Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults

Time perception at resting state and during active motion: The role of anxiety and depression

BACKGROUND

Time perception and motion intensity are interrelated factors that may influence symptom expression and severity in case of various psychiatric conditions, including anxiety and depression.

AIMS

The present study aimed to 1) explore the associations between the intensity of physical activity, time perception, impulsivity, anxiety and depressive symptoms, and to 2) investigate the extent to which resting state motion intensity can be used to identify the assessed psychiatric conditions.

METHODS

20 healthy controls and 20 psychiatric patients (with either anxiety or depression-related diagnoses) were included in the study and filled out a questionnaire consisting of validated anxiety, depression and impulsivity measures. Time perception was measured by a computerized time production task, whereas motion intensity was analyzed by a motion capture and analysis software. Respondents were randomly assigned to an experimental (with active motion task) and non-experimental group (resting state conditions). Both subgroups were repeatedly assessed, in order to explore changes in motion intensity, time perception and psychiatric symptom levels.

RESULTS

Random forest regression analysis identified the level of impulsivity, depression and anxiety as the strongest predictors of resting state motion intensity, while a path analysis model indicated that controls and psychiatric patients show different pathways regarding the connection between motion intensity changes, time production ratio alterations and symptom reduction.

CONCLUSIONS

Our study implies the importance of distinguishing between clinical and subclinical severity of psychiatric symptoms when considering the association between motion intensity, time perception, anxiety and depression. Potential transdiagnostic relevance of resting state motion intensity is also addressed.

Understanding Pain and Agitation Through System Analysis Algorithms in People With Dementia. A Novel Explorative Approach by the DIGI.PAIN Study

Background

Many people with dementia (PwD) live and die with undiagnosed and untreated pain and are no longer able to report their suffering. Several pain assessment tools have been developed, tested, and implemented in clinical practice, but nursing home patients are reported to be still in pain. Clinicians and research groups worldwide are seeking novel approaches to encode the prediction, prevalence, and associations to pain in PwD.

Participants

The data in this analysis are acquired from the COSMOS study, a cluster-randomized controlled trial (2014 to 2015), aimed to improve the quality of life in nursing home patients (N = 723) through the implementation of a multicomponent intervention. We utilize baseline data of PwD (N = 219) with complete datasets of pain and agitation.

Method

Systems analysis explores the relationship between pain and agitation using the Mobilization-Observation-Behavior-Intensity-Dementia (MOBID-2) Pain Scale, Cohen-Mansfield Agitation Inventory (CMAI), and Neuropsychiatric Inventory-Nursing Home version (NPI-NH). For each patient, the individualized continuous time trajectory, and rates of change of pain and agitation are estimated. We determine the relationship between these rates by analyzing them across the entire group.

Results

We found that the new analysis method can generate individualized estimations for pain and agitation evolution for PwD, as well as their relationship. For 189 of 219 PwD, results show that whenever pain increases or decreases, agitation does too, with the same rate. The method also identifies PwD for whom pain or agitation remains constant while the other varies over time, and patients for whom agitation and pain do not change together. The algorithm is scalable to other variables and compatible with wearable devices and digital sensors.

Conclusion

We presented a new approach to clinical data analysis using systems concepts and algorithms. We found that it is possible to quantify and visualize relationships between variables with a precision only dependent on the precision of measurements. This method should be further validated, but incipient results show great potential, especially for wearable-generated continuous data.

Smart homes for the older population: particularly important during the COVID-19 outbreak

Osteoporosis, one of the leading causes of disability in older adults, significantly reduces the quality of life and leads to loss of independence. Dynamic development of “smart” solutions based on artificial intelligence more and more commonly applied in older people’s houses may be an answer to the above issues.

The aim of this study is to present selected “smart home” solutions for the diagnosis and prevention of falls in the older population through a literature review. The conducted meta-analysis based on a review of the scientific literature available in English and Polish in the Medline/PubMed, Embase, Scopus, and GBL databases was undertaken from 01.01.2015 to 01.10.2020 with the string search method using key words.

According to the authors of this study, the development of new technology based on artificial intelligence allows older people to live independently, which contributes to a higher level of life satisfaction and quality.

Wearables bei Demenzerkrankungen

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.

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 5^th 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 3^rd 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.

Systematic Review Registration

PROSPERO, identifier CRD42019134313.

From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology

Mood disorders affect more than 300 million people worldwide and can cause devastating consequences. Elderly people and patients with neurological conditions are particularly susceptible to depression. Gait and body movements can be affected by mood disorders, and thus they can be used as a surrogate sign, as well as an objective index for pervasive monitoring of emotion and mood disorders in daily life. Here we review evidence that demonstrates the relationship between gait, emotions and mood disorders, highlighting the potential of a multimodal approach that couples gait data with physiological signals and home-based monitoring for early detection and management of mood disorders. This could enhance self-awareness, enable the development of objective biomarkers that identify high risk subjects and promote subject-specific treatment.

Mobilizing mHealth Data Collection in Older Adults: Challenges and Opportunities

Worldwide, there is an unprecedented and ongoing expansion of both the proportion of older adults in society and innovations in digital technology. This rapidly increasing number of older adults is placing unprecedented demands on health care systems, warranting the development of new solutions. Although advancements in smart devices and wearables present novel methods for monitoring and improving the health of aging populations, older adults are currently the least likely age group to engage with such technologies. In this commentary, we critically examine the potential for technology-driven data collection and analysis mechanisms to improve our capacity to research, understand, and address the implications of an aging population. Alongside unprecedented opportunities to harness these technologies, there are equally unprecedented challenges. Notably, older adults may experience the first-level digital divide, that is, lack of access to technologies, and/or the second-level digital divide, that is, lack of use/skill, alongside issues with data input and analysis. To harness the benefits of these innovative approaches, we must first engage older adults in a meaningful manner and adjust the framework of smart devices to accommodate the unique physiological and psychological characteristics of the aging populace. Through an informed approach to the development of technologies with older adults, the field can leverage innovation to increase the quality and quantity of life for the expanding population of older adults.