Timed Up-and-Go Dual-Task Testing in the Assessment of Cognitive Function: A Mixed Methods Observational Study for Development of the UDDGait Protocol

Cedervall, Ylva; Stenberg, Anna M.; Åhman, Hanna Bozkurt; Giedraitis, Vilmantas; Tinmark, Fredrik; Berglund, Lars; Halvorsen, Kjartan; Ingelsson, Martin; Rosendahl, Erik; Åberg, Anna Cristina

doi:10.3390/ijerph17051715

Cited by 24 publications

(26 citation statements)

References 62 publications

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“…The dual-task test procedure used in the current study was previously tested in a pilot study, which resulted in improvements to the test procedures [45]. In the current study, five physical therapists were trained to lead the standardized test procedure, and led all TUG tests.…”

Section: Clinical Diagnostic Assessmentmentioning

confidence: 99%

“…The recorded data collection consisted of three different TUG tests; first TUGst, followed by TUG while simultaneously naming different animals (TUGdt NA), and finally TUG while simultaneously reciting months in reverse order (TUGdt MB). The choice of the verbal task NA was based on previous research [29,45,46], while to the best of our knowledge MB has not been used as a part of dual-task research [45]. Participants were instructed to execute all TUG tests at their own pace, with the simultaneous performance of the verbal task at a self-selected speed.…”

Section: Clinical Diagnostic Assessmentmentioning

confidence: 99%

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Dual-task tests discriminate between dementia, mild cognitive impairment, subjective cognitive impairment, and healthy controls – a cross-sectional cohort study

et al. 2020

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Background: Discrimination between early-stage dementia and other cognitive impairment diagnoses is central to enable appropriate interventions. Previous studies indicate that dual-task testing may be useful in such differentiation. The objective of this study was to investigate whether dual-task test outcomes discriminate between groups of individuals with dementia disorder, mild cognitive impairment, subjective cognitive impairment, and healthy controls. Methods: A total of 464 individuals (mean age 71 years, 47% women) were included in the study, of which 298 were patients undergoing memory assessment and 166 were cognitively healthy controls. Patients were grouped according to the diagnosis received: dementia disorder, mild cognitive impairment, or subjective cognitive impairment. Data collection included participants' demographic characteristics. The patients' cognitive test results and diagnoses were collected from their medical records. Healthy controls underwent the same cognitive tests as the patients. The mobility test Timed Up-and-Go (TUG single-task) and two dual-task tests including TUG (TUGdt) were carried out: TUGdt naming animals and TUGdt months backwards. The outcomes registered were: time scores for TUG single-task and both TUGdt tests, TUGdt costs (relative time difference between TUG single-task and TUGdt), number of different animals named, number of months recited in correct order, number of animals per 10 s, and number of months per 10 s. Logistic regression models examined associations between TUG outcomes pairwise between groups.

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Section: Clinical Diagnostic Assessmentmentioning

confidence: 99%

Section: Clinical Diagnostic Assessmentmentioning

confidence: 99%

Dual-task tests discriminate between dementia, mild cognitive impairment, subjective cognitive impairment, and healthy controls – a cross-sectional cohort study

et al. 2020

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“…The current study forms part of the Uppsala-Dalarna Dementia and Gait (UDDGait) project [28,32,33]. UDDGait is an ongoing, longitudinal cohort project with the overall aim of investigating dual-task test outcomes as markers for dementia disorders.…”

Section: Setting and Participantsmentioning

confidence: 99%

“…The data collection procedures used in UDDGait have been described in detail previously [28,32,33]. All participants reported demographic characteristics including educational level (university education or not) and marital status.…”

Section: Data Collection At Baselinementioning

confidence: 99%

“…The test is timed from the individual's back leaving the backrest to sitting down (their posterior touching the seat) [30]. The TUGdt test procedure used in the current study has previously been tested and developed [33]. The testing was performed in the following order: TUGst, TUGdt naming animals (TUGdt NA), and TUGdt reciting months backwards (TUGdt MB).…”

