jpad journal

AND option

OR option



F. Kalligerou1, G. Paraskevas2, I. Zalonis1, M.H. Kosmidis3, M. Yannakoulia4, E. Dardiotis5, G. Hadjigeorgiou6, P. Sakka7, N. Scarmeas1


1. 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens, Medical School, Greece; 2. 2nd Department of Neurology, National and Kapodistrian University of Athens, »Attikon » General University Hospital, Athens Greece; 3. Laboratory of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece; 4. Department of Nutrition and Diatetics, Harokopio University, Athens, Greece; 5. School of Medicine, University of Thessaly, Larissa, Greece; 6. Department of Neurology, Medical School, University of Cyprus, Cyprus; 7. Athens Association of Alzheimer’s disease and Related Disorders, Athens, Greece

Corresponding Author: Nikolaos Scarmeas, M.D., M.Sc., Ph.D. Professor of Neurology, National and Kapodistrian University of Athens, Aiginition Hospital, Leof. Vasilissis Sofias 72, Athens 115 28, Phone: + 30 2107289310, Email:

J Prev Alz Dis 2022;
Published online August 29, 2022,



BACKGROUND: Slow gait speed has recently emerged as a potential prodromal feature of cognitive decline and dementia. Besides objective measurements, subjective motor function (SMF) difficulties might be present prior to the manifestation of gait disorders.
Objectives: To examine the association of walking time and the presence of SMF with future cognitive decline in cognitively normal individuals.
Design: Longitudinal study.
Settings: Athens and Larissa, Greece.
Participants: 931 cognitively normal individuals over the age of 64 with longitudinal follow-up from the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD).
Measurements: We used a simple chronometer for recording objective walking time (OWT) and SMF was assessed using a self-reported physical functioning questionnaire. Generalized estimating equations (GEE) models were deployed to explore the associations between baseline OWT and SMF difficulties and the rate of change of performance scores on individual cognitive domains over time. Models were adjusted for age, years of education and sex.
Results: Each additional second of OWT was associated with 1.1% of a standard deviation more decline per year in the composite z-score, 1.6% in the memory z-score, 1.1% in the executive z-score and 1.8% in the attention-speed z-score. The presence of SMF difficulties was not associated with differential rates of decline in any cognitive domain.
Conclusion: Gait speed can be indicative of future cognitive decline adding credence to the notion that gait speed might serve as a simple and easily accessible clinical tool to identify a larger pool of at risk individuals and improve the detection of prodromal dementia.

Key words: Gait speed, subjective motor function, cognition, dementia.



Early treatment and prevention may be the key in the effort to reduce the global burden of dementia (1). Detection of people at risk is essential to appropriately target early treatments, but the current available screening tools are too invasive and costly to be used on a population level or on everyday clinical practice. Therefore, early indicators of dementia risk are needed in order to accurately capture the pathological trajectory as early as possible. To deal with this challenge, there is an increasing effort to discover inexpensive, non-invasive and reliable markers of prodromal dementia (2).
During the last two decades, data deriving from large epidemiological studies demonstrate that changes in gait, particularly slowing gait, may be present at the early stages of dementia (3, 4). Furthermore, several studies (5-9) using several methods, have shown that motor slowing precedes and may predict the onset of cognitive impairment. However, it is unclear which is the best way to record gait speed measurements in clinical settings, which is totally different from the context in which we operate when conducting research studies. It is important to prove the use of easy and accessible tools to evaluate motor performance in order to be able to use gait speed as a motor indicator in everyday clinical practice. Taking in to consideration the above mentioned restrictions, in our study we used a simple chronometer to record gait speed for our participants.
Cognitive performance in dementia is believed to follow a continuum starting with the preclinical stage, in which biomarkers such as amyloid β and tau protein increase followed by mild cognitive impairment (MCI) and finally dementia (10). In the effort to identify clinical indicators that could detect changes in the earliest possible stages of neurodegeneration, the term subjective cognitive decline (SCD) (11) has been proposed as an early marker of dementia. SCD is characterized by a very early and subtle cognitive decline, prior to the appearance of objective cognitive dysfunction, and has been linked to future cognitive impairment (12).
In the same direction, prior to the appearance of objective gait difficulties, subjective motor function (SMF) changes, only perceived by individuals themselves, may be present in an early non symptomatic stage of the disease. Using a simple questionnaire regarding an individual’s motor ability outdoors and indoors, we wanted to investigate what type of self-reported motor difficulties, similar to the concept of SCD, might have a predictive value for future cognitive decline. In our study, subjective motor symptoms were associated with a higher probability of prodromal Parkinson’s disease in older adults. To our knowledge, SMF in the form of self-reported motor difficulties has never been explored so far in regard to cognitive performance.
In this paper we examine: 1) the association of walking time with future cognitive decline in cognitively normal (CN) individuals 2) the presence of SMF as an indicator of future cognitive decline in CN individuals.



