Validation of a smartphone based approach to in-situ cognitive fatigue assessment

Edward Price, George Moore, Leo Galway, Mark Linden

Research output: Contribution to journalArticle

Abstract

Background:Acquired Brain Injury can result in multiple detrimental cognitive effects such as reduced memory capability, concentration and planning. These effects can lead to cognitive fatigue, which can exacerbate the symptoms of Acquired Brain Injury and hinder management and recovery. Assessing cognitive fatigue is difficult due to the largely subjective nature of the condition and existing assessment approaches. Traditional methods of assessment use self-assessment questionnaires delivered in a medical setting. However, recent work has attempted to employ more objective cognitive tests as a way of evaluating cognitive fatigue. However, these tests are still predominantly delivered within a medical environment, limiting their utility and efficacy. Objective:The aim of the research was to investigate how cognitive fatigue can be accurately assessed in-situ, during the quotidian activities of life. It was hypothesised that this could be achieved through the use of mobile assistive technology to assess: working memory, sustained attention, information processing speed, reaction time, and cognitive throughput. Methods:The study used a bespoke smartphone application to track daily cognitive performance in order to assess potential levels of cognitive fatigue. 21 participants with no prior reported brain injuries took place in a two-week study, resulting in 81 individual testing instances being collected. The smartphone application delivered three cognitive tests on a daily basis: (1) Spatial Span to measure visuospatial working memory; (2) Psychomotor Vigilance Task to measure sustained attention, information processing speed and reaction time; (3) a Mental Arithmetic test to measure cognitive throughput. A smartphone optimised version of the Mental Fatigue Scale self-assessment questionnaire was used as a baseline to assess the validity of the three cognitive tests, as the questionnaire has already been validated in multiple peer reviewed studies. Results: Highest correlated results were from the Psychomotor Vigilance Task, and showed a positive correlation with those from the pre-validated Mental Fatigue Scale, measuring 0.342, p <.008. Scores from the cognitive tests were entered into a regression model and showed that only reaction time in the Psychomotor Vigilance Task was a significant predictor of fatigue (p = .016, F = 2.682, 95% CI 9.0 to 84.2). Higher scores on the Mental Fatigue Scale were related to increases in reaction time during our mobile variant of the Psychomotor Vigilance Task.
LanguageEnglish
Pages1-13
JournalJMIR mHealth and uHealth
Volume5
Issue number8
DOIs
Publication statusPublished - 17 Aug 2017

Fingerprint

Fatigue
Mental Fatigue
Reaction Time
Brain Injuries
Automatic Data Processing
Short-Term Memory
Self-Help Devices
Intelligence Tests
Smartphone
Research
Surveys and Questionnaires
Self-Assessment

Keywords

  • Smartphone
  • mental fatigue
  • fatigue
  • acquired brain injury
  • cognitive tests.

