Internet-based self-help smoking cessation and alcohol moderation interventions for cancer survivors: a study protocol of two RCTs

Mujcic, Ajla; Blankers, Matthijs; Boon, Brigitte; Engels, Rutger C. M. E.; Laar, Margriet van

doi:10.1186/s12885-018-4206-z

Cited by 17 publications

(23 citation statements)

References 73 publications

(88 reference statements)

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“…We aimed to explore cancer survivors’ views on AM and SC, their preferences regarding digital support for AM and SC, and experts’ recommendations for successful AM and SC interventions for cancer survivors. This information was used to inform the development of SC and AM digital interventions [ 15 ], considering that digital interventions may provide a low threshold for seeking help and reach a large population and that they have shown their effectiveness in the general population [ 6 , 7 ]. Our findings are also largely in line with findings from previous qualitative studies on health behaviour change and SC.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we aimed to explore the preconditions for successful digital alcohol and tobacco interventions for cancer survivors. These findings were collected and used to inform the development of digital interventions MyCourse – Quit Smoking and MyCourse – Moderate Drinking, both specifically designed for cancer survivors [ 15 ]. This study explored the following research questions: What are cancer survivors’ views on SC and AM?…”

Section: Introductionmentioning

confidence: 99%

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Cancer survivors’ views on digital support for smoking cessation and alcohol moderation: a survey and qualitative study

et al. 2021

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Background Digital interventions may provide low-threshold support for smoking cessation (SC) and alcohol moderation (AM) to the growing population of cancer survivors. The objective was to explore preconditions of successful AM and SC digital interventions for cancer survivors. Methods Using a multi-method approach we conducted a survey (n = 240), a qualitative study consisting of four focus groups (n = 15) and semi-structured interviews with Dutch cancer survivors (n = 8). To help interpretation of our results we interviewed experts in the field of eHealth and cancer survivors (n = 6) and we organized an expert meeting (n = 7). Qualitative data were analysed using the Framework approach and were double-coded by two coders. Results Survey results show the majority of drinkers had not previously considered AM (n = 158, 84.9%), often because they deemed their alcohol use to be non-problematic. All current smokers in the survey had considered SC before. In focus groups and interviews it became clear that SC efforts did not always stem from their own willingness to quit smoking, but originated from a wish to please their social environment. Main themes to be addressed in digital SC and AM that emerged from the interviews and focus groups, centred on the different ways of identification as cancer survivors, need for autonomy, differential beliefs about SC and AM, and the importance of a positive, non-patronizing tone-of-voice. Several specific preferences for digital interventions were formulated, although some cancer survivors prefer no support or face-to-face contact. Conclusions Cancer survivors are a diverse group with diverse preferences for AM and SC support. Digital AM and SC interventions for cancer survivors are perceived to be of value by some, especially when they incorporate a positive, non-judgemental and non-patronizing tone-of-voice, address concerns specifically relevant to cancer survivors, offer possibilities for personalization, and emphasize autonomy throughout. To encourage AM specifically, problem recognition and awareness of the health benefits of AM should be improved.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cancer survivors’ views on digital support for smoking cessation and alcohol moderation: a survey and qualitative study

et al. 2021

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“…Alcohol, tobacco, and other drugs (ATOD) use are among the leading risk factors for morbidity and mortality worldwide ( Degenhardt et al, 2013 ; Shield et al, 2016 ; Volkow and Boyle, 2018 ) and can be a major cause of negative social, economic, and medical effects ( Degenhardt and Hall, 2012 ). Digital self-help interventions for ATOD use have been broadly explored as a tool to help mitigate substance use and related harm, often with positive results ( Riper et al, 2008 ; Tait et al, 2014 ; Mujcic et al, 2018 ; Berman et al, 2019 ; Olthof et al, 2021 ). Nonetheless, participant adherence remains a major issue in digital interventions for mental disorders, either due to not using the intervention (non-adherence) or due to not completing follow-up measures (study dropout) ( Khadjesari et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“…Cognitive Behavioral Therapy (CBT) and motivational interviewing (MI) based digital self-help interventions have been developed to help treat people suffering from diverse conditions including problem drinking ( Riper et al, 2008 ; Mujcic et al, 2020 ), tobacco smoking ( Mujcic et al, 2018 ), and cannabis ( Olthof et al, 2021 ). Some central elements from CBT self-help interventions are: exploring and exploiting ambiguity regarding behavior change, stimulus control, stress management, social support, goal setting and pursuit through monitoring and exercises ( Foreyt and Poston, 1998 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

