Long-term manned spaceflight imparts significant physiological impairments that compromise astronaut health and safety during and after exposure to μ
Recent versions of in-flight resistive exercise hardware, such as the ARED, are typically designed to enable performance of the squat. Prime movers for the squat are the lower body extensors, which are the muscles most prone to atrophy and strength loss from actual and simulated spaceflight (Bamman et al., 1998; Stauber, 1989). It is the lower body extensors that incur the greatest degree of pre-stretch as squat depth (descent) increases, which leads to more motor unit recruitment when the aforementioned muscles shorten during the ascent phase of each exercise repetition. Thus the squat may be particularly efficacious as an in-flight countermeasure to μ
For a new or novel device, validity is essential to establish before its use is accepted by the population at large (Keppel et al., 1992). Validity denotes how well new or novel devices elicit similar responses to those derived from equipment deemed a criterion or “gold standard” by industry experts (Keppel et al., 1992). As it pertains to weight training, standard free weight exercises are known to elicit muscle mass and strength gains under ambulatory conditions, as well as attenuate atrophy and strength losses in simulated μ
The University of Tulsa's Institutional Review Board (IRB) approved our protocol in advance of data collection. All current study data were collected at the University of Tulsa. For the current study, subjects performed two squat workouts spaced 14 days apart. Their first workout was done with the exoskeleton, followed by an identical exercise protocol 14 days later with free weights. Due to the late date of the current study's IRB approval, in relation to the date of the exoskeleton's arrival and length of stay in the principal investigator's laboratory, we could not counterbalance the sequence of workouts. For our study, the exoskeleton was transported, and remained in the principal investigator's laboratory for a short (~4 days) period of time before it was returned to the IHMC. The brevity of the exoskeleton's stay required the investigators to schedule and collect data from all 14 subjects over that time period. Healthy, college-age female athletes (n=14) provided written informed consent before their participation. None had injuries that compromised their involvement. They had (mean ± sd) 3.1 ± 0.8 years of experience with the squat exercise prior to their participation. As part of their regular preparation for athletic competition, they routinely performed the back squat exercise 1–2 times per week. Their 1-repetition maximum (1RM) in the back squat at the time of their current study involvement was 76.4 ± 9.7 kg. Absolute strength measurements per current study exercise device were not performed before the start of each workout so that muscle fatigue would not impact workout results. Our subjects’ varsity sports participation was as follows: soccer-8, rowing-4, tennis-1, and golf-1. They were told to avoid stimulants, such as caffeine and those contained within dietary supplements, on days they performed current study workouts. They were told to come to workouts well-rested and to avoid lower body resistive exercise 24 hours prior to current study workouts. Subjects ate their pre-exercise meal 1–3 hours before workouts, arrived to our laboratory in athletic attire, and had their data collected between 1300–1700 hours to limit circadian effects. Subjects were instructed to consume their normal lunchtime meal before workouts. Per subject, they were also told to consume identical pre-exercise meals before each workout. Pre-exercise meals had an average energy intake (mean ± sd) of 520 ± 75 kcals, with a macronutrient breakdown as follows: carbohydrates 80 ± 19 g, protein 14 ± 9 g, and fat 16 ± 11 g.
To begin data collection, subjects submitted to a series of anthropometric measurements. Height, body mass, body fat percentage, hip width and circumference, as well as the lengths of their torso and upper and lower legs, were measured as they stood barefoot in an upright posture. Heights were measured by a stadiometer (Detecto Model 437, Webb City, MO). Body mass and composition were recorded with a calibrated bioimpedance scale (Model BF-350, Tanita Corporation, Tokyo, Japan). All hip, torso, and leg measurements were recorded in triplicate by the principal investigator (J. Caruso) with a cloth measuring tape to the nearest 0.1 cm. Hip width was measured as the lateral expanse between anterior superior iliac spines across the ventral surface of the body. Hip circumference was recorded at the level of the anterior superior iliac spines. Assessed along the left side of subjects’ bodies, torso length equaled the distance between the acromioclavicular joint and the anterior superior iliac spine. Upper leg length spanned the distance from the left femur's trochanter to the lateral condyle's lower border. Lower leg length equaled the distance from the left fibula's head to its lateral malleolus.