Cite this

@article{332b21bb0b0241548c62058451c498a4,
title = "Validation of a smartphone based approach to in-situ cognitive fatigue assessment",
abstract = "Background:Acquired Brain Injury can result in multiple detrimental cognitive effects such as reduced memory capability, concentration and planning. These effects can lead to cognitive fatigue, which can exacerbate the symptoms of Acquired Brain Injury and hinder management and recovery. Assessing cognitive fatigue is difficult due to the largely subjective nature of the condition and existing assessment approaches. Traditional methods of assessment use self-assessment questionnaires delivered in a medical setting. However, recent work has attempted to employ more objective cognitive tests as a way of evaluating cognitive fatigue. However, these tests are still predominantly delivered within a medical environment, limiting their utility and efficacy. Objective:The aim of the research was to investigate how cognitive fatigue can be accurately assessed in-situ, during the quotidian activities of life. It was hypothesised that this could be achieved through the use of mobile assistive technology to assess: working memory, sustained attention, information processing speed, reaction time, and cognitive throughput. Methods:The study used a bespoke smartphone application to track daily cognitive performance in order to assess potential levels of cognitive fatigue. 21 participants with no prior reported brain injuries took place in a two-week study, resulting in 81 individual testing instances being collected. The smartphone application delivered three cognitive tests on a daily basis: (1) Spatial Span to measure visuospatial working memory; (2) Psychomotor Vigilance Task to measure sustained attention, information processing speed and reaction time; (3) a Mental Arithmetic test to measure cognitive throughput. A smartphone optimised version of the Mental Fatigue Scale self-assessment questionnaire was used as a baseline to assess the validity of the three cognitive tests, as the questionnaire has already been validated in multiple peer reviewed studies. Results: Highest correlated results were from the Psychomotor Vigilance Task, and showed a positive correlation with those from the pre-validated Mental Fatigue Scale, measuring 0.342, p <.008. Scores from the cognitive tests were entered into a regression model and showed that only reaction time in the Psychomotor Vigilance Task was a significant predictor of fatigue (p = .016, F = 2.682, 95{\%} CI 9.0 to 84.2). Higher scores on the Mental Fatigue Scale were related to increases in reaction time during our mobile variant of the Psychomotor Vigilance Task.",
keywords = "Smartphone, mental fatigue, fatigue, acquired brain injury, cognitive tests.",
author = "Edward Price and George Moore and Leo Galway and Mark Linden",
