Keywords: chronic adaptations, electromyography, local muscle endurance, near‐infrared spectroscopy, strength training

2.1. Ethical approval

This study was performed in line with the principles of theDeclaration of Helsinki without being registered. Approval was granted by the Ethical Review Committee for Experimental Research involving Human Subjects, the Graduate School of Arts and Sciences, the University of Tokyo (no. 797). All the participants were informed of the procedures and possible risks and gave their informed consent prior to the experiment.

2.2. Participants

We recruited healthy men aged 18–50 years, for whom the exclusion criteria were as follows: resting systolic/diastolic blood pressure ≥140/90 mmHg, 1RM of unilateral elbow flexion <6 kg (to ensure that the exercise load is equal to or above the minimum in our equipment) and having an exercise habit (only for experiment 2). The sample size in experiment 1 was determined by the following procedure. Initially, we estimated an effect size (Cohen'sd) in Student's pairedt‐test to be 1.5 from previous studies investigating the acute effect of BFR on the maximum number of repetitions (Farup et al.,2015; Kolind et al.,2023; Wernbom et al.,2009). Using power analysis software (G*Power v.3.1.9.6, University of Düsseldorf, Germany), the minimal sample size required for an 80% statistical power and an α error of 5% (two tailed) resulted in seven participants. To allow for dropout, the sample size was increased to eight. This sample size is comparable to those of previous studies investigating the chronic effects of BFR training with the upper limbs (Dankel et al.,2016). The same sample size was used in experiment 2 because we assumed that the change in the number of repetitions attributable to BFR in each exercise session has a cumulative effect on the training‐induced muscle adaptation. As a result, eight healthy right‐handed males (26.9 ± 6.6 years of age, 178.3 ± 6.2 cm tall and weighing 78.5 ± 9.9 kg) participated in experiment 1, and another eight healthy right‐handed males (20.8 ± 3.1 years of age, 170.3 ± 3.6 cm tall and weighing 58.0 ± 5.7 kg) participated in experiment 2.

2.3. Procedures

2.4. Experiment 1

In accordance with previous studies examining the acute effects of BFR (Farup et al.,2015; Kolind et al.,2023; Wernbom et al.,2009), we used a within‐participant crossover design. The exercise was performed with the right arm, except for one participant who had a history of injury in the right arm. Each participant visited the laboratory three times. We instructed participants to refrain from consuming alcohol and caffeine for 24 h before the experiment. During the initial visit, 1RM of unilateral elbow flexion, resting blood pressure and upper‐arm circumference were measured. The participants then performed 20 repetitions of elbow flexion at 30% of 1RM with BFR and, subsequently, 20 repetitions with free blood flow (FBF) for practice. Next, the participants were scheduled for the two testing visits to be conducted at the same time of day, ≥5 days apart.

During the second and third visits, they performed the unilateral elbow flexion exercise with either BFR or FBF after the re‐evaluation of 1RM. There was a rest of ≥5 min between the 1RM measurement and the exercise. The order of exercise conditions was randomized and counterbalanced across participants.

During the exercise, muscle activity and tissue oxygenation were recorded from the biceps brachii muscle by using surface EMG and near‐infrared spectroscopy, respectively. Bipolar electrodes with an inter‐electrode distance of 12 mm (DL‐141, S&ME, Japan) were placed on the short head of the biceps brachii, two‐thirds of the distance from the medial acromion to the antecubital fossa (Hermens et al.,1999). An earth electrode was placed on the medial clavicle. Before electrode placement, the skin was shaved (if needed), abraded, and cleaned with alcohol wipes. The EMG signals were amplified (gain: ×400), band‐pass filtered (10–500 Hz), and sampled at a rate of 2 kHz using a data acquisition system (PowerLab/16SP, ADInstruments, New Zealand). For the recording of tissue oxygenation, we used a small, spatially resolved near‐infrared spectroscopy device (Oxy‐Pro, Astem, Japan). The measurement characteristics of its prototype were analysed previously using Monte Carlo simulation and a phantom experiment (Niwayama & Unno,2021). Optical probes consisting of light‐emitting diodes with wavelengths of 770 and 830 nm and photodetectors located 20 and 30 mm from the light source were placed on the long head of the biceps brachii, two‐thirds of the distance from the medial acromion to the antecubital fossa (Kojima et al.,2020). We used surgical tape to secure the device and a light‐shielding band to minimize the influence of ambient light. The oxygenated and deoxygenated haemoglobin (Hb) data were obtained after correcting for the thickness of the fat layer (Niwayama et al.,2000) measured with a B‐mode ultrasound device (MiruCube II, Global Health, Japan). The tissue oxygen saturation ($\mathrm{St}{O}_2$), a ratio of oxygenated to total Hb (the sum of oxygenated and deoxygenated Hb) concentrations, was monitored continuously and recorded at a sampling rate of 10 Hz. Immediately after termination of the exercise, the rating of perceived exertion (RPE) was measured with a 0–10 category scale. This scale is considered more useful than the 6–20 Borg scale for assessing the intensity of resistance exercise (Hackett et al.,2012).

