Guest Editorial to accompany Vasomotor responses in glabrous and nonglabrous skin during sinusoidal exercise

In the current issue of the journal, Yamazaki (5) describes research on vasomotor responses of glabrous and nonglabrous skin during exercise, and the possible underlying mechanisms accounting for these differences. The research is noteworthy not only for the novel and important observations but also for the unique experimental approach and data analyses used to study temporal characteristics of the vasomotor response to exercise. Yamazaki’s innovative use of exercise to study the temporal interrelationship of vasomotor responses for two skin types by rhythmically varying the intensity while subjects exercised presumably results in intermittent activation of the sympathetic nervous system and intermittent changes in blood pressure as exercise intensity waxes and wanes. This approach facilitated the observation that glabrous (palm) and nonglabrous (dorsal hand and forearm) skin respond to exercise differently from each other. Perhaps the most important aspect of the exercise construct was that sinusoidal exercise was not started until after warm-up exercise (35% peak V̇O2) had been done for 20 min so as to activate the subjects’ vasomotor and sudomotor responses. To begin the sinusoidal exercise period, exercise intensity was increased to 60% peak V̇O2 for 2 min and then reduced to 10% for 2 min. This sinusoidal pattern of changing exercise intensity continued until 10 cycles were completed. The vasomotor and sudomotor responses for each exercise bout were pooled and analyzed together. This approach proved to be effective in resolving skin vasomotor response differences between nonglabrous and glabrous skin. The other noteworthy aspect of Yamazaki’s experimental approach was the use of time series analyses to tease out temporal relationships within the data. Time series analyses have historically yielded fundamental insights into identifying physiological patterns (1,3,4) of change in humans so that mechanisms controlling these state changes could be understood (1,3). One recent example of this approach was used to identify gender differences in heart rate regulation based on analyses of R-R intervals by Evans et al. (2). Sinusoidal exercise was effective to observe the temporal characteristics and the magnitude of responses for skin perfusion (1). Yamazaki et al. (6) have used this experimental approach previously to study differences in sweating responses of trained and untrained men. Other researchers might also consider using sinusoidal exercise model, because it allows for several minutes of low-intensity exercise interspersed with more intense exercise. This trade-off should make sinusoidal exercise easier to perform by populations with reduced exercise capacity. The amplitude reported for cardiac frequency averaged 131 beats·min−1 by the end of the 60% peak V̇O2 bout. Further, this exercise method resulted in only a modest increase in esophageal temperature. By limiting heat storage associated with exercise, sinusoidal exercise may be safer to use than steady state exercise for studies of patients with cardiac disease, hypertension or diabetes. Thus, the exercise model provides a tool for researchers to study how disease states affect the temporal characteristics of vasomotor regulation, and Yamazaki demonstrates that researchers now have desktop access to the computing power necessary for complex time series analyses of large data sets. However, let’s not forget the importance of replicating novel research findings by other investigators before the exercise construct is broadly applied to study exercise-compromised populations. The research reported by Yamazaki shows an innovative use for exercise that I believe could be used to discover how physiological adaptations to chronic disease, aging and gender, and other states affect the temporal relationship and magnitude of vasomotor responses with different skin types and with other systemic responses, such as cardiac responses. Keep in mind what novel and important information might be derived from data by looking at its temporal characteristics as you design your next research study or complete analysis of current research.