Abstract
Introduction:
Studies have shown that Text2Quit and other mobile cessation programs increase quit rates in adult smokers, but the mechanism of effects and user experiences are not well understood.
Aims:
This study reports on participants’ experiences with the program and explores aspects of the program that they liked and disliked.
Methods:
Self-reported experiences of the program were collected through a follow-up survey conducted one month after enrollment (n=185). Participant responses to open-ended items were dual coded by independent coders.
Results:
Overall participants agreed that they liked the program (4.2/5), that the program was helpful (4.1/5) and that they would recommend the program to a friend (4.3/5). Top reasons for liking the program included that it served as a constant reminder of quitting (17.8%), the content (16.7%), the encouragement provided (13.3%), and the on-demand tools (12.2%). Top reasons for disliking the program were message frequency (20.5%), content (7.0%), and the lack of personal interaction (7.0%).
Conclusions:
The constancy of messaging was both liked as a reminder and disliked as an annoyance. Future programs might be improved by pre-testing and customizing the content based on user preferences, and by adding in human interactions, while keeping a supportive tone and offering on-demand tools.
INTRODUCTION
Smoking is the leading preventable cause of death in the United States (U.S. Department of Health and Human Services, 2014). Automated, text messaging smoking cessation programs have been found to increase abstinence among adult smokers (Abroms, Boal, Simmens, Mendel, & Windsor, 2014; Free et al., 2011; Whittaker et al., 2016). For example, in a randomized controlled trial (RCT) of the Text2Quit text messaging program, 11% of the intervention group were found to be biochemically confirmed quitters compared to 5% of the control group (relative risk =2.22, 95% confidence interval (CI) = 1.16 to 4.26, p < .05) (Abroms et al., 2014).
Such programs generally send text messages timed around a quit date that include advice, tips, and coping strategies for quitting (Abroms et al., 2012). Messages in these programs also generally include recommendations to call a quitline for additional help and to consider the use of approved quit smoking medications (Abroms et al., 2012). Engagement in these programs has been found to be high. In Text2Quit, users on average remain in the program for 144 days, sent in 24 keywords over the course of the intervention, reported reading most of the texts, and unsubscribed at low rates (Heminger, Boal, Zumer, & Abroms, 2016). However, little is known about how text messaging programs confer benefits to their users and what theoretical constructs are most altered from their use (Riley et al., 2011).
It may be that these programs are effective simply as referral systems to other proven methods of quitting such as calling a quitline or using pharmacotherapy (e.g. Nicotine Replacement Therapy). One study of a text messaging program used multiple mediation analysis to test the pathways of change. This study concluded that the benefit of the program was conferred primarily through psychosocial mechanisms such as by increasing self-efficacy rather than because of being a referral service to other external methods of quitting such as the quitline or medications (Hoeppner, Hoeppner, & Abroms, 2017). This is consistent with findings from a large RCT in the U.K., which found that participants had similar rates of NRT use in both the text messaging group and the control group, though the text messaging group was more likely to have quit (Free et al., 2011).
While engagement is high and effects appear to be related to psychosocial changes from the text messaging program, the nature of these effects are unclear. Qualitative analysis of user experiences can contribute to an improved understanding of these effects, but few analyses have been conducted to date. Prior qualitative analyses of two text messaging programs identified aspects of the programs that were liked and disliked by participants (Douglas & Free, 2013; Sloan, Hopewell, Coleman, Cooper, & Naughton, 2017). Participants liked the programs for their convenience and the encouragement, emotional support and information they provided. Negative aspects noted by the participants were that receiving texts about smoking stimulated cravings, and that the timing, frequency, and duration of messages were not optimal.
The goal of this analysis is to extend the work on user experience with smoking cessation text messaging programs by examining the user experience in a sample of participants who participated in large trial of Text2Quit in the U.S. (Abroms et al., 2014). Text2Quit is one of the largest text messaging programs in the United States with over 400,000 people enrolled since 2012. Of interest are the ways in which participants enrolled in the study over the course of a month experienced the program. It is hoped that by analyzing user experiences with text messaging programs, aspects of the psychosocial mechanisms that led to behavior change can be identified and further tested in future studies.
