Sensing Technology to Monitor Behavioral and Psychological Symptoms and to Assess Treatment Response in People With Dementia. A Systematic Review

Background: The prevalence of dementia is expected to rapidly increase in the next decades, warranting innovative solutions improving diagnostics, monitoring and resource utilization to facilitate smart housing and living in the nursing home. This systematic review presents a synthesis of research on sensing technology to assess behavioral and psychological symptoms and to monitor treatment response in people with dementia. Methods: The literature search included medical peer-reviewed English language publications indexed in Embase, Medline, Cochrane library and Web of Sciences, published up to the 5th of April 2019. Keywords included MESH terms and phrases synonymous with “dementia”, “sensor”, “patient”, “monitoring”, “behavior”, and “therapy”. Studies applying both cross sectional and prospective designs, either as randomized controlled trials, cohort studies, and case-control studies were included. The study was registered in PROSPERO 3rd of May 2019. Results: A total of 1,337 potential publications were identified in the search, of which 34 were included in this review after the systematic exclusion process. Studies were classified according to the type of technology used, as (1) wearable sensors, (2) non-wearable motion sensor technologies, and (3) assistive technologies/smart home technologies. Half of the studies investigated how temporarily dense data on motion can be utilized as a proxy for behavior, indicating high validity of using motion data to monitor behavior such as sleep disturbances, agitation and wandering. Further, up to half of the studies represented proof of concept, acceptability and/or feasibility testing. Overall, the technology was regarded as non-intrusive and well accepted. Conclusions: Targeted clinical application of specific technologies is poised to revolutionize precision care in dementia as these technologies may be used both by patients and caregivers, and at a systems level to provide safe and effective care. To highlight awareness of legal regulations, data risk assessment, and patient and public involvement, we propose a necessary framework for sustainable ethical innovation in healthcare technology. The success of this field will depend on interdisciplinary cooperation and the advance in sustainable ethic innovation.publishedVersio

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

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

Review of Methods for Data Collection Experiments with People with Dementia and the Impact of COVID-19

The development of a wearable-based system for detecting difficulties in the daily lives of people with dementia would be highly useful in the day-to-day management of the disease. To develop such a system, it would be necessary to identify physiological indicators of the difficulties, which can be identified by analyzing physiological datasets from people with dementia. However, there is no such data available to researchers. As such, it is vital that data is collected and made available in future. In this paper we perform a review of past physiological data collection experiments conducted with people with dementia and evaluate the methods used at each stage of the experiment. Consideration is also given to the impacts and limitations imposed by the COVID-19 pandemic and lockdowns both on the people with dementia- such people being one of the most at risk and affected groups- and on the efficacy and safety of each of the methods. It is concluded that the choice of method to be utilized in future data collection experiments is heavily dependent on the type and severity of the dementia the participants are experiencing, and that the choice of remote or COVID-secure methods should be used during the COVID-19 pandemic; many of the methods reviewed could allow for the spread of the virus if utilized during a pandemic

The Impact of Using Measurements of Electrodermal Activity in the Assessment of Problematic Behaviour in Dementia

Background: A major and complex challenge when trying to support individuals with dementia is meeting the needs of those who experience changes in behaviour and mood. Aim: To explore how a sensor measuring electrodermal activity (EDA) impacts assistant nurses’ structured assessments of problematic behaviours amongst people with dementia and their choices of care interventions. Methods: Fourteen individuals with dementia wore a sensor that measured EDA. The information from the sensor was presented to assistant nurses during structured assessments of problematic behaviours. The evaluation process included scorings with the instrument NPI-NH (Neuropsychiatric Inventory-Nursing Home version), the care interventions suggested by assistant nurses to decrease problematic behaviours, and the assistant nurses’ experiences obtained by focus group interviews. Results: The information from the sensor measuring EDA was perceived to make behavioural patterns more visual and clear, which enhanced assistant nurses’ understanding of time-related patterns of behaviours. In turn, this enhancement facilitated timely care interventions to prevent the patterns and decrease the levels of problematic behaviour. Conclusion: With the addition of information from the sensor, nursing staff could target causes and triggers in a better way, making care interventions more specific and directed towards certain times throughout the day to prevent patterns of problematic behaviours

