In this paper we present a new method for passively measuring walking speed using a small array of radio transceivers positioned on the walls of a hallway within a home. As a person walks between a radio transmitter and a receiver, the received signal strength (RSS) detected by the receiver changes in a repeatable pattern that may be used to estimate walking speed without the need for the person to wear any monitoring device. The transceivers are arranged as an array of 4 with a known distance between the array elements. Walking past the first pair of transceivers will cause a peak followed by a second peak when the person passes the second pair of transceivers. The time difference between these peaks is used to estimate walking speed directly. We further show that it is possible to estimate the walking speed by correlating the shape of the signal using a single pair of transceivers positioned across from each other in a hallway or doorframe. RMSE performance was less than 15 cm/s using a 2-element array, and less than 8 cm/s using a 4-element array relative to a gait mat used for ground truth.
Language is being increasingly harnessed to not only create natural human-machine interfaces but also to infer social behaviors and interactions. In the same vein, we investigate a novel spoken language task, of inferring social relationships in two-party conversations: whether the two parties are related as family, strangers or are involved in business transactions. For our study, we created a corpus of all incoming and outgoing calls from a few homes over the span of a year. On this unique naturalistic corpus of everyday telephone conversations, which is unlike Switchboard or any other public domain corpora, we demonstrate that standard natural language processing techniques can achieve accuracies of about 88%, 82%, 74% and 80% in differentiating business from personal calls, family from non-family calls, familiar from unfamiliar calls and family from other personal calls respectively. Through a series of experiments with our classifiers, we characterize the properties of telephone conversations and find: (a) that 30 words of openings (beginnings) are sufficient to predict business from personal calls, which could potentially be exploited in designing context sensitive interfaces in smart phones; (b) our corpus-based analysis does not support Schegloff and Sack’s manual analysis of exemplars in which they conclude that pre-closings differ significantly between business and personal calls – closing fared no better than a random segment; and (c) the distribution of different types of calls are stable over durations as short as 1–2 months. In summary, our results show that social relationships can be inferred automatically in two-party conversations with sufficient accuracy to support practical applications.
conversation telephone speech; social networks; social relationships
Mild disturbances of higher order activities of daily living are present in people diagnosed with mild cognitive impairment (MCI). These deficits may be difficult to detect among those still living independently. Unobtrusive continuous assessment of a complex activity such as home computer use may detect mild functional changes and identify MCI. We sought to determine whether long-term changes in remotely monitored computer use differ in persons with MCI in comparison to cognitively intact volunteers.
Participants enrolled in a longitudinal cohort study of unobtrusive in-home technologies to detect cognitive and motor decline in independently living seniors were assessed for computer usage (number of days with use, mean daily usage and coefficient of variation of use) measured by remotely monitoring computer session start and end times.
Over 230,000 computer sessions from 113 computer users (mean age, 85; 38 with MCI) were acquired during a mean of 36 months. In mixed effects models there was no difference in computer usage at baseline between MCI and intact participants controlling for age, sex, education, race and computer experience. However, over time, between MCI and intact participants, there was a significant decrease in number of days with use (p=0.01), mean daily usage (~1% greater decrease/month; p=0.009) and an increase in day-to-day use variability (p=0.002).
Computer use change can be unobtrusively monitored and indicate individuals with MCI. With 79% of those 55–64 years old now online, this may be an ecologically valid and efficient approach to track subtle clinically meaningful change with aging.
Mild Cognitive Impairment; Assessment of cognitive disorders/dementia; Cohort studies; Activities of daily living; Computer use
We present a device-free indoor tracking system that uses received signal strength (RSS) from radio frequency (RF) transceivers to estimate the location of a person. While many RSS-based tracking systems use a body-worn device or tag, this approach requires no such tag. The approach is based on the key principle that RF signals between wall-mounted transceivers reflect and absorb differently depending on a person’s movement within their home. A hierarchical neural network hidden Markov model (NN-HMM) classifier estimates both movement patterns and stand vs. walk conditions to perform tracking accurately. The algorithm and features used are specifically robust to changes in RSS mean shifts in the environment over time allowing for greater than 90% region level classification accuracy over an extended testing period. In addition to tracking, the system also estimates the number of people in different regions. It is currently being developed to support independent living and long-term monitoring of seniors.
