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The purpose of this study is (1) to develop the decision rules and evaluate the feasibility of a just-in-time intervention to delay or prevent smoking relapse in smokers attempting to quit (2) to predict the timing of lapses following a quit attempt (3) to develop a model to detect single puff episodes of smoking using wrist-worn inertial sensors.
This study uses mCerebrum software to collect daily weight, blood pressure, chest sensor, and wrist sensor data (as well as medication reporting and EMA survey data) from congestive heart failure patients.
This study is designed to combine objective and dynamic indices of smoking lapse/smoking abstinence, stress, key environmental influences, and neuroimaging data in the study of smoking cessation.
This study is designed to target the gaps in empirical evidence; use state of the science mobile technology and analytic advances to test fundamental hypotheses linking SES to objective, real time, real world measures of stress and smoking lapse/abstinence; and address key recommendations from major reports on both reducing smoking among low SES populations and addressing health disparities.
This study uses mCerebrum software to collect and interpret health data from a Microsoft Band.