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. Author manuscript; available in PMC: 2018 Aug 1.
Published in final edited form as: Psychol Addict Behav. 2017 Jul 13;31(5):601–607. doi: 10.1037/adb0000292

Smartphone-Based, Momentary Intervention for Alcohol Cravings amongst Individuals with an Alcohol Use Disorder

Patrick L Dulin 1, Vivian M Gonzalez 1
PMCID: PMC5548428  NIHMSID: NIHMS877033  PMID: 28703611

Abstract

Smartphone-based alcohol interventions represent an innovative strategy for providing in-the-moment intervention to individuals with an alcohol use disorder. While early research into their overall effectiveness is promising, little is known about the efficacy of specific intervention tools in reducing drinking subsequent to a cued craving. This study examined the influence of smartphone-delivered in-the-moment coping strategies on drinking after experiencing a craving amongst participants utilizing the Location-Based Monitoring and Intervention for Alcohol Use Disorders (LBMI-A). The LBMI-A was utilized by 28 adults (18 to 45 years old) who met criteria for an alcohol use disorder and were interested in changing their drinking. Participants utilized the system for 6 weeks and responded to a daily interview of craving, type of LBMI-A coping strategy utilized in response, and whether or not they subsequently drank. Mixed model analyses of 744 total observations revealed that craving cue type, craving strength, and coping strategies were significantly related to drinking in response to a craving. Results suggested that coping strategies delivered by the LBMI-A (i.e., listening to an urge surfing audio file, viewing reasons for changing drinking) were superior to using a non-LBMI-A strategy. Simple contrast analyses indicated that cues related to being around alcohol and time of day were the most potent elicitors of subsequent drinking. Results suggest smartphone-delivered coping strategies for alcohol cravings are effective in reducing craving cued drinking and that craving cues related to drinking environments and drinking times of day represent fruitful areas of intervention focus.

Keywords: smartphone application, technology, craving, coping

Introduction

Utilizing smartphones to deliver real time assessment and intervention services to individuals struggling with an alcohol addiction has the potential to bring needed intervention to large numbers of individuals who are not receiving help through traditional means. Ecological momentary assessment and intervention (EMAI) delivered through smartphones is particularly exciting as it provides the opportunity to assess in real time and immediately intervene to ameliorate potentially problematic psychological states, such as having a cued craving for alcohol or experiencing negative affect (Beckjord & Shiffman, 2014). Early research into the efficacy of smartphone-based alcohol intervention systems suggests that such systems can produce meaningful benefit amongst individuals with a variety of alcohol problems (Fowler, Sidney, & Deepti, 2016). Smartphone-based intervention systems have been shown to reduce percentage of heavy drinking days by over 50% (Dulin, Gonzalez, & Campbell, 2014) and to substantially reduce drinking amongst individuals post-discharge from a residential treatment facility (Gustafson et al., 2014). One of the many unanswered questions in this emerging area is whether a smarphone-based intervention system aimed at providing ongoing monitoring and immediate help with craving for alcohol can reduce drinking subsequent to a cued craving.

Craving – a subjective state of wanting or a desire to drink (Kozlowski, Mann, Wilkinson & Paulos, 1989) – is considered to be a fundamental component of an alcohol use disorder as reflected in its recent inclusion as a diagnostic criterion for a Substance Use Disorder in the DSM-5 (American Psychiatric Association, 2013). Alcohol craving is an important focus of intervention as it has been shown to be predictive of relapse during and after treatment (Flannery, Poole, Gallop, & Volpicelli, 2003; Gordon et al., 2006; Monti et al., 1993; Oslin et al., 2009; Yoon et al., 2006). Recent research utilizing interactive voice response data collection (IVR) suggests that craving can reliably predict subsequent drinking amongst heavy drinking individuals who received a brief alcohol intervention (Fazzino, Harder, Rose, & Helzer, 2013), underscoring craving as an important target for alcohol intervention amongst heavy drinkers. Ramirez and Miranda (2014) also found that craving was a clinically meaningful motivator for drinking amongst adolescents in a study utilizing EMA methodology.

There is limited empirical understanding regarding which types of alcohol-related craving cues are predictive of subsequent drinking, although it is widely accepted that cues can lead to the experience of craving and increased physiological arousal (Carter & Tiffany, 1999). While research has indicated that exposure to alcohol-related cues elicits more craving amongst alcohol dependent samples than neutral cues (Streeter et al., 2002), there is little research that has examined whether cravings triggered by particular types of cues differentially predict subsequent drinking amongst individuals in alcohol treatment.

