Technology-guided assessment of vocalisations and their diagnostic value as pain indicators for people living with dementia

Background: during pain assessment in persons unable to self-report, such as people living with dementia, vocalisations are commonly used as pain indicators. However, there is a lack of evidence from clinical practice regarding their diagnostic value and relationship with pain. We aimed to explore vocalisations and pain in people with dementia undergoing pain assessments in clinical practice settings. Methods: a total of 22,194 pain assessments were reviewed in people with dementia (n = 3,144) from 34 different Australian aged care homes and two dementia specific programs. Pain assessments were conducted by 389 purposely trained health care professionals and cares using PainChek pain assessment tool. Vocalised expressions were determined based on nine vocalisation features included in the tool. Linear mixed models were used to examine the relationship of pain scores with vocalisation features. Using a single pain assessment for each of the 3,144 people with dementia, additional data analysis was conducted via Receiver Operator Characteristic (ROC) analysis and Principal Component Analysis. Results: vocalisation scores increased with increasing pain intensity. High pain scores were more likely with the presence of sighing and screaming (8 times). The presence of vocalisation features varied depending on the intensity of pain. The ROC optimal criterion for the voice domain yielded a cut-off score of ≥2.0 with a Youden index of 0.637. The corresponding sensitivity and specificity were 79.7% [confidence interval (CI): 76.8–82.4%] and 84.0% (CI: 82.5–85.5%), respectively. Conclusion: we describe vocalisation features during presence of different levels of pain in people with dementia unable to self-report, therefore providing evidence in regard to their diagnostic value in clinical practice

Technology-guided assessment of vocalisations and their diagnostic value as pain indicators for people living with dementia

Background during pain assessment in persons unable to self-report, such as people living with dementia, vocalisations are commonly used as pain indicators. However, there is a lack of evidence from clinical practice regarding their diagnostic value and relationship with pain. We aimed to explore vocalisations and pain in people with dementia undergoing pain assessments in clinical practice settings. Methods a total of 22,194 pain assessments were reviewed in people with dementia (n = 3,144) from 34 different Australian aged care homes and two dementia specific programs. Pain assessments were conducted by 389 purposely trained health care professionals and cares using PainChek pain assessment tool. Vocalised expressions were determined based on nine vocalisation features included in the tool. Linear mixed models were used to examine the relationship of pain scores with vocalisation features. Using a single pain assessment for each of the 3,144 people with dementia, additional data analysis was conducted via Receiver Operator Characteristic (ROC) analysis and Principal Component Analysis. Results vocalisation scores increased with increasing pain intensity. High pain scores were more likely with the presence of sighing and screaming (8 times). The presence of vocalisation features varied depending on the intensity of pain. The ROC optimal criterion for the voice domain yielded a cut-off score of ≥2.0 with a Youden index of 0.637. The corresponding sensitivity and specificity were 79.7% [confidence interval (CI): 76.8–82.4%] and 84.0% (CI: 82.5–85.5%), respectively. Conclusion we describe vocalisation features during presence of different levels of pain in people with dementia unable to self-report, therefore providing evidence in regard to their diagnostic value in clinical practice

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

Affective Computing for Late-Life Mood and Cognitive Disorders

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer\u27s disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed

Impact of COVID-19 restrictions on behavioural and psychological symptoms in home-dwelling people with dementia: A prospective cohort study (PAN.DEM)

Objectives To investigate the impact of the COVID-19 restrictions on behavioural and psychological symptoms of dementia (BPSD). Design Prospective cohort study (PAN.DEM) nested within the halted parent trial ([email protected]). Setting Households in Norway immediate before and 6–9 weeks into the COVID-19 restrictions. Participants 104 dyads (persons with mild to moderate dementia aged ≥65 and their informal carers) completed both prepandemic and pandemic assessments, among 237 in the parent trial. Mini-Mental Status Examination score 15–26 or Functional Assessment Staging score 3–7 covered dementia severity. Main outcome measures Neuropsychiatric Inventory (NPI-12) total (range 0–144), psychosis (range 0–24), hyperactive behaviour (range 0–60) and mood subsyndrome (range 0–48) scores; Cornell Scale for Depression in Dementia (CSDD) total score (range 0–38). Results We found an overall increase in BPSD by NPI-12 total score comparing prepandemic to pandemic levels (median 16 IQR (4.5–29) to 20 (7–32.5), p=0.03) over a mean of 86 days (SD 19). NPI-12 total score worsened in 57 (55%) of people with dementia and was associated with postponed or averted contacts with healthcare professionals (logistic regression, OR 3.96, 95% CI 1.05 to 14.95). Psychosis subsyndrome levels increased (0 (0–3) to 0.5 (0–6), p=0.01) in 37 (36%) persons; this worsening was associated with partial insight (9.57, 1.14 to 80.71) and reduced informal carer contact (4.45, 1.01 to 19.71). Moreover, depressive symptoms increased as assessed by CSDD total score (5 (3–9) to 7 (4–12), p=0.01) and worsened for 56 (54%), which was inversely associated with psychotropic drugs on-demand (0.16, 0.03 to 0.75). Conclusions BPSD worsened during the first months of the COVID-19 restrictions, most pronounced for psychosis and depression. These BPSD exacerbations have implications for pandemic policies, emphasising that restrictions must balance COVID-19 morbidity and mortality against dementia deterioration.publishedVersio

Returning Individual Research Results from Digital Phenotyping in Psychiatry

Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants’ locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant’s real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas