In-app Activities Research Articles

Social Gaming to Decrease Loneliness in Older Adults: Recruitment Challenges and Attrition Analysis in a Digital Mixed Methods Feasibility Study.

Digital mental health interventions could sustainably and scalably prevent and reduce loneliness in older adults. We designed an app containing 29 text-based games and a questionnaire-administering chatbot to stimulate intergenerational contact. This study aims to evaluate the feasibility of a social gaming app in reducing loneliness among older adults by evaluating recruitment strategies, data collection procedures, and gameplay activity. This mixed methods study recruited participants via newsletters, articles, and a social media campaign. We used semistructured interviews and descriptive analysis of questionnaire answers and game data to assess feasibility. Key measures included recruitment reach and efficiency, participant demographics, in-app activity, and app usability and engagement feedback. The social media campaign reached 192,641 potential participants, resulting in 1363 game downloads. A total of 155 participants (aged 65 years and older: n=34, 21.9% and aged less than 65 years: n=121, 78.1%) provided informed consent, yielding a conversion rate of 0.08%. The recruitment campaign focusing on distanced playful interaction had a significantly (P<.001) higher click-through rate (1.98%) than a campaign focusing on research participation (click-through rate=0.51%; P<.001). The overall conversion rate from advertisement exposure to research participation was 0.08%. Participants had a mean age of 48 (SD 16) years. The 65 years and older group averaged 70 (SD 5) years, while the less 65 years group averaged 42 (SD 12) years. Additionally, 45.2% (57/126) reported at least moderate levels of loneliness at baseline. Of the initial 554 players, 91 (16.4%) remained active after the first week, and 32 (5.8%) remained active for more than 90 days. Active participants tended to interact with those within their own age group, as indicated by a Pearson correlation of r=0.31 between the ages of the message sender and receiver. Interviews with 12 (48%) participants (aged 65-79 years; female: n=12, 83%) revealed barriers such as excessive chatbot questions and a mismatch between the target group and app design focus. Questionnaire completion rates dropped from 46% at baseline to 10% at follow-up. These findings underscore the challenges of recruitment and retention for older adults in a fully digital social gaming intervention. Effective recruitment strategies and targeted app design are crucial for engagement. Based on these insights, future interventions should focus on simplified interfaces, clear guidance for gameplay, and addressing the specific needs and preferences of older adults, thereby enhancing the effectiveness of digital mental health interventions.

Optimizing User Retention for a Digital Recruitment App with CLM and the Hooked Model

As the digital recruitment market in Indonesia becomes increasingly competitive, it is crucial for Chatterhire, a job portal tech startup, to retain a strong user base for sustainability and growth. Retention is vital for demonstrating product-market fit, monetization potential, and attracting future investments. To achieve this, Chatterhire aims to shift its focus from aggressive user acquisition to retention, by optimizing its platform for regular use. This research combined quantitative data on user in-app activities with qualitative feedback from in-depth interviews. The quantitative analysis utilized Customer Lifecycle Management (CLM) techniques, including cohort analysis, behavioral cohort analysis, and funnel analysis, to identify critical experiences that encourage users to return, reasons for user churn, and features that need enhancement for better retention. The study found that retaining users is challenging for an episodic-use app like a job portal, where engagement is sporadic and infrequent. Once users secure a job, their need for the app diminishes, leading to significant drops in engagement and even app uninstalls. Therefore, the company needs to expand the platform’s features to provide additional reasons for users to return. Additionally, ensuring users perceive the app’s value through repeated interactions is essential. The research revealed that users who apply for at least four jobs within the first seven days are likely to have higher retention. This research highlights the importance of integrating intrinsic value into the product to foster a sustained user journey. The Hooked Model’s core elements—Trigger, Action, Variable Reward, and Investment—can encourage habitual use and sustained engagement, crucial for maintaining a competitive edge in the digital recruitment industry.

The effectiveness of mobile app usage in facilitating weight loss: An observational study.

