Meta-session autoregulation, a person-adaptive form of exercise prescription that adjusts training variables according to daily fluctuations in performance considering an individual's daily fitness, fatigue, and readiness-to-exercise is commonly used in sports-related training and may be beneficial for non-athlete populations to promote exercise adherence. To guide refinement of meta-session autoregulation, it is crucial to examine the existing literature and synthesize how these procedures have been practically implemented. Following PRIMSA guidelines a scoping review of two databases was conducted from August 2021 to September 2021 to identify and summarize the selected measures of readiness-to-exercise and decision-making processes used to match workload to participants in meta-session autoregulatory strategies, while also evaluating the methodological quality of existing study designs using a validated checklist. Eleven studies reported utilizing a form of meta-session autoregulation for exercise. Primary findings include: (i) readiness-to-exercise measures have been divided into either objective or subjective measures, (ii) measures of subjective readiness measures lacked evidence of validity, and (iii) fidelity to autoregulatory strategies was not reported. Results of the risk of bias assessment indicated that 45% of the studies had a poor-quality score. Existing implementations of meta-session autoregulation are not directly translatable for use in health promotion and disease prevention settings. Considerable refinement research is required to optimize this person-adaptive strategy prior to estimating effects related to exercise adherence and/or health and fitness outcomes. Based on the methodological deficits uncovered, researchers implementing autoregulation strategies would benefit reviewing existing models and frameworks created to guide behavioral intervention development.
Keywords: Individualization; behavioral treatment development; health behavior; optimization; precision behavioral medicine; training variability.