METHODS
Sample
This study was approved by The George Washington University’s Institutional Review Board in 2011, IRB #040810. To be eligible for the study, participants were required to (1) be 18 years of age or older; (2) smoke five or more cigarettes a day; (3) have a U.S. mailing address; (4) have a working e-mail address; (5) have a cell phone number with an unlimited short messaging service (SMS) plan; (6) express an interest in quitting smoking within the next month; and (7) not be pregnant. Additional details about the Text2Quit recruitment procedures have been reported previously (Abroms et al., 2014). The current study includes a sub-sample of the 262 individuals who were randomized to the intervention group of the larger randomized controlled trial and who answered the user experience section of the one month follow-up survey (n= 185, 70.6%).
Procedures & Intervention
Text2Quit was developed in 2010 by investigators at The George Washington University with technical support provided by Voxiva, Inc. The program consists primarily of automated, bidirectional text messages. The Text2Quit program provides advice and support on quitting smoking through text messages tailored around a participant’s first name, gender, their selected quit date, and their top three reasons for quitting (as input by the participant). Other tailored messages included statistics on money saved based on estimates calculated from their cigarettes smoked/day and average price paid for a pack of cigarettes, references to their self-identified social support person, reminders of up to five selected triggers for smoking, and discussion of their use of selected quit smoking medications from the list of seven first-line FDA approved medications (Tobacco Use and Dependence Guideline Panel, 2008). Messages are based on social cognitive theory (Bandura, 1989) and developed to be consistent with the U.S. Public Health Service Clinical Practice Guidelines (Tobacco Use and Dependence Guideline Panel, 2008). Messages regularly recommended calling a quitline and considering the use of approved quit smoking medications.
Participants were regularly prompted to interact with the system through surveys. Specific messages in the “pre-quit protocol” that prompted interaction included periodic surveys to assess readiness to quit and to track the number of cigarettes smoked per day against a pre-set goal and requests to take a weekly smokefree pledge. Once a participant replied to a text message, the system would send an additional text message with the appropriate feedback (e.g., a participant who replied and indicated that they had met their goal for cutting down on cigarettes would receive the following feedback, “Text2Quit: Great! You reached your goal of cutting down to 16 cigs. Visit Text2Quit.com to see a graph of your progress.”). Interactive messages in the “post-quit protocol” included prompts to reply and get help with cravings through a trivia game or a craving tip, and periodic surveys to assess quit status after a participant’s self-selected quit date.
Throughout the program, participants could send in a variety of keywords at any time to receive additional help or respond to survey items for further information or support. Keywords included the ability to reset a quit date (DATE), get help with a craving with a tip or by playing a trivia game (CRAVE), get a summary of their quitting statistics (STATS), and to indicate that they had smoked (SMOKED).
Participants in the Text2Quit intervention group picked a quit date in the next month, and then immediately started to receive messages timed around that date. Messages were sent for the following 3 months, with additional on-demand support available for 6 months.
Outgoing messages peaked in the period just prior to and following the quit date. Participants received five SMSs on their quit date and approximately two SMSs per day in the week after the quit date. Frequency declined in the subsequent weeks to approximately three SMSs per week for the next two months and then less than one per week for the remaining portion of the program. Further details regarding the Text2Quit program elements, interactive features, and intervention procedures have been reported previously (Abroms et al., 2012; Abroms et al., 2014).
