Less Daily Computer Use is Related to Smaller Hippocampal Volumes in Cognitively Intact Elderly

Can mobile health apps with smartphones and tablets be the new frontier of cognitive rehabilitation in older individuals? A narrative review of a growing field

INTRODUCTION

A recent interesting field of application of telemedicine/e-health involved smartphone apps. Although research on mHealth began in 2014, there are still few studies using these technologies in healthy elderly and in neurodegenerative disorders. Thus, the aim of the present review was to summarize current evidence on the usability and effectiveness of the use of mHealth in older adults and patients with neurodegenerative disorders.

METHODS

This review was conducted by searching for recent peer-reviewed articles published between June 1, 2010 and March 2023 using the following databases: Pubmed, Embase, Cochrane Database, and Web of Science. After duplicate removal, abstract and title screening, 25 articles were included in the full-text assessment.

RESULTS

Ten articles assessed the acceptance and usability, and 15 articles evaluated the efficacy of e-health in both older individuals and patients with neurodegenerative disorders. The majority of studies reported that mHealth training was well accepted by the users, and was able to stimulate cognitive abilities, such as processing speed, prospective and episodic memory, and executive functioning, making smartphones and tablets valuable tools to enhance cognitive performances. However, the studies are mainly case series, case-control, and in general small-scale studies and often without follow-up, and only a few RCTs have been published to date.

CONCLUSIONS

Despite the great attention paid to mHealth in recent years, the evidence in the literature on their effectiveness is scarce and not comparable. Longitudinal RCTs are needed to evaluate the efficacy of mHealth cognitive rehabilitation in the elderly and in patients with neurodegenerative disorders.

Leisure-Time Television Viewing and Computer Use, Family History, and Incidence of Dementia

INTRODUCTION

Time spent on screen-based sedentary activities is significantly associated with dementia risk, however, whether the associations vary by family history (FHx) of dementia is currently unknown. We aimed to examine independent associations of two prevalent types of screen-based sedentary activities (television [TV] viewing and computer use) with dementia and assess the modifying effect of FHx.

METHODS

We included 415,048 individuals free of dementia from the UK Biobank. Associations of TV viewing, computer use, and FHx with dementia risk were determined using Cox regression models. We estimated both multiplicative- and additive-scale interactions between TV viewing and computer use and FHx.

RESULTS

During a median follow-up of 12.6 years, 5,549 participants developed dementia. After adjusting for potential confounding factors, we observed that moderate (2-3 h/day; hazard ratio [HR] 1.13, 95% confidence interval 0.03-1.23) and high (>3 h/day; 1.33, 1.21-1.46) TV viewing was associated with a higher dementia risk, compared with low (0-1 h/day) TV viewing. Using restricted cubic spline models, the relationship of TV viewing with dementia was nonlinear (relative to 0 h/day; p for nonlinear = 0.005). We found that >3 h/day of TV viewing was associated with a 42% (1.42, 1.18-1.71) higher dementia risk in participants with FHx while a 30% (1.30, 1.17-1.45) in those without FHx. For computer use, both low (0 h/day; 1.41, 1.33-1.50) and high (>2 h/day; 1.17, 1.05-1.29) computer use were associated with elevated dementia risk, compared with moderate (1-2 h/day) computer use. We observed a J-shaped relationship with dementia (relative to 2 h/day; p for nonlinear <0.001). Compared with 1-2 h/day of computer use, the HRs of dementia were 1.46 (1.29-1.65) and 1.10 (0.90-1.36) for 0 h/day and >2 h/day of computer use in participants with FHx, respectively, while the corresponding HRs were 1.40 (1.30-1.50) and 1.19 (1.06-1.33) in those without FHx. We observed a positive additive interaction (RERI 0.29, 0.06-0.53) between computer use and FHx, while little evidence of interaction between TV viewing and FHx.

CONCLUSIONS

The time spent on TV viewing and computer use were independent risk factors for dementia, and the adverse effects of computer use and FHx were additive. Our findings point to new behavioral targets for intervention on preventing an early onset of dementia, especially for those with FHx.

