A mobile application for cognitive screening of dementia

Extended ambulatory assessment of executive function: within-person reliability of working memory and inhibitory control tasks

INTRODUCTION

Ambulatory assessment of executive function - particularly in the form working memory (WM) - is increasingly common. Few studies to date, however, have also incorporated ambulatory measures of inhibitory control. Critically, the extended within-person reliability of ambulatory tasks tapping each of these constructs has been largely overlooked.

METHOD

Participants (N = 283, Mage = 23.74 years, SD = 9.04) received notifications every 3 days (for 4 weeks) to undertake ambulatory assessment versions of the n-Back and Stop-Signal Tasks (SST) via the smartphone application CheckCog. Within-person reliability of these measures was explored.

RESULTS

Compliance ranged from 66% (for eight sessions) to 89% (for four sessions). Our results reveal significant changes in performance within the first two sessions for both the n-Back and SST, with performance remaining largely consistent across the remaining (two to eight) sessions. In terms of test-retest reliability, the ICC (C, 1) values ranged from .29 to .68 on the n-Back (with overall accuracy being .51) and .31-.73 on the SST (with stop-signal reaction time being .53).

CONCLUSION

The results of the current study contribute to the literature by demonstrating the reliability of brief measures of executive function - in the form of inhibitory control and WM - delivered using smartphones in participants' natural environments. Based on our findings, the CheckCog app reliability tracks baseline systematic changes in WM and response inhibition across multiple time points and for an extended period in healthy individuals.

Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.

A study about the impact of indoor air pollution on cognitive function among middle-aged and older adult people in India

BACKGROUND

In the 21st century, people still use solid fuel for cooking at home, resulting in poor indoor air quality. Prolonged exposure to such conditions may negatively affect one's cognitive function. So, the present study examines the possible association between IAP and the cognitive function of individuals aged 45 years or above in India.

METHODS

The study utilizes secondary data, procured from the longitudinal ageing study in India (2017-18). Treatment effects through regression-adjusted models were applied to represent the association between IAP and cognitive health and the results are represented by beta coefficient (β). Three separate models with a 95% confidence interval adjusting with the other factors like housing environment, individual and behavioural were framed.

RESULTS

The study revealed that households without a separate kitchen (β = -0.64; 95%CI: -0.90 to -0.39) and electricity (β = -0.97; 95%CI: -1.34 to -0.61) significantly affect cognitive strength. Cognitive decline is likely more pronounced among older adults (β = -1.19; 95%CI: -1.42 to -0.96) than the middle-aged population. Moreover, the cognitive ability of rural residents (β = -1.11; 95%CI: -1.49 to -0.73) and women (β = -2.05; 95%CI: -2.29 to -1.81) is negatively associated with IAP exposure. Older adults with no reading habits (β = -6.28; 95%CI: -6.72; to -5.85) and physical inactivity (β = -0.96; 95%CI: -1.22 to -0.70) had a sign of notable decline in cognitive ability.

CONCLUSIONS

Findings revealed that cognitive function is negatively associated with IAP, demanding a deep intervention plan to minimize the detrimental effect.

eHealth tools to assess the neurological function for research, in absence of the neurologist - a systematic review, part I (software)

BACKGROUND

Neurological disorders remain a worldwide concern due to their increasing prevalence and mortality, combined with the lack of available treatment, in most cases. Exploring protective and risk factors associated with the development of neurological disorders will allow for improving prevention strategies. However, ascertaining neurological outcomes in population-based studies can be both complex and costly. The application of eHealth tools in research may contribute to lowering the costs and increase accessibility. The aim of this systematic review is to map existing eHealth tools assessing neurological signs and/or symptoms for epidemiological research.

METHODS

Four search engines (PubMed, Web of Science, Scopus & EBSCOHost) were used to retrieve articles on the development, validation, or implementation of eHealth tools to assess neurological signs and/or symptoms. The clinical and technical properties of the software tools were summarised. Due to high numbers, only software tools are presented here.

FINDINGS

A total of 42 tools were retrieved. These captured signs and/or symptoms belonging to four neurological domains: cognitive function, motor function, cranial nerves, and gait and coordination. An additional fifth category of composite tools was added. Most of the tools were available in English and were developed for smartphone device, with the remaining tools being available as web-based platforms. Less than half of the captured tools were fully validated, and only approximately half were still active at the time of data collection.

