Mattsson, C. M. | bioRxiv Science

bioRxiv2017

Physiological factors of importance for load carriage

The energy expenditure during carrying no load, 20, 35 and 50 kg at two walking speeds, 3 and 5 km h-1, was studied in 36 healthy participants, 19 men (30 {+/-} 6 yrs, 82.5 {+/-} 7.0 kg) and 17 women (29 {+/-} 6 yrs, 66.1 {+/-} 8.9 kg). Anthropometric data, leg muscle strength as well as trunk muscle endurance and muscle fibre distribution of the thigh were also obtained. To load the participant a standard backpack filled with extra weight according to the carrying weight tested was used. Extra Load Index (ELI), the oxygen uptake (VO2) during total load over no-load-exercise, was used as a proxy for load carrying ability. In addition to analyzing factors of importance for the ELI values, we also conducted mediator analyzes using sex and long term carrying experience as causal variables for ELI as the outcome value.\n\nFor the lowest load (20 kg), ELI20, was correlated with body mass but no other factors. Walking at 5 km h-1 body mass, body height, leg muscle strength and absolute VO2max were correlated to ELI35 and ELI50, but relative VO2max, trunk muscle endurance and leg muscle fibre distribution were not.\n\nSex as causal factor was evaluated in a mediator analyses with ELI50 as outcome. ELI50 at 5 km h-1 differed between the sexes. The limit for acceptable body load, 40 % of VO2max (according to [A]strand, 1967), was nearly reached for women carrying 35 kg (39%) and surpassed at 50 kg at 3 km h-1, and for men carrying 50 kg at 5 km h-1. This difference was only mediated by difference in body mass. Neither muscle fibre distribution, leg muscle strength, trunk muscle endurance and body height nor did absolute or relative VO2max explain the difference.\n\nParticipants with long term experience of heavy load carrying had significant lower ELI20 and ELI50 values than those with minor or non-experience, but none of the above studied factors could explain this difference.\n\nThe study showed that body mass and experience of carrying heavy loads are important factors for the ability to carry heavy loads.\n\nFundingThis study was founded by the Swedish Military Forces{acute} Research Authority and The Research Funds of The Swedish School of Sport and Health Sciences.\n\nAbbreviations

physiology↗

bioRxiv2016

Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort

BackgroundThe ability to measure activity and physiology through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown.\n\nObjectiveTo assess the accuracy of seven commercially available wrist-worn devices in estimating heart rate (HR) and energy expenditure (EE) and to propose a wearable sensor evaluation framework.\n\nMethodsWe evaluated the Apple Watch, Basis Peak, Fitbit Surge, Microsoft Band, Mio Alpha 2, PulseOn, and Samsung Gear S2. Participants wore devices while being simultaneously assessed with continuous telemetry and indirect calorimetry while sitting, walking, running, and cycling. Sixty volunteers (29 male, 31 female, age 38 {+/-} 11 years) of diverse age, height, weight, skin tone, and fitness level were selected. Error in HR and EE was computed for each subject/device/activity combination.\n\nResultsDevices reported the lowest error for cycling and the highest for walking. Device error was higher for males,greater body mass index, darker skin tone, and walking. Six of the devices achieved a median error for HR below 5% during cycling. No device achieved an error in EE below 20 percent. The Apple Watch achieved the lowest overall error in both HR and EE, while the Samsung Gear S2 reported the highest.\n\nConclusionsMost wrist-worn devices adequately measure HR in laboratory-based activities, but poorly estimate EE, suggesting caution in the use of EE measurements as part of health improvement programs. We propose reference standards for the validation of consumer health devices (http://precision.stanford.edu/).\n\nAbbreviations

bioinformatics↗