Feature-level analysis of a smoking cessation smartphone app that uses a positive psychology approach (Preprint)

Transformative impact of positive psychotherapy-based hope placement program on life goals and hope level of individuals with multiple sclerosis

AIM

This study aimed to assess the impact of a positive psychotherapy-based hope placement program on life goal setting and hope levels in individuals diagnosed with multiple sclerosis (MS).

METHOD

A Solomon Four-Group experimental design was used, involving 50 individuals diagnosed with MS who sought treatment at a university hospital's neurology outpatient clinic. Experimental (1-2) and control (1-2) groups were randomized, and pretests were administered exclusively to the experimental and control groups. Data were collected using a Personal Information Form, the Herth Hope Scale, and the Scale for Determining Life Goals in the Context of Positive Psychotherapy. A 5-week/10-session Positive Psychotherapy-Based Hope Placement Program was conducted online for the Experimental groups. The research was conducted in the form of group therapy. Post-tests were administered to all groups upon program completion.

RESULTS

The experimental 1-2 group exhibited statistically significant improvements in the Scale for Identifying Life Goals in the Context of Positive Psychotherapy and Herth Hope Scale scores (p < .05). The Positive Psychotherapy-Based Hope Placement Program effectively enhances hope levels and life goal setting for individuals diagnosed with MS.

CONCLUSION

The results indicate that the program increased the sense of purpose in life and elevated levels of hope among MS patients. These findings suggest that positive psychotherapy-based interventions significantly improve the quality of life for MS patients.

CLINICAL OR METHODOLOGICAL SIGNIFICANCE OF THIS ARTICLE

This study examines the impact of a Positive Psychotherapy-based hope placement program on determining hope and life purpose in patients with Multiple Sclerosis (MS). This research shows that HOPP (Hope Placement Program), an intervention based on Positive Psychotherapy, can significantly improve the quality of life of MS patients. These findings support the consideration of Positive Psychotherapy-based approaches as a potential therapeutic option that can positively influence the life experiences of MS patients. Therefore, this article makes an important contribution to researchers interested in using Positive Psychotherapy-based interventions in clinical practice and related research.

Evaluation of effectiveness and acceptability of a psychological treatment for smoking cessation combined with a smartphone App: A pilot study

Despite the increasing number of mobile-based interventions to quit smoking over the last years, few studies have investigated the efficacy of smoking cessation interventions blended with smartphone Apps. The present pilot study aims to examine the preliminary effectiveness and acceptability of a cognitive-behavioral treatment combined with a smartphone App, compared to the same psychological treatment without the App. The sample comprised 206 treatment-seeking smokers, who were assigned to: 1) an experimental group receiving a cognitive-behavioral intervention combined with the "Non Fumo" App (n = 102), and 2) a control group receiving only the cognitive-behavioral intervention to quit smoking (n = 104). Results concerning the primary outcomes showed no significant differences between conditions in point-prevalence abstinence rates at 12-month follow-up (35.30 % in the experimental group vs. 31.70 % in the control group) and in treatment acceptability. Regarding the secondary outcomes, both groups obtained similar point-prevalence abstinence rates at the end of treatment (61.80 % vs. 65.40 %), at 3-month (42.20 % vs. 45.20 %, respectively) and 6-month follow-ups (37.30 % vs. 37.50 %). No significant differences were found between conditions in prolonged abstinence rates at 6-month (35.3 % vs. 35.6 %) and 12-month follow-ups (30.4 % vs. 26.9 %). Overall, good abstinence rates and treatment acceptability were obtained, although there were no significant differences between conditions. More research is needed to establish clear conclusions about the efficacy of psychological smoking cessation treatments blended with smartphone Apps.

Identifying Patterns of Smoking Cessation App Feature Use That Predict Successful Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning

BACKGROUND

Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness.

OBJECTIVE

We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors).

METHODS

Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the National Cancer Institute's quitSTART app. Participants' (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants' probability of cessation from 28 variables reflecting participants' use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals' SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation.