note = "Reference text: [1] D. J. Thurman, C. Alverson, K. A. Dunn, J. Guerrero, and J. E. Sniezek, “Traumatic brain injury in the United States: A public health perspective.,” J. Head Trauma Rehabil., vol. 14, no. 6, pp. 602–15, Dec. 1999. [2] F. Tagliaferri, C. Compagnone, M. Korsic, F. Servadei, and J. Kraus, “A systematic review of brain injury epidemiology in Europe,” Acta Neurochir. (Wien)., vol. 148, no. 3, pp. 255–267, 2006. [3] C. V. Faul M, Xu L, Wald MM, “Traumatic brain injury in the United States: emergency department visits, hospitalizations, and deaths,” Centers Dis. Control Prev. Natl. Cent. Inj. Prev. Control, pp. 891–904, 2010. [4] S. L. Hillier, J. E. Hiller, and J. Metzer, “Epidemiology of traumatic brain injury in South Australia.,” Brain Inj., vol. 11, no. 9, pp. 649–59, Sep. 1997. [5] R. Escorpizo, S. Brage, D. Homa, and G. Stucki, Eds., “Handbook of Vocational Rehabilitation and Disability Evaluation.” Springer International Publishing, Cham, 2015. [6] L. S. Aaronson, C. S. Teel, V. Cassmeyer, G. B. Neuberger, L. Pallikkathayil, and A. W. Janet Pierce, Allan N. Press, Phoebe D. Williams, “Defining and Measuring Fatigue,” J. Nurs. Scholarsh., vol. 31, no. 1, pp. 45–50, 1999. [7] B. Johansson and L. R{\"o}nnb{\"a}ck, “Mental Fatigue and Cognitive Impairment after an Almost Neurological Recovered Stroke,” ISRN Psychiatry, vol. 2012, pp. 1–7, 2012. [8] K. a Lee, G. Hicks, and G. Nino-Murcia, “Validity and reliability of a scale to assess fatigue.,” Psychiatry Res., vol. 36, no. 3, pp. 291–298, 1991. [9] B. Johansson, A. Starmark, P. Berglund, M. R{\"o}dholm, and L. R{\"o}nnb{\"a}ck, “A self-assessment questionnaire for mental fatigue and related symptoms after neurological disorders and injuries,” Dec. 2009. [10] K. LB, L. NG, M.-N. J, and S. AD, “The fatigue severity scale: Application to patients with multiple sclerosis and systemic lupus erythematosus,” Arch. Neurol., vol. 46, no. 10. [11] A. Shahid, K. Wilkinson, S. Marcu, and C. Shapiro, “Visual Analogue Scale to Evaluate Fatigue Severity (VAS-F),” in STOP, THAT and One Hundred Other Sleep Scales SE - 100, A. Shahid, K. Wilkinson, S. Marcu, and C. M. Shapiro, Eds. Springer New York, 2012, pp. 399–402. [12] B. Johansson and L. R{\"o}nnb{\"a}ck, “Mental Fatigue ; A Common Long Term Consequence After a Brain Injury,” 2009. [13] M. R{\"o}dholm, J.-E. Starmark, E. Svensson, and C. Von Essen, “Astheno-emotional disorder after aneurysmal SAH: reliability, symptomatology and relation to outcome,” Acta Neurol. Scand., vol. 103, no. 6, pp. 379–385, 2001. [14] N. S. King, S. Crawford, F. J. Wenden, N. E. G. Moss, and D. T. Wade, “The Rivermead Post Concussion Symptoms Questionnaire: a measure of symptoms commonly experienced after head injury and its reliability,” J. Neurol., vol. 242, no. 9, pp. 587–592, 1995. [15] A. H. van Zomeren and W. van den Burg, “Residual complaints of patients two years after severe head injury.,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 48, no. 1. pp. 21–28, Jan-1985. [16] K. Herlofson and J. P. Larsen, “Measuring fatigue in patients with Parkinson’s disease - the Fatigue Severity Scale.,” Eur. J. Neurol., vol. 9, no. 6, pp. 595–600, 2002. [17] J. B. and R. L., “Mental fatigue scale and its relation to cognitive, social and emotional functioning after a TBI or stroke,” Brain Inj., vol. 28, no. 1, pp. 572–573, 2014. [18] D. A. Grant, K. A. Honn, M. E. Layton, S. M. Riedy, H. P. A. Van Dongen, and D. A. Grant, “vigilance tests for the assessment of reduced alertness due to sleep deprivation,” Behav. Res. Methods, 2016. [19] M. Kay, K. Rector, S. Consolvo, B. Greenstein, J. O. Wobbrock, N. F. Watson, and