INTERPRETATION

The identified tools often presented limitations either due to language barriers or lack of proper validation. Maintenance and durability of most tools were low. The present mapping exercise offers a detailed guide for epidemiologists to identify the most appropriate eHealth tool for their research.

FUNDING

The current study was funded by a PhD position at the University of Groningen. No additional funding was acquired.

The RODI mHealth app Insight: Machine-Learning-Driven Identification of Digital Indicators for Neurodegenerative Disorder Detection

Neurocognitive Disorders (NCDs) pose a significant global health concern, and early detection is crucial for optimizing therapeutic outcomes. In parallel, mobile health apps (mHealth apps) have emerged as a promising avenue for assisting individuals with cognitive deficits. Under this perspective, we pioneered the development of the RODI mHealth app, a unique method for detecting aligned with the criteria for NCDs using a series of brief tasks. Utilizing the RODI app, we conducted a study from July to October 2022 involving 182 individuals with NCDs and healthy participants. The study aimed to assess performance differences between healthy older adults and NCD patients, identify significant performance disparities during the initial administration of the RODI app, and determine critical features for outcome prediction. Subsequently, the results underwent machine learning processes to unveil underlying patterns associated with NCDs. We prioritize the tasks within RODI based on their alignment with the criteria for NCDs, thus acting as key digital indicators for the disorder. We achieve this by employing an ensemble strategy that leverages the feature importance mechanism from three contemporary classification algorithms. Our analysis revealed that tasks related to visual working memory were the most significant in distinguishing between healthy individuals and those with an NCD. On the other hand, processes involving mental calculations, executive working memory, and recall were less influential in the detection process. Our study serves as a blueprint for future mHealth apps, offering a guide for enhancing the detection of digital indicators for disorders and related conditions.

An update on mobile applications collecting data among subjects with or at risk of Alzheimer's disease

Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer's disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature (n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition-related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, it may serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped.

Screening of Mild Cognitive Impairment Through Conversations With Humanoid Robots: Exploratory Pilot Study

BACKGROUND

The rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction.

OBJECTIVE

This study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination.

METHODS

This was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test.

RESULTS

We obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P<.001) and met the reference value r=0.1 (small) of the effect size. Additionally, filler periods (P<.01) and the proportion of fillers (P=.02) showed a significant difference; however, these did not meet the reference value of the effect size. In contrast, we obtained significant differences in 16 (94%) of 17 types of features obtained from the everyday conversations with the humanoid robot. The duration of response time, the duration of speech periods, jitter (local, relative average perturbation [rap], 5-point period perturbation quotient [ppq5], difference of difference of periods [ddp]), shimmer (local, amplitude perturbation quotient [apq]3, apq5, apq11, average absolute differences between the amplitudes of consecutive periods [dda]), and F0cov (coefficient of variation of the fundamental frequency) showed a significant difference (P<.001). In addition, the duration of response time, the duration of silent periods, the filler period, and the proportion of fillers showed significant differences (P<.05). However, only jitter (local) met the reference value r=0.1 (small) of the effect size. In the automatic classification experiment for the classification of participants into CN and MCI groups, the results showed 66.0% accuracy in the MMSE conversational speech and 68.1% accuracy in everyday conversations with the humanoid robot.

CONCLUSIONS

This study shows the possibility of early and simple screening for patients with MCI using prosodic and acoustic features from everyday conversations with a humanoid robot with the same level of accuracy as the MMSE.

Validation of a Computerized Cognitive Training Tool to Assess Cognitive Impairment and Enable Differentiation Between Mild Cognitive Impairment and Dementia

BACKGROUND

Age-related cognitive decline is a chronic, progressive process that requires active clinical management as cognitive status changes. Computerized cognitive training (CCT) provides cognitive exercises targeting specific cognitive domains delivered by computer or tablet. Meanwhile, CCT can be used to regularly monitor the cognitive status of patients, but it is not clear whether CCT can reliably assess cognitive ability or be used to diagnose different stages of cognitive impairment.

OBJECTIVE

To investigate whether CCT can accurately monitor the cognitive status of patients with cognitive impairment as well as distinguish patients with dementia from patients with mild cognitive impairment (MCI).

METHOD

We included 116 patients (42 dementia and 74 MCI) in final analysis. Cognitive ability was assessed by averaging the patient performance on the CCT to determine the Cognitive Index. The validity of the Cognitive Index was evaluated by its correlation with neuropsychological tests, and internal consistency was measured to assess the reliability. Additionally, we determined the diagnostic ability of the Cognitive Index to detect dementia using receiver operating characteristic (ROC) analysis.

