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Background: More people who smoke and are living with HIV now die from tobacco-related diseases than HIV itself. Most people are ambivalent about quitting smoking and want to quit someday but not yet. Scalable, effective interventions are needed to motivate and support smoking cessation among people ambivalent about quitting smoking (PAQS) who are living with HIV., Objective: This study aims to develop an app-based intervention for PAQS who are living with HIV and assess its feasibility, acceptability, and potential impact. Results of this study will inform plans for future research and development., Methods: In phase 1, PAQS living with HIV (n=8) participated in user-centered design interviews to inform the final intervention app design and recruitment plan for a subsequent randomized pilot study. In phase 2, PAQS living with HIV were randomized to either a standard care control app or a similar experimental app with additional content tailored for PAQS and those with HIV. Participants were followed for 3 months. Feasibility focused on recruitment, retention, and participants' willingness to install the app. The study was not powered for statistical significance. Indices of acceptability (satisfaction and use) and impact (smoking behavior change and treatment uptake) were assessed via automated data and self-report among those who installed and used the app (n=19)., Results: Recruitment for both study phases was a challenge, particularly via web-based and social media platforms. Enrollment success was greater among people living with HIV recruited from a health care provider and research registry. Once enrolled, retention for the phase 2 randomized study was good; 74% (14/19) of the participants completed the 3-month follow-up. Phase 1 findings suggested that PAQS living with HIV were receptive to using an app-based intervention to help them decide whether, when, and how to stop smoking, despite not being ready to quit smoking. Phase 2 findings further supported this conclusion based on feedback from people who agreed to use an app, but group differences were observed. Indices of acceptability favored the experimental arm, including a descriptively higher mean number of sessions and utilization badges. Similarly, indices of potential impact were descriptively higher in the experimental arm (proportion reducing smoking, making a quit attempt, or calling free tobacco quitline). No participants in either arm quit smoking at the 3-month follow-up., Conclusions: On the basis of this formative work, PAQS living with HIV may be receptive to using a mobile health-based app intervention to help them decide whether, when, or how to stop using tobacco. Indices of acceptability and impact indicate that additional research and development are warranted., Trial Registration: ClinicalTrials.gov NCT05339659; https://clinicaltrials.gov/study/NCT05339659., (©Jennifer B McClure, Jaimee L Heffner, Chloe Krakauer, Sophia Mun, Sheryl L Catz. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.07.2024.)

Background: The majority of cigarette smokers want to quit someday but are not ready to commit to long-term abstinence. However, available smoking cessation treatments are not well-suited to meet the needs of these ambivalent smokers. Low-cost, high-reach mobile health (mHealth) interventions may be a cost-efficient means of offering assistance to ambivalent smokers, yet there are currently no evidence-based options available for this group., Objective: The aim of this study was to develop and preliminarily evaluate the core content for an mHealth program targeting adult smokers who are ambivalent about quitting. The core content consisted of a series of "personal experiments" similar to those tested as part of a counseling intervention in prior work, including brief cognitive or behavioral tasks designed to boost readiness for changing smoking behavior., Methods: We conducted individual user interviews (N=3) to refine program content, and then conducted a one-arm pilot study (N=25) to assess user receptivity and the potential impact of the experiments on motivation and self-efficacy to quit or reduce smoking., Results: In user interviews, participants liked the concept of the personal experiments. Participants in the pilot study found a medium-fidelity prototype to be highly acceptable. After watching a brief orientation video that explained how the program works, most participants (80%, 20/25) indicated that it sounded interesting, primarily because it did not require any commitment to quit. All participants (100%, 25/25) completed all 7 experiments, including a 24-hour quit attempt, although not all were able to refrain from smoking for a full day based on qualitative feedback on the experiment. The mean rating of usefulness of the overall program was 4.12 (SD 1.09) out of 5, and the average rating of the difficulty of the experiments was 2.16 (SD 1.18) out of 5. At the last assessment point, 92% (23/25) of the participants indicated that they were more interested in either quitting or cutting back than when they began the program, and 72% (18/25) said that if the program had included a free trial of nicotine replacement therapy, they would have used it to try to quit smoking., Conclusions: This formative work confirmed that ambivalent smokers are willing to use and will remain engaged with an mHealth intervention that employs the novel concept of personal experiments to enhance their motivation for and ability to quit smoking. The addition of action-oriented treatment (self-help and free nicotine replacement therapy, quitline referral) could further support users' efforts to stop smoking and remain quit., (©Jaimee L Heffner, Sheryl L Catz, Predrag Klasnja, Brooks Tiffany, Jennifer B McClure. Originally published in JMIR Formative Research (http://formative.jmir.org), 27.08.2020.)

