Background and aims
Black adults who smoke are less likely to seek treatment and to succeed in quitting compared with other racial groups. The lack of efficacious and engaging trials for smoking ...cessation further contributes to this disparity. This study explored whether an acceptance and commitment therapy (ACT)‐based smartphone application (iCanQuit) was more efficacious for smoking cessation than a United States Clinical Practice Guidelines (USCPG)‐based smartphone application (QuitGuide) among Black adults.
Design
Secondary analysis of a two‐arm randomized trial with 12‐month follow‐up.
Setting
United States (US).
Participants
A total of 554 Black adults who smoke daily were recruited from 34 US states and enrolled between May 2017 and September 2018.
Interventions
Participants were randomized to receive iCanQuit (n = 274) or QuitGuide (n = 280) for 12 months.
Measurements
Smoking cessation outcomes were measured at 3, 6, and 12 months. The primary outcome was self‐reported complete‐case 30‐day point prevalence abstinence (PPA) at 12 months. Secondary outcomes were 7‐day PPA, missing‐as‐smoking imputation, multiple imputation, prolonged abstinence, and cessation of all tobacco products at 12 months. Study retention, treatment engagement, and change in ACT‐based processes were also compared between arms.
Findings
Study retention was 89% at 12 months and did not differ by arm (P > 0.05). The complete‐case 30‐day PPA was 28% for iCanQuit versus 20% for QuitGuide at 12 months (odds ratio OR = 1.60; 95% confidence interval CI = 1.03, 2.46). Similar associations were observed for the missing‐as‐smoking imputation, although non‐significant (25% iCanQuit vs 18% QuitGuide; OR = 1.50; 95% CI = 0.98, 2.30). iCanQuit vs QuitGuide participants were significantly more engaged with iCanQuit application as measured by the number of logins from baseline to 6 months (incidence rate ratio = 3.26; 95% CI = 2.58, 4.13). Increased acceptance of cues to smoke mediated the effect of treatment on cessation (indirect effect: OR = 0.20; 95% CI = 0.05, 0.29).
Conclusions
Among Black adults, an acceptance and commitment therapy‐based smartphone application appeared to be more efficacious and engaging for smoking cessation than the United States Clinical Practice Guidelines‐based QuitGuide application.
Background and aims
iCanQuit is a smartphone application (app) proven efficacious for smoking cessation in a Phase III randomized controlled trial (RCT). This study aimed to measure whether ...medications approved by the US Food and Drug Administration (FDA) for smoking cessation would further enhance the efficacy of iCanQuit, relative to its parent trial comparator—the National Cancer Institute's (NCI's) QuitGuide app.
Design
Secondary analysis of the entire parent trial sample of a two‐group (iCanQuit and QuitGuide), stratified, doubled‐blind RCT.
Setting
United States.
Participants
Participants who reported using an FDA‐approved cessation medication on their own (n = 619) and those who reported no use of cessation medications (n = 1469).
Interventions
Participants were randomized to receive iCanQuit app or NCI's QuitGuide app.
Measurements
Use of FDA‐approved medications was measured at 3 months post‐randomization. Smoking cessation outcomes were measured at 3, 6 and 12 months. The primary outcome was 12‐month self‐reported 30‐day point prevalence abstinence (PPA).
Findings
The data retention rate at the 12‐month follow‐up was 94.0%. Participants were aged 38.5 years, 71.0% female, 36.6% minority race/ethnicity, 40.6% high school or less education, residing in all 50 US States and smoking 19.2 cigarettes/day. The 29.6% of all participants who used medications were more likely to choose nicotine replacement therapy (NRT; 78.8%) than other cessation medications (i.e. varenicline or bupropion; 18.3 and 10.5%, respectively) and use did not differ by app treatment assignment (all P > 0.05). There was a significant (P = 0.049) interaction between medication use and app treatment assignment on PPA. Specifically, 12‐month quit rates were 34% for iCanQuit versus 20% for QuitGuide odds ratio (OR) = 2.36, 95% confidence interval (CI) = 1.59, 3.49 among participants reporting any medication use, whereas among participants reporting no medication use, quit rates were 28% for iCanQuit versus 22% for QuitGuide (OR = 1.41, 95% CI = 1.09, 1.82). Results were stronger for those using only NRT: 40% quit rates for iCanQuit versus 18% quit rates for QuitGuide (OR = 3.57, 95% CI = 2.20, 5.79).
