Bettina Sandgathe Husebø

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.

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: 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.

Background: Music-based intervention has been used as first-line non-pharmacological treatment to improve cognitive function for people with mild cognitive impairment (MCI) or dementia in clinical practice. However, evidence regarding the effect of music-based intervention on general cognitive function as well as subdomains of cognitive functions in these individuals is scarce. Objective: To evaluate the efficacy of music-based interventions on a wide range of cognitive functions in people with MCI or dementia. Method: We searched the effect of various music therapies using randomized controlled trials on cognitive function using several databases. Studies based on any type of dementia or MCI were combined. The effects of music-based intervention on each cognitive function were pooled by meta-analysis. Results: A total of 19 studies involving n = 1024 participants (mean age ranged from 60 to 87 years old) were included. We found statistically significant improvements in MMSE (general cognitive function), the Frontal Assessment Battery (executive function), and the Auditory Verbal Learning Test (episodic memory). Conclusions: This study provides positive evidence to support music-based interventions for improving a wide range of cognitive functions in older adults with MCI and dementia. Therefore, we recommend increased use of music in people’s homes, day care centers and nursing homes. This study was registered with PROSPERO, number 250383.

As a complex system, crowd dynamics emerge bottom-up from the local interactions between pedestrians as component subsystems. This article proposes a predictive agent-based crowd simulation model to analyze the outcomes of emergency evacuation scenarios taking into account collisions between pedestrians, smoke, fire sprinklers, and exit indicators. The crowd model is based on a decentralized control system structure, where each pedestrian agent is governed through a deliberative-reactive control architecture. The simulation model for evacuation includes a routing-based control system for dynamic-guided evacuation. A design case illustrates the modeling process. Results show that the crowd simulation model based on agent autonomy and local interactions is able to generate higher level crowd dynamics through emergence.