Background: Web-based cessation programs are now common for intervening with smokers. However, it remains unclear how acceptable or effective these interventions are among people with affective disorders and symptoms (ADS; eg, depression and anxiety). Research examining this is extremely limited, with mixed results on cessation rates. Additional large studies are needed to more fully understand whether Web-based interventions are similarly used and equally effective among people with and without affective disorder symptomology. If not, more targeted Web-based interventions may be required., Objective: The goal of the research was to compare Web-based treatment acceptability (defined by satisfaction and use) and 12-month cessation outcomes between smokers with and without ADS., Methods: Participants (N=2512) were adult smokers enrolled in a randomized, comparative effectiveness trial of two Web-based smoking interventions designed for the general population of smokers. At baseline, participants reported demographic and smoking characteristics and completed measures assessing ADS. Participants were then classified into subgroups based on their self-reported ADS-either into a no ADS group or into six nonmutually exclusive subgroups: depression, posttraumatic stress disorder (PTSD), panic disorder (PD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), and more than one ADS. Surveys at 12 months postrandomization included subjective ratings of treatment acceptability and self-reported smoking cessation. Treatment use (ie, number of log-ins and total duration of exposure) was assessed via automated records., Results: Relative to the no ADS group, all six ADS subgroups reported significantly greater satisfaction with their assigned Web treatment program, but they spent less time logged in than those with no ADS. For number of log-ins, a treatment arm by ADS group interaction was observed across all ADS subgroups except GAD, suggesting that relative to the no ADS group, they logged in less to one website but not the other. At the 12-month follow-up, abstinence rates in the no ADS group (153/520, 29.42%) were significantly higher than for participants who screened positive for depression (306/1267, 24.15%; P=.03), PTSD (294/1215, 24.19%; P=.03), PD (229/1003, 23.83%; P=.009), and two or more ADS (323/1332, 24.25%; P=.03). Post hoc analyses suggest the lower quit rates may be associated with differences in baseline nicotine dependence and levels of commitment to resist smoking in difficult situations. Website use did not explain the differential abstinence rates., Conclusions: Despite reporting higher levels of treatment satisfaction, most smokers with ADS used their assigned intervention less often and had lower quit rates than smokers with no ADS at treatment onset. The results support the need for developing more targeted interventions for smokers with ADS., Trial Registration: Clinical Trials.gov NCT01812278; https://clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/78L9cNdG4)., (©Noreen L Watson, Jaimee L Heffner, Kristin E Mull, Jennifer B McClure, Jonathan B Bricker. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.06.2019.)