Measures
Data for this analysis were drawn from the online baseline survey and the one month follow-up phone survey. The baseline survey captured demographic, smoking, and mobile phone use characteristics, including: age, race/ethnicity, education, presence of other smokers in the household, average number of cigarettes smoked per day, and the Fagerstrom Test for Nicotine Dependence (FTND) score (Heatherton, Kozlowski, Frecker, & Fagerstrom, 1991). Program acceptability was assessed with items from the follow-up survey, measured on a Likert scale from 1/completely disagree to 5/completely agree. These included agreement with the statements: “I liked the program,” “The program was helpful in getting me to try to quit smoking” and “I would recommend the program to a friend”. Additionally, open-ended survey questions were used to elicit a more qualitative understanding of user experience. At the one month survey, all participants were asked to explain: “What did you like about Text2Quit?” and “What did you not like about Text2Quit?” Verbatim responses by participants were taken down by research staff and entered into an open text field in the survey.
Analysis
Descriptive statistics were used to determine the demographic profile and smoking history of the participants, as well as the Likert scale ratings of program acceptability. In addition, a thematic analysis was conducted to sort into categories the responses to the open ended questions about what participants liked and/or disliked about Text2Quit. Each response was independently coded by two coders between January and June 2015. To create response categories, one coder reviewed all responses and identified major response categories in a codebook for liking or disliking the program.
Response categories for liking the program included the following: constant reminder (program liked because regularly reminds me that I am quitting), on-demand tools (program liked because the interactive tools in the program are helpful), convenient (program liked because the program’s format made it easy to participate), general liking (program liked because liked everything), helpful (program liked because gave help and support for quitting), social support (program liked because made me feel cared for and supported), encouragement (program liked because encouraged/motivated me to quit), incentive (program like for study giftcards), content (program liked because it gave good content for quitting), social control (program liked because made me feel accountable), message timing (program liked because messages came at right time of day), message frequency (program liked because messages came at the right frequency), tailoring (program liked because program was personalized for me), and self-efficacy (program liked because gave me the confidence for quitting).
Responses for not liking the program included the following: Nothing (program disliked for no reason), message frequency (program disliked because too many or too few messages), message timing (program disliked because messages were sent at the wrong time of day), technical problems (program disliked because of the presence of technical problems), lacked human interaction (program disliked because program did not offer human interaction), content (program disliked because it gave bad or unhelpful content for quitting), program-related technical problems (program disliked because Text2Quit had technical problems), personal technical problems (program disliked because participant’s cell phone had technical problems like a loss of service), not ready to quit (program disliked because user was not ready to quit), text as trigger (program disliked because messages were smoking triggers, program disliked because messages reminded user of smoking), and tailoring (program disliked because program was not tailored or personalized enough or too tailored). For both liking and disliking, responses that did not fit into any category or that were unclear were coded as “other/not clear”. Once a range of categories was identified that captured the diversity of responses, a final coding was performed using the codebook by two independent coders.
As some of the participants’ open-ended responses contained more than one reason for liking or disliking the program, a decision was made to allow up to two categories of reasons for each participant’s response to the question. Quotes that were particularly illustrative of a category were also noted. The two coders reached a moderate level of concordance in rating, with 78.3% agreement (kappa= 0.83) in categorizing the participants’ responses. When discrepancies between the two coders were identified (n=57 for liking and n=19 for disliking), a third coder was used to make a final determination. Analyses were conducted using STATA Version 12 (StataCorp. 2011. Stata Statistical Software: Release 12. College Station, TX: StataCorp LP) and Microsoft Excel 2010.
RESULTS
On average, participants were 36.3 (SD = 10.8) years of age and were predominantly female (74.6%). Most participants were white (81.6%) and the majority had attended some college or trade school (56.2%). Slightly more than half of the participants (54.9%) reported living with a smoker. The average number of cigarettes smoked per day at baseline was 17.2 (SD = 8.5) and the mean FTND score was 5.1 (SD = 2.4). Overall participants agreed that they liked the program (4.2/5), that the program was helpful (4.1/5) and that they would recommend the program to a friend (4.3/5).
