Effect of total sleep deprivation on egocentric distance estimation following a fatiguing task

Baati, Hamza; Hmani, Moufida Shell; Jarraya, Mohamed; Chtourou, Hamdi; Masmoudi, Liwa; Trabelsi, Khaled; Hakim, Ahmad; Marrakchi, Rim; Moalla, Wassim

doi:10.1080/09291016.2014.985003

Cited by 4 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, t statistics (or p values) derived from paired t tests were used to calculate the SD of the percentage change in exercise performance (SD Δ ). Where an exact value was quoted [45,76,88,92,[109][110][111], the calculation was performed using the following formula [112]:…”

Section: Weighted Mean Effectmentioning

confidence: 99%

“…where SD Δ is the SD of the net exercise performance change and R is the correlation coefficient. R was approximated (0.71) as the mean correlation coefficient calculated using raw exercise performance data from nine outcome measurements derived from seven publications [45,76,88,92,[109][110][111], as indicated by Higgins and Green [112]. Sensitivity analyses were performed using R = 0.30 and 0.80 to…”

Section: Weighted Mean Effectmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Acute Sleep Loss on Physical Performance: A Systematic and Meta-Analytical Review

et al. 2022

View full text Add to dashboard Cite

Background Sleep loss may influence subsequent physical performance. Quantifying the impact of sleep loss on physical performance is critical for individuals involved in athletic pursuits. Design Systematic review and meta-analysis. Search and Inclusion Studies were identified via the Web of Science, Scopus, and PsycINFO online databases. Investigations measuring exercise performance under ‘control’ (i.e., normal sleep, > 6 h in any 24 h period) and ‘intervention’ (i.e., sleep loss, ≤ 6 h sleep in any 24 h period) conditions were included. Performance tasks were classified into different exercise categories (anaerobic power, speed/power endurance, high-intensity interval exercise (HIIE), strength, endurance, strength-endurance, and skill). Multi-level random-effects meta-analyses and meta-regression analyses were conducted, including subgroup analyses to explore the influence of sleep-loss protocol (e.g., deprivation, restriction, early [delayed sleep onset] and late restriction [earlier than normal waking]), time of day the exercise task was performed (AM vs. PM) and body limb strength (upper vs. lower body). Results Overall, 227 outcome measures (anaerobic power: n = 58; speed/power endurance: n = 32; HIIE: n = 27; strength: n = 66; endurance: n = 22; strength-endurance: n = 9; skill: n = 13) derived from 69 publications were included. Results indicated a negative impact of sleep loss on the percentage change (%Δ) in exercise performance (n = 959 [89%] male; mean %Δ = − 7.56%, 95% CI − 11.9 to − 3.13, p = 0.001, I2 = 98.1%). Effects were significant for all exercise categories. Subgroup analyses indicated that the pattern of sleep loss (i.e., deprivation, early and late restriction) preceding exercise is an important factor, with consistent negative effects only observed with deprivation and late-restriction protocols. A significant positive relationship was observed between time awake prior to the exercise task and %Δ in performance for both deprivation and late-restriction protocols (~ 0.4% decrease for every hour awake prior to exercise). The negative effects of sleep loss on different exercise tasks performed in the PM were consistent, while tasks performed in the AM were largely unaffected. Conclusions Sleep loss appears to have a negative impact on exercise performance. If sleep loss is anticipated and unavoidable, individuals should avoid situations that lead to experiencing deprivation or late restriction, and prioritise morning exercise in an effort to maintain performance.

show abstract

Section: Weighted Mean Effectmentioning

confidence: 99%

Section: Weighted Mean Effectmentioning

confidence: 99%

Effects of Acute Sleep Loss on Physical Performance: A Systematic and Meta-Analytical Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…All experiments evaluated verbal distance estimation data. Pooling of two experiments ( Baati et al, 2020 ; Baati et al, 2015 ) that compared distance perception pre- vs post-exercise using a sprint cycling task (10 × 6 s maximal cycling) found no effect of exercise at any of the three distances measured, for either the normal ( Fig. 3A ) or the sleep deprived conditions ( Fig.…”

