Monica Patrascu

Background: Proxy-rated questionnaires remain the standard for the assessment of activity and sleep for people with dementia living in nursing homes. Sensing technologies, such as wearables, can generate continuous data that provide quantitative insights into daily activities and behavioral and psychological symptoms, such as sleep disturbance. This study explores the use of sensing technologies in the detection of changes in physical activity levels and sleep behaviors over time. Objective: This study aims to explore the long-term capabilities of multimodal sensing technologies for assessing physical activity levels and sleep quality using selected digital biomarkers for nursing home residents with dementia. Objectives were for observation to be aligned with real-world conditions in which such sensing technologies would be applied within a nursing home environment and to assess whether distinct differences in selected digital biomarkers can be observed accurately and reliably longitudinally. Methods: This study included 11 participants (79‐93 y) recruited from 2 dementia care units in Norway. A smartwatch (Garmin Vivoactive 5 or Garmin Venu 3) and a radar-based system (Vital Things, Somnofy) were used to collect 7 days and 6 nights of data on physical activity levels and sleep quality at baseline, 6 months, and 1 year. The Personal Self-Maintenance Score and Neuropsychiatric Inventory–Nursing Home Version (nighttime behaviors section K) were also administered. Digital biomarkers included Euclidean norm minus one (ENMO), sleep efficiency (SE), wake after sleep onset (WASO), sleep regulatory index (SRI), sleep fragmentation index (SFI), total sleep time (TST), and time out of bed (no presence). Results: A total of 9 participants were included in the final analysis. Differences were found in nighttime ENMO ( P =.01) and in 4 of the sleep biomarkers: TST ( P =.02), SE ( P =.02), WASO ( P =.01), and SRI ( P =.01). The long-term reliability of the group ENMO was poor (intraclass correlation coefficient 0.00‐0.02); however, it was moderate to strong (0.58‐0.79) between individuals at each time point. The adherence and acceptability of the technologies was high (88%-96%), and the application of the devices was well tolerated by the participants, with no adverse events. Conclusions: The use of sensing technologies could enable more objective, data-driven future care models for people with dementia residing in nursing homes; however, the results emphasized in this study require the recommendation for cautious, well-designed use of digital biomarkers for clinical decision-making.

BACKGROUND Proxy-rated questionnaires remain the standard for assessment of activity and sleep for people with dementia living in nursing homes. Sensing technologies, such as wearables, can generate continuous data that provides quantitative insights into daily activities and behavioral and psychological symptoms, such as sleep disturbance. This study explores the utility of sensing technologies in the detection of changes in physical activity levels and sleep behaviors over time. OBJECTIVE This study aims to explore the long-term capabilities of multi-modal sensing technologies for assessing physical activity levels and sleep quality using selected digital biomarkers for nursing home residents with dementia. Objectives were for observation to be aligned with real-world conditions in which such sensing technologies would be applied within a nursing home environment, and to assess whether distinct differences in selected digital biomarkers can be observed accurately and reliably longitudinally. METHODS This study included eleven participants (79–93 years) recruited from two dementia care units in Norway. A smartwatch (Garmin Vivoactive5/Garmin Venu3) and radar-based system (Vital Things, Somnofy) were used to collect 7-days and 6-nights of data on physical activity levels and sleep quality at baseline, 6 months, and 1-year. The Personal Self-Maintenance Score and Neuropsychiatric Inventory–Nursing Home Version (nighttime behaviors section K), were also administered. Digital biomarkers included Euclidean Norm Minus One (ENMO), Sleep Efficiency (SE), Wake After Sleep Onset (WASO), Sleep Regulatory Index (SRI), Sleep Fragmentation Index (SFI), Total Sleep Time (TST), and time out of bed (no presence). RESULTS Nine total participants were included in the final analysis. Differences were found in Nighttime ENMO (P= .01) and in four of the sleep biomarkers: TST (P=.02), SE (P= .02), WASO (P= .01), and SRI (P=.01). Long-term reliability of the group ENMO was poor (ICC: 0.00-0.02), however, between individuals at each timepoint was moderate - strong (0.58-0.79). Adherence and acceptability of the technologies was high (88-96%), and application of the devices was well tolerated by the participants with no adverse events. CONCLUSIONS The use of sensing technologies could enable more objective, data-driven future care models for people with dementia residing in nursing homes, however, the results emphasized in this study require the recommendation for cautious, well-designed use of digital biomarkers for clinical decision-making. CLINICALTRIAL The DIPH.DEM study was approved by the Regional Committee for Medical and Health Research Ethics (REK) in Norway in October 2023: approval number 634938.