In addition, a thematic analysis examined the responses to each of the open-ended questions about what participants liked (n=180) and disliked (n=171) about the Text2Quit program (see Table 2). For participants who liked the program, the most common reason was general or non-specific (18.9%). Beyond a general liking for the program, the top reason volunteered by almost one-fifth of participants was that they liked the program because it was a constant reminder of their desire to quit smoking (17.8%). Another commonly mentioned reason for liking the program was that the content was helpful for quitting (16.7%). For example, one participant stated that the “texts gave good ideas on how to fight cravings.…”. Other highly mentioned reasons for liking were because they liked the encouragement and motivation from the program (13.3%), because they liked the on-demand tools or interactive keywords for quitting (12.2%), and that the program was helpful for quitting (9.4%). One participant explained about the on-demand tools in the following way, “That you can [SMS] whenever you are feeling the urge to smoke and in that time frame you are actually not smoking…”. A smaller group of participants (8.3%) stated that they liked the program for the social support the program provided. They explained that the program was like “someone kind of there with you…”, and that the program was “like a constantly concerned friend”. Participants also noted liking the program for the message frequency and because the program provided a sense of social control. Examples that were illustrative of the social control reason included one participant who stated that he/she liked the program because it “made me feel accountable” and another who stated that the program was like “an electronic conscience.” Reasons mentioned by 5% or fewer of the participants included because the program format was convenient, because messages increased self-efficacy for quitting, because the program was tailored, because of the study incentives, because of the timing of messages and because of an “other” or unclear reason.
Table 2.
Participant reasons for liking and disliking Text2Quit at the one-month follow up survey
| Overall1 n (%) |
Quit Smoking at 1 Month2 n (%) |
Smoker at 1 Month2 n (%) |
||
|---|---|---|---|---|
| Liking (n = 180) |
General liking | 34 (18.9%) | 12 (35.3%) | 22 (64.7%) |
| Constant reminder | 32 (17.8%) | 11 (34.4%) | 21 (65.6%) | |
| Content | 30 (16.7%) | 9 (30.0%) | 21 (70.0%) | |
| Encouragement | 24 (13.3%) | 9 (37.5%) | 15 (62.5%) | |
| On-demand tools | 22 (12.2%) | 9 (40.9%) | 13 (59.1%) | |
| Helpful | 17 (9.4%) | 9 (52.9%) | 8 (47.1%) | |
| Social support | 15 (8.3%) | 7 (46.7%) | 8 (53.3%) | |
| Message frequency | 12 (6.7%) | 5 (41.7%) | 7 (58.3%) | |
| Social control | 10 (5.6%) | 3 (30.0%) | 7 (70.0%) | |
| Convenience | 9 (5.0%) | 7 (77.8%) | 2 (22.2%) | |
| Self-efficacy | 6 (3.3%) | 3 (50.0%) | 3 (50.0%) | |
| Tailoring | 5 (2.8%) | 2 (40.0%) | 3 (60.0%) | |
| Message timing | 5 (2.8%) | 2 (40.0%) | 3 (60.0%) | |
| Incentive | 2 (1.1%) | 0 (0%) | 2 (100.0%) | |
| Other/not clear | 5 (2.8%) | 1 (20.0%) | 4 (80.0%) | |
| Disliking (n = 171) |
Nothing | 83 (48.5%) | 37 (44.6%) | 46 (55.4%) |
| Message frequency | 35 (20.5%) | 8 (22.9%) | 27 (77.1%) | |
| Lacked human interaction | 12 (7.0%) | 5 (41.7%) | 7 (58.3%) | |
| Content | 12 (7.0%) | 1 (8.3%) | 11 (91.7%) | |
| Message timing | 8 (4.7%) | 4 (50.0%) | 4 (50.0%) | |
| Program technical problems | 8 (4.7%) | 2 (25.0%) | 6 (75.0%) | |
| Text as smoking trigger | 5 (2.9%) | 1 (20.0%) | 4 (80.0%) | |
| Personal technical problems | 2 (1.2%) | 0 (0%) | 2 (100.0%) | |
| Tailoring | 2 (1.2%) | 1 (50.0%) | 1 (50.0%) | |
| Not ready to quit | 2 (1.2%) | 1 (50.0%) | 1 (50.0%) | |
| Other/not clear | 7 (4.1%) | 2 (28.6%) | 5 (71.4%) |
Each comment could be coded with two labels, so variable totals will exceed number of participants.
