THE EFFECTS OF PROLONGED LOADED TREADMILL WALKING ON LOWER EXTREMITY AND TRUNK KINEMATICS, HEART RATE, AND SUBJECTIVE RESPONSES

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1 THE EFFECTS OF PROLONGED LOADED TREADMILL WALKING ON LOWER EXTREMITY AND TRUNK KINEMATICS, HEART RATE, AND SUBJECTIVE RESPONSES By Sivan Almosnino, Ph.D. David C. Kingston, M.Sc. Ryan B. Graham, Ph.D. Joan M. Stevenson, Ph.D. Ergonomics Research Group Queen s University Kingston, Ontario, Canada K7L 3N6 Project Manager: Joan M. Stevenson, Ph.D. (613) PWGSC # Research & Development of Load Carriage Tools Phase 7 on behalf of Department of National Defense As represented by Defense Research & Development Canada Toronto 1133 Sheppard Ave W. North York, ON M3M 3B9 January, 2014 Copyright 2014 S. Almosnino, D.C. Kingston, R.B. Graham, & J.M. Stevenson Copyright Disclaimer: This report contained on an Internet-accessible directory is included by the contributing authors as a mechanism to ensure timely dissemination of scholarly and technical information on a non-commercial basis. Copyright and all rights therein are maintained by the authors, despite their having offered this information electronically. Everyone copying this information must adhere to the terms and constraints invoked by each author's copyright. This report may not be copied for commercial redistribution, republication, or dissemination without the explicit permission of the Ergonomics Research Group at Queen's University and the authors.

2 Technical Abstract Introduction: Load carriage walking tasks performed by military personnel for extended time periods are thought to hamper performance as well as increase musculoskeletal injury risk. While the effect of different load configurations and equipment has been tested on acute biomechanical and physiological responses, few investigations have assessed the influence of these factors over a prolonged period of time. Purpose: The purpose of this investigation was to assess whether lower extremity and trunk kinematics, heart rate, and subjective exertion ratings are altered due to the performance of prolonged load carriage walking. Methods: Seventeen male participants performed 60 min of treadmill walking at a speed of 1.53 ms -1 while carrying a military-issue backpack weighing 32.5 kg. Differences in sagittal plane kinematic waveforms obtained during the initial and final phases of the task were assessed using principal component analyses, as well as changes in heart rate, rated perceived exertion, and body discomfort. Results: The only kinematic change observed across the one hour of walking was that the foot became slightly more dorsiflexed throughout the gait cycle. Perceived exertion and neck and shoulder discomfort were rated higher at the end of the walk. Conclusions: Although changes in rated perceived exertion and discomfort were small, these variables were influenced in an ideal setting with the best available equipment issued to soldiers. Therefore, backpacks currently used by soldiers could benefit from further development and design modifications to reduce shoulder and neck discomfort. Keywords: Military Ergonomics; Biomechanics; User Testing; Load Carriage; Backpack ii

3 Table of Contents Technical Abstract... ii 1. Introduction Methods Participants Instrumentation Experimental Protocol Data Analysis Statistical Analysis Results Discussion References iii