RESULTS

The SML model's sensitivity (0.67) and specificity (0.67) in the held-aside test set indicated that individuals' patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model-predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16).

CONCLUSIONS

Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736.

Effectiveness of the QuitSure Smartphone App for Smoking Cessation: Findings of a Prospective Single Arm Trial

BACKGROUND

Digital therapies, especially smartphone apps for active and continuous smoking cessation support, are strongly emerging as an alternative smoking cessation therapy. In the Indian context, there is a growing interest in the use of app-based smoking cessation programs; however, there is limited evidence regarding their effectiveness in achieving long-term continuous abstinence.

OBJECTIVE

This study aimed to evaluate the long-term abstinence effect (up to 30-d abstinence postprogram completion) of a smartphone app, QuitSure, for smoking cessation in active smokers from India.

METHODS

In this prospective single-arm study, participants who signed up for the QuitSure app were enrolled in this study. The primary end point was the prolonged abstinence (PA) rate from weeks 1 to 4 (day 7 to day 30). Furthermore, data for withdrawal symptoms, relapse reasons, and reasons for not continuing the program were also assessed.

RESULTS

The quit rate was calculated considering only the participants who followed up and completed the survey sent to them (per protocol) at day 7 and at day 30, respectively. The PA rate at day 7 was found to be 64.5% (111/172; 95% CI 56% to 72%), and the PA rate at day 30 was found to be 55.8% (72/129; 95% CI 45% to 65%). Within the 7-day abstinence period, 60.4% (67/111) of the participants did not have any withdrawal symptoms. The most common mild withdrawal symptoms were mild sleep disturbance (21/111, 18.9%), mild digestive changes (19/111, 17.1%), and coughing (17/111, 15.3%). Severe withdrawal symptoms were rare, with only 5.4% (6/111) experiencing them. For those achieving 30-day postprogram abstinence, 85% (61/72) had no mild withdrawal symptoms, and 99% (71/72) had no severe withdrawal symptoms. Among successful quitters at day 7, a total of 72.1% (80/111) reported minimal to no cravings, which increased to 88% (63/72) at day 30. Furthermore, 78% (56/72) of those with PA at day 30 reported no change in weight or reduced weight. Among participants experiencing relapse, 48% (28/58) cited intense cravings, 28% (16/58) mentioned facing a tragedy, and 26% (15/58) reported relapsing due to alcohol consumption. The PA rates as a result of the QuitSure program were found to be better than those reported in the results of other smoking-cessation app programs' studies.

CONCLUSIONS

The QuitSure app yields high PA rates and ameliorates symptoms associated with smoking cessation. In order to obtain conclusive evidence regarding the effectiveness and efficacy of the QuitSure program, future research should include appropriate control measures. Nevertheless, the QuitSure program can serve as a valuable adjunct to a conventional smoking cessation treatment program to aid sustained abstinence.

Use of the Therapy App Prescinde for Increasing Adherence to Smoking Cessation Treatment

Tobacco use poses major health risks and is a major contributor to causes of death worldwide. Mobile phone-based cessation apps for this substance are gaining popularity, often used as a component of traditional interventions. This study aimed to analyze adherence to an intervention using a mobile phone application (App-therapy Prescinde (v1)) as a function of sociodemographic variables (age, gender, educational level, and profession) as well as the primary activities supported by the app (reducing tobacco or cannabis use and increasing physical exercise). The participants were recruited through the web pages of the Occupational Risk Prevention Service and the Psychology Clinic of the University of Granada during the COVID-19 confinement period. The application's contents include three components (self-report, motivational phrases, and goal setting). Our findings indicate that being male, being aged between 26 and 62, having a high school education, and being unemployed increase the likelihood of adherence to the Prescinde therapy app three months after usage. Our findings highlight the importance of developing new therapeutic approaches and conducting in-depth studies on the factors associated with adherence to tobacco cessation and cannabis cessation treatments via mobile phone applications.