Conclusions
The iCanQuit smartphone app for smoking cessation was more efficacious than the QuitGuide smartphone app, regardless of whether participants used medications to aid cessation. Smoking cessation medications, especially nicotine replacement therapy, might enhance the efficacy of the iCanQuit app.
Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes.
In the context of a randomized trial comparing 2 ...smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership.
Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app.
For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio OR 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app.
Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could ...boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods.
This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual will become an iCanQuit early dropout while validating the approach using other statistical methods and randomized trial data from 3 other digital interventions for smoking cessation (combined randomized N=4529).
Standard logistic regression models were used to predict early dropouts for individuals receiving the iCanQuit smoking cessation intervention app, the National Cancer Institute QuitGuide smoking cessation intervention app, the WebQuit.org smoking cessation intervention website, and the Smokefree.gov smoking cessation intervention website. The main predictors were the number of times a participant logged in per day during the first 7 days following randomization. The area under the curve (AUC) assessed the performance of the logistic regression models, which were compared with decision trees, support vector machine, and neural network models. We also examined whether 13 baseline variables that included a variety of demographics (eg, race and ethnicity, gender, and age) and smoking characteristics (eg, use of e-cigarettes and confidence in being smoke free) might improve this prediction.
The AUC for each logistic regression model using only the first 7 days of log-in count variables was 0.94 (95% CI 0.90-0.97) for iCanQuit, 0.88 (95% CI 0.83-0.93) for QuitGuide, 0.85 (95% CI 0.80-0.88) for WebQuit.org, and 0.60 (95% CI 0.54-0.66) for Smokefree.gov. Replacing logistic regression models with more complex decision trees, support vector machines, or neural network models did not significantly increase the AUC, nor did including additional baseline variables as predictors. The sensitivity and specificity were generally good, and they were excellent for iCanQuit (ie, 0.91 and 0.85, respectively, at the 0.5 classification threshold).
Logistic regression models using only the first 7 days of log-in count data were generally good at predicting early dropouts. These models performed well when using simple, automated, and readily available log-in count data, whereas including self-reported baseline variables did not improve the prediction. The results will inform the early identification of people at risk of early dropout from digital health interventions with the goal of intervening further by providing them with augmented treatments to increase their retention and, ultimately, their intervention outcomes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
•Web-delivered interventions have the potential to help US Hispanic/Latinx smokers quit.•WebQuit (vs Smokefree) website was more engaging among US Hispanic/Latinx smokers.•WebQuit (vs Smokefree) ...website had higher quit rates among US Hispanic/Latinx smokers.
Hispanic/Latinx adult smokers in the United States (US) face barriers to receiving and utilizing evidenced-based cessation treatments compared with other racial/ethnic groups. The lack of efficacious and accessible smoking cessation treatments for this population further contributes to such smoking disparities. In a secondary analysis, we explored the efficacy of an Acceptance and Commitment Therapy (ACT)-based website (WebQuit.org) versus a US Clinical Practice Guidelines (USCPG)-based website (Smokefree.gov) for smoking cessation in a subset of Hispanic/Latinx adult participants enrolled in the WebQuit trial. Of the 2,637 participants who were randomized in the parent trial, 222 were Hispanic/Latinx (n = 101 in WebQuit, n = 121 in Smokefree). Smoking cessation outcomes were measured at 3, 6, and 12-months. The primary outcome was self-reported complete-case 30-day point prevalence abstinence (PPA) at 12-months. Treatment engagement and satisfaction, change in acceptance of urges to smoke, and commitment to quitting smoking were compared across conditions. Retention rate was 88% at 12-months. WebQuit participants had higher odds of smoking cessation compared to Smokefree participants at 12-months (40% vs. 25%; OR = 1.93 95% CI: 1.04, 3.59). Findings were similar using multiple imputation. WebQuit participants engaged more with the website than Smokefree participants through multiple indicators of engagement, including spending more time using the website (IRR = 2.32; 95% CI: 1.68, 3.20). Although WebQuit participants engaged more with the website than Smokefree participants, there was no evidence that differences in quit rates were mediated by engagement level. This study provides initial empirical evidence that digital interventions may be efficacious for helping Hispanic/Latinx adults quit smoking.