J. A. Kientz, “PVT-touch: adapting a reaction time test for touchscreen devices,” Pervasive Comput. Technol. Healthc., pp. 248–251, 2013. [20] C. Timmers, A. Maeghs, M. Vestjens, C. Bonnemayer, H. Hamers, and A. Blokland, “Ambulant cognitive assessment using a smartphone,” Appl. Neuropsychol. Adult, vol. 21, no. 2, pp. 136–142, 2014. [21] B. Johansson, P. Berglund, and L. R{\"o}nnb{\"a}ck, “Mental fatigue and impaired information processing after mild and moderate traumatic brain injury.,” Brain Inj., vol. 23, no. 13–14, pp. 1027–40, Dec. 2009. [22] H. Van Dongen, G. Maislin, J. M. Mullington, and D. F. Dinges, “The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total,” Sleep, vol. 26, no. 2, pp. 117–126, 2003. [23] D. Gartenberg and R. McGarry, “Development of a Neuroergonomic Application to Evaluate Arousal,” Adv. …, pp. 6378–6387, 2012. [24] S. A. Ferguson, B. P. Smith, M. Browne, and M. J. Rockloff, “Fatigue in Emergency Services Operations : Assessment of the Optimal Objective and Subjective Measures Using a Simulated Wildfire Deployment,” Int. J. Environ. Res. Public Health, vol. 13, no. 2, 2016. [25] K. Liu, B. Li, S. Qian, Q. Jiang, L. Li, and W. Wang, “Mental fatigue after mild traumatic brain injury : a 3D-ASL perfusion study,” Brain Imaging Behav., pp. 1–12, 2016. [26] D. A. Grant, K. A. Honn, M. E. Layton, S. M. Riedy, and H. P. A. Van Dongen, “3-Minute Smartphone-Based and Tablet-Based Psychomotor Vigilance Tests for the Assessment of Reduced Alertness Due To Sleep Deprivation,” Behav. Res. Methods, 2016. [27] D. Wechsler, “Wechsler adult intelligence scale–Fourth Edition (WAIS–IV),” San Antonio, TX NCS Pearson, 2008. [28] R. M. Reitan and D. Wolfson, The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation, vol. 4. Reitan Neuropsychology, 1985. [29] D. Swendeman, W. S. Comulada, N. Ramanathan, M. Lazar, and D. Estrin, “Reliability and Validity of Daily Self-Monitoring by Smartphone Application for Health-Related Quality-of-Life, Antiretroviral Adherence, Substance Use, and Sexual Behaviors Among People Living with HIV,” AIDS Behav., vol. 19, no. 2, pp. 330–340, 2014. [30] A.-L. L. Engstr{\"o}m, J. Lexell, and M. L. Lund, “Difficulties in using everyday technology after acquired brain injury: a qualitative analysis.,” Scand. J. Occup. Ther., vol. 17, no. 3, pp. 233–243, 2010. [31] S. P. a Drummond, A. Bischoff-Grethe, D. F. Dinges, L. Ayalon, S. C. Mednick, and M. J. Meloy, “The neural basis of the psychomotor vigilance task.,” Sleep, vol. 28, no. 9, pp. 1059–1068, 2005. [32] D. R. Thorne, S. G. Genser, H. C. Sing, and F. W. Hegge, “The Walter Reed performance assessment battery.,” Neurobehav. Toxicol. Teratol., 1985. [33] J. Brooke, “SUS-A quick and dirty usability scale,” Usability Eval. Ind., vol. 189, no. 194, pp. 4–7, 1996. [34] A. J. Thomson, A. F. Nimmo, B. Tiplady, and J. B. Glen, “Evaluation of a new method of assessing depth of sedation using two-choice visual reaction time testing on a mobile phone*,” Anaesthesia, vol. 64, no. 1, pp. 32–38, Jan. 2009.",
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Validation of a smartphone based approach to in-situ cognitive fatigue assessment. / Price, Edward; Moore, George; Galway, Leo; Linden, Mark.

