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Data retrieval and storage solution. After offloading the data from the bracelets (1), all data files are signed and encrypted (2), a backup on the NAS is created (3), on which a second backup to an external hard disk is made (4), the data are transferred securely to servers at the Computer Science Department of the University of Rostock (5), and finally data are analyzed (6). To provide the level of data protection required for meeting the privacy standards, only on these machines the data could be decrypted and analyzed. Abbreviation: NAS, network-attached storage.
Source publication
Introduction
Assessment of challenging behaviors in dementia is important for intervention selection. Here, we describe the technical and experimental setup and the feasibility of long-term multidimensional behavior assessment of people with dementia living in nursing homes.
Methods
We conducted 4 weeks of multimodal sensor assessment together wit...
Context in source publication
Citations
... Pattern characteristics included mean bout duration and alpha. Alpha is derived by logarithmic transformation of bout density and length and is based on shape and power-law distribution [44,45]. Alpha refers to the ratio of short to long walking bouts which are scaled relative to an individual's shortest walking bout. ...
... Strengths of this study were the large sample, drawn from multiple facilities, distributed across three levels of care and encompassing a broad spectrum of cognitive and physical capacities. This is particularly notable as there can be significant difficulties in collecting data using wearable technology from people with dementia in ARC facilities [45]. We employed a technically appropriate digital method to collect low volumes of walking data¸ meeting the recommendations from Mc Ardle, Sverdrup [8]. ...
Background
Walking is important for maintaining physical and mental well-being in aged residential care (ARC). Walking behaviors are not well characterized in ARC due to inconsistencies in assessment methods and metrics as well as limited research regarding the impact of care environment, cognition, or physical function on these behaviors. It is recommended that walking behaviors in ARC are assessed using validated digital methods that can capture low volumes of walking activity.
Objective
This study aims to characterize and compare accelerometry-derived walking behaviors in ARC residents across different care levels, cognitive abilities, and physical capacities.
Methods
A total of 306 ARC residents were recruited from the Staying UpRight randomized controlled trial from 3 care levels: rest home (n=164), hospital (n=117), and dementia care (n=25). Participants’ cognitive status was classified as mild (n=87), moderate (n=128), or severe impairment (n=61); physical function was classified as high-moderate (n=74) and low-very low (n=222) using the Montreal Cognitive Assessment and the Short Physical Performance Battery cutoff scores, respectively. To assess walking, participants wore an accelerometer (Axivity AX3; dimensions: 23×32.5×7.6 mm; weight: 11 g; sampling rate: 100 Hz; range: ±8 g; and memory: 512 MB) on their lower back for 7 days. Outcomes included volume (ie, daily time spent walking, steps, and bouts), pattern (ie, mean walking bout duration and alpha), and variability (of bout length) of walking. Analysis of covariance was used to assess differences in walking behaviors between groups categorized by level of care, cognition, or physical function while controlling for age and sex. Tukey honest significant difference tests for multiple comparisons were used to determine where significant differences occurred. The effect sizes of group differences were calculated using Hedges g (0.2-0.4: small, 0.5-0.7: medium, and 0.8: large).
Results
Dementia care residents showed greater volumes of walking ( P <.001; Hedges g =1.0-2.0), with longer ( P <.001; Hedges g =0.7-0.8), more variable ( P =.008 vs hospital; P <.001 vs rest home; Hedges g =0.6-0.9) bouts compared to other care levels with a lower alpha score (vs hospital: P <.001; Hedges g =0.9, vs rest home: P =.004; Hedges g =0.8). Residents with severe cognitive impairment took longer ( P <.001; Hedges g =0.5-0.6), more variable ( P< .001; Hedges g =0.4-0.6) bouts, compared to those with mild and moderate cognitive impairment. Residents with low-very low physical function had lower walking volumes (total walk time and bouts per day: P <.001; steps per day: P =.005; Hedges g =0.4-0.5) and higher variability ( P =.04; Hedges g =0.2) compared to those with high-moderate capacity.
