Simultaneous measurement of biomechanical energetics and heart

rates during a community-based high-intensity exercise intervention

Iain R. Spears

Larger Project Team: Tom McBain, Alan M. Batterham, Matthew Weston, Paul Crawshaw,

Liane Azevedo, Catherine Haighton

My Background

• Computational biomechanics• Computer-based interventions (gaming)• Training interventions

• Meaningful exercise• Load muscles in an effective way• Development and evaluation of a high-intensity

exergame in a region of lower socio-economic status

3

Introduction• Males have a lower life expectancy and a higher

morbidity rate than females.• Such trends are exacerbated in regions of social

deprivation.• Males in general are reluctant to engage with formal

health services and perform less than the recommended guidelines for physical activity

Aims

Larger project objective: • To develop and evaluate a purpose built high-intensity video

game intervention targeting males from a region of social deprivation in the North East of England

• (Research Councils UK, Research in the Wild Call (EP/I001891/1))

This presentation:• Can we do more to understand the mechanisms underpinning

the health outcomes – new form of physical activity measurement – muscle force

calculations)?

Gaming system hardware

• Boxing-specific resistance bands (ShadowBoxer Pty, Australia)• 2x Heart-rate monitor (Polar, Finland)• 2 Xbox Kinect Sensors (Microsoft, USA)• 2 Windows PCs connected by Router• Wireless inertial measurement units – combining triaxial accelerometer, gyroscope and

magnetometer (developed ‘in-house’)

Game development in Unity3D gaming engine (Unity3d.com, USA)

Next to see video

Gaming system software

Participants

• Intention-to-treat – Average %HRmax for work periods across all exercise sessions was 69

± 17%– Mean peak %HRmax during work periods across all exercise sessions

was 71.5 ± 18.6%• Per-protocol-analysis,

– Average %HRmax for work periods across all exercise sessions was 87 ± 3%

– Mean %peak HRmax during work periods across all exercise sessions was 90 ± 3%

• Sessions attended– Average %HRmax for work periods across all exercise sessions was 86 ± 3%

– Mean peak %HRmax during work periods across all exercise sessions was 90 ± 2%

Intervention Fidelity

Summary of findings (and my next step)• Data collection and exercise delivery are feasible in this

‘hard-to-reach’ group.• Adherence and retention good• Training at desired intensity• Generally beneficial results, i.e. moving in right direction• However,

– Unclear effects for most outcome variables– Large confidence intervals– Manipulate intensity, duration and frequency – More trials = time-consuming

.

My Current Work (Musculoskeletal Modelling)

• Muscle metabolic adaptations critical component to preventing diseases such as coronary heart disease, type 2 diabetes, and obesity

• Metabolic adaptation and energy costs are primarily determined by muscle contraction.

• Little is known about mechanics of generating muscular force and/or work in humans. – muscle actions involved, speed of movement, range of

motion, muscle groups trained, energy systems involved, intensity and volume of training

• Musculoskeletal modelling– Further understand the cause of large confidence intervals? – Enables ‘what if’ mode of enquiry, possibly on an individual basis– Time-savings

Next Steps• Heart rates and full-body kinematic data recorded in over

200 exercise sessions• Model Input

• Health outcomes recorded for intervention group (outcome variables) • Model validity

Joint reaction forcesfrom Newton, Sum of Horizontal Forces, SFx = m.ax:Rxp = m.ax - Rxd ... (1)(where p = proximal, d = distal joint, ay = acceleration of segment centre of mass, CoM, in y direction;Note that d = force platform when p = wrist)from Newton, Sum of Vertical Forces, SFy = m.ay: Ryp = m.ay + mg - Ryd ... (2)

About segment CoM:from Euler, Sum of Moments, SMz = Ia (where a = angular acceleration)Mzp = Iz.a - Mzd - Rxp.rp.sinq + Ryp.rp.cosq + Rxd.rd.sinq - Ryd.rd.cosq(where rp = distance from segment CoM to proximal joint) (q = angle of segment to the right-hand horizontal)or, using motion co-ordinates (more usual),Mzp = Iza - Mzd - Rxp.(yp-yCoM) + Ryp.(xCoM-xp) + Rxd.(yCoM-yd) - Ryd.(xd-xCoM) ... (3)

Musculoskeletal modelling

Musculoskeletal modelling

• Green Arrow = External force on wrist• Black Sphere radius = Joint Torque• Coloured Cylinders =- Joint torques in medial-lateral, superior-

inferior, anterior-posterior directions

• Next to see video

Musculoskeletal modelling

130 muscle units modelled so far

Complications Many degrees of freedom Many muscles Multi-segment joints Antagonistic contractions Muscles wrapping

References• Hamilton MT, Booth FW (2000 ). Skeletal muscle adaptation to exercise: a century of

progress. J Appl Physiol. 2000, 88(1):327-31.• American College of Sports Medicine position stand (2009). Progression models in

resistance training for healthy adults. American College of Sports Medicine. Med Sci Sports Exerc. 41(3):687-708.

• Willems PA, Cavagna GA, Heglund NC. (1995). External, internal and total work in human locomotion. J Exp Biol. 1995 Feb;198(Pt 2):379-93.

• Hopker JG, Passfield L. (2000). Is it time to re-evaluate the training study? J Sci Cycling. Vol. 3(3), 1-2. 88(1):327-31.

• Taylor KL, Weston M, Batterham AM (2015). Evaluating Intervention Fidelity: An Example from a High-Intensity Interval Training Study. PlosOne (DOI: 0.1371/journal.pone.0125166)

• Spears IR, McBain T, Lagadec P, Read S, Bateson SW, Azevedo L (2013). Quantification of net power output during exergaming using a single-mass, multi-segmental kinetics and joint power model: A comparison with indirect calorimetry. ISB 2013 Brazil, XXIV Congress of the ISB, Brazil.