Treadmill Test

All tests were performed in climate-controlled laboratory environment. When arriving to the test centre, body mass (measured while wearing in light-weight clothes to the nearest 0.1 kg) and height (to the nearest 0.1 cm) were measured. The participants were informed about the test procedure and equipped with a HR monitor (Polar Electro, Kempele, Finland). After individual adjustments of the seat and handlebar of the cycle ergometer and an introduction of the Borg´s scale of perceived exertion (RPE) (Borg 1970 (link)), the participant performed an EB-test according to the original 2012 test procedure (Ekblom-Bak et al. 2014 (link)). The test was performed on a mechanically braked cycle ergometer (Monark model 828E, Varberg, Sweden). Test procedure included 4 min of cycling on a standard and low work rate of 0.5 kilopond (kp) with a pedal frequency of 60 rpm (≈30 W when 1 W = 6.116 kpm/min), directly followed by 4 min of cycling on a higher individually chosen work rate (aiming at a RPE of ≈14 on the Borg scale). Mean steady-state HR during the last minute on the low and high work rates, respectively, was recorded by taking the mean of the observed HR at 3:15, 3:30, 3:45, and 4:00 min at each work rate. In addition, VO₂max was also estimated by the Åstrand test method by applying the work rate and HR of the high work rate to the Åstrand nomogram (Åstrand and Ryhming 1954 (link)) and associated age-correction factors (Åstrand 1960 ). The same way of obtaining Åstrand test results from the EB-test procedure was used in the original publication of the first EB-test prediction equation, and is further described and discussed in the previous article (Ekblom-Bak et al. 2014 (link)). Direct measurement of VO₂ during the submaximal cycle test was conducted in a subsample (n = 110) in the model group, using a computerised metabolic system (Jaeger Oxycon pro, Hoechberg, Germany) connected to a face mask worn by the participant. Before each test, ambient temperature, humidity, and barometric pressure were measured with built-in automatic procedures and a handheld instrument (HygroPalm, Rotronic, Bassersdorf, Schweiz). Gas analyzers and inspiratory flowmeter were calibrated with the metabolic system’s built-in automatic procedures, where high-precision calibration gases (15.00 ± 0.01 % O₂ and 6.00 ± 0.01 % CO₂, Air Liquid, Kungsängen, Sweden), and ambient indoor air was used for the gas analyses.
After a short rest, a 5 min warm-up on the treadmill preceded a graded maximal treadmill test to measure VO₂max. The individually designed protocol for the VO₂max test started off at 1° incline and a velocity corresponding to approximately 60–65 % of the participant’s estimated VO₂max (usually the speed that the participant felt comfortable with during the warm-up). The speed increased 1 km/h during the first 3 to 4 min of the test, and thereafter, there was an increase in incline with +1° every minute until voluntary exhaustion. For some of the well-trained participants, running to an incline of 5°–6°, there was an additional increase in speed (+1 km h⁻¹ per minute) to avoid too steep inclination on the treadmill. Direct measurements of VO₂ were obtained during the test with the same computerised system as mentioned above (Jaeger Oxycon pro). Criteria for acceptance of the VO₂max measurement were levelling off of VO₂ despite an increase in speed or incline, a respiratory exchange ratio >1.1, RPE above 16, work time above 6 min, supported by a maximal HR within ±15 beats min⁻¹ (bpm) from age-predicted maximal HR (ref Åstrand Rodahl). A test was accepted as VO₂max when a minimum of three out of the five criteria was achieved. In the model group, nine participants were tested but later excluded due to non-fulfilling the requirements for acceptance of test (five participants failed the VO₂max test and four participants had non-valid EB test). The corresponding values in the cross-validation group were four excluded participants in total, two with non-valid VO₂max test and two with non-valid EB-test.
VO₂max (L min⁻¹) and maximal HR (bpm) were recorded into 30 and 5 s epochs, respectively. We have previously shown that there is no mean difference and a small variation (CV: 2.7 %) between test–retest of VO₂max according to the above procedure in a mixed population (Ekblom-Bak et al. 2014 (link)), indicating no need for a second VO₂max test on a separate test day to verify the first accepted measurement.