Background: Despite having many advantages, online eHealth trials are not without challenges-notably, participant recruitment, and outcome data retention. Moreover, publications from these trials rarely provide detailed information on the methods used for recruitment and retention or discuss implications of the methods for future studies., Objective: To address this need for empirical guidance regarding recruitment and outcome data retention planning, we aim to describe the methods and lessons learned from the recruitment and retention procedures used in a large randomized trial of 2 Web-based smoking cessation interventions., Methods: To ensure a demographically and geographically diverse participant sample, we used the recruitment strategies (1) traditional, (2) Web-based, and (3) online survey panel methods and adaptively modified each in response to recruitment success. At baseline, participants indicated how they heard about the study and answered demographic questions. To maximize trial retention at each of the 3-, 6-, and 12-month assessment points, 4 survey modalities (first Web, followed by phone, mail, and postcard) were sequentially timed over a 30-day period. Participants received US $25 for submitting their responses, regardless of modality, and received an additional US $10 bonus for completing the Web survey within 24h of electronic notification., Results: We randomized 2637 smokers in 16 months and achieved 88% retention at 12-months. Participants (79.26% female, 72.60% Caucasian) were recruited from all 50 states. The majority of participants were recruited through Facebook (49.43%), followed by the survey panel (20.85%), free internet sources (14.54%), traditional media (11.34%), and Google ads (3.84%). Descriptively, participant demographics varied by recruitment source. Of the completed follow-up surveys, most were completed by Web (92%). Retention rates did not vary by recruitment source., Conclusions: Continuous monitoring and refinement of multiple recruitment methods, particularly of online advertising campaigns, allowed us to maximize the effectiveness of recruitment strategies in recruiting a large, diverse sample of smokers. Likewise, offering multiple follow-up survey modalities in sequential order along with time-dependent bonus incentives enabled us to obtain outcome data from a very high level of enrolled participants for the duration of the trial protocol. These strategies may be similarly useful in other trials., Trial Registration: ClinicalTrials.gov NCT01812278; https://clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/71gy5GLvO)., (©Noreen L Watson, Kristin E Mull, Jaimee L Heffner, Jennifer B McClure, Jonathan B Bricker. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.08.2018.)

Background: Little is known about how individuals engage with electronic health (eHealth) interventions over time and whether this engagement predicts health outcomes., Objective: The objectives of this study, by using the example of a specific type of eHealth intervention (ie, websites for smoking cessation), were to determine (1) distinct groups of log-in trajectories over a 12-month period, (2) their association with smoking cessation, and (3) baseline user characteristics that predict trajectory group membership., Methods: We conducted a functional clustering analysis of 365 consecutive days of log-in data from both arms of a large (N=2637) randomized trial of 2 website interventions for smoking cessation (WebQuit and Smokefree), with a primary outcome of 30-day point prevalence smoking abstinence at 12 months. We conducted analyses for each website separately., Results: A total of 3 distinct trajectory groups emerged for each website. For WebQuit, participants were clustered into 3 groups: 1-week users (682/1240, 55.00% of the sample), 5-week users (399/1240, 32.18%), and 52-week users (159/1240, 12.82%). Compared with the 1-week users, the 5- and 52-week users had 57% higher odds (odds ratio [OR] 1.57, 95% CI 1.13-2.17; P=.007) and 124% higher odds (OR 2.24, 95% CI 1.45-3.43; P<.001), respectively, of being abstinent at 12 months. Smokefree users were clustered into 3 groups: 1-week users (645/1309, 49.27% of the sample), 4-week users (395/1309, 30.18%), and 5-week users (269/1309, 20.55%). Compared with the 1-week users, 5-week users (but not 4-week users; P=.99) had 48% higher odds (OR 1.48, 95% CI 1.05-2.07; P=.02) of being abstinent at 12 months. In general, the WebQuit intervention had a greater number of weekly log-ins within each of the 3 trajectory groups as compared with those of the Smokefree intervention. Baseline characteristics associated with trajectory group membership varied between websites., Conclusions: Patterns of 1-, 4-, and 5-week usage of websites may be common for how people engage in eHealth interventions. The 5-week usage of either website, and 52-week usage only of WebQuit, predicted a higher odds of quitting smoking. Strategies to increase eHealth intervention engagement for 4 more weeks (ie, from 1 week to 5 weeks) could be highly cost effective., Trial Registration: ClinicalTrials.gov NCT01812278; https://www.clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/6yPO2OIKR)., (©Jonathan B Bricker, Vasundhara Sridharan, Yifan Zhu, Kristin E Mull, Jaimee L Heffner, Noreen L Watson, Jennifer B McClure, Chongzhi Di. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.04.2018.)