Physiological measurements were obtained by preparing subjects’ bodies for data collection. With aseptic techniques, pre-exercise saliva was obtained with oral swabs (Salimetrics, State College, PA) that were used to quantify cortisol concentrations ([C]) at a later date with enzyme-linked immunosorbent assay (ELISA) kits, and 1–2 fingertip blood drops were placed on test strips inserted within a calibrated device (Accupsort, Hawthorne, NY) to measure blood lactate concentrations ([BLa−]). Subjects then had a torso monitor and wrist strap (Model FT4, Polar, Kempele, Finland) attached to their bodies to record heart rate (HR) values. After pre-exercise [C], [BLa−], and HR data were obtained, they sat quietly for five minutes. Subjects’ first visits continued with self-administered, passive lower body stretching, which lasted five minutes and focused on areas most heavily engaged (lower back, hips, knees, and ankles) by the squat exercise.
When stretching concluded, subjects stood next to the exoskeleton as the final preparations for data collection began. Surface EMG signals were obtained with a computer-based oscillograph and acquisition system (Model MEB-7102A, Horizon Bio-Medical, Mooresville, NC). A bipolar Ag/AgCl collection electrode, with an inter-electrode distance of 3 cm, was applied to skin marked with ink over the left vastus lateralis. A ground electrode covered the fibular head of subjects’ left legs. The electrode was placed 20 cm superior to the fibular head, along the examined muscle's ventro-lateral surface, in order to monitor behavior closer to the knee, a major articulation where movement occurs during squat repetitions. Conduction paste (Elefix, Nihon Kohden, Foothill Ranch, CA) was applied to electrodes to enhance signal quality. Athletic tape was used to adhere the electrodes to the surface of subjects’ skin. EMG data were amplified at a bandwidth of 10–1000 Hz and sampled at 2048 Hz. Signals were full wave rectified and low pass filtered at a cut-off frequency of 250 Hz. The gain was adjusted so that the entire signal was captured and viewed as squats were performed. Per subject, EMG procedures were standardized across workouts, which included electrode placement at the same marked location along the thigh, to provide real-time waveforms of muscle activity.
After EMG preparations concluded, subjects performed a few practice repetitions as they wore the exoskeleton. Practice repetitions were performed with no added pre-programmed load. For exoskeleton workouts, its inventor (P. Neuhaus) and the principal investigator were present to ensure repetitions were done correctly. Verified by a photoelectric sensor (Automationdirect, Cumming, GA) for each repetition, subjects descended to a depth whereby their femurs were parallel to the ground before they ascended. All squats were done in cadence with a metronome (MR-600, Matrix, South Korea) at a rate of three seconds per repetition. After practice repetitions concluded, subjects donned a neoprene mask for the collection and analysis of their respiratory gases by a metabolic cart (Model K2b4, Cosmed, Rome, Italy) to quantify their peak O2 uptakes. Before the first set began, pre-exercise O2 uptake rates were measured as subjects stood motionless for at least five minutes before their first set. Subjects also wore the mask during and after workouts until their O2 consumption rates returned to pre-exercise levels.