HR data were pre-processed using a Bayesian approach as described elsewhere [43 (link)]. Briefly, auxiliary variables (HR signal indicator, fastest and slowest heart beats) were used to assign data points to noise clusters, following which Gaussian Process Robust regression incorporating short- and long-term (circadian) covariance functions (priors) was used to infer the true latent HR time-series along with uncertainty estimates. ACC data were checked for anomalies (baseline bleed, axis freezing) but as none occurred in this dataset, these data were analysed in their raw form. Segments of data with continuous zero acceleration lasting >90 minutes were treated as ‘monitor not worn’ if also accompanied by non-physiological HR data (large and prolonged heart rate uncertainty).
PAEE (in kJ·day^-1·kg^-1) was calculated by time-integration of the activity intensity (in J·min^-1·kg^-1) time-series, estimated from HR and ACC separately and combined ACC+HR in a branched equation model [13 (link),18 (link)]. We accounted for any potential diurnal imbalance of wear time by weighting all hours of the day equally in the summation [44 ].
For single-signal HR estimates, the flex-HR method as described elsewhere [27 (link),28 (link)] was used. Briefly, this method translates HR to EE estimates according to an individually established calibration (as described above for each of the five levels) but only for time points where HR is above an individually determined flex point (flex HRaS); below this point activity intensity is estimated to be 0 J·min^-1·kg^-1). Specifically for this study, we used a flex HRaS of 10bpm +50% of lowest exercise HRaS, defined as lowest HRaS after 2-min of walking at 3.2 km·hr^-1 for treadmill and walk-calibrated models and 80% of the 2-min HRaS value while stepping for step calibrated models; the latter is roughly equivalent to the HRaS value after 1-min of stepping but easier to determine reliably, and also comparable to the level of exertion used to define flex HR for the treadmill test. For non-exercise (group) calibrated models, predicted flex HRaS from sleeping HR was used [13 (link)].
The branched equation modelling technique assigns different weightings to the HR-PAEE and ACC-PAEE relationships, depending on the epoch-by-epoch observed values [13 (link),18 (link)]. Weightings for the two most extreme branches vary slightly between previous evaluations [14 (link),16 (link),18 (link),19 (link)]; as this matters most for the lower branch, we consolidated the two weightings, 0% and 10%, in the current study by applying the 0% weighting when average acceleration for the previous 2 min was lower than the movement (“X”) branching point, otherwise the 10% weighting was used.
Estimates of total energy expenditure (TEE, in MJ·day^-1) were calculated from each model by multiplying PAEE by body weight, adding resting energy expenditure, and dividing this sum by 0.9 to account for diet-induced thermogenesis [45 (link)]. For the two models which use individual-level indirect calorimetry in the dynamic calibration, measured RMR from indirect calorimetry (ventilated hood) would be a likely method combination and so this was used to calculate daily REE; for all remaining models, predicted RMR [46 (link)] was used to calculate daily REE.

Each DoF measured using the CAS system was expressed as a function of the knee flexion–extension angle (coupling curves) [14 (link)]. Then, the CAS kinematics were expressed as a theoretical gait cycle by matching the CAS knee flexion measurement with the corresponding average knee flexion angle measured using the KneeKG™ system during gait (Fig 2). The matching was performed in four steps: 1) at each frame of the treadmill gait cycle, the knee flexion-extension angle was identified, 2) all the frames from the CAS measurements with this knee flexion-extension angle were identified, 3) the values of the different degrees of freedom of the knee at those frames were identified, 4) those values were reported as the CAS values at this instant of the gait cycle. In this way, the theoretical CAS kinematics during treadmill gait are determined and can be compared to the knee kinematics assessed with the KneeKG^TM. To increase the number of corresponding points between systems, the kinematic measurements from the CAS and the KneeKG™ were upsampled from 100 Hz to 300 Hz, and a distinction was made between the extension and flexion phases.
Each patient’s adduction–abduction (AA) angle, internal–external rotation (IER) angle and anterior–posterior (AP) displacement [5 (link)], as measured during the treadmill gait test were averaged over the gait cycles and compared using a Bland–Altman analysis [15 (link)] to the corresponding CAS measurements averaged over the gait cycles. Next, bias and limits of agreement tests assessed how well their anatomical axes corresponded, while Spearman’s correlation coefficient assessed the consistency of their kinematics pattern. Correlation coefficients were categorised as weak (0–0.30), moderate (0.31–0.50), good (0.51–0.70) and high (> 0.70) [16 ]. Each DoF’s RoM was assessed as a reference for the limits of agreement. Finally, each system and patient’s variability was assessed using the square root of the standard deviation (SD). A non-parametric Wilcoxon test was performed to assess differences in variability between systems. These analyses were performed over the whole gait cycle, for the single support phase and for the swing phase at two timepoints: before and after definitive TKA. Analyses at the two latter phases were selected to avoid any STAs due to foot contact in KneeKG™ measurements.
Calculations were made using the open-source Biomechanical ToolKit package [17 (link)], the 3D Kinematics and Inverse Dynamics toolbox [18 ], the Bland–Altman and Correlation Plot toolbox [19 ], and Matlab R2016b (MathWorks, USA). The workflow of measurements and data analysis is summarised in Fig 2.