Indoor localization; indoor tracking; device-free passive localization; tag-free tracking; machine learning; neural network; health care; mobility
Trials aimed at preventing cognitive decline through cognitive stimulation among those with normal cognition or mild cognitive impairment are of significant importance in delaying the onset of dementia and reducing dementia prevalence. One challenge in these prevention trials is sample recruitment bias. Those willing to volunteer for these trials could be socially active, in relatively good health, and have high educational levels and cognitive function. These participants’ characteristics could reduce the generalizability of study results and, more importantly, mask trial effects. We developed a randomized controlled trial to examine whether conversation-based cognitive stimulation delivered through personal computers, a webcam and the internet would have a positive effect on cognitive function among older adults with normal cognition or mild cognitive impairment. To examine the selectivity of samples, we conducted a mass mail-in survey distribution among community-dwelling older adults, assessing factors associated with a willingness to participate in the trial.
Two thousand mail-in surveys were distributed to retirement communities in order to collect data on demographics, the nature and frequency of social activities, personal computer use and additional health-related variables, and interest in the prevention study. We also asked for their contact information if they were interested in being contacted as potential participants in the trial.
Of 1,102 surveys returned (55.1% response rate), 983 surveys had complete data for all the variables of interest. Among them, 309 showed interest in the study and provided their contact information (operationally defined as the committed with interest group), 74 provided contact information without interest in the study (committed without interest group), 66 showed interest, but provided no contact information (interest only group), and 534 showed no interest and provided no contact information (no interest group). Compared with the no interest group, the committed with interest group were more likely to be personal computer users (odds ratio (OR) = 2.78), physically active (OR = 1.03) and had higher levels of loneliness (OR = 1.16).
Increasing potential participants’ familiarity with a personal computer and the internet before trial recruitment could increase participation rates and improve the generalizability of future studies of this type.
The trial was registered on 29 March 2012 at ClinicalTirals.gov (ID number NCT01571427).
Sample recruitment selection bias; Volunteer bias; Behavioral randomized controlled trial; PC; Internet; Webcam; Conversation-based social interaction; Cognitive function; Mild cognitive impairment
Incidental white matter hyperintensities (WMHs) are common findings on T2-weighted magnetic resonance images of the aged brain and have been associated with cognitive decline. While a variety of pathogenic mechanisms have been proposed, the origin of WMHs and the extent to which lesions in the deep and periventricular white matter reflect distinct etiologies remains unclear. Our aim was to quantify the fractional blood volume (vb) of small WMHs in vivo using a novel magnetic resonance imaging (MRI) approach and examine the contribution of blood–brain barrier disturbances to WMH formation in the deep and periventricular white matter.
Twenty-three elderly volunteers (aged 59–82 years) underwent 7 Tesla relaxographic imaging and fluid-attenuated inversion recovery (FLAIR) MRI. Maps of longitudinal relaxation rate constant (R1) were prepared before contrast reagent (CR) injection and throughout CR washout. Voxelwise estimates of vb were determined by fitting temporal changes in R1 values to a two-site model that incorporates the effects of transendothelial water exchange. Average vb values in deep and periventricular WMHs were determined after semi-automated segmentation of FLAIR images. Ventricular permeability was estimated from the change in CSF R1 values during CR washout.
In the absence of CR, the total water fraction in both deep and periventricular WMHs was increased compared to normal appearing white matter (NAWM). The vb of deep WMHs was 1.8 ± 0.6 mL/100 g and was significantly reduced compared to NAWM (2.4 ± 0.8 mL/100 g). In contrast, the vb of periventricular WMHs was unchanged compared to NAWM, decreased with ventricular volume and showed a positive association with ventricular permeability.
Hyperintensities in the deep WM appear to be driven by vascular compromise, while those in the periventricular WM are most likely the result of a compromised ependyma in which the small vessels remain relatively intact. These findings support varying contributions of blood–brain barrier and brain-CSF interface disturbances in the pathophysiology of deep and periventricular WMHs in the aged human brain.
