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Busis NA, Marolia D, Montgomery R, Balcer LJ, Galetta SL, Grossman SN. Navigating the U.S. regulatory landscape for neurologic digital health technologies. NPJ Digit Med 2024; 7:94. [PMID: 38609447 PMCID: PMC11014948 DOI: 10.1038/s41746-024-01098-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 03/29/2024] [Indexed: 04/14/2024] Open
Affiliation(s)
- Neil A Busis
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA.
| | | | - Robert Montgomery
- Clinical Affairs and Ambulatory Care, NYU Langone Health System, New York, NY, USA
| | - Laura J Balcer
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
- Department of Ophthalmology, NYU Grossman School of Medicine, New York, NY, USA
| | - Steven L Galetta
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Ophthalmology, NYU Grossman School of Medicine, New York, NY, USA
| | - Scott N Grossman
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
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2
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Huang Z, Duan X, Zhu G, Zhang S, Wang R, Wang Z. Assessing the data quality of AdHawk MindLink eye-tracking glasses. Behav Res Methods 2024:10.3758/s13428-023-02310-2. [PMID: 38168041 DOI: 10.3758/s13428-023-02310-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
Most commercially available eye-tracking devices rely on video cameras and image processing algorithms to track gaze. Despite this, emerging technologies are entering the field, making high-speed, cameraless eye-tracking more accessible. In this study, a series of tests were conducted to compare the data quality of MEMS-based eye-tracking glasses (AdHawk MindLink) with three widely used camera-based eye-tracking devices (EyeLink Portable Duo, Tobii Pro Glasses 2, and SMI Eye Tracking Glasses 2). The data quality measures assessed in these tests included accuracy, precision, data loss, and system latency. The results suggest that, overall, the data quality of the eye-tracking glasses was lower compared to that of a desktop EyeLink Portable Duo eye-tracker. Among the eye-tracking glasses, the accuracy and precision of the MindLink eye-tracking glasses were either higher or on par with those of Tobii Pro Glasses 2 and SMI Eye Tracking Glasses 2. The system latency of MindLink was approximately 9 ms, significantly lower than that of camera-based eye-tracking devices found in VR goggles. These results suggest that the MindLink eye-tracking glasses show promise for research applications where high sampling rates and low latency are preferred.
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Affiliation(s)
- Zehao Huang
- Center for Psychological Sciences, Zhejiang University, 148 Tianmushan Rd., Hangzhou, 310028, China
| | - Xiaoting Duan
- Center for Psychological Sciences, Zhejiang University, 148 Tianmushan Rd., Hangzhou, 310028, China
| | - Gancheng Zhu
- Center for Psychological Sciences, Zhejiang University, 148 Tianmushan Rd., Hangzhou, 310028, China
| | - Shuai Zhang
- Center for Psychological Sciences, Zhejiang University, 148 Tianmushan Rd., Hangzhou, 310028, China
| | - Rong Wang
- Center for Psychological Sciences, Zhejiang University, 148 Tianmushan Rd., Hangzhou, 310028, China
| | - Zhiguo Wang
- Center for Psychological Sciences, Zhejiang University, 148 Tianmushan Rd., Hangzhou, 310028, China.
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3
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Holmqvist K, Örbom SL, Hooge ITC, Niehorster DC, Alexander RG, Andersson R, Benjamins JS, Blignaut P, Brouwer AM, Chuang LL, Dalrymple KA, Drieghe D, Dunn MJ, Ettinger U, Fiedler S, Foulsham T, van der Geest JN, Hansen DW, Hutton SB, Kasneci E, Kingstone A, Knox PC, Kok EM, Lee H, Lee JY, Leppänen JM, Macknik S, Majaranta P, Martinez-Conde S, Nuthmann A, Nyström M, Orquin JL, Otero-Millan J, Park SY, Popelka S, Proudlock F, Renkewitz F, Roorda A, Schulte-Mecklenbeck M, Sharif B, Shic F, Shovman M, Thomas MG, Venrooij W, Zemblys R, Hessels RS. Eye tracking: empirical foundations for a minimal reporting guideline. Behav Res Methods 2023; 55:364-416. [PMID: 35384605 PMCID: PMC9535040 DOI: 10.3758/s13428-021-01762-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/29/2021] [Indexed: 11/08/2022]
Abstract
In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").
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Affiliation(s)
- Kenneth Holmqvist
- Department of Psychology, Nicolaus Copernicus University, Torun, Poland.