Objective: Tremor symptoms in Parkinson’s disease (PD) are challenging to assess due to low resolution and subjectivity from standard clinical scales. To address this, wearable devices have been used, but algorithms have been relying on controlled or limited activity conditions. Our objective is to create a context-independent metric quantifying tremor in free-living conditions to bridge the gap between biomedical engineering and the PD field. Methods and procedures: We designed an algorithm which computes a tremor index (TI) from accelerometer data, collected via the Empatica E4 worn on the wrist by home dwelling people with PD. For validation, we use a within-participant design, comparing the TIs of the most and least tremor-affected hand. We included seven participants with unilateral tremor, monitored for two weeks each. The algorithm is able to compute TIs for a set of frequencies identified in literature as associated with different tremor types (3–12 Hz), over adjustable sampling time windows. Results: We show that the most tremor-affected hand yields a higher TI than the other hand for frequency sets that are individual to each person, in particular around 5-6 Hz where rest tremor typically occurs. We find that we can disambiguate tremor across 3-12 Hz from general movement and resting states. The number of frequencies with inter-hand separation correlate with the MDS-UPDRS part III tremor items. Conclusion: The designed tremor quantification algorithm can quantify tremor symptoms over time for people with PD and can be used to identify the individualized frequency ranges where these movements happen, in free-living conditions. Clinical and translational impact: By utilizing pervasive, easy-to-use wrist-wearable devices equipped with accelerometers, the proposed index has the potential to change how clinicians and researchers track tremor symptoms in the daily lives of patients.

In an era where digital communication accelerates the global spread of false narratives, understanding how misinformation and disinformation propagate, especially during crises such as the COVID-19 pandemic, is vital to public health and policy. To delve into the diffusion mechanisms of misinformation (unintentionally false information) and disinformation (intentionally false information), we introduce a novel enhanced agent-based model (ABM) that integrates psycho-social factors and communication networks, which are elements often overlooked in traditional equation-based models (EBMs). We assess the two distinct techniques (ABMs and EBMs) through the lens of six classical SEIRS-class models (S susceptible, E exposed, I infected, R recovered). Beside the enhanced ABM, we also develop a simple ABM to emulate the EBM structure. We compare the ABMs with the EBMs over their entire parameter ranges in a total of 11110 experiments. Results show an overall weak equivalence between the two types of models, even if, under certain conditions, the outcomes of the EBMs and ABMs are similar. Furthermore, we evaluate the two model types by fitting them to real-world infodemic data on vaccine acceptance over 36 weeks using a multi-objective optimization procedure. The enhanced ABM shows an exceptionally better fit to real-world data (Pearson’s correlation coefficient ρ   =   0.872 and normalized root mean of square error NRMSE = 0.055) than the EBM ( ρ = − 0.067 , NRMSE = 0.418) and the simple ABM ( ρ = 0.391 , NRMSE = 0.103). These findings underscore the critical role of model structure in capturing infodemic dynamics, and advocate for the use of ABMs when psycho-social influences and network interactions are central to the phenomenon.

Inactivity and increases in psychological and behavioral symptoms are common for people with dementia, and current assessment relies on proxy-rated tools. We investigate the feasibility and adherence of the use of sensor technology by exploring the relationship between daytime activity and sleep quality. For a total of 42 day–night data pairs in nursing home residents with dementia (N = 11), Garmin Vivoactive5 and Somnofy monitored continuous physical activity levels, sleep efficiency (SE), sleep score, sleep regularity index (SRI), and wake after sleep onset (WASO). Using the Spearman coefficient, we explored correlations between digital and proxy-rated tools (Personal Self Maintenance Scale (PSMS) and Neuropsychiatric Inventory–Nursing Home version (NPI-NH)) and the relationships between the digital biomarkers (SE, SRI, WASO, sleep score, physical activity). Participants (mean age 84 years) had moderate to severe degrees of dementia. Daytime activity levels correlated to sleep quality parameters WASO (−0.34, p = 0.03), and SRI (0.43, p = 0.01), and traditional sleep measures were associated with digital biomarkers (WASO/NPI-NH-K, p = 0.03). We found a relationship between daytime activity and sleep quality; however, the bidirectional relationship remains ambiguous and should be further investigated. The use of sensing technologies for people with dementia residing in a nursing home is feasible, although not without limitations, and has the potential to identify subtle changes, improving clinical assessment and the corresponding care recommendations.

Inactivity and increases in psychological and behavioral symptoms are common for people with dementia and current assessment relies on proxy-rated tools. We investigate the feasibility and adherence of the use of sensor technology by exploring the relationship between daytime activity and sleep quality. For a total of 40 day-night data pairs in nursing home residents with dementia (N=11), Garmin Vivoactive5 and Somnofy monitored continuous physical activity levels, Sleep Efficiency (SE), Sleep Score, Sleep Regularity Index (SRI), and Wake After Sleep Onset (WASO). Using Spearman coefficient, we explored correlations between digital and proxy-rated tools (Personal Self Maintenance Scale (PSMS), and Neuropsychiatric Inventory-Nursing Home version (NPI-NH)) and the relationships between the digital biomarkers (SE, SRI, WASO, Sleep Score, physical activity). Participants (mean age 84 years, 8 female) had moderate to severe degrees of dementia. Daytime activity levels correlated to sleep quality parameters SE (0.43, p=0.05), WASO (-0.35, p=0.02), and SRI (0.46, p=0.007), and traditional sleep measures were associated with digital biomarkers (WASO/NPI-NH-K, p=0.04). We find a relationship between daytime activity and sleep quality; however, the bidirectional relationship remains ambiguous and should be further investigated. Sensing technologies have the potential to more accurately identity subtle changes, improving clinical assessment and corresponding care recommendations.