Study Population

This work is part of the Hellenic Longitudinal Investigation of Ageing and Diet study (HELIAD). HELIAD is a population-based, multidisciplinary, collaborative study designed to estimate, in the Greek population over the age of 64 years, prevalence and incidence of MCI, Alzheimer’s disease (AD), other forms of dementia and other neuropsychiatric conditions of aging and to investigate associations between nutrition and cognitive dysfunction or age-related neuropsychiatric diseases. More information regarding the participants΄ characteristics and study methodology can be found in previously published articles (13-18). The participants are reevaluated at intervals of approximately 3 years, repeating the baseline examination and consensus diagnosis at each follow-up. Two evaluations per person have been completed so far. CN participants at baseline with available cognitive follow-up were included in the analyses of the present study.


Participation included a structured interview, a full neurological examination and a comprehensive neuropsychological assessment. Sociodemographic information including a detailed medical and family history was also collected. Participants responded to validated questionnaires about their sleeping habits, daily activities, physical exercise, and dietary habits. They were also screened for anxiety and depression (Geriatric Depression scale (19)). Information collected from all evaluations was reviewed at expert consensus meetings, including the neurologists who examined the participants clinically (junior neurologists and ED, GMH, NS: senior neurologists) and the neuropsychologists (psychometricians and MHK: senior neuropsychologist). The diagnosis of dementia was based on DSM-IV-TR criteria (20). The diagnosis of probable or possible AD was made according to the National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer Disease and Related Disorders Association (NINCDS/ADRDA) criteria (21). Using standard criteria [22], MCI and MCI subtypes (memory, executive-speed, visual spatial, language and combinations) were assigned when participants had subjective memory complaints and objective impairment in at least one cognitive domain, but preserved instrumental activities of daily living (23).

Neuropsychological evaluation

Trained psychometricians administered a battery of neuropsychological tests assessing five cognitive domains: memory, language, attention-speed, executive functioning and visuospatial perception. We decided on grouping of neuropsychological tests based on a priori neuropsychological knowledge of particular cognitive functions that each test reflects.
Global cognitive performance: Mini-Mental State Exam (24).
Nonverbal and Verbal Memory: 1) Medical College of Georgia Complex Figure Test (MCG) (25); 2) Greek Verbal Learning Test (26).
Language: 1) semantic and phonological verbal fluency (27); 2) subtests of the Greek version of the Boston Diagnostic Aphasia Examination short form, namely, the Boston Naming Test-short form; 3) selected items from the Complex Ideational Material Subtest, to assess verbal comprehension and repetition of words and phrases (28).
Visuoperceptual Ability: 1) Judgment of Line Orientation (29, 30) abbreviated form; 2) MCG Complex Figure Test copy condition; 3) Clock Drawing Test (31).
Attention and Information Processing Speed: Trail Making Test part A (TMT) (32).
Executive Functioning: 1) TMT-Part B, Verbal fluency, Anomalous Sentence Repetition, Graphical Sequence Test, Motor Programming (25); 2) a gross estimate of intellectual level (a Greek multiple-choice vocabulary test) (33).


Participants’ scores on each cognitive test were converted into z-scores using mean and SD values derived from the CN group of the total study sample. Subsequently, these individual neuropsychological test scores were used to produce an average domain score for memory, language, attention-speed, executive and visual-spatial functioning. These domain scores were then averaged in order to calculate a global neuropsychological composite score. If there was missing data in the z-score of a test or cognitive domain, the composite z-score was calculated as an average of the other tests or cognitive domain scores. A higher score indicated better performance.


1. Gait speed was assessed with a 4-meter measured walk. After a demonstration, we instructed participants to walk a 4-meter distance at their normal walking speed recording time using a conventional timer. We used a static start with timing commencing at the start. No verbal or other encouragement was given during the procedure. Each participant had two timed trials to complete the 4-meter distance. From these two trials each participant was assigned an average objective walking time (OWT). A high degree of reliability was found between the two measurements of walking time. The average measure ICC was 0.997 with a 95% confidence interval from 0.996 to 0.997 (F(1889,1889)=306.815, p<0.001).
2. SMF (13) was assessed using a self-reported physical functioning questionnaire which includes twelve items concerning motor ability. The response options tο ten of the twelve items included in the questionnaire were in a no/yes format. The response options to two items were formed in a dichotomous way in order to be compatible with the rest of the questionnaire (Supplementary Table 1). One item (This last month did you ever use a wheelchair in order to move either within the house or outside?) had no positive answers in the CN population and was excluded from the analyses. Based on the distribution of responses, two final groups of SMF (dichotomous form) were formed for the assessment: the first group included those who reported no SMF complaints (responded no to all eleven items) and the second those who reported at least one SMF complaint (responded yes to any one or more than one of the eleven items). Regarding internal consistency of the 12 item SMF scale, the value for Cronbach’s Alpha was 0.73.

