Information and Communication Technologies, a Promising Way to Support Pharmacotherapy for the Behavioral and Psychological Symptoms of Dementia

Background Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms. Objective The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying depressive symptom severity. Methods A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. Results A number of features from GPS data were related to depressive symptom severity, including circadian movement (regularity in 24-hour rhythm; r=-.63, P=.005), normalized entropy (mobility between favorite locations; r=-.58, P=.012), and location variance (GPS mobility independent of location; r=-.58, P=.012). Phone usage features, usage duration, and usage frequency were also correlated (r=.54, P=.011, and r=.52, P=.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with depressive symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants’ PHQ-9 scores obtained an average error of 23.5%. Conclusions Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to depressive symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach.

Background A longstanding barrier to progress in psychiatry, both in clinical settings and research trials, has been the persistent difficulty of accurately and reliably quantifying disease phenotypes. Mobile phone technology combined with data science has the potential to offer medicine a wealth of additional information on disease phenotypes, but the large majority of existing smartphone apps are not intended for use as biomedical research platforms and, as such, do not generate research-quality data. Objective Our aim is not the creation of yet another app per se but rather the establishment of a platform to collect research-quality smartphone raw sensor and usage pattern data. Our ultimate goal is to develop statistical, mathematical, and computational methodology to enable us and others to extract biomedical and clinical insights from smartphone data. Methods We report on the development and early testing of Beiwe, a research platform featuring a study portal, smartphone app, database, and data modeling and analysis tools designed and developed specifically for transparent, customizable, and reproducible biomedical research use, in particular for the study of psychiatric and neurological disorders. We also outline a proposed study using the platform for patients with schizophrenia. Results We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities. Conclusions Smartphone sensors and phone usage patterns, when coupled with appropriate statistical learning tools, are able to capture various social and behavioral manifestations of illnesses, in naturalistic settings, as lived and experienced by patients. The ubiquity of smartphones makes this type of moment-by-moment quantification of disease phenotypes highly scalable and, when integrated within a transparent research platform, presents tremendous opportunities for research, discovery, and patient health.

Behavioral and psychological symptoms of dementia (BPSD) are nearly universal in dementia, a condition occurring in more than 40 million people worldwide. BPSD present a considerable treatment challenge for prescribers and healthcare professionals. Our purpose was to prioritize existing and emerging treatments for BPSD in Alzheimer's disease (AD) overall, as well as specifically for agitation and psychosis. International Delphi consensus process. Two rounds of feedback were conducted, followed by an in-person meeting to ratify the outcome of the electronic process. 2015 International Psychogeriatric Association meeting. Expert panel comprised of 11 international members with clinical and research expertise in BPSD management. Consensus outcomes showed a clear preference for an escalating approach to the management of BPSD in AD commencing with the identification of underlying causes. For BPSD overall and for agitation, caregiver training, environmental adaptations, person-centered care, and tailored activities were identified as first-line approaches prior to any pharmacologic approaches. If pharmacologic strategies were needed, citalopram and analgesia were prioritized ahead of antipsychotics. In contrast, for psychosis, pharmacologic options, and in particular, risperidone, were prioritized following the assessment of underlying causes. Two tailored non-drug approaches (DICE and music therapy) were agreed upon as the most promising non-pharmacologic treatment approaches for BPSD overall and agitation, with dextromethorphan/quinidine as a promising potential pharmacologic candidate for agitation. Regarding future treatments for psychosis, the greatest priority was placed on pimavanserin. This international consensus panel provided clear suggestions for potential refinement of current treatment criteria and prioritization of emerging therapies.