bluuebunny/crossref_metadata_2025_split huggingface dataset:
Convergent and clinical validity of a remote smartphone‐based self‐assessment of cognition, function, and behavior in early Alzheimer’s disease
Authors: Perumal Thanneer M , Wolfer Arnaud M , Veloso Miguel , Kurniawan Irma T , Aponte Eduardo A , Keita Goullou , Hagenbuch Niels , Shi Beijue , Orfaniotou Foteini , Colell Macarena Garcia Valdecases , Chatham Christopher H , Rey‐Riek Simone , Sink Kaycee M. , Watson David , Boada Mercè , Taylor Kirsten I 
Published: December 2023

AbstractBackgroundTrials in early Alzheimer’s disease (eAD) require the quantification of cognitive and functional progression. Compared with traditional in‐clinic assessments, remote assessments via digital health technologies (DHT) enable the collection of a broader palette of cognitive and functional measures at higher frequency and in patients' home environments. However, these must demonstrate validity against standard clinical outcome measures and relevant biomarkers. To this end, the present study aims to determine the reliability, convergent clinical, and functional neuroanatomic validity of a novel DHT, the Alzheimer’s Disease Digital Assessment Suite (AD‐DAS), for individuals on the AD continuum.MethodIn total, 123 participants from 2 countries across 5 sites (3 USA, 2 Spain) participated (https://www.isrctn.com/ISRCTN17035495): 32 healthy controls (HC), 31 amyloid‐PET negative and 30 amyloid‐PET positive subjective cognitive decline (SCDn, SCDP, respectively), and 30 eAD. All participants completed standard in‐clinic neuropsychological assessments and remotely performed the AD‐DAS tasks daily for 28 days without supervision. AD‐DAS comprised 9 active tasks of cognitive/motor functioning and 4 survey questionnaires. Convergent and clinical validity analysis were performed using linear and proportional odds logistic regression modeling with age, sex, education, and site as covariates, respectively. Functional neuroanatomic validity was tested with voxel‐based morphometry (VBM) analyses with anatomic MRIs.ResultOutcome metrics from all AD‐DAS cognitive/motor tasks correlated with their corresponding clinical comparators (ρpartial (range) = 0.16 ‐ 0.52), supporting their convergent validity. Similarly, outcome metrics from all tasks significantly differentiated the eAD from all the other groups in the expected directions (odds ratio (range) = 0.01 ‐ 61), supporting their clinical validity. The reliability of the outcome metrics ranged from good to excellent (interclass correlation coefficient (range) = 0.71 ‐ 0.91). VBM analyses provided independent and largely confirmatory results, further validating the AD‐DAS.ConclusionThe AD‐DAS remote, smartphone‐based assessments of cognition show good to excellent test‐retest reliability, and preliminary analyses indicate that they may show convergent, clinical and exploratory functional neuroanatomic validity. Thus, these results provide a foundation for building towards the overarching goals of remote screening, prediction of progression, and monitoring of cognitive decline for future clinical trials and clinical care for preclinical and eAD.
Comparing the Classic Paired Associates Learning Task with a new Smartphone‐Based Version of the Task
Authors: Kaula Alexander J , Taptiklis Nick , Cormack Francesca K 
Published: December 2023