4 1. Introduction Load carriage walking with a backpack is a common task in military combat and training settings (Knapik, Reynolds, & Harman, 2004; Quesada, Mengelkoch, Hale, & Simon, 2000). This task is often performed over an extended period of time; a factor that may induce fatigue and consequently decrease performance and increase injury risk (Knapik et al., 2004). From a biomechanical stand-point, load carriage walking tasks have been extensively investigated with respect to biomechanical alterations resulting from: participant characteristics; absolute or relative load magnitude; load location within the backpack and with respect to the bearer s center of mass; different types of backpacks; walking speed; walking surface (e.g. over ground vs. treadmill); and more (see review by Knapik et al., 2004). However, the influences of these aforementioned factors have primarily been assessed using data collected over a short time period after donning of the loaded pack, as well as making use of only a small number of gait cycles. An evaluation of current literature on the topic reveals that only a few investigations have assessed the influence of prolonged walking time period (Table 1), or the influence of fatigue (Table 2) on biomechanical-related gait load carriage measures. With regards to the studies summarized in Tables 1 and 2; it seems that a major focus has been previously placed on the assessment of lower extremity kinematics. However, the kinematic assessments performed in these prior investigations utilized data from only a small number of steps (typically less than 3) for extraction of dependent study variables, and no explanation is usually given to the selection of variables. Also evident from examination of prior studies is the use of artificial protocols to induce a fatigued state (e.g. Blacker et al., 2010; Qu & Yeo, 2011; Wang et al., 2013). Study designs inducing fatigue by way of simulations are arguably of merit, as they allow understanding the effects of fatiguing a particular muscle group on subsequent task performance. However, such protocols do not seem to simulate the type of muscular fatigue that may result from actual prolonged loaded walking (Blacker et al., 2010; Patton et al., 1990), or the type and level of psychological demands associated with performance of this particular task (Dishman & Gettman, 1980; Patton et al., 1990). As such, exploration of the effects of prolonged load carriage walking utilizing data from multiple steps seems warranted in order to address current gaps in the literature. Given the limited and sometimes conflicting information pertaining to changes in walking biomechanics as a result of prolonged load carriage task performance (Table 1 and 2), an exploratory approach to the study is arguably in order. In this context, a popular technique previously suggested and used for exploratory purposes is waveform-based Principal Component Analysis (PCA) (Chau, 2001; Daffertshoffer et al., 2004; Wang et al., 2013). The use of PCA as an analysis method is of interest since it: 1) Utilizes data from the entire waveform and from multiple trials; 2) the technique objectively extracts waveform characteristics that explain the variation amongst groups or conditions. That is, there is no necessity to choose outcome variables prior to study commencement, and; 3) Several methods have been developed to interpret the extracted waveform features in a manner that is communicable (i.e. relate certain features to differences in shape and magnitude in specific poritions of the movment cycle) (Almosnino, Kingston, & Graham, 2013; Deluzio & Astephen, 2007; Deluzio, Wyss, Zee, Costigan, & Sorbie, 1997; Deluzio et al., 1997; Reid, Graham, & Costigan, 2010). It should be noted that PCA has been useful in identifying biomechanical waveform features that were undetected if using standard type analysis with discrete variable (e.g. Almosnino, Kingston, & Graham, 2013; Astephen et al., 2008). As such, the primary aim of this investigation was to explore whether lower extremity joint and trunk kinematics are altered in response to one hour of prolonged load carriage treadmill walking. In addition, a secondary aim of this study was to assess whether the task results in alterations to heart rate (HR), selfreported exertion levels, and physical comfort levels. 1

5 Table 1 Summary of load carriage papers with primary focus on biomechanical outcomes. Authors (year) Subjects Protocol Outcome Measures Main Results Attewlls et al. (2006) 20M, AS 8, 16, 40, 50 kg loads Military boots 2 steps bilateral Lower limb joint RoM Gait characteristics Trunk angle Heavy load decreased stride length/frequency RoM in knee increased with heavy loads Trunk angle increased with heavy loads Birrell & Haslam (2009) 10M, PE 8, 16, 24, 32 kg loads Leather military boots 1 step Lower limb joint RoM Gait characteristics RoM in the knee decreased with heavy loads Decrease of pelvic rotation with heavy loads Birrell & Haslam (2010) 12M, PE 8, 16, 24, 32 kg loads Leather military boots 1 step GRF: impact peak force, impulse, stance time Distribution and configuration of load had limited effect on GRF parameters Harman et al. (1999 a ) 11M, AS 12.5, 25, 35, 40, 50 kg loads Combat boots Step speed 1.33ms -1 1 step, post-outdoor run Time to complete run O 2 uptake GRF Trunk, lower limb joint angles Run time increased ~ 25% with heavy load O 2 uptake increased ~ 10% with heavy load RoM of trunk, knee, ankle, and stance time increased with heavy load RoM of hip decreased with heavy load Harman et al. (2000) 16M, AS 6, 20, 33, 74 kg loads Leather military boots 1.17, 1.33, 1.5 ms -1 speeds 1 step GRF Trunk, lower limb kinematics back, lower limb EMG Heavy load increased step frequency, Decreased stride length Increased RoM in knee with heavy loads Trunk angle increased with load Kinoshita (1985) 10M, RA 0, 20%, 40% of bodyweight in back and front load configurations (wooden frame, not rucksack) 1 step Gait characteristics RoM of hip, knee, angle increased with heavy load No change in stance phase between loads Ling et al. (2004) 6F, RA 0, 9.1, 13.6, 18.2, 22.7 kg loads 1.34 ms -1 speeds 2 steps on pressure mat Perceived exertion, HR General discomfort level Isokinetic strength of lower limbs High discomfort in 18.2 and 22.7 kg loads Longer stance with two highest loads Hip adductor strength was predictive of task performance Sharpe et al. (2008) 7M 5F, RA 40% of subject bodyweight load 0.5, 0.9, 1.3, 1.7 ms -1 speeds Instrumented treadmill < 3 min of data Phase of lower limb segments Effect of no hip belt vs. hip belt packs on kinematics Hip belt packs resulted in increased stability in coordination between the thorax and pelvis 2