RESULTS

The Cognitive Index was highly correlated with the Montreal Cognitive Assessment (r = 0.812) and the Mini-Mental State Examination (r = 0.694), indicating good convergent validity, and the Cronbach's alpha coefficient was 0.936, indicating excellent internal consistency. The area under the ROC curve, sensitivity, and specificity of the Cognitive Index to diagnose dementia were 0.943, 83.3%, and 91.9%, respectively.

CONCLUSIONS

CCT can be used to assess cognitive status and detect dementia in patients with cognitive impairment.

Touchscreen Cognitive Tools for Mild Cognitive Impairment and Dementia Used in Primary Care Across Diverse Cultural and Literacy Populations: A Systematic Review

BACKGROUND

Touchscreen cognitive tools opened new promising opportunities for the early detection of cognitive impairment; however, most research studies are conducted in English-speaking populations and high-income countries, with a gap in knowledge about their use in populations with cultural, linguistic, and educational diversity.

OBJECTIVE

To review the touchscreen tools used in primary care settings for the cognitive assessment of mild cognitive impairment (MCI) and dementia, with a focus on populations of different cultures, languages, and literacy.

METHODS

This systematic review was conducted following the PRISMA guidelines. Studies were identified by searching across MEDLINE, EMBASE, EBSCO, OVID, SCOPUS, SCIELO, LILACS, and by cross-referencing. All studies that provide a first-level cognitive assessment for MCI and dementia with any touchscreen tools suitable to be used in the context of primary care were included.

RESULTS

Forty-two studies reporting on 30 tools and batteries were identified. Substantial differences among the tools emerged, in terms of theoretical framework, clinical validity, and features related to the application in clinical practice. A small proportion of the tools are available in multiple languages. Only 7 out of the 30 tools have a multiple languages validation. Only two tools are validated in low-educated samples, e.g., IDEA and mSTS-MCI.

CONCLUSION

General practitioners can benefit from touchscreen cognitive tools. However, easy requirements of the device, low dependence on the examiner, fast administration, and adaptation to different cultures and languages are some of the main features that we need to take into consideration when implementing touchscreen cognitive tools in the culture and language of underrepresented populations.

A Neuropsychological Perspective on Defining Cognitive Impairment in the Clinical Study of Alzheimer’s Disease: Towards a More Continuous Approach

The global fight against Alzheimer’s disease (AD) poses unique challenges for the field of neuropsychology. Along with the increased focus on early detection of AD pathophysiology, characterizing the earliest clinical stage of the disease has become a priority. We believe this is an important time for neuropsychology to consider how our approach to the characterization of cognitive impairment can be improved to detect subtle cognitive changes during early-stage AD. The present article aims to provide a critical examination of how we define and measure cognitive status in the context of aging and AD. First, we discuss pitfalls of current methods for defining cognitive impairment within the context of research shifting to earlier (pre)symptomatic disease stages. Next, we introduce a shift towards a more continuous approach for identifying early markers of cognitive decline and characterizing progression and discuss how this may be facilitated by novel assessment approaches. Finally, we summarize potential implications and challenges of characterizing cognitive status using a continuous approach.

VIPCare: Understanding the support needed to create affective interactions between new caregivers and residents with dementia

Introduction

In this paper, we study the support needed by professional caregivers of those with dementia, and present a first step toward development of VIPCare, a novel application with the goal of assisting new caregivers at care-centres in interacting with residents with dementia.

Methods

A mixed-methods study including two questionnaires, two focus groups, and seven co-design sessions with 17 professional caregivers was conducted to (a) understand caregivers’ challenges/approaches used to reduce negative interactions with persons with dementia, (b) identify the existing gaps in supporting information for improving such interactions, and (c) co-design the user interface of an application that aims to help improve interactions between a new professional caregiver and persons with dementia. A pre-questionnaire assessed knowledge of smartphones and attitude toward technology. A post-questionnaire provided an initial evaluation of the designed user interface.

Results

Focus groups emphasized the importance of role-playing learned through trial and error. The layout/content of the application was then designed in four iterative paper-prototyping sessions with professional caregivers. An iOS/Android-based application was developed accordingly and was modified/improved in three iterative sessions. The initial results supported efficiency of VIPCare and suggested a low task load index.

