Digital neuropsychological test performance in a large sample of uninjured collegiate athletes

Saalfield, Jessica; Piersol, Kelsey L.; Esopenko, Carrie; Bates, Marsha E.; Weismiller, Scott; Brostrand, Kyle; Todaro, Sabrina M.; Conway, Fiona; Wilde, Elisabeth A.; Buckman, Jennifer F.

doi:10.1080/23279095.2021.2003365

Cited by 3 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, with the development of artificial intelligence technology, many shortcomings of traditional cognition assessments can be overcome [ 13 , 15 ]. Several studies have found that a comprehensive assessment of cognition can reflect a patient’s real status [ 16 ]. Although many studies have focused on developing models based on the Clock Drawing Test [ 17 , 18 ] and Rey Complex Figure Test [ 19 , 20 ], there is a relative scarcity of studies based on the TMT.…”

Section: Introductionmentioning

confidence: 99%

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study

Zhang¹,

Zheng²,

Tang³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method. Objective This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices. Methods This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant’s hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment–screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22). Results We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data. Conclusions The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants’ cognitive impairment compared to conventional paper-based feature assessment.

show abstract

Section: Introductionmentioning

confidence: 99%

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study

Zhang¹,

Zheng²,

Tang³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study (Preprint)

Zhang¹,

Zheng²,

Tang³

et al. 2022

Preprint

View full text Add to dashboard Cite

BACKGROUND Computer-aided detection used in screening and diagnostic cognitive impairment becomes increasingly the focus of attention, which allows objective, ecologically valid, and convenient assessment. Among that, sensor technology based on digital devices is potential method for detection. v OBJECTIVE The aim of this study was to develop and validate a novel trail-making test (TMT) based on a mixing of paper and electronic devices. METHODS In this research, a community-dwelling sample of older adults (n = 297) was included, classified as cognitively normal control (NC, n = 100), diagnosed with mild cognitive impairment (MCI, n = 98) or Alzheimer's disease (AD, n = 99). We used an electromagnetic tablet to record each participant's hand-painted strokes. Meanwhile, a sheet of A4 paper was placed on top of the tablet to keep the traditional interaction style for subjects who are not familiar or comfortable with electronic devices, e.g., touchscreen. In this way, all participants were instructed to perform the TMT-square and circle (TMT-S&C). Furthermore, we developed an efficient and interpretable cognitive impairment screening model to automated analyse cognitive impairment level, which was dependent on demographic, time-related, pressure-related, jerk-related, and template-related features. Among that, the novel template-based features were based on vector quantization (VQ) algorithm. Firstly, the model singled out a candidate trajectory as the standard answer (template) from the normal group. Then the distance between the recorded trajectories and the reference was computed as the important evaluation index. To verify the effectiveness of our method, we compared the performance of well-trained machine learning model using the extracted evaluation index with conventional demographic characters and time related features. In addition, the well-trained model also was validated on follow-ups’ data (NC: 38, MCI: 32, and AD: 22). RESULTS We compared five candidate machine learning methods, screened out the random forest as the ideal model with the best performance (Accuracy: 0.777 for NC vs. MCI, 0.929 for NC vs. AD, and 0.805 for AD vs. MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method and high stability and accuracy on follow-ups’ data. CONCLUSIONS The study demonstrated that the mixing of paper and electronic TMT produced more reliable and valid data for evaluating subjects' cognitive impairment compared with conventional paper-based assessment. CLINICALTRIAL The study was approved by Medical Ethics Committee of Shanghai Tongji Hospital (ChiCTR2000039550)

show abstract

Daytime Napping Benefits Passing Performance and Scanning Activity in Elite Soccer Players

Nishida¹,

Okano²,

Ichinose³

et al. 2023

jsportscimed

View full text Add to dashboard Cite

The present study explored the effect of a daytime napping opportunity on scanning activity, which is an essential component of successful soccer performance. Fourteen male elite collegiate soccer players performed the Trail Making Test (TMT), which was used to assess complex visual attention. In addition, a soccer passing test, modified with reference to the Loughborough Soccer Passing Test, was used to evaluate passing performance as well as scanning activity. A cross-over design was applied to examine nap and no-nap interventions. Participants (N = 14, mean age: 21.6 SD = 0.5 years, height: 1.73 ± 0.06 m, body mass: 67.1 ± 4.5 kg) were randomly allocated to a midday nap (40 minutes) or no-nap group. Subjective sleepiness was measured using the Karolinska Sleepiness Scale, and perceptive fatigue was evaluated by the visual analog scale. There were no significant differences in subjective measurements or TMT between the nap and no-nap groups. However, the performance time for the passing test and scanning activity was significantly shorter (p < 0.001) and scanning activity was significantly more frequent in the nap condition than in the no-nap condition (p < 0.00005). These results suggest that daytime napping is beneficial for soccer-related cognitive function, represented by visuospatial processing and decision-making, and particularly as a potential countermeasure to mental fatigue. Considering that inadequate sleep and residual fatigue are commonly observed in elite soccer, this finding may have practical implications for the preparation of players.

show abstract

Digital neuropsychological test performance in a large sample of uninjured collegiate athletes

Cited by 3 publications

References 35 publications

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study (Preprint)

Daytime Napping Benefits Passing Performance and Scanning Activity in Elite Soccer Players

Contact Info

Product

Resources

About