6 Simpson et al. (2011) 12F, RA 0%, 20%, 30%, 40% of bodyweight in and outdoor level walking 2 steps Perceived exertion, HR Anatomical area discomfort scores Trunk angle Loads below 40% of bodyweight resulted in postural, HR, and discomfort differences Not all subjects could complete 40% trials Simpson et al. (2012) 12F, RA Identical to Simpson et al Gait characteristics Increased stance phase with larger loads Tilbury-Davis & Hooper (1999) 10M, AS 0, 20, 40 kg loads Barefoot (0 kg) and military boots (all 3 loads) Self-selected speed 1 step GRF Gait characteristics 2-D analysis Contests the findings of Kinoshita (1985) no change in hip or knee flexion Subjects: M male; F female; AS active military service; PE previous backpack experience; RA recreationally active. Protocol: step refers to a step on a force plate; lab controlled laboratory settings; HR heart rate; footwear not controlled unless indicated. Outcome measures: GRF ground reaction force; EMG electromyography; RoM range of motion; gait characteristics stride length, stride frequency, stance time, and speed; RPE ratings of perceived exertion. 3

7 Table 2 Summary of load carriage articles with primary focus on fatigue and physiological/comfort effects. Authors (year) Subjects Protocol Outcome Measures Main Results Beekley et al. (2007) 10M, AS Fit subjects (<14 min 2 mile run) Combat boots 30 min at 1.81ms -1 speed with 30%, 50%, 70% bodyweight LBM Instrumented treadmill Borg (6-20) RPE Subject motivation RER Self-Motivation Inventory * RER was higher in the 70% load only 4 subjects could not complete 70% trial 30% load was not different than 50% RPE No change in exercise motivation post trial Blacker et al. (2010) 10M, RA 2h L, 25 kg backpack at 1.81ms -1 2h D, 25 kg backpack at 1.81ms -1 Isokinetic testing of lower limb Isokinetic vs. treadmill exhaustion methods Treadmill fatigue resulted in longer recovery time than isokinetic induced fatigue No difference in downhill vs. level walking fatigue methods Harman et al. (1999 b ) 11M, AS Identical to Harman et al. (1999 a Table 1) Marksmanship testing Identical to Harman et al. (1999 a ) Rifle marksmanship post-exercise Comfort measures of load carriage systems post-run No difference in grenade throw accuracy MOLLE configuration had best rifle accuracy High discomfort with heavy load in shoulders Patton et al. (1990) 15M, AS 5.2, 31.5, 49.4 kg loads (performed over 11 trials in 7 weeks) 1.1, 1.35, 1.6 ms -1 speeds 12 km treadmill walk VO 2 RPE Blood lactate Upper/lower body power (Wingate) RER higher at 49.4 kg load HR increased with 31.5 kg loads at top two speeds and overall with heavy load Increased RPE across all loads/speeds No change in blood content Pigrrynowsi et al. (1981) 6M, RA 0, 15, 19, 23, 29, 34 kg loads Quite standing then12 min followed by 12 min walking at1.54ms -1 3 steps RER Mechanical work 100W work in quiet standing for all loads Trunk and load movements not in phase, resulting in energy loss in the system Qu, Yeo (2011) 12M, PE 0, 7.5, 15 kg loads Self-selected walking pace after fatigue run at 3.58 ms -1 < 2 min instrumented treadmill Perceived exertion (Borg 6-20) Trunk, lower limb kinematics Hip RoM increased with 15 kg load Step width increased after fatigue trial and with 15 kg load Quesada et al. (2000) 12M, AS 0%, 15%, 30% bodyweight loads 40 min at 1.81ms -1 speed 2 steps pre/post walking Perceived exertion (Borg 6-20) VO 2 work rate HR Lower limb kinematics 0% load was not different than 30% RER 30% load higher RPE Peak knee flexion higher with increasing load 4

