(2022) 8:154
Fukuoka et al. Pilot and Feasibility Studies
https://doi.org/10.1186/s40814-022-01122-z

Open Access

STUDY PROTOCOL

The relationship between spatiotemporal
gait parameters and cognitive functionin healthy adults: protocol for a cross‑sectional
study
Tatsuya Fukuoka1†, Shun Irie1,2*† , Yoshiteru Watanabe3†, Toshiki Kutsuna3 and Akiko Abe4

Abstract
Background: Motor dysfunctions, such as slower walking speed, precede the occurrence of dementia and mild
cognitive impairment, suggesting that walking parameters are effective biomarkers for detecting early sub-clinicalcognitive risk. It is often also concurrent with self-complained cognitive dysfunction, called motoric cognitive risk
(MCR) syndrome. Our preliminary study found several walking parameters, obtained by a three-dimensional motion
capture system, to be correlated with computer-based assessments of various cognitive function modalities, although
the sample size was small. The Cognitive-Gait (CoGait) Database Project, described in the current protocol, aims toestablish a database of multi-dimensional walking and cognitive performance data, collected from a large sample ofhealthy participants, crucial for detecting early sub-clinical cognitive risk.
Methods: We will recruit healthy volunteers, 20 years or older, without any neurological musculoskeletal or psychiatric disorders. The estimated sample size is 450 participants, including a 10% attrition rate. Using computer-based
cognitive assessments, participants will perform six tasks: (i) the simple reaction time task, (ii) Go/No-Go task, (iii)
Stroop Color–Word Test, (iv) N-back test, (v) Trail Making Test, and (vi) digit span test. We will also conduct paper-based
cognitive assessments such as the Mini-Mental State Examination, Montreal Cognitive Assessment, and the GeriatricDepression Scale-15 for assessing MCR. Gait will be measured through joint kinematics and global positioning inparticipants’ lower legs while walking at a comfortable and faster pace, using pants with an inertial measurement
unit-based three-dimensional motion capture system. Finally, we will establish a prediction model for various cognitive performance modalities based on walking performance.
Discussion: This will be the first study to reveal the relationship between walking and cognitive performance using
multi-dimensional data collected from a large sample of healthy adults, from the general population. Despite certain methodological limitations such as the accuracy of measurements, the CoGait database is expected to be thestandard value for both walking and cognitive functions, supporting the evaluation of psychomotor function in early
sub-clinical cognitive risk identification, including motoric-cognitive risk syndrome.
Keywords: Mild cognitive impairment, Motoric-cognitive risk syndrome, Locomotor functions†

T. Fukuoka, S. Irie, and Y. Watanabe contributed equally to this work.

*Correspondence: s-irie360@dokkyomed.ac.jp
2

Division of Smart Healthcare Research, Dokkyo Medical University, 880
Kita‑Kobayashi, Mibu, Tochigi 321‑0293, Japan
Full list of author information is available at the end of the article

Background
Motoric-cognitive risk (MCR) syndrome, characterized
by self-reported cognitive complaints and slower walking
speed, is associated with an increased risk for dementia
and mild cognitive impairment (MCI) [1–4]. Detection© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

of MCR syndrome can facilitate early intervention, suchas pharmacological treatment, rehabilitation for sustained cognitive and physical functions, and preventionof MCI and dementia [5–9]. In addition to patient education for lifestyle modification, addressing issues suchas diet, physical, and social activities reduces the risk ofthese cognitive disorders [10–15]. In a recent survey, theincidence rate of MCR in people in their 60s was 54.9 per1000 persons, which strongly suggests that preventive
activities for these cognitive disorders should be started
at working age, before the initial presentation of earlycognitive decline [16].
There are many risk factors for dementia, such as
genetic factors, lifestyle habits, sleep quality, education,and physical and social activities, even in the absence ofdetectable cognitive risk [6, 7, 10, 11, 17–19]. Thus, we
argue that cognitive risk screening in healthy participantsrequires additional multimodal parameters. Establishinga database of multimodal parameters that include theserisk factors is necessary to distinguish individuals withhigher cognitive risks from the general population.
Specifically, walking performance is the most notable marker of cognitive risk [20, 21]. Although walking
speed is a known risk factor for cognitive decline, what
domains of walking parameters (i.e., cadence, speed, toeclearance, joint angle) relate to early cognitive decline
remain unknown. Therefore, a motion capture techniquethat can collect information on various parameters ofwalking functions is a potential screening methodology
for early cognitive decline. Motion capture systems are
divided into two categories: one is a conventional optical system with infrared cameras and reflective markers, and the other is an IMU (inertial measurementunit)-based system. It is easier to utilize the IMU-based
motion capture system to monitor walking functions indaily environments because it is easy to assemble andmeasure in any place compared to the conventional optical motion capture system [22] (summarized in Table 1).
Ultimately, we aim to establish a database of cognitive
and walking functions, enabling us to detect cognitive
risks in earlier stages based on walking performances inPage 2 of 13

daily environments collected by the wearable IMU-based
motion capture system, including the e-skin MEVA
(Fig. 1). The conventional optical motion capture system cannot be utilized to achieve our aim as it requires
special facilities and techniques. Even if there are limitations related to the accuracy of tracking data when using
the IMU-based system, this system is best suited for ourstudy objectives.
For cognitive assessments, MMSE (Mini-Mental State
Examination) and MoCA-J (Japanese version of Montreal Cognitive Assessment) are frequently used to detect
dementia and MCI. Although these are powerful screening tools for the screening of dementia and MCI, ceiling
effects have frequently been reported among healthy participants in these paper-based tests [23–26]. Thus, webbased cognitive assessment has recently gained attention,
as it is more feasible for detecting earlier stages of cognitive decline as compared to paper-based testing [27].
Therefore, we preliminarily examined walking parameters using the e-skin MEVA and cognitive function
scores, using web-based assessments (CogEvo, Total
Brain Care, Kobe, Japan) in healthy participants (5women and 6 men; 28–80 years). The CogEvo consistedof five cognitive function domains: spatial recognition,orientation, memory, planning, and attention. These
domains were measured by pattern recognition task,
confirming the date, flashlight task, maze task, and Trail
Making Test, respectively [28, 29]. Resultantly, our preliminary observations, even in a small sample, showed

Table 1 Comparison of specifications between conventional
optical and IMU-based motion capture system
Optical (conventional)
systemIMU-based system

Accuracy

High

Low

Cost

High

Low

Place

Specialized facilities

Anywhere

Time

Long

Quick

Daily useNo

Yes (as wearable devices)Fig. 1 Schematic diagram for the utilization of the database ofcognitive and walking functions. This is the ultimate goal of thisstudy: First, a person wears the IoT clothes that can collect thewalking parameters in daily environments (1). Parameters of walking
functions are uploaded to the cloud, and cloud computing is
performed to define the risks of cognitive impairment and frailty (2).
When the risks are above standard value, notifications will be sent to
the person’s smartphone (3). Finally, that person will start preventive
activities according to the notifications

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

a significant relationship between some cognitive andwalking performance modalities in healthy volunteers(see Supplementary Figure S1, Additional file 1). Fromthese results, we establish the study protocol to morecomprehensively investigate the relationship between
multi-dimensional walking parameters and cognitive
function using a custom-based web-application which
comprises more cognitive domains than CogEvo (see“Methods” section) and which can be utilized to predictand screen for cognitive function decline. Our study willcollect the detailed walking parameters and cognitive
assessment scores of a sample of healthy volunteers, aged
20 years or older, to establish more accurate predictivemodels for cognitive function.

