Mental Load and Fatigue Assessment Instruments: A Systematic Review

Association of brain-autonomic activities and task accuracy under cognitive load: a pilot study using electroencephalogram, autonomic activity measurements, and arousal level estimated by machine learning

The total amount of mental activity applied to working memory at a given point in time is called cognitive load, which is an important factor in various activities in daily life. We have proposed new feature quantities that reflect the time-series changes in the power of typical frequency bands in electroencephalogram (EEG) for use in examining the relationship between brain activity and behavior under cognitive load. We also measured heart rate variability (HRV) and spontaneous skin conductance responses (SCR) to examine functional associations among brain activity, autonomic activity, and behavior under cognitive load. Additionally, we applied our machine learning model previously developed using EEG to the estimation of arousal level to interpret the brain-autonomic-behavior functional association under cognitive load. Experimental data from 12 healthy undergraduate students showed that participants with higher levels of infra-slow fluctuations of alpha power have more cognitive resources and thus can process information under cognitive load more efficiently. In addition, HRV reflecting parasympathetic activity correlated with task accuracy. The arousal level estimated using our machine learning model showed its robust relationship with EEG. Despite the limitation of the sample size, the results of this pilot study suggest that the information processing efficiency of the brain under cognitive load is reflected by time-series fluctuations in EEG, which are associated with an individual's task performance. These findings can contribute to the evaluation of the internal state of humans associated with cognitive load and the prediction of human behaviors in various situations under cognitive load.

Action-rule-based cognitive control enables efficient execution of stimulus-response conflict tasks: a model validation of Simon task performance

Introduction

The human brain can flexibly modify behavioral rules to optimize task performance (speed and accuracy) by minimizing cognitive load. To show this flexibility, we propose an action-rule-based cognitive control (ARC) model. The ARC model was based on a stochastic framework consistent with an active inference of the free energy principle, combined with schematic brain network systems regulated by the dorsal anterior cingulate cortex (dACC), to develop several hypotheses for demonstrating the validity of the ARC model.

Methods

A step-motion Simon task was developed involving congruence or incongruence between important symbolic information (illustration of a foot labeled "L" or "R," where "L" requests left and "R" requests right foot movement) and irrelevant spatial information (whether the illustration is actually of a left or right foot). We made predictions for behavioral and brain responses to testify to the theoretical predictions.

Results

Task responses combined with event-related deep-brain activity (ER-DBA) measures demonstrated a key contribution of the dACC in this process and provided evidence for the main prediction that the dACC could reduce the Shannon surprise term in the free energy formula by internally reversing the irrelevant rapid anticipatory postural adaptation. We also found sequential effects with modulated dip depths of ER-DBA waveforms that support the prediction that repeated stimuli with the same congruency can promote remodeling of the internal model through the information gain term while counterbalancing the surprise term.

Discussion

Overall, our results were consistent with experimental predictions, which may support the validity of the ARC model. The sequential effect accompanied by dip modulation of ER-DBA waveforms suggests that cognitive cost is saved while maintaining cognitive performance in accordance with the framework of the ARC based on 1-bit congruency-dependent selective control.

Measurement properties of instruments to measure the fatigue domain of vitality capacity in community-dwelling older people: an umbrella review of systematic reviews and meta-analysis

BACKGROUND

Vitality capacity (VC) is a key domain of intrinsic capacity (IC) and is the underlying biophysiological aspect of IC. Energy and metabolism (E&M) is one of the domains of VC. Fatigue is one of the main characteristics of E&M.

OBJECTIVE

The aims of this umbrella review are (i) to identify the available instruments suitable for measuring fatigue in community-dwelling older adults and (ii) to critically review the measurement properties of the identified instruments.

DESIGN

Umbrella review.

SETTING

Healthcare.

SUBJECTS

Community-dwelling older adults.

METHODS

PubMed and Web of Knowledge were systematically screened for systematic reviews and meta-analysis reporting on fatigue instruments resulting in 2,263 articles (last search 5 December 2022). The COSMIN checklist was used to appraise psychometric properties and the AMSTAR for assessing methodological quality. Data on fatigue instruments, construct, reference period, assessment method, validated population, reliability, validity, responsiveness and predictive validity on negative health outcomes were extracted.

RESULTS

10 systematic reviews and 1 meta-analysis were included in this study. 70 fatigue instruments were identified in the literature and 21 were originally designed for fatigue. The Fatigue Severity Scale (FSS), Pittsburgh Fatigability Scale (PFS) and Visual Analogue scale (VAS-F), Fatigue Impact Scale (FIS) and the Functional Assessment of Chronic Illness Therapy Fatigue (FACIT-F) presented good psychometric properties.

CONCLUSIONS

The FSS, FIS, FACIT-F, PFS and the VAS-F presented good psychometric properties in various conditions. Therefore, these instruments could be used to quantify trajectories in the domain E&M in the context of VC in community-dwelling older adults.