AbstractBackgroundPAL is a well‐known and trusted task with good sensitivity to deficiencies in memory capabilities as has been shown in hundreds of studies over the past two decades. There has been considerable interest in a smartphone version of PAL that can be used remotely, and Cambridge Cognition has now completed work on the first production version of this task. Here, we show how the task compares with classic PAL, in an online within‐subjects study. PAL may be used in repeated assessments, and one notable feature is that performance tends to improve most strongly between a first and second attempt at the task. We therefore included a first smartphone ‘Familiarisation’ session on Day One, before the Days Two and Three web and smartphone sessions.MethodWe obtained PAL scores from 76 adults (43m, 33f) aged 50+ (M = 56.3, SD = 5.46) using the Prolific online cognitive testing platform for this three‐day crossover‐design study. Participants were divided into two groups. In a first ‘Familiarisation’ session, all participants used their smartphones to complete a PAL assessment, then on Day Two, one group used the web‐based assessment, and the other used smartphone assessment, and on the last day each group changed device from the previous day’s session, as well as a questionnaire about their experience and comparison of the task in different devices.ResultWe found that test‐retest reliability between the tasks was very good (Pearson’s r > .7) between the first (‘Familiarisation’) session and Day Two, and then excellent (Pearson’s r > .8) between mobile and web devices on Day Two and Day Three of the study. Next, when comparing performance across the two groups (web‐first, and smartphone‐first), bias was not significant and numerically a very small 0.2 adjusted error points. A significant improvement was seen between Familiarisation and Day Two, but not second and third, supporting the value of a familiarisation session.ConclusionWe found that the new smartphone version is highly comparable to the classic version of the task, and that familiarisation using PAL on smartphone is effective, generalising across both task versions.
Detecting Unrecognized Cognitive Impairment and Dementias Using Clinical Health Systems Bioinformatics and Electronic Health Records from a National Population
Authors: Khachaturian Ara S , Beeri Michal S , Li Gang , Ravona‐Springer Ramit 
Published: December 2023

AbstractGlobally there are upwards of 35 million people living with dementia and other cognitive impairments, with total annual costs of approximately US$ 1 trillion by 2025. Yet, this estimate does not consider the impact due to the lack of specialized personnel and health care infrastructure to provide timely access for dementia diagnosis, treatment, and care. These factors are complicated by the lack of valid and equitable means for detecting signs of impending cognitive impairment or dementia. Also, these same symptoms are not easily recognized by many primary care physicians who do not make referrals to specialists. These negative dynamics on access to quality brain healthcare may impact under‐represented, underserved, and vulnerable populations at disproportionately greater levels. Although more AD/ADRD interventions will be approved, this will not assure equal market access. There is an immediate need to provide accurate, affordable, appropriate, and equitable clinical care profiles. One approach is to develop digital tools to access electronic health record (EHR) data. Yet, generic EHR data on dementia status are limited to people who have received a formal diagnosis. Worse, in the community setting, most people enduring cognitive impairment or living with dementia will not receive generally a timely diagnosis to support useful clinical care decision‐making. Fortunately, several international health data reservoirs exist that integrate routine EHR data with dementia research‐driven cohort studies with affiliated biobanks, including Sheba Medical Center (SMC) with its ongoing ‐omics and neuroimaging research. As Israel has socialized medicine, and all residents have healthcare provided through the Israeli National Health Insurance law, the Sheba data offers a unique opportunity to develop valid, reliable, and transportable EHR derived assessment tools for cognitive impairment and dementia. The project in collaboration with SMC and Maccabi Healthcare Service—one of four national Israeli medical service providers—demonstrates the advanced capabilities to use electronic medical records linked with a national network of primary care physicians, and the ability to conduct deep‐source data mining in the absence of a clinical diagnosis of cognitive impairment or dementia. The project reports the performance characteristics (i.e., sensitivity and specificity) of assessment tools to detect cognitive impairment or dementia.
Humanoid Mobile Robots and wearable sensors improve the quality of care and address worker shortages in nursing homes
Authors: Khan Arshia A 
Published: December 2023

