Abstract
Introduction:
The use of dual-task model such as dual-task gait has been extensively studied to assess cognitive-motor performance among older adults. However, space restriction and safety factor limit its applications in remote assessment. To address the gap, we propose a video processing-based approach to remotely quantify cognitive-motor performance using a 20-second repetitive elbow flection-extension test with dual-task condition, called video-based motoric-cognitive meter (MCM).
Methods:
Eighteen older participants (age: 78.6±6.5 years) who were clinically diagnosed either as having mild cognitive impairment (MCI) or dementia were included in this study. Participants were asked to perform 20-second repetitive elbow flexion-extension exercise with a memory exercise by counting backward from a two-digit number. During the test, all movements of the forearm were recorded by a video camera. As a comparator, a validated wrist-worn sensor was used, which allowed quantifying upper-extremity kinematics.
Results:
The results showed a good agreement (r ≥ 0.530 and ICC2,1 ≥305389MC 0.681) between the derived dual-task upper-extremity motor performance from the proposed video-based MCM and a clinically validated sensor-based MCM. We also observed moderate correlations (|r| ≥ 0.496) between some measures of video-based MCM (flexion time, extension time, and flexion-extension time) and clinical cognitive scale (Minimum Mental State Examination, abbreviation: MMSE). Additionally, some measures of dual-task upper-extremity motor performance (speed, flexion time, extension time, and flexion-extension time) were associated with dual-task gait speed (|r| ≥ 0.557), which has been found to be correlated with cognitive impairment. Lastly, the selected dual-task motor performance metric (flexion time) was sensitive to predict MMSE scores in linear regression analyses with statistical significance (adjusted R2 = 0.306, p = 0.025).
Conclusion:
This study proposes a video processing-based approach to analyze dual-task upper-extremity motor performance from a simple and convenient upper-extremity function test. The results indicate concurrent validity of the proposed video-based MCM compared with the sensor-based MCM, and associations between dual-task upper-extremity motor performance and clinically validated cognitive markers (MMSE scores and dual-task gait). Future studies are warranted to explore sensitivity of this solution to promote remote assessment of cognitive-motor performance among older adults in telehealth applications.
Keywords: Dementia, cognitive-motor performance, dual-task condition, telehealth, remote patient monitoring
Introduction
Dementia causes great stress to our society, healthcare system, and family caregivers [1]. Currently, dementia is affecting approximately 47.5 million persons worldwide, and this number is projected to increase to 75.6 million by 2030 and 135.5 million by 2050 [2]. This has created an urgent need for robust and quickly administered cognitive assessment tools that could serve as surrogate measures for disease progression in people suffering from Alzheimer’s disease (AD) and Alzheimer’s-disease-related dementias (ADRD). These measures should be sensitive to detect and track the earliest clinical manifestations of Alzheimer’s disease and to predict long-term clinical and functional outcomes, which are urgently needed to decide on ‘Whom to Treat, When to Treat, and What Outcomes to Measure” [3].
The dual-task motor paradigm (e.g., dual-task walking) is a method for assessing executive function and divided attention performance [4–8]. Poor dual-task gait performance has been found to be associated with cognitive impairment [4, 9], and has been identified as an increased risk to dementia [10, 11]. However, dual-task gait test may not be suitable for telehealth applications, since a significant number of older adults have mobility impairment and the home environment often lacks adequate space for conducting a gait test safely [12].
To address practical barriers for telehealth use of dual-task walking paradigm, we have developed a cognitive assessment tool (called motor-cognitive meter, MCM), based on a quick and safe test of 20-second repetitive elbow flexion and extension under dual-task condition. This tool has been shown to be sensitive to distinguish cognitive impairment [13]. However, in our previous studies, a wrist-worn sensor and a tablet (worth at least $200) was needed for acquiring data and extracting kinematic information of the upper-extremity. This solution (sensor-based MCM) has several limitations for remote monitoring and telehealth applications, including purchasing cost, difficulty of sensor shipment, sensitivity to sensor orientation, and difficulty of the use for non-tech savvy individuals. To overcome these limitations, in this paper we proposed video processing techniques to capture dual-task upper-extremity motor performance as surrogate cognitive-motor markers for older adults. This solution (video-based MCM) would be easily integrated into a smartphone platform as a convenient access for remote assessment of cognitive-motor performance in home environment.
