About Cookies on this site

This site uses Cookies to improve your online experience. By continuing to use this site without changing your cookie preferences we will assume that you are agreeing to our use of cookies. For more information, or to change your cookie preferences, visit our cookie policy.

Accept
Choose your platform and buy
Try if one month free of charge with 10 licenses.
What is the account for?
Sign Up!

Confirm that the use of assessments and training is for yourselfYou are going to create a personal account. This type of account is specially designed to help you evaluate and train your cognitive skills

Please confirm that the use of cognitive training and assessment is for your patients. You are going to create a patient management account. This account is specially designed to help health professionals (doctors, psychologists, etc.) with the diagnosis, and intervention of cognitive disorders.

Confirm that you want to offer training and/or cognitive assessments to your family or friends.You are going to create a family account. This account is designed to give your family members access to CogniFit evaluations and training.

Please confirm that the use of cognitive training and assessment is for research study participants.You are going to create a research account. This account is specially designed to help researchers with their studies in the cognitive areas.

Please confirm that the use of cognitive training and assessment is for your students.You are going to create a student management account. This account is designed to assist in the diagnosis and intervention of cognitive disorders in children and young students.

For users 16 years and older. Children under 16 can use CogniFit with a parent on one of the other platforms.

loading

By clicking Sign Up or using CogniFit, you are indicating that you have read, understood, and agree to CogniFit's Terms & Conditions and Privacy Policy.

corporativelanding_STUDY-FATIGUE-ASSESS-NAVY-PILOTS_social_picture

CogniFit Helps U. S. Navy Increase Pilot Safety

Scientific publication on cognitive evaluation of pilot fatigue

  • Conveniently manage research patients from the researcher's platform

  • Evaluate and train up to 23 cognitive skills for your study participants

  • Check and compare participants' cognitive development for your study data

Start Now
loading

Name: Predicting Individual Differences in Response to Sleep Loss: Application of Current Techniques.

Authors: Joseph F. Chandler1, Richard D. Arnold1, Jeffrey B. Phillips1, Ashley E. Turnmire1.

  • 1. Naval Medical Research Unit.

Journal: Aviation, Space and Environmental Medicine (2013), vol. 84 (9): 927-937.

References to this article (APA style)::

  • Chandler, J. F., Arnold, R. D., Phillips, J. B., Turnmire, A. E. (2013). Predicting Individual Differences in Response to Sleep Loss: Application of Current Techniques. Aviat Space Environ Med., vol.84, pp.927-937.

Study Conclusion

CogniFit can measure highly relevant variables to predict individual user fatigue performance. This can help reduce the risk of accidents for military pilots and civilians, as tiredness is a recurring variable in various types of accidents. . Including some variables, such as response time (p=0.009), short-term memory (p=0.023), divided attention (p=0.026) or cognitive flexibility (p=0.002) in predictive models, the percentage of variance explained goes from 13.8% to 35.7%.

Study Summary

The fatigue is one of the main factors that puts safety at risk during military transport. Models predicting the response to fatigue have been made, but are not yet sufficiently accurate, as they do not take into account the individual differences in fatigue sensitivity. Instead, it is proposed that the predictive capacity of these models could be improved if cognitive measurements were performed using tools such as CogniFit and oculometric to account for individual differences.

The different cognitive and oculometric variables were measured in rested participants and every 3 hours during a 25-hour wakefulness process (so the group and individual scores were obtained). It was also possible to compare actual performance with expected performance. The results indicated that by adding these measures to the pre-existing models, they went from explaining 13.8% to 35.7% of the variance. This means that, using CogniFit and other measures to detect individual differences, can greatly improve the prediction of performance during fatigue and thus improve safety.

Context

Fatigue due to lack of sleep is one of the main risks they face in both military and civilian transport. In principle, the solution to these problems would be the sleeping properly and/or resort to drugs. However, sometimes this is not enough. Much of this can be prevented by predicting a person's performance or directly measuring an individual's ability to act at the right time. This prediction model, on the other hand, has a moderate effectiveness by itself.

The relative lack of success of this model may be because it assumes that all individuals have a similar circadian rhythm and response to fatigue, while studies suggest that individual differences in these settings are significant. Some of the aspects that interfere with this response to fatigue are a person's cognitive functioning.

