This exploratory study found that while subjective mental fatigue and reaction time significantly increased during a 60-min 2-back task under nap-deprivation conditions, no HRV features showed statistically significant temporal phase effects after false-discovery-rate correction, and the study does not establish fatigue-specific HRV phases or diagnostic markers.
Key Findings
Results
Subjective mental fatigue scores increased significantly over the 60-minute 2-back task.
FSAS mental-fatigue subscale scores increased from 18.99±17.09 to 46.88±24.89.
Mean change was 27.88 (95% CI 19.34–36.43).
Paired t-test: t(25)=6.72, p<0.001, Cohen's dz=1.32, indicating a large effect size.
Twenty-six healthy graduate students completed pre- and post-task assessments.
Results
Simple reaction time increased significantly from pre- to post-task, indicating behavioral performance decrements.
Reaction time increased from 371.63±37.74 ms to 407.62±66.70 ms.
Mean change was 35.98 ms (95% CI 21.17–50.80).
Paired t-test: t(25)=5.00, p<0.001, Cohen's dz=0.98, indicating a large effect size.
Both subjective fatigue and objective reaction-time measures confirmed task-induced fatigue in the sample.
Results
None of the 19 HRV features showed statistically significant temporal phase effects after false-discovery-rate correction.
Nineteen HRV features were estimated in 5-minute windows with a 1-minute step.
Several nominal phase effects were observed across the 19 features prior to correction.
Benjamini-Hochberg false-discovery-rate correction eliminated all nominally significant results.
The study therefore does not establish fatigue-specific HRV phases or diagnostic markers.
Results
Exploratory clustering of HRV feature trajectories revealed heterogeneous dynamics with limited reproducibility.
Exploratory ranking and dynamic time-warping clustering were used to characterize HRV temporal dynamics.
A fixed five-cluster solution was applied but was described as 'not the best-separated solution.'
Participant-level reproducibility was characterized as 'limited.'
Feature trajectories were described as 'heterogeneous,' indicating no consistent HRV temporal pattern across participants.
Results
A BCG-derived respiratory surrogate showed a phase effect for relative respiratory amplitude but not for respiratory rate.
Ballistocardiogram (BCG) signals were acquired using a 1000-Hz fiber-optic sensing cushion.
The respiratory surrogate demonstrated a significant temporal phase effect specifically for relative respiratory amplitude.
No significant phase effect was found for respiratory rate.
Four recordings were excluded due to insufficient compliance with the seated protocol, leaving 22 participants in the HRV analysis.
Background
The study was conducted during the habitual afternoon nap period, meaning observed physiological changes could reflect multiple confounding factors beyond mental fatigue alone.
The task was performed during the habitual afternoon nap period specifically to induce nap-deprivation conditions.
The authors note that physiological changes in single-group paradigms 'may also reflect time-on-task, circadian variation, nap deprivation, posture, and cognitive load.'
The study design was exploratory and single-group, without a control or comparison condition.
The study population comprised 26 healthy graduate students.
Conclusions
The study demonstrates the feasibility of unobtrusive fiber-optic BCG sensing for continuous physiological monitoring during prolonged cognitive tasks.
BCG signals were acquired continuously at 1000 Hz using a fiber-optic sensing cushion, an unobtrusive measurement approach.
The system enabled derivation of both HRV features and a respiratory surrogate without direct body-contact electrodes.
Authors conclude the findings 'support the feasibility of unobtrusive BCG for describing continuous physiological variation during prolonged cognitive engagement.'
Despite feasibility, the authors explicitly state the approach does not establish 'individual-level monitoring performance.'
What This Means
This research suggests that while a 60-minute mentally demanding memory task (called a 2-back task) reliably made participants feel more fatigued and slowed their reaction times, the heart rate variability (HRV) signals recorded during the task did not show consistent, statistically reliable patterns that could be linked specifically to mental fatigue. The study was conducted in the early afternoon — the time when participants would normally nap — so the participants were also sleep-deprived of their habitual nap. Subjective fatigue scores roughly doubled and reaction times slowed by about 36 milliseconds on average, both strong and statistically significant effects. However, when the researchers analyzed 19 different measures of HRV over time, none of these physiological signals showed a significant change pattern once the analysis was corrected for the large number of comparisons being made.
The study used an innovative, unobtrusive sensor: a cushion embedded with fiber-optic technology that could detect tiny body movements caused by the heartbeat (a technique called ballistocardiography, or BCG), without requiring any electrodes attached to the body. This approach was shown to be technically feasible for continuous monitoring during an hour-long seated task, and it could even estimate breathing patterns. However, the patterns of HRV change varied considerably from person to person, meaning the system could not reliably track fatigue at the individual level.
This research suggests that using HRV alone — at least as measured in this way and under these conditions — is insufficient to build a reliable real-time fatigue detection system. The results highlight that many factors beyond fatigue, including time of day, circadian rhythms, nap deprivation, and posture, all influence physiological signals simultaneously, making it difficult to isolate fatigue-specific markers. The work establishes a foundation for future controlled studies that would need to separate these competing influences to determine whether HRV can genuinely serve as a fatigue biomarker.
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Meng D, Yu L, Min T, Zhang J, Zhu L, Gong H, et al.. (2026). Exploratory Temporal Dynamics of Heart-Rate Variability During a Prolonged 2-Back Task Under Nap-Deprivation Conditions.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26196099