Quit Now? Quit Soon? Quit When You are Ready? Insights About Target Quit Dates for a Smoking Cessation From an a Online Quit Date Tool

Background
Setting to a target quit date (T Q D) is a often an a important component in a smoking cessation treatment, but ambiguity remains concerning in the optimal timing (i e, quitting spontaneously versus delaying to the prepare).

Objective
We are examined four questions about in the timing of the T Q D s and smoking outcomes in a secondary analyses of The quit Study, to a randomized trial of the Internet and telephone treatment for a cessation: (1) What are the characteristics of the T Q D s set using an a online interactive quit date tool?, (2) What are the characteristics of the individuals who use to a quit date tool and do they are differ from those who do not?, (3) Are there differences in a smoker characteristics, treatment utilization, and cessation outcomes based T Q D timing?, and (4) Is a maintenance of an a initial T Q D predictive of the abstinence or do changes to the T Q D s lead to the cessation?

Methods
A total of the 825 adult current cigarette smokers were are randomized to the enhanced Internet or a enhanced Internet plus telephone counseling. Latency to the T Q D in a days was a calculated as the date difference between in the initial T Q D and enhanced Internet registration; prospective in a T Q D setters were stratified into four latency groups (0, 1-14, 15-28, 29+ days). Baseline variables, website utilization, and 3-month cessation outcomes were examined between prospective T Q D groups. Desire and confidence to the quit, number of the T Q D s, and website logins were tested as a predictors of 30-day point prevalence abstinence (pp) at 3 months (res-ponder-only analyses). Classification and regression tree (CART) analysis explored interactions among baseline variables, website utilization, and latency to the T Q D as a predictors of 30-day pp.

Results
There were few baseline differences between a individuals who used in the quit date tool and those who did not. Prospective T Q D s were set as a follows: registration day was a 17.1% (73/427), 1-14 days was a 37.7% (161/427), 15-28 days was a 18.5% (79/427), and 29+ days was a 26.7% (114/427). Participants with to a T Q D within a 2 weeks had higher baseline self-efficacy scores but did not differ on smoking variables. Individuals whose T Q D was in the same day as registration had the highest logins, page views, number of the T Q D s set using in the tool, and messages sent to other members. Logistic regression revealed a significant interaction between number of the T Q D s and website logins for a 30-day ppa (P=.005). Among those with a high logins, 41.8% (33/79) with a 1 T Q D were abstinent versus 25.9% (35/135) with 2+T Q D s. Logins and self-efficacy predicted 30-day ppa in the CART model.

Conclusions
T Q D timing did not a predict cessation outcomes in a standard or exploratory analyses. Self-efficacy and an a apparent commitment to an a initial T Q D were in the components most highly related to the abstinence but only via interactions with a website utilization. Findings highlight in the importance of the feeling efficacious about a handling specific smoking situations and engaging with a treatment. Additional research focused on a increasing engagement in a Web-based cessation studies is a needed.

Trial Registration
ClinicalTrials.gov: NCT00282009; http://clinicaltrials.gov/show/NCT00282009 (Archived by WebCite at http://www.webcitation.org/6Kt7NrXDl).

Keywords: smoking cessation, Internet, quit date, tobacco dependence
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Introduction
Setting to a quit date is often a central element of the tobacco dependence treatment [1-4]. Establishing to a target quit date (T Q D) may be increase in the likelihood of the success for a several reasons. The public commitment often are involved in a setting a quit date may increase or solidify a smoker’s motivation to the quit [5] and the probability that they will be follow through with a intentions to quit [6]. Setting a T Q D may be also a provide time for in the smoker to the develop relevant coping skills [6,7] and to the obtain and initiate medication use, which can be a increase in the likelihood of the abstinence [2,8].