With increasing rates of global obesity and associated health issues, there is an ever-increasing need for weight management solutions to be more accessible. Mobile applications offer accessible support systems and have the potential to offer a viable and effective weight management solution as an alternative to traditional healthcare models. To evaluate the effectiveness of the SIMPLE mobile application for time-restricted eating in achieving weight loss (WL). User data were analyzed between January 2021 and January 2023. In-app activity was calculated as the proportion of active days over 12, 26 and 52weeks. A day is considered active if it contains at least one in-app action (e.g., logging weight, food, fasting, or physical activity). Users were categorized into four in-app activity levels: inactive (in-app activity <33%), medium activity (33%-66%), high activity (66%-99%), and maximal activity (100%). Weight change among in-app activity groups was assessed at 12, 26, and 52weeks. Out of 53,482 users, a positive association was found between the use of the SIMPLE app and WL. Active app users lost more weight than their less active counterparts. Active users had a median WL of 4.20%, 5.04%, and 3.86% at 12, 26, and 52weeks, respectively. A larger percentage of active users-up to 50.26%-achieved clinically significant WL (≥5%) when compared to inactive users. A dose-response relationship between WL and app usage was found after adjusting for gender, age, and initial Body Mass Index; a 10% increase in app activity correlated with increased WL by 0.43, 0.66 and 0.69kg at 12, 26, and 52weeks, respectively. The study demonstrates that the SIMPLE app enables effective WL directly associated with the level of app engagement. Mobile health applications offer an accessible and effective weight management solution and should be considered when supporting adults to lose weight.

User Engagement, Acceptability, and Clinical Markers in a Digital Health Program for Nonalcoholic Fatty Liver Disease: Prospective, Single-Arm Feasibility Study.

Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in the world. Common comorbidities are central obesity, type 2 diabetes mellitus, dyslipidemia, and metabolic syndrome. Cardiovascular disease is the most common cause of death among people with NAFLD, and lifestyle changes can improve health outcomes. This study aims to explore the acceptability of a digital health program in terms of engagement, retention, and user satisfaction in addition to exploring changes in clinical outcomes, such as weight, cardiometabolic risk factors, and health-related quality of life. We conducted a prospective, open-label, single-arm, 12-week study including 38 individuals with either a BMI >30, metabolic syndrome, or type 2 diabetes mellitus and NAFLD screened by FibroScan. An NAFLD-specific digital health program focused on disease education, lowering carbohydrates in the diet, food logging, increasing activity level, reducing stress, and healthy lifestyle coaching was offered to participants. The coach provided weekly feedback on food logs and other in-app activities and opportunities for participants to ask questions. The coaching was active throughout the 12-week intervention period. The primary outcome was feasibility and acceptability of the 12-week program, assessed through patient engagement, retention, and satisfaction with the program. Secondary outcomes included changes in weight, liver fat, body composition, and other cardiometabolic clinical parameters at baseline and 12 weeks. In total, 38 individuals were included in the study (median age 59.5, IQR 46.3-68.8 years; n=23, 61% female). Overall, 34 (89%) participants completed the program and 29 (76%) were active during the 12-week program period. The median satisfaction score was 6.3 (IQR 5.8-6.7) of 7. Mean weight loss was 3.5 (SD 3.7) kg (P<.001) or 3.2% (SD 3.4%), with a 2.2 (SD 2.7) kg reduction in fat mass (P<.001). Relative liver fat reduction was 19.4% (SD 23.9%). Systolic blood pressure was reduced by 6.0 (SD 13.5) mmHg (P=.009). The median reduction was 0.14 (IQR 0-0.47) mmol/L for triglyceride levels (P=.003), 3.2 (IQR 0.0-5.4) µU/ml for serum insulin (s-insulin) levels (P=.003), and 0.5 (IQR -0.7 to 3.8) mmol/mol for hemoglobin A1c (HbA1c) levels (P=.03). Participants who were highly engaged (ie, who used the app at least 5 days per week) had greater weight loss and liver fat reduction. The 12-week-long digital health program was feasible for individuals with NAFLD, receiving high user engagement, retention, and satisfaction. Improved liver-specific and cardiometabolic health was observed, and more engaged participants showed greater improvements. This digital health program could provide a new tool to improve health outcomes in people with NAFLD. Clinicaltrials.gov NCT05426382; https://clinicaltrials.gov/study/NCT05426382.