AbstractBackgroundTechnology has been identified as a solution to address the growing healthcare worker shortages(Harp, 2023). Humanoid robots have great potential to help augment care in elderly and people affected with dementia (Khan & Anwar, 2020; Joseph et.al, 2018). Humanoid robots and wearable sensors were deployed in eight nursing homes to help address healthcare worker shortages and augment care of elderly and people affected with dementia. The resident satisfaction and quality of life annual survey conducted by the Minnesota Department of Human Services revealed an improved satisfaction and quality of life in the domain of activities in the nursing homes that deployed robots when compared to the nursing homes that did not deploy the robots. The pre and post data gathered revealed that the residents felt happier, more cared for, less tired and less frustrated after interacting with the robots. The staff feedback indicates that the staff had time to address their chores while the robots engaged teh residents.MethodTwo humanoid robots and ten wearable sensors per facility were deployed in 8 different nursing homes. The robots were programmed to address the domains of physical, emotional/social and cognitive wellbeing. Four forms of data ‐ 1) nursing home resident satisfaction and quality of life data gathered by the Minnesota Department of Human Services; 2) pre and post surveys conducted before and after interactions with the robots; 3) weekly feedback was from the healthcare staff and wearable sensor data.ResultThe nursing home resident satisfaction and quality of life data gathered annually by the Minnesota Department of Human Services revealed improvement in the satisfaction and the quality of life domain of activities in the nursing homes that deployed humanoid robots versus the nursing homes that did not deploy humanoid robots. The pre and post surveys conducted before and after the interactions with the robots showed 17.4% improvement in their happiness, 4.3% more cared for,10% less tired and 14.5% less frustrated after engaging in the activities with humanoid robots.ConclusionHumanoid robots and wearable sensors have proved to help improve the quality of life of elderly.
Identifying modifiable psychosocial outcomes to maintain physical ACTIVity and promote independence in DEMentia: the ActivDem study
Authors: Ardle Ríona Mc , Din Silvia Del , Hicks Ben , Kingston Andrew , Banerjee Sube , Thomas Alan J , Robinson Louise , Rochester Lynn 
Published: December 2023

AbstractBackgroundPromoting habitual physical activity (HPA; e.g. walking) may help decelerate dependency and disability in people with dementia (PwD). HPA refers to any movement the skeletal muscles make which requires energy expenditure, including daily tasks (e.g. gardening/housework). Research regarding physical activity in PwD mainly focuses on exercise‐based activities, which may not be feasible for people with chronic health conditions or frailty, or who cannot afford or travel to exercise classes. Promoting participation in HPA may be an inclusive, accessible strategy to support PwD to remain physically active and mobile. PwD participate in less HPA than recommended and disease‐specific factors (e.g. cognition) do not sufficiently explain this. In healthy ageing, HPA participation is associated with multiple socio‐ecological factors (e.g. psychosocial wellbeing/social support/environment) but this has not been explored in dementia. The ActivDem study aims to identify key socio‐ecological influences on HPA change over time in community‐dwellers with dementia. Figure 1 presents ActivDem’s core objectives.MethodPwD and their carers will be recruited from the ongoing cohort study, DETERMIND, they will participate in a continuous seven‐day HPA assessment. This involves wearing an inertial measurement unit on their lower back at baseline and one‐year follow up. Digital mobility outcomes relating to volume (e.g. steps per day), pattern (e.g. mean ambulatory bout length), and variability (i.e. of bout length) will be derived via validated algorithms. PwD will also complete the LASA Physical Activity Questionnaire and Falls Efficacy Scale. Carers will complete the Dementia Management Strategies Scale. Demographic, clinical and psychosocial characteristics and information on care support will be assessed as part of the DETERMIND study (1).ResultLongitudinal statistical methods will identify key predictors of HPA change in PwD, and examine the relationship between HPA and independenceConclusionThe ActivDem study will identify key socio‐ecological predictors of HPA change, these will inform key interventional components to support PwD to remain physically active and mobile following diagnosis of dementia. References • Farina et al., 2020. DETERMinants of quality of life, care and costs, and consequences of INequalities in people with Dementia and their carers (DETERMIND): A protocol paper. Int. J. Geriatr.
Comparing intra‐individual variability on executive functioning tasks in individuals with Mild Cognitive Impairment and Dementia of the Alzheimer Type: Results from the ARMADA Study
Authors: Ho Emily , Karpouzian‐Rogers Tatiana , Dworak Elizabeth , Weintraub Sandra , Gershon Richard C. 
Published: December 2023