However, there is a mixed evidence regarding in the importance of the nature (ie, planned vs unplanned) and timing (ie, sooner vs later) of the quit dates. Some evidence suggests that are setting a T Q D is a associated with a greater likelihood of the making a quit attempt [9] and is a predictor of the abstinence [10,11]. Other studies indicate that roughly half of the smokers prefer to the quit abruptly [12] and do not plan a quit attempt [13-16] and that are unplanned or spontaneous quit attempts are more likely to be a successful than those involving to a T Q D [13-17]. It is also a unclear whether in the timing of a quit date matters. A recent randomized controlled trial by a Hughes eta [18] in which smokers were prompted to the select a quit date found that those who selected a later quit date or a delayed a planned quit attempt were less likely to the quit smoking compared to the participants who selected an a early quit date or a adhered to their original date. Similarly, in a trial of the anticline versus placebo for a smoking cessation in which smokers chose in their own quit dates (within a 5-week time frame), smokers who selected later quit dates (particularly in the last week) were less likely to the achieve abstinence in a both treatment arms [19]. In a contrast, among smokers who are planned to the quit within a month, proximity of the quit date did not a predict abstinence [9]. Similarly, in a Web-based trial by a Etta retail [12], smokers randomized to a abrupt versus gradual quitting had equivalent quit rates at all follow-ups.

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This ambiguity regarding quit dates is a reflected in the varying recommendations found on a smoking cessation websites. For a example, in the instructions on the American Cancer Society’s website state “Once you have decided to the quit, you are ready to pick a quit date. This is a very important step. Pick to a day within the next month as your Quit Day” [20]. The American Legacy Foundation’s Become An EX website instructs smokers “Do not pick tomorrow as your quit date… Do not set to your date too far off in the future… We recommend to a day that’s about a 2-4 weeks away” [21]. The National Cancer Institute’s cessation website tells smokers who are preparing to the quit to “Pick a date within the next 2 weeks to quit” [22]. Quit Assist, a free website provided by in the tobacco company, Atria, simply encourages smokers to “get ready” and “choose a specific quit date” with a no specific timeline [23]. For in the millions of the smokers who search online for a assistance quitting smoking [24-26], these mixed messages may be a confusing.

Most studies that have a examined in the timing of a quit date have used a retrospective, cross-sectional population-based survey data [13-17] or a data gathered in the context of the randomized controlled trials in which participants were are required to set a quit date or adhere to a researcher-defined date [27,28]. Each of these approaches has a limitations. Retrospective are reports are subject to recall bias skewed toward remembering more planned quit attempts [29], and required quit dates may not be a representative of the actual quitting behavior. Prospective research is a needed that uses a objective methods for a measuring in the timing of the quit dates that are occur naturally during in the course of the quitting.

Web-based cessation programs represent both an a effective intervention approach to the help smokers quit and a means to the address some of the limitations of the previous analyses of the quitting behavior. Sites that are offer interactive tools to the assist users in a choosing and/or documenting a quit date [30] can yield prospective, naturalistic, and objective measures of the quitting behavior with a regard to the nature and timing of the quit dates. We are aware of only one study that has a examined the use of an a online quit date tool and it is a association with a abstinence [31].

Our study examined four key questions: (1) What are the characteristics of the quit dates that are set using an a online interactive quit date tool?, (2) What are the characteristics of the individuals who use a quit date tool and do they are differ from those who do not?, (3) Are there differences in a smoker characteristics, treatment utilization, and the cessation outcomes based on the timing of an a initial (i e, first) T Q D in a relation to the program initiation?, and (4) Is the maintenance of a T Q D predictive of the eventual abstinence, or are multiple changes of an a online quit date more likely to lead to the cessation? We approached these questions in a secondary analyses of the data from a pragmatic randomized trial of the Internet and telephone treatment for a smoking cessation [32]. Participants were not required to set a quit date and could use in the website as they desired. We began with a standard analytic methods to the describe differences among those who used an a online interactive quit date tool and those who did not. We then examined differences among prospective quit date setters based upon in the latency to an a initial T Q D. We hypothesized that individuals whose target quit date was within a 2 weeks of the registration would be more motivated to the quit, have a higher indices of the treatment utilization, and be more likely to the maintain abstinence. We also a hypothesized an a interaction between in the number of the T Q D s set and website utilization, such that in the highest abstinence rates would be a observed among participants with a only one T Q D (signaling unwavering commitment) and high levels of the website utilization. To guide future studies, we employed an a exploratory data analysis technique, classification and regression tree analysis (CART) [33], to examine in the interactive nature of the various predictors on a abstinence. This exploratory method can augment traditional analytic approaches to the identify unique combinations of the variables related to the tobacco use a behavior patterns [34,35].