In this study, we examined the concurrent validity of video-based MCM compared to prior validated upper-extremity function assessment tools (e.g., sensor-based MCM) and association between dual-task upper-extremity motor performance and traditional cognitive markers (e.g., Minimum Mental State Examination, abbreviation: MMSE, and dual-task gait). Our key hypotheses are: 1) There is a high agreement between video-based and sensor-based MCM tools; and 2) there is significant correlation between video-based MCM and MMSE, as well as dual-task gait.
Methods
Participants
Older Adults (age 65 years or older) with either mild cognitive impairment, mild dementia, or Alzheimer’s disease diagnosed by certificated neurologists were recruited from the Geriatrics Clinic and Alzheimer’s Disease and Memory Disorders Center at Baylor College of Medicine. The diagnosis of cognitive disorders (mild cognitive impairment (MCI) and dementia) was done by an expert panel at the Baylor St Luke’s Medical Center, Section of Neuropsychology. For diagnosis of dementia and its severity, a validated procedure [14] was used, including a physical and neurological examination, medical history, neuropsychological assessment, examination of mental disorders for the elderly, visual association test, memory impairment screen, assessment of depression, and evaluation of activity of daily living, as well as extensive laboratory blood testing. Several of these measures would not always converge on the same answer, particularly between MCI and mild AD. However, the expert panel determined the final diagnosis based on all available information [15].
Participants were excluded from the study if they were non-ambulatory or had a severe gait impairment (e.g., unable to walk 10-meter independently with or without an assistive device); had major foot problems (e.g., major amputation, severe neuropathy, foot deformity, foot arthritis, foot pain, etc.); had other neurological conditions associated with cognitive impairment (stroke, Parkinson disease, Huntington disease, etc.); had any clinically significant medical or psychiatric condition or laboratory abnormality; had severe visual and/or hearing impairment; with changes in psychotropic or sleep medications in the last 6 weeks; or were unwilling to participate. All participants signed a consent form for this study. This study was approved by the Institutional Review Board of the Baylor College of Medicine (Houston, TX, USA).
Upper-Extremity Function Test
In the upper-extremity function test, the participant was asked to repetitively flex and extend their dominant elbow to full flexion and extension as quickly as possible for 20 seconds, while wearing a color band and a wearable motion sensor (tri-axial gyroscope, sample frequency = 100 Hz, BioSensics LLC, Cambridge, MA) on the forearm. The following video-based MCM extracted dual-task upper-extremity motor performance metrics from tracking trajectories of the color band in the video. At the same time, the participant conducted a cognitive task: counting backward by ones from a random two-digit number. During the test, all movements of the forearm were recorded by a video camera (Vixia HF R800 Video Camera, Canon U.S.A. Inc., Melville, NY) from the view of the trunk’s sagittal plane in the dominant-hand side. The video’s frame rate is 30 Hz.
Video Conversion Algorithm
The algorithm to extract upper-extremity motor performance from the video includes two procedures: video conversion and feature extraction. In the first procedure, the video is converted to a one-dimension angular velocity of the color band’s motion. In the second procedure, motor performance parameters were extracted from the angular velocity signal outputted from the first procedure. The algorithm was coded and implemented in MATLAB R2019b (Mathworks, Natick, MA, USA).