Smoking status determined by 1-month follow up survey response to “Have you smoked a cigarette, even a puff, in the last 7 days?” with missing responses coded as current smokers.
The most common reason identified for why participants did not like the program was for no specific reason (48.5%). This was followed by not liking the program for its message frequency (20.5%). While this included participants who thought the program had too few messages, most participants mentioned finding that the program had too many messages. One example was a participant who stated her reason for not liking the program was “OMG. Too many texts per day.” Participants (7.0%) also stated that they did not like the program because it was computer automated and lacked human interaction, and as one participant put it, “There wasn’t an actual person, only a computer, and I wish it was a real person”. Participants (7.0%) stated that they did not like the content, in some cases because it was too repetitive or not helpful, “Some of the messages gave me dumb ideas to get through a craving.” Reasons for not liking the program mentioned by 5% or fewer of the participants included because the messages came at the wrong time, because of technical problems related to the program, because the texts were triggers for their smoking, because of technical problems related to their cell phone service, because the program did not have the right level of tailoring, because they were not ready to quit, and because of an “other” or unclear reason. One example of a reason for not liking the program related to the text as a trigger came from a participant who said that getting the texts “reminded me [that] I can’t smoke…[and] made me think of smoking.”
DISCUSSION
Analysis of user experiences with the Text2Quit program found that overall participants liked the program, found it helpful and would recommend the program to a friend. The most common reasons provided for liking the program included that the program served as a constant reminder of quitting, the content, the encouragement provided by the program, and the on-demand tools. The most common reasons for disliking the program were message frequency, the content, and that it lacked personal interaction.
It is surprising that the top reason offered for liking the program was not related to the content (e.g. information and quitting skills) or the on-demand tools, but simply related to the constancy of the text messages. The mechanism by which the reminders could have helped users is by serving as a reminder or a cue to quit smoking. While the construct of cue to action is present in the Health Belief Model (Rosenstock, 1974), this construct of constancy is absent from most prominent behavioral theories. Given 24/7 access to mobile phones, researchers have noted a need for new behavioral theories that capture the dynamic and adaptive nature of mHealth interventions (Riley et al., 2011).
While many participants found the reminders helpful, some participants noted that the constancy of text messages had a boomerang effect by functioning as a trigger, which was also reported by some participants in a prior study (Douglas and Free, 2013). Future programs should monitor these unintended effects and explore whether certain kinds of text messages (e.g. messages that use the word smoking or cigarette) stimulate cravings for some individuals.
Though less common, some participants mentioned the concepts of self-efficacy and social support as reasons for liking the program. These findings align with a prior qualitative study (Sloan et al., 2017) as well as a quantitative analysis, which demonstrated that the benefits of the text messaging program was manifested through its impact on psychosocial constructs, such as increases in self-efficacy (Hoeppner et al., 2017).
Another important finding from this study was the desire for human interaction, with some participants perceiving automated messages as impersonal. This mirrors findings from text messaging programs to address other health behaviors, such as physical activity (Horner, Agboola, Jethwani, Tan-McGrory, & Lopez, 2017). Although automation enables scale up by reducing the cost of these programs, the trade-offs need to be taken in to account. Recent studies have tested programs that combine automated messaging along with messaging from a live person and this has been found to be promising (White et al., 2019). These kinds of programs could also be scaled, though more expensive, and may be appropriate for priority groups like pregnant smokers or HIV+ smokers, if not all adult smokers. More research is needed to determine engagement with and efficacy and cost effectiveness of these hybrid programs in comparison to programs that are entirely automated.
In addition to constancy of messages, other features of the program, such as the content and timing of messages were liked by some participants and disliked by others. Advances in technology have led to mHealth programs that can easily be customized. In this way, program elements can be tailored to suit individual user preferences. For instance, similar to the findings from Douglas and Free (2013), one of the aspects of the program with conflicting feedback was message frequency. In the future, the timing and frequency of messages can be individually tailored at enrollment based on user preference, and modifications can be made through the course of the program.