In: JMIR mHealth and uHealth, Vol. 5, No. 8, 17.08.2017, p. 1-13.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Validation of a smartphone based approach to in-situ cognitive fatigue assessment

AU - Price, Edward

AU - Moore, George

AU - Galway, Leo

AU - Linden, Mark

N1 - Reference text: [1] D. J. Thurman, C. Alverson, K. A. Dunn, J. Guerrero, and J. E. Sniezek, “Traumatic brain injury in the United States: A public health perspective.,” J. Head Trauma Rehabil., vol. 14, no. 6, pp. 602–15, Dec. 1999. [2] F. Tagliaferri, C. Compagnone, M. Korsic, F. Servadei, and J. Kraus, “A systematic review of brain injury epidemiology in Europe,” Acta Neurochir. (Wien)., vol. 148, no. 3, pp. 255–267, 2006. [3] C. V. Faul M, Xu L, Wald MM, “Traumatic brain injury in the United States: emergency department visits, hospitalizations, and deaths,” Centers Dis. Control Prev. Natl. Cent. Inj. Prev. Control, pp. 891–904, 2010. [4] S. L. Hillier, J. E. Hiller, and J. Metzer, “Epidemiology of traumatic brain injury in South Australia.,” Brain Inj., vol. 11, no. 9, pp. 649–59, Sep. 1997. [5] R. Escorpizo, S. Brage, D. Homa, and G. Stucki, Eds., “Handbook of Vocational Rehabilitation and Disability Evaluation.” Springer International Publishing, Cham, 2015. [6] L. S. Aaronson, C. S. Teel, V. Cassmeyer, G. B. Neuberger, L. Pallikkathayil, and A. W. Janet Pierce, Allan N. Press, Phoebe D. Williams, “Defining and Measuring Fatigue,” J. Nurs. Scholarsh., vol. 31, no. 1, pp. 45–50, 1999. [7] B. Johansson and L. Rönnbäck, “Mental Fatigue and Cognitive Impairment after an Almost Neurological Recovered Stroke,” ISRN Psychiatry, vol. 2012, pp. 1–7, 2012. [8] K. a Lee, G. Hicks, and G. Nino-Murcia, “Validity and reliability of a scale to assess fatigue.,” Psychiatry Res., vol. 36, no. 3, pp. 291–298, 1991. [9] B. Johansson, A. Starmark, P. Berglund, M. Rödholm, and L. Rönnbäck, “A self-assessment questionnaire for mental fatigue and related symptoms after neurological disorders and injuries,” Dec. 2009. [10] K. LB, L. NG, M.-N. J, and S. AD, “The fatigue severity scale: Application to patients with multiple sclerosis and systemic lupus erythematosus,” Arch. Neurol., vol. 46, no. 10. [11] A. Shahid, K. Wilkinson, S. Marcu, and C. Shapiro, “Visual Analogue Scale to Evaluate Fatigue Severity (VAS-F),” in STOP, THAT and One Hundred Other Sleep Scales SE - 100, A. Shahid, K. Wilkinson, S. Marcu, and C. M. Shapiro, Eds. Springer New York, 2012, pp. 399–402. [12] B. Johansson and L. Rönnbäck, “Mental Fatigue ; A Common Long Term Consequence After a Brain Injury,” 2009. [13] M. Rödholm, J.-E. Starmark, E. Svensson, and C. Von Essen, “Astheno-emotional disorder after aneurysmal SAH: reliability, symptomatology and relation to outcome,” Acta Neurol. Scand., vol. 103, no. 6, pp. 379–385, 2001. [14] N. S. King, S. Crawford, F. J. Wenden, N. E. G. Moss, and D. T. Wade, “The Rivermead Post Concussion Symptoms Questionnaire: a measure of symptoms commonly experienced after head injury and its reliability,” J. Neurol., vol. 242, no. 9, pp. 587–592, 1995. [15] A. H. van Zomeren and W. van den Burg, “Residual complaints of patients two years after severe head injury.,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 48, no. 1. pp. 21–28, Jan-1985. [16] K. Herlofson and J. P. Larsen, “Measuring fatigue in patients with Parkinson’s disease - the Fatigue Severity Scale.,” Eur. J. Neurol., vol. 9, no. 6, pp. 595–600, 2002. [17] J. B. and R. L., “Mental