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Wearables bei Demenzerkrankungen
Abstract
Zusammenfassung
Demenzerkrankungen führen durch den schleichenden Abbau kognitiver, sozialer und emotionaler Fähigkeiten, auch zu einem Verlust von Autonomie und Selbstbestimmtheit. Wearables sind am Körper getragene Sensoren: Akzelerometer und GPS-Tracker sind im Freizeit- und Fitnessbereich allgegenwärtig – sie zeichnen Bewegungs- und Positionsdaten auf. Das Potenzial, diese bei Demenzpatienten einzusetzen ist groß und wird intensiv beforscht. Wearables sind tlw. auch am Markt erhältlich (bspw. GPS-Tracker in Schuhsohlen). Informationen über Gangbild und Bewegungsdaten können auch Hinweise auf das Sturzrisiko, Verhaltensstörungen/Life-Events oder differenzialdiagnostische Aspekte geben. Trotz des großen Potenzials dürfen ethische Aspekte betreffend die Privatsphäre und den Datenschutz in der Entwicklung nicht außer Acht gelassen werden. Dieser Artikel gibt einen Überblick über die aktuelle Entwicklung von Wearables und damit verbundene ethische Aspekte.
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Purpose
This study aims to examine the factors affecting caregivers’ acceptance of the use of wearable devices by patients with dementia by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model with 2 additional constructs: resistance to change (RC) and technology anxiety (TA).
Methods
A structured online questionnaire was developed and distributed to caregivers who have either dealt previously with or were currently taking care of patients with dementia in sub-Saharan Africa. The selection of countries for the study was purely based on the availability of caregivers of patients with dementia and their readiness to take part in the survey. Questionnaire data from 350 respondents were analyzed and the hypothesis tested using partial least squares based on structural equation modeling.
Result
The results of the extended model show that social influence, effort expectancy, facilitating conditions, and behavioral intention (BI) all had P < .05, thus were statistically significant in explaining the perceived BI and actual use of health-care wearable devices among patients with dementia as reported by caregivers. However, P value for RC and TA was greater than .05, hence were insignificant.
The detection of significant moments can support the care of individuals with dementia by making visible what is most meaningful to them and maintaining a sense of interpersonal connection. We present a novel intelligent assistive technology (IAT) for the detection of significant moments based on patterns of physiological signal changes in individuals with dementia and their caregivers. The parameters of the IAT are tailored to each individual’s idiosyncratic physiological response patterns through an iterative process of incorporating subjective feedback on videos extracted from candidate significant moments identified through the IAT algorithm. The IAT was tested on three dyads (individual with dementia and their primary caregiver) during an eight-week movement program. Upon completion of the program, the IAT identified distinct, personal characteristics of physiological responsiveness in each participant. Tailored algorithms could detect moments of significance experienced by either member of the dyad with an agreement with subjective reports of 70%. These moments were constituted by both physical and emotional significances (e.g., experiences of pain or anxiety) and interpersonal significance (e.g., moments of heighted connection). We provide a freely available MATLAB toolbox with the IAT software in hopes that the assistive technology community can benefit from and contribute to these tools for understanding the subjective experiences of individuals with dementia.