Figure 2 depicts a subject as her data was collected from the exoskeleton workout. They performed a four-set, four-repetition squat protocol with 60-second rests between sets. Subjects did sets against progressively heavier loads in the following order: 22.5 kg, 34 kg, 45.5 kg, and 57 kg. The loads chosen were based on our sample's mean 1RM back squat value, so that repetitions could be safely performed by our subjects. Between each set, subjects stood motionless in an upright posture. Per set, an accelerometer (Myotest Inc., Royal Oak, MI) attached to the back of the exoskeleton measured peak force, peak velocity, and peak power. The accelerometer was shown previously to evoke high levels of validity for the squat exercise (Comstock et al., 2011). EMG data were recorded throughout each set. Peak EMG amplitudes were quantified from each set and used for analysis. HR was measured 30 seconds after each set. Five minutes after the last set concluded, post-exercise [BLa−] and HR were recorded, as values usually peak for the former dependent variable at that time. Our metabolic cart provided breath-by-breath analysis of O2 samples. Once O2 values returned to pre-exercise levels, the mask was removed from their faces and another oral swab was placed in their mouths. Swabs were subsequently analyzed for [C] (Figure 2).
At the conclusion of the exercise bout, subjects provided a rate of perceived exertion (RPE), or their perceptual index of the workout's rigor, with the range of possible values from 1 to 10. Fourteen days after their exoskeleton workout, they performed an identical back squat protocol in our laboratory with free weights. Their data were obtained using identical procedures and methods, with measurements of the same dependent variables. The principal investigator was also present at all free weight exercise bouts to ensure there were no inter-workout differences with respect to the procedures and methods employed. In addition, subjects used the same pre-exercise preparations (meal, avoidance of stimulants, etc.) as those employed before exoskeleton workouts. Thus our study was able to assess the degree of data similarity for values from the exoskeleton squat workouts to those derived from free weight exercise bouts.
We collected numerous dependent variables, mostly of an instantaneous nature, which depicted subjects’ efforts over a brief period for each workout. The variety of dependent variables spans a large range of absolute values. Thus to assess the comparative similarity of data and the exoskeleton's validity as exercise hardware, we compared the same dependent variables from both workouts with three statistical tests. First, however, our data were analyzed with Z scores to identify outliers. They were computed as: (individual score – mean)/sd. Absolute Z score values that exceeded −1.96 or +1.96 were excluded from further analyses. Outlier data, as well as its corresponding value from the other workout, were excluded from further analyses.
Our analyses then proceeded to assess the comparative similarity of our data and thereby the validity of the exoskeleton. As used in a prior study that assessed validity, we computed Pearson correlation coefficients to compare values per dependent variable derived from the two workouts (Andresen et al., 1999). We also examined each dependent variable using paired t-tests to assess absolute (unstandardized) inter-workout differences with a Bonferroni adjustment to control for type I error. Per dependent variable, our t-test values with the Bonferroni adjustment were calculated as a ratio to the number of similar dependent variables (metabolic, HR, accelerometry, EMG, etc.) also examined in the current study. Finally, we computed Cohen's d effect size as the relative standardized difference between mean values. To exhibit a high degree of comparative inter-workout similarity and to affirm our hypothesis, current dependent variables had to yield higher (r values from 0.50–1.00) Pearson correlation coefficients, as well as lower t-test (<1) and Cohen's d effect size (<0.4) values.
All subjects successfully completed both workouts and none were injured through their project participation. Anthropometric dimensions (mean ± sd) were as follows: height 171 ± 5 cm, mass 70.2 ± 5.0 kg, body fat 22.8 ± 3.4%, hip width 29.3 ± 3.8 cm, hip circumference 83.6 ± 4.4 cm, torso length 40.6 ± 4.5 cm, upper leg length 41.8 ± 5.3 cm, and lower leg length 42.4 ± 2.5 cm. Z score analyses revealed approximately 10% of our total data set were outliers. Most outliers were peak power and peak EMG values produced from the exoskeleton workout; such instantaneous measurements are more prone to elicit outliers than dependent variables that exhibit more stability over time (Caruso et al., 2013; Davidson et al., 2013). EMG, in particular, is a very sensitive measure; despite the similarity of inter-workout EMG preparations to our subjects’ left legs, which included replication of electrode placement over a marked skin site, there is inherently high variability when such data is obtained from dynamic exercise (Davidson et al., 2013). Table 3 and Table 4 each include a column that displays the number of subjects who provided data (with outliers and corresponding values excluded) per dependent variable. Our raw data (mean ± sd; range) from each workout appear in Table 1 and Table 2.