fatigue scale and its relation to cognitive, social and emotional functioning after a TBI or stroke,” Brain Inj., vol. 28, no. 1, pp. 572–573, 2014. [18] D. A. Grant, K. A. Honn, M. E. Layton, S. M. Riedy, H. P. A. Van Dongen, and D. A. Grant, “vigilance tests for the assessment of reduced alertness due to sleep deprivation,” Behav. Res. Methods, 2016. [19] M. Kay, K. Rector, S. Consolvo, B. Greenstein, J. O. Wobbrock, N. F. Watson, and J. A. Kientz, “PVT-touch: adapting a reaction time test for touchscreen devices,” Pervasive Comput. Technol. Healthc., pp. 248–251, 2013. [20] C. Timmers, A. Maeghs, M. Vestjens, C. Bonnemayer, H. Hamers, and A. Blokland, “Ambulant cognitive assessment using a smartphone,” Appl. Neuropsychol. Adult, vol. 21, no. 2, pp. 136–142, 2014. [21] B. Johansson, P. Berglund, and L. Rönnbäck, “Mental fatigue and impaired information processing after mild and moderate traumatic brain injury.,” Brain Inj., vol. 23, no. 13–14, pp. 1027–40, Dec. 2009. [22] H. Van Dongen, G. Maislin, J. M. Mullington, and D. F. Dinges, “The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total,” Sleep, vol. 26, no. 2, pp. 117–126, 2003. [23] D. Gartenberg and R. McGarry, “Development of a Neuroergonomic Application to Evaluate Arousal,” Adv. …, pp. 6378–6387, 2012. [24] S. A. Ferguson, B. P. Smith, M. Browne, and M. J. Rockloff, “Fatigue in Emergency Services Operations : Assessment of the Optimal Objective and Subjective Measures Using a Simulated Wildfire Deployment,” Int. J. Environ. Res. Public Health, vol. 13, no. 2, 2016. [25] K. Liu, B. Li, S. Qian, Q. Jiang, L. Li, and W. Wang, “Mental fatigue after mild traumatic brain injury : a 3D-ASL perfusion study,” Brain Imaging Behav., pp. 1–12, 2016. [26] D. A. Grant, K. A. Honn, M. E. Layton, S. M. Riedy, and H. P. A. Van Dongen, “3-Minute Smartphone-Based and Tablet-Based Psychomotor Vigilance Tests for the Assessment of Reduced Alertness Due To Sleep Deprivation,” Behav. Res. Methods, 2016. [27] D. Wechsler, “Wechsler adult intelligence scale–Fourth Edition (WAIS–IV),” San Antonio, TX NCS Pearson, 2008. [28] R. M. Reitan and D. Wolfson, The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation, vol. 4. Reitan Neuropsychology, 1985. [29] D. Swendeman, W. S. Comulada, N. Ramanathan, M. Lazar, and D. Estrin, “Reliability and Validity of Daily Self-Monitoring by Smartphone Application for Health-Related Quality-of-Life, Antiretroviral Adherence, Substance Use, and Sexual Behaviors Among People Living with HIV,” AIDS Behav., vol. 19, no. 2, pp. 330–340, 2014. [30] A.-L. L. Engström, J. Lexell, and M. L. Lund, “Difficulties in using everyday technology after acquired brain injury: a qualitative analysis.,” Scand. J. Occup. Ther., vol. 17, no. 3, pp. 233–243, 2010. [31] S. P. a Drummond, A. Bischoff-Grethe, D. F. Dinges, L. Ayalon, S. C. Mednick, and M. J. Meloy, “The neural basis of the psychomotor vigilance task.,” Sleep, vol. 28, no. 9, pp. 1059–1068, 2005. [32] D. R. Thorne, S. G. Genser, H. C. Sing, and F. W. Hegge, “The Walter Reed performance assessment battery.,” Neurobehav. Toxicol. Teratol., 1985. [33] J. Brooke, “SUS-A quick and dirty usability scale,” Usability Eval. Ind., vol. 189, no. 194, pp. 4–7, 1996. [34] A. J. Thomson, A. F. Nimmo, B. Tiplady, and J. B. Glen, “Evaluation of a new method of assessing depth of sedation using two-choice visual reaction time testing on a mobile phone*,” Anaesthesia, vol. 64, no. 1, pp. 32–38, Jan. 2009.