Aging; Blood–brain barrier; Blood volume; Relaxographic imaging; Periventricular; White matter hyperintensity; 7T
This report describes the baseline experience of the multi-center, Home Based Assessment (HBA) study, designed to develop methods for dementia prevention trials using novel technologies for test administration and data collection. Non-demented individuals ≥ 75 years old were recruited and evaluated in-person using established clinical trial outcomes of cognition and function, and randomized to one of 3 assessment methodologies: 1) mail-in questionnaire/live telephone interviews (MIP); 2) automated telephone with interactive voice recognition (IVR); and 3) internet-based computer Kiosk (KIO). Brief versions of cognitive and non-cognitive outcomes, were adapted to each methodology and administered at baseline and repeatedly over a 4-year period. “Efficiency” measures assessed the time from screening to baseline, and staff time required for each methodology. 713 individuals signed consent and were screened; 640 met eligibility and were randomized to one of 3 assessment arms and 581 completed baseline. Drop out, time from screening to baseline and total staff time were highest among those assigned to KIO. However efficiency measures were driven by non-recurring start-up activities suggesting that differences may be mitigated over a long trial. Performance among HBA instruments collected via different technologies will be compared to established outcomes over this 4 year study.
Alzheimer’s disease; clinical trials; in-home assessment; prevention studies
To determine which vascular pathology measure most strongly correlates with white matter hyperintensity (WMH) accumulation over time, and whether Alzheimer disease (AD) neuropathology correlates with WMH accumulation.
Sixty-six older persons longitudinally followed as part of an aging study were included for having an autopsy and >1 MRI scan, with last MRI scan within 36 months of death. Mixed-effects models were used to examine the associations between longitudinal WMH accumulation and the following neuropathologic measures: myelin pallor, arteriolosclerosis, microvascular disease, microinfarcts, lacunar infarcts, large-vessel infarcts, atherosclerosis, neurofibrillary tangle rating, and neuritic plaque score. Each measure was included one at a time in the model, adjusted for duration of follow-up and age at death. A final model included measures showing an association with p < 0.1.
Mean age at death was 94.5 years (5.5 SD). In the final mixed-effects models, arteriolosclerosis, myelin pallor, and Braak score remained significantly associated with increased WMH accumulation over time. In post hoc analysis, we found that those with Braak score 5 or 6 were more likely to also have high atherosclerosis present compared with those with Braak score 1 or 2 (p = 0.003).
Accumulating white matter changes in advanced age are likely driven by small-vessel ischemic disease. Additionally, these results suggest a link between AD pathology and white matter integrity disruption. This may be due to wallerian degeneration secondary to neurodegenerative changes. Alternatively, a shared mechanism, for example ischemia, may lead to both vascular brain injury and neurodegenerative changes of AD. The observed correlation between atherosclerosis and AD pathology supports the latter.
The demand for rapidly administered, sensitive, and reliable cognitive assessments that are specifically designed for identifying individuals in the earliest stages of cognitive decline (and to measure subtle change over time) has escalated as the emphasis in Alzheimer’s disease clinical research has shifted from clinical diagnosis and treatment toward the goal of developing presymptomatic neuroprotective therapies. To meet these changing clinical requirements, cognitive measures or tailored batteries of tests must be validated and determined to be fit-for-use for the discrimination between cognitively healthy individuals and persons who are experiencing very subtle cognitive changes that likely signal the emergence of early mild cognitive impairment. We sought to collect and review data systematically from a wide variety of (mostly computer-administered) cognitive measures, all of which are currently marketed or distributed with the claims that these instruments are sensitive and reliable for the early identification of disease or, if untested for this purpose, are promising tools based on other variables. The survey responses for 16 measures/batteries are presented in brief in this review; full survey responses and summary tables are archived and publicly available on the Campaign to Prevent Alzheimer’s Disease by 2020 Web site (http://pad2020.org). A decision tree diagram highlighting critical decision points for selecting measures to meet varying clinical trials requirements has also been provided. Ultimately, the survey questionnaire, framework, and decision guidelines provided in this review should remain as useful aids for the evaluation of any new or updated sets of instruments in the years to come.
Cognition; Neuropsychological assessment; Alzheimer’s disease; Mild cognitive impairment; Clinical trials
Using novel monitoring technologies, we sought to ascertain the association between self-report of low mood and unobtrusively measured behaviors (walking speed, time out of residence, frequency of room transitions, and computer use) in community-dwelling older adults.