The effect of bright light on rest-activity rhythms and behavioural and psychological symptoms of dementia

De fleste som lever med demens har også atferdsmessige- og psykologiske symptomer ved demens (APSD) som for eksempel depresjon, angst, agitasjon, og søvnforstyrrelser. APSD påvirker livskvalitet og pleiebehov. Aktivitetsrytmen er ofte endret hos personer med demens. For eksempel kan søvn og våkenhet forekomme uregelmessig, med rastløshet og atferdsforstyrrelser på kvelds- og nattestid, og søvn på dagtid. Forstyrrelser i søvn og våkenhet har negative konsekvenser for daglig fungering, kognisjon, og affekt. I tillegg er det trolig at denne typen problemer gjenspeiler forstyrrelse av den endogene cirkadiane rytmen. APSD, inkludert søvnproblemer, behandles ofte medikamentelt, på tross av at slik behandling har begrenset effekt og kan medføre alvorlige bivirkninger. Lys påvirker den cirkadiane rytmen, og kan i tillegg ha en innvirkning på våkenhet og humør. Disse omtales som ikke-visuelle effekter av lys. Lysterapi er en ikke-medikamentell behandling som ifølge noen tidligere studier kan ha en positiv effekt på affekt, agitasjon, søvnforstyrrelser og aktivitetsrytmer hos personer med demens, men resultatene fra ulike studier har ikke vært entydige. Målet med denne avhandlingen var å undersøke effekten av lysterapi på APSD og aktivitetsrytmer, gjennom en klynge-randomisert placebo-kontrollert studie over 24 uker – DEM.LIGHT studien. Et sekundært mål, og et forarbeid til hovedstudien, var å undersøke lysforholdene ved demensenheter på sykehjem. Artikkel 1 presenterte en undersøkelse av lys på 15 demensenheter i Bergen kommune, gjennomført ved to årstider og med lysmålinger i ulike retninger. Lysmålingene ble sammenlignet med grenseverdier basert på anbefalinger og tidligere forskning. Lysverdiene ble oppgitt i måleenheter som er relevante for ikkevisuelle effekter av lys. Artikkel 2 og 3 rapporterte resultater fra DEM.LIGHTstudien, gjennomført på 8 sykehjem med 69 deltagere. Intervensjonen besto av takmonterte LED-lys i fellesstuen på 4 demensenheter, som gav lys av ulik styrke og fargetemperatur gjennom dagen. Maksimalt nivå for intervensjonen var ~1000 lx og 6000 K, mellom kl. 10:00 og 15:00, målt vertikalt 1.2 m over gulvet. Kontrollgruppen (4 demensenheter) hadde standard innendørsbelysning (~150–300 lx, 3000 K). Data ble innhentet ved baseline, og etter 8, 16 og 24 uker. Artikkel 2 undersøkte effekten av lysbehandlingen på aktivitetsrytmer registrert med aktigrafi, og artikkel 3 undersøkte effekten på proxy-vurderte APSD-mål (Cornell Scale for Depression in Dementia, CSDD og Neuropsychiatric Inventory – Nursing Home Version, NPI-NH). Effekten av behandlingen ble analysert ved bruk av blandede regresjonsmodeller (multilevel models), med demensstadium (Functional Assessment Staging Tool, FAST skåre) ved baseline som en a priori bestemt kovariat. I tillegg ble baselineskårer på utfallsmålene inkludert som kovariater i analysene til artikkel 3. I artikkel 1 fant vi at de fleste målingene av lyset på demensenhetene var under terskelverdiene, uavhengig av årstid og måleretning. I artikkel 2 fant vi ingen forbedring av aktivitetsrytmen etter BLT hos personer med demens når vi korrigerte for multippel testing. Uten slik korreksjon var akrofasen (tidspunktet for aktivitetrytmens makspunkt) signifikant mindre forsinket (med en time) i uke 16 i intervensjonsgruppen sammenlignet med kontrollgruppen. Artikkel 3 rapporterte blandede resultater for effekten av lysintervensjonen på APSD. Det var en signifikant effekt på underskalaer som måler affektive symptomer i uke 16, men ikke i uke 8 eller 24, etter korreksjon for multippel testing. Det var en signifikant effekt på CSDD og NPI-NH total-skårer i uke 16 før, men ikke etter, korreksjon for multippel testing. Det var ingen signifikant effekt på andre underskalaer. Oppsummert peker funnene fra artikkel 1 mot at lyset på demensenheter er utilstrekkelig sett opp mot terskelverdier for ikke-visuelle effekter av lys. Likevel var resultatene fra DEM.LIGHT-studien, som økte belysningen på demensenheter, blandede. Basert på disse resultatene kan vi ikke anbefale takmontert lysterapi ved demensenheter. Det er imidlertid flere metodologiske utfordringer og karakteristikker ved utvalget som begrenser