mCrave

Abstract

Craving usually precedes a lapse for impulsive behaviors such as overeating, drinking, smoking, and drug use. Passive estimation of craving from sensor data in the natural environment can be used to assist users in coping with craving. In this paper, we take the first steps towards developing a computational model to estimate cigarette craving (during smoking abstinence) at the minute-level using mobile sensor data. We use 2,012 hours of sensor data and 1,812 craving self-reports from 61 participants in a smoking cessation study. To estimate craving, we first obtain a continuous measure of stress from sensor data. We find that during hours of day when craving is high, stress associated with self-reported high craving is greater than stress associated with low craving. We use this and other insights to develop feature functions, and encode them as pattern detectors in a Conditional Random Field (CRF) based model to infer craving probabilities.

Identifying Smoking Environments From Images of Daily Life With Deep Learning

“…11 The JITAI framework requires dynamic, ongoing assessment of the probability that a target behavior will occur to trigger an intervention at the most opportune moment. 12 However, in most JITAIs, this assessment has been based on the internal state of the patient—for example, by using physiological measurements or self-report to estimate smoking risk 12,13,14,15 or to support dieting 16 —without also considering the effects of the external environment.…”

Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research

“…In heroin and cocaine users, Kennedy and colleagues (2015) showed that heart rate changed with EMA-reported mood and stress (as well as drug use). Work has also been done to develop/apply algorithms to detect stress from cardiovascular and respiratory parameters in different populations of substance users (Hovsepian et al 2015; Rahman et al 2014; Sarker et al 2016), as well as to detect craving in smokers by interactions of craving, stress, and time of day (Chatterjee et al 2016). Considerable work in non-substance-users has also been done on stress and negative emotions, given their links to cardiovascular disease, as well as how potential protective factors (e.g., self-esteem, relaxation, positive social interactons) could buffer the cardiovascular effects of negative events (e.g., Brondolo et al 2008; Enkelmann et al 2005; Gump et al 2011; Kamarck et al 1998, 2002, 2005; Ottaviani et al 2015; Plarre et al 2011; Schwerdtfeger & Scheel 2012; Verkuil et al 2015).…”

“…In their pioneering description of ambulatory physiological monitoring, Holter and Generelli (1949) proposed using ambulatory EEG to develop an “epilepsy alarm” (p. 750) that would sound before seizures began. It may soon be possible to combine EMA with physiological information, as well as location/environmental information, to predict and prevent stress, drug craving and use (e.g., Chatterjee et al 2016; Hossain et al 2014; McClernon & Choudhury 2013; Sarker et al 2016). One often-noted promise of mHealth is its giving clinicians the ability to intervene wherever and whenever needed—“just in time” (e.g., Carreiro et al 2017).…”

Low-Burden Mobile Monitoring, Intervention, and Real-Time Analysis Using the Wear-IT Framework: Example and Usability Study

“…Alternative approaches to improve engagement have used individualized feedback and visualization [14], badges [15], self-tracking [16], or self-experimentation [17]. Participant burden can be reduced, for example, via passive sensing tools [18]. Methods also exist to balance data cost with burden, for example, by reducing survey size through feature selection [19] or by modeling adherence propensity [20].…”