Metabolic, heart rate (HR), and rate of perceived exertion (RPE) data from exoskeleton and free weight workouts. Sets 1–4 entailed heavier loads (22.5, 34, 45.5, and 57 kg, respectively) for each workout.
pre-exercise [BLa−] (mmol · L−1) | 1.7 ± 0.8 | 0.8 – 3.7 | 1.8 ± 0.9 | 0.9 – 3.7 |
post-exercise [BLa−] (mmol · L−1) | 2.4 ± 1.4 | 1.0 – 6.1 | 2.1 ± 1.0 | 1.0 – 3.9 |
pre-exercise [C] (μg · dl−1) | 0.21 ± 0.08 | 0.10 – 0.37 | 0.22 ± 0.08 | 0.10 – 0.42 |
post-exercise [C] (μg · dl−1) | 0.25 ± 0.11 | 0.10 – 0.45 | 0.21 ± 0.15 | 0.10 – 0.76 |
peak O2 (ml · min−1) | 1144 ± 174 | 909 – 1457 | 1278 ± 173 | 947 – 1535 |
pre-exercise HR (beats · min−1) | 65.1 ± 8.0 | 52 – 75 | 68.4 ± 9.7 | 46 – 80 |
post-set 1 HR (beats · min−1) | 94.8 ± 14.0 | 75 – 126 | 104.9 ± 13.4 | 84 – 126 |
post-set 2 HR (beats · min−1) | 101.1 ± 12.9 | 83 – 125 | 111.1 ± 16.3 | 80 – 134 |
post-set 3 HR (beats · min−1) | 110.6 ± 18.2 | 83 – 151 | 117.2 ± 17.1 | 81 – 140 |
post-set 4 HR (beats · min−1) | 107.4 ± 17.7 | 82 – 146 | 123.4 ± 20.0 | 89 – 153 |
post-exercise HR (beats · min−1) | 78.6 ± 16.0 | 58 – 115 | 77.4 ± 14.2 | 51 – 109 |
RPE | 6.3 ± 1.5 | 3 – 8 | 4.1 ± 1.5 | 2 – 7.5 |
Accelerometry and EMG data from exoskeleton and free weight workouts. Sets 1–4 entailed heavier loads (22.5, 34, 45.5, and 57 kg, respectively) per workout.
Peak force set 1 (newtons) | 283 ± 25 | 257 – 338 | 329 ± 55 | 213 – 440 |
Peak velocity set 1 (cm · sec−1) | 71 ± 31 | 39 – 118 | 96 ± 13 | 81 – 122 |
Peak power set 1 (watts) | 169 ± 78 | 85 – 317 | 247 ± 52 | 187 – 349 |
Peak EMG set 1 (μV) | 521 ± 143 | 275 – 680 | 322 ± 101 | 170 – 500 |
Peak force set 2 (newtons) | 443 ± 43 | 390 – 514 | 486 ± 122 | 201 – 663 |
Peak velocity set 2 (cm · sec−1) | 69 ± 24 | 38 – 124 | 89 ± 27 | 53 – 140 |
Peak power set 2 (watts) | 251 ± 99 | 129 – 476 | 322 ± 89 | 180 – 479 |
Peak EMG set 2 (μV) | 413 ± 231 | 95 – 700 | 350 ± 238 | 110 – 810 |
Peak force set 3 (newtons) | 618 ± 70 | 531 – 789 | 673 ± 151 | 358 – 879 |
Peak velocity set 3 (cm · sec−1) | 413 ± 231 | 95 – 700 | 350 ± 238 | 110 – 810 |
Peak power set 3 (watts) | 352 ± 153 | 135 – 576 | 394 ± 95 | 282 – 531 |
Peak EMG set 3 (μV) | 616 ± 306 | 300 – 1100 | 511 ± 321 | 130 – 1100 |
Peak force set 4 (newtons) | 761 ± 101 | 657 – 1060 | 752 ± 125 | 479 – 1030 |
Peak velocity set 4 (cm · sec−1) | 77 ± 28 | 37 – 124 | 86 ± 30 | 30 – 124 |
Peak power set 4 (watts) | 387 ± 111 | 220 – 591 | 526 ± 202 | 170 – 734 |
Peak EMG set 4 (μV) | 645 ± 331 | 100 – 1005 | 550 ± 378 | 80 – 1270 |
