“Kind and Grateful”: A Context-Sensitive Smartphone App Utilizing Inspirational Content to Promote Gratitude

Background Previous research has shown that gratitude positively influences psychological wellbeing and physical health. Grateful people are reported to feel more optimistic and happy, to better mitigate aversive experiences, and to have stronger interpersonal bonds. Gratitude interventions have been shown to result in improved sleep, more frequent exercise and stronger cardiovascular and immune systems. These findings call for the development of technologies that would inspire gratitude. This paper presents a novel system designed toward this end. Methods We leverage pervasive technologies to naturally embed inspiration to express gratitude in everyday life. Novel to this work, mobile sensor data is utilized to infer optimal moments for stimulating contextually relevant thankfulness and appreciation. Sporadic mood measurements are inventively obtained through the smartphone lock screen, investigating their interplay with grateful expressions. Both momentary thankful emotion and dispositional gratitude are measured. To evaluate our system, we ran two rounds of randomized control trials (RCT), including a pilot study (N = 15, 2 weeks) and a main study (N = 27, 5 weeks). Studies’ participants were provided with a newly developed smartphone app through which they were asked to express gratitude; the app displayed inspirational content to only the intervention group, while measuring contextual cues for all users. Results In both rounds of the RCT, the intervention was associated with improved thankful behavior. Significant increase was observed in multiple facets of practicing gratitude in the intervention groups. The average frequency of practicing thankfulness increased by more than 120 %, comparing the baseline weeks with the intervention weeks of the main study. In contrast, the control group of the same study exhibited a decrease of 90 % in the frequency of thankful expressions. In the course of the study’s 5 weeks, increases in dispositional gratitude and in psychological wellbeing were also apparent. Analyzing the relation between mood and gratitude expressions, our data suggest that practicing gratitude increases the probability of going up in terms of emotional valence and down in terms of emotional arousal. The influences of inspirational content and contextual cues on promoting thankful behavior were also analyzed: We present data suggesting that the more successful times for eliciting expressions of gratitude tend to be shortly after a social experience, shortly after location change, and shortly after physical activity. Conclusions The results support our intervention as an impactful method to promote grateful affect and behavior. Moreover, they provide insights into design and evaluation of general behavioral intervention technologies.Robert Wood Johnson FoundationMIT Media Lab Consortiu

Predicting students' happiness from physiology, phone, mobility, and behavioral data

In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.MIT Media Lab ConsortiumRobert Wood Johnson Foundation (Wellbeing Initiative)National Institutes of Health (U.S.) (Grant R01GM105018)Samsung (Firm)Natural Sciences and Engineering Research Council of Canad

A Multimodal Mediated Work Environment

Atmosphere - the sensorial qualities of a space, shaped by the composition of light, sound, objects, people, etc. - has remarkable influence on our experiences and behavior. Manipulating it has been shown to be powerful, affecting cognitive performance, mood and even physiology, our work envisions and implements a smart office prototype, capable of digitally transforming its atmosphere - creating what we call Mediated Atmospheres (MA) - using computationally controlled lighting, video projection and sound. Additionally, we equipped this space with a modular real-time data collection infrastructure, integrating a set of biosignal sensors. Through a user study (N=29) we demonstrate MA's effects on occupants’ ability to focus and to recover from a stressful situation. Our evaluation is based on subjective measurements of perception, as well as objective measurements, extracted from recordings of heart rate variability and facial features. We compare multiple signal processing approaches for quantifying changes in occupant physiological state. Our findings show that MA significantly (p<0.05) affect occupants’ perception as well as physiological response, which encouragingly correlate with occupants’ perception. Our findings is a first step towards personalized control of the ambient atmosphere to support wellbeing and productivity