Statistical analysis

Nominally significant α values were defined as p ≤ 0.05. Differences between groups were tested through analysis of variance for continuous variables and Pearson chi-square for categorical variables. For the purposes of comparison of participants’ baseline socio-demographic characteristics, we ranked the baseline OWT and composite cognitive z-score, according to their relative frequencies, into two equal groups (low-high).
We used generalized estimating equations (GEE) models to test whether baseline walking time was associated with differential rates of cognitive change over time. GEE takes into account the multiple visits per subject and the fact that the characteristics of the same individual over time are likely to be correlated. In the GEE models the z-scores were treated as the dependent variable and OWT, time (in years from baseline assessment), as well as OWT x time interaction were the main predictors. In these GEE models, exchangeable (compound symmetry) was defined as the working correlation matrix. A significant interaction term indicates differential rates of change in cognitive function, as a function of baseline OWT. Same separate GEE models were tested using OWT as a dichotomous variable, divided into two equal groups.
Further analyses were performed for the individual items included in the SMF assessment and their associations with cognitive trajectories. Specifically, we constructed GEE models including all eleven SMF subcomponents, as well as SMF subcomponent x time interaction terms as the main predictors, and cognitive z-scores as the outcome. A significant interaction term indicates differential rates of change in cognitive function, as a function of baseline SMF subcomponent. Additional GEE models were tested including the SMF variable in its dichotomous form with SMF dichotomous x time interaction term as the main predictor.
We finally computed a GEE model including both objective and subjective measures: all eleven SMF items and OWT as a continuous variable. All GEE analyses were adjusted for age, sex and education. Age and education were treated as continuous variables, whereas sex was treated as a categorical variable (male, female). We opted to include the confounders mentioned above because of their influence on both cognition and motor function.
To better interpret the association of OWT with differential rates of cognitive change, we calculated an adjusted (for sex, years of education, OWT) GEE model, in order to estimate the effect of cognitive aging on global cognitive score in the entire HELIAD study sample.

Supplementary analysis

Additional sensitivity analyses were performed using two linear mixed models (LMMs) to test whether baseline OWT was associated with differential rates of cognitive change over time. Model 1 uses the correlation structure method to account for within-participant neighborhood effects and Model 2 is a random effect model including both a random intercept (considering that different participants might have different baseline Z scores) as well as a random slope (considering that different participants might have different rates of change of Z scores over time). In both models, appropriate covariance types were specified using the Akaike information criterion (AIC).



In total, 1088 participants of the HELIAD study had completed both evaluations and from those 945 were CN at baseline. We excluded 14 individuals with missing information related to their gait speed measurements. Therefore, all analyses were conducted using a sample of 931 individuals.

Clinical and socio-demographic characteristics at baseline

The participants were followed longitudinally over time with a mean follow-up of 3.06 years (SD=0.84). Participants’ baseline clinical and socio-demographic characteristics based on OWT measurements (median=3.85sec), as well as based on global cognitive performance (median z-score=0.116) can be found in Tables 1 and 2, respectively. Those who had a higher OWT were older, less educated, had a worse baseline cognitive performance and reported more SMF difficulties compared to those who walked faster. Compared to males, more females tended to have higher OWT. Participants with slower OWT responded positively more frequently to seven of the eleven SMF items (Do you have difficulty walking in the house?, Do you have any difficulty walking outside?, Does anyone helps you walk outdoors?, Do you often use a stick as a help for walking?, Do you drag tour feet or make small steps when walking?, Is your balance poor?, How many meters can you walk without rest?) (Table 1).
Regarding cognitive performance in the baseline visit, individuals with higher composite z-scores were younger, better educated, had a lower OWT at baseline and reported less SMF difficulties compared to the other group while there was no difference for sex. Those with higher composite z-scores responded positively less frequently to six of the eleven SMF items (Do you have difficulty walking in the house?, Do you have any difficulty walking outside?, Do you often use a stick as a help for walking?, Do you drag tour feet or make small steps when walking?, Is your balance poor?, How many meters can you walk without rest?) (Table 2).