Conclusions
ARC residents across different levels of care, cognition, and physical function demonstrate different walking behaviors. However, ARC residents often present with varying levels of both cognitive and physical abilities, reflecting their complex multimorbid nature, which should be considered in further work. This work has demonstrated the importance of considering a nuanced framework of digital outcomes relating to volume, pattern, and variability of walking behaviors among ARC residents.
... Lockdowns and social distancing worsen the cognitive and motor performances of these patients and delay their monitoring, making it necessary to provide severely demented patients with pain assessment and neurorehabilitation by means of telemedicine. An excellent example to follow is represented by the development of intelligent assistive systems, to obtain high-quality behavior data from real-world environments [56]. Here, we report the case of an 85-year-old woman, suffering from mild cognitive impairment, who died from an extensive brain hemorrhage during the last period of the health emergency, because of the lack of accurate pain management, leading to institutionalization and off-label treatment with atypical antipsychotics, known to double the risk of death of these fragile patients. ...
The coronavirus disease 2019 (COVID-19) pandemic imposes an unprecedented lifestyle, dominated by social isolation. In this frame, the population to pay the highest price is represented by demented patients. This group faces the highest risk of mortality, in case of severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection, and they experience rapid cognitive deterioration, due to lockdown measures that prevent their disease monitoring. This complex landscape mirrors an enhancement of neuropsychiatric symptoms (NPSs), with agitation, delirium and reduced motor performances, particularly in non-communicative patients. Due to the consistent link between agitation and pain in these patients, the use of antipsychotics, increasing the risk of death during COVID-19, can be avoided or reduced through an adequate pain treatment. The most suitable pain assessment scale, also feasible for e-health implementation, is the Mobilization-Observation-Behaviour-Intensity-Dementia (MOBID-2) pain scale, currently under validation in the Italian real-world context. Here, we report the case of an 85-year-old woman suffering from mild cognitive impairment, subjected to off-label treatment with atypical antipsychotics, in the context of undertreated pain, who died during the pandemic from an extensive brain hemorrhage. This underscores the need for appropriate assessment and treatment of pain in demented patients.
... There have been very few studies on detecting agitation and aggression in PLwD using multi-modal sensors [9]. A systematic review found that most studies focused on changes in motor activity, and that there is some correlation between accelerometer and motor agitation [8]. ...
People living with dementia (PLwD) often exhibit behavioral and psychological symptoms, such as episodes of agitation and aggression. Agitated behavior in PLwD causes distress and increases the risk of injury to both patients and caregivers. In this paper, we present the use of a multi-modal wearable device that captures motion and physiological indicators to detect agitation in PLwD. We identify features extracted from sensor signals that are the most relevant for agitation detection. We hypothesize that combining multi-modal sensor data will be more effective to identify agitation in PLwD in comparison to a single sensor. The results of this unique pilot study are based on 17 participants’ data collected during 600 days from PLwD admitted to a Specialized Dementia Unit. Our findings show the importance of using multi-modal sensor data and highlight the most significant features for agitation detection.
... The exclusion criterion is that the patient has other neurological disabilities or has significantly reduced ability to complete instrumental activities of daily living due to other non-dementia conditions. Informed and written consent shall be obtained from both the participant and their caregiver for both of their participations, however if the participant is incapable of consenting, their Next of Kin (NoK) or Power of Attorney (POA) will be asked to provide written and informed consent for them, with verbal assent being sought from the participant [11,13,14,[33][34][35][36][37]. All consenting and assenting parties will be informed they can withdraw from the study at any time without being required to give a reason or justification. ...
Dementia is a neurodegenerative disease which leads to the individual experiencing difficulties in their daily lives. Often these difficulties cause a large amount of stress, frustration and upset in the individual, however identifying when the difficulties are occurring or beginning can be difficult for caregivers, until the difficulty has caused problematic behavior or undeniable difficulty to the person with dementia. Therefore, a system for identifying the onset of dementia-related difficulties would be helpful in the management of dementia. Previous work highlighted wearable computing-based systems for analyzing physiological data as particularly promising. In this paper, we outline the methodology used to perform a systematic search for a relevant dataset. However, no such dataset was found. As such, a methodology for collecting such a dataset and making it publicly available is proposed, as well as for using it to train classification models that can predict difficulties from the physiological data. Several solutions to overcome the lack of available data are identified and discussed: data collection experiments to collect novel datasets; anonymization and pseudonymization to remove all identifiable data from the dataset; and synthetic data generation to produce a larger, anonymous training dataset. In conclusion, a combination of all the identified methods should ideally be employed in future solutions. Future work should focus on the conductance of the proposed experiment and the sharing of the collected data in the manner proposed, with data ideally being collected from as many people as possible with as many different types of dementia as possible.