generaliserbarheten til disse funnene.Most people living with dementia have behavioural and psychological symptoms of dementia (BPSD), such as depression, anxiety, agitation, and disturbed sleep, that strongly affect well-being and care needs. The rest-activity rhythm (RAR), i.e., the diurnal pattern of activity, is often altered in individuals with dementia. Sleep and wakefulness may, for instance, occur at irregular intervals, characterised by restlessness and behavioural disturbances at night, and napping during the day. This disruption of the sleep-wake pattern is detrimental to functioning and well-being. It is also thought to reflect deterioration of the endogenous circadian rhythm. Pharmacotherapy is often used to treat BPSD, including sleep disturbances, but has limited efficacy and is associated with severe side effects. Light influences the circadian rhythm, and can also have effects on alertness and mood. These are collectively referred to as non-image forming (NIF) effects of light. Bright light treatment (BLT) is a non-pharmacological intervention that has been found to improve affective symptoms, agitation, sleep disorders, and RARs in people with dementia in some studies, but results have been mixed. The main aim of this thesis was to investigate the effect of BLT on RARs and BPSD in a 24-week cluster randomised controlled trial - the DEM.LIGHT trial (ClinicalTrials.gov identifier: NCT03357328). A secondary aim, and preparation for the trial, was to investigate the illumination in nursing home dementia units. Paper 1 was a field study investigating nursing home illumination in 15 dementia units across seasons and gaze directions. Measured illuminances were compared to thresholds suggested by industry standards and research, and measurement units relevant to NIF effects of light were used. Paper 2 and 3 reported results from the DEM.LIGHT trial, conducted at 8 dementia units, with 69 participants. In the intervention group (4 units), ceiling mounted LED-panels provided ambient light of varying illuminance and correlated colour temperature throughout the day, with a peak of ~1000 lx and 6000 K (measured vertically at 1.2 m) between 10:00 and 15:00. In the control group (4 units), standard indoor light of ~150–300 lx, 3000 K was used. Data were collected at baseline and at 8, 16, and 24 weeks. Paper 2 investigated the effect of the intervention on actigraphy-measured RARs, and paper 3 investigated the effect on proxy-rated BPSD measures: the Cornell Scale for Depression in Dementia (CSDD) and the Neuropsychiatric Inventory - Nursing Home Version (NPI-NH). Treatment effects were analysed using multilevel regression models, with dementia stage (score on the Functional Assessment Staging Tool, FAST) at baseline as a pre-determined covariate. In addition, baseline scores on the outcome measures were included as covariates in the models in paper 3. In paper 1 we found that, regardless of season and gaze direction, nearly all measured illuminances in dementia units fell below the thresholds. In paper 2, we found that there was no effect of BLT on RAR outcomes in people with dementia when controlling for multiple testing. Without controlling for multiple testing, the acrophase (i.e., timing of the activity peak) was significantly less delayed (by one hour) in the intervention group compared to the control group, in week 16. Paper 3 found mixed results for the effect of BLT on BPSD. There was a significant reduction of scores on affective subscales in the intervention group in week 16, but not at other follow-ups, after controlling for multiple testing. There was a significant effect on the NPI-NH and CSDD total scores in week 16 before, but not after, controlling for multiple testing. There were no significant effects on other subscales. In conclusion, the findings in paper 1 suggest that illumination in dementia units is inadequate compared to thresholds suggested for NIF effects of light. However, the results of the DEM.LIGHT trial, which increased the indoor illumination in dementia units, were mixed. Based on our results, we cannot make clear recommendations regarding the use of ambient BLT in dementia units. Several methodological challenges and sample characteristics may limit the generalisability of these results.Doktorgradsavhandlin