Table 1. Participants’ baseline clinical and socio-demographic characteristics by groups of OWT at baseline

OWT: objective walking time, ZCO: composite z-score, SMF: subjective motor function

Table 2. Participants’ baseline clinical and socio-demographic characteristics by cognitive performance at baseline

OWT: objective walking time, ZCO: composite z-score, SMF: subjective motor function


Motor function and trajectories of cognitive scores

Each additional second of OWT was associated with 1.1% of a standard deviation more decline per year (Table 3, Figure 1). Associations between OWT measurements and the rate of change of individual cognitive domain scores revealed that each additional second of OWT was associated with 1.6% of a standard deviation more decline per year in the memory z-score, 1.1% in the executive z-score and 1.8% in the attention-speed z-score. No associations were observed between OWT and visuospatial perception or language z-scores. When using OWT as a dichotomous variable, compared to the low OWT group, the high OWT one was associated with 2.4% of a standard deviation more decline per year in the composite z-score and 3.4% of a standard deviation more decline per year in the memory z-score (Table 3).

Table 3. Adjusted GEE models. Results from the associations between baseline OWT and differential rates of change of cognitive z-scores

OWT: objective walking time; Models adjusted for age, sex, education; * Further adjusted for all eleven SMF items

Figure 1. GEE predicted composite z-scores (y-axis) over the course of follow-up in years (x-axis) separately for the low and high OWT group

The figure is derived from an adjusted for age, sex and education model.


Regarding SMF decline, the presence of SMF difficulties (dichotomous form) was not associated with difference in decline in any cognitive domain. Considering all SFM questions simultaneously, a positive answer to the item “is there anyone who helps you walk in the house” was associated with 31.9% of a standard deviation more decline per year. All of the other items from the SMF evaluation were not associated with global cognition (Table 4).
In models considering both subjective and objective measurements, OWT was associated with 1.5% of a standard deviation more decline per year in the composite z-score, 2.1% in the memory z-score, 2.3% in the executive z-score and 1.7% in the attention-speed z-score (Table 3).
In order to better interpret the association between motoric measures and cognition we translated changes in z scores to changes in cognitive aging using the β coefficients of the chronological age predictor (together with time and sex) in a GEE model with global cognitive z scores as the outcome. Using the entire HELIAD study sample (n=1853), there was a 6.1% of a standard deviation reduction in global cognitive z score for each additional year of chronological aging [β=-0.061(-0.066 – -0.056); p<0.001]. For example, a two second increase in the OWT in three years [1.1% per year x 2 second increase in OWT x 3 years=6.6%], corresponds to a decline associated with 1 year of cognitive aging.

Table 4. Adjusted GEE models. Results from the associations between SMF items and differential rates of change of cognitive z-scores

SMF: subjective motor function; Answers to the items are in a no/yes format except for: * more than 1000m/less than 1000m, ** moves without any help/ moves with any kind of help


Supplementary analysis

When using LMMs to perform sensitivity analysis, the results were similar to our original GEE model. Each additional second of OWT was associated with 1.1% of a standard deviation more decline per year in the global cognitive z-score in both models (Model 1 95% CI -0.017 – -0.005, p ≤ 0.001, Model 2 95% CI -0.018 – -0.005, p = 0.001).