Longitudinal cohort study of older adults whose homes were outfitted with activity sensors. The participants completed internet-based weekly health questionnaires with questions about mood.
Apartments and homes of older adults living in the Portland, Oregon metropolitan area.
157 adults, average age 84, followed for an average of 3.7 years.
Mood was assessed by self-report each week. Walking speed, time spent out of residence, and room transitions were estimated using data from sensors; computer use was measured by timing actual use. We ascertained the association between global or weekly low mood and the four behavior measures, adjusting for baseline characteristics.
18,960 weekly observations of mood were analyzed; 2.6% involved low mood. Individuals who reported low mood more often showed no average differences in any behavior parameters compared to those who reported low mood less often. During weeks when they reported low mood, participants spent significantly less time out of residence and on the computer, but showed no change in walking speed or room transitions.
Low mood in these community-dwelling older adults involved going out of the house less and using the computer less, but no consistent changes in movements. Technologies to monitor in-home behavior may have potential for research and clinical care.
Psychomotor; mood; sensors; behaviors; monitoring; technologies
To evaluate the efficacy of cognitive rehabilitation therapies (CRTs) for mild cognitive impairment (MCI). Our review revealed a need for evidence-based treatments for MCI and a lack of a theoretical rehabilitation model to guide the development and evaluation of these interventions. We have thus proposed a theoretical rehabilitation model of MCI that yields key intervention targets - cognitive compromise, functional compromise, neuropsychiatric symptoms, and modifiable risk and protective factors known to be associated with MCI and dementia. Our model additionally defines specific cognitive rehabilitation approaches that may directly or indirectly target key outcomes - restorative cognitive training, compensatory cognitive training, lifestyle interventions, and psychotherapeutic techniques.
Fourteen randomized controlled trials met inclusion criteria and were reviewed.
Studies markedly varied in terms of intervention approaches and selected outcome measures and were frequently hampered by design limitations. The bulk of the evidence suggested that CRTs can change targeted behaviors in individuals with MCI and that CRTs are associated with improvements in objective cognitive performance, but the pattern of effects on specific cognitive domains was inconsistent across studies. Other important outcomes (i.e., daily functioning, quality of life, neuropsychiatric symptom severity) were infrequently assessed across studies. Few studies evaluated long-term outcomes or the impact of CRTs on conversion rates from MCI to dementia or normal cognition.
Overall, results from trials are promising but inconclusive. Additional well-designed and adequately powered trials are warranted and required before CRTs for MCI can be considered evidence based.
mild cognitive impairment; cognitive rehabilitation therapy; cognitive training; systematic review; neuropsychological; dementia
Fundamental laws governing human mobility have many important applications such as forecasting and controlling epidemics or optimizing transportation systems. These mobility patterns, studied in the context of out of home activity during travel or social interactions with observations recorded from cell phone use or diffusion of money, suggest that in extra-personal space humans follow a high degree of temporal and spatial regularity – most often in the form of time-independent universal scaling laws. Here we show that mobility patterns of older individuals in their home also show a high degree of predictability and regularity, although in a different way than has been reported for out-of-home mobility. Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. These results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.
To test for an association between the apolipoprotein E (APOE) ε4 allele and dementias with synucleinopathy.
Genetic case-control association study.
Autopsied subjects were classified into 5 categories: dementia with high-level Alzheimer disease (AD) neuropathologic changes (NCs) but without Lewy body disease (LBD) NCs (AD group; n=244), dementia with LBDNCs and high-level ADNCs (LBD-AD group; n=224), dementia with LBDNCs and no or low levels of ADNCs (pure DLB [pDLB] group; n=91), Parkinson disease dementia (PDD) with no or low levels of ADNCs (n=81), and control group (n=269).
Main Outcome Measure
The APOE allele frequencies.
The APOE ε4 allele frequency was significantly higher in the AD (38.1%), LBD-AD (40.6%), pDLB (31.9%), and PDD (19.1%) groups compared with the control group (7.2%; overall χ42=185.25; P=5.56×10−39), and it was higher in the pDLB group than the PDD group (P=.01). In an age-adjusted and sex-adjusted dominant model, ε4 was strongly associated with AD (odds ratio, 9.9; 95% CI, 6.4–15.3), LBD-AD (odds ratio, 12.6; 95% CI, 8.1–19.8), pDLB (odds ratio, 6.1; 95% CI, 3.5–10.5), and PDD (odds ratio, 3.1; 95% CI, 1.7–5.6).