Numerous strategies have been developed to help clients manage the experience of craving subsequent to exposure to cues without giving in to drinking. These include cognitively-focused strategies, such as reminding oneself of the consequences of use; mindfulness-based strategies that focus on allowing and tolerating the experience of craving; and behavior strategies, such as physically leaving a triggering environmental circumstance (Monti et al., 2002; Marlatt & Gordon, 1985). These strategies have varying levels of empirical support. Mindfulness-based strategies have been shown to increase awareness and the ability to tolerate cravings without subsequent drinking (Witkiewitz, Marlatt, & Walker, 2005) and are associated with reduced alcohol and drug use (Bowen et al., 2006; Bowen & Marlatt, 2009). Research into differences in effectiveness between different strategies for managing cravings has produced equivocal findings. Murphy and MacKillop (2014) found in an experimental setting that behavioral and cognitive distraction techniques were superior to a mindfulness-based strategy with regard to subsequent craving to drink and with distress associated with cravings. However, there is also evidence to suggest little difference among the various types of coping strategies. Dolan, Rosenhow, Martin, and Monti (2013) evaluated the influence of craving-specific coping skills taught to a sample of alcohol dependent individuals in an inpatient treatment context. At up to 12 months discharge, they found that five behavioral (e.g., engage in another activity) and seven cognitive (e.g., think of the positive and negative consequences of drinking) strategies for coping with cravings were related to reduced drinking. That study did not find substantial predictive differences amongst the various coping strategies. A limitation of prior studies is that they did not have the benefit of delivering the strategies in-the-moment in which they were happening, which is a potential advantage of smartphone-based interventions.

While there has been little to no prior study into the effectiveness of EMAI assessed coping strategies for managing cravings amongst individuals participating in an alcohol intervention, there is some evidence from the smoking cessation literature. Utilizing EMA methodology with a group of smokers trying to quit, O’Connel, Hosein, Schwartz, and Leibowitz (2007) found that while multiple coping strategies are helpful in resisting the urge to smoke and that the number of strategies used by smokers is positively associated with effectiveness, no particular strategy was better than another. To our knowledge, no studies have similarly utilized this methodology to compare coping strategies for alcohol cravings.

This report is part of a larger study examining the effectiveness of the Location-Based Monitoring and Intervention for Alcohol Use Disorders (LBMI-A; Dulin, Gonzalez, King, Giroux, & Bacon, 2013), an EMAI system for alcohol use disorder. We previously reported that participants who utilized the system evidenced moderate to large improvements in percentage of days spent drinking heavily and percentage of days abstinent during the 6 weeks in which they used the intervention (Gonzalez & Dulin, 2015). The current study specifically focused on examining whether the LBMI-A coping strategies for a cued craving, which were presented in the moment when cravings were taking place, were effective in reducing drinking subsequent to the craving. We reported previously that the craving module of the LBMI-A, was rated by participants as one of the most helpful modules of the system (Dulin, Gonzalez, & Campbell, 2015), but we have not previously examined if this aspect of the intervention was effective in reducing drinking subsequent to a cued craving. Secondary aims of this study were to determine which craving cue types (e.g., being around alcohol) were the most potent predictors of subsequent drinking and which LBMI-A coping strategies were chosen most frequently by participants.

Method

Participants and Procedures

This study is a secondary analysis of a larger study (see Gonzalez & Dulin, 2015) to determine the effectiveness of the LBMI-A in comparison to an internet-based, brief motivational intervention with established support for reducing alcohol use among problem drinkers, the Drinker’s Check-up (DCU; Hester, Delaney, & Campbell, 2012; Hester, Squires, & Delaney, 2005), which was supplemented with a brief bibliotherapy (National Institute on Alcohol Abuse and Alcoholism, 2010) that provided strategies for reducing or eliminating drinking. The overarching goal of the study was to determine if providing smartphone-based, in-the-moment intervention produced increased effectiveness over self-administered interventions that were not momentarily responsive to participants.