Limitations
This study had some limitations. Only 70.6% of participants enrolled in the intervention completed the 1-month follow-up survey, and of these participants, 97.3%, and 92.4% responded to the questions eliciting open-ended feedback on aspects of the programs that they liked and disliked respectively. As a result, selection bias could have been present as participants who successfully quit smoking or liked the program may have been more likely to complete the follow-up survey and provide feedback to the open-ended questions. The sample was also predominantly female and Caucasian, so findings may not generalize to other demographic groups. A strength of the study was the use of open-ended questions to elicit feedback. By using an open-ended format, participants were not limited to choosing from a set of options, resulting in responses across a variety of domains. Additionally, as the follow-up survey was conducted over the phone, participants may have felt more comfortable sharing negative feedback.
Conclusions
The main reason for liking and disliking the program was the constancy of messaging. Other favorable aspects were the content, encouragement and on-demand tools. Negative aspects cited included the content and lack of human interaction. More research is needed on identifying optimal message frequency that balances salience with habituation and fatigue, as well as on other factors such as the efficacy of combined automated and live-counseling programs.
Table 1.
Demographics and ratings of Text2Quit at the 1-month follow-up survey (N = 185)
| n (%) / M (SD) | |
|---|---|
| Mean Age (years) | 36.26 (SD = 10.81) |
| Gender | |
| Male | 47 (25.41%) |
| Female | 138 (74.59%) |
| Race/Ethnicity | |
| White | 151 (81.62%) |
| African American | 18 (9.73%) |
| Latino | 5 (2.70%) |
| Asian | 3 (1.62%) |
| American Indian/Alaska Native | 3 (1.62%) |
| Other | 5 (2.70%) |
| Education | |
| High school or lower | 26 (14.05%) |
| Some college or trade school | 104 (56.22%) |
| College degree or higher | 55 (29.73%) |
| Presence of 1+ smokers in household | 84 (54.90%) |
| Mean # of cigarettes per day at baseline | 17.23 (SD = 8.45) |
| Mean number of past quit attempts | 5.77 (SD = 7.92) |
| Mean baseline nicotine dependence (FTND)1 | 5.11 (SD = 2.36) |
| Reported number of texts received as “just right”. | 126 (68.11%) |
| “I liked the program.”2 | 4.22 (SD = 1.07) |
| “The program was helpful in getting me to try to quit smoking.”2 | 4.08 (SD = 1.02) |
| “I would recommend the program to a friend.” 2 | 4.28 (SD = 1.07) |
The Fagerstrom Test for Nicotine Dependence ranged from 1-10, with the resulting categories: 1-2 = low dependence; 3-4 = low to moderate dependence; 5-7 = moderate dependence; 8+ = high dependence.
Likert items collected at 1-month with scale ranging 1 (completely disagree) to 5 (completely agree).
ACKNOWLEDGMENTS
Dr. Abroms would like to thank the research staff at The George Washington University for their dedication to the study, including Amanda Davis, MPH, and Meenakshi Ahuja, MPH.
FINANCIAL SUPPORT
Research reported in this publication was supported by grant number 5K07 CA124579-02 and the American Recovery and Reinvestment Act supplement to Dr. Lorien Abroms, from the National Cancer Institute of the NIH.
Footnotes
DECLARATIONS OF INTEREST
The George Washington University/Dr. Lorien Abroms has licensed the Text2Quit program to Voxiva, Inc.; Dr. Lorien Abroms has stock options in Voxiva, Inc.
ETHICAL STANDARDS
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.” and “The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional guides on the care and use of laboratory animals.
