The Validity of Physical Therapy Assessment of Low Back Pain via Telerehabilitation in a Clinical Setting

Truter, Piers; Russell, Trevor; Fary, Robyn

doi:10.1089/tmj.2013.0088

Cited by 80 publications

(102 citation statements)

References 41 publications

(60 reference statements)

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“…Truter et al investigated the validity of the assessment of low back pain by means of telehealth as compared to face‐to‐face physical therapist assessments of spinal posture, active lumbar spine movements, and passive straight leg raise (SLR). They reported high levels of agreement in identifying pain with specific lumbar movements, eliciting symptoms, and with discerning response to SLR; moderate agreement with detecting which the relationship of the direction of spine movement and pain, SLR range of motion, and active lumbar spine range of motion and poor agreement with postural analysis and identifying limitations to lumbar movements . Correlations between telehealth and in‐person assessment of functional outcome measures or special musculoskeletal exam maneuvers have been assessed.…”

Section: Specific Telehealth Applications For Neuromuscular or Musculmentioning

confidence: 99%

“…They reported high levels of agreement in identifying pain with specific lumbar movements, eliciting symptoms, and with discerning response to SLR; moderate agreement with detecting which the relationship of the direction of spine movement and pain, SLR range of motion, and active lumbar spine range of motion and poor agreement with postural analysis and identifying limitations to lumbar movements. 62 Correlations between telehealth and inperson assessment of functional outcome measures or special musculoskeletal exam maneuvers have been assessed. A pilot study found good reliability for 7 of their 9 outcome measures, including the Oswestry Disability Index, anterior SLR test, 12-Item Short Form Health Survey questionnaire, and visual analog scale for pain.…”

Section: Specific Telehealth Applications For Neuromuscular or Musculmentioning

confidence: 99%

See 1 more Smart Citation

Telehealth applications for outpatients with neuromuscular or musculoskeletal disorders

Howard

Kaufman

2018

Muscle and Nerve

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Telehealth describes the provision of medical services remotely through technology, and may enhance patient access to specialty care services. Although teleneurology has expanded widely since the introduction of telestroke in 1999, telehealth services for outpatients with neuromuscular or musculoskeletal disorders are less widespread. In this narrative review, we will describe the current technology, applications, outcomes, and limitations of this dynamically growing field. Evidence for telehealth applications related to neuromuscular diseases, palliative care, specialized multidisciplinary services, and musculoskeletal care are reviewed. With growing demand for specialized services and finite resources, telehealth provides a promising avenue to promote access to high-quality care, decrease the cost and burden of travel for patients, and with the expansion of software to personal computing and mobile devices, offer flexible, low-overhead practice opportunities for clinicians. Providers embarking on careers in telehealth should be aware of current legal restrictions impacting care to minimize risk and avoid liability. Muscle Nerve 58: 475-485, 2018.

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Section: Specific Telehealth Applications For Neuromuscular or Musculmentioning

confidence: 99%

Section: Specific Telehealth Applications For Neuromuscular or Musculmentioning

confidence: 99%

Telehealth applications for outpatients with neuromuscular or musculoskeletal disorders

Howard

Kaufman

2018

Muscle and Nerve

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“…In a study on remote assessment of low back pain, Truter et al also addressed the practicality of TR, in particular by PTs in rural clinical settings. The participant satisfaction was good overall, with acceptable performance in assessments, strong correlation with in-person results, and high reliability scores [16]. This emerging evidence is encouraging for the use of TR as a supplement to or even in place of face-to-face sessions.…”

Section: Literature Reviewmentioning

confidence: 65%

Development of telerehabilitation application with designated consultation categories

Tan¹,

Narayanan²,

Koh³

et al. 2014

J Rehabil Res Dev

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Abstract-Telerehabilitation (TR) is getting ever more popular because it is effective in bringing rehabilitation services to rural populations by means of audiovisual systems and its initial implementation studies presented encouraging results. TR is proven to be helpful, with benefits in terms of reduced travel time, cost, and availability of specialists' support in local communities. However, TR systems that are usable under low-bandwidth network environments are rare. This article introduces the development of a TR system with customized consultation categories for users to choose from, depending on requirements. Each category, with its preset parameter values, is discussed in detail by demonstrating relevant rehabilitation exercises. A novel bandwidth adaptation algorithm is also presented for optimal utilization of the dynamic network conditions, which ensures the system functionality even under narrow-bandwidth environments. Experiment results show that the system is able to perform effectively in each consultation category while the rehabilitation exercises are being performed. The proposed algorithm is also verified for its ability to adapt the content quality and effectively utilize the network under constrained conditions. A survey conducted on the video quality of the system under low-bandwidth conditions shows encouraging results for a large-scale deployment of the application.

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“…Besides, it only requires a wearable device equipped with IMUs, which is the most frequently used sensors for fitness trackers or smart watches. The simplicity of the proposed method is what makes it possible to incorporate the method into tele-rehabilitation applications that deliver rehabilitation services-including rehabilitation interventions [55], clinical assessments [56], and consultations [57]-over tele-communication networks as well as the Internet [58].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

Jungyeon

Park

Lee

et al. 2020

Sensors

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Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms—including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons—were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.

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The Validity of Physical Therapy Assessment of Low Back Pain via Telerehabilitation in a Clinical Setting

Cited by 80 publications

References 41 publications

Telehealth applications for outpatients with neuromuscular or musculoskeletal disorders

Telehealth applications for outpatients with neuromuscular or musculoskeletal disorders

Development of telerehabilitation application with designated consultation categories

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

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