Behavioral and psychological symptoms of dementia are common in Alzheimer’s disease (AD) and associated with a more rapid decline in cognitive function. Psychotropic substances are frequently used in AD, but we lack conclusive evidence of their efficacy in this setting. SSRI and trazodone were reported to have positive effects on cognition. Based on the prospective registry of dementia in Austria (PRODEM), we investigated the effects of psychotropic substances on cognition, behavioral symptoms, and caregiver burden (CB) in patients with AD, followed up prospectively over a 12-month period. We used the Mini-Mental State Examination (MMSE), the Neuropsychiatric Inventory (NPI), and the Zarit caregiver burden interview. The study cohort consisted of 309 patients. Patients taking no psychotropic drugs (NO) or those undergoing consistent monotherapy with a psychotropic drug for 12 months were analyzed further (NO 101 patients, SSRI 22, trazodone 8, atypical neuroleptics or benzodiazepines (ANL/BZD) 18). Additionally, the subgroup of patients who started taking any of the substances during the study period were analyzed further to determine the effects before versus six months after the start of medication. MMSE, NPI, and CB at baseline and during follow-up did not differ between the groups. MMSE and CB declined over 12 months in the overall group (MMSE: 21.2±4 versus 19.7±5, p = 0.001 and CB 20.3±12 versus 24.7±14.2, p = 0.007), but no statistically significant changes were registered within groups over 12 months. When trazodone was started, only NPI improved significantly after 6 months (33.4±18 versus 18.9±22.7, p < 0.01). ANL/BZD or SSRI, when started, did not alter MMSE, NPI, or CB. SSRI had no beneficial effect on cognition. We conclude that trazodone might be helpful in the treatment of behavioral symptoms.
In today’s world, 46.8 million people suffer from brain related diseases. Dementia is most prevalent of all. In general scenario, a dementia patient lacks proper guidance in searching out the way to return back at his/her home. Thus, increasing the risk of getting damaged at individual-health level. Therefore, it is important to track their movement in more sophisticated manner as possible. With emergence of wearables, GPS sensors and Internet of Things (IoT), such devices have become available in public domain. Smartphone apps support caregiver to locate the dementia patients in real-time. RF, GSM, 3G, Wi-Fi and 4G technology fill the communication gap between patient and caregiver to bring them closer. In this paper, we incorporated 7 most popular wearables for investigation to seek appropriateness for dementia tracking in recent times in systematic manners. We performed an in-depth review of these wearables as per the cost, technology wise and application wise characteristics. A case novel study i.e. IoT-based Force Sensor Resistance enabled System-FSRIoT, has been proposed and implemented to validate the effectiveness of IoT in the domain of smarter dementia patient tracking in wearable form factor. The results show promising aspect of a whole new notion to leverage efficient assistive physio-medical healthcare to the dementia patients and the affected family members to reduce life risks and achieve a better social life.
Background:
Among areas that have challenged the progress of dementia care has been the assessment of change in symptoms over time. Digital biomarkers are defined as objective, quantifiable, physiological, and behavioral data that are collected and measured by means of digital devices, such as embedded environmental sensors or wearables. Digital biomarkers provide an alternative assessment approach, as they allow objective, ecologically valid, and long-term follow-up with continuous assessment. Despite the promise of a multitude of sensors and devices that can be applied, there are no agreed-upon standards for digital biomarkers, nor are there comprehensive evidence-based results for which digital biomarkers may be demonstrated to be most effective.
Objective:
In this review, we seek to answer the following questions: (1) What is the evidence for real-life, home-based use of technologies for early detection and follow-up of mild cognitive impairment (MCI) or dementia? And (2) What transformation might clinicians expect in their everyday practices?
Methods:
A systematic search was conducted in PubMed, Cochrane, and Scopus databases for papers published from inception to July 2018. We searched for studies examining the implementation of digital biomarker technologies for mild cognitive impairment or mild Alzheimer disease follow-up and detection in nonclinic, home-based settings. All studies that included the following were examined: community-dwelling older adults (aged 65 years or older); cognitively healthy participants or those presenting with cognitive decline, from subjective cognitive complaints to early Alzheimer disease; a focus on home-based evaluation for noninterventional follow-up; and remote diagnosis of cognitive deterioration.
Results:
An initial sample of 4811 English-language papers were retrieved. After screening and review, 26 studies were eligible for inclusion in the review. These studies ranged from 12 to 279 participants and lasted between 3 days to 3.6 years. Most common reasons for exclusion were as follows: inappropriate setting (eg, hospital setting), intervention (eg, drugs and rehabilitation), or population (eg, psychiatry and Parkinson disease). We summarized these studies into four groups, accounting for overlap and based on the proposed technological solutions, to extract relevant data: (1) data from dedicated embedded or passive sensors, (2) data from dedicated wearable sensors, (3) data from dedicated or purposive technological solutions (eg, games or surveys), and (4) data derived from use of nondedicated technological solutions (eg, computer mouse movements).