Using Continuous Passive Assessment Technology to Describe Health and Behavior Patterns Preceding and Following a Cancer Diagnosis in Older Adults: Proof-of-Concept Case Series Study

BACKGROUND

Describing changes in health and behavior that precede and follow a sentinel health event, such as a cancer diagnosis, is challenging because of the lack of longitudinal, objective measurements that are collected frequently enough to capture varying trajectories of change leading up to and following the event. A continuous passive assessment system that continuously monitors older adults' physical activity, weight, medication-taking behavior, pain, health events, and mood could enable the identification of more specific health and behavior patterns leading up to a cancer diagnosis and whether and how patterns change thereafter.

OBJECTIVE

In this study, we conducted a proof-of-concept retrospective analysis, in which we identified new cancer diagnoses in older adults and compared trajectories of change in health and behaviors before and after cancer diagnosis.

METHODS

Participants were 10 older adults (mean age 71.8, SD 4.9 years; 3/10, 30% female) with various self-reported cancer types from a larger prospective cohort study of older adults. A technology-agnostic assessment platform using multiple devices provided continuous data on daily physical activity via wearable sensors (actigraphy); weight via a Wi-Fi-enabled digital scale; daily medication-taking behavior using electronic Bluetooth-enabled pillboxes; and weekly pain, health events, and mood with online, self-report surveys.

RESULTS

Longitudinal linear mixed-effects models revealed significant differences in the pre- and postcancer trajectories of step counts (P<.001), step count variability (P=.004), weight (P<.001), pain severity (P<.001), hospitalization or emergency room visits (P=.03), days away from home overnight (P=.01), and the number of pillbox door openings (P<.001). Over the year preceding a cancer diagnosis, there were gradual reductions in step counts and weight and gradual increases in pain severity, step count variability, hospitalization or emergency room visits, and days away from home overnight compared with 1 year after the cancer diagnosis. Across the year after the cancer diagnosis, there was a gradual increase in the number of pillbox door openings compared with 1 year before the cancer diagnosis. There was no significant trajectory change from the pre- to post-cancer diagnosis period in terms of low mood (P=.60) and loneliness (P=.22).

CONCLUSIONS

A home-based, technology-agnostic, and multidomain assessment platform could provide a unique approach to monitoring different types of behavior and health markers in parallel before and after a life-changing health event. Continuous passive monitoring that is ecologically valid, less prone to bias, and limits participant burden could greatly enhance research that aims to improve early detection efforts, clinical care, and outcomes for people with cancer.

Correlating continuously captured home-based digital biomarkers of daily function with postmortem neurodegenerative neuropathology

BACKGROUND

Outcome measures available for use in Alzheimer's disease (AD) clinical trials are limited in ability to detect gradual changes. Measures of everyday function and cognition assessed unobtrusively at home using embedded sensing and computing generated "digital biomarkers" (DBs) have been shown to be ecologically valid and to improve efficiency of clinical trials. However, DBs have not been assessed for their relationship to AD neuropathology.

OBJECTIVES

The goal of the current study is to perform an exploratory examination of possible associations between DBs and AD neuropathology in an initially cognitively intact community-based cohort.

METHODS

Participants included in this study were ≥65 years of age, living independently, of average health for age, and followed until death. Algorithms, run on the continuously-collected passive sensor data, generated daily metrics for each DB: cognitive function, mobility, socialization, and sleep. Fixed postmortem brains were evaluated for neurofibrillary tangles (NFTs) and neuritic plaque (NP) pathology and staged by Braak and CERAD systems in the context of the "ABC" assessment of AD-associated changes.

RESULTS

The analysis included a total of 41 participants (M±SD age at death = 92.2±5.1 years). The four DBs showed consistent patterns relative to both Braak stage and NP score severity. Greater NP severity was correlated with the DB composite and reduced walking speed. Braak stage was associated with reduced computer use time and increased total time in bed.

DISCUSSION

This study provides the first data showing correlations between DBs and neuropathological markers in an aging cohort. The findings suggest continuous, home-based DBs may hold potential to serve as behavioral proxies that index neurodegenerative processes.

Unobtrusive Sensing Technology Detects Ecologically Valid Spatiotemporal Patterns of Daily Routines Distinctive to Persons With Mild Cognitive Impairment

BACKGROUND

The ability to capture people's movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI).