Methods
Study design

The current study, called the Cognitive-Gait Database(CoGait) project, will follow a cross-sectional design
aimed at elucidating the relationship between walking
and cognitive performance in healthy adults. The sample size was estimated to be 410 persons, calculatedusing the effect size (f = 0.074) of a similar study [20]under the following conditions: multiple linear model,
df = 24, P = 0.01, 1-β= 0.8. The upwardly corrected
sample size of 450 people accounts for an attrition rateof 10%. The sample size calculation was performed
using G*power [30, 31]. Before submitting this article,
we confirmed whether the design was suitable for the
STROBE checklist for cross-sectional studies (see Additional file 2). After publication of our study results, wewill immediately publish all datasets except personal
information in a secured database server.

Page 3 of 13

TK, and AA), and Dokkyo Medical University (SI). To
recruit participants, we will post advertisements about
the study through our partners, such as universities(Tokyo University of Technology and Dokkyo Medical
University), local governments (Ota-City in Tokyo), local
corporate associations, and companies for recruitment
testing. To reduce any age-related bias, our advertisement partners will be assigned to collect data from different age generations. For example, younger participantsfrom universities, younger to middle-aged individuals
from the local corporate associations, and the elderly
from the local governments. Our advertisements willassure prospective participants of their right to withdraw
at any time and the financial benefit for participating(approximately 1000 JPY). The study interval is set to 3
years. If we are unable to obtain 450 samples within thistime frame, the study will be concluded, and the database will be published as soon as possible. The location of
measurement will be gymnasiums and halls in universities and local governments.
Study participants
Inclusion criteria

Healthy participants older than 20 years, without any
neurological, musculoskeletal, or psychiatric disorders
potentially affecting walking and cognitive functions,will be eligible for participation. Only native Japanese
speakers will be recruited. Informed consent will besought from all participants before they declare their
medical histories. In the absence of exclusion criteria(see the “Exclusion criteria” section), participants’ walking, and cognitive performance will be measured (seethe “Measures” section).

Study setting and recruitment

Exclusion criteria

Healthy volunteers, older than 20 years and without
any neurological or musculoskeletal disorders, will be
recruited for the study. The research team consists of a
research scientist at Xenoma Inc. (TF), as well as faculty
members at the Tokyo University of Technology (YW,

Participants will be excluded from the study if their
medical histories include the disorders or conditions
listed in Table 2. Participants with visible abnormalities
in walking function (i.e., a mobility function score of <7on the Functional Independent Measure), as assessed by

Table 2 Exclusion criteria
Exclusion criteria
People who cannot walk independently
People with any amputations.
People with disabilities in vision, hearing, and/or equilibrium.
People who cannot use an electronic tablet device owing to disabilities in their upper limbs.
People at a high risk of falling.
People with detectable (MMSE <24 and MoCA-J ≤25) or self-complaint cognitive dysfunctions assessed by GDS-15.

People with other orthopedic, neurological, or psychiatric disorders that potentially affect walking and cognitive functions (i.e., osteoarthritis, stroke,
depression).

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

a skilled physician, physiotherapist, nurse, or researchscientist, will also be excluded [32]. Additionally, participants with cognitive problems (i.e., MMSE <24 andMoCA-J ≤25) or self-complaint cognitive dysfunctions
assessed by the Geriatric Depression Scale 15 (GDS-15)
will be excluded [33].
Measures
General procedure

First, we will obtain written informed consent from allparticipants. Second, we will obtain their personal information (Table 3), medical histories using the checklists,
MMSE and MoCA-J scores, and the questionnaire forsubjective memory complaints using GDS-15, in order
to exclude participants with pre-diagnostic cognitive
disorders (Table 2) [33]. Medical history includes neurological, orthopedic, and psychiatry disorders that potentially affect the walking and cognitive functions (see the“Exclusion criteria” section) [34, 35]. Medical histories
Table 3 Items for collecting participants’ personal information
Personal information

Body information

Name

Body height

GenderBody weight

Date of birth

Sex

Contact

Other disorders

Education

Medications

Mother tongue

Page 4 of 13

will be recorded, and cognitive assessments will be performed by a skilled physician, physiotherapist, nurse, or
research scientist face-to-face. Unique personal identities(IDs) will be generated for participants who do not meetthe exclusion criteria and printed as QR codes, required
during registration for the walking and cognitive assessments. Upon receiving their IDs, participants’ walking
will be measured (see the “Gait measurement” section).
After a 10-min break, they will participate in the cognitive assessment (see the “Cognitive assessment” section). The walking and cognitive function datasets will be
securely stored in online cloud storage. An overview ofthe study procedures and measurements is presented inFig. 2.
Gait measurement

We will measure walking parameters using pants, fitted with seven inertial measurement unit (IMU) sensors (e-skin MEVA; Xenoma Inc., Tokyo, Japan),located in the e-textile segments, as shown in Fig. 3A–
C. IMU sensors contain triaxial accelerometers andtriaxial gyroscopes, enabling the estimation of threedimensional joint kinematics and global positioning according to a known algorithm [36]. Prior to thedevelopment of the current study protocol, we conducted a validity check comparing joint angles acrossthe e-skin MEVA and the conventional optical motion
capture system (VICON Nexus ver. 2.1.1, VICON,
Oxford, UK). Overall, the systems were comparable

Fig. 2 Schematic diagram of the experimental procedure. The experiments consist of four phases or sessions: informed consent, registrationof personal information (i.e., height, gender, and date of birth), gait measurement with e-skin MEVA, and cognitive assessment using the tablet.
Datasets will be automatically uploaded to online cloud storage