Therefore, an increase in the effectiveness of the predictive model could be expected if we include measures that take into account individual differences, such as cognitive and oculometric measures.

Methodology

Participants

The participants consisted of 15 volunteers from the Naval Aviation Preflight Indoctrination (API) program on board the Naval Air Station Pensacola, serving military personnel (13 men and 2 women, with an average age of 24.7 and 21.5 years respectively). To participate in the study, alcohol, caffeine, and tobacco use were controlled, and they had to be free of neurological, psychiatric, or sleep-related problems.

Procedure

A design of repeated measures was applied to learn about the effects of sleep deprivation on cognitive and oculometric performance, both at a group and individual levels. First, the baseline was recorded and then the data was taken during sleep deprivation.

CogniFit personalized cognitive assessment with CogniFit: Researchers Platform.

To assess cognitive skills relevant to fatigue, they used the CogniFit cognitive assessment tool. Since the complete cognitive evaluation is 30 minutes long and participants could not spend as much time, the evaluation was adapted to the needs of the experiment, reducing its duration to 7 or 8 minutes, and measuring exclusively the response time, visual scanning, divided attention, cognitive flexibility, focused attention and short-term memory. However, CogniFit is a cognitive assessment and training tool that measures and trains more than 20 cognitive abilities. It also has a platform for researchers. This platform helps participant and research data management and helps to improve participants' cognitive abilities.

  • CogniFit has several evaluations that will allow us to know the complete user profile in one session. These assessments help measure the baseline level of each participant, which is very useful in the case of an intervention since it allows the user to adjust from the beginning.
  • As well as the assessment, CogniFit also has cognitive training programs that will allow us to strengthen and optimize the cognitive abilities of users. One of CogniFit's most distinctive features is that training sessions are customized. This means that the activities and their difficulty will be automatically chosen by the program to adapt to the needs of each participant. In each session the participant's performance is measured, so we will be able to know the strong and weak points of the user at all times. CogniFit automatically decides the best training plan for that particular user. In addition, this makes it unlikely that two participants will carry out exactly the same training.
  • It's a very accessible online tool, since it is available in 18 languages and only requires a device with Internet access to access the platform.The CogniFit tool has been validated by scientific studies from a number of countries. This makes it a robust and reliable tool for training our participants' cognitive status.
  • CogniFit currently has 18 different assessment tasks that measure over 20 cognitive skills. Thus, researchers will be able to know the state of the participants' cognitive profile.

The platform for CogniFit researchers allows to conveniently manage research carried out with this tool. For create a free account just follow the steps below. This researchers account gives the possibility of:

  • Manage participants and the tests that will be applied to measure and/or train their cognitive functions.
  • Collect the participants' data and monitor cognitive progress.
  • To carry out experimental studies on the effects of cognitive stimulation through computer programs.
  • Design research with training for specific cognitive areas.
CogniFit – Image Sign up

When we are logged in, we may buy licenses for evaluations, trainings or both. CogniFit tool has 9 different evaluations and 15 different training programs .

CogniFit – Image Main screen

CogniFit's two main products help measure or train all the cognitive skills that we work with wide scientific support:

  • Assessments: Through 18 different tasks we can accurately evaluate more than 20 cognitive abilities. This allows us to create a complete profile of the participant's current cognitive status. The most complete assessment is the General Assessment Battery (CAB), but CogniFit also offers more specific assessment batteries: for Parkinson, Depression, Dyslexia, ADHD and others. At the beginning of each task, the participant will be interactively explained what to do.
  • Training: With more than 30 training games, it is possible to stimulate all cognitive skillstrained in CogniFit. Personalized brain training allows us to strengthen the cognitive abilities of our participants in an entertaining and comfortable way. CogniFit also offers training for specific pathologies. The games include an interactive explanation so that participants can easily understand how they work. Each training session lasts approximately 15 to 20 minutes. During this time, our participants will carry out three activities (two games and one evaluation task). In addition, the tool automatically adapts the difficulty of the activities to the participants' level. We will also have the option to indicate the number of hours we want each participant to rest between training sessions.