Conclusions

We presented a first step toward understanding caregiver needs and developing an application that can help reduce negative interactions between professional caregivers and those with dementia.

Encouraging Digital Technology in Neuropsychology: The Theory of Visual Attention on Tablet Devices

OBJECTIVE

Visual attention helps us to selectively process relevant information and is crucial in our everyday interactions with the environment. Not surprisingly, it is one of the cognitive domains that is most frequently affected by acquired brain injury. Reliable assessment of attention deficits is pivotal to neuropsychological examination and helps to optimize individual rehabilitation plans. Compared with conventional pen-and-paper tests, computerized tasks borrowed from the field of experimental psychology bring many benefits, but lab-based experimental setups cannot be easily incorporated in clinical practice. Light-weight and portable mobile tablet devices may facilitate the translation of computerized tasks to clinical settings. One such task is based on the Theory of Visual Attention (TVA), a mathematical model of visual attention. TVA-based paradigms have been widely used to investigate several aspects of visual attention in both fundamental and clinical research, and include measures for general processing capacity as well as stimulus-specific attentional parameters.

METHODS

This article discusses the benefits of TVA-based assessments compared with frequently used neuropsychological tests of visual attention, and examines the reliability of a tablet-based TVA-based assessment in 59 neurologically healthy participants.

RESULTS

Pearson's correlations indicate that the tablet-based TVA assessment and the conventional lab-based TVA assessment have a comparable parallel-form (range: .67-.93), test-retest (range: .61-.78), and internal reliability (range: .56-.97).

CONCLUSION

Our results suggest that tablet-based TVA assessment may be a promising tool to acquire clinical measures of visual attention at low cost at the bedside of the patient.

Mobile health and neurocognitive domains evaluation through smartphones: A meta-analysis

BACKGROUND

Mobile health (mHealth) have significantly advanced evaluating neurocognitive functions; but, few reports have documented whether they validate neurocognitive impairments as well as paper-and-pencil neuropsychological tests.

OBJECTIVE

To meta-analyze the correlation between mobile applications for neuropsychological tests and validated paper-and-pencil neuropsychological tests for evaluating neurocognitive impairments.

METHOD

We used PubMed, Embase, Cochrane, Web of Science, and IEEE Explorer through January 2020 to identify studies that compared mobile applications for neuropsychological tests vs. paper-and-pencil neurophysiological tests. We used random-effects models via the DerSimonian and Laird method to extract pooled Pearson's correlation coefficients and we stratified by study design.

RESULT

Nine out of 4639 screened articles (one RCT and eight prospective longitudinal case series) were included. For the observational studies, there was a statistically significant strong and direct correlation between mobile applications for neuropsychological test scores and validated paper-and-pencil neuropsychological assessment scores (r = 0.70; 95% CI 0.59, 0.79; I² = 74.5%; p- heterogeneity <0.001). Stronger results were seen for the RCT (r = 0.92; 95% CI 0.77, 0.97).

CONCLUSION

This meta-analysis showed a statistically significant correlation between mobile applications and the validated paper-and-pencil neuropsychological assessments analyzed for the evaluation of neurocognitive impairments.

Human Factors and Requirements of People with Cognitive Impairment, Their Caregivers, and Healthcare Professionals for mHealth Apps Including Reminders, Games, and Geolocation Tracking: A Survey-Questionnaire Study

Background:

Mobile Health (mHealth) apps can delay the cognitive decline of people with dementia (PwD), by providing both objective assessment and cognitive enhancement.

Objective:

This patient involvement survey aims to explore human factors, needs and requirements of PwD, their caregivers, and Healthcare Professionals (HCPs) with respect to supportive and interactive mHealth apps, such as brain games, medication reminders, and geolocation trackers through a constructive questionnaire.

Methods:

Following the principles of user-centered design to involve end-users in design we constructed a questionnaire, containing both open-ended and closed-ended questions as well as multiple choice and Likert scale, in order to investigate the specific requirements and preferences for mHealth apps. We recruited 48 participants including people with cognitive impairment (n = 15), caregivers (n = 16), and HCPs (n = 17) and administered the questionnaire.

Results:

All participants are likely to use mHealth apps, with the primary desired features being the improvement of memory and cognition, assistance on medication treatment, and perceived ease to use. HCPs, caregivers, and PwD consider brain games as an important technology-based, non-pharmaceutical intervention. Both caregivers and patients are willing to use a medication reminder app frequently. Finally, caregivers are worried about the patient wandering. Therefore, global positioning system tracking would be particularly important to them. On the other hand, patients are concerned about their privacy, but are still willing to use a geolocation app for cases of emergency.