The study protocol was approved by the Institutional Review Board of the university. Participants were recruited from a Northwest community of approximately 300,000 individuals using radio and newspaper advertisements, as well as flyers. In the study advertising we sought people experiencing problems with alcohol and advertised a self-managed, technology-based intervention to help people change their drinking habits. To be included in the study participants had to meet Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-5) diagnostic criteria for an alcohol use disorder (American Psychiatric Association, 2013) and be at least minimally motivated to change their drinking. Minimal motivation was defined as scoring above a mean of 3.0 on the contemplation, action, or maintenance subscales of the University of Rhode Island Change Assessment Scale (DiClemente & Hughes, 1990) and below a mean of 3.0 on the precontemplation subscale. Selected participants also needed to be drinking a minimum of: (a) ≥14 standard drinks (females) or ≥21 standard drinks (males) on average per week over a consecutive 30 days in the 90 days prior to the eligibility evaluation, and (b) ≥2 heavy drinking days (4 or more standard drinks—females, 5 or more—males) in the same 30 day period as above. Further eligibility criteria included being between the age of 18 to 45 years old and having a basic working knowledge of technology (i.e., could text and use email).

Exclusion criteria included being more than 21 days abstinent at the baseline interview; currently in alcohol or drug abuse treatment, except mutual self-help (e.g., Alcoholics Anonymous); pregnant or nursing; legally mandated to attend treatment; needing alcohol detoxification; severe alcohol dependence as indicated by a score of 30 or above on the Severity of Alcohol Dependence Questionnaire (Stockwell, Murphy, & Hodgson, 1983); having delusions, hallucinations, or Bipolar I Disorder; or having another substance use disorder, with the exceptions of nicotine or marijuana. Participants were compensated $5 for each day they completed a daily interview of alcohol consumption, craving type, craving strength and coping strategy.

A total of 28 participants completed the study, of which 25 provided sufficient data on alcohol consumption measures on the LBMI-A. Three participants were excluded due to lack of compliance with the LBMI-A daily interviews (more than 10% of daily interviews were missing). The 25 participants included men (52%, n =13) and women (48%, n =12) between ages 22 and 45 (M = 33.6, SD = 6.5). The sample was 50.0% White, 25.0% multiethnic, 10.7% Alaska Native or American Indian, 7.1% African American, 3.6% Hispanic, and 3.6% Pacific Islander. In regard to drinking history, the average age of participants’ first drink was 14.57 (SD = 2.17) and average age at which they reported first having a problem with alcohol was 22.86 (SD = 5.07). Approximately 90% of the sample reported at least one prior attempt to stop or reduce their drinking, with a mean of 18.35 (SD = 28.95) such attempts in their lifetime and 4.93 (SD = 8.00) attempts in the past year. Approximately 46% reported prior outpatient treatment for alcohol problems, while only 7% reported prior residential treatment.

Momentary Intervention and Assessment

This study utilized a smartphone-based, ecological momentary assessment and intervention system (LBMI-A) that assessed for and intervened with alcohol cravings. In addition to addressing cravings, this system had numerous other intervention features, which are describing briefly below (see Dulin et al., 2014 for further detail).

Participants were provided with a customized LBMI-A enabled smartphone that included cellular data plans. The overall intervention was based on existing cognitive and behavioral alcohol use disorder interventions that have empirical support for efficacy. The LBMI-A provided seven psychoeducation modules, or steps: (1) assessment and feedback; (2) high-risk locations for drinking; (3) selecting and using supportive people for change, (4) cravings and their management; (5) problem-solving skills; (6) communication and drink refusal skills; and (7) pleasurable, non-drinking activities. Following completion of a step an associated tool became available. These tools led users through immediate coping strategies during times of need (e.g., while experiencing a craving or negative mood), as well as monitoring alcohol consumption and cravings. The craving tool, which is of particular interest in this study, was accessed by participants by pressing on the craving icon when they were experiencing a craving to drink alcohol. After they pressed this icon, participants were prompted to record the strength and trigger of their craving in real time (see Measure below), they were then able to choose amongst seven different options for managing the craving: (a) urge surfing provided an audio file leading them through an urge surfing exercise, (b) distract myself encouraged the user to engage in a non-drinking alternative activity, (c) view reasons for change displayed participant entered reasons for changing their drinking, (d) view reminder photos displayed photos taken by participants reminding them of why they wanted to change their drinking, (e) contact a support person provided text or phone contact to individuals selected by the participant in the supportive person module, (f) escape the situation encouraged the user to simply leave the triggering environment, (g) contact a friend or family member provided a link to their smartphone contact list to contact individuals not selected as supportive people, and (h) other strategy was any other strategy used not on the LBMI-A list of options. If users chose other strategy, no other functionality was delivered.