The present findings confirm the relationship of slower gait speed with more rapid cognitive decline in a population of cognitively intact elderly individuals as OWT was associated with both global and domain-specific cognitive functioning trajectories. Overall, the presence of SMF difficulties was not convincingly linked to future cognitive decline.
Gait is a complex task requiring the coordinated integration of widespread brain regions and the intact functioning and participation of multiple cognitive domains (34). In longitudinal studies, slow gait speed was able to predict the onset of cognitive decline in individuals with and without cognitive deficits (5). Moreover, gait speed has been shown to predict future onset of dementia and MCI (6). It has been even proposed that changes in gait speed precede the onset of poor cognitive status (7) as evidence indicates that gait speed decline may occur as early as 12 years before the onset of MCI (8). In the effort to capture as much information as possible, several alternative forms for recording gait speed have been used in previous studies. Computerized tools which cover a wide range of quantifiable parameters have been used in research settings, thought their use is rather challenging on everyday clinical practice (35, 36). Alternative ways, seen in other studies, include more demanding commands from a simple “walk on your normal pace” which require complex neuropsychological organization and intact cognitive resources (8, 37). In our study we were able to demonstrate an association between gait speed and future cognitive decline using a simple chronometer which can be easily incorporated in everyday clinical practice.
Gait speed is a commonly used outcome across different types of studies and patient populations. The methodology used to assess gait speed as well as the description in testing procedures, however, is quite variable. Certainly, there is a need for a standardized protocol to record gait speed and such recommendations have been made in the effort to establish the optimal design of gait speed assessments. In our study, we used the 4-meter distance, preferred in several studies in the past (38) and presented a detailed description of the testing conditions.
Further supporting the predictive value of gait performance, additional parameters of gait have also been linked to future cognitive decline. There is evidence that gait parameters present differential associations with various cognitive domains and predict different cognitive trajectories (39), implying that specific gait parameters may be linked to different types of dementia (40). The fact that no association was observed for visuospatial perception and language in our study could be attributed to the fact that we recorded no detailed gait parameters except than simple gait speed.
Cognitive and motor decline share a number of pathophysiological pathways (41). The observed association between gait and cognition is further supported by neuroimaging findings showing that slow gait speed has been found to be associated with higher amyloid β burden in key brain areas in asymptomatic people at risk for AD, suggesting that slower gait speed may represent an early sign of the underlying neuropathology (42). Besides brain pathology, in older people, gait speed is the result of the interplay between several factors such as demographic factors, lifestyle-related conditions and medications that perhaps need to be taken into account when slow gait speed is used as a predictor of cognitive decline and incident dementia. Although gait speed is a comprehensive measure of mobility, untangling the role played by each of the above mentioned factors remains challenging. Age, sex and education were used as cofounders in our analysis in an effort to eliminate possible interactions between gait speed and the above mentioned factors.
Our results are in general agreement with previous studies (43) supporting the notion that slow gait speed is predictive of decline in cognitive function. It is notable that we demonstrated this association in a group of Greek participants, using a simple chronometer for recording OWT without relying on expensive and complicated computerized tools, difficult to incorporate in everyday clinical settings.
We found associations with most cognitive domains but not with visuospatial perception and language. This may reflect differences in the neuropsychological tests used, or the way in which they were combined to form the cognitive domain scores used in the analyses. In most previous studies (5, 8, 44), cognition was assessed using simple tests of global cognitive performance. In our study, we performed a very extensive neuropsychological evaluation using a very detailed battery of tests for each cognitive domain.
In the same way that SCD represents a preclinical marker of cognitive impairment, we hypothesized that SMF difficulties might also represent a more sensitive measure in the detection of motor impairment, prior to the manifestation of gait disorders. We opted to investigate whether minimal modifications of gait, not detected by objective measurements but only perceived by individuals themselves, may be indicative of future cognitive decline. One item from the SMF questionnaire, “the need for help to walk indoors” was associated with future decline in most cognitive domains while three others “does anyone helps you walk outdoors?”, “is your balance poor?” and “do you lie down or get up from your bed and sit down or get up from a chair without help?” were related to individual cognitive domains (data not shown). However, the very low frequencies of the positive answers to those four questions limit confidence in the findings. Generally, most SMF difficulties recorded a very low frequency in the CN population of our study. We tried to overcome this challenge by examining the presence of any kind of SMF complaints versus none but not significant associations with cognitive changes were noted.
However, the fact that the relationship between OWT and cognitive performance remained significant (both in terms of significance but also in terms of coefficient magnitude) even after adjusting for all SMF items, demonstrates that most likely SMF captures different than OWT aspects and dimensions of motor ability. The use of these eleven items when exploring SMF did not produce fruitful results, perhaps because these specific items were not sensitive enough to detect preclinical changes. It is also likely that a longer duration of follow up might be needed in order to better investigate the use of SMF as a very early and subtle predictor of motor decline. Similar to the concept of SCD, where many different ways of recording them have been proposed in the literature, further investigation of SMF might provide new insights in the future.
As mentioned above, a two second increase in the OWT in three years [1.1% per year x 2 second increase in AWT x 3 years=6.6%], corresponds to a decline associated with 1 year of cognitive aging. Taking in to consideration that our analysis included only participants with normal cognition and the long preclinical trajectory of dementia, we believe that the long term cognitive burden is noticeable and cumulatively can result to objective future cognitive decline. More specifically, considering the report that gait speed decline may occur as early as 12 years before the onset of MCI, a steady increase in gait speed can lead to up to 4 years of cognitive aging and the progression to objectively measured cognitive decline.
There are potential limitations to the present findings. The dichotomous format of the SMF questionnaire may have restricted the amount of information that could be gleaned from the participants in relation to their level of motor ability. Furthermore, SMF questions did not come from a questionnaire with demonstrated psychometric properties in our population. Additionally, our study had a relatively short duration of follow-up, while the preclinical and prodromal stage of dementia begins decades before the onset of clinical symptoms [10]. Although we adjusted our models for important confounders, the effect of other factors not assessed in this study (i.e. residual confounding) cannot be excluded.
Confidence in our findings is strengthened by many factors. The HELIAD study includes a large number of participants, selected through random sampling, from both rural and urban areas collating a substantial and representative pool of subjects. One of the strongest features of our study was the thorough neuropsychological evaluation that each participant underwent in order to assess their cognitive status. Diagnoses were assigned using published criteria at expert consensus meetings including the neurologists and neuropsychologists who examined the participants and senior neurologists and neuropsychologists specialized in dementia. We administered a quite long list of SMF questions addressing many different aspects of mobility including balance, speed and gait. We were able to simultaneously examine associations of objective and subjective estimates of motor function adjusting for each other. We excluded not only demented subjects but also participants with MCI, considering in our analyses only those with intact cognitive function.
In this study we opted to investigate the predictive value of objective and subjective measurements of motor function in relation to cognitive performance. As shown in previous papers, gait speed can be indicative of cognitive decline, which is the first step for cognitive impairment, adding credence to the notion that gait speed may improve the detection of prodromal dementia. The potential applicability of such a measure, in both clinical and research settings, points at the importance of expanding our knowledge about the common underlying mechanisms of both cognitive and motor decline.