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

Page 5 of 13

Fig. 3 The wearable motion capture system “e-skin MEVA.” A–C The IMU pants, from the frontal (A), lateral (B), and posterior (C) views. IMU sensors
are placed on the lateral surfaces of the upper and lower legs, and the dorsal surfaces of the foot and sacrum. The sensor on the sacrum is equippedwith a removable attachment, with Bluetooth® wireless communication. D, E The poses for calibration. Prior to the measurement, participants mustpose in two ways: leaning forward (D) and standing upright (E)(root mean square error: 3.57±1.50°; r = 0.96±0.03,
on both sides of the hip, knee, and ankle joints; see
Supplementary Figure S2, Additional file 1).
To calibrate the three-dimensional model calculation prior to gait measurement, each participant will
be asked to adopt two postures: leaning forward with
their hands pressed against a wall (Fig. 3D) and standing upright (Fig. 3E). Next, the participants will be
asked to walk in a straight line on a 16-m walkway,
including 3-m inlet zones, at the start and end points.
The measurements will be conducted under two conditions: fast (maximum speed) and comfortable (selfselected speed). Participants will practice the walking
task under each condition several times before themeasurements to ensure they understand the requirements of the experimental tasks. In the fast condition,
we will instruct participants to walk at their maximum
speed, without running or falling. In the comfortablecondition, we will instruct them to walk at their regular, comfortable speed.
The measurement datasets will comprise raw IMU
sensor signals (acceleration and angular velocity),
global positioning of each sensor and anatomical landmark, and joint angles in the pelvis, hip, knee, andankle (347 parameters in total). The datasets will be
automatically uploaded to cloud storage (Fig. 2). The
data processing methods are described below (see the“Data analysis” section).

Cognitive assessment

Cognitive assessments will be conducted using the conventional paper-based screening tests: MMSE, MoCAJ, and GDS-15, and a custom-developed web-based
software application. Computer-based cognitive assessments cover a wide range of cognitive functions andminimizes floor and ceiling effects [37]. Moreover, suchassessments can collect data not only in terms of accuracy of each task, but also in terms of temporal, spatial,
and spatiotemporal domains, differentiating them fromconventional paper-pencil-based cognitive assessments[38, 39]. Thus, we believe that the combination of conventional paper-based and web-based assessments willassist further studies extended to MCI and MCR.
The software was coded using JavaScript® and runs
on a web browser (Safari, Apple, Cupertino, CA). To
ensure visual conformity, all tests will be conductedusing tablets with the same model number (iPad (8th),
Apple, Cupertino, CA). The measure consists of six
subtests: (i) simple reaction time (SRT) task, (ii) Go/
No-Go task, (iii) Stroop Color–Word Test, (iv) N-back
test, (v) Trail Making Test (TMT), and (vi) digit span(DS) test. During the tests, the tablets will be positioned in a landscape orientation and tilted at 20°.
Participants’ right index fingers will be placed 2.0 cm
behind the tablet. Before any tests, all participants willpractice the tasks at least twice, with verbal instructions from the expert staff, using a tablet.

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154(i) Simple reaction time task The flow of the SRT task
is illustrated in Fig. 4A. Participants will be asked to fix
their gaze on the center of the white cross (fixation point),
and after the warning signal (1000 Hz, 50 ms), the target
signal (red circle) will appear on a black background at

Page 6 of 13

random timings (1–3 s after the warning signal). Participants will be asked to press the “はい (Yes)” button with
their right index finger when the signal appears [40]. The
SRT task comprises 10 trials.

Fig. 4 Cognitive assessments using the reaction time paradigms. A Simple reaction time (SRT) task: In addition to the presence of the fixation point,
the warning signal (WS) rings (1000 Hz, 50 ms). Participants should tap “はい (Yes)” as soon as possible after the WS. Feedback is presented after the
responses. B Go/No-Go task: The flow of the task is almost the same as that of the SRT. However, in this task, participants are asked not to respond toa non-target presentation (triangle and cross). C Stroop Color–Word Test: All experimental paradigms and flows are similar to those in the Go/No-Go
task. Participants responded only to the red Chinese letters

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154(ii) Go/No‑Go task The flow of the Go/No-Go task
is shown in Fig. 4B. The procedure of this task is similar to the SRT task, but the target signal is either a
red circle, red triangle, or red cross, on a black background with a 2-s presentation time. The participants
will be asked to respond only when the red circle
appears on the display. The task consists of 10 trials
each, for the “Go” and “No-Go” paradigms [41]. The
Go/No-Go ratio was determined such that the reaction time was prolonged compared to SRT in healthy
volunteers (n = 4; M = 38.5 years, SD = 12.2; Supplementary Figure S3A, Additional file 1).
(iii) Stroop Color–Word Test The Stroop Color–Word
Test was translated from a previous study [42] and converted into a digitalized test, using a tablet. The color
words are displayed as target signals after the warning
signal. The target signals are the Japanese words (kanji)[RED], [BLUE], and [GREEN], with the font color set
to one of these colors, on a black background, with a
2-s presentation time (Fig. 4C). Participants will be
asked to respond only when any word in a red fontappears (Go trial). The Go trial consists of congruent(color and word match) and incongruent (color andword mismatch) conditions [43]. The ratio between
congruent and incongruent groups is 1:1 [43], and thetask consists of 10 trials each, for the Go and No-Go
paradigms. In addition to the Go/No-Go task, the reaction time was prolonged compared to SRT in healthy
volunteers (n = 4; M = 38.5 years, SD =12.2; Supplementary Figure S3A, Additional file 1).
(iv) N‑back task The N-back task is a major approach
used for assessing working memory capacity [44, 45].
Single-digit numbers will be displayed on the tablet astarget signals (Fig. 5A), with a presentation time of 2 s foreach target signal.
In the one-back condition, the participants will be asked
to respond only when the target signal is the same as thelast number displayed (congruent condition). In the twoand three-back conditions, they should respond only
when the target signals are the same as the second-to-lastand third-to-last numbers displayed, respectively. The
frequency of the congruent condition will be set to 44%of the target signals, and the task consists of 15 trials foreach n-back condition. In this condition, reaction times
in healthy volunteers (n = 5; M = 34.4 years, SD = 4.98)
were prolonged in the N = 1 and 2, compared to N =1
(Supplementary Figures S3B and C, Additional file 1).
(v) Trail making test We adapted the Japanese version of the paper-based TMT, so that the data could be