The APOE ε4 allele is a strong risk factor across the LBD spectrum and occurs at an increased frequency in pDLB relative to PDD. This suggests that ε4 increases the likelihood of presenting with dementia in the context of a pure synucleinopathy. The elevated ε4 frequency in the pDLB and PDD groups, in which the overall brain neuritic plaque burden was low, indicates that apoE might contribute to neurodegeneration through mechanisms unrelated to amyloid processing.
Revised diagnostic criteria for Alzheimer disease (AD) acknowledge a key role of imaging biomarkers for early diagnosis. Diagnostic accuracy depends on which marker (i.e., amyloid imaging, 18F-fluorodeoxyglucose [FDG]-PET, SPECT, MRI) as well as how it is measured (“metric”: visual, manual, semiautomated, or automated segmentation/computation). We evaluated diagnostic accuracy of marker vs metric in separating AD from healthy and prognostic accuracy to predict progression in mild cognitive impairment. The outcome measure was positive (negative) likelihood ratio, LR+ (LR−), defined as the ratio between the probability of positive (negative) test outcome in patients and the probability of positive (negative) test outcome in healthy controls. Diagnostic LR+ of markers was between 4.4 and 9.4 and LR− between 0.25 and 0.08, whereas prognostic LR+ and LR− were between 1.7 and 7.5, and 0.50 and 0.11, respectively. Within metrics, LRs varied up to 100-fold: LR+ from approximately 1 to 100; LR− from approximately 1.00 to 0.01. Markers accounted for 11% and 18% of diagnostic and prognostic variance of LR+ and 16% and 24% of LR−. Across all markers, metrics accounted for an equal or larger amount of variance than markers: 13% and 62% of diagnostic and prognostic variance of LR+, and 29% and 18% of LR−. Within markers, the largest proportion of diagnostic LR+ and LR− variability was within 18F-FDG-PET and MRI metrics, respectively. Diagnostic and prognostic accuracy of imaging AD biomarkers is at least as dependent on how the biomarker is measured as on the biomarker itself. Standard operating procedures are key to biomarker use in the clinical routine and drug trials.
Oxidative stress, inflammation, and increased cholesterol levels are all mechanisms that have been associated with Alzheimer’s disease (AD) pathology. Several epidemiologic studies have reported a decreased risk of AD with fish consumption. This pilot study was designed to evaluate the effects of supplementation with omega-3 fatty acids alone (ω-3) or omega-3 plus alpha lipoic acid (ω-3 +LA) compared to placebo on oxidative stress biomarkers in AD. The primary outcome measure was peripheral F2-isoprostane levels (oxidative stress measure). Secondary outcome measures included performance on: Mini-Mental State Examination (MMSE), Activities of Daily Living/Instrumental Activities of Daily Living (ADL/IADL), and Alzheimer Disease Assessment Scale-cognitive subscale (ADAS-cog). Thirty-nine AD subjects were randomized to one of three groups: 1) placebo, 2) ω-3, or 3) ω-3 + LA for a treatment duration of 12 months. Eighty seven percent (34/39) of the subjects completed the 12-month intervention. There was no difference between groups at 12 months in peripheral F2-isoprostane levels (p = 0.83). The ω-3 +LA and ω-3 were not significantly different than the placebo group in ADAS-cog (p = 0.98, p = 0.86) and in ADL (p = 0.15, p = 0.82). Compared to placebo, the ω-3+LA showed less decline in MMSE (p< 0.01) and IADL (p= 0.01) and the ω-3 group showed less decline in IADL (p < 0.01). The combination of ω-3+LA slowed cognitive and functional decline in AD over 12 months. Because the results were generated from a small sample size, further evaluation of the combination of omega-3 fatty acids plus alpha-lipoic acid as a potential treatment in AD is warranted.
