Identifying Prognostic Variables for Persistent Upper Limb Dysfunctions After Breast Cancer Treatment: Longitudinal Cohort Study
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Breast Cancer
- Sponsor
- KU Leuven
- Enrollment
- 150
- Locations
- 1
- Primary Endpoint
- Construct validity for the assessment of movement functions
- Status
- Recruiting
- Last Updated
- 2 years ago
Overview
Brief Summary
Breast cancer is the most frequently occurring cancer, assuming that it accounts for 29% of all new cancers in women (European Cancer Information System). The number of long-term survivors is increasing rapidly due to improving accuracy of the detecting methods, the early diagnosis and advances in cancer treatment.
The International Consortium for Health Outcomes Measurement Initiative described upper limb (UL) function as the health outcome that matters most for breast cancer survivors (BCS). 50% of BCS at 6 months post-radiotherapy suffer from of decreased UL function, i.e. difficulties in performing activities of daily living with the upper limb. Patients experiencing UL dysfunctions and other problems are less likely to be physically active. Given that physical inactivity is associated with an increased risk of mortality after breast cancer, taking away the barriers to physical activity (e.g. UL dysfunctions) is very important. Identifying these factors contributing to chronic UL dysfunction is important in terms of identifying targets for prospective evaluation and specific treatment approaches at specific time points during breast cancer treatment.
This study aims to find reliable and valid assessment methods for the prognostic factors and upper limb function itself in a small group of BCS and healthy volunteers.
Investigators
An De Groef
Prof. Dr
KU Leuven
Eligibility Criteria
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Construct validity for the assessment of movement functions
Time Frame: 1 week
The validity of goniometer, digital goniometer (EasyAngle®, Meloq AB, Stockholm) and inclinometer will be assessed for the following tasks: shoulder abduction, flexion, external rotation, and the ULIFT functional task. The inertial sensors are easy to use in daily practice, portable but come with a high cost in comparison to a goniometer, digital goniometer or inclinometer. The scapular movement assessment of the inertial sensors will be compared with the results of the inclinometer and digital goniometer. The glenohumeral movements will be compared with the results of the goniometer, digital goniometer and inclinometer. approximately 1 week between test \& retest
Validity of the use of hot/cold tubes for temperature sensations
Time Frame: 1 week
Hot/cold tubes will be used for the cold and heat detection, but to validate this assessment method is will be compared with thermorollers. approximately 1 week between test \& retest
Validity of the assessment of upper limb activities: quickDASH
Time Frame: 1 week
The quickDASH will be used as comparable sign and dependent variable to evaluate the upper limb function to capture the 'activity and participation level' of the ICF model. The QuickDASH is a shortened version of the DASH Outcome Measure. The QuickDASH uses 11 items to measure physical function and symptoms in people with any or multiple musculoskeletal disorders of the upper limb. The resultant score is reported on a scale of 0 to 100, where 0 represents no disability and 100 represents total disability approximately 1 week between test \& retest
Reliability of measurements with MyotonPro
Time Frame: 1 week
Inter- and intra-rater reliability will be tested. The MyotonPRO assessment for soft-tissue stiffness and elasticity will take place at the pectoralis major, upper trapezius, infraspinatus and teres major region, from which a mean stiffness index (N/m) will be calculated. approximately 1 week between test \& retest
Validity of palpation for general stiffness
Time Frame: 1 week
Palpation for general tissue stiffness scored on a Linkert Scale (0-5) at the different locations will be explored and the sum of total score of all locations will be calculated (0-35). results will be compared with the results of the Myoton pro. approximately 1 week between test \& retest
Reliability for the assessment of movement functions
Time Frame: 1 week
The reliability of goniometer, digital goniometer (EasyAngle®, Meloq AB, Stockholm) and inclinometer will be assessed for the following tasks: shoulder abduction, flexion, external rotation, and the ULIFT functional task. The inertial sensors are easy to use in daily practice, portable but come with a high cost in comparison to a goniometer, digital goniometer or inclinometer. The scapular movement assessment of the inertial sensors will be compared with the results of the inclinometer and digital goniometer. The glenohumeral movements will be compared with the results of the goniometer, digital goniometer and inclinometer. approximately 1 week between test \& retest
Validity of the evaluation tool for Myofascial Adhesions in Patients after Breast Cancer
Time Frame: 1 week
The validity of the evaluation tool for Myofascial Adhesions in Patients after Breast Cancer (MAP-BC evaluation tool), developed during PhD project of Prof. An De Groef will be investigated. This clinical assessment tool consists of the assessment of soft-tissue adhesion at 7 locations at the upper limb region: axillary and breast region scars, pectoral region, axilla, frontal chest wall, lateral chest wall and the inframammary fold. At each location, the degree of the adhesions will be scored at three levels (skin, superficial and deep) on a 4-point scale (between no adhesions and very stiff adhesions). results will be compared with the results of the Myoton pro. approximately 1 week between test \& retest
Validity of machine learning model for Upper limb functioning from ActiGraph sensors for breast cancer survivors
Time Frame: 1 week
The accelerometer to evaluate upper limb functioning will be validated against video annotated data as proposed by Lum and colleagues. Established methods use a machine learning pipeline to accurately and automatically recognize functional upper limb activities from accelerometer data in healthy subjects. These models will be evaluated against video annotated data to assess accuracy and correlation for our subsample of breast cancer survivors. approximately 1 week between test \& retest
Validity of the assessment of upper limb activities through accelerometry
Time Frame: 1 week
The accelerometer will be used as comparable sign and dependent variable to evaluate the upper limb function to capture the 'activity and participation level' of the ICF model. approximately 1 week between test \& retest