Page 7 of 13

uploaded to our cloud storage [46]. The TMT consistsof the TMT-A and TMT-B. The TMT-A contains 25
circled numbers, ranging from 1 to 25; participants will
be asked to tap the circled numbers in order, from 1 to
25 (Fig. 5B). The TMT-B contains 13 circled numbers,
ranging from 1 to 13, and 12 circled Japanese kana letters; participants will be asked to tap the numbers andletters following the rule 1-[あ]-2-[い]-3-[う]…[し]-13, corresponding to the original alphabet version of TMT-B(Fig. 5B) [46, 47]. In our adapted TMT, the participants
will not need to draw a line on the screen to prevent misrecognition of tapping on non-target symbols. From preliminary experiments, the process time for the TMT-B
in healthy volunteers (n = 4; M = 30.3 years, SD = 4.03)
was markedly prolonged compared to that for the TMTA, which was similar to previous results (Supplementary
Figure S3D, Additional file 1) [48].
(vi) Digit span test The DS test is a well-established measure of working memory capacity [49, 50]. The digit indicator and numeric keypad used in the DS are shown in
Fig. 5C. Numerical digits are presented individually andsequentially, and the participants will be asked to remember the sequence of the presentation. Next, the participants
will be asked to recall the sequence, using the numeric
keypad. The DS test will be conducted under forward andbackward conditions. The participants will be required
to recall the sequences in forward or backward direction,
depending on the task condition; sequence length ranges,
from two to nine numerical digits, in both tasks. Participants will be required to perform the test under bothconditions, and the trials will be repeated three times foreach sequence length. When participants record three mistakes in the same sequence, the DS test will be completed.
In our preliminary experiment with healthy volunteers(n = 4; 29.8±2.68 years), the matching rate for the backward condition was relatively low for longer target number
sequences (Supplementary Figure S3E, Additional file 1).

Data analysis
Gait analysis

From the gait measurement datasets, we will calculate thegeneral walking parameters, such as stride length and minimum toe clearance, using the built-in software (e-skin LETS
WALK, Xenoma Inc., Tokyo, Japan). The general parameters are presented in Table 4. The sweeps of raw signals,
such as IMU data, joint angle, and global positioning of eachsensor or anatomical landmark, will be averaged with thetime normalized by the percentage of the stride cycle. Whenthe stride cycles cannot be defined because of poor data
quality, the dataset will be excluded.

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

Page 8 of 13

Fig. 5 Cognitive assessments using the process time paradigms. A N-back test: The N-back test is one of the most established assessments ofworking memory capacity. Participants are asked to respond when the target signal is the same as the last n (1–3) digit number. B Trail Making Test
(TMT): The TMT consists of TMT-A (numeric numbers alone) and TMT-B (a mixture of numeric numbers and Japanese kana letters). In the TMT-A,
participants are instructed to tap the numerical digits in ascending order. In the TMT-B, participants also tap the targets in ascending order, but theyshould tap the numerical digits and kana letters alternately. C Digit span (DS) test: This test uses a numeric keypad and numeric number indicator.
Participants are asked to remember the target sequence of numerical digits individually shown in the indicator. After the presentation, they are
asked to recall the sequence in the same or reversed orientation, using the keypad. After inputting the digit sequences, they tap the Enter (Etr) key.
In addition, they can fix the input using the All Clear (AC) and Delete (Del) keys

Psychological analysis

A cognitive performance is evaluated by reaction time,process time, and task accuracy. The reaction time will
be calculated for the SRT, Go/No-Go task, Stroop Color–
Words Test, and N-back test. The reaction time is definedas the interval between the onset of the target signal andthe participant’s response. The process time is defined as
the interval between the task onset and completion in the
TMT and DS test. Furthermore, we will compare the cognitive performance with the scores of the conventional

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

Table 4 Items related to the general walking parameters
Spatial parameters

Temporal parameters Spatiotemporalparameters

Stride length

Cadence

Base width

Stance-swing ratio

Step width

Double support time

Speed

Minimum toe clearance
Joint angles (hip, knee, andankle)
Left-right asymmetry

paper-based assessments (MMSE and MoCA-J) using
multiple linear regression analysis in order to confirm thevalidity of our custom-made application.
Statistical analysis

In this study, we will establish statistical models for predicting cognitive functions based on walking characteristics in healthy participants. At first, we will exclude
the data obtained by MCR participants for the statistical analysis. MCR will be defined using the algorithm byMarquez et al. [33]. According to this study, we defined
MCR syndrome as slower comfortable walking speed(-1SD below the mean of whole dataset) and subjective
memory complaint as detected by the GDS-15. After the
exclusion of the data obtained from MCR participants,
we will perform the statistical analyses using the following processes.
The dependent variables are reaction time, processtime, and task accuracy, and the independent variables
are walking parameters and averaged signal traces, inboth the fast and comfortable conditions. Prior to thesubstitution of the independent variables in the statistical model, we will sift the variables, to prevent problems
related to multi-covariance [51].
The walking parameters will be reduced to less than 25
dimensions, by principal component analysis (PCA), as
needed. The statistical model will be used for multiple
linear regression, with and without the random sample
consensus (RANSAC) algorithm. The accuracy of thestatistical model will be evaluated using Akaike’s information criterion (AIC) [52]. All statistical analyses will
be performed using the Python script, with the scikitlearn library.

Discussion
To the best of our knowledge, this will be the first study
to use a three-dimensional motion capture system toreveal the relationship between walking and cognitive
functions in healthy adults. This database will be essential
for developing a system that detects early cognitive risks

Page 9 of 13

based on walking performances in a daily environment.
As described in the “Introduction” section, the conventional optical motion capture system requires more than
three cameras and several reflective markers, which arelocated in laboratory environments and not daily situations. Thus, the IMU-based motion capture system ismore suitable for the objectives of this study as it canbe embedded into wearable devices, such as the e-skin
MEVA in this study.
From a methodological perspective, the accuracy of
the algorithm for three-dimensional bone modeling in
the e-skin MEVA has already been confirmed in a previous study and was also supported by the findings ofour preliminary experiment, comparing it with conventional motion capture systems (see Supplementary Figure S2, Additional file 1) [36]. In fact, the e-skin MEVA
and LETS WALK have already been applied in clinicalfields to detect abnormal walking patterns owing to spinal cord injury (Higashibaba and Irie, in submission).
Thus, we believe that the accuracy of three-dimensionalmotion capture, using the e-skin MEVA, is adequate forthis study. Furthermore, the raw IMU data during walking will be published with our study database, which
would enable users and developers of other IMU devices
to use the database for their own healthcare applications. However, it is important to assess the validity of
the IMU-based system compared to the conventional
optical motion capture system to improve the comparability of the database. Thus, we also plan future validity
studies of the e-skin MEVA in multiple locations and to
publish those datasets simultaneously as the databasefrom this study.
For cognitive assessments, we used both the custommade web-based- and paper-based cognitive assessmentsin this protocol. As described in the “Introduction” section, conventional paper-based testing methods, such as
MMSE and MoCA, are not suitable for this study becauseof their ceiling effects in healthy participants [23–26].
Moreover, web-based cognitive assessment has recently
gained attention because of its feasibility in detecting
early stage cognitive decline compared to paper-based
testing [27].
In particular, our custom-made application could better assess higher dimensional cognitive performances,including the temporal, spatial, and spatio-temporal
domains, than conventional applications. Additionally,
we will obtain scores of the conventional paper-based
assessments (MMSE, MoCA-J, and GDS-15). Thesescores will be used for the screening of pre-diagnostic
dementia, MCI, and MCR and for comparison with variables obtained by the web-based application for validity
testing, which will assist further studies aiming to detect
MCI and MCR.