Background: Older adults have high rates of obesity and are prone to chronic health conditions. These conditions are in part due to high rates of sedentary time (ST). As such, reducing ST could be an innovative strategy for improving health outcomes among obese older adults. To test this theory, we developed a novel, technology-enhanced intervention to reduce sitting time (I-STAND) and pilot tested it to assess the feasibility, acceptability, and preliminary effects of the intervention on ST and biometric outcomes., Objective: The current paper aims to describe the rationale, design, and methods of the I-STAND sitting reduction pilot trial., Methods: Older adults with obesity (n=60) were recruited from a large health care system and randomized to receive I-STAND or a healthy living intervention. I-STAND combined personal coaching with a technology-enhanced intervention (Jawbone UP band) to cue breaks from sitting. Participants completed self-report and biometric assessments at baseline and 3 months. Additional qualitative results were collected from a subset of I-STAND participants (n=22) to further inform the feasibility and acceptability of the interventions. The primary outcome was total hours of daily sitting time measured by the activPAL device. Secondary outcomes included sit-to-stand transitions, bouts of sitting longer than 30 minutes, physical function, blood pressure, fasting glucose, cholesterol, and depressive symptoms., Results: Study enrollment has ended and data processing is underway., Conclusions: Data from randomized trials on sitting reduction are needed to inform novel approaches to health promotion among older adults with obesity. Our trial will help fill this gap. The methods used in our study can guide future research on using technology-based devices to assess or prompt sedentary behavior reduction, or those interested in behavioral interventions targeting obese older adults with novel approaches., Trial Registration: ClinicalTrials.gov: NCT02692560; https://clinicaltrials.gov/ct2/show/NCT02692560 (Archived by WebCite at http://www.webcitation.org/6wppLTWAl)., (©Dori E Rosenberg, Amy K Lee, Melissa Anderson, Anne Renz, Theresa E Matson, Jacqueline Kerr, David Arterburn, Jennifer B McClure. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 12.02.2018.)

Background: Accurately estimating the period of time that individuals are exposed to online intervention content is important for understanding program engagement. This can be calculated from time-stamped data reflecting navigation to and from individual webpages. Prolonged periods of inactivity are commonly handled with a time-out feature and assigned a prespecified exposure duration. Unfortunately, this practice can lead to biased results describing program exposure., Objective: The aim of the study was to describe how multiple imputations can be used to better account for the time spent viewing webpages that result in a prolonged period of inactivity or a time-out., Methods: To illustrate this method, we present data on time-outs collected from the Q(2) randomized smoking cessation trial. For this analysis, we evaluate the effects on intervention exposure of receiving content written in a prescriptive versus motivational tone. Using multiple imputations, we created five complete datasets in which the time spent viewing webpages that resulted in a time-out were replaced with values estimated with imputation models. We calculated standard errors using Rubin's formulas to account for the variability due to the imputations. We also illustrate how current methods of accounting for time-outs (excluding timed-out page views or assigning an arbitrary viewing time) can influence conclusions about participant engagement., Results: A total of 63.00% (1175/1865) of participants accessed the online intervention in the Q(2) trial. Of the 6592 unique page views, 683 (10.36%, 683/6592) resulted in a time-out. The median time spent viewing webpages that did not result in a time-out was 1.07 minutes. Assuming participants did not spend any time viewing a webpage that resulted in a time-out, no difference between the two message tones was observed (ratio of mean time online: 0.87, 95% CI 0.75-1.02). Assigning 30 minutes of viewing time to all page views that resulted in a time-out concludes that participants who received content in a motivational tone spent less time viewing content (ratio of mean time online: 0.86, 95% CI 0.77-0.98) than those participants who received content in a prescriptive tone. Using multiple imputations to account for time-outs concludes that there is no difference in participant engagement between the two message tones (ratio of mean time online: 0.87; 95% CI 0.75-1.01)., Conclusions: The analytic technique chosen can significantly affect conclusions about online intervention engagement. We propose a standardized methodology in which time spent viewing webpages that result in a time-out is treated as missing information and corrected with multiple imputations., Trial Registration: Clinicaltrials.gov NCT00992264; http://clinicaltrials.gov/ct2/show/NCT00992264 (Archived by WebCite at http://www.webcitation.org/6Kw5m8EkP).