2.5. Experiment 2

In accordance with previous studies examining the effects of training with BFR on muscle adaptations (Counts et al.,2016; Farup et al.,2015), we used a within‐participant unilateral model, with each training condition assigned to either the left or the right arm. The two conditions were counterbalanced by arm dominance (half of the participants were assigned to BFR with the dominant arm and vice versa) and by the maximal number of repetitions achieved during submaximal elbow flexions (R_max; see below) at pre‐intervention measurement. The order of BFR and FBF conditions was alternated for each exercise session. There was a rest of ≥10 min between the BFR and FBF conditions.

Experiment 2 consisted of a 6 week training intervention (Bjørnsen et al.,2019; Farup et al.,2015; Groennebaek et al.,2018) and pre‐ and post‐intervention measurements of muscle endurance, muscle strength, muscle thickness and echo intensity. We instructed participants to maintain their regular diet during the experimental period. The pre‐intervention measurements involved three visits, and the post‐intervention measurements involved two visits, with an interval of 2–5 days between each visit. Participants performed exercise training twice a week during the intervention period (at intervals of ≥72 h). This is based on the ACSM position statement, which recommends 2–3 days per week as the training frequency for gaining muscle mass and endurance for novice participants (ACSM,2009).

We used two different measures of muscle endurance (note that both these measurements were always performed without restricting blood flow). One measure wasR_max, representing the number of repetitions to failure during the unilateral elbow flexion exercise. The same load (30% of 1RM determined before intervention) was used for pre‐ and post‐intervention measurements ofR_max. The other measure was half‐time (Figure 1), defined as the estimated time for peak torque output to decay by 50% during a series of maximum voluntary isometric contractions (MVCs). The protocol for half‐time measurement consisted of 60 MVCs performed on a dynamometer (AO‐500N, Applied Office, Japan) with a cadence of one ballistic contraction every 2 s (Huczel & Clarke,1992), when the participants were instructed to contract the elbow flexor muscles as hard as they could. During the measurement, the participants sat on a chair, with the shoulder abducted to 90°, the elbow flexed to 90° and the forearm supinated to 90°. To ensure an isometric contraction, the wrist and the upper arm were secured with a metal cuff and an inelastic strap, respectively. Elbow flexion torque was sampled at 2 kHz using the data acquisition system. A real‐time torque output and the maximal torque achieved during an isometric strength test (described below) were displayed on an oscilloscope (SS‐7604, Iwatsu Electric, Japan) to provide visual feedback to the participants. During the measurement, surface EMG was recorded from the biceps brachii muscle in a manner similar to that of experiment 1.

We measured both dynamic (1RM) and static (isometric torque) muscle strengths. The protocol of 1RM testing was the same as in experiment 1. The isometric strength of elbow flexor muscles was measured as the peak torque applied to the dynamometer. The participants were instructed to produce the elbow flexion torque as strongly as possible for 3 s. Measurements were taken three times, with a 3 min rest between each, prior to the measurement of half‐time. The highest value was used for further analysis.

Muscle thickness and echo intensity of elbow flexors were measured using the same ultrasound device as used in experiment 1. The measurement site was at 60% of the upper arm length (i.e., the distance from the acromion process of the scapular to the lateral epicondyle of the humerus; Miyatani et al.,2004). During the measurement, the participants stood in an upright position, with the elbow joint fully extended and the forearm supinated to 90°. A transducer with a 6 MHz scanning head coated with water‐soluble transmission gel was placed perpendicular to the underlying muscle and bone tissues. The muscle thickness was defined as the distance from the border of adipose and muscle tissues including the biceps brachii and the brachialis to the upper edge of the humerus, which showed a strong correlation with the muscle volume measured by MRI (Miyatani et al.,2004). The echo intensity was determined by 8‐bit grey‐scale analysis (0, black; 256, white) of the transverse ultrasound images using the standard histogram function in ImageJ (v.1.46r; National Institutes of Health, USA). A region of interest was selected as a rectangle between the uppermost part of the bone echo of the humerus and the superficial fascia of the biceps brachii, with a fixed width of 10 mm.