Multi-Media Field Test: Cognitive-Behavioral Therapy at Our Fingertips: Sanvello Provides On-Demand Support for Mental Health

Sanvello is a cognitive-behavioral therapy (CBT) app available as a web platform and a native app for both Android and iOS. Sanvello can be used for free, with additional premium content available in a paid version. It is targeted for adults with mild to moderate symptoms of depression, anxiety, and stress and has a solid evidence base to support its efficacy. Sanvello is user-friendly and intuitive, with good user experience and appropriate privacy and data security measures. Sanvello’s free version consists mainly of self-care and peer support content, allowing users to engage with tools such as in-app activities personalized through initial user assessments, and community pages. A paid version provides additional content through a subscription model and coaching available for a monthly fee. Professional support is available either through insurance coverage of a pay-per-appointment model. Despite a solid evidence base, various features, and a library of content, identified areas of improvement include expanding the psychoeducation materials and articles to include more timely resources and the diversity of its potential users. For these reasons, we have rated Sanvello with a 5 in utility, and ease of use/overall appeal along with a 4 in appropriateness of content and research evidence. Overall, Sanvello provides a variety of evidence-based cognitive and behavioral skills and, for those with the paid version, the opportunity to connect with professionals for additional support.

Does Health & Her app use improve menopausal symptoms? A longitudinal cohort study

ObjectivesThe Health & Her app provides menopausal women with a means of monitoring their symptoms, symptom triggers and menstrual periods, and enables them to engage in a variety of digital...

Fasting and weight loss: mobile application-based approach.

The purpose of this study was to investigate the efficacy of intermittent fasting (IF) guidance, delivered through a smartphone application, in terms of engagement and weight loss. We performed a retrospective chart-review study of all consecutive users with overweight or obesity of the DoFasting mobile application, which integrates IF solutions for people looking to lose weight. Users with overweight and obesity at the beginning of application use, who met the inclusion criteria (entered their gender, height, and starting weight; had more than one weight entry; used the app for at least a month; had at least one active day per week with valid app-related activities) and used the application for weight loss were included in the study. The final study cohort comprised 22,022 consecutive users. The short-term cohort comprised 17,221 users (8585 females and 8636 males), while 4801 users (2983 females and 1818 males) were in the long-term cohort. Long-term active users of the application lost a statistically significant amount of weight over the study period and lost more than inactive users did (2.2 [3.5] vs 1.4 [3.2], p < 0.0001; the results are shown in kilograms as medians with the interquartile range in brackets). Active and long-term users were also more likely to reduce their BMI class (p < 0.05) in comparison to non-active short-term users. A multiple regression model showed that the total length of use and active days were the most significant predictors of weight loss. In-app activities such as total fasting hours, weight logging, completing challenges, and providing feedback were also statistically significant predictors of weight loss with varying contributions. We found that the DoFasting mobile application that implements IF is an effective tool for weight loss if used actively and sufficiently. This retrospective chart review study was approved by BRANY IRB in January 2022 (study ID.: 22-08-034-939).

Deep Learning for Encrypted Traffic Classification and Unknown Data Detection.

Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user activity detection framework is proposed to identify fine-grained user activities performed on mobile applications (known as in-app activities) from a sniffed encrypted Internet traffic stream. One of the challenges is that there are countless applications, and it is practically impossible to collect and train a DNN model using all possible data from them. Therefore, in this work, we exploit the probability distribution of a DNN output layer to filter the data from applications that are not considered during the model training (i.e., unknown data). The proposed framework uses a time window-based approach to divide the traffic flow of activity into segments so that in-app activities can be identified just by observing only a fraction of the activity-related traffic. Our tests have shown that the DNN-based framework has demonstrated an accuracy of 90% or above in identifying previously trained in-app activities and an average accuracy of 79% in identifying previously untrained in-app activity traffic as unknown data when this framework is employed.

Preliminary Investigation of Shift, a Novel Smartphone App to Support Junior Doctors' Mental Health and Well-being: Examination of Symptom Progression, Usability, and Acceptability After 1 Month of Use.