Middleware-Driven Intelligent Glove for Industrial Applications

It is estimated that by the year 2020, 700 million wearable technology devices will be sold worldwide. One of the reasons is the industries’ need to increase their productivity. Some of the tools welcomed by industries are handheld devices such as tablets, PDAs and mobile phones. However, handheld devices are not ideal for industrial applications because they often subject users to fatigue during their long working hours. A viable solution to this problem is wearable devices. The advantage of wearable devices is that they become part of the user. Hence, they subject the user to less fatigue, thereby increasing their productivity. This chapter presents the development of an intelligent glove, which is designed to control actuators in an industrial environment. This system utilizes RTI connext data distributed service middleware to facilitate communication over WiFi. Our experiments show very promising results with maximum power consumption of 310 mW and latency as low as 23 ms. These results make the proposed system a perfect fit for most industrial applications

Promotors and barriers to the implementation and adoption of assistive technology and telecare for people with dementia and their caregivers: a systematic review of the literature

Background One of the most pressing issues in our society is the provision of proper care and treatment for the growing global health challenge of ageing. Assistive Technology and Telecare (ATT) is a key component in facilitation of safer, longer, and independent living for persons with Dementia (PwD) and has the potential to extend valuable care and support for caregivers (formal and informal) globally. Results of this systematic review are of key importance because well-executed ATT implementation, leading to habitual usage and adoption, can assist and strengthen current healthcare services, improve access to healthcare and decrease societal and caregiver burden. Objective The objective of this study is to identify promotors and barriers to implementation and adoption of ATT for PwD and their informal (family and friends) and formal (healthcare professionals) caregivers. In addition, we aim to provide valuable insight for municipalities and healthcare organizations for improved implementation strategies. Methods The study was registered in PROSPERO 25th of February, 2021: CRD42021239448. NVivo was utilized for synthesis and analysis of article content. As the results were from diverse disciplines using varied methods of analysis, a semi-systematic approach with narrative synthesis was used for the review. PICO criteria and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines have been used to guide all processes and results. Rayyan and NVivo were utilized for selection of articles and analysis of found themes. In addition, the Critical Appraisal Skills Programme (CASP) has been used to visualize meta-synthesis and meta-analysis results and overall quality of included literature. Results This review encompasses relevant information regarding the implementation and adoption of ATT for PwD and their caregivers from five continents and sixty-five countries. It is a true global representation of the growing challenge of ageing. In total, 32 publications were included for review. Identified primary promotors for the implementation and adoption of ATT were as follows: personalized (tailored) training and co-designed solutions, safety for the PwD, involvement of all relevant stakeholders (multi-faceted approach including PwD), ease of use and support (design and follow up), and cultural relevance. Main barriers for the implementation and adoption of ATT included: unintended adverse consequences, timing and disease progress, technology anxiety, system failures (connectivity, errors, etc.), digital divide and lack of access to or knowledge of available ATT. Conclusions The most crucial elements for the adoption of ATT in the future will be a focus on co-design, improved involvement of both the PwD and their caregivers, and the adaptability (tailoring related to context) of ATT solutions over time (disease process). 94% of the literature presented in the review comes from high income countries. There is a significant need for more quality research to be conducted in the regions of the world where population growth and prevalence of dementia is expected to grow most rapidly over the next 30 years.M.Phil. in Global Health - ThesisINTH395AMAMD-GLO