REFERENCES
- Abroms LC, Ahuja M, Kodl Y, Thaweethai L, Sims J, Winickoff JP, et al. (2012). Text2Quit: Results from a pilot test of a personalized, interactive mobile health smoking cessation program. Journal of Health Communication, 17 Suppl 1, 44–53. doi: 10.1080/10810730.2011.649159 [doi] [DOI] [PMC free article] [PubMed] [Google Scholar]
- Abroms LC, Boal AL, Simmens SJ, Mendel JA, & Windsor RA (2014). A randomized trial of Text2Quit: A text messaging program for smoking cessation. American Journal of Preventive Medicine, 47(3), 242–250. doi: 10.1016/j.amepre.2014.04.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bandura A (1989). Human agency in social cognitive theory. The American Psychologist, 44(9), 1175–1184. [DOI] [PubMed] [Google Scholar]
- Douglas N, & Free C (2013). ‘Someone batting in my corner’: Experiences of smoking-cessation support via text message. The British Journal of General Practice : The Journal of the Royal College of General Practitioners, 63(616), e768–76. doi: 10.3399/bjgp13X674459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Free C, Knight R, Robertson S, Whittaker R, Edwards P, Zhou W, et al. (2011). Smoking cessation support delivered via mobile phone text messaging (txt2stop): A single-blind, randomised trial. Lancet (London, England), 378(9785), 49–55. doi: 10.1016/S0140-6736(11)60701-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Heatherton TF, Kozlowski LT, Frecker RC, & Fagerstrom KO (1991). The Fagerstrom test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119–1127. [DOI] [PubMed] [Google Scholar]
- Heminger CL, Boal AL, Zumer M, & Abroms LC (2016). Text2Quit: An analysis of participant engagement in the mobile smoking cessation program. The American Journal of Drug and Alcohol Abuse, 42(4), 450–458. doi: 10.3109/00952990.2016.1149591. [DOI] [PubMed] [Google Scholar]
- Hoeppner BB, Hoeppner SS, & Abroms LC (2017). How do text-messaging smoking cessation interventions confer benefit? A multiple mediation analysis of Text2Quit. Addiction (Abingdon, England), 112(4), 673–682. doi: 10.1111/add.13685. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Horner GN, Agboola S, Jethwani K, Tan-McGrory A, & Lopez L (2017). Designing patient-centered text messaging interventions for increasing physical activity among participants with type 2 diabetes: Qualitative results from the text to move intervention. JMIR mHealth and uHealth, 5(4), e54. doi: 10.2196/mhealth.6666. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, & Mermelstein R (2011). Health behavior models in the age of mobile interventions: Are our theories up to the task? Translational Behavioral Medicine, 1(1), 53–71. doi: 10.1007/s13142-011-0021-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rosenstock IM (1974). The health belief model and preventive health behavior. Health education Monographs, 2(4), 354–386. [DOI] [PubMed] [Google Scholar]
- Sloan M, Hopewell S, Coleman T, Cooper S, & Naughton F (2017). Smoking cessation support by text message during pregnancy: A qualitative study of views and experiences of the MiQuit intervention. Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco, 19(5), 572–577. doi: 10.1093/ntr/ntw241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tobacco Use and Dependence Guideline Panel. Treating Tobacco Use and Dependence: 2008 Update. (2008). Rockville (MD): US Department of Health and Human Services. Retrieved from https://www.ahrq.gov/professionals/clinicians-providers/guidelines-recommendations/tobacco/index.html [Google Scholar]
- U.S. Department of Health and Human Services. The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General. (2014). Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. [Google Scholar]
- White JS, Toussaert S, Thrul J, Bontemps-Jones J, Abroms L, & Westmaas JL (2019). Peer mentoring and automated text messages for smoking cessation: A randomized pilot trial. Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco, pii: ntz047. doi: 10.1093/ntr/ntz047. [DOI] [PubMed] [Google Scholar]
- Whittaker R, McRobbie H, Bullen C, Rodgers A, & Gu Y (2016). Mobile phone-based interventions for smoking cessation. The Cochrane Database of Systematic Reviews, 4, CD006611. doi: 10.1002/14651858.CD006611.pub4. [DOI] [PMC free article] [PubMed] [Google Scholar]