Background Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot. Objective We aim to ...report results of the user-centered design development process and randomized controlled trial for a novel and comprehensive quit smoking conversational chatbot called QuitBot. Methods The 4 years of formative research for developing QuitBot followed an 11-step process: (1) specifying a conceptual model; (2) conducting content analysis of existing interventions (63 hours of intervention transcripts); (3) assessing user needs; (4) developing the chat’s persona (“personality”); (5) prototyping content and persona; (6) developing full functionality; (7) programming the QuitBot; (8) conducting a diary study; (9) conducting a pilot randomized controlled trial (RCT); (10) reviewing results of the RCT; and (11) adding a free-form question and answer (QnA) function, based on user feedback from pilot RCT results. The process of adding a QnA function itself involved a three-step process: (1) generating QnA pairs, (2) fine-tuning large language models (LLMs) on QnA pairs, and (3) evaluating the LLM outputs. Results We developed a quit smoking program spanning 42 days of 2- to 3-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing Food and Drug Administration–approved cessation medications, coping with triggers, and recovering from lapses and relapses. In a pilot RCT with 96% three-month outcome data retention, QuitBot demonstrated high user engagement and promising cessation rates compared to the National Cancer Institute’s SmokefreeTXT text messaging program, particularly among those who viewed all 42 days of program content: 30-day, complete-case, point prevalence abstinence rates at 3-month follow-up were 63% (39/62) for QuitBot versus 38.5% (45/117) for SmokefreeTXT (odds ratio 2.58, 95% CI 1.34-4.99; P=.005). However, Facebook Messenger intermittently blocked participants’ access to QuitBot, so we transitioned from Facebook Messenger to a stand-alone smartphone app as the communication channel. Participants’ frustration with QuitBot’s inability to answer their open-ended questions led to us develop a core conversational feature, enabling users to ask open-ended questions about quitting cigarette smoking and for the QuitBot to respond with accurate and professional answers. To support this functionality, we developed a library of 11,000 QnA pairs on topics associated with quitting cigarette smoking. Model testing results showed that Microsoft’s Azure-based QnA maker effectively handled questions that matched our library of 11,000 QnA pairs. A fine-tuned, contextualized GPT-3.5 (OpenAI) responds to questions that are not within our library of QnA pairs. Conclusions The development process yielded the first LLM-based quit smoking program delivered as a conversational chatbot. Iterative testing led to significant enhancements, including improvements to the delivery channel. A pivotal addition was the inclusion of a core LLM–supported conversational feature allowing users to ask open-ended questions. Trial Registration ClinicalTrials.gov NCT03585231; https://clinicaltrials.gov/study/NCT03585231
To evaluate the association between the dietary inflammatory index (DII
) and incident cardiovascular disease (CVD) in Hispanic women from the Women's Health Initiative (WHI), and to determine if ...body mass index (BMI) interacted with the DII scores.
Secondary analysis of baseline dietary data and long-term CVD outcomes among 3,469 postmenopausal women who self-identified as Hispanic enrolled in WHI. DII scores were calculated from self-administered food frequency questionnaires. The CVD outcomes included coronary heart disease (CHD) and stroke. Stratified Cox regression models were used to assess the relationship between DII scores and CVD in women with and without obesity. Models were adjusted for age, lifestyle risk factors, known risk factors, and neighborhood socioeconomic status.
