A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study.

BACKGROUND: A major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. OBJECTIVE: We sought to (1) assess smokers' compliance with reporting their smoking in real time and identify reasons for noncompliance, (2) assess the app's accuracy in identifying user-specific high-risk locations for smoking, (3) explore the feasibility and user perspective of geofence-triggered support, and (4) identify any technological issues or privacy concerns. METHODS: An explanatory sequential mixed-methods design was used, where data collected by the app informed semistructured interviews. Participants were smokers who owned an Android mobile phone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, end-of-day (EoD) surveys of smoking beliefs and behavior, support message ratings, and app interaction data. Interviews were undertaken and analyzed thematically (N=13). Quantitative and qualitative data were analyzed separately and findings presented sequentially. RESULTS: Out of 15 participants, 3 (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 37.8 (SD 21.2) per participant, or 2.0 (SD 2.2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56.2% of days. Geolocation was collected in 97.0% of smoking reports with a mean accuracy of 31.6 (SD 16.8) meters. A total of 5 out of 9 (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50.0% (137/274) of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 78.2% (158/202) of delivered messages were rated by participants. Qualitative findings identified multiple reasons for noncompliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app's identification of their smoking locations, were largely positive about the value of geofence-triggered support, and had no privacy concerns about the data collected by the app. CONCLUSIONS: User-initiated self-report is feasible for training a cessation app about an individual's smoking behavior, although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants

My Phone and Me:Understanding People's Receptivity to Mobile Notifications

none5siNotifications are extremely beneficial to users, but they often demand their attention at inappropriate moments. In this paper we present an in-situ study of mobile interruptibility focusing on the effect of cognitive and physical factors on the response time and the disruption perceived from a notification. Through a mixed method of automated smartphone logging and experience sampling we collected 10372 in-the-wild notifications and 474 questionnaire responses on notification perception from 20 users. We found that the response time and the perceived disruption from a notification can be influenced by its presentation, alert type, sender-recipient relationship as well as the type, completion level and complexity of the task in which the user is engaged. We found that even a notification that contains important or useful content can cause disruption. Finally, we observe the substantial role of the psychological traits of the individuals on the response time and the disruption perceived from a notification.noneMehrotra, A and Pejovic, V and Vermeulen, J and Hendley, RJ and Musolesi, MMehrotra, A and Pejovic, V and Vermeulen, J and Hendley, RJ and Musolesi,

Understanding the Relationship Between People and Their Environments Using Smartphone Data: A Study of Personality, Places Visited, and Emotional Experiences

Much has been theorized about the relationship between people and their environments. Certain people may be more inclined to visit certain types of places (e.g., campus, pub) and display different patterns of mobility as they move among them (e.g., number of places visited, distances traveled). Moreover, even the same place may affect people differently, depending on their psychological characteristics (e.g., personality). In this dissertation, I draw upon recent technological advances in smartphone-sensing methods to investigate the relationship between people’s psychological characteristics and their physical movements through space. I begin by reviewing the existing psychological literature. I next describe features that can be extracted from GPS data and categorize them to provide a framework for collecting, analyzing, and discussing mobility. Then, I conduct an empirical investigation demonstrating this methodology at work. One-hundred and eighteen participants provided ecological momentary assessments, reporting their places visited and emotional states (e.g., feeling stressed, relaxed, sad) four times per day for two to four weeks. In addition to these ecological momentary assessments, place and mobility data were also automatically collected for forty students using their smartphone’s GPS sensors. I supplemented these data by collecting place attributions from an independent sample of 267 participants who evaluated the situational characteristics (e.g., sociality, positivity) of the most commonly visited locations. Lastly, I look at how people perceive places and whether their judgments about a location (e.g., predictions about the personality of those most likely to visit a location) demonstrate consensus or accuracy. A lens model analysis highlights the cues underlying these perceptions. The results show how places visited (based on self-reported places) and mobility patterns (based on sensed GPS data) are related to people’s in-the-moment emotional experiences and their enduring psychological characteristics, such as their personality and wellbeing. I also examine how one’s personality interacts with the situational characteristics of a place to affect emotional states. For instance, one key finding reveals that, in general, participants experienced more positive emotions in social places (e.g., common rooms, pubs) but that this was especially true for more extraverted individuals. Lastly, I find that though people demonstrate consensus in their judgments when virtually visiting a place, they do not show significant accuracy. My discussion focuses on the benefits of using place and GPS-based mobility measures to understand the relationship between people and their environments, as well as the unique methodological and logistical challenges inherent to this. I conclude by discussing potential implications for privacy and research ethics and point to promising directions for future research

Open source smartphone libraries for computational social science

The ubiquity of sensor-rich and computationally powerful smartphones makes them an ideal platform for conducting social and behavioural research. However, building sensor data collection tools remains arduous and challenging: it requires an understanding of the varying sensor programming interfaces as well as the research issues related to building sensor-sampling systems. To alleviate this problem and facilitate the development of social sensing and data collection applications, we are developing a set of open-source smartphone libraries to collect, store and transfer, and query sensor data. Furthermore, we have also developed a library that can trigger notifications based on time or sensor events to assist experience sampling methods. This paper presents these libraries' architecture, initial feedback from developers using it, and a sensing application that we built using them to study daily affect. Copyright © 2013 ACM

Open source smartphone libraries for computational social science. In UbiComp (Adjunct Publication)

Abstract The ubiquity of sensor-rich and computationally powerful smartphones makes them an ideal platform for conducting social and behavioural research. However, building sensor data collection tools remains arduous and challenging: it requires an understanding of the varying sensor programming interfaces as well as the research issues related to building sensor-sampling systems. To alleviate this problem and facilitate the development of social sensing and data collection applications, we are developing a set of open-source smartphone libraries to collect, store and transfer, and query sensor data. Furthermore, we have also developed a library that can trigger notifications based on time or sensor events to assist experience sampling methods. This paper presents these libraries' architecture, initial feedback from developers using it, and a sensing application that we built using them to study daily affect