Conflictc of interest: The author have no conflict of interest to report.

Funding: This study was supported by the grants: IIRG-09-133014 from the Alzheimer’s Association, 189 10276/8/9/2011 from the ESPA-EU program Excellence Grant (ARISTEIA) and the ΔΥ2β/οικ.51657/14.4.2009 of the Ministry for Health and Social Solidarity (Greece). The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Ethical standard: The HELIAD study has been approved by the National and Kapodistrian University of Athens Ethics Committee (256/10-05-2021). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.





1. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C (2014) Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol 13, 788-794. DOI: 10.1016/s1474-4422(14)70136-x.
2. Montero-Odasso M, Almeida QJ, Bherer L, Burhan AM, Camicioli R, Doyon J, Fraser S, Muir-Hunter S, Li KZH, Liu-Ambrose T, McIlroy W, Middleton L, Morais JA, Sakurai R, Speechley M, Vasudev A, Beauchet O, Hausdorff JM, Rosano C, Studenski S, Verghese J (2019) Consensus on Shared Measures of Mobility and Cognition: From the Canadian Consortium on Neurodegeneration in Aging (CCNA). J Gerontol A Biol Sci Med Sci 74, 897-909. DOI: 10.1093/gerona/gly148.
3. Duff K, Mold JW, Roberts MM (2008) Walking speed and global cognition: results from the OKLAHOMA Study. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 15, 31-39. DOI: 10.1080/13825580701531904.
4. Verghese J, Lipton RB, Hall CB, Kuslansky G, Katz MJ, Buschke H (2002) Abnormality of gait as a predictor of non-Alzheimer’s dementia. N Engl J Med 347, 1761-1768. DOI: 10.1056/NEJMoa020441.
5. Grande G, Triolo F, Nuara A, Welmer AK, Fratiglioni L, Vetrano DL (2019) Measuring gait speed to better identify prodromal dementia. Exp Gerontol 124, 110625. DOI: 10.1016/j.exger.2019.05.014.
6. Dumurgier J, Artaud F, Touraine C, Rouaud O, Tavernier B, Dufouil C, Singh-Manoux A, Tzourio C, Elbaz A (2017) Gait Speed and Decline in Gait Speed as Predictors of Incident Dementia. J Gerontol A Biol Sci Med Sci 72, 655-661. DOI: 10.1093/gerona/glw110.
7. Mielke MM, Roberts RO, Savica R, Cha R, Drubach DI, Christianson T, Pankratz VS, Geda YE, Machulda MM, Ivnik RJ, Knopman DS, Boeve BF, Rocca WA, Petersen RC (2013) Assessing the temporal relationship between cognition and gait: slow gait predicts cognitive decline in the Mayo Clinic Study of Aging. J Gerontol A Biol Sci Med Sci 68, 929-937. DOI: 10.1093/gerona/gls256.
8. Buracchio T, Dodge HH, Howieson D, Wasserman D, Kaye J (2010) The trajectory of gait speed preceding mild cognitive impairment. Arch Neurol 67, 980-986. DOI: 10.1001/archneurol.2010.159.
9. Hoogendijk EO, Rijnhart JJM, Skoog J, Robitaille A, van den Hout A, Ferrucci L, Huisman M, Skoog I, Piccinin AM, Hofer SM, Muniz Terrera G (2020) Gait speed as predictor of transition into cognitive impairment: Findings from three longitudinal studies on aging. Exp Gerontol 129, 110783. DOI: 10.1016/j.exger.2019.110783.
10. Jack CR, Jr., Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, Shaw LM, Vemuri P, Wiste HJ, Weigand SD, Lesnick TG, Pankratz VS, Donohue MC, Trojanowski JQ (2013) Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 12, 207-216. DOI: 10.1016/s1474-4422(12)70291-0.
11. Jessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Chételat G, Dubois B, Dufouil C, Ellis KA, van der Flier WM, Glodzik L, van Harten AC, de Leon MJ, McHugh P, Mielke MM, Molinuevo JL, Mosconi L, Osorio RS, Perrotin A, Petersen RC, Rabin LA, Rami L, Reisberg B, Rentz DM, Sachdev PS, de la Sayette V, Saykin AJ, Scheltens P, Shulman MB, Slavin MJ, Sperling RA, Stewart R, Uspenskaya O, Vellas B, Visser PJ, Wagner M (2014) A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement 10, 844-852. DOI: 10.1016/j.jalz.2014.01.001.