et al. 2021

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BackgroundDigital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant’s goal achievement.MethodsWe included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy.ResultsFrom the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69–0.73) and (0.71 95%CI 0.67–0.76), respectively, followed by cannabis (0.67 95%CI 0.59–0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success.DiscussionUsing log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention.

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“…ACT's efficacy has been studied in several conditions, namely anxiety, depression and chronic pain and its application to the cancer setting is increasing ( Hulbert-Williams et al, 2015 ). More recently, ACT-based internet-interventions are emerging and some positive preliminary results have been published ( Low et al, 2016 ; Köhle et al, 2015 ; Arch and Mitchell, 2016 ; Mujcic et al, 2018 ; Köhle et al, 2017 ). Nevertheless, stronger evidence of efficacy and cost-effectiveness is needed.…”

Section: Introductionmentioning

confidence: 99%

A guided internet-delivered individually-tailored ACT-influenced cognitive behavioural intervention to improve psychosocial outcomes in breast cancer survivors (iNNOVBC): Study protocol

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Santana

et al. 2019

Internet Interventions

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BackgroundInternet-delivered interventions can provide remarkable opportunities in addressing breast cancer survivors' unmet support care needs, as they present an effective strategy to improve care coordination and provide access to efficacious, cost-efficient and convenient survivorship care. Nevertheless, research focusing on improving survivors' psychosocial needs using internet-based tools is scarce and its practical implementation is limited.ObjectivesTo study the acceptability, feasibility, efficacy and cost-effectiveness of iNNOVBC, a 10 weeks guided internet-delivered individually-tailored Acceptance and Commitment Therapy (ACT)-influenced cognitive behavioural (CBT) intervention developed to improve mild to moderate anxiety and depression in Breast cancer survivors when compared to treatment as usual (TAU) in a waiting list control group (WLC).MethodsA two-arm, parallel, open label, multicentre, waiting list randomized controlled trial will be conducted to investigate the efficacy and cost-effectiveness of INNOVBC. The primary outcomes in this research will be anxiety and depression. Secondary outcomes will include psychological flexibility, fatigue, insomnia, sexual dysfunction and Health Related Quality of Life (HRQoL).Ethical approvalThis study has been reviewed and approved by Comissão Nacional de Proteção de Dados; Instituto Português de Oncologia do Porto Francisco Gentil; Unidade Local de Saúde de Matosinhos, EPE; Centro Hospitalar de São João and Ordem dos Psicólogos ethical committees.Expected resultsIt is anticipated that iNNOVBC will show to be an efficacious and cost-effective program in improving the outcomes of interest in this study, as opposed to a WLC under TAU. The results of this research will be published in accordance with CONSORT-EHEALTH guidelines.ConclusionsThis study will inform on the acceptability, feasibility, efficacy and cost-effectiveness of iNNOVBC, in improving psychosocial outcomes in breast cancer survivors when compared to TAU in a WLC. Its conclusions will contribute to understand the idiosyncrasies of designing and implementing internet-delivered interventions in breast cancer survivors.Trial Registration code: INNOVBC (NCT03275727).

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Internet-based self-help smoking cessation and alcohol moderation interventions for cancer survivors: a study protocol of two RCTs

Cited by 17 publications

References 73 publications

Cancer survivors’ views on digital support for smoking cessation and alcohol moderation: a survey and qualitative study

Cancer survivors’ views on digital support for smoking cessation and alcohol moderation: a survey and qualitative study

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

A guided internet-delivered individually-tailored ACT-influenced cognitive behavioural intervention to improve psychosocial outcomes in breast cancer survivors (iNNOVBC): Study protocol

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