8 Wang et al. (2013) 18M, RA 32 kg load at 1.67ms min total Step-up and heel raises to fatigue Combat boots Instrumented treadmill Gait characteristics Muscle fatigue Increased pelvic anterior tilt, hip and knee flexion with fatigue Decrease of ankle dorsiflexion with fatigue Subjects: M male; AS active military service; PE previous backpack experience; RA recreationally active. Protocol: LBM lean body mass; HR heart rate; footwear not controlled unless indicated; L level walking; I inclined walking, D declined walking. Outcome measures: GRF ground reaction force; RoM range of motion; RER repertory exchange ratio; RPE rated perceived exertion; gait characteristics stride length, stride frequency, stance time, and speed. * (Dishman & Gettman, 1980) 5

9 2. Methods 2.1 Participants Seventeen males participated in the study (mean ± SD: age 25.9 ± 4.5 years; weight 80.6 ± 10.0 kg; height 177 ± 6 cm). All participants were healthy, and did not report current or previous musculoskeletal injuries to the lower extremities or spine. Participant s physical activity levels comprised of 4-6 weekly workout sessions, each lasting a minimum of 45 minutes, involving individual and team sports, as well as weightlifting activities. Participant experience with carrying loads varied. Five participants had extensive familiarity with task demands through current military service or recreational hiking, while the remainder had intermediate experience with seasonal hiking. Written informed consent was obtained from all participants prior to testing, with the investigative procedures approved by the University s General Research Ethics Board. 2.2 Instrumentation Three-dimensional kinematic data for the right lower extremity, pelvis, and trunk were obtained using a 9 camera Vicon 512 motion capture system (Vicon Motion Systems, Oxford, UK) sampling at 120 Hz. Marker clusters containing 3 non-collinear spherical markers affixed to rigid plastic surfaces were attached using flexible neoprene bands to: dorsal aspect of the shoe; proximal lateral shank; thigh, approximately 2 inches above the lateral epicondyle; anterior pelvis, centered between the anterior superior iliac spines); and the sternum, approximately 5 cm superior to the xyphoid process (Figure 1). For definition of the lower extremity joint center of rotations and segmental coordinate systems, virtual marker positions were recorded during a standing reference trial (Winter, 2009). Reference markers were placed on the 1 st and 5 th metatarsal heads; calcaneus; ankle malleoli, femoral epicondyles, and anterior and posterior superior iliac spines (LaFiandra, Wagenaar, Holt, & Obusek, 2003). Figure 1 Subject performing walking trial wearing standardized boots, clothing, and backpack with centralized 32.5 kg load. 6