Background: The increasingly older world population presents new aging-related challenges, especially for persons with dementia unable to express their suffering. Pain intensity and the effect of pain treatment are difficult to assess via proxy rating and both under- and overtreatment lead to neuropsychiatric symptoms, inactivity, care-dependency and reduced quality of life. In this debate piece, we provide a rationale on why valid digitalization, sensing technology, and artificial intelligence should be explored to improve the assessment of pain in people with dementia. Main text: In dementia care, traditional pain assessment relies on observing the manifestations of typical pain behavior. At the same time, pain treatment is complicated by polypharmacy, potential side effects, and a lack of around-the-clock, timely measures. But proper pain treatment requires objective and accurate measures that capture both the levels of pain and the treatment effects. Sensing systems research for personalized pain assessment is underway, with some promising results regarding associations between physiological signals and pain. Digital phenotyping, making use of everyday sensor data for monitoring health behaviors such as patterns of sleep or movement, has shown potential in clinical trials and for future continuous observation. This emerging approach requires transdisciplinary collaboration between medical and engineering sciences, with user involvement and adherence to ethical practices. Conclusion: Digital phenotyping based on physiological parameters and sensing technology may increase pain assessment objectivity in older adults with dementia. This technology must be designed with user involvement and validated; however, it opens possibilities to improve pain relief and care.

Background: With a progressively aging global population, the prevalence of Parkinson’s Disease and dementia will increase, thus multiplying the healthcare burden worldwide. Sensing technology can complement the current measures used for symptom management and monitoring. The aim of this umbrella review is to provide future researchers with a synthesis of the current methodologies and metrics of sensing technologies for the management and monitoring of activities and behavioral symptoms in older adults with neurodegenerative disease. This is of key importance when considering the rapid obsolescence of and potential for future implementation of these technologies into real-world healthcare settings. Methods: Seven medical and technical databases were searched for systematic reviews (2018–2024) that met our inclusion/exclusion criteria. Articles were screened independently using Rayyan. PRISMA guidelines, the Cochrane Handbook for Systematic Reviews, and the Johanna Briggs Institute Critical Appraisal Checklist for Systematic Reviews were utilized for the assessment of bias, quality, and research synthesis. A narrative synthesis combines the study findings. Results: After screening 1458 articles, 9 systematic reviews were eligible for inclusion, synthesizing 402 primary studies. This umbrella review reveals that the use of sensing technologies for the observation and management of activities and behavioral symptoms is promising, however diversely applied, heterogenous in the methods used, and currently challenging to apply within clinical settings. Conclusions: Human activity and behavioral recognition requires true interdisciplinary collaborations between engineering, data science, and healthcare domains. The standardization of metrics, ethical AI development, and a culture of research-friendly technology and support are the next crucial developments needed for this rising field.

BACKGROUND With a progressively aging global population, the prevalence of Parkinson’s Disease and dementia will increase, thus multiplying healthcare burden worldwide. Sensing technology can complement current measures used for symptom management and monitoring. The aim of this umbrella review is to provide future researchers with a synthesis of current methodologies and metrics for use of sensing technologies for the management and monitoring of activities and behavioral symptoms in older adults with neurodegenerative disease. This is of key importance when considering the rapid obsolescence of and potential for future implementation of these technologies into real-world healthcare settings. METHODS Seven medical and technical databases were searched for systematic reviews (2018-2024) which met inclusion/exclusion criteria. Articles were screened independently using Rayyan. PRISMA guidelines, the Cochrane Handbook for Systematic Reviews, and the Johanna Briggs Institute Critical Appraisal Checklist for Systematic Reviews were utilized for assessment of bias, quality, and research synthesis. A narrative synthesis combines the study findings. RESULTS After screening 1458 articles, 9 systematic reviews were eligible for inclusion, synthesizing 402 primary studies. This umbrella review reveals that use of sensing technologies for observation and management of activities and behavioral symptoms is promising, however diversly applied, heterogenous in methods used, and currently challenging to apply within clinical settings. CONCLUSIONS Human Activity and Behavioral Recognition requires true interdisciplinary collaborations between engineering, data science, and healthcare domains. Standardization of metrics, ethical AI development, and a culture of research-friendly technology and support are the next crucial developments needed for this rising field.