Introduction: Brief vigorous exercise can enhance glycemic control. Limited work has investigated the effect of simple, practical interventions that require no specialized equipment. We examined the effect of bodyweight exercise (BWE) on acute glycemic control using continuous glucose monitoring (Abbott Libre Sense) under controlled dietary conditions This study was registered as a clinical trial (NCT05144490). Methods: Twenty-seven healthy adults (8 males, 19 females; age: 23±3 y) completed two virtually supervised trials in random order ~1 wk apart. The trials involved an 11-min BWE protocol that consisted of five, 1-min bouts performed at a self-selected pace interspersed with 1-min active recovery periods or a non-exercise sitting control period (CON). Food intake was standardized for each participant using pre-packaged meals supplied over 24 h. Results: Mean rating of perceived exertion for BWE was 14±2 (6-20 scale). Mean HR over the 11-minute the BWE protocol was 147±14 bpm which corresponded to 75% of age-predicted maximal HR. Mean 24-h glucose after BWE and CON was not different (5.0±0.4 vs 5.0±0.5 mM respectively; p=0.39). Postprandial glucose responses were also not different between trials after ingestion of a 75 g glucose drink, lunch, dinner and breakfast meals after each intervention. Measures of glycemic variability were not different between conditions. Conclusion: A single session of BWE did not alter acute glycemic control in healthy, young adults. This study demonstrates the feasibility of conducting a remotely supervised BWE intervention using CGM under free-living conditions. Future studies should investigate the effect of repeated sessions of BWE training as well as responses in people with impaired glycemic control. / Thesis / Master of Science (MSc) / We investigated the effect of brief bodyweight exercise (BWE) on glycemic control. This refers to the ability to maintain blood sugar within a healthy range. Glycemic control was assessed with a small device called a continuous glucose monitor (CGM) that is inserted just below the skin. Healthy adults completed a virtually supervised 11-minute BWE protocol or an equivalent period of sitting. There was no difference in glycemic control measured over 24 hours following the BWE compared to sitting under standardized dietary conditions. Future studies should investigate the effect of repeated sessions of BWE training as well as responses in people with impaired glycemic control.

Home-based exercise is a popular physical activity of maintaining fitness, health andwellness in general. However, without proper supervision and basic knowledge of theexercises in the workout plan, there is an increased risk of injury. Considering that noteveryone is willing to attend crowded gyms or schedule professional personal trainingsessions, in this study, a novel feedback system is proposed, in the form of a mobileapplication. Accelerometer and gyroscope data were collected from 10 volunteersperforming 3 exercises, squats, lunges and bridges, with inertial sensors attachedto their back lumbar region, on both shanks and on both thighs. Each participantperformed 5 repetitions of the correct technique and 5 repetitions of 4 mistakes foreach exercise. The accuracies of 3 classifiers, a SVM, a RF and DT were comparedwith the SVM performing the best across all 3 exercises. The best location and numberof sensors was determined by examining the accuracy of a SVM model for 15 uniquemulti-sensor configurations. The best performing setup, being the configuration with 2sensors, one at the lumbar area and one at the shank, was used in exploring the efficacyof different data processing techniques. Time-domain statistical features, sensor angletimeseries and the filtered signal timeseries were evaluated as input to a NN. The timedomainfeatures performed the best achieving the highest accuracy in all 3 exercises,with an accuracy of 67% for the squats, 87% for the lunges and 75% for the hip bridges.Overall, the final model demonstrated promising capabilities of classifying exercisetechnique of basic lower-body exercises, with a real-time feedback implementationbeing a feasible solution for self-efficient fitness. / Hemmaträning är en populär typ av fysisk aktivitet för att upprätthålla kondition,hälsa och välbefinnande. Dock utan övervakning och basal kunskap om hur olikaövningar bör utföras så finns det en ökad risk för skador. Alla människor går intefrivilligt till trånga och fullsatta gym eller bokar in pass med personlig tränare. Därförföreslås i denna studie ett nytt återkopplingssytem vid träning som kan användas via enmobilapp. Data från en accelerometer och ett gyroskop har samlats in från tio frivilligapersoner. De har utfört tre olika styrkeövningar; knäböj, utfallssteg och höftlyft medtröghetssensorer placerade på deras ländrygg, på underbenen och på låren. Varjedeltagare utförde fem repetitioner med korrekt teknik och sedan fem repetitionermed fyra olika typer av felaktig teknik för varje styrkeövning. Noggrannheten förtre klassificerare, SVM, RF och DT jämfördes sedan med det SVM som presteradebäst i alla tre styrkeövningarna. Det optimala antalet sensorer tillsammans med bästplacering av dessa räknades ut genom att undersöka en SVM modell med 15 unikamultisensorkonfigurationer. Det visade sig att kombinationen med två sensorer, envid ländryggen och en på underbenet var den bästa och därför användes den föratt undersöka effektiviteten av olika databehandlingstekniker. Tidsdomänsstatistiskafunktioner, sensorvinkeltidsserier och filtrerade signaltidsserier utvärderades sominmatning till ett NN. Tidsdomänsfunktionerna presterade bäst och uppnådde högstnoggrannhet i alla tre övningarna. Detta med ett korrekt utfall av 67% för knöböj,87% för utfallsteg och 75% för höftlyft. Sammantaget visade den slutliga modellenen lovande förmåga att klassificera träningsteknik för basala styrkeövningar för nedredelen av kroppen. Samtidigt som användaren får feedback i realtid vilket gör detmöjligt att utföra effektiv träning själv hemma.