Measures

Cravings and drinking in response to cravings

There were two different paths within the LBMI-A that users could utilize to record a craving, its strength, and how they coped with it. The first option was for the participant to immediately record the craving when they were having it by pressing on the craving icon. The second option was through the Daily Interview. Completion of the Daily Interview was prompted by the system every day at a pre-determined time. This interview displayed the cravings participants had recorded in the moment during the previous day through the Craving Tool and queried if they experienced other cravings during the prior day that they did not record in the moment. The data for this study were garnered from the Daily Interview feature, thus included craving-related data recorded in the moment utilizing the Craving Tool, as well as cravings reported during the Daily Interview that were not reported momentarily. Participants were prompted to take the Daily Interview every day, regardless of whether or not they had previously reported experiencing a craving. In both assessment options, participants were asked to record the strength of each craving from 1 (extremely weak) to 10 (extremely strong), what triggered the craving (e.g., seeing others drinking), and how they coped with the craving. Additionally, during the Daily Interview participants were asked if they drank in response to the cravings recorded. The craving types participants could select were: thinking about drinking, an emotion I am having, people drinking or offering me a drink, a physical feeling, being around alcohol, reminded of drinking, and other type of craving (i.e., not on the list).

Alcohol dependence severity

Alcohol dependence severity was measured with the Severity of Alcohol Dependence Questionnaire (SADQ; Stockwell et al., 1983). The SADQ is a 20-item self-report measure of alcohol dependence severity. Items are rated from 0 (almost never) to 3 (nearly always). Scores over 30 have been shown to correlate with clinicians’ ratings of severe dependence (Stockwell, Hodgson, Edwards, Taylor, & Rankin, 1979).

Analyses

Generalized Linear Mixed Modelling (GLMM), using SPSS 21 was utilized due to its ability to model effects of multiple observations within each participant (Singer & Willett, 2003; West, 2009). In this analysis, there were a total of 744 observations garnered from the 28 participants. The target (outcome) variable was categorical; whether or not a participant drank subsequent to a reported craving (coded as yes = 1, no = 0). A binomial probability distribution and the logit link function were utilized in the model. The fixed effects included in the analysis were craving trigger type and coping strategy and the random effects included gender (male = 0, female = 1), SADQ score (0–20), and craving strength (1–10). Gender and alcohol dependence were included in the random effects model as they have been shown to be significant covariates in other studies examining the influence of craving on subsequent drinking (Fazzino, Harder, Rose, & Helzer, 2013). The effect of time was also included as a random effect in order to determine if drinking in response to a craving changed over the course of the 6 week trial. The time variable reflected scores from 1 to 6 with a 1 reflecting data collected during week 1 of the trial and a 6 reflecting week 6 trial data.

Results

The analysis of the mixed model fixed effects indicated that coping strategies contributed significant variability to the model predicting drinking subsequent to a craving. All of the LBMI-A coping strategies were significant in comparison to the other/non-LBMI-A strategy, with the exception of contact a friend or family member. There was also significant variability in craving trigger types regarding the prediction of subsequent drinking to a craving. The fixed effects coefficients in Table 1 show that the craving trigger types of time of day or week, people drinking near me, and being around alcohol were all significantly more predictive of subsequent drinking compared with the reference category of other or not sure.

Table 1.

Fixed and Random Effects Predicting Drinking in Response to a Craving

Fixed Effects Coefficient S.E. Random Effects Estimate S.E.
Coping Strategy Dependence .66 1.70
Escape the Situation 3.02*** .65 Gender <.001
Urge Surf 3.69*** . 48 Time 3.49* 1.81
Distract Myself 3.19*** .37 Craving Strength 2.37 1.35
View Reasons for Change 3.35*** .56
Contact a Support Person 3.22*** .69
View Reminder Photos 4.12*** .95
Contact a Friend/Family 1.32 .87
Other/Non-LBMI-A 0
Craving Cue Type
Other or Not Sure 0
Reminded of Drinking .21 .73
An Emotion .43 .64
Time of Day or Week 1.21* .62
People Drinking Near Me 1.47* .70
Being Around Alcohol 2.20** .77
Thinking About Drinking .62 .63
A Physical Feeling 1.20 .84
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Note: The outcome variable in this analysis is binary, drank (1) or not (0) in response to a reported craving. Types of cravings contrasts were performed between all craving types and the other craving category. Coping strategies contrasts were performed between all coping strategies and other/non-LBMI-A strategy category.

*

p < .05.

**

p < .01.

***

p < .001.