AbstractBackgroundCognitive decline is most often assessed mean group differences on static measures of cognitive abilities (MacDonald et al., 2009). Another, perhaps more dynamic, method to understanding cognitive functioning is to evaluate variability in performance within an individual, termed intraindividual variability (IIV), which has been associated with reduction in gray matter and less efficient cognitive processing. Using results from a multi‐site, longitudinal ARMADA study that validated NIH Toolbox across the cognitive aging spectrum, we investigated whether IIV in reaction time processing can differentiate between older adults with normal cognition, mild cognitive impairment (MCI), or dementia of the Alzheimer type (DAT), as well as the relative magnitude of differences between groups.MethodAs part of the Advancing Reliable Measurement in Alzheimer’s Disease and cognitive Aging study (ARMADA; Weintraub et al 2020), participants completed the NIH Toolbox Cognition Battery, including two measures of executive functioning, Dimensional Change Card Sort and Flanker Inhibitory Control, which include granular reaction time data in milliseconds. We analyzed standard deviation and mean absolute deviation of item‐level data of all trials, correct trials only, and incorrect trials only for three groups: normal controls, those with amnestic MCI, and those with mild DAT.ResultWe observe large effect sizes of reaction time by clinical group, with the mild DAT group demonstrating mean standard error across trials of approximately .5 seconds throughout, compared with .25 seconds for MCI and .10 seconds for the control group (Figure 1). This translates to an approximate 5‐fold increase in processing speed in the most severe clinical group. Examining time trends, we see that while the NC and MCI group were faster as the test progressed; there is no similar learning effect for the mild DAT group. Similar effect sizes were observed for the Flanker test.ConclusionThis study represents one of the first studies to examine clinical performance using process data from two executive functioning measures on the NIH Toolbox across the cognitive aging spectrum. Future work will explore more definitions of IIV to determine which measure will maximally differentiate between established clinical groups and examine longitudinal differences.
Effect of COVID‐19 and telehealth implementation on Patient Centered Outcome Measures completion by patients with Alzheimer’s and related disorders in an academic memory center
Authors: Gubler Boston , Finch Kelly , Holden Samantha K. , Mullen Rebecca , Matlock Daniel D , Pressman Peter S. 
Published: December 2023

AbstractBackgroundPatient Centered Outcome Measures (PCOMs) are increasingly recognized as a means of improving clinical efficiency, particularly when completed electronically ahead of clinic visits. However, PCOM completion is often far from consistent. The shift to telehealth introduced by the Covid‐19 pandemic changed how patients and caregivers engaged with medical care, possibly encouraging increased participation in electronic assessments. We questioned how the pandemic influenced electronic PCOM completion by our patients with Alzheimer’s disease (AD) and their caregivers.MethodsPatients and caregivers were routinely invited to fill electronic PCOM questionnaires prior to visits both before and during the Covid‐19 pandemic. PCOMs assessed the predominant cognitive domain of concern, other diagnostically useful symptoms, disease severity, neuropsychiatric symptoms, and caregiver wellbeing. A date of 3/15/2020 was set as the onset of the pandemic for this study, after which time our clinic converted to telehealth visits only. We tallied rates of completion of electronic surveys by patients and/or caregivers three and a half months before and after that time, comparing completion rates by two proportion Z‐test.ResultsDuring this timeframe, across a total of 402 visits (total: 333 individuals, 69 returns; AD: 73 individuals, 6 returns), caregivers and/or patients were offered an opportunity to fill PCOMs ahead of the visit. Across all clinical diagnoses combined, the probability of a visit being associated with filled PCOMS was greater after switching to only telehealth (caregivers: P = 0.005, patients: P = 0.016). Additionally, after the shift to telehealth, AD‐related visits were twice as likely to have completed preclinical electronic PCOMs (caregivers: P = 0.002, patients: P = 0.008). Approximately 33.3% of PCOMS to be filled by AD caregivers were completed before the telehealth shift, and 67.5% were afterwards. Approximately 26.7% of patient PCOMs were filled before the change, and afterwards this percentage rose to 55.0%.ConclusionThe beginning of the Covid‐19 pandemic and subsequent shift towards telehealth‐only practice was associated with greater use of electronic PCOMs by all patients, including those with AD and their caregivers. Increased acceptance of electronic PCOMs seems to offer promising opportunities for remote assessment in the future of dementia diagnosis and care.
TELL: A web‐based app to capture speech and language markers of neurodegeneration
Authors: Garcia Adolfo M 
Published: December 2023

