Significant progress has been made in the development of countermeasures to attenuate the negative consequences of prolonged exposure to microgravity on astronauts’ bodies. Deconditioning of several organ systems during flight includes losses to cardiorespiratory fitness, muscle mass, bone density and strength. Similar deconditioning also occurs during prolonged bedrest; any protracted time immobile or inactive, especially for unwell older adults (e.g., confined to hospital beds), can lead to similar detrimental health consequences. Due to limitations in physiological research in space, the six-degree head-down tilt bedrest protocol was developed as ground-based analogue to spaceflight. A variety of exercise countermeasures have been tested as interventions to limit detrimental changes and physiological deconditioning of the musculoskeletal and cardiovascular systems. The Canadian Institutes of Health Research and the Canadian Space Agency recently provided funding for research focused on Understanding the Health Impact of Inactivity to study the efficacy of exercise countermeasures in a 14-day randomized clinical trial of six-degree head-down tilt bedrest study in older adults aged 55–65 years old (BROA). Here we will describe the development of a multi-modality countermeasure protocol for the BROA campaign that includes upper- and lower-body resistance exercise and head-down tilt cycle ergometry (high-intensity interval and continuous aerobic exercise training). We provide reasoning for the choice of these modalities following review of the latest available information on exercise as a countermeasure for inactivity and spaceflight-related deconditioning. In summary, this paper sets out to review up-to-date exercise countermeasure research from spaceflight and head-down bedrest studies, whilst providing support for the proposed research countermeasure protocols developed for the bedrest study in older adults.

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Oxygen consumption ($$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 ) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 from wearable sensors during cycle ergometer exercise using a temporal convolutional network (TCN). Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 . Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (−22 ml min−1, [−262, 218]), spanning transitions from low–moderate (−23 ml min−1, [−250, 204]), low–high (14 ml min−1, [−252, 280]), ventilatory threshold–high (−49 ml min−1, [−274, 176]), and maximal (−32 ml min−1, [−261, 197]) exercise. Second-by-second classification of physical activity across 16,090 s of predicted $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 was able to discern between vigorous, moderate, and light activity with high accuracy (94.1%). This system enables quantitative aerobic activity monitoring in non-laboratory settings, when combined with tidal volume and heart rate reserve calibration, across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.