Measuring the Motor Aspect of Cancer-Related Fatigue using a Handheld Dynamometer

Summary

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.

Abstract

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.

Introduction

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 injuries^6,7. 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 trackers^8,9,10. 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 complete^14,15. 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.

Protocol

The current study (NCT00852111) was approved by the Institutional Review Board (IRB) of the National Institutes of Health (NIH). Participants enrolled in this study were 18 years of age or older, diagnosed with non-metastatic prostate cancer with or without prior prostatectomy, and scheduled to receive external beam radiation therapy at the Radiation Oncology Clinic of the NIH Clinical Center. Potential participants were excluded if they had a progressive disease that could cause significant fatigue, had psychiatric dise…

Representative Results

Representative force (kg) versus time (s) traces are shown in Figure 1. During the static fatigue test, subjects typically reach maximal strength (Fmax) within 2–3 s23. Self-reported fatigue in subjects was measured based on previous studies3. The absence of Fmax (±10% MVIC) within 3 s indicates insufficient effort23. To prevent this issue, verbal encouragement should be provided. Both su…

Discussion

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 reliability^7</s…

Materials

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