcomes such as physical function [ 4 , 5 , 48 ].…”

Section: Resultsmentioning

confidence: 99%

Effects of physical activity interventions using wearables to improve objectively-measured and patient-reported outcomes in adults following orthopaedic surgical procedures: A systematic review

et al. 2022

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Objective To synthesize evidence on physical activity interventions that used wearables, either alone or in combination with education or rehabilitation, in adults following orthopaedic surgical procedures. Methods PubMed, CINAHL, PsycINFO and EMBASE were searched for randomized controlled trials of wearable-based interventions from each database’s inception to August 2021 in patients undergoing orthopaedic surgery. Relevant outcomes included physical activity, physical function, pain, psychological distress, or general health. PEDro scale scoring ranges from 0 to 10 and was used to appraise studies as high (≥7), moderate (5–6), or poor (<5) quality. Results Of 335 articles identified, 6 articles met eligibility criteria. PEDro scores ranged from 2 to 6, with 3 studies of moderate quality and 3 of poor quality. Studies included patients undergoing total knee (number; n = 4) or total knee or hip (n = 1) arthroplasty and lumbar disc herniation surgery (n = 1). In addition to wearables, intervention components included step diary (n = 2), motivational interviewing (n = 1), goal setting (n = 2), tailored exercise program (n = 2), or financial incentives (n = 1). Interventions were delivered in-person (n = 2), remotely (n = 3) or in a hybrid format (n = 1). Intervention duration ranged from 6 weeks to 6 months. Compared to controls, 3 moderate quality studies reported greater improvement in steps/day; however, 1 moderate and 2 poor quality studies showed no between-group difference in physical function, pain, or quality of life. No serious adverse events related to the use of wearable were reported. Conclusions The effects of physical activity interventions using wearables, either delivered in-person or remotely, appear promising for increasing steps per day after joint arthroplasty; however, this finding should be viewed with caution since it is based on 3 moderate quality studies. Further research is needed to determine the therapeutic effects of using wearables as an intervention component in patients undergoing other orthopaedic surgical procedures. Trial registration PROSPERO Registration Number: CRD42020186103

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“…Physical capacity correlates more strongly with PROs, while physical performance during free-living may afford additional insight into a patient's functional state and lived experience. 21,33…”

Section: Methodsmentioning

confidence: 99%

Digital Biomarkers and the Evolution of Spine Care Outcomes Measures: Smartphones and Wearables

Bi,

Kurland,

Ber

et al. 2023

Neurosurgery

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Over the past generation, outcome measures in spine care have evolved from a reliance on clinician-reported assessment toward recognizing the importance of the patient's perspective and the wide incorporation of patient-reported outcomes (PROs). While patient-reported outcomes are now considered an integral component of outcomes assessments, they cannot wholly capture the state of a patient's functionality. There is a clear need for quantitative and objective patient-centered outcome measures. The pervasiveness of smartphones and wearable devices in modern society, which passively collect data related to health, has ushered in a new era of spine care outcome measurement. The patterns emerging from these data, so-called “digital biomarkers,” can accurately describe characteristics of a patient's health, disease, or recovery state. Broadly, the spine care community has thus far concentrated on digital biomarkers related to mobility, although the researcher's toolkit is anticipated to expand in concert with advancements in technology. In this review of the nascent literature, we describe the evolution of spine care outcome measurements, outline how digital biomarkers can supplement current clinician-driven and patient-driven measures, appraise the present and future of the field in the modern era, as well as discuss present limitations and areas for further study, with a focus on smartphones (see Supplemental Digital Content, http://links.lww.com/NEU/D809, for a similar appraisal of wearable devices).

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Wearable Technology in Spine Surgery

Cited by 16 publications

References 27 publications

Spine Medical Image Segmentation Based on Deep Learning

Spine Medical Image Segmentation Based on Deep Learning

Effects of physical activity interventions using wearables to improve objectively-measured and patient-reported outcomes in adults following orthopaedic surgical procedures: A systematic review

Digital Biomarkers and the Evolution of Spine Care Outcomes Measures: Smartphones and Wearables

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