12. Mitchell AJ, Beaumont H, Ferguson D, Yadegarfar M, Stubbs B (2014) Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta-analysis. Acta Psychiatr Scand 130, 439-451. DOI: 10.1111/acps.12336.
13. Dardiotis E, Kosmidis MH, Yannakoulia M, Hadjigeorgiou GM, Scarmeas N (2014) The Hellenic Longitudinal Investigation of Aging and Diet (HELIAD): rationale, study design, and cohort description. Neuroepidemiology 43, 9-14. DOI: 10.1159/000362723.
14. Anastasiou CA, Yannakoulia M, Kosmidis MH, Dardiotis E, Hadjigeorgiou GM, Sakka P, Arampatzi X, Bougea A, Labropoulos I, Scarmeas N (2017) Mediterranean diet and cognitive health: Initial results from the Hellenic Longitudinal Investigation of Ageing and Diet. PLoS One 12, e0182048. DOI: 10.1371/journal.pone.0182048.
15. Kosmidis MH, Vlachos GS, Anastasiou CA, Yannakoulia M, Dardiotis E, Hadjigeorgiou G, Sakka P, Ntanasi E, Scarmeas N (2018) Dementia Prevalence in Greece: The Hellenic Longitudinal Investigation of Aging and Diet (HELIAD). Alzheimer Dis Assoc Disord. DOI: 10.1097/wad.0000000000000249.
16. Ntanasi E, Yannakoulia M, Kosmidis MH, Anastasiou CA, Dardiotis E, Hadjigeorgiou G, Sakka P, Scarmeas N (2018) Adherence to Mediterranean Diet and Frailty. J Am Med Dir Assoc 19, 315-322.e312. DOI: 10.1016/j.jamda.2017.11.005.
17. Tsapanou A, Gu Y, O’Shea DM, Yannakoulia M, Kosmidis M, Dardiotis E, Hadjigeorgiou G, Sakka P, Stern Y, Scarmeas N (2017) Sleep quality and duration in relation to memory in the elderly: Initial results from the Hellenic Longitudinal Investigation of Aging and Diet. Neurobiol Learn Mem 141, 217-225. DOI: 10.1016/j.nlm.2017.04.011.
18. Tsapanou A, Gu Y, O’Shea DM, Yannakoulia M, Kosmidis MH, Dardiotis E, Hadjigeorgiou G, Sakka P, Stern Y, Scarmeas N (2017) Dataset on the associations between sleep quality/duration and cognitive performance in cognitively healthy older adults. Data Brief 14, 720-723. DOI: 10.1016/j.dib.2017.08.028.
19. Yesavage JA (1988) Geriatric Depression Scale. Psychopharmacol Bull 24, 709-711.
20. American Psychiatric Association (2000) Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), APA.
21. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939-944.
22. Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, Ritchie K, Rossor M, Thal L, Winblad B (2001) Current concepts in mild cognitive impairment. Arch Neurol 58, 1985-1992.
23. Lawton MP, Brody EM (1969) Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9, 179-186.
24. Folstein MF, Folstein SE, McHugh PR (1975) « Mini-mental state ». A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189-198.
25. Lezak MD, Howieson DB, Loring DW, Fischer JS (2004) Neuropsychological assessment, Oxford University Press, USA.
26. Vlahou CH, Kosmidis MH, Dardagani A, Tsotsi S, Giannakou M, Giazkoulidou A, Zervoudakis E, Pontikakis N (2013) Development of the Greek Verbal Learning Test: reliability, construct validity, and normative standards. Arch Clin Neuropsychol 28, 52-64. DOI: 10.1093/arclin/acs099.
27. Kosmidis MH, Vlahou CH, Panagiotaki P, Kiosseoglou G (2004) The verbal fluency task in the Greek population: normative data, and clustering and switching strategies. J Int Neuropsychol Soc 10, 164-172. DOI: 10.1017/s1355617704102014.
28. Tsapkini K, Vlahou CH, Potagas C (2010) Adaptation and validation of standardized aphasia tests in different languages: Lessons from the Boston Diagnostic Aphasia Examination – Short Form in Greek. Behav Neurol 22, 111-119. DOI: 10.3233/ben-2009-0256.