10 For trial performance, participants were fitted with a 32.5 kg loaded military backpack (USMC MarPat ILBE rucksack, Arc teryx Equipment Ltd., North Vancouver, BC, Canada). This load magnitude corresponded to 41% ± 5% of each participant s bodyweight. The load magnitude is similar to the traditional upper limit recommendation of the US Army approach march, as well as being the upper limit of studies with traditional style backpacks (Table 1) (Knapik, Ang, Meiselman, & Johnson, 1997). The load consisted of steel plates placed in the middle of the pack. Styrofoam padding was securely fit around the plates to prevent plate movement during trial performance. All participants donned the same type of individually fitted military boots (Desert TFX Rough-Out GTX Military Boots, Danner Boots Inc., Portland, OR, USA), and compressive undershirt (Under Armor HeatGear, Under Armor Inc., Baltimore, MD, USA). 2.3 Experimental Protocol Testing was conducted in a climate-controlled laboratory. Prior to testing, the participants performed a warm-up and familiarization protocol consisting of examiner-guided stretching of the lower extremities and trunk musculature, as well as unloaded level treadmill walking. For familiarization with task demands, the participants performed walking trials at different speeds whilst carrying the loaded backpack for a total duration of 10 min. Following a 3-5 minute recovery period, the participants performed 60 minutes of continuous treadmill walking at a speed of 1.53 ms -1. A fixed walking speed was selected to simulate forced march conditions, which are routinely employed during military training (Knapik et al., 2004). The particular speed magnitude was chosen as it corresponds to the upper echelon of speeds reported in previous load carriage studies (Harman et al., 2000; Knapik et al., 2004; LaFiandra, Holt, Wagenaar, & Obusek, 2002; Polcyn et al., 2002; Sharpe et al., 2008). As well, the walking speed of 1.53 ms -1 approximately corresponds to reports of forced march speeds employed during initial basic training (Hetsroni et al., 2008). The 60 minute duration in our study was chosen to replicate the brief rest period typically provided following 60 minutes of continuous marching in standard military training (Bartlett & Robusto, 2012). Two data trials were recorded, each lasting 3 minutes in duration. The first trial was made between minutes 7 and 10 from the start of the walking trial to ensure that participants had reached steady state walking (Quesada et al., 2000). The second trial was recorded during the last 3 minutes of the walk. At the end of each trial, participant heart rate (HR) was recorded using a commercial wrist monitor (RS100, Polar Electro Inc., Kempele, Finland), subjective estimates of exertion were obtained using a Borg 6-20 RPE scale (Borg 1982), and discomfort of the neck/upper trapezius, shoulders, upper back, lower back, hip, thighs, shank, and feet were documented using a modified body discomfort map (Simpson et al., 2011). 2.4 Data Analysis Kinematic data were filtered prior to calculations using a 2 nd order, zero-phase-shift, low-pass Butterworth filter with cut-off frequencies ranging between 4-6 Hz, as determined by residual analysis (Winter, 2009). Joint center of rotations for the ankle and knee joints were defined as the midpoint between the medial and lateral malleoli and epicondyles, respectively. The hip joint center was defined using a sphere fitting algorithm (Gamage & Lasenby, 2002), employing recordings of participant thigh motion about the pelvis combining flexion/extension and abduction/adduction followed by an arc motion (Camomilla, Cereatti, Vannozzi, & Cappozzo, 2006). Segmental anatomical coordinate systems were established for the lower extremity, and three-dimensional lower extremity and trunk joint angles were calculated using an Euler rotation sequence corresponding to flexion-extension, abduction-adduction, and axial rotation (Deluzio, Wyss, & Zee, 1997). Trunk rotation was defined as movement of the trunk technical coordinate system relative to the pelvis technical coordinate system (Sadler, Graham, & Stevenson, 2011; Smallman, Graham, & Stevenson, 2013). All trial angles were referenced to a standing trial without the backpack. Subsequently, data were divided into individual strides based on the heel marker velocity data (Zeni, Richards, & Higginson, 2008) and normalized to 101 data points using a shape-preserving spline routine. The first 100 strides from each individual trial were ensemble averaged to yield representative waveforms for each participant. 7

11 2.5 Statistical Analysis For principal component analysis (PCA) calculations, mean sagittal plane waveforms for each joint and the trunk were arranged in a 34 x 101 matrix corresponding to 17 participants by 2 conditions (start and end of walk). Waveform PCA calculations were then performed as previously described (Deluzio & Astephen, 2007; Deluzio et al., 1997; Deluzio et al., 1999; Reid et al., 2010). For data reduction purposes, the number of PCs retained for further analysis was determined using a 90% trace criterion (Deluzio & Astephen, 2007; Reid, Graham, & Costigan, 2010). For each joint, differences in average individual PC scores were assessed using Bonferroni-corrected paired two-tailed t-tests given a preset α = PC interpretation was achieved via examination of the associated loading vector and single PC reconstruction plots (Brandon, Graham, Almosnino, Stevenson, & Deluzio, In Press; O Connor & Bottum, 2009). Differences in heart rate and RPE scores were assessed using paired two-tail t-tests (α = 0.05). Heart rate data was expressed as a percentage of estimated maximum (Inbar & Oren, 1994). Lastly, differences in body region discomfort were assessed using paired two-tailed t tests (α = 0.05). All comparisons are accompanied with effect size estimates (Pearson s r) (Rosnow, Rosenthal, & Rubin, 2000). 3. Results Following principal component model construction for each lower extremity joint and the trunk, a total of 8 PCs were retained using the 90% trace criterion (Table 3). Of these, only PC1 of the ankle joint exhibited statistically significant differences across the two measurement trials. Examination of the associated loading vector and PC reconstruction plot (Figure 2) suggests that at the final stage of the walking task, participants adopted a slightly more dorsiflexed foot throughout the gait cycle. However, note that the magnitude of this systematic difference is only 1-2 degrees at any given instant in the gait cycle. Table 3 Description of principal component (PC) models for sagittal plane lower extremity joints angles and trunk. Mean ± SD PC scores Joint/Segment PC Number Explained Variance (%) Trial 1 (Start) Trial 2 (End) p value (effect size) Ankle ± ± (0.06) ± ± (0.04) ± ± (-0.04) Knee ± ± (-0.07) ± ± (0.05) Hip ± ± (0.00) ± ± (0.01) Trunk* ± ± (0.06) * Analysis based on data from 16 participants. Trunk data for 1 participant was unusable due to corruption. 8