METHODS

One hundred and sixty-one older adults (26 with MCI) living alone (age = 78.9 ± 9.2) were included from 2 study cohorts-the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory.

RESULTS

Latent trajectory models identified 2 diurnal patterns of bathroom usage (high and low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low-usage group (odds ratio [OR] = 4.1, 95% CI [1.3-13.5], p = .02). For kitchen activity, 2 diurnal patterns were identified (high and low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR = 3.2, 95% CI [1.1-9.1], p = .03).

CONCLUSIONS

The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.

Unobtrusive, in-home assessment of older adults' everyday activities and health events: associations with cognitive performance over a brief observation period

In-home assessment of everyday activities over many months to years may be useful in predicting cognitive decline in older adulthood. This study examined whether a comparatively brief data collection period (3 months) may yield similar diagnostic information. A total of 91 community-dwelling older adults without dementia underwent baseline neuropsychological testing and completed weekly computer-based surveys assessing health-related events/activities. A subset of participants wore fitness tracker watches assessing daily sleep and physical activity patterns, used a sensor-instrumented pillbox, and had their computer use frequency recorded on a daily basis. Similar patterns in computer use, sleep and medication use were noted in comparison to prior literature with more extensive data collection periods. Greater computer use and sleep, as well as self-reported pain and independence, were also linked to better cognition. These activities and symptoms may be useful correlates of cognitive function even when assessed over a relatively brief monitoring period.

A Phenome-wide Association and Mendelian Randomization Study for Alzheimer's Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank

BACKGROUND

Considerable uncertainty remains regarding associations of multiple risk factors with Alzheimer's disease (AD). We aimed to systematically screen and validate a wide range of potential risk factors for AD.

METHODS

Among 502,493 participants from the UK Biobank, baseline data were extracted for 4171 factors spanning 10 different categories. Phenome-wide association analyses and time-to-event analyses were conducted to identify factors associated with both polygenic risk scores for AD and AD diagnosis at follow-up. We performed two-sample Mendelian randomization analysis to further assess their potential causal relationships with AD and imaging association analysis to discover underlying mechanisms.

RESULTS

We identified 39 factors significantly associated with both AD polygenic risk scores and risk of incident AD, where higher levels of education, body size, basal metabolic rate, fat-free mass, computer use, and cognitive functions were associated with a decreased risk of developing AD, and selective food intake and more outdoor exposures were associated with an increased risk of developing AD. The identified factors were also associated with AD-related brain structures, including the hippocampus, entorhinal cortex, and inferior/middle temporal cortex, and 21 of these factors were further supported by Mendelian randomization evidence.

CONCLUSIONS

To our knowledge, this is the first study to comprehensively and rigorously assess the effects of wide-ranging risk factors on AD. Strong evidence was found for fat-free body mass, basal metabolic rate, computer use, selective food intake, and outdoor exposures as new risk factors for AD. Integration of genetic, clinical, and neuroimaging information may help prioritize risk factors and prevention targets for AD.

Development of a Questionnaire to Evaluate Older Adults' Total Sedentary Time and Sedentary Time With Cognitive Activity

This study aimed to develop a questionnaire for evaluating total sedentary time (ST) and ST with cognitive activity, and to examine the association between ST and cognitive function among Japanese older adults. The questionnaire to evaluate ST comprised 12 items regarding behavior in specific settings, including 8 items on ST with cognitive activity, in a usual week. Older adults aged ≥75 years who participated in a health check-up assessing cognitive function completed the developed questionnaire and subsequently wore an accelerometer and recorded a diary of ST with cognitive activity for a week as validity measures. Cognitive function was assessed with neuropsychological tests covering 4 domains: memory, attention, executive function, and processing speed. Fifty-two participants were included in the validity analysis. Spearman's correlation coefficient indicated fair-to-good agreement between the questionnaire-measured and the diary-measured time for ST with cognitive activity (r = 0.59, p < 0.001), but this was not the case for total ST. Bland-Altman plots showed that the questionnaire-measured total ST contained proportional bias (r = 0.51, p < 0.001). Multiple regression analysis (n = 49) showed longer questionnaire-measured ST with cognitive activity was significantly associated with better neuropsychological test scores (attention: β = -0.38, p = 0.025; executive function: β = -0.46, p = 0.003; and processing speed: β = 0.31, p = 0.041), while total ST was not associated with better cognitive performance. The developed questionnaire showed acceptable validity to measure ST with cognitive activity, which was found to be protectively associated with cognitive function.