Alpha-lipoic acid; Alzheimer’s disease; clinical trial; omega-3 fatty acids
This study aims to infer the social nature of conversations from their content automatically. To place this work in context, our motivation stems from the need to understand how social disengagement affects cognitive decline or depression among older adults. For this purpose, we collected a comprehensive and naturalistic corpus comprising of all the incoming and outgoing telephone calls from 10 subjects over the duration of a year. As a first step, we learned a binary classifier to filter out business related conversation, achieving an accuracy of about 85%. This classification task provides a convenient tool to probe the nature of telephone conversations. We evaluated the utility of openings and closing in differentiating personal calls, and find that empirical results on a large corpus do not support the hypotheses by Schegloff and Sacks that personal conversations are marked by unique closing structures. For classifying different types of social relationships such as family vs other, we investigated features related to language use (entropy), hand-crafted dictionary (LIWC) and topics learned using unsupervised latent Dirichlet models (LDA). Our results show that the posteriors over topics from LDA provide consistently higher accuracy (60-81%) compared to LIWC or language use features in distinguishing different types of conversations.
With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders’ behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.
To describe the attitudes of U.S. neurologists specializing in dementia toward the use of amyloid imaging in the diagnosis of Alzheimer’s Disease (AD).
A cross-sectional electronic physician survey of dementia specialists at U.S. medical schools was performed.
The response rate for the survey was 51.9% (135/260). Greater than 83% of respondents plan to use amyloid imaging to evaluate patients for Alzheimer disease. Most respondents intend to use amyloid imaging as an adjunctive diagnostic modality to confirm (77%) or rule-out (73%) a diagnosis of Alzheimer dementia; 24% plan to use amyloid imaging to screen asymptomatic individuals for evidence of cerebral amyloid. Specialists who do not intend to use amyloid imaging (16%) express concern about the cost (73%), the usefulness (55%), and likelihood of patient (55%) and clinician (59%) misinterpretation of findings. The need for patient pre-test counseling was endorsed by a large percentage (92%) of dementia specialists (higher than for genetic testing (82%)).
Dementia specialists, particularly young specialists, are likely to be early adopters of amyloid imaging. Assuming ready availability, this new technology would be used as a confirmatory test in the evaluation of Alzheimer disease, as well as a screening tool for asymptomatic pathology. Specialists recognize the complexity of interpreting amyloid imaging findings and the need for patient counseling before undergoing testing.
Alzheimer’s disease; dementia; amyloid; PET; neuroimaging; biomarker; diagnosis
Remote telepresence provided by tele-operated robotics represents a new means for obtaining important health information, improving older adults' social and daily functioning and providing peace of mind to family members and caregivers who live remotely. In this study we tested the feasibility of use and acceptance of a remotely controlled robot with video-communication capability in independently living, cognitively intact older adults.
Materials and Methods:
A mobile remotely controlled robot with video-communication ability was placed in the homes of eight seniors. The attitudes and preferences of these volunteers and those of family or friends who communicated with them remotely via the device were assessed through survey instruments.
Overall experiences were consistently positive, with the exception of one user who subsequently progressed to a diagnosis of mild cognitive impairment. Responses from our participants indicated that in general they appreciated the potential of this technology to enhance their physical health and well-being, social connectedness, and ability to live independently at home. Remote users, who were friends or adult children of the participants, were more likely to test the mobility features and had several suggestions for additional useful applications.
Results from the present study showed that a small sample of independently living, cognitively intact older adults and their remote collaterals responded positively to a remote controlled robot with video-communication capabilities. Research is needed to further explore the feasibility and acceptance of this type of technology with a variety of patients and their care contacts.
robotics; tele-operated; aging; technology; video-communication
Mutations in the GBA gene occur in 7% of patients with Parkinson disease (PD) and are a well-established susceptibility factor for PD, which is characterized by Lewy body disease (LBD) neuropathologic changes (LBDNCs). We sought to determine whether GBA influences risk of dementia with LBDNCs, Alzheimer disease (AD) neuropathologic changes (ADNCs), or both.
We screened the entire GBA coding region for mutations in controls and in subjects with dementia and LBDNCs and no or low levels of ADNCs (pure dementia with Lewy bodies [pDLB]), LBDNCs and high-level ADNCs (LBD-AD), and high-level ADNCs but without LBDNCs (AD).