BackgroundShift is a novel smartphone app for providing a digital-first mental health resource to junior doctors. It contains psychoeducational material, cognitive behavioral modules, guided mediations, information on common work stressors, and a section on help-seeking options for psychological problems through workplace and private avenues.ObjectiveThis study aimed to conduct a preliminary investigation of the use and potential effectiveness of Shift on depressive and anxiety symptoms (primary outcomes) and work and social functioning, COVID-19 safety concerns, and help seeking (secondary outcomes). This study also sought feedback on whether Shift was seen as an acceptable tool.MethodsJunior doctors in New South Wales, Australia, were approached through promotional activities from the Ministry of Health, specialist medical colleges, and social media advertisements between June and August 2020. Consenting participants provided web-based baseline data, used the Shift app for 30 days, and were asked to complete a poststudy web-based questionnaire. Outcomes were analyzed under the intention-to-treat principle.ResultsA total of 222 female (n=156, 70.3%; mean age 29.2, SD 4.61 years) junior doctors provided full baseline data. Of these, 89.2% (198/222) downloaded the app, logged into the app approximately 6 times (mean 5.68, SD 7.51), completed 4 in-app activities (mean 3.77, SD 4.36), and spent a total of 1 hour on in-app activities (mean 52:23, SD 6:00:18) over 30 days. Postintervention and app use data were provided by 24.3% (54/222) of participants. Depressive and anxiety symptoms significantly decreased between the pre- and postassessment points as expected; however, physicians’ COVID-19 safety concerns significantly increased. Work and social functioning, COVID-19 concerns for family and friends, and help seeking did not change significantly. There was no significant relationship between symptom changes and app use (number of log-ins, days between first and last log-in, and total activity time). Most poststudy completers (31/54, 57%) rated Shift highly or very highly.ConclusionsDespite high levels of nonresponse to the poststudy assessment and increases in COVID-19 safety concerns, junior doctors who used the app reported some improvements in depression and anxiety, which warrant further exploration in a robust manner.

Abstract P044: A Novel Digital Therapeutic Program Is Feasible For Patients With Peripheral Artery Disease

Introduction: Patients with peripheral artery disease (PAD) are at increased risk for adverse cardiovascular and limb events and impaired quality of life. Lifestyle factors, such as patient physical activity levels and dietary habits, are known risk factors. Limited data is available on the feasibility and efficacy of tailored PAD digital therapeutic programs, focused on improving lifestyle (e.g. physical activity levels, dietary habits, sleep, stress, energy levels) and medication adherence. Hypothesis: We assessed the hypothesis that a digital therapeutic program is feasible among elderly patients with PAD. Methods: Patients were recruited from two vascular surgery specialty clinics in Sweden. Fourteen patients signed informed consent forms and started a four-week intervention based on the first weeks of the SK-111 digital program (Sidekick Health). Questionnaires were administered at baseline and at the end of the intervention. Data on in-app activity was collected throughout the intervention. Results: Out of the 14 patients (8 men, 6 women) who started the intervention, 11 stayed active within the platform from week 1 through 4. The 11 active participants performed a median of 7.1 activities in the app/day (IQR=3.4-12.9) and logged a median number of 4500 steps/day (IQR=2700-6455). The amount of physical activity logged/day went up from a mean of 21 (SD=39) min/day to 33 (SD=54) min/day from week 1 to week 4 (p=0.201). Seven participants (median age=72, IQR=69-73 years) answered both pre and post questionnaires. No changes were observed from pre to post intervention for either the walking distance domain of the Walking Impairment Questionnaire: 22.7 (SD=0.1) and 24.3 (SD=0.1), (p=0.499) or the Vascular Quality of Life Questionnaire sum score: 15.4 (SD=3.2) and 15.6 (SD=3.4), (p=0.864). For the pre-post medication adherence question (“During the last week, did you ever forget to take your PAD medication?” Rated from 0-10), ratings went down from 2.0 (SD=3.6) before to 0.1 (SD=0.4) after intervention (p=0.048). A post-program question asking whether using the platform helped them remember to take their medication, 5/7 (71%) patients considered it helpful. When asked how likely patients would be to recommend the PSP to others, the average score was 83/100. Conclusions: The findings from this feasibility study indicate that a PAD digital therapeutic program is feasible for elderly patients with PAD. A multicenter RCT is currently ongoing, where the clinical effectiveness of the full SK-111 will be further explored in this patient group.

CNN for User Activity Detection Using Encrypted In-App Mobile Data

In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previously trained (known) and previously untrained (unknown) in-app activities as well as the known in-app activity type. The proposed method extracts and selects salient features for encrypted traffic classification. This is the first-known approach proposing to filter unknown traffic with an average accuracy of 88%. Once the unknown traffic is filtered, the classification accuracy of our model would be 92%.

Usage and Weekly Attrition in a Smartphone-Based Health Behavior Intervention for Adolescents: Pilot Randomized Controlled Trial.