Table 1 also shows the random effects in the model. Gender and alcohol dependence level were not significant predictors of drinking in response to cravings, but craving strength (β = 2.37, p <.05) and time (β = .3.49 p <.01) were. In this study, having more severe cravings was related to a greater likelihood of subsequent drinking in response to a craving.

Table 2 shows the reported frequencies of craving trigger types, coping strategies and their estimated means. It is noteworthy that time of day or week, thinking about drinking, and an emotion were the most frequently reported craving triggers. They were not, however, the most potent in relation to subsequent drinking. The triggers of being around alcohol (M = .52) and people drinking around me or offering me a drink (M = .34) produced the highest amount of subsequent drinking. The craving trigger of time of day or week was noted to be experienced with high frequency and was the third most potent trigger regarding subsequent drinking (M = .29).

Table 2.

Types of Cravings and Coping Strategies (frequencies and % of subsequent drinking)

Types of Craving # of cravings reported % of subsequent drinking Coping Strategies # of times strategy was selected % of subsequent drinking
Time of day or week 225 29 Other Strategy (non-LBMI based) 315 82
Thinking about drinking 157 18 Distract myself 213 16
An emotion 128 16 Urge surfing 87 10
People drinking near me/offering drinks 58 34 View reasons for change 41 14
A physical feeling 47 28 Escape the situation 33 19
Being around alcohol 46 52 Contact LBMI support people 19 16
Reminded of Drinking 40 13 Contact other friend/family 16 55
Other craving not on list 43 10 View reminder photos 13 7
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Note: Percentages of subsequent drinking results are based on mixed models estimated means.

Regarding coping strategies, the most common strategies participants reported were other/non-LBMI strategy, distract myself, and urge surfing. As can be seen in Table 2, other/non-LBMI strategy (M = .82) and contact a friend or family member (M =.55) resulted in the highest levels of subsequent drinking in response to a craving.

Contrary to expectations, despite participants using the LBMI-A in an effort to reduce their drinking, there was a significantly greater likelihood of drinking subsequent to a craving over time. The estimated mean percentage of drinking in response to a craving for week 1 was .22 and was .27 for week 6, suggesting that time using the system produced a modest increase in likelihood of drinking subsequent to a craving. It should also be noted that the overall number of cravings reported during each week decreased markedly from week 1 (M =10.6) to week 6 (M =5.2). This suggests that while the number of cravings decreased over time during the intervention, those that remained were perhaps more likely to trigger drinking in response.

Discussion

The results of this study suggest that the immediately available coping strategies provided by the LBMI-A reduced the likelihood of drinking in response to a craving and may have played a part in the overall effectiveness of the LBMI-A intervention in reducing drinking over the course of 6 weeks of LBMI-A use (Gonzalez & Dulin, 2015). Results also suggest that if participants utilized one of the specific smartphone-based intervention strategies, the percentage of subsequent drinking was in the 10–20% range, whereas if they chose a non-LBMI-A based strategy, the percentage of subsequent drinking was in the 50–80% range. The only exception to this was the coping strategy of call a friend or family member, which was an LBMI-A option to manage a craving that was shown to be significantly less effective in reducing subsequent drinking than the other LBMI-A options. This strategy differed from the other options in that the other strategies had particular psychoeducational modules that provided instruction on how to use them and how they could be helpful. For instance, the coping strategy of escape the situation referred to information in the cravings module that provided a rationale and specific instruction on how to escape a triggering situation. Contact a friend or family member also differed from contact a supportive person in that the app had users choose specific individuals and provided guidance regarding who would make a good support person to include in their list of supportive people. The supportive people were sent informational materials in the form of an email about how to be supportive to someone who is trying to change their drinking. These results may suggest that in order to be successful, smartphone-based coping strategies need specific instruction and a rationale for their use. Further research into this possibility is warranted.

We were also interested in which coping strategies were utilized most frequently amongst participants. The LBMI-A based coping strategies that were utilized most frequently were distract myself and urge surfing. They also resulted in low levels of subsequent drinking (16% and 10% subsequent drinking, respectively). Providing individuals with an alcohol use disorder other activity options and an urge surfing (Witkiewitz, Marlatt, & Walker, 2005) audio file in the moment when they experience a craving cue appear to be both popular and effective means of technological intervention with alcohol cravings. Other strategies that were chosen less frequently included view reasons for change, escape the situation, contact support people, and view reminder photos. These all resulted in less than a 20% chance of drinking subsequent to a cued craving. Regarding which coping strategies were most effective, results of this study are consistent with those of Dolan et al. (2013) and O’Connell et al. (2007) wherein they show that engaging in a specific, learned coping strategy to manage cravings for a substance is effective in reducing subsequent use, but there was little evidence for differential effectiveness.

