WB
Walter Baccinelli
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8 records found
1
Journal article
(2026)
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Milon van Vliet, Eline Meijer, N. Albers, Kristell M. Penfornis, Walter Baccinelli, Bouke Scheltinga, Roxy van Eersel, Niels H. Chavannes, Anke Versluis
Background: Mobile health (mHealth) interventions with virtual coaches offer scalable and potentially cost-effective solutions for health behavior change. However, these interventions commonly present challenges, such as limited personalization and insufficient grounding in evidence-based strategies. Perfect Fit (PF; Perfect Fit consortium), a personalized mHealth intervention with a text-based virtual coach, supports adults in quitting smoking and becoming more physically active. By combining innovative techniques, including sensor technology, end user involvement, and evidence-based strategies, PF aims to address common challenges faced by mHealth interventions, including those with virtual coaches. Objective: The study primarily investigated the feasibility and acceptability of PF. The secondary aim was to explore associations between sociodemographic, smoking-, and physical activity–related characteristics and the feasibility and acceptability outcomes. The third aim was to evaluate the feasibility of conducting the research study. Methods: A single-arm, pre-post, mixed methods study was conducted in the Netherlands with 100 adults who smoked. The intervention lasted approximately 16 weeks. Data were collected at baseline, during the intervention, and postintervention (4 months). Quantitative data included usage data and self-report questionnaires on feasibility, acceptability, and baseline characteristics. Qualitative data were gathered through postintervention semistructured interviews. Analyses included descriptive and inferential analyses, as well as the framework approach for the qualitative data. Results: PF usage varied considerably across participants (n=87). The mean satisfaction rating was 2.79 (SD 0.73; scale range 1‐4), and perceived usability had a median score of 67.50 (range 12.50‐87.50; scoring range 0‐100), indicating OK-to-good usability. The mean virtual coach acceptance rating was –0.27 (SD 1.30; scale range −3 to 3; n=77). Higher PF usage was associated with greater satisfaction, usability, and coach acceptance (all P≤.004). Frequent connection issues with the smartwatch were a disruptive factor. Qualitative findings (n=12) provided in-depth insights into PF’s feasibility and acceptability, encompassing both positive and negative experiences. For instance, some participants valued the virtual coach for its anonymity, low-threshold access, and the sense of control it offered, while others preferred a human coach for greater accountability. Suggested improvements included more varied content and enhanced adaptability of the coach to users’ input and personal situations. Exploratory analyses suggested that high PF users were older than moderate (P=.01) and low PF users (P=.05). Importantly, PF was perceived as similarly feasible and acceptable across socioeconomic groups (P>.05), aligning with one of the project’s goals. Finally, research procedures and recruitment strategies proved feasible. Conclusions: PF shows potential as an accessible and inclusive strategy for multiple health behavior changes, contributing to public health. Findings highlight areas for improvement and can guide the future development of virtual coach interventions.
...
Background: Mobile health (mHealth) interventions with virtual coaches offer scalable and potentially cost-effective solutions for health behavior change. However, these interventions commonly present challenges, such as limited personalization and insufficient grounding in evidence-based strategies. Perfect Fit (PF; Perfect Fit consortium), a personalized mHealth intervention with a text-based virtual coach, supports adults in quitting smoking and becoming more physically active. By combining innovative techniques, including sensor technology, end user involvement, and evidence-based strategies, PF aims to address common challenges faced by mHealth interventions, including those with virtual coaches. Objective: The study primarily investigated the feasibility and acceptability of PF. The secondary aim was to explore associations between sociodemographic, smoking-, and physical activity–related characteristics and the feasibility and acceptability outcomes. The third aim was to evaluate the feasibility of conducting the research study. Methods: A single-arm, pre-post, mixed methods study was conducted in the Netherlands with 100 adults who smoked. The intervention lasted approximately 16 weeks. Data were collected at baseline, during the intervention, and postintervention (4 months). Quantitative data included usage data and self-report questionnaires on feasibility, acceptability, and baseline characteristics. Qualitative data were gathered through postintervention semistructured interviews. Analyses included descriptive and inferential analyses, as well as the framework approach for the qualitative data. Results: PF usage varied considerably across participants (n=87). The mean satisfaction rating was 2.79 (SD 0.73; scale range 1‐4), and perceived usability had a median score of 67.50 (range 12.50‐87.50; scoring range 0‐100), indicating OK-to-good usability. The mean virtual coach acceptance rating was –0.27 (SD 1.30; scale range −3 to 3; n=77). Higher PF usage was associated with greater satisfaction, usability, and coach acceptance (all P≤.004). Frequent connection issues with the smartwatch were a disruptive factor. Qualitative findings (n=12) provided in-depth insights into PF’s feasibility and acceptability, encompassing both positive and negative experiences. For instance, some participants valued the virtual coach for its anonymity, low-threshold access, and the sense of control it offered, while others preferred a human coach for greater accountability. Suggested improvements included more varied content and enhanced adaptability of the coach to users’ input and personal situations. Exploratory analyses suggested that high PF users were older than moderate (P=.01) and low PF users (P=.05). Importantly, PF was perceived as similarly feasible and acceptable across socioeconomic groups (P>.05), aligning with one of the project’s goals. Finally, research procedures and recruitment strategies proved feasible. Conclusions: PF shows potential as an accessible and inclusive strategy for multiple health behavior changes, contributing to public health. Findings highlight areas for improvement and can guide the future development of virtual coach interventions.
