Estimating Proximity To Failure Using Perceived Repetitions In Reserve In Adults Treated For Cancer

PURPOSE: There is a consensus from governing bodies that more evidence is needed in understanding how to properly prescribe, monitor and progress resistance training (RT) in oncology populations. Repetitions in reserve (RIR) has been demonstrated as a quick/easy tool to monitor proximity to failure in older adults and apparently healthy populations. However, the validity of RIR in RT in oncology has yet to be examined. Therefore, the purpose of this study was to assess the accuracy of RIR predications in individuals treated for cancer in a pragmatic setting. METHODS: Individuals treated for cancer (n = 25) enrolled in an 8-week RT intervention, completed RIR testing sessions at weeks 2 and 8 of the study. Each testing session involved the participant estimating a 2 RIR prediction, and then continuing to complete repetitions until momentary failure was achieved. Generalized linear mixed models (GLMM) were performed on both relative and absolute RIR difference values and a binary logistic regression model was performed on number of accurate predictions (±2). All statistical analyses were performed in R. RESULTS: Individuals treated for cancer (n = 25, 52% breast, 32% colorectal; age 62 ± 10.16 y; BMI 30.91 ± 5.62) participated in the program with 48% of the sample identifying as black and 44% as white non-Hispanic origin, 8% identifying as “Other”. The raw prediction (the difference between predicted RIR and actual RIR) was -2.22 at week-2, and was -2.48 at week-8, with no significant differences between timepoints. Preliminary GLMM data revealed time had negligible effects on both raw RIR values (estimate = -0.011, p = 0.787) and absolute RIR values (estimate = -0.040, p = 0.699). The median scaled residuals for the raw RIR model (0.100) and absolute RIR model (-0.171) suggest a satisfactory fit between performed and predicted values. The binary logistic regression model demonstrated negligible effects of time on the probability of accurate predictions (estimate = 0.036, p = 0.636). CONCLUSIONS: Raw RIR prediction at weeks 2 and 8 were outside the range of acceptable accuracy (±2), indicating a need for caution when solely relying on RIR to prescribe RT load in oncology. More research is warranted to understand RIR predictions with different training loads, anchoring procedures/scale familiarization, and intervention lengths.