Conclusion:

This research contributes to mHealth app design and potential adoption. All three groups agree that mHealth services could facilitate care and ameliorate behavioral and cognitive disturbances of patients.

Validation of SATURN, a free, electronic, self-administered cognitive screening test

BACKGROUND

Cognitive screening is limited by clinician time and variability in administration and scoring. We therefore developed Self-Administered Tasks Uncovering Risk of Neurodegeneration (SATURN), a free, public-domain, self-administered, and automatically scored cognitive screening test, and validated it on inexpensive (<$100) computer tablets.

METHODS

SATURN is a 30-point test including orientation, word recall, and math items adapted from the Saint Louis University Mental Status test, modified versions of the Stroop and Trails tasks, and other assessments of visuospatial function and memory. English-speaking neurology clinic patients and their partners 50 to 89 years of age were given SATURN, the Montreal Cognitive Assessment (MoCA), and a brief survey about test preferences. For patients recruited from dementia clinics (n = 23), clinical status was quantified with the Clinical Dementia Rating (CDR) scale. Care partners (n = 37) were assigned CDR = 0.

RESULTS

SATURN and MoCA scores were highly correlated (P < .00001; r = 0.90). CDR sum-of-boxes scores were well-correlated with both tests (P < .00001) (r = -0.83 and -0.86, respectively). Statistically, neither test was superior. Most participants (83%) reported that SATURN was easy to use, and most either preferred SATURN over the MoCA (47%) or had no preference (32%).

DISCUSSION

Performance on SATURN-a fully self-administered and freely available (https://doi.org/10.5061/dryad.02v6wwpzr) cognitive screening test-is well-correlated with MoCA and CDR scores.

Implementing Remote Memory Clinics to Enhance Clinical Care During and After COVID-19

Social isolation is likely to be recommended for older adults due to COVID-19, with ongoing reduced clinical contact suggested for this population. This has increased the need for remote memory clinics, we therefore review the literature, current practices and guidelines on organizing such remote memory clinics, focusing on assessment of cognition, function and other relevant measurements, proposing a novel pathway based on three levels of complexity: simple telephone or video-based interviews and testing using available tests (Level 1), digitized and validated methods based on standard pen-and-paper tests and scales (Level 2), and finally fully digitized cognitive batteries and remote measurement technologies (RMTs, Level 3). Pros and cons of these strategies are discussed. Remotely collected data negates the need for frail patients or carers to commute to clinic and offers valuable insights into progression over time, as well as treatment responses to therapeutic interventions, providing a more realistic and contextualized environment for data-collection. Notwithstanding several challenges related to internet access, computer skills, limited evidence base and regulatory and data protection issues, digital biomarkers collected remotely have significant potential for diagnosis and symptom management in older adults and we propose a framework and pathway for how technologies can be implemented to support remote memory clinics. These platforms are also well-placed for administration of digital cognitive training and other interventions. The individual, societal and public/private costs of COVID-19 are high and will continue to rise for some time but the challenges the pandemic has placed on memory services also provides an opportunity to embrace novel approaches. Remote memory clinics' financial, logistical, clinical and practical benefits have been highlighted by COVID-19, supporting their use to not only be maintained when social distancing legislation is lifted but to be devoted extra resources and attention to fully potentiate this valuable arm of clinical assessment and care.

A Mobile-Based Screening System for Data Analyses of Early Dementia Traits Detection

Existing early detection methods that deal with the pre-diagnosis of dementia have been criticised as not being comprehensive as they do not measure certain cognitive functioning domains besides being inaccessible. A more realistic approach is to develop a comprehensive outcome that includes cognitive functioning of dementia, as this will offer a robust and unbiased outcome for an individual. In this research, a mobile screening application for dementia traits called DementiaTest is proposed, which adopts the gold standard assessment criteria of Diagnostic and Statistical Manual of Mental Disorders (DSM-V). DementiaTest is implemented and tested on the Android and IOS stores. More importantly, it collects data from cases and controls using an easy, interactive, and accessible platform. It provides patients and their family with quick pre-diagnostic reports using certain cognitive functioning indicators; these can be utilized by general practitioners (GPs) for referrals for further assessment in cases of positive outcomes. The data gathered using the new application can be analysed using Artificial Intelligence methods to evaluate the performance of the screening to pinpoint early signs of the dementia.