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

The associations between walking and cognitive functions have been well described in studies on cognitive
interference in walking. Killeen et al. reported that minimum toe clearance decreased while a Stroop Color-Word
test was administered under experimental conditions[53]. In addition, there have been several reports aboutsuch interferences, assessed using the well-known dual
task paradigm [54–56], considered to be compensatory
mechanisms and/or overlapping functional localization[57]. These common neural mechanisms in walking andcognition are related to changes in walking characteristics that precede cognitive decline. However, the relationship between these interferences in dual-task paradigms
and natural walking parameters without any cognitive
loading is unclear. Further studies are required to elucidate this relationship.
This study has several limitations. First, although wechecked the reliability of the e-skin MEVA comparedto the conventional motion capture system, there areseveral errors in the three-dimensional model calculation because the MEVA algorithm calculates a threedimensional model based on gender and body height, notincluding the length of each segment [36]. Moreover, theaccuracy of the IMU-based motion capture system relieson the pitch (cadence) of walking [58]. Second, our cognitive assessment tools have not been compared to conventional tools, such as the MMSE and MoCA, whichmight decrease the reliability of the overall experimentfrom a methodological perspective.
For gait measurement, we posit that this study
focuses on revealing the relationship between walking and cognitive functions in healthy participants. We
also aim to utilize the database to detect early cognitive risks for MCI and MCR patients from walking ina daily environment, which will be realized by further
studies. Thus, the IMU-based motion capture system ismore suitable for gait measurement in this study thanthe conventional optical motion capture system fromthe perspective of the daily monitoring, even though
the accuracy of the IMU-based system is inferior tothe conventional optical system [59]. Additionally, it
is important that the comparison between the IMUbased (e-skin MEVA) and conventional optical systems
is published on open repositories in order to assurecompatibility with other measurement systems. In thisstudy, we will publish the dataset of this comparison
simultaneous to the publication of the whole database,which will improve the utility of the database so that it
will be available for any device and environment [60].
For cognitive assessments, our preliminary experiments indicated a significant relationship between partsof walking and cognitive parameters (see Supplementary
Figure S3, Additional file 1) [42, 48, 60, 61]. Additionally,

Page 10 of 13

our web-based applications were created based on a wellestablished psychological paradigm that is sensitive toearly cognitive decline, even in healthy participants, inwhom functional localizations and relationship between
cognitive impairments have already been detected[62–70]. Thus, we believe that our cognitive assessment
application has adequate reliability to detect the relationships between the walking and cognitive functions [42,48, 62, 63]. Additionally, we will compare the results ofthe paper-based and our custom-made application in thisstudy. Thus, it will allow us to observe the compatibility,
superiority, and inferiority of our application comparing
with conventional paper-based tests.
For clinical implications, the database developed in thisstudy could provide standard values for both walking andcognitive functions, which would support the evaluationof psychomotor function, including the MCR syndrome.
Our research team also plans to conduct a cohort study
for participants, to define the risks of MCR and MCI.
This CoGait project database will be developed for use asa worldwide platform, for cross-sectional and longitudinal studies on cognition, walking, and frailty, as well asother studies in the field of geriatrics.
Abbreviations
AIC: Akaike’s information criterion; DS: Digit span; GDS-15: Geriatric Depression Scale 15; IMU: Inertial measurement unit; MCI: Mild cognitive impairment;
MCR: Motoric cognitive risk; MMSE: Mini-Mental State Examination; MoCA:
Montreal cognitive assessment; RANSAC: Random sample consensus; SRT:
Simple reaction time; TMT: Trail Making Test.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40814-​022-​01122-z.
Additional file 1: Preliminary results. We performed preliminary experiments before developing this study protocol. Figure S1. depicts theresult of a pilot observation of the relationship between the walking andcognitive parameters in a small population. Figure S2. shows the resultof comparisons of joint angles between MEVA and VICON (conventionalmotion capture system). Figure S3. presents the results of preliminary
experiments of cognitive performance using a custom-made web-based
application. Figure S4. shows the rule for the Japanese kana letters in the
TMT-B test.
Additional file 2. STROBE checklist. The STROBE checklist for crosssectional studies. Each cell in the line numbers column indicates theline numbers within the manuscript where each item of the checklist is
located.
Acknowledgements
The authors wish to acknowledge Dr. Amimori (CEO of Xenoma Inc.), Dr.
Amano (Chief Scientist at Xenoma Inc.), Mrs. Amimori (Engineering Specialistat Xenoma Inc.), and Mr. Hirasawa, Ms. Tatsuyama (Chief Designer at XenomaInc.), Mr. Ueda (Chief Engineers at Xenoma Inc.), and Dr. Sakata (Professor of
Dokkyo Medical University) for their help with information technology andmanagement.

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

Page 11 of 13

Authors’ contributions
TF, SI, YW, TK, and AA contributed to the study design. TF, SI, and YW wereinvolved in the preliminary experiments of gait measurements. TF, SI, and AA
were involved in the preliminary cognitive assessments. All authors criticallyrevised the report, commented on drafts of the manuscript, and approved thefinal report. The authors read and approved the final manuscript.

6.

Funding
This work was supported by the Strategic International Research Cooperative
Program (SICORP) and Japan Science and Technology Agency (JST) for the“Continuous health status monitoring of elderly people, using flexible skinpatch sensors” (TF and SI; Grant no. JPMJSC1812), and Xenoma Inc. (SI and
YW).

8.

Availability of data and materials
After publication, the database of walking and cognitive parameters developed in this study will be published as a public repository. Access permissions
will be managed by the Tokyo University of Technology and Xenoma Inc.

Declarations
Ethics approval and consent to participate
This research protocol was approved by the local ethics committee of theTokyo University of Technology (approval no. E20HS-038) and Dokkyo Medical
University (approval no. 2021-011), in accordance with the Declaration of
Helsinki. Prior to conducting our study, we will obtain participants’ written
informed consent and assure them of their right to withdraw their consent at
any time, for any reason.
Consent for publication
At registration, we will obtain participants’ consent for publication. When aparticipant withdraws consent, we will exclude their data from the published
data.

7.

9.

10.

11.

12.
13.
14.
15.

Competing interests
The authors declare that they have no competing interests. YW received
research funding from Xenoma Inc., associated with the Cooperative Researchand Development Agreement. SI received research funding from Xenoma Inc.,
as a donation to Dokkyo Medical University.

16.