Section: Resultsmentioning

confidence: 95%

“…However, our risk of bias appraisal identified potential issues with fatigue paradigms ( e.g ., insufficient fatigue for perceptual scaling to occur) and/or distance estimation tasks ( e.g ., distances not sufficiently challenging for the population). For example, in both pooled studies ( Baati et al, 2020 ; Baati et al, 2015 ) that found null results, a relatively easy sprint cycling task (6 s of sprinting × 10), particularly for a young population (mean = 23 years), was used to induce fatigue. In contrast, Hunt, Hunt & Park (2017) recruited an older population (mean = 54 years), used a challenging exercise task for that population (stepper task), and used longer distances (92.5 m), and did show findings consistent with EoA hypothesis: fatigued participants estimated distance as further than non-fatigued participants.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, given high risk of bias for participant blinding and given the populations recruited ( e.g ., often athletes vs non-athletes), social effects on compliance with experimental demands and knowledge effects cannot be ruled out. In particular, athletes (such as the soccer players in both Baati et al (2015 , 2020) experiments), may have substantial knowledge/experience in estimating distances. Thus, we cannot rule out the effect of such knowledge on the results.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Does who I am and what I feel determine what I see (or say)? A meta-analytic systematic review exploring the influence of real and perceived bodily state on spatial perception of the external environment

MacIntyre¹,

Braithwaite²,

Mouatt³

et al. 2022

PeerJ

View full text Add to dashboard Cite

Background Bodily state is theorised to play a role in perceptual scaling of the environment, whereby low bodily capacity shifts visuospatial perception, with distances appearing farther and hills steeper, and the opposite seen for high bodily capacity. This may play a protective role, where perceptual scaling discourages engaging with the environment when capacity is low. Methodology Our protocol was pre-registered via Open Science Framework (https://osf.io/6zya5/) with all amendments to the protocol tracked. We performed a systematic review and meta-analysis examining the role of bodily state/capacity on spatial perception measures of the environment. Databases (Medline, PsychINFO, Scopus, Embase, and Emcare) and grey literature were searched systematically, inclusive to 26/8/21. All studies were assessed using a customised Risk of Bias form. Standard mean differences and 95% CIs were calculated via meta-analysis using a random-effects model. Results A total of 8,034 studies were identified from the systematic search. Of these, 68 experiments (3,195 participants) met eligibility and were included in the review. These were grouped into the following categories: fatigue; pain; age; embodiment; body size/body paty size; glucose levels; fitness; and interoception, and interoceptive accuracy. We found low level evidence (limited studies, high risk of bias) for the effect of bodily state on spatial perception. There was consistent evidence that both glucose manipulations and age influence spatial perception of distances and hills in a hypothesised direction (lower capacity associated with increased distance and hill steepness). Mixed evidence exists for the influence of external loads, embodiment, body/body-part size manipulations, pain, and interoceptive accuracy. Evidence for fitness and/or fatigue influencing spatial perception was conflicting; notably, methodological flaws with fitness and fatigue paradigms and heterogenous spatial perception measures may underlie null/conflicting results. Conclusion We found limited evidence for bodily state influencing spatial perception of the environment. That all studies had high risk of bias makes conclusions about reported effects reflecting actual perceptual shifts (vs merely reflecting experimental demands or error due to inadequate study design) pre-emptive. Rigorous evaluation is needed to determine whether reported effects reflect more than bias (e.g., experimental demands, inadequate blinding). Future work using reliable measures of spatial perception, comprehensive evaluation of relevant confounders, and methodologically robust (and experimentally confirmed) bodily state experimental paradigms is warranted.

show abstract

Effects of a 30 min nap opportunity on cognitive and short-duration high-intensity performances and mood states after a partial sleep deprivation night

Souissi

Bayoudh

et al. 2020

Journal of Sports Sciences

View full text Add to dashboard Cite

Effect of total sleep deprivation on egocentric distance estimation following a fatiguing task

Cited by 4 publications

References 34 publications

Effects of Acute Sleep Loss on Physical Performance: A Systematic and Meta-Analytical Review

Effects of Acute Sleep Loss on Physical Performance: A Systematic and Meta-Analytical Review

Does who I am and what I feel determine what I see (or say)? A meta-analytic systematic review exploring the influence of real and perceived bodily state on spatial perception of the external environment

Effects of a 30 min nap opportunity on cognitive and short-duration high-intensity performances and mood states after a partial sleep deprivation night

Contact Info

Product

Resources

About