In the feature extraction procedure, the angular velocity signal is integrated and differentiated into angle and angular acceleration signals, respectively. Then the angular velocity signal is segmented as flexion and extension periods using zero-crossing. In each period, peak-to-peak magnitudes of angular velocity, angle, and angular acceleration are defined as speed, range of motion, and power, respectively. These features represent spatial properties of the upper-extremity motor performance. In another aspect, flexion time is defined as the time length of the positive part of a period. Extension time is defined as the time length of the negative part of a period. Flexion-extension time is defined as the length of a complete period. These features represent temporal properties of the upper-extremity motor performance. Figure 1 displays the flowchart of the video conversion algorithm and definitions of all these features. Then, the mean of each feature across all periods in one test is outputted as a dual-task motor performance metric.
Figure 1.
The video conversion algorithm of the video-based MCM: (a) Flowchart of the vide conversation algorithm. (b) The procedure to convert a frame of the input video to trajectories of the color band during the video. (c) A typical pattern of angular velocity, range of motion, and angular acceleration for sensor-based (dashed line) and video-based (solid line) MCM.
The feature extraction procedure was also used to analyze the angular velocity collected by the wearable motion sensor. The dual-task upper-extremity motor performance measured by the wearable sensor (sensor-based MCM) signal were used as a benchmark to validate the accuracy of the same motor performance from our proposed video-based MCM.
Clinical Cognitive Scale and Dual-Task Gait
The participant’s cognitive performance was assessed using MMSE test. Participants’ gait performance was assessed under dual-task walking condition (counting backward by 1, starting from a random two-digit number) with supervision of a trained examiner. Participants walked 10 m at a self-selected speed. Gait speed was measured using shank- and thigh-attached wearable sensors (LEGSys™, Biosensics, Boston, US) and validated gait analysis algorithms, which have been described previously in details [16–18]. Average of gait speed during steady state walking was used for this study [19].
Statistical Analyses
Statistical analyses were performed using IBM SPSS Statistics version 24 (IBM, Armonk, NY, USA). For all statistical analyses, p < 0.05 was considered statistically significant.
To validate the accuracy of the video-based MCM, absolute and relative agreements between dual-task upper-extremity motor performance measured by the video-based MCM and the one measured by the sensor-based MCM were tested using intraclass correlation coefficients (ICC2,1) and using Pearson’s correlation coefficients (r), respectively. The cutoff of ICC2,1 is that values below 0.50 indicate poor accuracy, values between 0.50 and 0.75 indicate moderate accuracy, values between 0.75 and 0.90 indicate good accuracy, and values above 0.90 indicate excellent accuracy [20]. The cutoff of r is that values below 0.35 indicate weak correlation, values between 0.35 and 0.67 indicate moderate correlation, and values between 0.68 and 1.0 indicate strong correlation [21]. The Bland-Altman plots were also used to compare data from two different types of MCM tools [22].
The associations between the clinical cognitive scale (MMSE) and dual-task upper-extremity motor performance measured by the video-based MCM were tested using partial correlation coefficients (r) with control of covariates of age[23]. The cutoff of partial correlation coefficient ts (r) is same with Pearson’s correlation coefficients (r). Similarly, the association between dual-task gait speed and dual-task upper-extremity motor performance measured by the video-based MCM were tested using partial correlation coefficients (r) with the same covariate.
Then, the metrics of dual-task upper-extremity motor performance that are moderately or strongly correlated with MMSE scores are included as candidate predictors of multivariate linear regression models (MMSE scores as dependent variable) adjusted with covariates of age information. Collinearity between the candidate predictors is checked using the variance inflation factor (VIF). When two or more predictors show that their VIFs are higher than 10, the predictor with the lowest r will be removed from the model until all predictors’ VIFs are below 10. For the multivariate linear regression model, feature selection of stepwise method was used, and model performance (F-ratio and p value of Analysis of Variance (ANOVA), adjusted R2, and standard error of the estimate), and each predictor’s coefficient (B) and significance (p-value) were reported.
Results
Eighteen older participants who were clinically diagnosed either as mild cognitive impairment (MCI) or dementia were included in this study. Their demographic information is shown in Table 1.
