2.1. Participants
The participants of this study included 13 cancer survivors who had completed their oxaliplatin-containing chemotherapy and had not received any other neurotoxic agents and 13 healthy controls who were cancer-free and chemotherapy naïve (Table. 1). Participants were included in the study if they were 18 years of age or older, could understand task instruction, and did not have any diagnoses of sensory disorders (e.g., Guillain-Barre syndrome, B12 sensory neuropathy), central nervous system disorders (e.g., spinal cord injury, brain injury, multiple sclerosis, Parkinson’s disease), other systemic medical conditions (e.g., fibromyalgia, rheumatoid arthritis, diabetes), or upper limb injuries. All participants provided written informed consent before data collection. The scientific review committee of Robert H. Lurie Comprehensive Cancer Center and the Institutional Review Board of Northwestern University approved this study.
2.2. Assessment of signs, symptoms, and quality of life related to chemotherapy-induced peripheral neuropathy (CIPN)
Recommended clinical measures, including the European Organization for Research and Treatment of Cancer Quality of Life (EORTC QLQ) CIPN20 and C30 questionnaires, the modified Total Neuropathy Score (mTNS), and a visual analog pain scale, were used to assess OX-related signs and symptoms and quality of life [16].
The EORTC QLQ-CIPN20 questionnaire is a validated instrument that assesses sensory, motor, and autonomic symptoms and functional limitations related to CIPN. It consists of 20 items, and each item is scored from 1 (not at all) to 4 (very much) based on the severity of symptoms experienced by patients. The sensory, motor, and autonomic subscale scores were linearly transformed to a 0–100 scale, with higher scores indicating more severe symptoms [17].
Version 3.0 of the EORTC QLQ-C30 questionnaire is a well-validated and widely used questionnaire designed to assess the impact of cancer and its treatments on the core set of quality of life issues. It consists of 30 items that cover six functional domains (physical, role, emotional, cognitive, social, and global health status) and nine symptoms (fatigue, nausea/vomiting, pain, dyspnea, sleep disturbance, appetite loss, constipation, diarrhea, and financial impact). Except for two global health items that are rated from 1 (very poor) to 7 (excellent), all other items are rated from 1 (not at all) to 4 (very much). Scores from the C30 were linearly transformed to a 0–100 scale, with a higher score indicating worse function and quality of life [17].
The mTNS is a validated measure that consists of both subjective and objective items designed to assess the severity of CIPN. The subjective items ask participants to rate the severity of the sensory, motor, and autonomic symptoms. A licensed physical therapist administered the objective items to test for the presence and severity of the deficits in pinprick sensitivity, vibration sensitivity, muscle strength, and deep tendon reflexes. All items were rated on a 4-point scale. The total score was linearly transformed to a 0–100 scale with a higher score corresponding to worse symptoms [17].
2.3. Proprioception-focused sensorimotor assessment
Three multidirectional sensorimotor tasks (target reaching, force matching, and postural stability) were used to assess the use of proprioceptive information. These sensorimotor tasks were adapted from previous experimental paradigms used to investigate the functional consequence of proprioceptive deficits [13, 15, 18, 19]. Although both kinesthetic and force components of proprioception are needed to complete the tasks, target reaching relies more on limb kinesthesia and force matching on muscular force, whereas maintaining postural stability requires information about both kinesthesia and force. We chose to investigate these tasks in six different directions within the arm workspace to more completely span the range of muscle activations and movements relevant to daily tasks. Multidirectional tasks are likely to involve coordination of multiple arm joints, which are more complex than single-joint movement due to the human arm's anisotropic biomechanical characteristics [20]; thus, they are likely harder to compensate if there are proprioceptive deficits.
Equipment and setup: A three-degree-of-freedom robotic manipulator (Haptic Master; Moog SCS, Nieuw-Vennep, The Netherlands) was used to implement the sensorimotor tasks and record kinematics and force data (Fig. 1). Details of the equipment have been provided previously [21]. Participants sat in a Biodex chair (Biodex Medical Systems, Shirley, NY) with the trunk secured. The hand and wrist were constrained and securely attached to the robot using a custom-fitted plastic orthosis mounted to a gimbal at the end of the manipulator. The orthosis fixed the hand and wrist in a neutral position and extended approximately one-third of the distance from the wrist to the elbow so that the generated forces and motions resulted primarily from actions at the elbow and shoulder. The tested arm was supported against gravity by a passive multilink device (Jaeco, Hot Spring, AR) to avoid fatigue. In the target reaching and postural stability tasks, the robot was used in an admittance control mode so participants could move their arms freely in the horizontal plane. The start position, target position, and hand location were displayed on a monitor covering the arm. A similar arrangement was used in the force matching task except that the robot was used in an isometric mode, and the display showed the voluntary force exerted by the subject on the robot rather than hand position. The feedback cursor of the hand location or force can be switched on and off to emphasize vision or proprioception use. All participants completed a practice session to get familiar with the tasks and conditions. These practice data were not used in subsequent analyses. Participants completed the three tasks in random orders.