Our results, whereby dependent variables from the exoskeleton workout were compared to the same indices obtained from the free weight exercise bout, appear in Table 3 and Table 4. Table 3 includes metabolic, HR, and RPE results. Table 3 Pearson correlation coefficient results show post-exercise [BLa−], all HR, as well as RPE dependent variables produced higher r values and thus the most inter-workout agreement. In contrast, pre-exercise [BLa−] and [C], post-exercise [C], and peak O2 display more inter-workout variability. The variability in post-exercise [C] may be due to time differences at which saliva was obtained. Post-exercise saliva collection was delayed until the mask, worn to assess O2 uptake, was removed so that a swab could be placed in subjects’ mouths. Since post-exercise collection times between workouts varied slightly, that is a potential source of variability. Table 3 t-test results exhibit non-significant inter-workout differences for [BLa−], [C], peak O2, and some of our HR data. However, Table 3 t-test results also include significant inter-workout differences for post-set 2 HR and RPE, with higher HR data from free weight, yet greater RPE values from exoskeleton workouts. Table 3 Cohen's d results show pre- and post-exercise [BLa−] and [C] values produced the smallest standardized inter-workout differences. Yet most Table 3 Cohen's d results include far higher values, indicative of greater inter-workout variability and less similarity for responses obtained from both workouts.
Metabolic, HR, and RPE results, whereby dependent variable values from the exoskeleton and free weight workouts were compared to note the degree of similarity. Included are the number of subjects (n) who provided paired values for analysis.
pre-exercise [BLa−] | 0.01 | 0.11 | 0.04 | 14 |
post-exercise [BLa−] | 0.51 | 0.56 | 0.26 | 14 |
pre-exercise [C] | 0.07 | 0.32 | 0.13 | 14 |
post-exercise [C] | 0.24 | 0.80 | 0.30 | 14 |
peak O2 | 0.14 | 1.94 | 0.73 | 14 |
pre-exercise HR | 0.57 | 1.50 | 0.37 | 14 |
post-set 1 HR | 0.61 | 3.10 | 0.70 | 14 |
post-set 2 HR | 0.79 | 3.80* | 0.65 | 14 |
post-set 3 HR | 0.55 | 1.50 | 0.38 | 14 |
post-set 4 HR | 0.50 | 3.10 | 0.79 | 14 |
post-exercise HR | 0.78 | 1.70 | 0.37 | 14 |
RPE | 0.51 | 5.40* | 1.17 | 14 |
statistically (p<0.05) different inter-workout values
Accelerometry and EMG results, whereby dependent variables from the exoskeleton and free weight workouts were compared to note the degree of similarity. Included are the number of subjects (n) who provided paired values for analysis.
