Spatiotemporal Patterns of Pain During Exercise

1. Intro Pain recently was defined as distressing experience associated with actual or potential tissue damage with sensory, cognitive, emotional, and social components. Pain can be seen as perceptual marker within physical exercise. Individuals can report on a number of perceptual markers such as the changes in exertive pain which was shown to be a distinct source of information than reports of PE during cycling for instance. The perceptual markers of pain are still little investigated within exercise settings. From another side, to best understand exertive pain, one needs to consider several dimensions of pain including the sensory-discriminative, affective-motivational, and the cognitive-evaluative dimensions. Several number of studies within the exercise science have focused on the sensory-discriminative dimension of pain. Results from these have suggested a linear relationship between pain perception and the power output in diverse effort settings including cycling, static and dynamic handgrip squeezing, and incremental treadmill running. Indeed, the comparison of ratio of leg pain to arm pain during cycling and running has also demonstrated that the ratio of leg to arm pain increases across higher intensities. Most of the extant work however centred on pain and PE from a group-based standpoint to lead to group-based conclusions. Traditionally, within exercise science, variables are measured at distinct, low-frequency time points, and then are either averaged over the course of the trial or reported at the measured time. In turn, such practices make it difficult to measure dynamic feed-forward and feedback control mechanisms inherent in these variables. Taken these together, studying pain and discomfort from an individual, case-by-case standpoint, in a high-frequency time points setting, may present unique benefits. Considering that the physiological and psychological variables (e.g., PE) present fluctuations during heavy constant-power cycling when reporting at a measurement frequency of 15s it seems plausible to assume that pain locations can also fluctuate. Consequently, it may be useful to distinguish amongst differential types of spatiotemporal pain fluctuations patterns during exercise. Mapping these dynamics can help predict individual-specific fatigue in the course of exercise. The functional role of fluctuating dynamics has already been studied in psychological (e.g., volitional states, thought processes) and kinematic variables during constant-power exercise performed until volitional exhaustion. These approaches are important in that the suppression of the intra- individual variability performed by a statistical averaging may hide the properties of individual systems. In this vein, we tend to study and delineate individualized pain and discomfort patterns in exercise. The purpose of this study will be to study the individual spatiotemporal structure and dynamics of topologically defined areas of perceived body discomfort and pain during exercise.
2. Methods 2.1 Participants To determine the sample size for this study a power analysis was conducted using G*Power 3.1. In studies of thought processes larger effect sizes have been reported. Thus, using an effect size of d = 1.0, α < 0.05, power (1 - β) = 0.80, a sample size of n = 11 emerged. Caucasian physical education students (age range: 18 - 30 years) undergoing regular aerobic exercise will be recruited to participate voluntarily in the study. Students will receive oral and written information regarding the study. They will be additionally encouraged to ask questions if there would still any ambiguities.

2.2 Materials and procedure Completion of this study will took a total of three sessions, separated by one-week intervals, and each of approximately 30 minutes. At week one, participants will complete a baseline incremental cycling and running tests to determine the workload and velocity values corresponding to their RPE (6-20 Borg's scale) = 15 (i.e., heavy). At this time, participants also will be familiarized with the use of body maps and reporting procedures. At week two and three, participants will complete the constant-power cycling and constant velocity running tasks, respectively, in a counter-balanced assigned order.

2.2.1. Monitoring for Discomfort and Pain Every 15s during exercise, upon the researcher's prompts, participants will report bodily locations with discomfort and pain on a body map scale.

2.2.2. Constant-power Cycling Task The task will include an incremental warm-up session and a constant-power cycling which will be performed up to volitional exhaustion. The constant-power cycling will begin when participants will report RPE = 15 during the incremental warm-up and the cycling task will last up to volitional exhaustion. The end will be set when participants won't be able to cycle longer at the fixed cadence for five consecutive seconds at the sitting position.

2.2.3. Constant-velocity Running Task The task will include an incremental warm-up (identical to the running baseline test) and a constant velocity run which will be performed up to volitional exhaustion. The constant velocity run will start when participants will achieve and report RPE = 15 during the incremental warm-up and will last up to volitional exhaustion when they could no longer be able to maintain the imposed velocity.

2.2.4. Commitment check Upon the completion of all testing procedures participants will answer questions about complete commitment to measure their commitment levels to the reporting tasks (a) and commitment to task (b), on an 11-point Likert-type scale with anchors ranging from 0 (not at all) to 10 (greatly).

2.3 Data collection and analysis All the locations for each participant will be transformed to binary vectors. Investigators will analyse the effects of the time steps using repeated measures ANOVAs. Principal component analysis (PCA) will be used to reduce the dimensionality of the local discomfort and/or pain data (that will be obtained from the 50-item pain map). The data and time of each testing session will be reported in a list. All data can be reconstructed for each participants.