How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators

This piece of research is situated in the domain of multi-modal analytics. New commercial off the shelf wearables, such as smartwatches or wristbands, are becoming popular and increasingly used for fitness and wellness in a new trend known as the quantified-self movement. The sensors included in these devices (e.g. accelerometer, heart rate) in conjunction with data analytics algorithms are used to provide information such as steps walked, calories consumed, etc. The main goal of this piece of research is to check if new wearable technologies could be used to estimate sleep indicators in an automatic way. The available medical literature proposes several sleep-related features and methods to calculate them involving direct user observation, interviews or specific medical instrumentation. Off the shelf wearable vendors also provide some sleep indicators, such as the sleep duration, the number of awakes or the time to fall asleep. Taking as a reference the results and methods described in the medical literature and the data available in commercial off the shelf wearables, we propose new sleep indicators offering a greater interpretative value: sleep quality, sleepiness level, chronotype. The results obtained after initial experiments demonstrate the feasibility of this approach to be applied in real contexts. Eventually, we plan to apply these solutions to support educational scenarios related to self-regulated learning and teaching support.Agencia Estatal de Investigación | Ref. TIN2016-80515-RXunta de Galicia | Ref. GRC2013-006Universidade de Vig

Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables

This paper discusses the possibility of detecting personal stress making use of popular wearable devices available in the market. Different instruments found in the literature to measure stress-related features are reviewed, distinguishing between subjective tests and mechanisms supported by the analysis of physiological signals from clinical devices. Taking them as a reference, a solution to estimate stress based on the use of commercial-off-the-shelf wrist wearables and machine learning techniques is described. A mobile app was developed to induce stress in a uniform and systematic way. The app implements well-known stress inducers, such as the Paced Auditory Serial Addition Test, the Stroop Color-Word Interference Test, and a hyperventilation activity. Wearables are used to collect physiological data used to train classifiers that provide estimations on personal stress levels. The solution has been validated in an experiment involving 19 subjects, offering an average accuracy and F-measures close to 0.99 in an individual model and an accuracy and F-measure close to 0.85 in a global 2-level classifier model. Stress can be a worrying problem in different scenarios, such as in educational settings. Thus, the last part of the paper describes the proposal of a set of stress related indicators aimed to support the management of stress over time in such settings.Agencia Estatal de Investigación | Ref. TIN2016-80515-RUniversidade de Vig

Comparison of subjective and physiological stress levels in home and office work environments

Work stress is a major problem to individuals and society, with prolonged periods of stress often leading to health issues and reduced productivity. COVID-19 has increased the incidence of individuals working in a mixture of home and office-based environments, with each location presenting its own stressors. Identification of stress levels in each environment will allow individuals to better plan how to mitigate stress and boost productivity. In this project, differences in stress levels are predicted in each work environment from individuals’ physiological responses and subjectively reported stress and productivity. Initial work on the project focused upon development of a system for the detection of dementia-related difficulties through the wearable-based tracking of physiological indicators. As such, a review of the available commercial and laboratory devices available for tracking physiological indicators of dementia-related difficulties was conducted. Furthermore, no publicly available physiological dataset for predicting difficulties in dementia currently exists. However, a review of the methods for collecting such a dataset and the impact of COVID-19 found that it is impractical and potentially unethical to conduct an experiment with people with dementia during the pandemic. As such, a pivot in research was necessitated. Comparing the stress levels of individuals working in home and office environments was selected. A data collection experiment was then performed with 13 academics working in combinations of home and office environments. Descriptive statistical features were then extracted from both the physiological and questionnaire data, with the relationships between attributes and features calculated using various advanced data analytics and statistical approaches. The resultant correlation coefficients and statistical summaries of stress were used to evaluate relationships between stress and work environment at different times of day, different days of the week, and while performing different activities. A bagged tree machine learning model was trained over the data, achieving 99.3% accuracy when evaluated using 10-fold cross validation. When tested on the purely unseen instances it achieved 56% accuracy corresponding to inter-class stress classification, however a testing accuracy of 73.7% was achieved using principal component analysis for dimensionality reduction and the dataset is balanced using Synthetic Minority Oversampling Technique