Peak force set 1 | 0.44 | 2.20 | 0.95 | 14 |
Peak velocity set 1 | 0.34 | 2.40 | 0.91 | 13 |
Peak power set 1 | 0.01 | 2.40 | 1.02 | 11 |
Peak EMG set 1 | 0.66 | 2.30 | 1.31 | 11 |
Peak force set 2 | 0.01 | 1.00 | 0.47 | 14 |
Peak velocity set 2 | 0.20 | 1.50 | 0.73 | 13 |
Peak power set 2 | 0.73 | 2.70 | 0.71 | 11 |
Peak EMG set 2 | 0.05 | 0.20 | 0.46 | 11 |
Peak force set 3 | 0.55 | 0.90 | 0.47 | 14 |
Peak velocity set 3 | 0.31 | 2.30 | 0.55 | 13 |
Peak power set 3 | 0.46 | 0.00 | 0.32 | 13 |
Peak EMG set 3 | 0.12 | 0.30 | 0.16 | 13 |
Peak force set 4 | 0.50 | 0.30 | 0.08 | 14 |
Peak velocity set 4 | 0.38 | 0.70 | 0.32 | 13 |
Peak power set 4 | 0.01 | 1.44 | 0.82 | 13 |
Peak EMG set 4 | 0.28 | 0.01 | 0.27 | 13 |
Table 4 displays our accelerometry and EMG results. Table 4 Pearson correlation coefficient results show peak EMG set 1, peak power set 2, and peak forces for sets 3 and 4 each yielded r values of 0.50 or greater. Yet most Table 4 dependent variables exhibited far weaker inter-workout correlations. In contrast to Table 3, our Table 4 t-test results exhibit non-significant inter-workout differences for all its dependent variables. Table 4 Cohen's d results show low standardized differences for peak EMG for sets 3 and 4, as well as peak force for set 4. Yet the majority of our Table 4 Cohen's d results include far higher values, which exhibit greater inter-workout variability and less comparative similarity between data obtained from both workouts.
For our study, the similarity of results from both workouts was used to assess the validity of data provided by the exoskeleton. Validity refers to the extent data from new and established devices are similar (Keppel et al., 1992). Different forms of validity exist. Our study compared the similarity in responses derived from exoskeleton and free weight workouts, and is best described as an examination of convergent validity, which refers to the degree two data sets that in theory should be related, actually are (Keppel et al., 1992; Measurement Validity Types, 2015).
Research examined the ability of in-flight exercise hardware to mitigate muscle mass and strength losses produced by long-term stays aboard the ISS (Smith et al., 2012; Trappe et al., 2009). Despite use of in-flight aerobic hardware 5 days/week at moderate intensities, and concurrent strength training on an interim resistive exercise device (iRED) 3–6 days/week with several lower body resistive exercises, crewmembers experienced significant plantar flexor mass and strength losses after six months on the ISS (Trappe et al., 2009). In addition, there were in-flight changes to muscle fibers associated with a reduced resistance to fatigue (Trappe et al., 2009). It was concluded the ISS should be equipped with hardware that offers a greater mechanical loading stimulus to better attenuate muscle mass and strength losses (Trappe et al., 2009). That conclusion was affirmed by a bone study done on the ISS that compared the merits of different forms of in-flight hardware (Smith et al., 2012). With no crossover, crewmembers were assigned to resistive exercise done with either the iRED or ARED for their 4–6 month stays (Smith et al., 2012). Unlike the iRED, which employed elastic bands for resistance, the ARED uses pneumatic cylinders and flywheels to simulate the manner in which weights are lifted on Earth. Bone losses, measured before and after flights, were best abated in those who exercised on the ARED, which provided comparatively more resistance (Smith et al., 2012).
Ground-based studies also examined the utility of actual and potential in-flight hardware as prospective exercise countermeasures (Beck et al., 2014; Loehr et al., 2011; Rea et al., 2013). A training study compared physiological changes from strength training on the ARED to free weights (Loehr et al., 2011). Assigned to one of two groups with no crossover, subjects did identical workouts, which included the squat exercise, three days/week for 16 weeks. Results showed both groups incurred similar improvements over time. It was concluded physiological changes from ARED workouts were like those of free weights (Loehr et al., 2011). Yet it is important to interpret differences between the current and ground-based ARED results with caution. For instance, the ARED study examined chronic changes in physiology; over time, actual inter-group differences could be assessed. In contrast, the current trial quantified acute changes to dependent variables of a far more instantaneous nature, in which recorded values were attained for only a very brief time period. Thus our dependent variables inherently exhibit more data variability. In addition, the ARED study did not assess convergent validity, but rather compared changes over time in both groups (Loehr et al., 2011).