Background: Participant engagement influences treatment effectiveness, but it is unknown which intervention design features increase treatment engagement for online smoking cessation programs., Objective: We explored the effects of 4 design features (ie, factors) on early engagement with an Internet-based, motivational smoking cessation program., Methods: Smokers (N=1865) were recruited from a large health care organization to participate in an online intervention study, regardless of their interest in quitting smoking. The program was intended to answer smokers' questions about quitting in an effort to motivate and support cessation. Consistent with the screening phase in the multiphase optimization strategy (MOST), we used a 2-level, full-factorial design. Each person was randomized to 1 of 2 levels of each factor, including message tone (prescriptive vs motivational), navigation autonomy (dictated vs not), proactive email reminders (yes vs no), and inclusion of personally tailored testimonials (yes vs no). The effects of each factor level on program engagement during the first 2 months of enrollment were compared, including number of visits to the website resulting in intervention content views (as opposed to supplemental content views), number of intervention content areas viewed, number of intervention content pages viewed, and duration of time spent viewing this content, as applicable to each factor., Results: Adjusting for baseline readiness to quit, persons who received content written in a prescriptive tone made the same number of visits to the website as persons receiving content in a motivational tone, but viewed 1.17 times as many content areas (95% CI 1.08-1.28; P<.001) and 1.15 times as many pages (95% CI 1.04-1.28; P=.009). Time spent viewing materials did not differ among groups (P=.06). Persons required to view content in a dictated order based on their initial readiness to quit made the same number of visits as people able to freely navigate the site, but viewed fewer content areas (ratio of means 0.80, 95% CI 0.74-0.87; P<.001), 1.17 times as many pages (95% CI 1.06-1.31; P=.003), and spent 1.37 times more minutes online (95% CI 1.17-1.59; P<.001). Persons receiving proactive email reminders made 1.20 times as many visits (95% CI 1.09-1.33; P<.001), viewed a similar number of content areas as persons receiving no reminders, viewed 1.58 times as many pages (95% CI 1.48-1.68; P<.001), and spent 1.51 times as many minutes online (95% CI 1.29-1.77; P<.001) as those who did not receive proactive emails. Tailored testimonials did not significantly affect engagement., Conclusions: Using a prescriptive message tone, dictating content viewing order, and sending reminder emails each resulted in greater program engagement relative to the contrasting level of each experimental factor. The results require replication, but suggest that a more directive interaction style may be preferable for online cessation programs., Trial Registration: clinicaltrials.gov NCT00992264; http://clinicaltrials.gov/ct2/show/NCT00992264 (Archived by WebCite at http://www.webcitation.org/6F7H7lr3P).

Background: The Internet provides us with tools (user metrics or paradata) to evaluate how users interact with online interventions. Analysis of these paradata can lead to design improvements., Objective: The objective was to explore the qualities of online participant engagement in an online intervention. We analyzed the paradata in a randomized controlled trial of alternative versions of an online intervention designed to promote consumption of fruit and vegetables., Methods: Volunteers were randomized to 1 of 3 study arms involving several online sessions. We created 2 indirect measures of breadth and depth to measure different dimensions and dynamics of program engagement based on factor analysis of paradata measures of Web pages visited and time spent online with the intervention materials. Multiple regression was used to assess influence of engagement on retention and change in dietary intake., Results: Baseline surveys were completed by 2513 enrolled participants. Of these, 86.3% (n = 2168) completed the follow-up surveys at 3 months, 79.6% (n = 2027) at 6 months, and 79.4% (n = 1995) at 12 months. The 2 tailored intervention arms exhibited significantly more engagement than the untailored arm (P < .01). Breadth and depth measures of engagement were significantly associated with completion of follow-up surveys (odds ratios [OR] = 4.11 and 2.12, respectively, both P values < .001). The breadth measure of engagement was also significantly positively associated with a key study outcome, the mean increase in fruit and vegetable consumption (P < .001)., Conclusions: By exploring participants' exposures to online interventions, paradata are valuable in explaining the effects of tailoring in increasing participant engagement in the intervention. Controlling for intervention arm, greater engagement is also associated with retention of participants and positive change in a key outcome of the intervention, dietary change. This paper demonstrates the utility of paradata capture and analysis for evaluating online health interventions., Trial Registration: NCT00169312; http://clinicaltrials.gov/ct2/show/NCT00169312 (Archived by WebCite at http://www.webcitation.org/5u8sSr0Ty).