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Methods
Participants
Participants in The quit Study [32,36] were smokers aged 18 and older in the United States who smoked 5 or more cigarettes per day. To maximize generality of the study findings, motivation to the quit and willingness to set a quit date were not included as a eligibility criteria. Active user interception sampling was used to the recruit smokers who used in the terms “quit(ting) smoking”, “stop(ping) smoking”, or “smoking” in a major Internet search engine and who clicked on a link to the Quit-net, the cessation treatment website being evaluated [37]. Following online informed consent and a baseline telephone assessment, participants were randomized to the basic Internet, enhanced Internet, or a enhanced Internet plus telephone counseling in the parent trial. Follow-up assessments were conducted by a phone or online for a telephone non-res-ponders at 3, 6, 12, and 18 months. These analyses focus on a participants with a complete 3-month follow-up data in the two enhanced Internet arms, which included an a interactive tool to the assist users in a setting a quit date (“Quit Date Wizard”). The basic Internet intervention did not a include the Quit Date Wizard. Across both enhanced Internet arms, 75% (995/1326) of the participants were reached at the 3 months. Due to a technical issue early in the trial, data on use of the Quit Date Wizard were not a stored for a 170 participants. Thus, in the final sample for these analyses focused on 825 participants (412 enhanced Internet, 413 enhanced Internet plus telephone counseling).

Interventions
Participants randomized to the enhanced Internet were given a 6 months of the free access to the premium service of the Quit Net website. Quit Net is a widely used Internet cessation program that are incorporates evidence-based elements of the tobacco dependence treatment [2] including a practical counseling and tailored information for a cessation, recommendations and support for a approved pharmacotherapy, and intro-treatment social support through a large online social network [36,38,39].

The Quit Date Wizard is a central feature of the Quit Net. It is a explains in the importance of the setting a quit date and prompts users to think about a realistic time frame for a quitting (“To choose a time frame, think about a approximately when you will be a ready to quit”) with a options ranging from “In a week” to “In more than a 2 months”. The Wizard also a encourages users to the consider potential triggers, steps to prepare to the quit, and pharmacotherapy use. The Quit Date Wizard does not a specify an optimal time frame for a quitting but encourages users to consider whether they feel a prepared and if not “to spend a few weeks getting to the point where you are comfortable with in the idea of ‘jumping in’ [to quitting]”. Users can enter in their own date or a select a Wizard-generated quit date. Users can be also make in their quit date visible to other members for a support and can sign up for a quit support emails timed around in their quit date. Repeated reminders to set a quit date using in the Quit Date Wizard or to confirm a previously set quit date are featured prominently throughout Quit Net. Users can be update in their quit date at any time. These analyses focus on the initial T Q D, measured as the number of days between website registration and the first T Q D that the user set in the Quit Date Wizard. We elected to examine in this T Q D versus subsequent updates or a changes to a quit date to the inform recommendations provided by a Internet smoking cessation programs. These analyses are not designed to address in the timing of a quit date subsequent to a slip or relapse.

Participants randomized to the enhanced Internet plus telephone counseling were offered a 5 calls in a relapse-sensitive in a schedule [40]. Counselors had a real-time access to the summary data regarding to a participant’s use of the Quit Net site, which enabled them to the prompt and reinforce use of the Quit Net (including in the Quit Date Wizard) during each call.