Little research exists on the convergent validity of exoskeleton data, with the goal of improved in-flight exercise hardware (Beck et al., 2014; Rea et al., 2013). Knee extension and flexion torque data from NASA's X1 exoskeleton were compared to those derived from an isokinetic dynamometer (Beck et al., 2014). Subjects performed one workout on each device. Results showed high levels of agreement for knee extension, but not knee flexion, torques obtained from each device (Beck et al., 2014). It was suggested the X1 could be used to assess lower body muscle strength (Beck et al., 2014). The validity of that data exceeds that of the current study's; yet it is important to note X1 and dynamometer torque values were derived from single joint exercises, unlike our study that entailed squats—a dynamic, multi-joint movement of far greater methodological rigor. Thus the validity of our data is expected to be less than that of the X1 paper based on the exercises examined (Beck et al., 2014).
The achievement of comparatively similar data from both of our workouts was made difficult by numerous factors inherent to the current study. They include performance of a multi-joint, multi-planar movement at relatively high velocities with a novel exoskeleton (Caruso et al., 2013; Caruso et al., 2012; Davidson et al., 2013). Yet the low degree of comparative similarity, as seen in some Table 3 and Table 4 results, is still disconcerting and the reason we did not address our study hypothesis. Due to the limited inter-workout similarity, our exoskeleton data does not exhibit an acceptable degree of convergent validity. This is most likely due to the brief amount of familiarization (a few practice repetitions) with the exoskeleton prior to actual data collection, which is a serious limitation. For our study, the exoskeleton was transported and stayed in the principal investigator's laboratory for a short (~4 days) period of time before it had to be returned to the IHMC. The brevity of the exoskeleton's stay, combined with our subjects’ busy schedules, did not afford them an ideal opportunity to familiarize themselves to squats done with the exoskeleton. Clearly, limited familiarization with a complex dynamic exercise performed on a novel device appears to have impacted our results.
Anecdotal claims from 13 subjects inferred free weight squats were easier. This claim is supported by our RPE data, despite the same number of sets and repetitions, rest periods, and loads used for each workout. Due to their considerable background squatting with a barbell, subjects could perhaps put more effort into repetitions for that exercise mode, which could in part explain the higher HR values seen with free weight squats. In contrast, subjects stated the exoskeleton distributed loads in a manner they were unaccustomed to as they performed repetitions. Differences in load distributions and the resultant kinesthetic and biomechanical changes, as well as limited familiarity with the exoskeleton, likely made those squats comparatively more difficult. This is supported by our post-exercise [BLa−] and [C] values, as well as peak EMG data, which show higher mean values from exoskeleton workouts. Higher peak EMG data from the exoskeleton exercise bout, despite generally greater performance-based values from free weight workouts, suggests subjects recruited more motor units for the exercise device they were less familiar with, which concurs with prior research (Jakobsen et al., 2013).