Cognitive impairment screening using m-health: an android implementation of the mini-mental state examination (MMSE) using speech recognition

PURPOSE

This paper describes an implementation of an Android application that allows cognitive functions to be tested.

METHODS

The application consists of a slightly modified Mini Mental state Examination (MMSE) test that provides examiner assistance in diagnosing cognitive impairments. The application deploys speech recognition techniques to allow easy and automated scoring of the test. The test results and test-related information are stored in a database providing easy access to data for follow-up and analysis, resulting in overall benefits to the examiner workflow. A small-scale pilot study of 5 months duration was conducted in a nursing home where 15 residents were tested with the MMSE app test and with the (paper) MMSE test with the aim of determining the agreement between the two test methods.

RESULTS

The final MMSE test scores, with a maximum score of 30, agree; the differences have a mean of 0.1, a standard deviation of 2.1 and fall in a [- 4, + 4] range as is illustrated in a Bland-Altman analysis. From examiner reflections, the motoric skills of the participant are indicated to contribute strongly to the time benefit of the assessment itself.

CONCLUSIONS

The findings of this study suggest that the mobile digital version of the slightly modified MMSE test has the potential to be used as an attractive alternative for the conventional paper version of the test.

Mobile-Health Applications for the Efficient Delivery of Health Care Facility to People with Dementia (PwD) and Support to Their Carers: A Survey

Dementia directly influences the quality of life of a person suffering from this chronic illness. The caregivers or carers of dementia people provide critical support to them but are subject to negative health outcomes because of burden and stress. The intervention of mobile health (mHealth) has become a fast-growing assistive technology (AT) in therapeutic treatment of individuals with chronic illness. The purpose of this comprehensive study is to identify, appraise, and synthesize the existing evidence on the use of mHealth applications (apps) as a healthcare resource for people with dementia and their caregivers. A review of both peer-reviewed and full-text literature was undertaken across five (05) electronic databases for checking the articles published during the last five years (between 2014 and 2018). Out of 6195 searches yielded articles, 17 were quantified according to inclusion and exclusion criteria. The included studies distinguish between five categories, viz., (1) cognitive training and daily living, (2) screening, (3) health and safety monitoring, (4) leisure and socialization, and (5) navigation. Furthermore, two most popular commercial app stores, i.e., Google Play Store and Apple App Store, were searched for finding mHealth based dementia apps for PwD and their caregivers. Initial search generated 356 apps with thirty-five (35) meeting the defined inclusion and exclusion criteria. After shortlisting of mobile applications, it is observed that these existing apps generally addressed different dementia specific aspects overlying with the identified categories in research articles. The comprehensive study concluded that mobile health apps appear as feasible AT intervention for PwD and their carers irrespective of limited available research, but these apps have potential to provide different resources and strategies to help this community.

Information visualizations of symptom information for patients and providers: a systematic review

Objective

To systematically synthesize the literature on information visualizations of symptoms included as National Institute of Nursing Research common data elements and designed for use by patients and/or healthcare providers.

Methods

We searched CINAHL, Engineering Village, PsycINFO, PubMed, ACM Digital Library, and IEEE Explore Digital Library to identify peer-reviewed studies published between 2007 and 2017. We evaluated the studies using the Mixed Methods Appraisal Tool (MMAT) and a visualization quality score, and organized evaluation findings according to the Health Information Technology Usability Evaluation Model.

Results

Eighteen studies met inclusion criteria. Ten of these addressed all MMAT items; 13 addressed all visualization quality items. Symptom visualizations focused on pain, fatigue, and sleep and were represented as graphs (n = 14), icons (n = 4), and virtual body maps (n = 2). Studies evaluated perceived ease of use (n = 13), perceived usefulness (n = 12), efficiency (n = 9), effectiveness (n = 5), preference (n = 6), and intent to use (n = 3). Few studies reported race/ethnicity or education level.

Conclusion

The small number of studies for each type of information visualization limit generalizable conclusions about optimal visualization approaches. User-centered participatory approaches for information visualization design and more sophisticated evaluation designs are needed to assess which visualization elements work best for which populations in which contexts.