Author details
1
R&D division, Xenoma Inc, Tokyo 143‑0013, Japan. 2 Division of Smart
Healthcare Research, Dokkyo Medical University, 880 Kita‑Kobayashi,Mibu, Tochigi 321‑0293, Japan. 3 Major of Physical Therapy, Departmentof Rehabilitation, School of Health Sciences, Tokyo University of Technology, Tokyo 144‑8535, Japan. 4 Major of Occupational Therapy, Departmentof Rehabilitation, School of Health Sciences, Tokyo University of Technology,
Tokyo 144‑8535, Japan.

18.

Received: 12 November 2021 Accepted: 15 July 2022

17.

19.

20.

21.
References
1. Verghese J. Motoric cognitive risk syndrome: next steps. Eur J Neurol.
2021;28:2467–8. https://​doi.​org/​10.​1111/​ene.​14949.
2. Camicioli R, Howieson D, Oken B, Sexton G, Kaye J. Motor slowing precedes cognitive impairment in the oldest old. Neurology. 1998;50:1496–8.
https://​doi.​org/​10.​1212/​wnl.​50.5.​1496.
3. Verghese J, Wang C, Lipton RB, Holtzer R. Motoric cognitive risk syndrome
and the risk of dementia. J. Gerontol. A Biol. Sci. Med J. 2013;68:412–8.
4. Verghese J, Annweiler C, Ayers E, Barzilai N, Beauchet O, Bennett DA, et al.
Motoric cognitive risk syndrome: multicountry prevalence and dementia
risk. Neurology. 2014;83:718–26.
5. Jedrziewski MK, Lee VM-Y, Trojanowski JQ. Physical activity and cognitive
health. Alzheimers Dement. 2007;3:98–108. https://​doi.​org/​10.​1016/j.​jalz.​2007.​01.​009.

22.
23.

24.

25.

Jedrziewski MK, Ewbank DC, Wang H, Trojanowski JQ. Exercise andcognition: results from the National Long Term Care Survey. Alzheimers
Dement. 2010;6:448–55. https://​doi.​org/​10.​1016/j.​jalz.​2010.​02.​004.
Jedrziewski MK, Ewbank DC, Wang H, Trojanowski JQ. The impact of exercise, cognitive activities, and socialization on cognitive function: resultsfrom the National Long-Term Care Survey. Am J Alzheimers Dis Other
Dement. 2014;29:372–8. https://​doi.​org/​10.​1177/​15333​17513​518646.
Petersen RC, Thomas RG, Grundman M, Bennett D, Doody R, Ferris S, et al.
Vitamin E and Donepezil for the treatment of mild cognitive impairment.
NEJM. 2005;352:2379–88. https://​doi.​org/​10.​1056/​NEJMo​a0501​51.
Rosenberg A, Mangialasche F, Ngandu T, Solomon A, Kivipelto M. Brief
reports multidomain interventions to prevent cognitive impairment,
Alzheimer’s disease, and dementia: from FINGER to World-Wide FINGERS.
J Prev Alzheimers Dis. 2020;7:29–36. https://​doi.​org/​10.​14283/​jpad.​2019.​
41.
Doi T, Mbbs JV, Makizako H, Tsutsumimoto K, Phd H, Nakakubo S, et al.
Effects of cognitive leisure activity on cognition in mild cognitive impairment: results of a randomized controlled trial. JAMDA. 2017;18:686–91.
https://​doi.​org/​10.​1016/j.​jamda.​2017.​02.​013.
Larson EB, Wang L, Bowen JD, McCormick WC, Teri L, Crane P, et al. Exercise is associated with reduced risk for incident dementia among persons
65 years of age and older. Ann Intern Med. 2006;144:73. https://​doi.​org/​
10.​7326/​0003-​4819-​144-2-​20060​1170-​00004.
Luchsinger JA, Tang M-X, Shea S, Mayeux R. Caloric intake and the risk of
Alzheimer disease. Arch Neurol. 2002;59:1258. https://​doi.​org/​10.​1001/​
archn​eur.​59.8.​1258.
Luchsinger JA, Tang M-X, Siddiqui M, Shea S, Mayeux R. Alcohol intake
and risk of dementia. J Am Geriatr Soc. 2004;52:540–6. https://​doi.​org/​10.​1111/j.​1532-​5415.​2004.​52159.x.
Scarmeas N, Stern Y, Mayeux R, Luchsinger JA. Mediterranean diet, Alzheimer disease, and vascular mediation. Arch Neurol. 2006;63:1709. https://​doi.​org/​10.​1001/​archn​eur.​63.​12.​noc60​109.
Scarmeas N, Stern Y, Tang M-X, Mayeux R, Luchsinger JA. Mediterranean
diet and risk for Alzheimer’s disease. Ann Neurol. 2006;59:912–21. https://​doi.​org/​10.​1002/​ana.​20854.
Verghese J, Ayers E, Barzilai N, Bennett DA, Buchman AS, Holtzer R, et al.
Motoric cognitive risk syndrome: multicenter incidence study. Neurology.
2014;83:2278–84. https://​doi.​org/​10.​1212/​WNL.​00000​00000​001084.
Norwitz NG, Saif N, Ariza IE, Isaacson RS. Precision nutrition for Alzheimer’s
prevention in ApoE4 carriers. Nutrients. 2021;13:1362. https://​doi.​org/​10.​3390/​nu130​41362.
Sabia S, Fayosse A, Dumurgier J, van Hees VT, Paquet C, Sommerlad A,
et al. Association of sleep duration in middle and old age with incidence
of dementia. Nat Commun. 2021;12:2289.
Matthews FE, Arthur A, Barnes LE, Bond J, Jagger C, Robinson L, et al. A
two-decade comparison of prevalence of dementia in individuals aged
65 years and older from three geographical areas of England: results ofthe cognitive function and ageing study I and II. Lancet. 2013;382:1405–
12. https://​doi.​org/​10.​1016/​S0140-​6736(13)​61570-6.
Jongki C, Jinse P, Byung-Inn L, Kyoung S, Sunmi Y, Hyoeun K, et al. The
correlation between cognition screening scores and Gait status fromthree-dimensional Gait analysis. J Clin Neurol. 2019;15:152–8. https://​doi.​
org/​10.​3988/​jcn.​2019.​15.2.​152.
Knapstad MK, Steihaug OM, Aaslund MK, Nakling A, Naterstad IF, Fladby
T, et al. Reduced walking speed in subjective and mild cognitive impairment: a cross-sectional study. J Geriatr Phys Ther. 2019;42:E122–8. https://​doi.​org/​10.​1519/​JPT.​00000​00000​00015​7qwqqw.
Maruyama T, Tada M, Toda H. Riding motion capture system using inertial
measurement units with contact constraints. Int J Autom Technol.
2019;13:506–16. https://​doi.​org/​10.​20965/​ijat.​2019.​p0506.
Breton A, Casey D, Arnaoutoglou NA. Cognitive tests for the detectionof mild cognitive impairment (MCI), the prodromal stage of dementia:
meta-analysis of diagnostic accuracy studies. Int J of Geriatr Psychiatry.
2019;34:233–42. https://​doi.​org/​10.​1002/​gps.​5016.
Kaufer DI, Williams CS, Braaten AJ, Gill K, Zimmerman S, Sloane PD. Cognitive screening for dementia and mild cognitive impairment in assisted
living: comparison of 3 tests. J Am Med Dir Assoc. 2008;9:586–93. https://​doi.​org/​10.​1016/j.​jamda.​2008.​05.​006.
Mitchell AJ. A meta-analysis of the accuracy of the mini-mental
state examination in the detection of dementia and mild cognitive

Fukuoka et al. Pilot and Feasibility Studies

26.