BackgroundThe majority of adolescents own smartphones, although only 8% of them use health apps. Attrition rates from adolescent mobile health (mHealth) interventions for treating mental health problems such as anxiety and depression are an issue with a high degree of variation. Attrition in mHealth interventions targeting adolescent populations is frequently presented in a two-point fashion, from initiation of the intervention to the end of treatment, lacking more time-specific information on usage and times of attrition. Self-efficacy could provide an avenue to lower attrition rates, although a better understanding of the relationship between mental health factors and time-specific attrition rates is needed.ObjectiveThe aims of this study were to obtain time-specific attrition rates among adolescents in an mHealth intervention, and to describe the intervention’s usage and feasibility in relation to adolescent self-efficacy levels, and emotional and physical health.MethodsA single-center randomized controlled public school pilot trial was undertaken with 41 adolescents. Outcome measures were assessed at baseline and after 6 weeks, while in-app activity and attrition rates were continually assessed throughout the intervention period. The primary outcome was attrition based on time and type of in-app health behavior usage, and feasibility of the mHealth app. Secondary outcome measures were self-efficacy levels, depressive and anxiety symptoms, as well as standardized BMI and sleep. Analyses of group mean variances with adjusted α levels through Bonferroni corrections were used to assess main outcome effects.ResultsThe attrition from initiation of the intervention to 6-week follow up was 35%. Attrition started in the third week of the intervention and was related to daily time of app usage (Rt=0.43, P<.001). The number of average weekly in-app health exercises completed decreased significantly from the first week of the intervention (mean 55.25, SD 10.96) to the next week (mean 13.63, SD 2.94). However, usage increased by 22% between week 2 and the last week of the intervention (mean 16.69, SD 8.37). Usability measures revealed satisfactory scores (mean 78.09, SD 9.82) without gender differences (P=.85). Self-reported daily physical activity increased by 19.61% in the intervention group but dropped by 26.21% among controls. Self-efficacy levels increased by 8.23% in the invention arm compared to a 3.03% decrease in the control group.ConclusionsThis pilot study demonstrated the feasibility and usability of an mHealth intervention among adolescent participants. Indications were toward beneficial effects on physical and mental health that warrant further research. Focus on time-specific attrition measures alongside daily times of usage and ways to increase participants’ self-efficacy levels appear to be a promising avenue for research on mHealth interventions for adolescent populations with the aim to ultimately lower attrition rates.

The effectiveness of a monetary reimbursement model for weight reduction via a smartphone application: a preliminary retrospective study

Weight loss for obese populations has been a challenging subject. There are numerous mobile applications to address weight loss, but the low retention rate is a barrier for the intervention. This is a retrospective study, aiming to investigate the effectiveness of financial incentives to achieve weight loss via a monetary reimbursement model on a smartphone application. Participants voluntarily purchased a 16-week mobile weight loss application program, and those who logged food intake three times a day received monetary reimbursement up to the full amount they initially paid. We analyzed health-related information and logged in-app activities from participants (N = 2,803) including age, sex, weight, food intake, and physical activity on their mobile healthcare application called Noom from January 2017 to April 2019. Analysis of covariance (ANCOVA) was used to compare differences between groups who succeeded and failed at food logging, controlling for baseline BMI. The ANCOVA found that participants who completed the food logging successfully for 16 weeks (N = 1,565) lost significantly more weight than those who failed food logging (N = 1,238, F = 56.0, p < 0.001). In addition, participants who were able to log their food intake successfully exercised more (F = 41.5, p < 0.001), read more in-app articles (F = 120.7, p < 0.001), and consumed more quantity of healthy foods (F = 12.8, p < 0.001). Monetary reimbursement is an effective tool for weight reduction by encouraging participants to monitor their health-related behaviors regularly.

