A Christian Guide to Body Stewardship, Diet and Exercise

Chapter 5: Training for Endurance 110 be used for race distances greater than a 10-km (6.2-mi.), it may not prove as effective, as it does for middistance events, due to the pace being too slow to produce the physiological adaptations associated with LT training. Even so, pace / tempo training may still be beneficial for ultra-endurance athletes as it has been shown to increase both running economy and lactate threshold (LT), which can result in improved run times for longer distance events. As depicted above, the number 60 is the dividend used to calculate the desired run time for 1.0-mile. Table 5.11 provides the required dividend and recommended seconds or minutes to subtract for several other mid-distance and long-distance endurance events. Table 5.11. Required Dividend and Recommended Seconds to Subtract for Various Endurance Events Race Distance (mi.) Required Dividend Seconds to Subtract Minutes to Subtract 1.0 60 10-20 - 1.5 90 15-30 - 2.0 120 20-45 - 3.0 180 30-60 - 3.1 (5-km) 180.6 30-60 - 6.2 (10-km) 361.2 60-90 - 13.1 (½ Marathon) 786 - 3-5 26.2 (Marathon) 1,572 - 10-20 50 3,000 - 45-60 100 6,000 - 90-120 Measuring Intensity for Endurance Training As depicted in Table 5.7, the intensity used for the different types of endurance training is typically based on maximum heart rate (MHR). However, unless you have a heart rate monitor, it can be difficult to accurately assess heart rate while exercising. Additionally, research has shown that MHR can vary significantly between individuals regardless of fitness level or age thereby calling into question the accuracy of using prediction equations to determine MHR (Prevost, 2015). A study by Robergs (2002) demonstrates the variability of the age-predicted maximum heart rate (APMHR) equation (i.e., 220 - Age = MHR). The blue dots in Figure 5.3 represent measured MHR for individuals of various ages. The red line represents estimated MHR for each age group as calculated by the APMHR equation. As demonstrated in the figure, the APMHR equation over-predicted MHR for some individuals and under-predicted MPR for others. This means that if the APMHR equation was used to