The incidence of CHD was 3.4 and 2.8% for stroke after a median follow-up of 12.9 years. None of the DIIs were associated with CVD risk in this sample of Hispanic women. BMI interacted with the DII (p < 0.20) and stratified models showed that the associations between the DII and CVD were only significant in women with overweight (p < 0.05). In this group, higher DII scores were associated with a higher risk of CHD (HR 1.27; 95% CI: 1.08, 1.51) and a higher risk of stroke (HR 1.32; 95% CI: 1.07, 1.64).
Among postmenopausal Hispanic women with overweight, greater adherence to pro-inflammatory diets was associated with higher risk of CVD. Additional research is needed to understand how to promote long-term heart-healthy dietary habits to reduce inflammation and prevent CVD in at-risk Hispanic women.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
The purpose of this study is to conduct validity and reliability testing of a new instrument, the Preferences and Self-Efficacy of Diet and Physical Activity Behaviors Questionnaire for Latina Women ...(PSEDPALW), which is for women who identify as Latina and are breast cancer survivors. PSEDPALW measures preferences and self-efficacy for four behaviors: physical activity (PA), fruit and vegetable (FV) intake, dietary fat (DF) intake, and added sugar (AS) intake (eight scales in total). Validity testing was conducted through an expert panel review and a cognitive interviewing focus group (n = 4). Reliability was tested via internal consistency reliability (n = 118) and test–retest reliability (n = 30). Validity testing was used to refine PSEDPALW. Reliability testing was conducted on three versions with 104, 47, and 41 items. PA scales had acceptable Cronbach’s α (>0.70) but low ICC (NS). FV and DF scales had acceptable Cronbach’s α (>0.70), with preferences for the shorter (47- and 41-item) versions (Cronbach’s α < 0.70), and all scales had moderate ICC (p < 0.05, except the FV scale on the 104-item version (p = 0.07)). The AS preferences scale had Cronbach’s α < 0.70, with self-efficacy > 0.70 for all versions and ICC moderate for all versions (p ≤ 0.01). PSEDPALW may be useful to assess diet and physical activity preferences and self-efficacy in theory-based diet and physical activity interventions in women who identify as Latina and are breast cancer survivors.
Engagement with digital interventions is a well-known predictor of treatment outcomes, but this knowledge has had limited actionable value. Instead, learning why engagement with digital interventions ...impact treatment outcomes can lead to targeted improvements in their efficacy.
This study aimed to test a serial mediation model of an Acceptance and Commitment Therapy (ACT) smartphone intervention for smoking cessation.
In this randomized controlled trial, participants (N=2415) from 50 US states were assigned to the ACT-based smartphone intervention (iCanQuit) or comparison smartphone intervention (QuitGuide). Their engagement with the apps (primary measure: number of logins) was measured during the first 3 months, ACT processes were measured at baseline and 3 months (acceptance of internal cues to smoke, valued living), and smoking cessation was measured at 12 months with 87% follow-up retention.
There was a significant serial mediation effect of iCanQuit on smoking cessation through multiple indicators of intervention engagement (ie, total number of logins, total number of minutes used, and total number of unique days of use) and in turn through increases in mean acceptance of internal cues to smoke from baseline to 3 months. Analyses of the acceptance subscales showed that the mediation was through acceptance of physical sensations and emotions, but not acceptance of thoughts. There was no evidence that the effect of the iCanQuit intervention was mediated through changes in valued living.
In this first study of serial mediators underlying the efficacy of smartphone apps for smoking cessation, our results suggest the effect of the iCanQuit ACT-based smartphone app on smoking cessation was mediated through multiple indicators of engagement and in turn through increases in the acceptance of physical sensations and emotions that cue smoking.
Clinical Trials.gov NCT02724462; https://clinicaltrials.gov/ct2/show/NCT02724462.
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