James Lee

Dr. Neal Lathia

Easy M

Overview

Easy M is an Android smartphone app for researchers and clinicians to conduct experience sampling studies that collect behavioral data from sensors. Easy M makes smartphone-based experience sampling studies easy, simple, and fun.

Smartphone Surveys with Configurable Notifications

Ask your participants to assess their mood, health, and context with a variety of questions using Easy M's reconfigurable interfaces, ranging from categorical to Likert scales. Prompt your participants to answer a survey at specific times of the day, or let the device pick random times to beep.

Behavioral data from Sensors

Study your participant's mobility, sociability, and physical activity by capturing data passively from their smartphones' sensors. Use machine learning algorithms to analyse participants' daily behaviours, such as meaningful places and dynamic communication networks.

Research that Used Easy M

2017 Dept of Education, University of Oxford, UK

Lars-Erik Malmberg, et al.
Activity, rest, and learning experiences in students.

2016 School of Medicine, University of Maryland, USA

James A. Waltz, et al.

2016 Dept of Psychiatry, University of Cambridge, UK

Julieta Galante, et al.
Online. The Cambridge University Mindful Student Study.

2015 Dept of Psychology, University of Texas at Austin, USA

Gabriella Harari, et al.
Daily mood, experiences, situations of hundreds of undergraduate psychology students.

2015 Dept of Psychology, University of Cambridge, UK

S. R. Müller, G. Sandstrom, N. Lathia, C. Mascolo, J. Rentfrow. Assessing the Relationship Between Personality and Adaption to University Life Using Mobile Phones. Poster presented at the European Conference on Personality. Timisoara, Romania. July 19-23, 2016.

S. R. Müller, G. Sandstrom, N. Lathia, C. Mascolo, J. Rentfrow. Using Mobile Technology to Understand Student Adjustment. Poster presented at the Society for Personality and Social Psychology. San Diego, California, USA. January 28-30, 2016.

2015 School of Engineering and Digital Arts, University of Kent, UK

Online. Lee, J. A., Efstratious, C., and Lu, B. (2016, September 12-16). OSN mood tracking: exploring the use of online social network activity as an indicator of mood changes. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Pages 1171-1179 .

2014 Dept of Psychology, University of Washington St. Louis, USA

Online. Moran, E. K., Culbreth, A. J., and Barth, D. M. (2016, November 28). Ecological Momentary Assessment of Negative Symptoms in Schizophrenia: Relationships to Effort-Based Decision Making and Reinforcement Learning. Journal of Abnormal Psychology.

2014 Dept of Psychology, University of Silesia, Poland

Irena Pilch, et al.
Socially aversive personality traits and behaviours.

2013 Dept of Psychology, University of Maryland, USA

Alex Shackman, et al.
Emotional levels of anxiety, mobility patterns, and social interactions.

2013 Dept of Psychology, University of Cambridge, UK

Alex Kogan, et al.
Emotional wellbeing and mindfulness.