When we have chosen the evaluations and training that interest us, we can invite our participants, assign them a group and activities that they will have to do. Participants will receive an invitation in their email and will only need to create an account as a normal user and accept that we researchers can see their results.

CogniFit – Image Invite participant

From our researcher's account, we will be able to observe the activity of our participants , view their profiles or cognitive evolution and export the study data. We will also have access to different data:

  • The state of the five cognitive areas in which the other cognitive skills are included.
.
CogniFit – Image Cognitive areas
  • An individual status of each of the cognitive abilities
CogniFit – Image Cognitive skills
  • Agraphic with the general evolution of the participant's cognitive state, or each of the cognitive abilities independently.
.
CogniFit – Image Cognitive evolution

Once we have completed data collection from the study, we will be able to download the results of each participant to our computer for analysis.

Statistical Analysis

The analysis was carried out in three steps:

  • Step 1: A series of ANOVAs were performed for each criterion and predictor variable measured in each trial. This determined what variables showed changes over time.
  • Step 2: A series of bivariate linear hierarchical models with fixed and random effects were carried out with the objective of predicting when fatigue would produce a lower yield and, in turn, discovering undetected differences in the group analysis level. A group effect (p<0.05) and individual differences within that overall effect (0<0.05) were detected. Following this, a multivariate multi-hierarchical linear model was carried out to know which predictor variables shared explanatory variance at the statistical level and relationship at the conceptual.
  • Step 3: A series of general linear models were made from the significant predictor variables of the previous step. Thus, the aim was to know the predictive capacity of the model by taking into account cognitive and oculometric factors.
.

Results and Conclusions

In Step 1 of the data analysis, the effects of group were obtained. It was observed that there were significant effects on response time (p=0.009), short-term memory (p=0.023), divided attention (p=0.026) and cognitive flexibility (p=0.002). With fatigue, there was a reduction in the performance of these cognitive abilities, so they were taken into account as predictive variables in the next step. In Step 2 of the analysis, the individual differences were obtained through significant relationships between different variables with fixed or random effects. Step 3 of data analysis, it was observed that when only classical prediction measures were used, predictions could only account for 13.8% of the variance. By contrast, adding significant cognitive variables, predictions could account for 35.7% of the variance.

These results indicate that adding some fatigue-sensitive variables to the usual predictive models, such as CogniFit measures, can help us predict more accurately when performance will be affected by fatigue. Knowing this information can be very useful to prevent accidents and take precautionary measures in both military and civilian airplanes.

Patient #141

Cate Brown

catebrown@mail.com

59 years old

Last activity: 02/01/2016 | 4:09 min

Registration date: 01/01/2013

Total number of logins: 23

Inhibition

598

Focused Attention

608

Auditory Short-term Memory

468

Spatial Perception

405

Set up a new training session

Custom Training

Session length

15 min

Personalized Training

Memory

Concentration

Reasoning

Chemotherapy

55 and Over

Mental Arithmetic

Perception

Insomnia

Darwin Science Institute

Participants: 135

Groups: 24

60 and Over

Control Group

Participants: 11

Add participants

60 and Over

Normal Group

Participants: 11

Add participants

Memory Test

Control Group

Participants: 5

Add participants

Memory Test

Normal Group

Participants: 5

Add participants

Create New Group

Name

Type of group

Control Group

Normal Group

Save

Settings: Manual

Daniel Foster

Memory Test

Control Group

0
Personalized Training
1
Memory
0
Concentration
1
Reasoning
0
Driving
0
55 and Over
0
Perception
0
Stroke
1
Insomnia
1
Mental Arithmetic
2
Delay between training sessions (hours)
General Cognitive Assessment

Number of training regime iterations

5

Send

Student #231

Paul Perkins

DaVinci High School

12 years old | Right handed

ADHD

DaVinci High School

Students: 357

Calculation

Logic

Writing

Reading

Working Memory

565

Naming

411

Visual Perception

355

Visual Short Term Memory

392

Processing Speed

450

Focused Attention

298

ADHD Intervention
Cognitive training
Cognitive Skills
Focused Attention
Spatial Perception
Visual Scanning
Send this training session to Paul Perkins
Strengths
Natural Sciences
Language and Literature
Art

Please type your email address