This study also examined the types of cravings participants experienced and whether or not they drank in response to them. Regarding the craving cue types, there were three that emerged as being particularly potent in predicting subsequent drinking: being around alcohol (52% subsequent drinking), people drinking near the participant (34% subsequent drinking), and the time of day or day of the week (29% subsequent drinking). While it has long been postulated that being in environments in which alcohol is available and other people are drinking is a trigger for ongoing drinking amongst people who are trying to change their drinking (Brown, Vik, & Creamer, 1989; Marlatt & Gordon, 1985), this study suggests that they may be among the most influential cues for subsequent drinking in comparison to other cue types. One of the primary attributes of smartphone intervention apps is their ability to monitor and intervene in high risk contexts such as times of day when individuals previously drank and risky geographic locations (e.g., bars and houses where individuals drank in the past). These findings indicate that drinking times and contexts are fruitful areas in which to provide real-time intervention to individuals engaged in alcohol treatment.

It was noteworthy that gender and alcohol dependence level were not shown to be related to drinking subsequent to a craving in this study. It was surprising that alcohol dependence level was not a significant predictor, but having restricted the range of alcohol dependence level in this study may explain the lack of significant effects. A previous study found gender to be a predictor of drinking in response to a craving with males having greater drinking, but it was a small effect so a null effect in this study is not particularly surprising (Fazzino et al., 2013). A curious finding in this study was that while the overall number of cravings reported was shown to drop substantially over the 6-week time frame that participants utilized the intervention system, drinking in response to a craving cue was shown to slightly increase over time. It may be that while the overall number of cravings drops over time while using this intervention system, the cravings that remained were particularly potent in producing subsequent drinking and, therefore, resistant to extinction.

This study also had numerous limitations. The most substantial limitation is the lack of a control group. The main outcome study had a comparison group, but that group did not have daily assessment of their drinking in response to a craving so it was not able to be utilized in this study. Having a control condition that assessed drinking in response to cravings without the support of the LBMI-A would provide an enhanced understanding of the effectiveness of the LBMI-A coping strategies and if the reduction in craving over time shown in this study was due to the intervention. In this study, we also were not able to separate out data that was reported “in-the-moment” from data that was reported in the daily interview. This would have allowed us to determine if there was a difference between immediate and retrospective reporting of craving and whether or not users drank in response. This could have been important as research suggests that retrospective reporting of drinking results in under reporting of consumption compared with in-the-moment reporting (Shiffman, 2009). There was also a limitation in the present study regarding the other/non-LBMI-A strategy. This strategy was endorsed most frequently amongst all coping strategies and accounted for 42% of strategies reported. It would have been informative to have provided participants the option to enter their own unique strategy in order to ascertain what participants did during those times. Given the high rate at which they drank in response to the craving cue if they chose other/non-LBMI-A strategy, it is possible that they utilized an avoidance strategy (i.e., avoid thinking about drinking), which have been shown to be both highly prevalent and predictive of increased drinking amongst individuals in recovery (Hasking, Lyvers, & Carlopio, 2011; Moos & Holohan, 2003). It is also possible that selection of this other strategy category reflected that no method of coping with the craving was used and that perhaps they simply drank in response to the cued craving.

Regarding craving cue types, it is not clear what type of cravings individuals experienced when they acknowledged other craving not on the list. This category accounted for a relatively minor amount (5.7%) of overall craving types, but it would have been more informative if participants had the opportunity to enter their own custom craving type that was not on the list. A final limitation revolves around the sample of research participants. Participants consisted of relatively younger adults (ages 18–45) who were motivated to change their drinking and who met criteria for an alcohol use disorder but were not severely physically dependent on alcohol. The results of this study likely do not generalize to the wider population of problem drinkers and individuals with severe alcohol use disorders. There was also a decidedly small sample size employed in this study. While the number of observations over time provided adequate statistical power to detect significant effects, results of this study should be considered preliminary. Examining EMAI-based coping strategies through smartphone delivery with a larger sample of a broad spectrum of drinkers over longer time frames would be a meaningful next step in this research domain.

Acknowledgments

This project was supported from a grant from the National Institute of Alcohol Abuse and Alcoholism, # RC2 AA019422-02.

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