Two-week step-reduction has limited negative effects on physical function and metabolic health in older adults.

PURPOSE: This study determined the effects of a 2-week step-reduction period followed by 4-week exercise rehabilitation on physical function, body composition, and metabolic health in 70-80-year-olds asymptomatic for injury/illness. METHODS: A parallel-group randomized controlled trial (ENDURE-study, NCT04997447) was used, where 66 older adults (79% female) were randomized to either intervention or control group. The intervention group reduced daily steps to < 2000, monitored by accelerometer, for two weeks (Period I) and then step-reduction requirement was removed with an additional exercise rehabilitation 4 times per week for 4 weeks (Period II). The control group continued their habitual physical activity throughout with no additional exercise intervention. Laboratory tests were performed at baseline, after Period I and Period II. The primary outcome measure was leg lean mass (LLM). Secondary outcomes included total lean and fat mass, blood glucose and insulin concentration, LDL cholesterol and HDL cholesterol concentration, maximal isometric leg press force (MVC), and chair rise and stair climb performance. RESULTS: LLM remained unchanged in both groups and no changes occurred in physical function nor body composition in the intervention group in Period I. HDL cholesterol concentration reduced after Period I (from 1.62 ± 0.37 to 1.55 ± 0.36 mmol·L-1, P = 0.017) and returned to baseline after Period II (1.66 ± 0.38 mmol·L-1) in the intervention group (Time × Group interaction: P = 0.065). MVC improved after Period II only (Time × Group interaction: P = 0.009, Δ% = 15%, P < 0.001). CONCLUSION: Short-term step-reduction in healthy older adults may not be as detrimental to health or physical function as currently thought.

Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network.

Oxygen uptake (V˙O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant use by consumers due to their costs, difficulty of operation and their intervening in the physical integrity of their users. Therefore, it is important to develop approaches for the indirect estimation of V˙O2-based measurements of motion parameters, heart rate data and application-specific measurements from consumer-grade sensors. Typically, these approaches are based on linear regression models or neural networks. This study investigates how motion data contribute to V˙O2 estimation accuracy during unconstrained running and walking. The results suggest that a long short term memory (LSTM) neural network can predict oxygen consumption with an accuracy of 2.49 mL/min/kg (95% limits of agreement) based only on speed, speed change, cadence and vertical oscillation measurements from an inertial navigation system combined with a Global Positioning System (INS/GPS) device developed by our group, worn on the torso. Combining motion data and heart rate data can significantly improve the V˙O2 estimation resulting in approximately 1.7-1.9 times smaller prediction errors than using only motion or heart rate data.