Unconscious physiological response of healthy volunteers to dynamic respiration-synchronized couch motion

Background Intrafractional motion can be a substantial uncertainty in precision radiotherapy. Conventionally, the target volume is expanded to account for the motion. Couch-tracking is an alternative, where the patient is moved to compensate for the tumor motion. However, the couch motion may influence the patient’s stress and respiration behavior decreasing the couch-tracking effectiveness. Methods In total, 100 volunteers were positioned supine on a robotic couch, which moved dynamically and respiration synchronized. During the measurement, the skin conductivity, the heartrate, and the gaze location were measured indicating the volunteer’s stress. Volunteers rated the subjective motion sickness using a questionnaire. The measurement alternated between static and tracking segments (three cycles), each 1 min long. Results The respiration amplitude showed no significant difference between tracking and static segments, but decreased significantly from the first to the last tracking segment (p < 0.0001). The respiration frequency differed significantly between tracking and static segments (p < 0.0001), but not between the first and the last tracking segment. The physiological parameters and the questionnaire showed mild signals of stress and motion sickness. Conclusion Generally, people tolerated the couch motions. The interaction between couch motion and the patient’s breathing pattern should be considered for a clinical implementation. Trial registration The study was registered at ClinicalTrials.gov (NCT02820532) and the Swiss national clinical trials portal (SNCTP000001878) on June 20, 2016. Electronic supplementary material The online version of this article (10.1186/s13014-017-0925-6) contains supplementary material, which is available to authorized users.

1. Sheet for Questionnaire 1 Fig. 1: The first questionnaire (in German), which was filled in by each volunteer before the measurement procedure.
The first part of the questionnaire shown in Fig. 1 asks for the gender of the volunteer (female, male), the body weight in kg, and the age in years. The second part asks for the history of motion sickness, whether it has happened before (yes, no), and if yes, how often. The possible answers to tick are: • once or twice • sometimes on a winding road • sometimes on a boat • often on a winding road • often on a boat • always on a winding road • always on a boat • Any other possible situations. In such a case, the volunteers were asked to describe the situation and write how often it happened.
2. Sheet for Questionnaire 2 Fig. 2: The second questionnaire (in German), which was filled in after the volunteers left the couch after the measurement procedure. This questionnaire is the German version of the questionnaire developed in [1].
The English version of the statements are (in the same order as shown in Fig. 2 and taken directly from

Normalization of external respiratory signal
The normalization of the peak-to-peak amplitude of the external respiration signal required an estimate of the actual peak-to-peak amplitude of the external signal as a normalization value. The estimation approach was to detect the inspiration and expiration peaks and sequentially computing their differences. Then, the normalization value was the mean of these differences. However, the real-time detection of the peaks tended to lag behind the actual peaks, especially the inspiration peaks (Fig. 5). The detection approach was to do a linear fit of the last 0.8 s and consider the zero-crossings of the slope of the linear fit. This approach caused a systematic underestimate of the peak-to-peak amplitude. The underestimate explains the difference between the resulting median fictitious tumor motion peak-to-peak amplitude of 14 mm and the preset multiplication value of 11 mm for the fictitious tumor motion (see section Motion trajectory).

Fig. 5:
Example for the peak detection results. The peak detection of the inspiration peaks lagged behind, which led to an estimate of the peak-to-peak amplitude smaller than the actual peak-to-peak amplitude.
6. Histograms of respiration characteristics and physiological signals

Questionnaire 2 scores and motion sickness history
The scores of Questionnaire 2 seem to be only very slightly dependent on motion sickness history. Volunteers with motion sickness history tended to report slightly higher scores. Fig. 13: Box plots of the four dimensions of motion sickness (Questionnaire 2). Each panel shows the scores (median, quartiles and whiskers, which include all data points below a given distance from the respective quartile) of volunteers that either reported in Questionnaire 1 to have experienced motion sickness before (yes) or did not (no).

Respiration characteristics and scores of questionnaire 2
The respiration characteristics (amplitude and frequency) do not seem to correlate to the scores of Questionnaire 2 (Fig. 14 and Fig. 15). The Pearson correlation coefficients are generally small and the corresponding p-values not significant.

Physiological measurements and scores of Questionnaire 2
The skin conductivity increase during the first tracking segment does not seem to correlate with Questionnaire 2 scores, see Fig. 16. The Pearson correlation coefficients are generally small and the corresponding p-values not significant. Fig. 16: Scatterplot of the difference of the skin conductivity during the first tracking segment and the mean of the first and second static segments, and the corresponding score of Questionnaire 2. Each blue cross corresponds to one volunteer. Above each panel is the linear Pearson correlation coefficient (rho) with the corresponding p-value (p). The black line shows the best linear fit.
The heartrate difference between tracking and static segments does not seem to correlate with Questionnaire 2 scores, see Fig. 17. The Pearson correlation coefficients are generally small and the corresponding p-values not significant. The gaze-point deviation difference between tracking and static segments does not seem to correlate with Questionnaire 2 scores, see Fig. 18. The Pearson correlation coefficients are generally small and the corresponding p-values not significant. Fig. 18: Scatterplot of the average difference of the gaze-point deviation during the tracking segments and the static segments, and the corresponding score of Questionnaire 2. Each blue cross corresponds to one volunteer. Above each panel is the linear Pearson correlation coefficient (rho) with the corresponding p-value (p). The black line shows the best linear fit.