- Department of Computer Science and Informatics, University of the Free State, Bloemfontein, South Africa.
- Department of Psychology, Regensburg University, Regensburg, Germany.
| | - Saga Lee Örbom
- Department of Psychology, Regensburg University, Regensburg, Germany
| | - Ignace T C Hooge
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Diederick C Niehorster
- Lund University Humanities Lab and Department of Psychology, Lund University, Lund, Sweden
| | - Robert G Alexander
- Department of Ophthalmology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | | | - Jeroen S Benjamins
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
- Social, Health and Organizational Psychology, Utrecht University, Utrecht, The Netherlands
| | - Pieter Blignaut
- Department of Computer Science and Informatics, University of the Free State, Bloemfontein, South Africa
| | | | - Lewis L Chuang
- Department of Ergonomics, Leibniz Institute for Working Environments and Human Factors, Dortmund, Germany
- Institute of Informatics, LMU Munich, Munich, Germany
| | | | - Denis Drieghe
- School of Psychology, University of Southampton, Southampton, UK
| | - Matt J Dunn
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
| | | | - Susann Fiedler
- Vienna University of Economics and Business, Vienna, Austria
| | - Tom Foulsham
- Department of Psychology, University of Essex, Essex, UK
| | | | - Dan Witzner Hansen
- Machine Learning Group, Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | | | - Enkelejda Kasneci
- Human-Computer Interaction, University of Tübingen, Tübingen, Germany
| | | | - Paul C Knox
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Ellen M Kok
- Department of Education and Pedagogy, Division Education, Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, The Netherlands
- Department of Online Learning and Instruction, Faculty of Educational Sciences, Open University of the Netherlands, Heerlen, The Netherlands
| | - Helena Lee
- University of Southampton, Southampton, UK
| | - Joy Yeonjoo Lee
- School of Health Professions Education, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Jukka M Leppänen
- Department of Psychology and Speed-Language Pathology, University of Turku, Turku, Finland
| | - Stephen Macknik
- Department of Ophthalmology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Päivi Majaranta
- TAUCHI Research Center, Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
| | - Susana Martinez-Conde
- Department of Ophthalmology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Antje Nuthmann
- Institute of Psychology, University of Kiel, Kiel, Germany
| | - Marcus Nyström
- Lund University Humanities Lab, Lund University, Lund, Sweden
| | - Jacob L Orquin
- Department of Management, Aarhus University, Aarhus, Denmark
- Center for Research in Marketing and Consumer Psychology, Reykjavik University, Reykjavik, Iceland
| | - Jorge Otero-Millan
- Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, CA, USA
| | - Soon Young Park
- Comparative Cognition, Messerli Research Institute, University of Veterinary Medicine Vienna, Medical University of Vienna, Vienna, Austria
| | - Stanislav Popelka
- Department of Geoinformatics, Palacký University Olomouc, Olomouc, Czech Republic
| | - Frank Proudlock
- The University of Leicester Ulverscroft Eye Unit, Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
| | - Frank Renkewitz
- Department of Psychology, University of Erfurt, Erfurt, Germany
| | - Austin Roorda
- Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, CA, USA
| | | | - Bonita Sharif
- School of Computing, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Frederick Shic
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, USA
- Department of General Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
| | - Mark Shovman
- Eyeviation Systems, Herzliya, Israel
- Department of Industrial Design, Bezalel Academy of Arts and Design, Jerusalem, Israel
| | - Mervyn G Thomas
- The University of Leicester Ulverscroft Eye Unit, Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
| | - Ward Venrooij
- Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
| | | | - Roy S Hessels
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
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Hecimovich M, Murphy M, Chivers P, Stock P. Evaluation and Utility of the King-Devick With Integrated Eye Tracking as a Diagnostic Tool for Sport-Related Concussion. Orthop J Sports Med 2022; 10:23259671221142255. [PMID: 36582931 PMCID: PMC9793019 DOI: 10.1177/23259671221142255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 09/15/2022] [Indexed: 12/24/2022] Open
Abstract
Background Eye-tracking technology for detecting eye movements has been gaining increasing attention as a possible assessment and monitoring tool for sport-related concussion (SRC). Purpose To determine the diagnostic accuracy of a rapid number-naming task with eye tracking, the King-Devick Eye Tracking (K-D ET) assessment, in identifying SRC. Study Design Cohort study. Methods One female and 1 male team of United States collegiate rugby-15 players competing during the 2018 season were recruited. Variables assessed were total saccades, saccade velocity, total fixations, fixation duration, fixation polyarea, and test duration. A generalized estimating equation was used to examine group (concussion vs nonconcussion), time (baseline vs postinjury/postseason), and sex-based differences for each outcome measure. In addition, the different components of diagnostic accuracy of the K-D ET were calculated. Results Baseline K-D ET assessment for 49 participants (25 male, 24 female) were assessed at the beginning of the season, with 28 participants who did not sustain a head injury during the season completing the postseason assessments and 6 participants completing a postinjury (suspected concussion) assessment. Significant differences were observed between concussed and nonconcussed groups for total saccades (P = .024), fixation duration (P = .007), and fixation polyarea (P = .030), with differences from baseline to follow-up observed for saccade velocity (P = .018) in both groups. Sex-based differences were noted for total fixations (P = .041), fixation polyarea (P = .036), and completion time (P = .035). No significant Group × Time interactions were noted. The K-D ET test duration indicated high specificity (0.86) but not high sensitivity (0.40). No other variables reported high sensitivity or specificity. Conclusion Other than completion time of the K-D ET test, no K-D ET oculomotor parameter was highly sensitive or specific in the diagnosis of concussion in this study.