12 Figure 2 PC loading vectors and reconstructed waveform plots of respective joints. Coloured bands in the top row graphs indicate ± 1 SD from the ensemble averaged waveform of that joint. 9

13 The standard deviation-bounded mean waveforms for the knee and hip show a high degree of performance similarity at the start and end of the task, in as much that the waveforms nearly completely overlap (Figure 2). The 1 st PC for the knee and hip joints, which explained 60.0% and 89.9% of the variance across trials respectively, can also be interpreted as magnitude operators, since the loading vector signs are consistent throughout the entire gait cycle. Examination of the associated PC reconstruction plots corroborate the insignificantly reduced knee flexion and increased hip flexion range of motion seen throughout the gait cycle due to the prolonged walking task. Finally, examination of mean trunk waveforms and associated loading vectors and PC reconstruction plots for first trunk PC, which explained nearly all the variation in scores across trials (99.3%), reveal a small and statistically insignificant tendency for an increased trunk flexion angle throughout the gait cycle as a function of prolonged walking. Descriptive statistics for RPE, HR, and discomfort scores are presented in Table 4. RPE scores were significantly greater at the end of the walk (11.1 ± 1.7) then at the start (10.2 ± 1.9, t(16) = -3.45, p<0.01, r = 0.65). Heart rate, expressed as a percentage of predicted maximum, did not differ between the final (63% ± 7%) and initial periods of the walk (63% ± 6%, t(16) = 0.60, p = 0.55, r = 0.15). Lastly, the only significant increases reported for body discomfort were for the neck/upper trapezius region (1.8 ± 1.1 vs. 3.9 ± 2.6, p<0.001, r = 0.45) and shoulders (1.4 ± 1.3 vs. 2.3 ± 1.6, p<0.001, r = 0.29). Table 4 Descriptive statistics of heart rate expressed as a percentage of predicted maximum, ratings of perceived exertion (RPE) (Borg 6-20 scale), and body region discomfort scores obtained during trial performance. Measure Trial 1 (Start) Mean ± SD Trial 2 (End) Mean ± SD p value (effect size) RPE 10.2 ± ± 1.7 <0.01 (0.65) Heart Rate 63% ± 7% 63% ± 6% 0.55 (0.15) Body Region Discomfort Scores Trial 1 (Start) Mean ± SD Trial 2 (End) Mean ± SD p value (effect size) Neck/Upper Trapezius 1.8 ± ± 2.6 <0.001 (0.45) Shoulders* 1.4 ± ± 1.6 <0.001 (0.29) Upper Back 2.1 ± ± (0.10) Lower Back 1.1 ± ± (0.03) Hip 0.8 ± ± (0.03) Thighs* 1.2 ± ± (0.03) Calves* 1.7 ± ± (-0.05) * Scores were pooled across limbs since no bilateral differences were evident (p > 0.05). 10