Current approaches to dementia screening and case finding in low- and middle-income countries: Research update and recommendations

Approximately 47 million people have dementia worldwide, with this figure, it is expected to almost triple by 2050. Most people with dementia (approximately two-thirds) live in low- and middle-income countries (LMICs). This presents a significant challenge for such countries that often have limited financial resources and less well-developed health and social care systems. In the absence of a cure, reducing the future costs of dementia care and burden of disease may be best achieved by a greater emphasis on (1) more timely diagnosis with earlier intervention to maintain functional independence and (2) undertaking "screening" in groups at high risk of developing dementia, case finding, and using brief cognitive assessment instruments. In clinical settings, a wide range of instruments for dementia screening and diagnosis are currently available; however, few cognitive assessment tools have been developed specifically for clinical use within LMIC settings. Screening for dementia and cognitive impairment in LMICs largely relies on tools adapted from high-income countries (HICs); these often lack validation in these settings leading to education, literacy, and cultural biases. Research is urgently needed to develop cognitive assessment tools and dementia diagnostic approaches that are appropriate and feasible for clinical use in LMIC settings.

Mobile Technology for Cognitive Assessment of Older Adults: A Scoping Review

BACKGROUND AND OBJECTIVES

The number of people diagnosed with dementia is rising appreciably as the population ages. In an effort to improve outcomes, many have called for facilitating early detection of cognitive decline. Increased use of mobile technology by older adults provides the opportunity to deliver convenient, cost-effective assessments for earlier detection of cognitive impairment. This article presents a review of the literature on how mobile platforms-smartphones and tablets-are being used for cognitive assessment of older adults along with benefits and opportunities associated with using mobile platforms for cognitive assessment.

RESEARCH DESIGN AND METHODS

We searched MEDLINE, Web of Science, PsycInfo, CINAHL, EMBASE, and Cochrane Central Register of Controlled Trials in October 2018. This search returned 7,024 articles. After removing 1,464 duplicates, we screened titles and abstracts then screened full-text for those articles meeting inclusion and exclusion criteria.

RESULTS

Twenty-nine articles met our inclusion criteria and were categorized into 3 groups as follows: (a) mobile versions of existing article or computerized neuropsychological tests; (b) new cognitive tests developed specifically for mobile platforms; and (c) the use of new types of data for cognitive assessment. This scoping review confirms the considerable potential of mobile assessment.

DISCUSSION AND IMPLICATIONS

Mobile technologies facilitate repeated and continuous assessment and support unobtrusive collection of auxiliary behavioral markers of cognitive impairment, thus allowing users to view trends and detect acute changes that have traditionally been difficult to identify. Opportunities include using new mobile sensors and wearable devices, improving reliability and validity of mobile assessments, determining appropriate clinical use of mobile assessment information, and incorporating person-centered assessment principles and digital phenotyping.

A Mobile App (iBeni) With a Neuropsychological Basis for Cognitive Stimulation for Elderly Adults: Pilot and Validation Study

BACKGROUND

Cognitive impairment is considered one of the most feared chronic conditions among the older adult population since its incidence is approximately twice more frequent than that of dementia. In Mexico, no studies or reports of older adults using technology for cognitive interventions have been published, given that institutions usually frame cognitive stimulation tasks in paper and pencil (ie, in the traditional manner).

OBJECTIVE

The objective of this study was to create and analyze the effect, viability, and impact of a mobile app for cognitive stimulation implemented among a group of elderly adults (over 60 years of age) from the state of Hidalgo in Mexico.

METHODS

This study was a nonprobabilistic pilot trial using convenience sampling. An intervention was implemented among a group of 22 older adults between 60 and 80 years of age over 12 weeks. Half of the older adults were stimulated with the mobile app (experimental group) and the other half followed the traditional paper and pencil training (control group). Assessments with the Mini-Mental State Examination (MMSE) and the Neuropsi, a neuropsychological test validated in Mexico, were done before and after both cognitive stimulations.

RESULTS

According to the analyzed data, 6/11 (55%) participants from the experimental group obtained better results in their cognitive skills, and 5 (45%) of the adults maintained their score, given that the participants were able to execute the exercises repetitively. Meanwhile, for the control group, only 3/11 (27%) participants obtained better results in the postevaluation. Significant values for results of the MMSE were obtained in the postevaluation for the experimental group compared to the control group, while results did not show significant differences in the Neuropsi. Regarding the validation of the app, all the participants evaluated its pertinence positively.

