Using Smartphones to Capture Social Network Dynamics in Young Adult Smoking
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Abstract
Background
There is a general consensus that smoking in young adults aged 18-25 presents a significant public health concern. We have enrolled 98 study participants nested within 19 social network clusters of young adult smokers. In this abstract, we report the technology and preliminary findings in using smartphones as social network sensors. The overall goal of the project is to examine the social network influences of young adult smoking.
Methods
Using the pioneering concepts developed by Eagle and colleagues (e.g., http://alumni.media.mit.edu/~nathan/bio.html), we have programmed a smartphone app to make the social network sensing assessments by periodically sampling the phone's chips to derive proximity data. For example, two phones share a proximity to a specific location if both detect the same Wifi access point(s) and each other's Bluetooth ids. Barring compatibility issues, participants were provided with a GSM-compatible smartphone with the app installed. They replaced their own phones with the study-provided phone by swapping the SIM card. They were instructed to use the phone as usual for up to 3 months and report each incidence of cigarette smoking.
Results
As of March, 2014, we have just completed most of the data collection, In this abstract we plan to share (primarily) the technology aspects of real-time data capture and analysis, including security and privacy issues, data encryption, secure data transmission, and analytics. Our top priority now is to process the vast amounts of phone sensing data as efficiently as possible. For example, our first network of 4 participants contributed over 400,000 data entries. Data analysis is ongoing. The goal is to share, when the conference commences, social network diagrams and the dynamics of network characteristics over time.
Conclusions
Our experience has shown that automatic sensing of social network proximity by smartphones provide a useful data collection method. However, there are limitations. Adherence to self-reporting of cigarette smoking is a main challenge. The main advantage of this assessment method is that social network proximity data can be detected automatically and unobtrusively, all without the need for user input.
There is a general consensus that smoking in young adults aged 18-25 presents a significant public health concern. We have enrolled 98 study participants nested within 19 social network clusters of young adult smokers. In this abstract, we report the technology and preliminary findings in using smartphones as social network sensors. The overall goal of the project is to examine the social network influences of young adult smoking.
Methods
Using the pioneering concepts developed by Eagle and colleagues (e.g., http://alumni.media.mit.edu/~nathan/bio.html), we have programmed a smartphone app to make the social network sensing assessments by periodically sampling the phone's chips to derive proximity data. For example, two phones share a proximity to a specific location if both detect the same Wifi access point(s) and each other's Bluetooth ids. Barring compatibility issues, participants were provided with a GSM-compatible smartphone with the app installed. They replaced their own phones with the study-provided phone by swapping the SIM card. They were instructed to use the phone as usual for up to 3 months and report each incidence of cigarette smoking.
Results
As of March, 2014, we have just completed most of the data collection, In this abstract we plan to share (primarily) the technology aspects of real-time data capture and analysis, including security and privacy issues, data encryption, secure data transmission, and analytics. Our top priority now is to process the vast amounts of phone sensing data as efficiently as possible. For example, our first network of 4 participants contributed over 400,000 data entries. Data analysis is ongoing. The goal is to share, when the conference commences, social network diagrams and the dynamics of network characteristics over time.
Conclusions
Our experience has shown that automatic sensing of social network proximity by smartphones provide a useful data collection method. However, there are limitations. Adherence to self-reporting of cigarette smoking is a main challenge. The main advantage of this assessment method is that social network proximity data can be detected automatically and unobtrusively, all without the need for user input.
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