Table 1.
Participants’ characteristics
| Variables | Values |
|---|---|
| Number of participants | 18 |
| Age, years, mean±SD | 78.6±6.5 |
| BMI, kg/m2, mean±SD | 25.8±4.3 |
| Female, n (%) | 5 (27.8) |
| MMSE, mean±SD | 26.4±4.9 |
| Clinically diagnosed MCI, n (%) | 14 (77.8) |
| Clinically diagnosed dementia, n (%) | 4 (22.2) |
MCI: mild cognitive impairment. BMI: body mass index. MMSE: mini-mental state examination. SD: Standard deviation
Table 2 shows intraclass correlation coefficients (ICC2,1) and Pearson’s correlation coefficients (r) for dual-task upper-extremity motor performance measured by the sensor-based MCM and the video-based MCM. The Pearson’s correlation coefficients indicated strong correlation with statistical significance for speed, power, flexion time, extension time, and flexion-extension time, as well as moderate correlation with statistical significance for range of motion. The intraclass correlation coefficients indicate good or excellent accuracy with statistical significance for speed, power, flexion time, extension time, and flexion-extension time, as well as moderate accuracy with statistical significance for range of motion. In Figure S1, we also observed that most of the differences between measurements of two types of MCM tools lay within two standard deviations of the mean.
Table 2.
Agreement of dual-task upper-extremity motor performance between video- and sensor-based MCM
| Correlation coefficients | ||
|---|---|---|
| r | ICC2,1 | |
| Speed | 0.772** | 0.823** |
| Range of motion | 0.530* | 0.681* |
| Power | 0.878** | 0.931** |
| Flexion time | 0.857** | 0.922** |
| Extension time | 0.910** | 0.952** |
| Flexion-extension time | 0.908** | 0.951** |
r: Pearson’s correlation coefficients. ICC2,1: intraclass correlation coefficients.
p < 0.05.
p < 0.01.
Table 3 shows partial correlation coefficients (ρ) between dual-task upper-extremity motor performance measured by video-based MCM and MMSE scores. For temporal metrics of dual-task motor performance, flexion time, extension time and flexion-extension time were moderately and negatively correlated with MMSE scores with statistical significance (p < 0.05). Table III also shows the correlations between dual-task measures of dual-task upper-extremity motor performance measured by video-based MCM and dual-task gait speed. Similarly, temporal metrics of dual-task motor performance (flexion time, extension time and flexion-extension time) and one spatial metric (speed) were moderately correlated with dual-task gait speed with statistical significance (p < 0.05).
Table 3.
Associations between video-based dual-task upper-extremity motor performance and MMSE scores as well as dual-task gait speed, with control of age as a covariate
| MMSE | Gait speed | |||
|---|---|---|---|---|
| r | p | r | p | |
| Speed | 0.471 | 0.056 | 0.558 | 0.020 |
| Range of motion | 0.139 | 0.594 | 0.355 | 0.162 |
| Power | 0.448 | 0.071 | 0.480 | 0.051 |
| Flexion time | −0.615 | 0.009 | −0.668 | 0.003 |
| Extension time | −0.496 | 0.043 | −0.557 | 0.020 |
| Flexion-extension time | −0.577 | 0.015 | −0.637 | 0.006 |
MMSE: mini-mental state examination. r: partial correlation coefficients. p: p-value for correlation.
In the model to predict MMSE scores, flexion time (B = −18.797, p = 0.009) was selected from metrics of dual-task upper-extremity motor performance as an independent predictor of linear regression model, with age (B = 0.290, p = 0.102) as a covariate. The model could predict MMSE scores with statistical significance (F (2, 15) = 4.741, p = 0.025, adjusted R2 = 0.306, standard error = 4.074). The establishment process of multivariate linear regression model is shown in Table S1.