Journal article
(2026)
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Milon H.M. van Vliet, Anke Versluis, Kristell M. Penfornis, Walter Baccinelli, Bouke Scheltinga, N. Albers, Niels H. Chavannes, Ralph C. A. Rippe, Eline Meijer
Objectives
Virtual coaches can support smoking cessation and physical activity (PA) promotion. Smoking and low PA often co-occur, and changes in one behavior may reinforce the other. Interventions targeting both behaviors are therefore promising. Perfect Fit (PF) is a smartphone-based virtual coach providing personalized support for smoking cessation and PA promotion. This study evaluates PF’s two-month preliminary effectiveness and explores associations of outcomes with baseline, psychosocial, and usage characteristics.
Methods
Single-arm, convergent mixed-methods design. Eligible adults smoked daily, intended to quit within six weeks, had B1-level Dutch, and a smartphone. Quantitative data were collected at baseline, post-intervention (16 weeks), and two-month follow-up (24 weeks), including smoking cessation, PA, and psychosocial factors (e.g., self-identity). Semi-structured interviews were held post-intervention. Quantitative analysis included descriptive statistics and (generalized) linear mixed models. Qualitative analysis followed the Framework Approach.
Results
Of 100 participants, 77 completed post-intervention and 78 follow-up. Seven-day point prevalence abstinence was 36.4% at post-intervention and 35.4% at follow-up. Median self-reported PA more than doubled post-intervention and remained above baseline at follow-up. Increased PA self-efficacy was significantly associated with higher PA. Descriptive patterns suggested that lower socioeconomic position and increased smoking abstinence self-efficacy were associated with abstinence (non-significant). Non-smoker and PA self-identity were minimally associated with outcomes. Qualitative findings (n = 12) indicated participants valued PF’s multi-behavior focus and preferred brief, actionable support during high-risk moments.
Conclusions
PF shows promise as an accessible, scalable, multi-behavior virtual coach supporting smoking cessation and PA promotion. Findings guide refinement of PF and future virtual coach interventions. ...
Virtual coaches can support smoking cessation and physical activity (PA) promotion. Smoking and low PA often co-occur, and changes in one behavior may reinforce the other. Interventions targeting both behaviors are therefore promising. Perfect Fit (PF) is a smartphone-based virtual coach providing personalized support for smoking cessation and PA promotion. This study evaluates PF’s two-month preliminary effectiveness and explores associations of outcomes with baseline, psychosocial, and usage characteristics.
Methods
Single-arm, convergent mixed-methods design. Eligible adults smoked daily, intended to quit within six weeks, had B1-level Dutch, and a smartphone. Quantitative data were collected at baseline, post-intervention (16 weeks), and two-month follow-up (24 weeks), including smoking cessation, PA, and psychosocial factors (e.g., self-identity). Semi-structured interviews were held post-intervention. Quantitative analysis included descriptive statistics and (generalized) linear mixed models. Qualitative analysis followed the Framework Approach.
Results
Of 100 participants, 77 completed post-intervention and 78 follow-up. Seven-day point prevalence abstinence was 36.4% at post-intervention and 35.4% at follow-up. Median self-reported PA more than doubled post-intervention and remained above baseline at follow-up. Increased PA self-efficacy was significantly associated with higher PA. Descriptive patterns suggested that lower socioeconomic position and increased smoking abstinence self-efficacy were associated with abstinence (non-significant). Non-smoker and PA self-identity were minimally associated with outcomes. Qualitative findings (n = 12) indicated participants valued PF’s multi-behavior focus and preferred brief, actionable support during high-risk moments.
Conclusions
PF shows promise as an accessible, scalable, multi-behavior virtual coach supporting smoking cessation and PA promotion. Findings guide refinement of PF and future virtual coach interventions. ...
