The effect of music tempo on movement flow

The study addresses how music tempo influences movement flow during exercise, a psychological state characterized by immersion, optimal performance, and intrinsic reward. Prior work shows flow arises from interactions among internal states (attention, arousal, motivation), external conditions, and behavior, and can vary over time. Flow has been linked to neural correlates measurable with EEG (increased alpha and theta; changes in delta; and typically reduced beta in some contexts), suggesting objective assessment is possible. Music is known to affect arousal, attention, emotions, and performance in sport, but evidence on the optimal tempo is mixed across contexts. This study aims to isolate the effect of music tempo (fast vs. slow vs. no music) on movement flow during brisk treadmill walking, combining subjective ratings (S-FSS-2) with objective EEG power across frequency bands. Hypotheses: H1, fast tempo leads to higher subjective flow; H2, fast tempo more effectively stimulates unconscious brainwaves during brisk walking.

Music attributes such as tempo, melody, and harmony influence exercise responses. Loud/fast music tends to increase arousal, whereas soft/slow reduces it; music can enhance tempo, efficiency, and decrease perceived exertion. Benefits include dissociation, positive affect, neuromuscular efficiency, and performance improvements, though effects may diminish at high intensities where internal cues dominate. Evidence on tempo and flow is mixed: fast/optimistic music may benefit untrained but not trained runners; soft/slow music reduced arousal and increased endurance; motivational characteristics of music correlate with flow; treadmill studies reported higher flow for musical conditions (fast, medium, mixed) than no music, with some suggesting medium tempo yields highest flow at moderate intensity; preferences often favor medium-to-fast tempos at moderate intensity and fast at high intensity, but relationships are not strictly linear. Neurophysiologically, rhythmic properties affect arousal and engage widespread cortical regions. Tempo is a key, easily manipulated factor, yet there is no consensus on how tempo affects movement flow. This motivates a controlled, single-factor test focusing on tempo while holding other musical elements constant.

Design: Single-factor repeated-measures experiment testing three conditions: fast tempo music, slow tempo music, and no-music control during brisk treadmill walking. Sessions were scheduled on three different days per participant, with order counterbalanced via a Latin square. Each session involved 10 minutes of brisk walking. Participants: 21 untrained university students (8 males, mean age 21.3±3.23; 13 females, mean age 23.5±1.15), aged 18–27, with normal hearing and no relevant health issues. One participant’s EEG was unusable, yielding 20 valid responses for subjective analyses. A priori power: Using GPower with α=0.05, power=0.80, medium effect size f=0.25, and within-measure correlation r=0.65, required total N=20 for repeated-measures ANOVA. Stimuli: Fast tempo 150–160 bpm; slow tempo 90–100 bpm; instrumental, upbeat, simple music without lyrics, presented at ~75 dBA, to minimize lyrical/cultural biases and emphasize rhythmic entrainment. Apparatus and environment: Laboratory at 23±2°C with fresh air circulation. Treadmill: SOLE F63. EEG: 32-channel Bitbrain hydroelectrode system (10–10 montage, REF at earlobe), 256 Hz sampling, 24-bit resolution, movement-resistant gyro. Music delivered by laptop and speakers. Procedure: Participants received instructions, completed a 2-minute warm-up at 5 km/h without music, then donned a moistened 32-channel EEG cap with signal quality verified. They walked 10 minutes at 6.5 km/h (females) or 7.0 km/h (males) under one randomly assigned condition per day, looking at a blank wall to avoid visual distractions. After each walk, EEG cap was removed and participants completed the 9-item S-FSS-2 (5-point Likert) and performed cool-down stretches. Total time per session ~30 minutes. Measures: Subjective flow via S-FSS-2 (nine dimensions). Objective EEG: mean power values for delta, theta, alpha, beta, and gamma bands. Signal processing: ErgoLAB acquisition with high-pass 1 Hz, low-pass 40 Hz, and 50 Hz notch. Artifact removal via EEGLAB (Matlab R2022a) using blind source separation to remove bad channels and motion artifacts. Power spectral density expressed in dB units. Statistics: Reliability assessed with Cronbach’s α (fast 0.735; slow 0.746; control 0.741). Repeated-measures one-way ANOVAs tested effects of tempo on S-FSS-2 scores and EEG band powers. Post-hoc pairwise comparisons used multiple-comparison adjustments (reported as Bonferroni/LSD in tables). Effect sizes: partial η², Cohen’s d, and adjusted d (Ezekiel’s correction) due to small sample size. Significance threshold p<0.05. Analyses performed in SPSS.