29. Benton AL (1994) Neuropsychological assessment. Annu Rev Psychol 45, 1-23. DOI: 10.1146/
30. Kosmidis MH, Tsotsi S, Karambela O, Takou E, Vlahou CH (2010) Cultural factors influencing performance on visuoperceptual neuropsychological tasks. Behav Neurol 23, 245-247. DOI: 10.3233/ben-2010-0306.
31. Bozikas VP, Giazkoulidou A, Hatzigeorgiadou M, Karavatos A, Kosmidis MH (2008) Do age and education contribute to performance on the clock drawing test? Normative data for the Greek population. J Clin Exp Neuropsychol 30, 199-203. DOI: 10.1080/13803390701346113.
32. Vlahou C, Kosmidis M (2002) The Greek Trail Making Test: Preliminary normative data for clinical and research use. Psychology: The Journal of the Hellenic Psychological Society 9, 336-352.
33. Giaglis G, Kyriazidou S, Paraskevopoulou E, Tascos N, Kosmidis M (2010) Evaluating premorbid level: preliminary findings regarding the vulnerability of scores on cognitive measures in patients with MS. Journal of the International Neuropsychological Society 15.
34. Rosso AL, Verghese J, Metti AL, Boudreau RM, Aizenstein HJ, Kritchevsky S, Harris T, Yaffe K, Satterfield S, Studenski S, Rosano C (2017) Slowing gait and risk for cognitive impairment: The hippocampus as a shared neural substrate. Neurology 89, 336-342. DOI: 10.1212/wnl.0000000000004153.
35. Darweesh SKL, Licher S, Wolters FJ, Koudstaal PJ, Ikram MK, Ikram MA (2019) Quantitative gait, cognitive decline, and incident dementia: The Rotterdam Study. Alzheimers Dement 15, 1264-1273. DOI: 10.1016/j.jalz.2019.03.013.
36. Savica R, Wennberg AM, Hagen C, Edwards K, Roberts RO, Hollman JH, Knopman DS, Boeve BF, Machulda MM, Petersen RC, Mielke MM (2017) Comparison of Gait Parameters for Predicting Cognitive Decline: The Mayo Clinic Study of Aging. J Alzheimers Dis 55, 559-567. DOI: 10.3233/jad-160697.
37. Jabourian A, Lancrenon S, Delva C, Perreve-Genet A, Lablanchy JP, Jabourian M (2014) Gait velocity is an indicator of cognitive performance in healthy middle-aged adults. PLoS One 9, e103211. DOI: 10.1371/journal.pone.0103211.
38. Graham JE, Ostir GV, Fisher SR, Ottenbacher KJ (2008) Assessing walking speed in clinical research: a systematic review. J Eval Clin Pract 14, 552-562. DOI: 10.1111/j.1365-2753.2007.00917.x.
39. Verghese J, Wang C, Lipton RB, Holtzer R, Xue X (2007) Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg Psychiatry 78, 929-935. DOI: 10.1136/jnnp.2006.106914.
40. Allali G, Ayers EI, Verghese J (2016) Motoric Cognitive Risk Syndrome Subtypes and Cognitive Profiles. J Gerontol A Biol Sci Med Sci 71, 378-384. DOI: 10.1093/gerona/glv092.
41. Ferrucci L, Fabbri E (2018) Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol 15, 505-522. DOI: 10.1038/s41569-018-0064-2.
42. Del Campo N, Payoux P, Djilali A, Delrieu J, Hoogendijk EO, Rolland Y, Cesari M, Weiner MW, Andrieu S, Vellas B (2016) Relationship of regional brain β-amyloid to gait speed. Neurology 86, 36-43. DOI: 10.1212/wnl.0000000000002235.
43. Beauchet O, Annweiler C, Callisaya ML, De Cock AM, Helbostad JL, Kressig RW, Srikanth V, Steinmetz JP, Blumen HM, Verghese J, Allali G (2016) Poor Gait Performance and Prediction of Dementia: Results From a Meta-Analysis. J Am Med Dir Assoc 17, 482-490. DOI: 10.1016/j.jamda.2015.12.092.
44. Veronese N, Stubbs B, Trevisan C, Bolzetta F, De Rui M, Solmi M, Sartori L, Musacchio E, Zambon S, Perissinotto E, Crepaldi G, Manzato E, Sergi G (2016) What physical performance measures predict incident cognitive decline among intact older adults? A 4.4year follow up study. Exp Gerontol 81, 110-118. DOI: 10.1016/j.exger.2016.05.008.