Behavioral tests were performed 1 week and 2 weeks after injury, to assess changes in motor functions and coordination, compared to evaluations performed 1 week prior to CCI TBI. All tests were performed during the animals’ light phase; cages were transported to testing rooms at least 30 min prior to testing.
Beam Walking Test (BWT): A BWT was used to assess CCI-induced deficits in fine motor coordination. Mice have a preference for a darkened enclosed environment, as compared to an open illuminated one. Each animal was placed in darkened goal box for a 2 min habituation and then the trial began from the other (light) end of the beam. The beam was constructed with the following dimensions: 1.2 cm (width) × 91 cm (length). The time taken for each animal to traverse the beam to reach the dark goal box and the immobility time spent between the moment when they were initially placed on the beam and when they started walking were documented. Five trials were recorded for each animal before CCI and at 1 and 2 weeks after CCI. The mean times to traverse the beam and the immobility times were calculated, and a plot was generated to evaluate treatment effects; these times were then used for statistical analysis.
Gait analysis: For the gait analysis, mice were tested on a fixed-speed treadmill apparatus (DigiGait; Mouse Specifics). Mice were habituated to the apparatus for 1 min, and then given a 1-min run at 5 cm/s. Following a 1-min rest, the treadmill speed was increased to 15 cm/s. Video was collected at high speed from a ventrally placed camera, and 3–5 s of representative gait video was selected by an experienced but blinded user for automated analysis.

This study examined more possible mechanisms through which exercise-related media may affect people’s exercise intention. There were no significant relationships in the women’s study with the exception that explicit attitudes were negatively related to believability. Previous researchers found that a factually incorrect blog post about how women’s appearance can change with exercise was rated as more believable than a factually correct blog post (Ori et al., 2021 (link)). However, the current study found no difference in believability between the Fitspiration compared to control media even though the control messages were largely about exercising for positive mental health and happiness, whereas the Fitspiration messages implied that the featured bodies could be achieved through exercise. It may be that both messages are equally palatable to women.
In the men’s models, higher ECEs were negatively related to believability regardless of which condition the men were in. This is a consistent finding in this research across both studies (albeit with small correlations) and is opposite to the hypothesized relationship. Locke and Brawley (2016) (link) found that participants who made more ECEs focused on the vignette aspects that would hinder exercise. Thus, it is possible that ECEs were negatively related to Fitspiration believability because the text included was about success and effort, which may have seemed unattainable for someone predisposed to worrying about being tired or being made fun of when exercising. FCEs were not related to believability among women or men.
There were no relationships between ECEs or FCEs and implicit associations which corroborates other research finding no relationship between ECEs and automatic affective evaluations of exercise (Locke and Berry, 2021 (link)). Further, none of the constructs tested were related to intention. Fitspiration targeting men and women, including the media used in the current research, tends to feature idealized bodies and highlight appearance as the reason to exercise (Boepple et al., 2016 (link)). Researchers have found that viewing such media was related to greater inspiration to exercise (Tiggemann and Zaccardo, 2015 (link)), but the current research did not find a relationship between Fitspiration and exercise intention. Other researchers also report that Fitspiration media featuring an athletic ideal may have influenced exercise motivation, but the motivation did not translate to short-term exercise behavior (measured as distance covered during a 10-min treadmill test; Robinson et al., 2017 (link)). Thus, this research again shows that it is unlikely that “fitspiration” inspires many people to exercise.