Simple and accessible methods were developed to measure the motor aspect of cancer-related fatigue objectively and quantitatively. We describe, in detail, ways to administer the physical fatigue test using a simple handgrip device as well as methods to calculate fatigue indices.
Cancer-related fatigue (CRF) is commonly reported by patients both during and after receiving treatment for cancer. Current CRF diagnoses rely on self-report questionnaires which are subject to report and recall biases. Objective measurements using a handheld dynamometer, or handgrip device, have been shown in recent studies to correlate significantly with subjective self-reported fatigue scores. However, variations of both the handgrip fatigue test and fatigue index calculations exist in the literature. The lack of standardized methods limits the utilization of the handgrip fatigue test in the clinical and research settings. In this study, we provide detailed methods for administering the physical fatigue test and calculating the fatigue index. These methods should supplement existing self-reported fatigue questionnaires and help clinicians assess fatigue symptom severity in an objective and quantitative manner.
Cancer-related fatigue (CRF) is an prevalent and debilitating symptom that is reported by up to 80% of cancer patients1. The National Comprehensive Cancer Network (NCCN) defines CRF as a persistent sense of physical, emotional, and cognitive exhaustion1. The main differentiating characteristics of CRF are the disproportionality to recent activity and the inability of CRF to be relieved by rest1. As a result, CRF severely impacts patients' participation in daily activities and their health-related quality of life1.
The current assessment of CRF relies primarily on self-report questionnaires2. As a result, symptom severity which is measured using self-reports is subject to recall and reporting biases and can be influenced by the specific questionnaire and cutoff scores used to assess CRF3. As a multidimensional construct, the physical dimension of CRF has been shown to correlate with daily activity changes and a need for daytime naps4, whereas the influence of CRF on physical functioning is less explored. To this date, CRF remains an underdiagnosed and undertreated symptom with no well-defined underlying mechanism or treatment option1. To better understand this debilitating condition, there is an increasing need to measure CRF and its dimensions objectively and quantitatively.
Physical fatigue refers to an inability to maintain the required force during sustained contractile activity5. The subsequent compromised daily functioning as a result of not being able to carry out daily tasks (e.g., carrying grocery bags, lifting and holding an object) greatly affects the health-related quality of life, especially in older adults, and contributes to future injuries6,7. Various tools have been developed to quantify physical impairment including physical performance tests, such as the 6 min walk test (6MWT) and sit-to-stand test (STS), as well as wearable physical activity monitors, such as actigraphy devices and fitness trackers8,9,10. Physical performance tests such as 6MWT and STS are easy to administer and do not require special equipment10. However, the reliability and success of such tests require clinician training and logistical requirements such as a 30 m corridor10. Wearable activity monitors allow for automated data collection and longitudinal symptom monitoring11. However, these activity monitors often need to be worn for multiple days, and patient compliance can be an issue11. In addition, the large amount of data collected using activity monitors can be challenging to process, making it difficult to derive clinically meaningful information11.
The handheld dynamometer, or instrumented handgrip device with computer-assisted data acquisition, is a portable apparatus that measures grip strength. Handheld dynamometry has been used to test motor fatigue and impairment in disease conditions that typically involve the motor system including motor neurons and muscular problems12. Recent work has demonstrated an association between self-reported subjective CRF scores and motor fatigue measured using a handgrip static fatigue test13. Handgrip fatigue tests are particularly suitable for clinical use due to their reliability and time efficiency, requiring a few minutes to complete14,15. Furthermore, handgrip fatigue tests can be pre-programed, ensuring data reproducibility7. Administering the handgrip test requires minimal training on the part of the test administrator and can be easily implemented in a clinical setting given a standardized protocol. Using self-reported fatigue questionnaires in conjunction with the handgrip fatigue test should provide additional tools for clinicians to screen, monitor, and manage fatigue symptoms in cancer patients.
The lack of standardized consensus methods has limited the adoption of the handgrip fatigue test in the clinics16. In this current work, we outline three different methods to use the handheld dynamometer to quantify motor fatigue objectively. The utility of each method should be tested in each cancer population to ensure it accurately distinguishes between fatigued and non-fatigued subjects. We also outline methods to calculate the fatigue index for each handgrip fatigue test. The goal of this work is to provide a comprehensive toolkit to supplement self-reported questionnaires and to standardize CRF physical performance measurement accurately and objectively.
Here, we provide three different methods for measuring the physical dimension of CRF. Motor fatigue tests using handheld dynamometers are simple and easily adaptable for clinical use. Since many variations of the test exist in the literature, our goal was to provide standardized methods to administer these tests and decrease the need for extensive in-person trainings for clinicians.
Although the fatigue tests outlined in this study demonstrate good test-retest reliability7</s…
The authors have nothing to disclose.
This study is fully supported by the Division of Intramural Research of the National Institute of Nursing Research of the NIH, Bethesda, Maryland.
Quantitative Muscle Assessment application (QMA) | Aeverl Medical | QMA 4.6 | Data acquisition software. NOTE: other brands/models can be used as long as the software records force over time. |
QMA distribution box | Aeverl Medical | DSTBX | Software distribution box which connects the handgrip to the software. |
Baseline hand dynamometer with analog output | Aeverl Medical | BHG | Instrumented handgrip device with computer assisted data acquisition. NOTE: other brands/models can be used as long as the instrument measures force over time |