AbstractBackgroundOver the last decade, the search for scalable markers of neurodegeneration has been boosted by automated speech and language analysis (ASLA). Participants are asked to produce natural discourse, generating acoustic and linguistic data that can be digitally extracted and analyzed with statistical or machine learning methods. This approach has revealed subtle patterns that differentiate among disorders, predict core symptom severity, correlate with anatomo‐functional brain alterations, and identify autopsy‐confirmed pathology years before death. Accordingly, multiple studies have described ASLA as a promising resource for patient assessment. However, this translational potential remains unfulfilled, as the underlying technologies are unavailable for immediate clinical use. Aiming to tackle this issue, we developed the Toolkit to Examine Lifelike Language (TELL), a web‐based app for collecting, storing, analyzing, and visualizing speech data.MethodTELL version 1.0 was developed by an interdisciplinary team of language, brain health, and data science experts. We designed a speech elicitation protocol comprised of validated spontaneous, semi‐spontaneous, and non‐spontaneous tasks with different cognitive demands. Each task is analyzed via ASLA algorithms capturing word frequency, affective valence, and emotional magnitude, as well as rhythm‐ and pitch‐related features. On‐the‐fly results are offered through intuitive visualizations for each metric. The measures are complemented with a language profile survey. Audio files, transcriptions, and data matrices can be downloaded for offline analysis. The app runs on multiple devices, requires no installation, and offers multi‐language capabilities.ResultTELL version 1.0 is now being used in 10 sites worldwide, mainly including low‐income countries. Preliminary results from persons with Alzheimer’s and Parkinson’s disease, as well as frontotemporal dementia, show that its metrics are sensitive for disease identification, discrimination, and monitoring. Updates have been implemented drawing from users’ feedback. Funds have been secured to develop TELL version 2.0, with novel metrics, improved speech‐to‐text algorithms, and a streamlined front end.ConclusionTELL turns basic language science methods into clinically usable tools, offering an objective, low‐cost, non‐invasive complement to classical assessments. More generally, it illustrates how digital technologies can foster equitable insights on brain health. Ongoing updates will further increase its capacity to establish scalable markers of diverse neurodegenerative diseases.
Age differentially mediates the relationship between plasma biomarkers and cognition detected by digital screening
Authors: Toniolo Sofia , Zhao Sijia , Scholcz Anna , Amein Benazir , Gallacher Anne , Ganse‐Dumrath Akke , Bukala Bernard R , Thompson Sian , Manohar Sanjay , Zetterberg Henrik , Husain Masud 
Published: December 2023

AbstractBackgroundPlasma biomarkers are emerging as a useful tool to measure neurodegeneration, particularly in patients with Alzheimer’s Disease (AD). Digital cognitive testing is also increasingly used to detect cognitive changes in AD. Age is a major determinant of performance at cognitive testing and can affect the levels of different plasma biomarkers. However, little is known about how age mediates the relationship between plasma biomarkers and cognition.Method38 AD and 48 age‐matched controls were recruited from the Oxford Centre for Cognitive Disorders. We used a fully remote digital visual short‐term memory (STM) task (Figure 1) and extracted eight cognitive metrics (Figure 1): Identification Accuracy (percentage of correctly identified items), Absolute Error (distance from the response location to the original item), Target detection (probability of correctly identifying the target), Misbinding (erroneously localizing an item to the remembered location of another item in memory), Guessing (random guessing), Identification and Localization Time (time to identify and drag the correct object to its remembered location), and memory Imprecision (width of the distribution around the target). Plasma p‐tau 181, GFAP, NFL, Aß40 and Aß42 were measured, and the Aß42/40 ratio was calculated. In Model 1 (Figure 2) the correlation between digital cognitive metrics and plasma biomarkers is shown, while in Model 2 age was regressed out. A mediation analysis was then performed to look at the effect of age as a mediator.ResultIn Model 1, most cognitive metrics on the STM task were strongly associated with p‐tau 181, GFAP and NFL. Identification Accuracy was also negatively correlated with the Aß42/40 ratio. No association with Aß42 or Aß40 was found. However, regressing out age had a profound impact on these correlations, particularly for GFAP and NFL, while p‐tau 181 was not significantly affected (Figure 2 and 3). The mediation analysis (Figure 3), shown for Identification Accuracy, confirmed that age had a more profound impact on GFAP and NFL compared to p‐tau 181.ConclusionThese new digital metrics show a strong correlation with plasma biomarkers. Finally, age is an important mediator between plasma biomarkers and cognition, but that varies across different biomarkers.