Target Reaching: Six positions separated by 15 cm in the workspace across the mid-chest area were used. Only two positions were shown in a trial – a home position and a target position. Participants initiated the trial by moving the hand cursor into the home position. After participants maintained the hand cursor in the home position for 0.5 second, a visual cue (hand cursor turns from red to green) and an audio sound signaled the participants to reach the target. Participants were instructed to use a self-selected reaching speed and maintain the hand cursor in the target until the end of the trial. In trials with visual feedback, the hand cursor was visible for the entire reach; in trials without visual feedback, the hand cursor became invisible after participants initiated the reach. The visible and invisible trials were separated tested in 4 blocks, and the orders of the blocks were randomized. Each reaching trial lasted 7 seconds, and each direction was repeated 6 times.
Force Matching: The Haptic Master robot was set to the isometric mode, preventing participants tested arm from moving freely. Each participant’s hand was set to a position ~ 20 cm across their mid-chest. Six force vectors of 10 N were used. Participants initiated the trial by relaxing their arm (staying in zero force) for 0.5 sec. Then the force feedback cursor turned green, and an audio sound signaled the participants to generate a force that matches the target force vector with visual feedback. Participants were instructed to maintain the force in the target, memorize the force vector, and relax when the target disappears (7 seconds after initiating the trial). After 3 seconds, the target reappeared, and participants were instructed to regenerate the remembered force without visual feedback and maintain the force until the trial ended (7 seconds after the target reappears). Seven seconds was selected to allow sufficient time for participants to generate and maintain a target force, but not too long to cause fatigue. Each force vector was repeated 6 times and completed in 3 testing blocks. Participants took rest breaks in between blocks.
Postural Stability: Participants maintained a static posture with their hand resting ~ 20 cm across their mid-chest while resisting a bias force vector. Participants initiated the trial by entering the home position. Then the HapticMaster robot applied a bias force vector of 5 N that would perturb the participants. Participants were instructed to maintain their hand cursor in the home position while resisting the force. After they stayed in the home position for 3 seconds, the hand cursor's visibility would be switched off during invisible trials and kept visible during visible trials. Participants would continue to hold the bias force until the trial ended after 5 seconds. The orders of visible and invisible trials were randomized. Six bias force vectors were used, and each was repeated 6 times and completed in 3 testing blocks. Participants took rest breaks in between blocks.
Data Analysis: Kinematic and force data of the sensorimotor tasks were sampled at 2 kHz and were smoothed using a 4th order, zero-lag, low-pass Butterworth filter with a cut-off frequency of 8 Hz. Given that participants were allotted 7 seconds to reach a target and match a force, the steady-state of the hand position and force were first identified for the target reaching and force matching tasks, respectively (Fig. 2). The steady hand position was defined as when the rate of position change was maintained below 8% of the maximal reaching speed for 1 second. The steady force was defined as when the rate of force change was maintained below 15% of the maximal force rate for 1 second. For trials with multiple steady states, the earliest one was used. Trials were discarded if the participants could not maintain a steady position or force for 1 second (7.7% of the trials). The data in the steady period were averaged and used to evaluate task performance. We evaluated the accuracy and precision of the performance. Performance accuracy quantifies the x and y errors relative to the target. Given that six targets were used for the force matching and target reaching tasks, we also quantified the radial and tangential errors relative to the target vector (Fig. 1b) to allow reasonable comparisons across different target directions for the two tasks. Performance precision quantified the spread of the data around the mean performance for the same task condition. The standard deviation of the distance of each point from the mean center was used to quantify the spread.
2.4. Statistical analysis
Our central hypothesis was that cancer survivors have impaired use of proprioceptive information in sensorimotor tasks compared to healthy controls. We compared the accuracy and precision of the sensorimotor performance between cancer survivors and controls using mixed-effect models. For performance accuracy, we implemented multivariate and multilevel mixed-effect models. We set the participant group (cancer survivors vs. controls), visual feedback condition (on vs. off), and direction as fixed effects, and subject as a random effect. Task accuracy, measured by the radial and tangential errors in the force matching and target reaching tasks, and x and y errors in the postural stability task, was set as the dependent variable. Separate analyses were performed for each task. For performance precision, we implemented a univariate mixed-effect model. We set the participant group, visual feedback condition, and direction as fixed effects, subject as a random effect, and task precision as the dependent variable. Separate analyses were performed for each task. All trials were considered in the analysis to account for the variability within each subject appropriately. Linear hypothesis tests on the fixed effects were performed using F-tests (implemented by the coefTest function in MATLAB) during the post-hoc analyses. We expected the effects of visual feedback condition and participant group to be significant. Significant interactions between the two factors would indicate that cancer survivors weighted the visual system differently during sensorimotor tasks, consistent with changes in proprioception use.
Lastly, to investigate if the sensorimotor deficits in cancer survivors were related to clinical signs and symptoms of CIPN, we completed a Pearson correlation analysis. We computed the subject mean of performance accuracy and precision for each sensorimotor task and correlated the proprioception-related changes in these parameters to the score of CIPN20, C30, and TNSc, and their sub-scores. We were specifically interested in the sensory and motor sub-scores of CIPN20 as sensory and motor symptoms are common, and these sub-scores might be more relevant to the sensorimotor function.
All statistical analyses were performed in MATLAB (2020b, Mathworks, Natick, MA). Significance was evaluated against a p-value of 0.05.