Discussion
In this paper, we proposed a novel sensor-less approach to quantify dual-task motor performance using a practical, safe, and quick (~20 seconds) upper-extremity function test. The key advantage of this solution is allowing quantifying cognitive-motor performance using a standard 2-D video recording and without requirement of administrating gait test and without the use of sensors. The association between dual-task upper-extremity motor performance and dual-task gait speed (clinically validated motor biomarker of cognitive impairment) may suggest that the video-based MCM can potentially provide a novel cognitive-motor tool for older adults in telehealth applications, and the video-based data collection pattern largely increasing practicability of cognitive assessment, since multiple studies have already shown an increasing trend for older adults to own and use smartphones or tablets with a video camera module [24]. In another aspect, the COVID-19 pandemic has rapidly increased the demand and need to telehealth for dementia care and facilitated a shift towards remote capture of digital biomarkers to minimize the risk to the vulnerable population, especially for those older adults who may live in low-income rural area.
From Table 2, we can observe a good agreement between the proposed video-based MCM and clinically validated sensor-based MCM. In our previous studies, it has been shown that the upper-extremity function under dual-task condition measured by a wearable inertial sensor is sensitive to distinguish older adults with cognitive impairment [13]. Our proposed video-based MCM is inspired by our previous sensor-based approach, replacing a specialized sensor with a common video camera which can be normally integrated in a smartphone in older adults’ daily lives. The correlation between two types of MCM supports concurrent validity of the proposed video-based MCM in this paper.
From Table 3, the negative correlation between MMSE scores and temporal metrics of dual-task upper-extremity motor performance (flexion time, extension time, and flexion-extension time) with statistical significance suggests that older participants with higher cognitive scales can perform the dual-task upper-extremity function test more quickly. These findings are in line with our previous studies [25, 26] about motor dysfunction of the upper-extremity due to cognitive impairment. The reason behind this association could be explained by previous image brain studies in older adults with MCI [27, 28]. These studies revealed that dual-task gait performance was associated with the neurochemistry and volume of the primary motor cortex, which is part of the executive network circuit of normal locomotion. Moreover, our previous study indirectly supported that a strong correlation was observed between upper-extremity motor performance and gait performance [25, 29]. It still however requires a future validation about the direct association between dual-task upper-extremity motor performance and brain mechanism.
In the linear regression analyses, only flexion time was finally selected as an independent predictor to predict MMSE scores, after stepwise feature selection. The coefficients in the linear regression model for MMSE scores indicate that MMSE score increases as flexion time decreases. Interestingly, we also observed a phenomenon that the older participants usually counted down number (i.e. performing cognitive task) when performing flexion motion during upper-extremity function test. The introduction of cognitive workload along with elbow flexion increased flexion time, especially for those older adults with poorer cognitive function. This fact may explain why the flexion time is the most sensitive cognitive-motor marker among dual-task upper-extremity motor performance metrics to predict traditional cognitive marker (MMSE)..
A limitation of this study is that the small sample size limited the use of validation techniques (e.g. hold-out or cross validation) to evaluate the performance of the assessment tool. In future, to validate the performance of this proposed video-based MCM in real telehealth scenario, we will conduct a clinical trial with larger sample size in various in-home environments with different light conditions and shot angles. We will also develop a smartphone application to integrate our proposed video-based MCM algorithm and friendly graphical user interface which can guide the older adult to perform upper-extremity function test without any human supervision.
Conclusion
This paper proposes a sensor-less dual-task testing model using a quick 20-second repetitive elbow flexion-extension test, which could be used as a practical solution to assess cognitive-motor performance using a standard 2-D video camera and video processing techniques. Results support concurrent validity of this solution to assess cognitive-motor performance among older adults suffering from MCI or dementia. These findings show a promising future of using a simple and affordable tool (i.e. a smartphone or tablet housing a video camera) to assess cognitive-motor performance of older adults irrespective of setting including remote patient monitoring. Future studies are recommended to explore the application of the proposed solution to remotely track changes in cognitive performance over time.