Conclusions:
Few publications dealt with home-based, real-life evaluations. Most technologies were far removed from everyday life experiences and were not mature enough for use under nonoptimal or uncontrolled conditions. Evidence available from embedded passive sensors represents the most relatively mature research area, suggesting that some of these solutions could be proposed to larger populations in the coming decade. The clinical and research communities would benefit from increasing attention to these technologies going forward.
Dementia is a critically important issue due to its wide impact on health services as well as personal and society costs. Limitations exists for current dementia protocols with ongoing calls to introduce modern technology to facilitate the creation and addition of digital biomarkers to routine clinical practice. Wearable technology (wearables) are nearly ubiquitous in everyday life, gathering discrete and continuous digital data of habitual activities, but their utility in modern medicine remains lacking. Due to advances in data analytics, wearables are now commonly discussed as pragmatic tools to aid the diagnosis and treatment of a range of neurological disorders. Inertial sensor-based wearables are one such technology that offer a low-cost approach to quantify routine activities that are fundamental to maintain normal activities of daily living, most notably postural control and gait. Here, we provide a narrative review of how wearables are providing useful postural control and gait data to facilitate the capture of digital markers to better aid dementia research. We present wearables from humble beginnings to the current context of using them beyond the clinic, exploring their integration to modern systems with ongoing standardisation and regulatory efforts to integrate their use in clinical trials.
People Living with Dementia (PLwD) often exhibit behavioral and psychological symptoms of dementia; with agitation being one of the most prevalent symptoms. Agitated behaviour in PLwD indicates distress and confusion and increases the risk to injury to both the patients and the caregivers. In this paper, we present the use of wearable devices to detect agitation in PLwD. We hypothesize that combining multi-modal sensor data can help in building better classifiers to identify agitation in PLwD in comparison to a single sensor. We present a unique study to collect motion and physiological data from PLwD. This multi-modal sensor data is subsequently used to build predictive models to detect agitation in PLwD. The results on Random Forest for 28 days of data from PLwD show a strong evidence to support our hypothesis and highlight the importance of using multi-modal sensor data for detecting agitation events amongst them.
Purpose of review:
Recent advances in technology have changed the landscape of treatment for adults with mental illness. This review highlights technological innovations that may improve care for older adults with mental illness and neurocognitive disorders through the measurement and assessment of physical motion. These technologies include wearable sensors (such as smart watches and Fitbits), passive motion sensors, and smart home models that incorporate both active and passive motion technologies.
Recent findings:
Clinicians have evaluated motion measurement technologies in older adults with depression, dementia, anxiety, and schizophrenia. Results from studies in dementia populations suggest that motion measurement technologies can assist clinicians in diagnosing dementia earlier through the evaluation of gait, balance, and postural kinematics. Motion detection technologies can also be used to identify mood episodes at an earlier stage by detecting subtle behavioral changes. Clinicians may use the objective data provided by technologies such as accelerometers to identify illnesses earlier, which may inform treatment decisions. The data may be used as a suitable surrogate marker for detecting depression in older adults, predicting the likelihood of falls, or quantifying physical activity in older adults with chronic mental illnesses or anxiety. Motion-based technologies also have the potential to detect physical activity for older adults residing in nursing homes. Wearable technologies are generally well tolerated in older adults, although the use of new technology and electronic health data could involve privacy and security concerns among this vulnerable population.