27.

28.
29.

30.

31.
32.

33.

34.
35.
36.

37.
38.
39.

40.
41.
42.
43.
44.
45.

46.

(2022) 8:154

impairment. J Psychiatr Res. 2009;43:411–31. https://​doi.​org/​10.​1016/j.​
jpsyc​hires.​2008.​04.​014.
Ruan Q, Zhang W, Ruan J, Chen J, Yu Z. Clinical and objective cognitive
measures for the diagnosis of cognitive frailty subtypes: a comparative
study. Front Psychol. 2021;12:603974. https://​doi.​org/​10.​3389/​fpsyg.​2021.​
603974.
Devenney KE, Sanders ML, Lawlor B, Olde Rikkert MGM, Schneider S. The
effects of an extensive exercise programme on the progression of Mild
Cognitive Impairment (MCI): study protocol for a randomised controlled
trial. BMC Geriatr. 2017;17:75. https://​doi.​org/​10.​1186/​s12877-​017-​0457-9.
CogEvoと認知機能 – CogEvo [Internet]. Available from: https://​cog-​evo.​jp/​cogni​tive-​funct​ion/. Cited 2022 Jun 6.
Ichii S, Nakamura T, Kawarabayashi T, Takatama M, Ohgami T, Ihara K, et al.
CogEvo, a cognitive function balancer, is a sensitive and easy psychiatric test battery for age-related cognitive decline. Geriatr GerontolInt.
2020;20:248–55. https://​doi.​org/​10.​1111/​ggi.​13847.
Faul F, Erdfelder E, Lang A-G, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical
sciences. Behav Res Methods. 2007;39:175–91. https://​doi.​org/​10.​3758/​
bf031​93146.
Faul F, Erdfelder E, Buchner A, Lang A-G. Statistical power analyses using
G*Power 3.1: tests for correlation and regression analyses. Behav Res
Methods. 2009;41:1149–60. https://​doi.​org/​10.​3758/​BRM.​41.4.​1149.
Granger CV, Hamilton BB, Linacre JM, Heinemann AW, Wright BD. Performance profiles of the functional independence measure. Am J Phys
Med Rehabil. 1993;72:84–9. https://​doi.​org/​10.​1097/​00002​060-​19930​
4000-​00005.
Marquez I, Garcia-Cifuentes E, Velandia FR, Iragorri A, Saavedra AM, Borda
MG, et al. Motoric cognitive risk syndrome: prevalence and cognitive
performance. A cross-sectional study. Lancet Regional Health Am.
2022;8:100162. https://​doi.​org/​10.​1016/j.​lana.​2021.​100162.
Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J,
Ames D, et al. Dementia prevention, intervention, and care. Lancet.
2017;390:2673–734. https://​doi.​org/​10.​1016/​S0140-​6736(17)​31363-6.
Perry J, Burnfield J. Gait analysis: normal and pathological function. 2nd
ed. Thorofare: Slack Inc; 2010.
Teufl W, Miezal M, Taetz B, Fröhlich M, Bleser G. Validity, test-retest reliability and long-term stability of magnetometer free inertial sensor based
3D joint kinematics. Sensors. 2018;18:1980. https://​doi.​org/​10.​3390/​s1807​
1980.
Wild K, Howieson D, Webbe F, Seelye A, Kaye J. Status of computerized
cognitive testing in aging: a systematic review. Alzheimers Dement.
2008;4:428–37. https://​doi.​org/​10.​1016/j.​jalz.​2008.​07.​003.
Makdissi M. Computerised cognitive assessment of concussed Australianrules footballers. Br J Sports Med. 2001;35:354–60. https://​doi.​org/​10.​1136/​bjsm.​35.5.​354.
Cysique LAJ, Maruff P, Darby D, Brew BJ. The assessment of cognitive
function in advanced HIV-1 infection and AIDS dementia complex using
a new computerised cognitive test battery. Arch Clin Neuropsychol.
2006;21:185–94. https://​doi.​org/​10.​1016/j.​acn.​2005.​07.​011.
Lasaponara S, Glicksohn J, Mauro F, Ben-Soussan TD. Contingent negative
variation and P3 modulations following mindful movement training. Prog
Brain Res. 2019;244:101–14. https://​doi.​org/​10.​1016/​bs.​pbr.​2018.​10.​017.
Baijot S, Cevallos C, Zarka D, Leroy A, Slama H, Colin C, et al. EEG Dynamics of a Go/Nogo task in children with ADHD. Brain Sci. 2017;7:167.
https://​doi.​org/​10.​3390/​brain​sci71​20167.
Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol.
1935;18:643–62. https://​doi.​org/​10.​1037/​h0054​651.
Shen C, Jiang Q, Luo Y, Long J, Tai X, Liu S. Stroop interference in children
with developmental dyslexia. Medicine. 2021;100:e26464. https://​doi.​org/​
10.​1097/​MD.​00000​00000​026464.
Craik FIM. Age differences in recognition memory. Q J Exp Psychol.
1971;23:316–23. https://​doi.​org/​10.​1080/​14640​74690​84018​27.
Grässler B, Herold F, Dordevic M, Gujar TA, Darius S, Böckelmann I, et al.
Multimodal measurement approach to identify individuals with mild
cognitive impairment: study protocol for a cross-sectional trial. BMJ
Open. 2021;11:46879. https://​doi.​org/​10.​1136/​bmjop​en-​2020-​046879.
Toyokura M, Sawatari M, Nishimura Y, Ishida A. Nondominant hand
performance of the Japanese trail making test and its mirror version. Arch
Phys Med Rehabil. 2003;84:691–3. https://​doi.​org/​10.​1016/​s0003-​9993(02)​04904-3.