Objectives
Virtual coaches can support smoking cessation and physical activity (PA) promotion. Smoking and low PA often co-occur, and changes in one behavior may reinforce the other. Interventions targeting both behaviors are therefore promising. Perfect Fit (PF) is a smartphone-based virtual coach providing personalized support for smoking cessation and PA promotion. This study evaluates PF’s two-month preliminary effectiveness and explores associations of outcomes with baseline, psychosocial, and usage characteristics.
Methods
Single-arm, convergent mixed-methods design. Eligible adults smoked daily, intended to quit within six weeks, had B1-level Dutch, and a smartphone. Quantitative data were collected at baseline, post-intervention (16 weeks), and two-month follow-up (24 weeks), including smoking cessation, PA, and psychosocial factors (e.g., self-identity). Semi-structured interviews were held post-intervention. Quantitative analysis included descriptive statistics and (generalized) linear mixed models. Qualitative analysis followed the Framework Approach.
Results
Of 100 participants, 77 completed post-intervention and 78 follow-up. Seven-day point prevalence abstinence was 36.4% at post-intervention and 35.4% at follow-up. Median self-reported PA more than doubled post-intervention and remained above baseline at follow-up. Increased PA self-efficacy was significantly associated with higher PA. Descriptive patterns suggested that lower socioeconomic position and increased smoking abstinence self-efficacy were associated with abstinence (non-significant). Non-smoker and PA self-identity were minimally associated with outcomes. Qualitative findings (n = 12) indicated participants valued PF’s multi-behavior focus and preferred brief, actionable support during high-risk moments.
Conclusions
PF shows promise as an accessible, scalable, multi-behavior virtual coach supporting smoking cessation and PA promotion. Findings guide refinement of PF and future virtual coach interventions.
Virtual coaches can support smoking cessation and physical activity (PA) promotion. Smoking and low PA often co-occur, and changes in one behavior may reinforce the other. Interventions targeting both behaviors are therefore promising. Perfect Fit (PF) is a smartphone-based virtual coach providing personalized support for smoking cessation and PA promotion. This study evaluates PF’s two-month preliminary effectiveness and explores associations of outcomes with baseline, psychosocial, and usage characteristics.
Methods
Single-arm, convergent mixed-methods design. Eligible adults smoked daily, intended to quit within six weeks, had B1-level Dutch, and a smartphone. Quantitative data were collected at baseline, post-intervention (16 weeks), and two-month follow-up (24 weeks), including smoking cessation, PA, and psychosocial factors (e.g., self-identity). Semi-structured interviews were held post-intervention. Quantitative analysis included descriptive statistics and (generalized) linear mixed models. Qualitative analysis followed the Framework Approach.
Results
Of 100 participants, 77 completed post-intervention and 78 follow-up. Seven-day point prevalence abstinence was 36.4% at post-intervention and 35.4% at follow-up. Median self-reported PA more than doubled post-intervention and remained above baseline at follow-up. Increased PA self-efficacy was significantly associated with higher PA. Descriptive patterns suggested that lower socioeconomic position and increased smoking abstinence self-efficacy were associated with abstinence (non-significant). Non-smoker and PA self-identity were minimally associated with outcomes. Qualitative findings (n = 12) indicated participants valued PF’s multi-behavior focus and preferred brief, actionable support during high-risk moments.
Conclusions
PF shows promise as an accessible, scalable, multi-behavior virtual coach supporting smoking cessation and PA promotion. Findings guide refinement of PF and future virtual coach interventions.
Abstract
(2025)
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Milon van Vliet, Anke Versluis, Niels H. Chavannes, Bouke Scheltinga, N. Albers, Kristell M. Penfornis, Walter Baccinelli, Eline Meijer
Journal article
(2024)
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Milon van Vliet, Anke Versluis, Niels H. Chavannes, Bouke Scheltinga, N. Albers, Kristell M. Penfornis, Walter Baccinelli, Eline Meijer
Journal article
(2024)
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Milon van Vliet, Anke Versluis, Niels H. Chavannes, Bouke Scheltinga, N. Albers, Kristell M. Penfornis, Walter Baccinelli, Eline Meijer
Objective
Adopting healthy behavior is vital for preventing chronic diseases. Mobile health (mHealth) interventions utilizing virtual coaches (i.e., artificial intelligence conversational agents) can offer scalable and cost-effective solutions. Additionally, targeting multiple unhealthy behaviors, like low physical activity and smoking, simultaneously seems beneficial. We developed Perfect Fit, an mHealth intervention with a virtual coach providing personalized feedback to simultaneously promote smoking cessation and physical activity. Through innovative methods (e.g., sensor technology) and iterative development involving end-users, we strive to overcome challenges encountered by mHealth interventions, such as shortage of evidence-based interventions and insufficient personalization. This paper outlines the content of Perfect Fit and the protocol for evaluating its feasibility, acceptability, and preliminary effectiveness, the role of participant characteristics, and the study's feasibility.