14 4. Discussion Load carriage is a regularly performed task in military training and operations (Knapik, Reynolds, & Harman, 2004). Past research has addressed physiological (Blacker et al., 2010; Patton et al., 1990; Table 2) and traditional biomechanical gait parameters (e.g. ranges, peak values, joint angles; Table 1), but such work has been limited in quantifying lower limb kinematics for less than 3 steps. Diverging from typical analyses, this study used PCA as a novel statistical approach in load carriage gait. PCA can objectively identify temporal waveform features, thus overcoming the need for an a-priori choice of primary outcome measures. As well, alterations to heart rate (HR), self-reported exertion levels, and physical comfort levels were assessed. Pertaining to the results of the investigation, the lack of meaningful changes in lower extremity and trunk kinematics may be explained by the following factors. First, the relative HR values obtained from our participants suggest that the task performed was only of light aerobic intensity. As such, we postulate that muscular fatigue, a factor that has been shown to alter biomechanical-related performance parameters during load carriage walking (Beekley et al., 2007; Blacker et al., 2010; Patton et al., 1990), was not induced by the experimental task. In this context, note that the choice of walking speed, duration, and load magnitude were consistent with current military training practices (Bartlett & Robusto, 2012; Hetsroni et al., 2008; Knapik et al., 2004; Polcyn et al., 2002). As well, the selected speed, duration, and load magnitude were close to the upper limit of previously reported studies with traditional style backpacks (Table 1). However, in the current study, walking was performed in a controlled environment with the backpack in isolation of other equipment typically carried by soldiers during combat training and operations. This load configuration is routinely used in military training and regular solider fitness regiments where full operational equipment is not needed or allowable (Jones, 2002). Operational configurations include donning of protective equipment and a fighting vest, as well as carrying of a rifle; the latter has been shown to affect trunk-pelvis kinematics (Seay, Hasselquist, & Bensel, 2011), but have little effect in lower limb kinematics (Birrell & Haslam, 2010). The use of PCA as an analysis method of kinematic variables produced contrasting results from that of many articles listed in Tables 1 and 2. These differences in findings may be explained by two main components; many research articles observing kinematic variation in load carriage report findings between load magnitudes (Tables 1 and 2) and previous studies report changes from 1 or 2 steps only (Table 1). Our study observed changes over time with a fixed load and the inclusion of temporal information, in addition to over 100 steps being averaged at the start and end of the trial. Due to a single load being used, comparative results between conditions is inappropriate. We are confident using these advanced statistical analyses that no significant changes in walking kinematics occurred over task performance in this particular population. We would expect that following longer periods of walking, or with loads in excess of 41% ± 5% of participant bodyweight, changes in lower limb kinematics would start to emerge. Changes were not observed in RPE values. While this study did not induce fatigue in our subjects, long duration walking studies (Beekley et al., 2007; Gordon, Goslin, Graham, & Hoare, 1983; Patton et al., 1990; Simpson et al., 2011, 2012), report conflicting results in RPE and discomfort scores within load conditions. Beekley et al. (2007) support our findings by reported no change of RPE in loads of 30-50% subject bodyweight. Gordon et al. (1983) do not report RPE and HR values in relation to time but only across loaded conditions; therefore, comparisons from our findings cannot be drawn from their work. Contrasting our RPE results, Patton et al. (1990) and Simpson et al. (2011, 2012) did find temporal changes in RPE within a loaded condition. The subject pool in Beekley et al. (2007) was fit males in active military service whereas subjects in Simpson et al. (2011, 2012) were recreationally active females. This study actively recruited fit males, which may also help explain the lack of change in RPE values. The second factor that may have contributed to the observed results relates to the equipment used in the investigation. The backpack, undershirt and footwear are state of the art, and in effect were designed to accommodate performance of tasks similar to the current one. Inferring from the relatively low discomfort experienced by the participants during trial performance in the current study, it may be that kinematic changes reported in previous studies were influenced, at least in part, by use of less accommodating equipment. However, and consistent with previous investigations, the highest level of discomfort was reported for the neck/upper trapezius and shoulder regions (Harman et al., 2000; Polcyn et al., 2002; Simpson et al., 2011, 2012), which may advise that this aspect of load carriage equipment design is still in need of improvement. 11

15 This study did not assess, due to technical limitations, the kinematics of the contra-lateral lower extremity. This topic may be of interest, as bilateral differences that may be developed due to task performance could help in understanding the occurrence of injuries in a particular lower extremity in populations performing tasks similar to the one in the current investigation (Hetsroni et al., 2008; Jones, 2002). As well, future work could address the level of muscular fatigue resulting from prolonged load carriage for the implications on gait stability and inter-joint/segmental coordination. 12

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