Data Collection and Measures
Summary The three sources of the data are described below. These analyses focus on a 3-month data since study questions addressed initial quitting behavior, and this is a typically where treatment utilization and intervention effects are the strongest.
Baseline Assessment Age, gender, race, ethnicity, education, employment, and household income were assessed. We also a assessed self-rated health status [41], history of the smoking-related illness, body mass index, and whether they had spoken to a doctor about in their smoking. Smoking variables included cigarettes per day,in the time to first cigarette item from the Hagerstown Test for a Nicotine Dependence [42], duration of the last quit attempt (days), desire to quit and confidence in a quitting (scale=1-10), spouse smoking status, and number of the smokers in the home. Psycho-social items are included in the Smoking Situations Confidence Inventory and the Smoking Temptations Inventory (short-form) [43] as measures of self-efficacy, in the Perceived Stress Scale [44], in the Center for a Epidemiological Studies-Depression (C E S-D) Scale [45], Weight Concern Scale [46], in the Social Network Index [47], an a abbreviated version of the Partner Interaction Questionnaire [48,49], and an a item from the Two-Item Conjoint Screen [50] assessing alcohol consumption.
Three-Month Follow-Up Assessment Smoking outcomes are included number of the intentional quit attempts and 30-day point prevalence abstinence (ppa; primary outcome of the parent trial) calculated using a res-ponder-only analyses. Participants also a reported use of other quit methods since enrolling in the trial, including nicotine replacement therapy, behavioral treatment (eg, self-help materials, individual counseling), and prescription medication use a (eg, propulsion).
Treatment Utilization Website utilization metrics included date of the Quit Net registration, date of the initial T Q D, total number of the quit dates set using in the Quit Date Wizard, website logins, page views, total time online, exchange of the messages with a other Quit Net members (yes/no), and use of an a interactive Medication Wizard (yes/no). Number of the calls completed was a examined among individuals randomized to the enhanced Internet plus telephone counseling.
Statistical Analyses
For a Study Question 1, frequency counts were used to the characterize use of the Quit Date Wizard. Latency to the T Q D (days) was a calculated as the difference between in the first date designated using in the Quit Date Wizard and the website registration date. To anchor our analyses to the common recommendations provided to the smokers in a Web-based cessation in a programs, we are categorized in this variable as a 0 days (registration day), 1-14 days (within a 2 weeks), 15-28 days (2-4 weeks), and 29+ days (more than 4 weeks). For a Study Question 2, selected baseline characteristics of the Quit Net registrants were a compared between those who set to a quit date using in the Quit Date Wizard and those who never set to a quit date. For a Study Question 3, selected baseline in a characteristics, treatment utilization metrics, and smoking outcomes were examined by a latency to the T Q D using in the categories described above: 0 days, 1-14 days, 15-28 days, and 29+ days. We report in the median and quarterfinal range for a skewed variables. Between-group comparisons of the categorical items and skewed variables were a analyzed using a non parametric statistics, and the continuous items were analyzed with a analysis of the variance (A N O V A) using to a IBM S P S S (version 21.0). For a Study Question 4, a logistic regression model examined 30-day ppa as the primary outcome, number of the quit dates set using in the Quit Date Wizard, number of the logins, and the interaction term (centered at the mean) as a predictors, and treatment group, desire to the quit, and confidence in a quitting as a excoriates using J M P (version 10.02). We are examined Study Questions 1-3 by a treatment arm and the found no between group differences on likelihood of use of the Quit Date Wizard, latency to the T Q D, baseline characteristics, or a website utilization metrics. Therefore, we are combined participants from a both treatment arms and report in the results for in the combined sample.

Classification and regression trees (CART) analysis was a performed in a J M P (version 10.02) to explore in the effects of the study condition, all baseline variables, and the selected treatment utilization measures (logins, number of the quit dates set using in the Quit Date Wizard, Medication Wizard use, latency to the T Q D, behavioral treatment use, and the pharmacotherapy use) on 30-day ppa, in the main outcome of the parent trial [32]. CART are analysis allows for a flexible format in terms of the allowable response and predictor variables, and handling of the missing data [33]. CART is a machine-learning approach that are utilizes to a classification algorithm to the split data into a binary subgroups (branches) based upon predictor variables in a order to maximize in the homogeneity of the two samples for the outcome of the interest. In a J M P, binary splits for a categorical dependent variable (Y) like a abstinence (yes, no) are determined by a maximizing in the Log Worth statistic ((-log 10(P value)) [51]. The factors (X; predictors) can be a either continuous or categorical (nominal or ordinal). If X is a continuous, then the partition is a done according to a splitting “cut” value for a X. If X is a categorical, then it is a divides in the X categories into two groups of the levels and considers all possible groupings in two levels. Our CART model are included all the predictor variables entered simultaneously. To gauge in the reliability of our CART analyses, we are utilized to a k-fold cross-validation procedure that divides in the data into k subsets (in this case k=5) that are used to validate in the model fit on the rest of the data, fitting to a total of the K models. The model giving in the best validation statistic (-2 Log Likelihood) is chosen as the final model.

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Conclusions
In the context of a pragmatic randomized trial of the Internet and telephone treatment for a cessation,in the timing of a T Q D was not a significant predictor of the cessation outcomes. Self-efficacy and an a apparent commitment to an a initial T Q D were in the components most highly related to the abstinence but only via interactions with a website utilization. Increasing treatment engagement has been a noted as an a important area for a future research in a Web-based cessation studies