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Affiliation(s)
- Mark Hecimovich
- Department of Athletic Training, 003C Human Performance Center,
University of Northern Iowa, Cedar Falls, Iowa, USA.,Mark Hecimovich, PhD, Department of Athletic Training, 003C
Human Performance Center, University of Northern Iowa, Cedar Falls, IA 50614,
USA ()
| | - Myles Murphy
- Nutrition and Health Innovation Research Institute, School of
Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia,
Australia
| | - Paola Chivers
- Institute for Health Research, The University of Notre Dame
Australia, Fremantle, Western Australia, Australia
| | - Payton Stock
- College of Health Sciences, Des Moines University, Des Moines, Iowa,
USA
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5
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Grossman SN, Calix R, Hudson T, Rizzo JR, Selesnick I, Frucht S, Galetta SL, Balcer LJ, Rucker JC. Accuracy of clinical versus oculographic detection of pathological saccadic slowing. J Neurol Sci 2022; 442:120436. [PMID: 36183516 DOI: 10.1016/j.jns.2022.120436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/23/2022] [Accepted: 09/18/2022] [Indexed: 10/31/2022]
Abstract
Saccadic slowing as a component of supranuclear saccadic gaze palsy is an important diagnostic sign in multiple neurologic conditions, including degenerative, inflammatory, genetic, or ischemic lesions affecting brainstem structures responsible for saccadic generation. Little attention has been given to the accuracy with which clinicians correctly identify saccadic slowing. We compared clinician (n = 19) judgements of horizontal and vertical saccade speed on video recordings of saccades (from 9 patients with slow saccades, 3 healthy controls) to objective saccade peak velocity measurements from infrared oculographic recordings. Clinician groups included neurology residents, general neurologists, and fellowship-trained neuro-ophthalmologists. Saccades with normal peak velocities on infrared recordings were correctly identified as normal in 57% (91/171; 171 = 9 videos × 19 clinicians) of clinician decisions; saccades determined to be slow on infrared recordings were correctly identified as slow in 84% (224/266; 266 = 14 videos × 19 clinicians) of clinician decisions. Vertical saccades were correctly identified as slow more often than horizontal saccades (94% versus 74% of decisions). No significant differences were identified between clinician training levels. Reliable differentiation between normal and slow saccades is clinically challenging; clinical performance is most accurate for detection of vertical saccade slowing. Quantitative analysis of saccade peak velocities enhances accurate detection and is likely to be especially useful for detection of mild saccadic slowing.
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Affiliation(s)
- Scott N Grossman
- Department of Neurology, New York University Grossman School of Medicine, United States of America.
