Purple Robot is a core component of CBITs' mobile offerings.
The app provides the following major features for constructing context-aware interventions and experiences:
- A full real-time sensor data acquisition platform for collecting information about the user and their immediate surroundings. Purple Robot provides
- Full access to the Android sensor framework, including the accelerometer, gyroscope, pressure sensor, and more.
- Access to other device information such as battery level, running software & apps, and hardware information.
- Options to scan for external devices such as wireless access points and visible Bluetooth devices.
- Location sensors that use the built-in GPS and cellular triangulation options to map the user’s location.
- Local environmental data sources such as solar event timing (sunrise & sunset) and weather conditions.
- Statistical summaries of the user’s communication patterns, including phone logs and text-message transcripts.
- Cryptographic anonymization of personally-identifiable information before it leaves the device.
- An embedded web server that exposes the scripting engine to put web-based applications on an equal footing with native applications.
- A complete programming framework that allows other applications (native & web-based) to store data, launch applications, update homescreen widgets, and create customized native prompts and dialogs.
- Networked configuration options built around the scripting engine that allow for remote configuration (with proper authorization).
- A robust store-and-forward architecture for transmitting sensor data and user-provided information to custom data warehouses.
- A full trigger framework for defining conditions when scripted actions should occur.
- Date & triggers fire at preselected intervals specified by the standard iCalendar format.
- Sensor-based triggers fire when a condition matching a predefined pattern occurs.
- Triggers have access to the full scripting runtime, providing rich interactions.
Design and evaluation of a peer network to support adherence to a web-based intervention for adolescents.
Ho, J, Corden, ME, Caccamo, L, Tomasino, KN, Duffecy, J, Begale, M, & Mohr DC.
Internet Interventions. 2016; 6, 50-56. doi:http://dx.doi.org/10.1016/j.invent.2016.09.005
Feasibility, acceptability, and preliminary efficacy of a technology-assisted psychosocial intervention for racially diverse men with advanced prostate cancer
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Cancer. 2015. doi: 10.1002/cncr.29658
Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study
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Web-based interventions for ulcerative colitis and crohn's disease: Systematic review and future directions
Stiles-Shields C, Keefer, L.
Clinical and Experimental Gastroenterology. 2015. doi: http://dx.doi.org/10.2147/CEG.S56069
The relationship between clinical, momentary, and sensor-based assessment of depression
Saeb, S, Zhang S, Karr CJ, Corden ME, Kwasny M, Mohr DC
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Mental health disorders in young urban sexual minority men
Burn MN, Ryan DT, Garofalo, R, Newcomb ME, Mustanski B.
Journal of Adolescent Health. 2015; 56, 52-58
Perspectives of young emerging adults with serious mental health conditions on vocational peer mentors
Klodnick VV, Sabella K, Brenner CJ, Krzos IM, Ellison ML, Kaiser SM, Davis M, Fagan MA.
Journal of Emotional and Behavioral Disorders. 2014. doi: 10.1177/1063426614565052
Affective forecasting in schizophrenia: Comparing predictions to real-time ecological momentary assessment (EMA) ratings
Brenner CJ, Ben-Zeev D.
Psychiatric Rehabilitation Journal. 2014; 37(4), 316 – 320
Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia
Ben-Zeev D, Brenner CJ, Begale M, Duffecy J, Mohr DC, Mueser KT.
Schizophrenia Bulletin. 2014. doi: 10.1093/schbul/sbu033
Comorbid anxiety as a differential treatment predictor for telephone vs. face-to-face administered cognitive behavioral therapy for depression
Stiles-Shields C, Kwasny MJ, Cai X, Mohr DC.
Depression and Anxiety. 2014; 31 (11), 934-940