Agitation and aggression are among the most challenging symptoms of dementia. Agitated persons with dementia can harm themselves, their caregivers or other patients in a care facility. Automatic detection of agitation would be useful to alert caregivers so that appropriate interventions can be performed. The building blocks in the automatic detection of agitation and aggression are appropriate sensing platforms and generalized predictive models. In this paper, we perform a systematic review of studies that use different types of sensors to detect agitation and aggression in persons with dementia. We conclude that
actigraphy shows some evidence of correlation with incidences of agitation and aggression; however, multi-modal sensing has not been fully evaluated for this purpose. Based on this systematic review, we provide guidelines and recommendations for future research directions in this field.
Distinguishing dementia subtypes can be difficult due to similarities in clinical presentation. There is increasing interest in discrete gait characteristics as markers to aid diagnostic algorithms in dementia. This structured review explores the differences in quantitative gait characteristics between dementia and healthy controls, and between four dementia subtypes under single-task conditions: Alzheimer’s disease (AD), dementia with Lewy bodies and Parkinson’s disease dementia, and vascular dementia. Twenty-six papers out of an initial 5,211 were reviewed and interpreted using a validated model of gait. Dementia was associated with gait characteristics grouped by slower pace, impaired rhythm, and increased variability compared to normal aging. Only four studies compared two or more dementia subtypes. People with AD are less impaired in pace, rhythm, and variability domains of gait compared to non-AD dementias. Results demonstrate the potential of gait as a clinical marker to discriminate between dementia subtypes. Larger studies using a more comprehensive battery of gait characteristics and better characterized dementia sub-types are required.
Background:
The sustained interest in electronic assistive technology in dementia care has been fuelled by the urgent need to develop useful approaches to help support people with dementia at home. Also the low costs and wide availability of electronic devices make it more feasible to use electronic devices for the benefit of disabled persons. Information Communication Technology (ICT) devices designed to support people with dementia are usually referred to as Assistive Technology (AT) or Electronic Assistive Technology (EAT). By using AT in this review we refer to electronic assistive devices. A range of AT devices has been developed to support people with dementia and their carers to manage their daily activities and to enhance safety, for example electronic pill boxes, picture phones, or mobile tracking devices. Many are commercially available. However, the usefulness and user-friendliness of these devices are often poorly evaluated. Although reviews of (electronic) memory aids do exist, a systematic review of studies focusing on the efficacy of AT for memory support in people with dementia is lacking. Such a review would guide people with dementia and their informal and professional carers in selecting appropriate AT devices.
Objectives:
Primary objectiveTo assess the efficacy of AT for memory support in people with dementia in terms of daily performance of personal and instrumental activities of daily living (ADL), level of dependency, and admission to long-term care. Secondary objectiveTo assess the impact of AT on: users (autonomy, usefulness and user-friendliness, adoption of AT); cognitive function and neuropsychiatric symptoms; need for informal and formal care; perceived quality of life; informal carer burden, self-esteem and feelings of competence; formal carer work satisfaction, workload and feelings of competence; and adverse events.
Search methods:
We searched ALOIS, the Specialised Register of the Cochrane Dementia and Cognitive Improvement Group (CDCIG), on 10 November 2016. ALOIS is maintained by the Information Specialists of the CDCIG and contains studies in the areas of dementia prevention, dementia treatment and cognitive enhancement in healthy people. We also searched the following list of databases, adapting the search strategy as necessary: Centre for Reviews and Dissemination (CRD) Databases, up to May 2016; The Collection of Computer Science Bibliographies; DBLP Computer Science Bibliography; HCI Bibliography: Human-Computer Interaction Resources; and AgeInfo, all to June 2016; PiCarta; Inspec; Springer Link Lecture Notes; Social Care Online; and IEEE Computer Society Digital Library, all to October 2016; J-STAGE: Japan Science and Technology Information Aggregator, Electronic; and Networked Computer Science Technical Reference Library (NCSTRL), both to November 2016; Computing Research Repository (CoRR) up to December 2016; and OT seeker; and ADEAR, both to February 2017. In addition, we searched Google Scholar and OpenSIGLE for grey literature.