The effectiveness of an intervention to reduce exposure to trace metals during or prior to pregnancy: A prospective study in urban and rural locations

Background

Prenatal exposure to trace metals can have adverse effects on health and increase the risk of developing certain diseases. This study aimed to determine the effectiveness of giving women advice to reduce their exposure to trace metals during pregnancy or prior to conception. The study also examined differences in exposure between rural and urban environments in southern France.

Methods

In this prospective study, pregnant women or those intending to conceive were recruited from two medical centers for gynecology/obstetrics (rural location: Saint-Rémy-de-Provence; urban location: Marseille). Hair samples were collected and analyzed to determine the levels of exposure to trace metals. Participants with 'risky' levels were given corresponding advice sheets on how to reduce their exposure or, for certain metals, they were encouraged to find out about potential sources of exposure. A second hair sample was collected and analyzed 3 months later.

Results

It was found that 109 women had 'risky' levels of exposure to trace metals, out of a total of 184 women (59.2 %). Cerium was the most frequently identified metal (N = 26), followed by nickel (N = 23), and titanium (N = 19). There were more women at the urban center with 'risky' levels (56/86; 65.1 %) than at the rural center (53/98; 54.1 %), but this difference was not statistically significant (p = 0.13). Advice sheets were given to 64 of the 109 participants with 'risky' levels (58.7 %), but only 21 returned for the second hair analysis. Of these, 14 were found to have reduced their exposure, which corresponds to just 12.8 % (14/109) of the participants with 'risky' levels.

Conclusions

These results indicate that it would be helpful to develop new interventions to reduce trace metal exposure during or prior to pregnancy.

Use of the Smoking Cessation App Ex-Smokers iCoach and Associations With Smoking-Related Outcomes Over Time in a Large Sample of European Smokers: Retrospective Observational Study

BACKGROUND

Digital interventions are increasingly used to support smoking cessation. Ex-smokers iCoach was a widely available app for smoking cessation used by 404,551 European smokers between June 15, 2011, and June 21, 2013. This provides a unique opportunity to investigate the uptake of a freely available digital smoking cessation intervention and its effects on smoking-related outcomes.

OBJECTIVE

We aimed to investigate whether there were distinct trajectories of iCoach use, examine which baseline characteristics were associated with user groups (based on the intensity of use), and assess if and how these groups were associated with smoking-related outcomes.

METHODS

Analyses were performed using data from iCoach users registered between June 15, 2011, and June 21, 2013. Smoking-related data were collected at baseline and every 3 months thereafter, with a maximum of 8 follow-ups. First, group-based modeling was applied to detect distinct trajectories of app use. This was performed in a subset of steady users who had completed at least 1 follow-up measurement. Second, ordinal logistic regression was used to assess the baseline characteristics that were associated with user group membership. Finally, generalized estimating equations were used to examine the association between the user groups and smoking status, quitting stage, and self-efficacy over time.

RESULTS

Of the 311,567 iCoach users, a subset of 26,785 (8.6%) steady iCoach users were identified and categorized into 4 distinct user groups: low (n=17,422, 65.04%), mild (n=4088, 15.26%), moderate (n=4415, 16.48%), and intensive (n=860, 3.21%) users. Older users and users who found it important to quit smoking had higher odds of more intensive app use, whereas men, employed users, heavy smokers, and users with higher self-efficacy scores had lower odds of more intensive app use. User groups were significantly associated with subsequent smoking status, quitting stage, and self-efficacy over time. For all groups, over time, the probability of being a smoker decreased, whereas the probability of being in an improved quitting stage increased, as did the self-efficacy to quit smoking. For all outcomes, the greatest change was observed between baseline and the first follow-up at 3 months. In the intensive user group, the greatest change was seen between baseline and the 9-month follow-up, with the observed change declining gradually in moderate, mild, and low users.

CONCLUSIONS

In the subset of steady iCoach users, more intensive app use was associated with higher smoking cessation rates, increased quitting stage, and higher self-efficacy to quit smoking over time. These users seemed to benefit most from the app in the first 3 months of use. Women and older users were more likely to use the app more intensively. Additionally, users who found quitting difficult used the iCoach app more intensively and grew more confident in their ability to quit over time.