Page 12 of 13

47. David Wechsler. WMS-III: Wechsler Memory scale administration and scoring manual. Psychological Corporation; place of pub: 1997.
48. Tombaugh T. Trail Making Test A and B: Normative data stratified by age
and education. Arch Clin Neuropsychol. 2004;19:203–14. https://​doi.​org/​
10.​1016/​S0887-​6177(03)​00039-8.
49. Cherry B. Better preservation of memory span relative to supraspan
immediate recall in Alzheimer’s disease. Neuropsychologia. 2002;40:846–
52. https://​doi.​org/​10.​1016/​S0028-​3932(01)​00173-7.
50. Belleville S, Peretz I, Malenfant D. Examination of the working memory
components in normal aging and in dementia of the Alzheimer type.
Neuropsychologia. 1996;34:195–207. https://​doi.​org/​10.​1016/​0028-​
3932(95)​00097-6.
51. Kumar TK. Multicollinearity in regression analysis. Rev Econ Stat.
1975;57:365–6.
52. Akaike H. Information theory and an extension of the maximum likelihood principle. In: 2nd International Symposium on Information Theory:
Akademiai Kiado; 1973. p. 267–81.
53. Killeen T, Easthope CS, Demkó L, Filli L, Lőrincz L, Linnebank M, et al.
Minimum toe clearance: probing the neural control of locomotion. Sci
Rep. 2017;7:1922. https://​doi.​org/​10.​1038/​s41598-​017-​02189-y.
54. Springer S, Giladi N, Peretz C, Yogev G, Simon ES, Hausdorff JM. Dualtasking effects on gait variability: the role of aging, falls, and executivefunction. Mov Disord. 2006;21:950–7. https://​doi.​org/​10.​1002/​mds.​20848.
55. Doi T, Makizako H, Shimada H, Park H, Tsutsumimoto K, Uemura K, et al.
Brain activation during dual-task walking and executive function amongolder adults with mild cognitive impairment: a fNIRS study. Aging Clin
Exp Res. 2013;25:539–44. https://​doi.​org/​10.​1007/​s40520-​013-​0119-5.
56. Teo WP, Rantalainen T, Nuzum N, Valente L, Macpherson H. Alteredprefrontal cortex responses in older adults with subjective memory complaints and dementia during dual-task gait: an fNIRS study. Eur J Neurosci.
2021;53:1324–33. https://​doi.​org/​10.​1111/​ejn.​14989.
57. Reuter-Lorenz PA, Cappell KA. Neurocognitive Aging and the compensation hypothesis. Curr Dir Psychol Sci. 2008;17:177–82. https://​doi.​org/​10.​1111/j.​1467-​8721.​2008.​00570.x.
58. Majumder S, Deen MJ. Wearable IMU-based system for real-time monitoring of lower-limb joints. IEEE Sensors J. 2021;21:8267–75. https://​doi.​org/​
10.​1109/​JSEN.​2020.​30448​00.
59. Poitras I, Dupuis F, Bielmann M, Campeau-Lecours A, Mercier C, Bouyer L,
et al. Validity and reliability of wearable Sensors for joint angle estimation:a systematic review. Sensors. 2019;19:1555. https://​doi.​org/​10.​3390/​s1907​
1555.
60. Miller JO, Low K. Motor processes in simple, go/no-go, and choice
reaction time tasks: a psychophysiological analysis. J Exp Psychol Hum
Percept Perform. 2001;27:266–89. https://​doi.​org/​10.​1037/​0096-​1523.​
27.2.​266.
61. Cho YS, Jang SH, Cho JS, Kim MJ, Lee HD, Lee SY, et al. Evaluation ofvalidity and reliability of inertial measurement unit-based gait analysis
systems. Ann Rehabil Med. 2018;42:872–83. https://​doi.​org/​10.​5535/​arm.​2018.​42.6.​872.
62. Monaco M, Costa A, Caltagirone C, Carlesimo GA. Forward and backward
span for verbal and visuo-spatial data: standardization and normative
data from an Italian adult population. Neurol Sci. 2013;34:749–54. https://​doi.​org/​10.​1007/​s10072-​012-​1130-x.
63. Tsai C-K, Kao T-W, Lee J-T, Wang C-C, Chou C-H, Liang C-S, et al. Globalcognitive health metrics: a novel approach for assessing cognition
impairment in adult population. PLoS One. 2018;13:e0197691.
64. Kumar A, Migliore S, Sollievo C, Sofferenza D, Kandimalla R, Guarino A,
et al. Executive functions in Alzheimer disease: a systematic review. Front
Aging Neurosci. 2019;1:437. https://​doi.​org/​10.​3389/​fnagi.​2018.​00437.
65. O’Callaghan C, Naismith SL, Hodges JR, Lewis SJG, Hornberger M.
Fronto-striatal atrophy correlates of inhibitory dysfunction in Parkinson’s
disease versus behavioural variant frontotemporal dementia. Cortex.
2013;49:1833–43. https://​doi.​org/​10.​1016/j.​cortex.​2012.​12.​003.
66. Specka M, Weimar C, Stang A, Jöckel K-H, Scherbaum N, Hoffmann SS,
et al. Trail making test normative data for the German older population.
Arch Clin Neuropsychol. 2021;00:1–13. https://​doi.​org/​10.​1093/​arclin/​
acab0​27.
67. Jäncke L, Gerson Unschuld P, Faraza S, Waldenmaier J, Dyrba M, Wolf D,
et al. Dorsolateral prefrontal functional connectivity predicts working
memory training gains. Front Aging Neurosci. 2021;13:592261. https://​doi.​org/​10.​3389/​fnagi.​2021.​592261.

Fukuoka et al. Pilot and Feasibility Studies(2022) 8:154

Page 13 of 13

68. Menon RS, Luknowsky DC, Gati JS. Mental chronometry using latencyresolved functional MRI. Neurobiology. 1998;95:10902–7. https://​doi.​org/​
10.​1073/​pnas.​95.​18.​10902.
69. Motes MA, Rao NK, Shokri-Kojori E, Chiang H-S, Kraut MA, Hart J. Triallevel regressor modulation for functional magnetic resonance imaging
designs requiring strict periodicity of stimulus presentations: illustratedusing a go/no-go task. Magn Reason Insights. 2017;10:1–7. https://​doi.​
org/​10.​1177/​11786​23X17​746693.
70. Nordlund A, Rolstad S, Klang O, Edman A, Hansen S, Wallin A. Two-year
outcome of MCI subtypes and aetiologies in the Goteborg MCI study.
J Neurol Neurosurg Psychiatry. 2010;81:541–6. https://​doi.​org/​10.​1136/​
jnnp.​2008.​171066.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ready to submit your research ? Choose BMC and benefit from:• fast, convenient online submission• thorough peer review by experienced researchers in your field• rapid publication on acceptance• support for research data, including large and complex data types• gold Open Access which fosters wider collaboration and increased citations• maximum visibility for your research: over 100M website views per year
At BMC, research is always in progress.
Learn more biomedcentral.com/submissions