Among white subjects, pathogenic GBA mutations were identified in 6 of 79 pDLB cases (7.6%), 8 of 222 LBD-AD cases (3.6%), 2 of 243 AD cases (0.8%), and 3 of 381 controls (0.8%). Subjects with pDLB and LBD-AD were more likely to carry mutations than controls (pDLB: odds ratio [OR] = 7.6; 95% confidence interval [CI] = 1.8–31.9; p = 0.006; LBD-AD: OR = 4.6; CI = 1.2–17.6; p = 0.025), but there was no significant difference in frequencies between the AD and control groups (OR = 1.1; CI = 0.2–6.6; p = 0.92). There was a highly significant trend test across groups (χ2(1) = 19.3; p = 1.1 × 10−5), with the likelihood of carrying a GBA mutation increasing in the following direction: control/AD < LBD-AD < pDLB.
GBA is a susceptibility gene across the LBD spectrum, but not in AD, and appears to convey a higher risk for PD and pDLB than for LBD-AD. PD and pDLB might be more similar to one another in genetic determinants and pathophysiology than either disease is to LBD-AD.
One of the recommendations of the 2010 Leon Thal Symposium, organized to develop strategies to prevent Alzheimer’s disease, was to build a global database of longitudinal aging studies. While several databases of longitudinal aging studies exist, none of these are comprehensive or complete. In this paper we review selected databases of longitudinal aging studies. We make recommendations on future steps to create a comprehensive database. Additionally, we discuss issues related to data harmonization.
This study examines differences in computer related self-efficacy and anxiety in subgroups of older adults, and changes in those measures following exposure to a systematic training program and subsequent computer use.
Participants were volunteers in the Intelligent Systems for Assessment of Aging Changes Study (ISAAC) carried out by the Oregon Center for Aging and Technology. Participants were administered two questionnaires prior to training and again one year later, related to computer self-efficacy and anxiety. Continuous recording of computer use was also assessed for a subset of participants.
Baseline comparisons by gender, age, education, living arrangement, and computer proficiency, but not cognitive status, yielded significant differences in confidence and anxiety related to specific aspects of computer use. At one-year follow-up, participants reported less anxiety and greater confidence. However, the benefits of training and exposure varied by group and task. Comparisons based on cognitive status showed that the cognitively intact participants benefited more from training and/or experience with computers than did participants with Mild Cognitive Impairment (MCI), who after one year continued to report less confidence and more anxiety regarding certain aspects of computer use.
After one year of consistent computer use, cognitively intact participants in this study reported reduced levels of anxiety and increased self-confidence in their ability to perform specific computer tasks. Participants with MCI at baseline were less likely to demonstrate increased efficacy or confidence than their cognitively intact counterparts.
To determine the time of acceleration in white matter hyperintensity (WMH) burden, a common indicator of cerebrovascular pathology, in relation to conversion to mild cognitive impairment (MCI) in the elderly.
A total of 181 cognitively intact elderly volunteers from the longitudinal, prospective, Oregon Brain Aging Study underwent yearly evaluations, including brain MRI, and cognitive testing. MRIs were analyzed for imaging markers of neurodegeneration: WMH and ventricular CSF (vCSF) volumes. The time before MCI, when the changes in WMH and vCSF burden accelerate, was assessed using a mixed-effects model with a change point for subjects who developed MCI during follow-up.
During a follow-up duration of up to 19.6 years, 134 subjects converted to MCI. Acceleration in %WMH volume increase occurred 10.6 years before MCI onset. On average, the annual rate of change in %WMH increased an additional 3.3% after the change point. Acceleration in %vCSF volume increase occurred 3.7 years before the onset of MCI. Out of 63 subjects who converted to MCI and had autopsy, only 28.5% had Alzheimer disease (AD) as the sole etiology of their dementia, while almost just as many (24%) had both AD and significant ischemic cerebrovascular disease present.
Acceleration in WMH burden, a common indicator of cerebrovascular disease in the elderly, is a pathologic change that emerges early in the presymptomatic phase leading to MCI. Longitudinal changes in WMH may thus be useful in determining those at risk for cognitive impairment and for planning strategies for introducing disease-modifying therapies prior to dementia onset.