| | - Rachel Calix
- Department of Neurology, New York University Grossman School of Medicine, United States of America
| | - Todd Hudson
- Department of Neurology, New York University Grossman School of Medicine, United States of America; Rusk Institute of Rehabilitation, New York University Grossman School of Medicine, United States of America
| | - John Ross Rizzo
- Department of Neurology, New York University Grossman School of Medicine, United States of America; Rusk Institute of Rehabilitation, New York University Grossman School of Medicine, United States of America
| | - Ivan Selesnick
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, United States of America
| | - Steven Frucht
- Department of Neurology, New York University Grossman School of Medicine, United States of America
| | - Steven L Galetta
- Department of Neurology, New York University Grossman School of Medicine, United States of America; Department of Ophthalmology, New York University Grossman School of Medicine, United States of America
| | - Laura J Balcer
- Department of Neurology, New York University Grossman School of Medicine, United States of America; Department of Ophthalmology, New York University Grossman School of Medicine, United States of America; Department of Population Health, New York University Grossman School of Medicine, United States of America
| | - Janet C Rucker
- Department of Neurology, New York University Grossman School of Medicine, United States of America; Department of Ophthalmology, New York University Grossman School of Medicine, United States of America
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6
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Hecimovich M, King D, Murphy M, Koyama K. An investigation into the measurement properties of the King-Devick Eye Tracking system. JOURNAL OF CONCUSSION 2022. [DOI: 10.1177/20597002221082865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Objectives Eye tracking has been gaining increasing attention as a possible assessment and monitoring tool for concussion. The King-Devick test (K-DT) was expanded to include an infrared video-oculography-based eye tracker (K-D ET). Therefore, the aim was to provide evidence on the reliability of the K-D ET system under an exercise condition. Methods Participants (N = 61; 26 male, 35 female; age range 19-25) were allocated to an exercise or sedentary group. Both groups completed a baseline K-D ET measurement and then either two 10-min exercise or sedentary interventions with repeated K-D ET measurements between interventions. Results The test-retest reliability of the K-D ET ranged from good to excellent for the different variables measured. The mean ± SD of the differences for the total number of saccades was 1.04 ± 4.01 and there was an observable difference (p = 0.005) in the trial number. There were no observable differences for the intervention (p = 0.768), gender (p = 0.121) and trial (p = 0.777) for average saccade’s velocity. The mean ± SD of the difference of the total fixations before and after intervention across both trials was 1.04 ± 3.63 and there was an observable difference in the trial number (p = 0.025). The mean ± SD of the differences for the Inter-Saccadic Interval and the fixation polyarea before and after intervention across both trials were 1.86 ± 22.99 msec and 0.51 ± 59.11 mm2 and no observable differences for the intervention, gender and trial. Conclusion The results provide evidence on the reliability of the K-D ET, and the eye-tracking components and demonstrate the relationship between completion time and other variables of the K-D ET system. This is vital as the use of the K-DT may be increasing and the combination of the K-DT and eye tracking as one single package highlights the need to specifically measure the reliability of this combined unit.
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Affiliation(s)
- M. Hecimovich
- Division of Athletic Training, University of Northern Iowa, Cedar Falls, Iowa, USA
| | - D. King
- Sports Performance Research Institute New Zealand (SPRINZ) at AUT Millennium, Faculty of Health and Environmental Science, Auckland University of Technology, Auckland, New Zealand
- Traumatic Brain injury Network (TBIN), Auckland University of Technology, Auckland, New Zealand
- Department of Science and Technology, University of New England, Sydney, Australia
| | - M. Murphy
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- SportsMed Subiaco, St John of God Health Care, Subiaco, Western Australia, Australia
| | - K. Koyama
- Department of Rehabilitation Medicine, Gunma University Graduate School of Medicine
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Griffith H, Lohr D, Abdulin E, Komogortsev O. GazeBase, a large-scale, multi-stimulus, longitudinal eye movement dataset. Sci Data 2021; 8:184. [PMID: 34272404 PMCID: PMC8285447 DOI: 10.1038/s41597-021-00959-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 05/20/2021] [Indexed: 11/09/2022] Open
Abstract
This manuscript presents GazeBase, a large-scale longitudinal dataset containing 12,334 monocular eye-movement recordings captured from 322 college-aged participants. Participants completed a battery of seven tasks in two contiguous sessions during each round of recording, including a - (1) fixation task, (2) horizontal saccade task, (3) random oblique saccade task, (4) reading task, (5/6) free viewing of cinematic video task, and (7) gaze-driven gaming task. Nine rounds of recording were conducted over a 37 month period, with participants in each subsequent round recruited exclusively from prior rounds. All data was collected using an EyeLink 1000 eye tracker at a 1,000 Hz sampling rate, with a calibration and validation protocol performed before each task to ensure data quality. Due to its large number of participants and longitudinal nature, GazeBase is well suited for exploring research hypotheses in eye movement biometrics, along with other applications applying machine learning to eye movement signal analysis. Classification labels produced by the instrument's real-time parser are provided for a subset of GazeBase, along with pupil area.