Section: Timed Up-and-go Single-and Dual-task Testsmentioning

confidence: 99%

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Dual-Task Tests Predict Conversion to Dementia—A Prospective Memory-Clinic-Based Cohort Study

Åhman

Berglund

Cedervall

et al. 2020

IJERPH

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The aim of this study was to investigate whether Timed Up-and-Go (TUG) dual-task (TUGdt) tests predict dementia incidence among patients with subjective or mild cognitive impairment (SCI; MCI). Other study objectives were to determine whether TUGdt improves dementia prediction compared to a) demographic characteristics and standard cognitive tests alone; and b) TUG and Verbal Fluency performed separately. Patients (n = 172, age range 39–91 years, 78 women) with SCI or MCI performed TUGdt tests, including 1) naming animals and 2) reciting months backwards, and clinical cognitive tests at baseline. Diagnoses were identified at follow-up after 2.5 years. Logistic regression was used to predict dementia incidence, receiver operating characteristic (ROC) curves and c-statistics for predictive capacity. Analyses were stratified by age and gender. At follow-up, 51 patients had developed dementia. The TUGdt result “animals/10 s” was associated with dementia incidence (standardized odds ratio (OR) = 4.06, 95% confidence interval (CI) 2.28–7.23, p < 0.001), more so among patients under the median age of 72 years (standardized OR = 19.4, 95% CI 3.53–106.17, p < 0.001). TUGdt “animals/10 s” improved dementia prediction compared to demographic characteristics and standard tests alone (c-statistics 0.88 to 0.94) and single-task tests (c-statistics 0.86 to 0.89), but only in the younger patient group. TUGdt has the potential to become a useful tool for dementia prediction.

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Two-step deep-learning identification of heel keypoints from video-recorded gait

Halvorsen,

Peng,

Olsson

et al. 2024

Med Biol Eng Comput

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Accurate and fast extraction of step parameters from video recordings of gait allows for richer information to be obtained from clinical tests such as Timed Up and Go. Current deep-learning methods are promising, but lack in accuracy for many clinical use cases. Extracting step parameters will often depend on extracted landmarks (keypoints) on the feet. We hypothesize that such keypoints can be determined with an accuracy relevant for clinical practice from video recordings by combining an existing general-purpose pose estimation method (OpenPose) with custom convolutional neural networks (convnets) specifically trained to identify keypoints on the heel. The combined method finds keypoints on the posterior and lateral aspects of the heel of the foot in side-view and frontal-view images from which step length and step width can be determined for calibrated cameras. Six different candidate convnets were evaluated, combining three different standard architectures as networks for feature extraction (backbone), and with two different networks for predicting keypoints on the heel (head networks). Using transfer learning, the backbone networks were pre-trained on the ImageNet dataset, and the combined networks (backbone + head) were fine-tuned on data from 184 trials of older, unimpaired adults. The data was recorded at three different locations and consisted of 193 k side-view images and 110 k frontal-view images. We evaluated the six different models using the absolute distance on the floor between predicted keypoints and manually labelled keypoints. For the best-performing convnet, the median error was 0.55 cm and the 75% quartile was below 1.26 cm using data from the side-view camera. The predictions are overall accurate, but show some outliers. The results indicate potential for future clinical use by automating a key step in marker-less gait parameter extraction. Graphical abstract

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Timed Up-and-Go Dual-Task Testing in the Assessment of Cognitive Function: A Mixed Methods Observational Study for Development of the UDDGait Protocol

Cited by 24 publications

References 62 publications

Dual-task tests discriminate between dementia, mild cognitive impairment, subjective cognitive impairment, and healthy controls – a cross-sectional cohort study

Dual-task tests discriminate between dementia, mild cognitive impairment, subjective cognitive impairment, and healthy controls – a cross-sectional cohort study

Dual-Task Tests Predict Conversion to Dementia—A Prospective Memory-Clinic-Based Cohort Study

Two-step deep-learning identification of heel keypoints from video-recorded gait

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