Supplementary Material
Acknowledgement:
The authors would like to acknowledge Ms. Maria Noun, Ms. Sogol Golafshan, and Ms. Sanam Sharafkhaneh who contributed to data management.
Funding:
This work was supported by the National Institutes of Health/National Institute on Agings (award number 2R44AG061951-02). The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.
Footnotes
Conflict of interest statement: Bijan Najafi is serving as a consultant for BioSensics LLC. However, his consultation is not relevant to the scope of this study. He was also not involved in data analysis from this study. Ashkan Vaziri is with BioSensics LLC. He has however only contributed in the technical aspect of this study and didn’t involve in patient recruitment, data analysis or interpretation of clinical data. None of other coauthors claimed conflict of interest relevant to the scope of this study.
Statement of Ethics: Eligible subjects signed a written informed consent form, approved by local institutional review boards of Baylor College of Medicine, Protocol #H-43917.
Data Availability:
All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.
References
- [1].Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. 2013;9(1):63–75 e2. [DOI] [PubMed] [Google Scholar]
- [2].World Health Organization. Mental health and older adults. [Internet]. 2016. [cited August, 24, 2017]. Available from: http://www.who.int/mediacentre/factsheets/fs381/en/.
- [3].Care AsANP, Workgroup SM, Borson S, Boustani MA, Buckwalter KC, Burgio LD, et al. Report on milestones for care and support under the US National Plan to Address Alzheimer’s Disease. Alzheimer’s & Dementia. 2016;12(3):334–69. [DOI] [PubMed] [Google Scholar]
- [4].Bahureksa L, Najafi B, Saleh A, Sabbagh M, Coon D, Mohler MJ, et al. The Impact of Mild Cognitive Impairment on Gait and Balance: A Systematic Review and Meta-Analysis of Studies Using Instrumented Assessment. Gerontology. 2017;63(1):67–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Schwenk M, Grewal GS, Honarvar B, Schwenk S, Mohler J, Khalsa DS, et al. Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults. J Neuroeng Rehabil. 2014;11:164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Dubost V, Annweiler C, Aminian K, Najafi B, Herrmann FR, Beauchet O. Stride-to-stride variability while enumerating animal names among healthy young adults: result of stride velocity or effect of attention-demanding task? Gait Posture. 2008;27(1):138–43. [DOI] [PubMed] [Google Scholar]
- [7].Dubost V, Kressig RW, Gonthier R, Herrmann FR, Aminian K, Najafi B, et al. Relationships between dual-task related changes in stride velocity and stride time variability in healthy older adults. Hum Mov Sci. 2006;25(3):372–82. [DOI] [PubMed] [Google Scholar]
- [8].Beauchet O, Kressig RW, Najafi B, Aminian K, Dubost V, Mourey F. Age-related decline of gait control under a dual-task condition. J Am Geriatr Soc. 2003;51(8):1187–8. [DOI] [PubMed] [Google Scholar]
- [9].Lee J, Park S. Effects of a priority-based dual task on gait velocity and variability in older adults with mild cognitive impairment. J Exerc Rehabil. 2018;14(6):993–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Montero-Odasso MM, Sarquis-Adamson Y, Speechley M, Borrie MJ, Hachinski VC, Wells J, et al. Association of Dual-Task Gait With Incident Dementia in Mild Cognitive Impairment: Results From the Gait and Brain Study. JAMA Neurol. 2017;74(7):857–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Ramírez F, Gutiérrez M. Dual-Task Gait as a Predictive Tool for Cognitive Impairment in Older Adults: A Systematic Review. Frontiers in aging neuroscience. 2021;13:769462–. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Najafi B, Zhou H, Nguyen H. Gait Test or No Gait Test: Do We Need Walking Assessment to Determine Physical Frailty? Gerontology. 2019;65(3):311–2. [DOI] [PubMed] [Google Scholar]