PY - 2017/8/17

Y1 - 2017/8/17

N2 - Background:Acquired Brain Injury can result in multiple detrimental cognitive effects such as reduced memory capability, concentration and planning. These effects can lead to cognitive fatigue, which can exacerbate the symptoms of Acquired Brain Injury and hinder management and recovery. Assessing cognitive fatigue is difficult due to the largely subjective nature of the condition and existing assessment approaches. Traditional methods of assessment use self-assessment questionnaires delivered in a medical setting. However, recent work has attempted to employ more objective cognitive tests as a way of evaluating cognitive fatigue. However, these tests are still predominantly delivered within a medical environment, limiting their utility and efficacy. Objective:The aim of the research was to investigate how cognitive fatigue can be accurately assessed in-situ, during the quotidian activities of life. It was hypothesised that this could be achieved through the use of mobile assistive technology to assess: working memory, sustained attention, information processing speed, reaction time, and cognitive throughput. Methods:The study used a bespoke smartphone application to track daily cognitive performance in order to assess potential levels of cognitive fatigue. 21 participants with no prior reported brain injuries took place in a two-week study, resulting in 81 individual testing instances being collected. The smartphone application delivered three cognitive tests on a daily basis: (1) Spatial Span to measure visuospatial working memory; (2) Psychomotor Vigilance Task to measure sustained attention, information processing speed and reaction time; (3) a Mental Arithmetic test to measure cognitive throughput. A smartphone optimised version of the Mental Fatigue Scale self-assessment questionnaire was used as a baseline to assess the validity of the three cognitive tests, as the questionnaire has already been validated in multiple peer reviewed studies. Results: Highest correlated results were from the Psychomotor Vigilance Task, and showed a positive correlation with those from the pre-validated Mental Fatigue Scale, measuring 0.342, p <.008. Scores from the cognitive tests were entered into a regression model and showed that only reaction time in the Psychomotor Vigilance Task was a significant predictor of fatigue (p = .016, F = 2.682, 95% CI 9.0 to 84.2). Higher scores on the Mental Fatigue Scale were related to increases in reaction time during our mobile variant of the Psychomotor Vigilance Task.

AB - Background:Acquired Brain Injury can result in multiple detrimental cognitive effects such as reduced memory capability, concentration and planning. These effects can lead to cognitive fatigue, which can exacerbate the symptoms of Acquired Brain Injury and hinder management and recovery. Assessing cognitive fatigue is difficult due to the largely subjective nature of the condition and existing assessment approaches. Traditional methods of assessment use self-assessment questionnaires delivered in a medical setting. However, recent work has attempted to employ more objective cognitive tests as a way of evaluating cognitive fatigue. However, these tests are still predominantly delivered within a medical environment, limiting their utility and efficacy. Objective:The aim of the research was to investigate how cognitive fatigue can be accurately assessed in-situ, during the quotidian activities of life. It was hypothesised that this could be achieved through the use of mobile assistive technology to assess: working memory, sustained attention, information processing speed, reaction time, and cognitive throughput. Methods:The study used a bespoke smartphone application to track daily cognitive performance in order to assess potential levels of cognitive fatigue. 21 participants with no prior reported brain injuries took place in a two-week study, resulting in 81 individual testing instances being collected. The smartphone application delivered three cognitive tests on a daily basis: (1) Spatial Span to measure visuospatial working memory; (2) Psychomotor Vigilance Task to measure sustained attention, information processing speed and reaction time; (3) a Mental Arithmetic test to measure cognitive throughput. A smartphone optimised version of the Mental Fatigue Scale self-assessment questionnaire was used as a baseline to assess the validity of the three cognitive tests, as the questionnaire has already been validated in multiple peer reviewed studies. Results: Highest correlated results were from the Psychomotor Vigilance Task, and showed a positive correlation with those from the pre-validated Mental Fatigue Scale, measuring 0.342, p <.008. Scores from the cognitive tests were entered into a regression model and showed that only reaction time in the Psychomotor Vigilance Task was a significant predictor of fatigue (p = .016, F = 2.682, 95% CI 9.0 to 84.2). Higher scores on the Mental Fatigue Scale were related to increases in reaction time during our mobile variant of the Psychomotor Vigilance Task.

KW - Smartphone

KW - mental fatigue

KW - fatigue

KW - acquired brain injury

KW - cognitive tests.

U2 - 10.2196/mhealth.6333

DO - 10.2196/mhealth.6333

M3 - Article

VL - 5

SP - 1

EP - 13

JO - JMIR mHealth and uHealth

T2 - JMIR mHealth and uHealth

JF - JMIR mHealth and uHealth

SN - 2291-5222

IS - 8

ER -