Background: Web-based behavioral programs efficiently disseminate health information to a broad population, and online tailoring may increase their effectiveness. While the number of Internet-based behavioral interventions has grown in the last several years, additional information is needed to understand the characteristics of subjects who enroll in these interventions, relative to those subjects who are invited to enroll., Objective: The aim of the study was to compare the characteristics of participants who enrolled in an online dietary intervention trial (MENU) with those who were invited but chose not to participate, in order to better understand how these groups differ., Methods: The MENU trial was conducted among five health plans participating in the HMO Cancer Research Network in collaboration with the University of Michigan Center for Health Communication Research. Approximately 6000 health plan members per site, between the ages of 21 and 65, and stratified by gender with oversampling of minority populations, were randomly selected for recruitment and were mailed an invitation letter containing website information and a US$2 bill with the promise of US$20 for completing follow-up surveys. Administrative and area-based data using geocoding along with baseline survey data were used to compare invitees (HMO members sent the introductory letter), responders (those who entered a study ID on the website), and enrollees (those who completed the enrollment process). Generalized estimating equation multivariate and logistic regression models were used to assess predictors of response and enrollment., Results: Of 28,460 members invited to participate, 4270 (15.0%) accessed the website. Of the eligible responders, 2540 (8.9%) completed the consent form and baseline survey and were enrolled and randomized. The odds of responding were 10% lower for every decade of increased age (P < .001), while the likelihood of enrolling was 10% higher for every decade increase in age (P < .001). Women were more likely to respond and to enroll (P < .001). Those living in a census tract associated with higher education levels were more likely to respond and enroll, as well as those residing in tracts with higher income (P < .001). With a 22% (n = 566) enrollment rate for African Americans and 8% (n = 192) for Hispanics, the enrolled sample was more racially and ethnically diverse than the background sampling frame., Conclusions: Relative to members invited to participate in the Internet-based intervention, those who enrolled were more likely to be older and live in census tracts associated with higher socioeconomic status. While oversampling of minority health plan members generated an enrolled sample that was more racially and ethnically diverse than the overall health plan population, additional research is needed to better understand methods that will expand the penetration of Internet interventions into more socioeconomically diverse populations., Trial Registration: Clinicaltrials.gov NCT00169312; http://clinicaltrials.gov/ct2/show/NCT00169312 (Archived by WebCite at http://www.webcitation.org/5jB50xSfU).

Background: The Internet is a promising venue for delivering smoking cessation treatment, either as a stand-alone program or as an adjunct to pharmacotherapy. However, there is little data to indicate what percent of smokers are interested in receiving online smoking cessation services or how best to recruit smokers to Internet-based programs., Objective: Using a defined recruitment sample, this study aimed to identify the percentage of smokers who expressed interest in or enrolled in Project Quit, a tailored, online, cognitive-behavioral support program offered with adjunctive nicotine replacement therapy patches. In addition, we examined the effectiveness of several individual-level versus population-level recruitment strategies., Methods: Members from two large health care organizations in the United States were invited to participate in Project Quit. Recruitment efforts included proactive invitation letters mailed to 34533 likely smokers and reactive population-level study advertisements targeted to all health plan members (> 560000 adults, including an estimated 98000 smokers across both health care organizations)., Results: An estimated 1.6% and 2.5% of adult smokers from each health care organization enrolled in Project Quit. Among likely smokers who received proactive study invitations, 7% visited the Project Quit website (n = 2260) and 4% (n = 1273) were eligible and enrolled. Response rates were similar across sites, despite using different sources to assemble the invitation mailing list. Proactive individual-level recruitment was more effective than other forms of recruitment, accounting for 69% of website visitors and 68% of enrollees., Conclusions: Smokers were interested in receiving online smoking cessation support, even though they had access to other forms of treatment through their health insurance. Uptake rates for this program were comparable to those seen when smokers are advised to quit and are referred to other forms of smoking cessation treatment. In this sample, proactive mailings were the best method for recruiting smokers to Project Quit.