Reproducibility and replicability of high-frequency, in-home digital biomarkers in reducing sample sizes for clinical trials

INTRODUCTION

Reproducibility and replicability of results are rarely achieved for digital biomarkers analyses. We reproduced and replicated previously reported sample size estimates based on digital biomarker and neuropsychological test outcomes in a hypothetical 4-year early-phase Alzheimer's disease trial.

METHODS

Original data and newly collected data (using a different motion sensor) came from the Oregon Center for Aging & Technology (ORCATECH). Given trajectories of those with incident mild cognitive impairment and normal cognition would represent trajectories of the control and experimental groups in a hypothetical trial, sample sizes to provide 80% power to detect effect sizes ranging from 20% to 50% were calculated.

RESULTS

For the reproducibility, identical P-values and slope estimates were found with both digital biomarkers and neuropsychological test measures between the previous and current studies. As for the replicability, a greater correlation was found between original and replicated sample size estimates for digital biomarkers (r = 0.87, P < .001) than neuropsychological test outcomes (r = 0.75, P < .001).

DISCUSSION

Reproducibility and replicability of digital biomarker analyses are feasible and encouraged to establish the reliability of findings.

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone (n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88-0.94]; 0.59[0.48-0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = -0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Older Adults' Daily Activity and Mood Changes Detected During the COVID-19 Pandemic Using Remote Unobtrusive Monitoring Technologies

Background and Objectives

The coronavirus disease 2019 (COVID-19) pandemic has limited older adults' access to in-person medical care, including screenings for cognitive and functional decline. Remote, technology-based tools have shown recent promise in assessing changes in older adults' daily activities and mood, which may serve as indicators of underlying health-related changes (e.g., cognitive decline). This study examined changes in older adults' driving, computer use, mood, and travel events prior to and following the COVID-19 emergency declaration using unobtrusive monitoring technologies and remote online surveys. As an exploratory aim, the impact of mild cognitive impairment (MCI) on these changes was assessed.

Research Design and Methods

Participants were 59 older adults (41 cognitively intact and 18 MCI) enrolled in a longitudinal aging study. Participants had their driving and computer use behaviors recorded over a 5-month period (75 days pre- and 76 days post-COVID emergency declaration) using unobtrusive technologies. Measures of mood, overnight guests, and frequency of overnight travel were also collected weekly via remote online survey.

Results

After adjusting for age, gender, and education, participants showed a significant decrease in daily driving distance, number of driving trips, highway driving, and nighttime driving, post-COVID-19 as compared to pre-COVID-19 (p < .001) based on generalized estimating equation models. Further, participants spent more time on the computer per day post-COVID-19 (p = .03). Participants endorsed increases in blue mood (p < .01) and loneliness (p < .001) and decreases in travel away from home and overnight visitors (p < .001) from pre- to post-COVID-19. Cognitive status did not impact these relationships.

Discussion and Implications

From pre- to post-COVID-19 emergency declaration, participants drove and traveled less, used their computer more, had fewer overnight visitors, and reported greater psychological distress. These results highlight the behavioral and psychological effects of stay-at-home orders on older adults who are cognitively intact and those with MCI.

Computer use and cognitive decline among Japanese older adults: A prospective cohort study

INTRODUCTION

This study aimed to examine the prospective association between computer use and cognitive decline among community-dwelling Japanese older adults, considering the characteristics of computer users.

METHODS

This four-year prospective cohort study was conducted in Obu, Japan. Participants who were cognitive intact at Wave 1 (2011-2012) were followed through the study period. Cognitive decline was defined as scoring below the standard threshold in at least one of four neuropsychological tests at Wave 2 (2015-2016). The association between computer use at Wave 1 and cognitive decline was examined using logistic regression for complete samples (n = 2010, 52.5% female, mean 71.0 ± 4.7 years) and imputed samples (n = 3435, 51.8% female, mean 71.5 ± 5.3 years).

