Measuring Adherence to Home Shoulder Physiotherapy With Artificial Intelligence

Study Overview
=================
Brief Summary
-----------------
An important part of recovery for shoulder injuries, is sticking to the exercise regimen that is prescribed by a physiotherapist. Currently, there is no proper way to measure whether patients are correcting doing their prescribed exercises at home. Researchers at Sunnybrook have tested out a Smart Physiotherapy Recognition System (SPARS), which consists of a watch that patients can wear while they are performing their physiotherapy exercises. The watch aims to learn how the exercises are done correctly when worn during supervised physiotherapy sessions, and then to record and compare whether those same exercises are being done correctly in a home setting. The main objectives of this study aims to test whether the SPARS system can effectively measure whether physiotherapy exercises are being done properly when they are done without physiotherapist supervision. Secondly, to examine whether the recovery process after shoulder injuries is improved if patients perform the physiotherapy exercises correctly.

Detailed Description
-----------------
A research assistant will conduct in-person data collection on 120 patients, by recording patients' exercises during their supervised physiotherapy sessions each week using the SPARS smart watch. Each patient will wear smart watch on their affected arm only when performing prescribed exercises, and the watch will start recording inertial sensor data once the patient puts the watch on. The injured worker population will be recruited from referrals made to the Sunnybrook Working Condition Program (WCP) at the Holland Centre for a work-related shoulder injury. The patient population that is being funded by OHIP, will be recruited from referrals Sunnybrook Shoulder and Upper Extremity Program. Data collection will be collected at baseline (at time of informed consent), and at each supervised physiotherapy session during the course of a patient's treatment. A research assistant will be required to attend a minimum of one supervised physiotherapy session for each patient every two weeks, and record exercise type and technique, alongside the inertial data collection, based on feedback from the treating physiotherapist. Follow-up outcomes such as surveys and shoulder testing will be done at 6 weeks, monthly at a maximum of 3 months (OHIP-patients) and 5 months (WSIB) patients, final treatment session (if different than maximum amount), and 1 year after.

Official Title
-----------------
Measuring Adherence to Home Shoulder Physiotherapy With Artificial Intelligence

Conditions
-----------------
Rotator Cuff Pathology

Intervention / Treatment
-----------------
* Device: Smart Physiotherapy Activity Recognition System (SPARS)


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: Males and females over the age of 18 Be diagnosed with rotator cuff tendinosis, shoulder impingement syndrome or a rotator cuff tear Will be undergoing planned conservative management Be able to participate in home physiotherapy exercises Exclusion Criteria: Upper extremity neurological deficit Have had a previous failed surgery on the shoulder that is currently being treated Undergoing simultaneous treatment for both shoulders

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Treatment
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Injured Worker Population<br>Participants that have a confirmed rotator cuff pathology, and are undergoing physiotherapy at the Holland Centre for a work-related shoulder injury as part of the Working Condition Program. | Device: Smart Physiotherapy Activity Recognition System (SPARS)<br>* Wearable smart watch that records inertial data such as (accelerometer, gyroscope magnetometer) while patients are performing physiotherapy exercises.<br>|
| Active Comparator: OHIP (funded) Patient Population<br>Participants that have a confirmed rotator cuff pathology, and are undergoing physiotherapy at the Holland Centre as part of the Shoulder Program. | Device: Smart Physiotherapy Activity Recognition System (SPARS)<br>* Wearable smart watch that records inertial data such as (accelerometer, gyroscope magnetometer) while patients are performing physiotherapy exercises.<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Physiotherapy participation (smart watch inertial data) | Watch will begin recording inertial sensor data when it is put on during supervised and home physiotherapy exercises, and will stop recording when it is removed. | Up to 3-5 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Work status | Clinical and return to work outcome data Full-time, part-time, off-work, modified or regular duties | Up to 1 year | 
| Numeric Pain Rating Scale (NPRS) | Scale range: 0 to 10 (whole number integers). Higher scores represent more pain. | Up to 1 year | 
| The Disabilities of the Arm, Shoulder and Hand (DASH) Score | Self administered questionnaire to self rate upper extremity disability and symptoms. Comprises of a 30-item disability/symptom scale, and higher scores represent more symptoms/disability. | Up to 1 year | 
| Strength testing | Rotator cuff strength testing using manual muscle testing as well as strength testing with a dynamometer | Up to 12 weeks | 
| Shoulder active range of motion | Measured by handheld goniometer | Up to 12 weeks | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Rehabilitation, Artificial Intelligence, Wearable Electronic Devices, Adherence