Selection criteria:
We intended to review randomised controlled trials (RCTs) and clustered randomised trials with blinded assessment of outcomes that evaluated an electronic assistive device used with the single aim of supporting memory function in people diagnosed with dementia. The control interventions could either be 'care (or treatment) as usual' or non-technological psychosocial interventions (including interventions that use non-electronic assistive devices) also specifically aimed at supporting memory. Outcome measures included activities of daily living, level of dependency, clinical and care-related outcomes (for example admission to long-term care), perceived quality of life and well-being, and adverse events resulting from the use of AT; as well as the effects of AT on carers.
Data collection and analysis:
Two review authors independently screened all titles and abstracts identified by the search.
Main results:
We identified no studies which met the inclusion criteria.
Authors' conclusions:
This review highlights the current lack of high-quality evidence to determine whether AT is effective in supporting people with dementia to manage their memory problems.
Objective:
To estimate daily total energy expenditure (TEE) using a physical activity monitor, combined with dietary assessment of energy intake to assess the relationship between daily energy expenditure and patterns of activity with energy intake in people with dementia living in care homes.
Design and setting:
A cross-sectional study in care homes in the UK.
Participants:
Twenty residents with confirmed dementia diagnosis were recruited from two care homes that specialised in dementia care.
Measurements:
A physical activity monitor (SensewearTM Armband, Body Media, Pittsburgh, PA) was employed to objectively determine total energy expenditure, sleep duration and physical activity. The armband was placed around the left upper triceps for up to 7 days. Energy intake was determined by weighing all food and drink items over 4 days (3 weekdays and 1 weekend day) including measurements of food wastage.
Results:
The mean age was 78.7 (SD ± 11.8) years, Body Mass Index (BMI) 23.0 (SD ± 4.2) kg/m2; 50% were women. Energy intake (mean 7.4; SD ± 2.6) MJ/d) was correlated with TEE (mean 7.6; SD ± 1.8 MJ/d; r=0.49, p<0.05). Duration of sleeping ranged from 0.4-12.5 (mean 6.1) hrs/d and time spent lying down was 1.3-16.0 (8.3) hrs/d. On average residents spent 17.9 (6.3-23.4) hrs/d undertaking sedentary activity. TEE was correlated with BMI (r=0.52, p<0.05) and body weight (r=0.81, p<0.001) but inversely related to sleep duration (r=-0.59, p<0.01) and time lying down (r=-0.62, p<0.01). Multiple linear regression analysis revealed that after taking BMI, sleep duration and time spent lying down into account, TEE was no longer correlated with energy intake.
Conclusions:
The results show the extent to which body mass, variable activity and sleep patterns may be contributing to TEE and together with reduced energy intake, energy requirements were not satisfied. Thus wearable technology has the potential to offer real-time monitoring to provide appropriate nutrition management that is more person-centred to prevent weight loss in dementia.
The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.
Introduction
Accurately and efficiently determining a participant's capacity to consent to research is critically important to protect the rights of patients with Alzheimer's disease (AD).
Methods
Understanding of the informed consent document was assessed in 613 community-dwelling patients with mild-to-moderate AD enrolled in a randomized, placebo-controlled trial. Associations were examined between clinically determined capacity to consent and (1) patient demographics and clinical characteristics and (2) the Informed Consent Questionnaire (ICQ), an objective measurement of a participant's factual understanding and perceived understanding.
Results
A total of 453 (74%) participants were determined to have capacity to consent by clinical judgment. ICQ perceived understanding, race, measures of cognitive function, and caregiver time were all significantly associated with the determination of capacity in multivariate analyses.
Discussion
We found a significant association between capacity and disease severity level, caregiver time, race, and ICQ perceived understanding.