RESULTS

The computer use group had a reduced adjusted odds ratio (aOR) of cognitive decline, after adjustment for covariates, in both the complete and imputed samples (complete samples: aOR 0.71, 95% confidence interval [CI] 0.52-0.97, p = 0.030; imputed samples: aOR 0.67, 95% CI 0.51-0.88, p < 0.003). Stratified analysis of both samples showed that computer users with ≥ 10 years' education, a GDS score of < 6, or a walking speed of ≥ 1.0m/s, showed reduced aOR for cognitive decline (aOR 0.61 to 0.69, p < 0.05). Those with < 10 years of education years, GDS scores ≥ 6 of GDS, or walking speed < 1.0m/s did not show significant association.

CONCLUSION

Computer use is longitudinally associated with protected cognitive function, based on computer user characteristics.

Passively-Measured Routine Home Computer Activity and Application Use Can Detect Mild Cognitive Impairment and Correlate with Important Cognitive Functions in Older Adulthood

BACKGROUND

Computer use is a cognitively complex instrumental activity of daily living (IADL) that has been linked to cognitive functioning in older adulthood, yet little work has explored its capacity to detect incident mild cognitive impairment (MCI).

OBJECTIVE

To examine whether routine home computer use (general computer use as well as use of specific applications) could effectively discriminate between older adults with and without MCI, as well as explore associations between use of common computer applications and cognitive domains known to be important for IADL performance.

METHODS

A total of 60 community-dwelling older adults (39 cognitively healthy, 21 with MCI) completed a neuropsychological evaluation at study baseline and subsequently had their routine home computer use behaviors passively recorded for three months.

RESULTS

Compared to those with MCI, cognitively healthy participants spent more time using the computer, had a greater number of computer sessions, and had an earlier mean time of first daily computer session. They also spent more time using email and word processing applications, and used email, search, and word processing applications on a greater number of days. Better performance in several cognitive domains, but in particular memory and language, was associated with greater frequency of browser, word processing, search, and game application use.

CONCLUSION

Computer and application use are useful in identifying older adults with MCI. Longitudinal studies are needed to determine whether decreases in overall computer use and specific computer application use are predictors of incident cognitive decline.

Neuroimaging in the Oldest-Old: A Review of the Literature

The oldest-old, those 85 years and older, are the fastest growing segment of the population and present with the highest prevalence of dementia. Given the importance of neuroimaging measures to understand aging and dementia, the objective of this study was to review neuroimaging studies performed in oldest-old participants. We used PubMed, Google Scholar, and Web of Science search engines to identify in vivo CT, MRI, and PET neuroimaging studies either performed in the oldest-old or that addressed the oldest-old as a distinct group in analyses. We identified 60 studies and summarized the main group characteristics and findings. Generally, oldest-old participants presented with greater atrophy compared to younger old participants, with most studies reporting a relatively stable constant decline in brain volumes over time. Oldest-old participants with greater global atrophy and atrophy in key brain structures such as the medial temporal lobe were more likely to have dementia or cognitive impairment. The oldest-old presented with a high burden of white matter lesions, which were associated with various lifestyle factors and some cognitive measures. Amyloid burden as assessed by PET, while high in the oldest-old compared to younger age groups, was still predictive of transition from normal to impaired cognition, especially when other adverse neuroimaging measures (atrophy and white matter lesions) were also present. While this review highlights past neuroimaging research in the oldest-old, it also highlights the dearth of studies in this important population. It is imperative to perform more neuroimaging studies in the oldest-old to better understand aging and dementia.

Choosing the right time granularity for analysis of digital biomarker trajectories

INTRODUCTION

The use of digital biomarker data in dementia research provides the opportunity for frequent cognitive and functional assessments that was not previously available using conventional approaches. Assessing high-frequency digital biomarker data can potentially increase the opportunities for early detection of cognitive and functional decline because of improved precision of person-specific trajectories. However, we often face a decision to condense time-stamped data into a coarser time granularity, defined as the frequency at which measurements are observed or summarized, for statistical analyses. It is important to find a balance between ease of analysis by condensing data and the integrity of the data, which is reflected in a chosen time granularity.

METHODS

In this paper, we discuss factors that need to be considered when faced with a time granularity decision. These factors include follow-up time, variables of interest, pattern detection, and signal-to-noise ratio.