2.6. Data analysis

In experiment 1, the number of repetitions, RPE, EMG data and tissue oxygenation data were analysed. For the EMG data, root‐mean‐square amplitude and mean power frequency were calculated over five repetitions at the beginning (First5), in the middle (Mid5) and at the end (Last5) of the exercise. The root‐mean‐square amplitude for each repetition (2 s time window) was normalized to that obtained from the 1RM trial (the highest 1 s) and used as an indicator of muscle activity. For the calculation of mean power frequency, fast Fourier transform with a Hamming window function was applied to a series of 0.256 s segments (frequency resolution, 3.91 Hz) overlapping each other by 50%. We did not separate the data into concentric and eccentric phases because the fatigue‐induced reduction in mean power frequency was observed in the biceps brachii, regardless of contraction mode (Potvin,1997). The tissue oxygenation data ($\mathrm{St}{O}_2$, oxygenated‐Hb, deoxygenated‐Hb and total‐Hb) were synchronized with the EMG data and averaged over First5, Mid5 and Last5.

In experiment 2, total exercise volume was obtained by multiplying the load and the total number of repetitions over the training period. For the measurement of half‐time, the peak torque output and the biceps brachii muscle activity were analysed for each contraction. The peak torque output was averaged for every three contractions (Figure 1). The averaged torque output was then regressed against time using an exponential function, from which the half‐time was calculated (Huczel & Clarke,1992). To confirm that the decrease in peak torque with time was attributable mainly to peripheral muscle fatigue, the muscle activity quantified as the root‐mean‐square amplitude for the highest 0.5 s was normalized to that recorded during the isometric strength test and averaged over the 60 MVCs.

2.7. Statistical analysis

Statistical analysis was performed using GraphPad Prism 9 software (GraphPad Software, USA). For all variables, data normality was assessed using the Kolmogorov–Smirnov test. The data with non‐normal distribution were logarithmically transformed. As a result, the data followed a normal or log‐normal distribution in all the variables and thus are expressed as the mean and SD. In experiment 1, Student's pairedt‐test was used to determine the effect of BFR on the number of repetitions to failure and RPE. Within‐participant two‐way ANOVA (condition × time) was used to determine the effects of BFR on the changes in EMG and tissue oxygenation data during the exercise. In experiment 2, Student's pairedt‐test was used to determine the effect of BFR on the total exercise volume. Within‐participant two‐way ANOVA (condition × time) was used to determine the effect of BFR onR_max, half‐time, average muscle activity during half‐time measurement, 1RM, maximum isometric torque, muscle thickness and echo intensity. When a significant interaction was found, post‐hoc multiple comparisons were conducted using Student's pairedt‐test, with the false discovery rate method (Benjamini & Hochberg,1995) used to correctP‐values. Linear regression analysis was performed to determine the association of the percentage changes inR_max and half‐time with the total exercise volume. AP‐value of <0.05 was considered significant. Cohen's effect size (d), generalizedη² (η_G²) and the coefficient of determination (R²) were reported as a measure of effect size for Student's pairedt‐test, within‐participant ANOVA and linear regression analysis, respectively. Usingη_G² as a measure of effect size is deemed statistically more suitable for repeated‐measures ANOVA than using partialη² (Olejnik & Algina,2003).

3.1. Estimated arterial occlusion pressure

The estimated arterial occlusion pressures on the upper arm were 158.2 ± 13.7 and 145.3 ± 8.0 mmHg in experiments 1 and 2, respectively, resulting in target pressures during exercise to be 63 ± 6 and 58 ± 3 mmHg, respectively.

3.2. Experiment 1

3.3. Experiment 2

4.1. Limitations

In the present study, the participants were limited to healthy men, whose characteristics differed to some extent between experiments 1 and 2. Despite the differences in age, body mass and training experience, the reduction in the number of repetitions attributable to BFR was consistent in both experiments, which has also been reported in previous studies including both sexes (Ganesan et al.,2015; Kolind et al.,2023; Wernbom et al.,2009). Additionally, although we could detect a significant impact of BFR on the exercise volume and the post‐training improvement inR_max, the small sample size in this study might prevent ‘less than a large effect’ from reaching statistical significance. Another limitation is the use of ultrasound‐based muscle thickness measurement, which is incapable of detecting three‐dimensional changes in muscle size with resistance training (Yasuda et al.,2012). Finally, and most importantly, further research is needed to determine whether our findings can be applied to multi‐joint and/or lower‐limb exercises. Nevertheless, recent studies using single‐leg squat (Petrick et al.,2019) and knee extension (Kolind et al.,2023) have shown that the application of BFR caused a reduction in the total number of repetitions, the magnitude of which was similar to that in the present study. We thus believe that the acute and chronic effects of BFR on muscle endurance performance and adaptations are consistent across types of exercises.

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Data Availability Statement

All data supporting the results in this paper are available as Supporting information.