Ambulatory measurements of trunk accelerations can provide valuable information on the amount and quality of daily-life activities and contribute to the identification of individuals at risk of falls. We compared associations between retrospective and prospective falls with potential risk factors as measured by daily-life accelerometry. In addition, we investigated predictive value of these parameters for 6-month prospective falls.
One week of trunk accelerometry (DynaPort MoveMonitor) was obtained in 169 older adults (mean age 75). The amount of daily activity and quality of gait were determined and validated questionnaires on fall-risk factors, grip strength, and trail making test were obtained. Six-month fall incidence was obtained retrospectively by recall and prospectively by fall diaries and monthly telephone contact.
Among all participants, 35.5% had a history of ≥1 falls and 34.9% experienced ≥1 falls during 6-month follow-up. Logistic regressions showed that questionnaires, grip strength, and trail making test, as well as the amount and quality of gait, were significantly associated with falls. Significant associations differed between retrospective and prospective analyses although odds ratios indicated similar patterns. Predictive ability based on questionnaires, grip strength, and trail making test (area under the curve .68) improved substantially by accelerometry-derived parameters of the amount of gait (number of strides), gait quality (complexity, intensity, and smoothness), and their interactions (area under the curve .82).
Daily-life accelerometry contributes substantially to the identification of individuals at risk of falls, and can predict falls in 6 months with good accuracy.
© The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Background:
There are unique signatures of gait impairments in different dementia disease subtypes, such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease (PDD). This suggests gait analysis is a useful differential marker for dementia disease subtypes, but this has yet to be assessed using inexpensive wearable technology.
Research question:
This study aimed to assess whether a single accelerometer-based wearable could differentiate dementia disease subtypes through gait analysis.
Methods:
80 people with mild cognitive impairment or dementia due to AD, DLB or PD performed six ten-metre walks. An accelerometer-based wearable (Axivity) assessed gait. Data was processed using algorithms validated in other neurological disorders and older adults. Fourteen spatiotemporal characteristic were computed, that broadly represent pace, variability, rhythm, asymmetry and postural control features of gait. One way analysis of variance and Kruskall Wallis tests identified significant between-group differences, and post-hoc independent t-tests and Mann Whitney U's established where differences lay. Receiver Operating Characteristics and Area Under the Curve (AUC) demonstrated overall accuracy for single gait characteristics.
Results:
The wearable was able to differentiate dementia disease subtypes (p ≤ .05) and demonstrated significant differences between the groups in 7 gait characteristics with modest accuracy. For reference the instrumented walkway showed 2 between-group differences in gait characteristics.
Significance:
This study found that a wearable device can be used to differentiate dementia disease subtypes. This provides a foundation for future research to investigate the application of wearable technology as a clinical tool to aid diagnostic accuracy, allowing the correct treatment and care to be applied. Wearable technology may be particularly useful as its use is less restricted to context, making it easier to implement.
Complexity of human physiology and physical behavior has been suggested to decrease with aging and disease and make older adults more susceptible to falls. The present study investigates complexity in daily life walking in community-dwelling older adult fallers and non-fallers measured by a 3D inertial accelerometer sensor fixed to the lower back. Complexity was expressed using new metrics of entropy: refined composite multiscale entropy (RCME) and refined multiscale permutation entropy (RMPE). The study re-analyses data of 3 days daily-life activity originally described by Weiss et al. (2013). The data set contains inertial sensor data from 39 older persons reporting less than 2 falls and 32 older persons reporting two or more falls during the previous year. The RCME and the RMPE were derived for trunk acceleration and velocity signals from walking epochs of 50s using mean and variance coarse graining of the signals. Discriminant abilities of the entropy metrics were assessed using a partial least square discriminant analysis. Both RCME and RMPE successfully distinguished between the daily-life walking of the fallers and non-fallers (AUC>0.8) and performed better than the 35 conventional gait features investigated by Weiss et al. (2013). Higher complexity was found in the vertical and mediolateral directions in the non-fallers for both entropy metrics. These findings suggest that RCME and RMPE can be used to improve the assessment of fall risk in older people.
