BackgroundA 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.ObjectiveOur 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.MethodsWe 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.ResultsWe demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities.ConclusionsSmartphone 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.

In modern psychopharmacology, the gold standard for measurement of depressive symptoms, as with most psychiatric outcomes, has been clinician-rated scales. However, reliance on such measures introduces substantial limitations: the need for trained clinician raters increases the cost of assessment (despite enthusiasm for measurement-based care and recognition of the importance

Introduction-Apostle Islands National Lakeshore (APIS) lies at the northern tip of Wisconsin and is home to a network of 21 islands along Lake Superior. The goal of this report is to investigate search and rescue (SAR) and emergency medical services (EMS) trends at APIS in an effort to improve visitor safety and resource allocation.Methods-This study is a retrospective analysis reviewing APIS SAR reports and annual EMS summary reports from January 1, 2006, to December 31, 2015. Information related to incident type, incident date, individual demographic characteristics and activities, injury/illness type, cost, and contributing factors were recorded and analyzed in frequency tables.Results-From 2006 to 2015, APIS SAR conducted 133 total missions assisting 261 individuals-200 not injured/ill, 57 injured/ill, and 4 fatalities. Median cost per SAR incident involving aircraft totaled $21,695 (range: $2,993À141,849), whereas incidents not involving aircraft had a median cost of $363 (range: $35À8,830). Nonmotorized boating was the most common activity resulting in SAR incidents. All 4 fatalities were attributed to drowning while kayaking or swimming. Cold-related injury/illness accounted for nearly half of all injuries/illnesses (45%) with the most commonly reported contributing factor being high winds. EMS responded to a total of 134 incidents. Trauma and first aid accounted for 43% and 34% of EMS workload, respectively.Conclusions-Overall, this study highlights the hazards associated with the frigid and rough conditions of Lake Superior. The reported results aim to help APIS personnel more saliently convey risks to visitors and plan appropriately in an effort to decrease the need for future rescues.

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