CONCLUSIONS

The intervention data show that the experimental group obtained better results in the postevaluation given that the participants were able to execute the exercises repetitively. The control group could not accomplish this since they had to respond on the manual and no further attempts were provided. However, both groups increased their score in the neuropsychological evaluations. This suggests that a longer and more frequent intervention is required.

REGISTERED REPORT IDENTIFIER

RR1-10.2196/9603.

Mobile technology for mental health assessment

Assessment and outcome monitoring are critical for the effective detection and treatment of mental illness. Traditional methods of capturing social, functional, and behavioral data are limited to the information that patients report back to their health care provider at selected points in time. As a result, these data are not accurate accounts of day-to-day functioning, as they are often influenced by biases in self-report. Mobile technology (mobile applications on smartphones, activity bracelets) has the potential to overcome such problems with traditional assessment and provide information about patient symptoms, behavior, and functioning in real time. Although the use of sensors and apps are widespread, several questions remain in the field regarding the reliability of off-the-shelf apps and sensors, use of these tools by consumers, and provider use of these data in clinical decision-making.

Effect of educational status on performance of older adults in digital cognitive tasks: A systematic review

As people age, cognitive abilities may decline resulting in serious disabilities. Neuropsychological instruments can provide information on the cognitive state of older adults. Researchers worldwide have been using digital cognitive tests to assess cognitive domains.

OBJECTIVE

To determine whether educational status affects the performance of older adults on digital cognitive tasks.

METHODS

A systematic review of articles in English, Portuguese, or Spanish published in the last 5 years was conducted. The databases searched were SCOPUS, PubMed, Lilacs, Scielo and PsychInfo. The PRISMA method was used.

RESULTS

A total of 7,089 articles were initially retrieved. After search and exclusion with justification, seven articles were selected for further review.

CONCLUSION

The findings revealed that researchers using digital tasks generally employed paper-based tests to compare results. Also, no association between years of education and test performance was found. Finally, a dearth of studies using digital tests published by Brazilian researchers was evident.

Spatial and dynamical handwriting analysis in mild cognitive impairment

Background and Objectives Standard clinical procedure of Mild Cognitive Impairment (MCI) assessment employs time-consuming tests of psychological evaluation and requires the involvement of specialists. The employment of quantitative methods proves to be superior to clinical judgment, yet reliable, fast and inexpensive tests are not available. This study was conducted as a first step towards the development of a diagnostic tool based on handwriting. Methods In this paper the handwriting sample of a group of 37 patients with MCI (mean age 76.1±5.8) and 37 healthy controls (mean age 74.8±5.7) was collected using a Livescribe Echo Pen while completing three tasks: (1) regular writing, (2) all-capital-letters writing, and (3) single letter multiply repeated. Parameters differentiating both groups were selected in each task. Results Subjects with confirmed MCI needed more time to complete task one (median 119.5s, IQR - interquartile range - 38.1 vs. 95.1s, IQR 29.2 in control and MCI group, p-value <0.05) and two (median 84.2s, IQR 49.2 and 53.7s, IQR 30.5 in control and MCI group) as their writing was significantly slower. These results were associated with a longer time to complete a single stroke of written text. The written text was also noticeably larger in the MCI group in all three tasks (e.g. median height of the text block in task 2 being 22.3mm, IQR 12.9 in MCI and 20.2mm, IQR 8.7 in control group). Moreover, the MCI group showed more variation in the dynamics of writing: longer pause between strokes in task 1 and 2. The all-capital-letters task produced most of the discriminating features. Conclusion Proposed handwriting features are significant in distinguishing MCI patients. Inclusion of quantitative handwriting analysis in psychological assessment may be a step forward towards a fast MCI diagnosis.

Scoring systems for the Clock Drawing Test: A historical review

The Clock Drawing Test (CDT) is a simple neuropsychological screening instrument that is well accepted by patients and has solid psychometric properties. Several different CDT scoring methods have been developed, but no consensus has been reached regarding which scoring method is the most accurate. This article reviews the literature on these scoring systems and the changes they have undergone over the years. Historically, different types of scoring systems emerged. Initially, the focus was on screening for dementia, and the methods were both quantitative and semi-quantitative. Later, the need for an early diagnosis called for a scoring system that can detect subtle errors, especially those related to executive function. Therefore, qualitative analyses began to be used for both differential and early diagnoses of dementia. A widely used qualitative method was proposed by Rouleau et al. (1992). Tracing the historical path of these scoring methods is important for developing additional scoring systems and furthering dementia prevention research.