... There have been very few studies on detecting agitation and aggression in PLwD using multi-modal sensors [34]. A systematic review found that most studies focused on changes in motor activity, and that there is some correlation between accelerometer and motor agitation [18]. ...
People Living with Dementia (PLwD) often exhibit behavioral and psychological symptoms, such as episodes of agitation and aggression. Agitated behaviour in PLwD causes distress and increases the risk of injury to both the patients and the caregivers. In this paper, we present the use of a multi-modal wearable device that captures motion and physiological indicators to detect agitation in PLwD. We hypothesize that combining multi-modal sensor data will be more effective to identify agitation in PLwD in comparison to a single sensor. This paper presents the results of a unique pilot study to collect motion and physiological data from PLwD admitted to a Specialized Dementia Unit. The classification results on 14 participants from 481 days of data collected from PLwD show strong evidence to support our hypothesis and highlight the importance of using multi-modal sensor data for detecting agitation events in this population.
... Assistenztechnologien könnten dabei z. B. auf die Bewältigung der Symptome abzielen oder dazu beitragen, diesen vorzubeugen, u. a. durch sogenannte verstehende Diagnostik (Teipel et al., 2017). Auch psychosoziale Interventionen, die z. ...
https://www.gkv-spitzenverband.de/pflegeversicherung/forschung/forschung.jsp
... There have been very few studies on detecting agitation and aggression in PLwD using multi-modal sensors [7]. The systematic review by Khan et al. [8] suggests that many previous studies found correlation between actigraphy (accelerometer based devices) and agitation among PLwD. ...
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.
... That is, the group with apathy showed significantly lower daytime activity [32]. When used in combination with measurement tools for other environmental factors, including noise, light, and air pressure, actigraphy data helps provide a holistic report of the patient that may allow clinicians to tailor their clinical interventions to each patient [33]. ...
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.
... analysis reports) are being developed on demand during all steps of the data lifecycle whenever a problem occurs in a trial [81]; a top-down strategy to ensure of data quality for ICT-based RWE is still missing. ...
Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials.
... Parece-nos por isso essencial executar, nas estruturas residenciais para idosos, uma avaliação objetiva das funções cognitivas e proceder a essa avaliação o mais precocemente possível. A par da avaliação cognitiva, uma avaliação multidimensional deve ser ativada para que a suspeita de demência seja progressivamente confirmada [30][31][32] e as suas consequências (e.g., alterações na funcionalidade) sejam devidamente acompanhadas 33 . Importa, contudo, referir que existem vários instrumentos de avaliação do estado mental, mas não existem instrumentos de padrão de ouro para o diagnóstico 34 . ...
This study aimed to screen the cognitive profile elderly people living in long-term care institutions in the municipality of Miranda do Corvo by evaluating 174 participants with the Mini Mental State Examination (MMSE) (n=96) and the clinical dementia diagnosis (n=78). According to the MMSE, 41.7% of respondents had scores suggestive of cognitive impairment. The percentage rose to 67.8% (n=118) by adding the diagnosis of dementia reported in individual medical records to this result. The comparison of our results with those obtained nationwide showed that this proportion was significantly higher (p<0.001). The educational level was a predictive factor for MMSE scores (p=0.001). We can conclude that the high prevalence of suspected cognitive impairment and dementia revealed in our study should lead us to reflect on the quality of care provided and on the lack/scarcity of cognitive stimulation programs in long-term care institutions for seniors. Thus, it is imperative to implement regular cognitive assessment and to apply intervention programs for the preservation and improvement of the cognitive functioning of institutionalized elderly of deprived areas.