• Subjective flow (S-FSS-2): Significant main effect of tempo, F(2,38)=4.243, p=0.022, partial η²=0.183. Pairwise comparisons: fast > no-music (MD=2.100±0.695, p=0.007, partial η²=0.324, d=0.675); slow > no-music (MD=1.800±0.848, p=0.047, partial η²=0.192, d=0.475); fast vs. slow not significant (p=0.708). Thus, both fast and slow tempos elevated subjective flow over no music; no difference between fast and slow.
• EEG band powers: Significant main effects of tempo for delta (F=4.609, p=0.016, partial η²=0.195), theta (F=3.784, p=0.032, partial η²=0.166), alpha (F=4.422, p=0.019, partial η²=0.189), and beta (F=3.698, p=0.034, partial η²=0.163). Gamma not significant (F=0.468, p=0.63, partial η²=0.024).
  • Pairwise comparisons (MD in dB): Fast > no-music for delta (MD=3.232, p=0.005, partial η²=0.346, d=0.709), theta (MD=2.312, p=0.010, partial η²=0.301, d=0.640), alpha (MD=2.649, p=0.015, partial η²=0.272, d=0.596), and beta (MD=2.092, p=0.011, partial η²=0.295, d=0.630).
  • Slow > no-music for delta (MD=2.194, p=0.034, partial η²=0.216, d=0.512); slow vs. no-music not significant for theta, alpha, or beta (all p>0.05).
  • Fast vs. slow not significant for any band (all p>0.05). Overall, fast tempo music produced reliable increases in delta, theta, alpha, and beta power relative to no music; slow tempo increased delta only.

Findings indicate music tempo modulates both subjective flow and EEG markers during brisk walking. Subjectively, both fast and slow tempos enhanced perceived flow relative to no music, with no difference between fast and slow, partially supporting H1. Objectively, fast tempo yielded higher mean power in delta, theta, alpha, and beta bands compared to no music, while slow tempo increased delta only, supporting H2 that fast tempo more effectively stimulates unconscious brainwave activity associated with immersion (delta), focused attention and concentration (theta), relaxed attentional engagement (alpha), and cognitive processing (beta). The divergence between subjective and objective results may stem from timing and sensitivity: subjective ratings were post hoc snapshots potentially influenced by additional musical features and time-varying experiences, whereas EEG captured continuous state changes throughout exercise. Integrating these perspectives suggests that across the exercise period, fast tempo music is more conducive to eliciting movement flow than slow or no music. These results align with prior evidence that music can benefit untrained exercisers and enhance exercise efficiency, while also highlighting contextual dependencies (e.g., exercise intensity) reported in the literature.

Combining subjective (S-FSS-2) and objective (EEG) measures, the study shows that fast tempo music enhances movement flow during brisk treadmill walking, elevating perceived flow versus no music and increasing EEG power in delta, theta, alpha, and beta bands. Practically, fast, upbeat, lyric-free music may be used to stimulate flow and improve training outcomes, particularly for repetitive movements and early training phases; slower music might be applied later depending on sport-specific needs. The study underscores EEG’s promise for monitoring flow over time during exercise.

• Flow is an elusive, dynamic state that is difficult to measure, and causal mechanisms remain incompletely understood.
• Task specificity: findings are tied to brisk treadmill walking and may not generalize to other activities or intensities.
• Music complexity and individual differences were not accounted for (e.g., self-selected preferences, cultural associations, lyrics/emotional content).
• Only tempo was manipulated; other musical attributes (melody, harmony), intensity adaptation, and mixed tempo conditions were not tested.
• Potential gender differences and other demographic moderators were not examined. Future research should explore musical complexity, mixed tempos, participant music preferences, adaptive intensity/tempo matching, and gender or individual differences across varied exercise intensities and modalities.