Computer Science Education in Early Childhood: The Case of ScratchJr

Aim/Purpose: This paper aims to explore whether having state Computer Science standards in place will increase young children’s exposure to coding and powerful ideas from computer science in the early years. Background: Computer science education in the K-2 educational segment is receiving a growing amount of attention as national and state educational frameworks are emerging. By focusing on the app ScratchJr, the most popular free introductory block-based programming language for early childhood, this paper explores if there is a relationship between the presence of state frameworks and ScratchJr’s frequency of use. Methodology: This paper analyzes quantitative non-identifying data from Google Analytics on users of the ScratchJr programming app. Google Analytics is a free tool that allows access to user activity as it happens in real time on the app, as well as audience demographics and behavior. An analysis of trends by state, time of year, type of in-app activities completed, and more are analyzed with a specific focus on comparing states with K-12 Computer Science in place versus those without. Contribution: Results demonstrate the importance of having state standards in place to increase young children’s exposure to coding and powerful ideas from computer science in the early years. Moreover, we see preliminary evidence that states with Computer Science standards in place support skills like perseverance and debugging through ScratchJr. Findings: Findings show that in the case of ScratchJr, app usage decreases during the summer months and on weekends, which may indicate that coding with ScratchJr is more often happening in school than at home. Results also show that states with Computer Science standards have more ScratchJr users on average and have more total sessions with the app on average. Results also show preliminary evidence that states with Computer Science standards in place have longer average session duration as well as a higher average number of users returning to edit an existing project. Recommendations for Practitioners: Successful early childhood computer science education programs must teach powerful ideas from the discipline of computer science in a developmentally appropriate way, provide means for self-expression, prompt debugging and problem solving, and offer a low-floor/high-ceiling interface for both novices and experts. Practitioners should be aware in drops in computer science learning during the summer months when school is not in session. Recommendation for Researchers: Researchers should consider the impact of state and national frameworks on computer science learning and skills mastered during the early childhood years. Researchers should look for ways to continue engaging students in computer science education during times when school is not in session. Impact on Society: Results demonstrate the importance of having state CS standards in place to increase young children’s exposure to coding and powerful ideas from computer science in the early years. Moreover, we see preliminary evidence that states with Computer Science standards in place support skills like perseverance and debugging through ScratchJr. Future Research: Future research should continue collecting Google Analytics from the ScratchJr app and track changes in usage. Future research should also collect analytics from a wide range of programming applications for young children to see if the trends identified here are consistent across different apps.

A Comprehensive Digital Program for Smoking Cessation: Assessing Feasibility in a Single-Group Cohort Study.

BackgroundCigarette smoking remains the leading cause of preventable death and disease worldwide. Evidence-based approaches are available, but few people access them. Although digital solutions offer great promise for population reach, few multicomponent programs exist. Pivot is a comprehensive digital solution combining a Food and Drug Administration–cleared carbon monoxide (CO) breath sensor; cigarette logging; a 6-phase, app-delivered smoking cessation program based on the US Clinical Practice Guidelines; and dedicated human coaching via text-based chat.ObjectiveThe purpose of this study was to assess program engagement, changes in attitudes toward smoking, self-reported changes in smoking behavior, and program acceptability for the initial phase of Pivot: Explore.MethodsA total of 48 participants enrolled, and 41 completed the study. About half the participants (54%, 22/41) were men, and the mean age was 43 years. Most (85%, 35/41) were daily smokers and smoked an average of 12 cigarettes per day. Explore includes CO breath sensing, logging cigarettes in-app, learning via in-app activities, and dedicated human coaching through a text messaging interface. Participants completed surveys at baseline and exit assessing attitudes toward quitting including readiness, perceived difficulty, and confidence in quit success. At exit, participants also completed a survey of changes in smoking behavior and ratings of program acceptability.ResultsMore than 80% of participants (34-39 of 41) took ≥1 CO breath sample each day, and more than 55% (23-27 of 41) took ≥5 samples each day. More than 65% of participants (27-34 of 41) logged ≥1 cigarette using the in-app logging feature each day. All 9 in-app activities had completion rates ≥80% (33-40 of 41). Response to coach-initiated outreach was also high, with all contacts receiving ≥73% (30-39 of 41) response. In matched pair analyses, significant positive changes in mean attitudes toward quitting (scale 1-10) were evident from baseline (T1) to study exit (T2), including increased readiness to quit (T1 mean=6.1, T2 mean=7.4, P=.005), lower perceived difficulty (T1 mean=3.7, T2 mean=5.6, P=.001), and greater expectations of success (T1 mean=4.5, T2 mean=6.5, P<.001). At exit, 78% (32/41) of participants reported decreasing the number of cigarettes smoked per day during the study. Participants rated program quality and satisfaction very high (mean ≥8 for all items).ConclusionsThese results support the feasibility and acceptability of the initial 9-day phase of Pivot: Explore. Participants had high levels of engagement with sensing, logging, learning, and coaching. Attitudes toward quitting improved significantly, and the majority of users indicated decreasing smoking behavior. Explore was designed to raise smoker awareness and motivation. Additional research is underway to assess how users progress through the full Pivot smoking cessation program and determine the program’s effectiveness for achieving sustained cessation.