- [13].Toosizadeh N, Najafi B, Reiman EM, Mager RM, Veldhuizen JK, O’Connor K, et al. Upper-Extremity Dual-Task Function: An Innovative Method to Assess Cognitive Impairment in Older Adults. Front Aging Neurosci. 2016;8:167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Mets T, Monsch A, Kellett J, Ousset P, Kuntzman F, Pellerin J, et al. Assessment of dementia in elderly outpatients: a comparative study of European centers and consensus statement. Archives of gerontology and geriatrics. 2000;30(1):17–24. [DOI] [PubMed] [Google Scholar]
- [15].Gabel MJ, Foster NL, Heidebrink JL, Higdon R, Aizenstein HJ, Arnold SE, et al. Validation of consensus panel diagnosis in dementia. Archives of neurology. 2010;67(12):1506–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Aminian K, Najafi B, Büla C, Leyvraz PF, Robert P. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. Journal of biomechanics. 2002;35(5):689–99. [DOI] [PubMed] [Google Scholar]
- [17].Aminian K, Trevisan C, Najafi B, Dejnabadi H, Frigo C, Pavan E, et al. Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement. Gait Posture. 2004;20(1):102–7. [DOI] [PubMed] [Google Scholar]
- [18].Najafi B, Khan T, Wrobel J. Laboratory in a box: wearable sensors and its advantages for gait analysis. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:6507–10. [DOI] [PubMed] [Google Scholar]
- [19].Lindemann U, Najafi B, Zijlstra W, Hauer K, Muche R, Becker C, et al. Distance to achieve steady state walking speed in frail elderly persons. Gait Posture. 2008;27(1):91–6. [DOI] [PubMed] [Google Scholar]
- [20].Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of chiropractic medicine. 2016;15(2):155–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Taylor R Interpretation of the Correlation Coefficient: A Basic Review. Journal of Diagnostic Medical Sonography. 1990;6(1):35–9. [Google Scholar]
- [22].Giavarina D Understanding Bland Altman analysis. Biochem Med (Zagreb). 2015;25(2):141–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].Schober P, Boer C, Schwarte LA. Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia and analgesia. 2018;126(5):1763–8. [DOI] [PubMed] [Google Scholar]
- [24].Berenguer A, Goncalves J, Hosio S, Ferreira D, Anagnostopoulos T, Kostakos V. Are Smartphones Ubiquitous?: An in-depth survey of smartphone adoption by seniors. IEEE Consumer Electronics Magazine. 2017;6(1):104–10. [Google Scholar]
- [25].Toosizadeh N, Najafi B, Reiman EM, Mager RM, Veldhuizen JK, O’Connor K, et al. Upper-Extremity Dual-Task Function: An Innovative Method to Assess Cognitive Impairment in Older Adults. Front Aging Neurosci. 2016;8:167–. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Ehsani H, Mohler MJ, O’Connor K, Zamrini E, Tirambulo C, Toosizadeh N. The association between cognition and dual-tasking among older adults: the effect of motor function type and cognition task difficulty. Clinical interventions in aging. 2019;14:659–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Annweiler C, Beauchet O, Bartha R, Wells JL, Borrie MJ, Hachinski V, et al. Motor cortex and gait in mild cognitive impairment: a magnetic resonance spectroscopy and volumetric imaging study. Brain : a journal of neurology. 2013;136(Pt 3):859–71. [DOI] [PubMed] [Google Scholar]
- [28].Sakurai R, Bartha R, Montero-Odasso M. Entorhinal Cortex Volume Is Associated With Dual-Task Gait Cost Among Older Adults With MCI: Results From the Gait and Brain Study. The journals of gerontology Series A, Biological sciences and medical sciences. 2019;74(5):698–704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Toosizadeh N, Mohler J, Najafi B. Assessing Upper Extremity Motion: An Innovative Method to Identify Frailty. J Am Geriatr Soc. 2015;63(6):1181–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.