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task

Introduction

Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.

Methods

We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.

Results

Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.

Discussion

The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

Mental Fatigue Is Associated with Subjective Cognitive Decline among Older Adults

Both Subjective Cognitive Decline (SCD) and mental fatigue are becoming increasingly prevalent as global demographics shifts indicate our aging populations. SCD is a reversible precursor for Alzheimer's disease, and early identification is important for effective intervention strategies. We aim to investigate the association between mental fatigue-as well as other factors-and SCD. A total of 707 old adults (aged from 60 to 99) from Shanghai, China, participated in this study and completed self-reported instruments covering their cognitive and mental status as well as demographic information. Mental fatigue status was assessed by using four items derived from the functional impairment syndrome of the Old Adult Self Report (OASR). SCD was assessed by using the Memory/Cognition syndrome of OASR. A total of 681 old adults were included in the current study. The means of SCD significantly differed between each group of factors (age, gender, and mental fatigue). The general linear regression models showed that SCD increased with age, females scored higher than males, and SCD was positively associated with mental fatigue factors including difficulty getting things done, poor task performance, sleeping more, and a lack of energy among old adults. The study also found that SCD is negatively associated with the high-income group among young-old (aged from 60 to 75) males and associated with good marital/living status with the companion of spouses/partners among young-old females. These results suggest that gender, income level, marital/living status, and mental fatigue are crucial factors in preventing SCD among old adults and are pivotal in developing early intervention strategies to preserve the mental health of an increasingly aging population.

Influence of Scoring Systems on Mental Fatigue, Physical Demands, and Tactical Behavior during Soccer Large-Sided Games

Constraints are common in soccer training to develop physical, technical-tactical, and mental training concurrently. This study examined how different scoring systems influence physical, tactical, and mental demands during large-sided games in soccer. Eighteen youth-elite male (17.39 ± 1.04 y) soccer players completed three 8 vs. 8 large-sided games where the different score systems were i) official score system (OSS; i.e., 1 goal = 1 goal), ii) double the value of the goal-4 min (DVx4; i.e., 1 goal = 1 goal from 0.00 to 7.59 min, and 1 goal = 2 goals from 8.00 to 12.00 min), and iii) double the value of the goal-8 min (DVx8; i.e., 1 goal = 1 goal from 0.00 to 3.59 min, and 1 goal = 2 goals from 4.00 to 12.00 min). Physical demands and tactical behaviors were recorded during tasks using a global positioning system and video camera. Mental fatigue was recorded pre- and post-task using a visual analogue scale. Also, the ratio of perceived exertion and mental load were recorded after tasks were finished. Results reported the highest values of mental and physical demands in DVx4. Mental fatigue increased during all three large-sided games, although this increase was significantly higher in DVx4 compared with OSS (p = 0.006) and DVx8 (p = 0.027). Tactical behavior showed a trend towards more direct play during DVx4, which was less observed during DVx8, and not at all during OSS. In conclusion, changing the scoring system affects physical, tactical, and mental demands.

[Asthenia, mental fatigue and cognitive dysfunction]

Conditions associated with asthenia are usually characterized by increased fatigue, impaired activities of daily living and decreased productivity. In clinical practice it is important to distinguish between idiopathic chronic fatigue (primary or functional asthenia) and chronic fatigue syndrome (CFS). Fatigue can also be classified by neuromuscular and/or cognitive and mental fatigue. The article discusses the neuroanatomical basis and focuses on the neurocognitive theory of pathological fatigue. In addition the relationship between mental stress, fatigue and cognitive impairments such as subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) are also discussed. We discuss the rationale that for treatment of asthenic conditions accompanied by cognitive dysfunction it is justified to use combination therapy - fonturacetam and a preparation containing nicotinoyl-GABA and Ginkgo Biloba.

Gender differences in professional drivers’ fatigue level measured with BAlert mobile app: A psychophysiological, time efficient, accessible, and innovative approach to fatigue management

Addressing fatigue is useful in a variety of scenarios and activities. Fatigue has recently been studied from a psychophysiological standpoint. As a result, the expression and impact of peripheral and central fatigue has been evaluated. Driving is one occupation where tiredness has disastrous consequences. BAlert is a smartphone app that approaches exhaustion with psychophysiological measures. More specifically, it evaluates the level of fatigue via heart rate variability (HRV) data and the cognitive compromise via Stroop effect. The goal of this study is to determine if there are gender differences in fatigue levels among professional drivers using the BAlert app. Statistically significant differences were found in the number of hours awake, in different parameters of HRV (AVNN, PNN50, RMSSD, and SDNN), in the level of stress, as well as in the cognitive response evaluated through the app. The results are discussed and their implications for the management of work fatigue are presented.

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain-Computer Interfaces (BCI) allows for unobtrusively monitoring one's cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human-computer interaction variables.