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Affiliation(s)
- Henry Griffith
- Texas State University, Department of Computer Science, San Marcos, TX, 78666, USA.
| | - Dillon Lohr
- Texas State University, Department of Computer Science, San Marcos, TX, 78666, USA
| | - Evgeny Abdulin
- Texas State University, Department of Computer Science, San Marcos, TX, 78666, USA
| | - Oleg Komogortsev
- Texas State University, Department of Computer Science, San Marcos, TX, 78666, USA
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Ivanchenko D, Rifai K, Hafed ZM, Schaeffel F. A low-cost, high-performance video-based binocular eye tracker for psychophysical research. J Eye Mov Res 2021; 14. [PMID: 34122750 PMCID: PMC8190563 DOI: 10.16910/jemr.14.3.3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
We describe a high-performance, pupil-based binocular eye tracker that approaches the performance
of a well-established commercial system, but at a fraction of the cost. The eye
tracker is built from standard hardware components, and its software (written in Visual C++)
can be easily implemented. Because of its fast and simple linear calibration scheme, the eye
tracker performs best in the central 10 degrees of the visual field. The eye tracker possesses
a number of useful features: (1) automated calibration simultaneously in both eyes while
subjects fixate four fixation points sequentially on a computer screen, (2) automated realtime
continuous analysis of measurement noise, (3) automated blink detection, (4) and realtime
analysis of pupil centration artifacts. This last feature is critical because it is known
that pupil diameter changes can be erroneously registered by pupil-based trackers as a
change in eye position. We evaluated the performance of our system against that of a wellestablished
commercial system using simultaneous measurements in 10 participants. We
propose our low-cost eye tracker as a promising resource for studies of binocular eye movements.
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Rochefort C, Legace E, Boulay C, Macartney G, Goulet K, Zemek R, Sveistrup H. Balance Markers and Saccadic Eye-Movement Measures in Adolescents With Postconcussion Syndrome. J Athl Train 2020; 55:475-481. [PMID: 32216658 DOI: 10.4085/1062-6050-548-18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
CONTEXT Deficits in both balance and oculomotor function, including impairments in saccadic eye movements, are observed in approximately 30% of patients postconcussion. Whereas balance and saccadic eye movements are routinely assessed separately, growing evidence suggests that they should be assessed concurrently. OBJECTIVE To compare balance measures and saccades between adolescents 1 to 3 months postconcussion and healthy uninjured adolescents. DESIGN Case-control study. SETTING Concussion clinic and 2 private schools. PATIENTS OR OTHER PARTICIPANTS Twenty-five adolescents (10 boys, 15 girls; median [interquartile range (IQR)] age = 14 years [11.5-16 years]) between 1 and 3 months postconcussion (median [IQR] time since injury = 39.5 days [30-56.75 days]) and 33 uninjured adolescents (18 boys, 15 girls; median [IQR] age = 13 years [11.5-14 years]). MAIN OUTCOME MEASURE(S) The center-of-pressure 95% ellipse area and medial-lateral and anterior-posterior velocity and the number of saccades in the dual-task balance conditions including a high cognitive load (cognitive condition), a low cognitive load and a gaze-shifting component (visual condition) or both a high cognitive load and a gaze-shifting component (combined condition). RESULTS Concussion-group participants swayed over larger center-of-pressure ellipse areas in the visual (P = .02; effect size = 0.73) and combined (P = .005; effect size = 0.86) conditions but not in the cognitive condition (P = .07; effect size = 0.50). No group differences were identified for anterior-posterior (F1,56 = 2.57, P = .12) or medial-lateral (F1,56 = 0.157, P = .69) velocity. Concussion-group participants also did not perform more saccades than the control-group participants (F1,56 = 2.04, P = .16). CONCLUSIONS Performing dual-task balance conditions for which the secondary task involved a gaze-shifting component or both a gaze-shifting component and a high cognitive load resulted in greater sway amplitude in adolescents with concussion. However, these larger amounts of postural sway were not associated with increased saccadic eye movements.
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Affiliation(s)
- Coralie Rochefort
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, ON, Canada
| | - Elizabeth Legace
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, ON, Canada
| | | | - Gail Macartney
- Research Institute, Children's Hospital of Eastern Ontario, Ottawa, Canada.,Concussion Clinic, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Kristian Goulet
- Concussion Clinic, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Roger Zemek
- Research Institute, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Heidi Sveistrup
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, ON, Canada.,Bruyère Research Institute and School of Rehabilitation Sciences, University of Ottawa, ON, Canada
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