RESULTS

We applied our procedure to real-world data which include longitudinal in-home monitored walking speed. The example shed lights on typical problems that data present and how we could use the above factors in exploratory analysis to choose an appropriate time granularity.

DISCUSSION

Further work is required to explore issues with missing data and computational efficiency.

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species.

Cognitive Function and Digital Device Use in Older Adults Attending a Memory Clinic

This study investigated cognitive function in relation to the use of a computer and a touchscreen device among older adults attending a memory clinic. The entire sample (n = 323) was categorized into four profiles, according to the frequency of digital device use (either daily or non-daily usage). Results showed that on a daily basis, 26% of the sample used both a computer and a touchscreen device, 26.9% used only a computer, 7.1% used only a touchscreen device, and 39.9% used neither type of digital device. There were significant group differences on age, education, and clinical diagnosis (p < .001). Non-daily users of digital devices had significantly lower performance, compared with daily users of both types of digital device, on measures of global cognitive function, processing speed, short-term memory, and several components of executive function (p < .001). Falling behind with regard to the use of digital devices might reflect underlying poor cognitive capacities.

Early Detection of Mild Cognitive Impairment With In-Home Monitoring Sensor Technologies Using Functional Measures: A Systematic Review

The aging of the world population is accompanied by a substantial increase in neurodegenerative disorders, such as dementia. Early detection of mild cognitive impairment (MCI), a clinical diagnostic that comes with an increased chance to develop dementias, could be an essential condition for promoting quality of life and independent living, as it would provide a critical window for the implementation of early pharmacological and nonpharmacological interventions. This systematic review aims to investigate the current state of knowledge on the effectiveness of smart home sensors technologies for the early detection of MCI through the monitoring of everyday life activities. This approach offers many advantages, including the continuous measurement of functional abilities in ecological environments. A systematic search of publications in MEDLINE, EMBASE, and CINAHL, before November 2017, was conducted. Seventeen studies were included in this review. Thirteen studies were based on real-life monitoring, with several sensors installed in participants' actual homes, and four studies included scenario-based assessments, in which participants had to complete various tasks in a research lab apartment. In real-life monitoring, the most used indicators of MCI were walking speed and activity/motion in the house. In scenario-based assessment, time of completion, quality of activity completion, number of errors, amount of assistance needed, and task-irrelevant behaviors during the performance of everyday activities predicted MCI in participants. Despite technological limitations and the novelty of the field, smart home technologies represent a promising potential for the early screening of MCI and could support clinicians in geriatric care.

Cognitive behavioral therapy (CBT) for preventing Alzheimer's disease

This review provides the rationale for implementing cognitive behavioral therapy (CBT) for the prevention of Alzheimer's disease (AD). There are known risk factors associated with the development of AD, some of which may be ameliorated with CBT. We posit that treating the risk factors of inactivity, poor diet, hyposmia and anosmia, sleep disorders and lack of regularly engaged challenging cognitive activity will modify the physiology of the brain sufficiently to avoid the accumulation of excess proteins, including amyloid beta, causal events in the development of AD. Further, the successful treatment of the listed risk factors is well within our technology to do so and, even further, it is cost effective. Also, there is considerable scientific literature to support the proposition that, if implemented by well-established practices, CBT will be effective and will be engaged by those of retirement age. That is, we present a biologically informed CBT for the prevention of the development of AD, i.e., an aspect of applied behavioral neuroscience.

Dietary Intake of Nutrients and Lifestyle Affect the Risk of Mild Cognitive Impairment in the Chinese Elderly Population: A Cross-Sectional Study