The electronic monitoring of human health behaviour using computer techniques has been an active research area for the past few decades. A wide array of different approaches have been investigated using various technologies including inertial sensors, Global Positioning System, smart homes, Radio Frequency IDentification and others. It is only in recent years that research has turned towards a sensor fusion approach using several different technologies in single systems or devices. These systems allow for an increased volume of data to be collected and for activity data to be better used as measures of behaviour. This change may be due to decreasing hardware costs, smaller sensors, increased power efficiency or increases in portability. This paper is intended to act as a reference for the design of multi-sensor behaviour monitoring systems. The range of technologies that have been used in isolation for behaviour monitoring both in research and commercial devices are reviewed and discussed. Filtering, range, sensitivity, usability and other considerations of different technologies are discussed. A brief overview of commercially available activity monitors and their technology is also included.
This study explores differences in the out-of-home behavior of community-dwelling older adults with different cognitive impairment. Three levels of complexity of out-of-home behavior were distinguished: (a) mostly automatized walking behavior (low complexity), (b) global out-of-home mobility (medium complexity), and (c) defined units of concrete out-of-home activities, particularly cognitively demanding activities (high complexity). A sample of 257 older adults aged 59 to 91 years (M = 72.9 years, SD = 6.4 years) included 35 persons with early-stage Alzheimer’s disease (AD), 76 persons with mild cognitive impairment (MCI), and 146 cognitively healthy persons (CH). Mobility data were gathered by using a GPS tracking device as well as by questionnaire. Predicting cognitive impairment status by out-of-home behavior and a range of confounders by means of multinomial logistic regression revealed that only cognitively demanding activities showed at least a marginally significant difference between MCI and CH and were highly significant between AD and CH.
Technology aimed at enhancing function and enabling independent living among older and disabled adults is a growing field of research. Privacy concerns are a potential barrier to adoption of such technology. Using data from a national Web survey (n=1,518), we focus on perceived acceptability of sharing information about toileting, taking medications, moving about the home, cognitive ability, driving behavior, and vital signs with five targets: family, healthcare providers, insurance companies, researchers, and government. We also examine acceptability of recording the behaviors using three methods: video with sound, video without sound, and sensors. Results show that sharing or recording information about toileting behavior; sharing information with the government and insurance companies; and recording the information using video were least acceptable. Respondents who reported current disability were significantly more accepting of sharing and recording of information than nondisabled adults, controlling for demographic variables, general technology attitudes, and assistive device use. Results for age were less consistent, although older respondents tended to be more accepting than younger respondents. The study provides empirical evidence from a large national sample of the implicit trade-offs between privacy and the potential for improved health among older and disabled adults in quality of life technology applications.
Accelerometry (ACC) shows promise as an easily implemented clinical measure of balance. The purpose of the study was to estimate test-retest reliability of ACC measures and determine the relationship between ACC measured at the pelvis and underfoot center of pressure (COP) measures during sensory organization test (SOT) conditions. Eighty-one subjects were recruited from the community with no known orthopedic or vestibular deficits (19-85 years). Subjects completed three consecutive, ninety second trials for each of the six SOT conditions, while wearing the accelerometer. ACC and COP time series were described by calculating the normalized path length, root mean square (RMS), and peak-to-peak values. The test-retest reliability of the three measures within each SOT condition was estimated over three trials using the intraclass correlation coefficient. ACC and COP test-retest reliability were similar, ranging from 0.63 to 0.80 using ACC and 0.42 to 0.81 using COP for the measure of normalized path length. Linear regression between ACC and COP measures showed significant correlation under almost every SOT condition using both single and average measures across trials. The degree of association between COP and ACC was equivalent when using the first trial or the 3-trial average, suggesting that one trial may be sufficient. The use of accelerometry may have value in estimating balance function and minimizing clinical evaluation time.