Familiarization requirements are based on the nature of the task and the length of inter-session time intervals (Donovan and Radosevich, 1999). The number of familiarization sessions done by human subjects prior to actual data collection certainly impacts convergent validity results. Less familiarization is required for tasks with a low methodological rigor (Sleivert and Wenger, 1994; Viitasalo et al., 1980); the opposite is true of squats, a complex dynamic motor skill that requires refined patterns of muscle activity executed in a specific sequential fashion (Donovan and Radosevich, 1999; Frost et al., 2012; Lee and Genovese, 1989). Recent work on the required number of familiarization sessions examined exercises less rigorous than the squat, and included dependent variables, such as subjects’ 1RM values, which tend to exhibit less variability than our study's performance-based dependent variables, which is in part due to the speed at which repetitions are performed. Current study repetitions occurred at faster velocities than are generally seen with 1RM attempts, which lead to higher rates of movement and more data variability (Caruso et al., 2013; Caruso et al., 2012; Davidson et al., 2013). In exercise studies with low rigor (e.g., vertical jump, isometric contractions, and elastic band exercise), familiarization occurred with a single session administered prior to actual test data collection (Calder and Gabriel, 2007; Colado et al., 2014; Frost et al., 2012), or no familiarization whatsoever for vertical jumps done by physically active men (Moir et al., 2004).
Familiarization requirements were compared among young (23 ± 4 years) and old (66 ± 5 years) women who each performed multiple knee extension 1RM tests (Ploutz-Snyder and Giamis, 2001). They engaged in at least two test sessions; if their 1RM values exceeded the prior sessions’ by more than 1 kg, they were required to perform an additional trial. Older women required more familiarization (8–9 sessions) than younger subjects (3–4 sessions) to achieve consistent 1RM values (Ploutz-Snyder and Giamis, 2001). While more familiarization improves validity, too many sessions may induce a training effect and thus not reflect subjects initial performance capabilities. Two such studies examined the number of familiarization sessions needed to achieve valid 1RM values for three exercises (bench press, squat, and arm curl) in women (Soares-Caldeira et al., 2009) and men (Dias et al., 2005). To derive 1RM values, subjects performed four (Dias et al., 2005) or five (Soares-Caldeira et al., 2009) sessions spaced 2–3 days apart. There was a consistent rise in 1RM values for each exercise over successive trials, which infers multiple tests induced a training stimulus and caused the required number of sessions recommended to be inflated (Dias et al., 2005; Soares-Caldeira et al., 2009). Multiple tests may evoke a 5–10% strength gain, which can be avoided by spacing sessions farther (7–10 days) apart (Schroeder et al., 2007).
Hardware development for in-flight exercise must address the adverse impacts long-term μ
With ~10% of our total data as outliers, which is usually higher than that seen in other trials, it appears to have foretold the rejection of our hypothesis. The lack of acceptable levels of convergent validity was made difficult by several limitations, which should be addressed in future exoskeleton studies. Some limitations are inherent to squats, which are complex motor skills with a high methodological rigor. A major limitation that prevented acceptable convergent validity, and could address the aforementioned concern with the squat exercise, is the brief amount of familiarization subjects had to the exoskeleton prior to data collection. Depending on the subjects employed and the methods by which data are obtained, future studies that involve exoskeleton squats should include one or more familiarization sessions with the device prior to actual data collection. Another current study limitation may include an order effect whereby due to its brief stay in the principal investigator's laboratory, we initially collected our exoskeleton squat data, followed by the free weight workout, 14 days later. Yet the occurrence of an order effect was likely blunted by the large disparities in the degree of familiarity subjects had with the two types of exercise devices examined. Future trials may wish to vary the sequence that subjects perform workouts in order to reduce the likelihood of an order effect. With respect to our post-exercise [C] results, the convergent validity of that data may have been higher if the time point at which those measurements were obtained was consistent. Since, for our study, we waited until O2 uptake rates returned to pre-exercise levels in order to remove the mask and subsequently insert the oral swab for saliva collection, this is a potential source of variability. New exoskeleton trials may wish to employ different methods in order to increase the convergent validity of post-exercise [C] measurements. Finally, most of our outliers came from dependent variables that entailed instantaneous measurements, in which recorded values were attained for only a very brief time period. Future studies may wish to assess the convergent validity of the exoskeleton with dependent variables whose values show greater stability over time. Adoption of these recommendations in future exoskeleton trials should increase convergent validity and thus improve the likelihood of its use as in-flight exercise hardware.