Mild cognitive impairment (MCI) is a pre-clinical stage of Alzheimer’s disease afflicting a large number of the elderly throughout the world. However, modifiable risk factors for the onset and progression of MCI remain unclear. A cross-sectional study was performed to explore whether and how daily dietary nutrients intake and lifestyle impacted the risk of MCI in the Chinese elderly. We examined 2,892 elderly subjects, including 768 MCI patients and 2,124 subjects with normal cognition in three different Provinces of China. Dietary intake of nutrients were collected by using a 33-item food frequency questionnaire and calculated based on the Chinese Food Composition database. The MCI patients were first screened by Montreal Cognitive Assessment and then diagnosed by medical neurologists. Multivariate logistic regression and exploratory factor analyses were applied to identify and rank the risk factors. Three dietary nutrient intake combination patterns were identified as the major protective factors of MCI, with eigenvalues of 14.11, 2.26, and 1.51 and adjusted odds ratios (OR) of 0.77, 0.81, and 0.83 (P < 0.05), respectively. The most protective combination was featured with eight vitamins and six minerals, and OR for the third and fourth quartiles of these nutrients intake ranged from 0.48 to 0.74 (P < 0.05). Carotenoids, vitamin C, and vitamin B₆ exhibited the highest protective factor loadings of 0.97, 0.95, and 0.92 (P < 0.05), respectively. Education, computer use, reading, and drinking represented the most protective lifestyle factors (OR = 0.25 to 0.85, P < 0.05), whereas smoking and peripheral vascular diseases were associated with higher (OR = 1.40 and 1.76, P < 0.05) risk of MCI. Adequate dietary intake of monounsaturated fatty acids and cholesterol were significantly associated with decreased risk of MCI. In conclusion, adequate or enhanced intake of micronutrients seemed to lower the risk of MCI in the Chinese elderly. In addition, improving education and lifestyle such as reading, computer use and moderate drinking might also help to decrease the risk of MCI.

Modeling Alzheimer Disease Through Functional Independence and Participation

INTRODUCTION

The relationship between cognitive and functional impairment in Alzheimer Disease (AD) at the earliest stages of the disease is not well characterized. This study aimed at investigating such relationships along AD evolution by means of the Disability Assessment for Dementia (DAD).

METHODS

Consecutive pairs of AD outpatients and their primary informal caregivers were enrolled. Patients were evaluated by means of the Mini Mental State Examination and neuropsychological tests. A clinician completed the Clinical Dementia Rating Scale to stage dementia severity and interviewed the caregivers to complete the Neuropsychiatric Inventory to assess behavioral disturbances and the DAD to evaluate patients' functional competence.

RESULTS

A total of 158 dyads were enrolled; the Mini Mental State Examination score was used to stratify patients into 4 groups (>24; 20 to 23.9; 10 to 19.9; <10) that were compared. The statistical analysis revealed that all the cognitive domains were positively related to functional independence, but only logical and executive functions seemed to predict autonomy. An intergroup comparison did not show significant differences in the DAD subscales measuring initiation, planning and organization, and performance. The role of education emerged, confirming the relevance of cognitive reserve.

DISCUSSION

As the field moves toward earlier intervention in preclinical AD, the detection of early functional changes may drive the definition of trials on prevention or intervention for dementia.

Neuroplasticity and Clinical Practice: Building Brain Power for Health

The focus of this review is on driving neuroplasticity in a positive direction using evidence-based interventions that also have the potential to improve general health. One goal is to provide an overview of the many ways new neuroscience can inform treatment protocols to empower and motivate clients to make the lifestyle choices that could help build brain power and could increase adherence to healthy lifestyle changes that have also been associated with simultaneously enhancing vigorous longevity, health, happiness, and wellness. Another goal is to explore the use of a focus in clinical practice on helping clients appreciate this new evidence and use evolving neuroscience in establishing individualized goals, designing strategies for achieving them and increasing treatment compliance. The timing is urgent for such interventions with goals of enhancing brain health across the lifespan and improving statistics on dementia worldwide.

Computer mouse movement patterns: A potential marker of mild cognitive impairment

INTRODUCTION

Subtle changes in cognitively demanding activities occur in MCI but are difficult to assess with conventional methods. In an exploratory study, we examined whether patterns of computer mouse movements obtained from routine home computer use discriminated between older adults with and without MCI.

METHODS

Participants were 42 cognitively intact and 20 older adults with MCI enrolled in a longitudinal study of in-home monitoring technologies. Mouse pointer movement variables were computed during one week of routine home computer use using algorithms that identified and characterized mouse movements within each computer use session.

RESULTS

MCI was associated with making significantly fewer total mouse moves (p<.01), and making mouse movements that were more variable, less efficient, and with longer pauses between movements (p<.05). Mouse movement measures were significantly associated with several cognitive domains (p's<.01-.05).

DISCUSSION

Remotely monitored computer mouse movement patterns are a potential early marker of real-world cognitive changes in MCI.