Methods
A single-arm, mixed-method, real-world evaluation study will be conducted in the Netherlands. We aim to recruit 100 adult daily smokers intending to quit within 6 weeks. The personalized intervention will last approximately 16 weeks. Primary outcomes include Perfect Fit's feasibility and acceptability. Secondary outcomes are preliminary effectiveness and study feasibility, and we will measure participant characteristics. Quantitative data will be collected through questionnaires administered at baseline, post-intervention and 2, 6, and 12 months post-intervention. Qualitative data will be gathered via semi-structured interviews post-intervention. Data analysis will involve descriptive analyses, generalized linear mixed models (quantitative) and the Framework Approach (qualitative), integrating quantitative and qualitative data during interpretation.
Conclusions
This study will provide novel insight into the potential of interventions like Perfect Fit, as a multiple health behavior change strategy. Findings will inform further intervention development and help identify methods to foster feasibility and acceptability. Successful mHealth interventions with virtual coaches will prevent chronic diseases and promote public health. ...
Adopting healthy behavior is vital for preventing chronic diseases. Mobile health (mHealth) interventions utilizing virtual coaches (i.e., artificial intelligence conversational agents) can offer scalable and cost-effective solutions. Additionally, targeting multiple unhealthy behaviors, like low physical activity and smoking, simultaneously seems beneficial. We developed Perfect Fit, an mHealth intervention with a virtual coach providing personalized feedback to simultaneously promote smoking cessation and physical activity. Through innovative methods (e.g., sensor technology) and iterative development involving end-users, we strive to overcome challenges encountered by mHealth interventions, such as shortage of evidence-based interventions and insufficient personalization. This paper outlines the content of Perfect Fit and the protocol for evaluating its feasibility, acceptability, and preliminary effectiveness, the role of participant characteristics, and the study's feasibility.
Methods
A single-arm, mixed-method, real-world evaluation study will be conducted in the Netherlands. We aim to recruit 100 adult daily smokers intending to quit within 6 weeks. The personalized intervention will last approximately 16 weeks. Primary outcomes include Perfect Fit's feasibility and acceptability. Secondary outcomes are preliminary effectiveness and study feasibility, and we will measure participant characteristics. Quantitative data will be collected through questionnaires administered at baseline, post-intervention and 2, 6, and 12 months post-intervention. Qualitative data will be gathered via semi-structured interviews post-intervention. Data analysis will involve descriptive analyses, generalized linear mixed models (quantitative) and the Framework Approach (qualitative), integrating quantitative and qualitative data during interpretation.
Conclusions
This study will provide novel insight into the potential of interventions like Perfect Fit, as a multiple health behavior change strategy. Findings will inform further intervention development and help identify methods to foster feasibility and acceptability. Successful mHealth interventions with virtual coaches will prevent chronic diseases and promote public health. ...
Objective
Adopting healthy behavior is vital for preventing chronic diseases. Mobile health (mHealth) interventions utilizing virtual coaches (i.e., artificial intelligence conversational agents) can offer scalable and cost-effective solutions. Additionally, targeting multiple unhealthy behaviors, like low physical activity and smoking, simultaneously seems beneficial. We developed Perfect Fit, an mHealth intervention with a virtual coach providing personalized feedback to simultaneously promote smoking cessation and physical activity. Through innovative methods (e.g., sensor technology) and iterative development involving end-users, we strive to overcome challenges encountered by mHealth interventions, such as shortage of evidence-based interventions and insufficient personalization. This paper outlines the content of Perfect Fit and the protocol for evaluating its feasibility, acceptability, and preliminary effectiveness, the role of participant characteristics, and the study's feasibility.
Methods
A single-arm, mixed-method, real-world evaluation study will be conducted in the Netherlands. We aim to recruit 100 adult daily smokers intending to quit within 6 weeks. The personalized intervention will last approximately 16 weeks. Primary outcomes include Perfect Fit's feasibility and acceptability. Secondary outcomes are preliminary effectiveness and study feasibility, and we will measure participant characteristics. Quantitative data will be collected through questionnaires administered at baseline, post-intervention and 2, 6, and 12 months post-intervention. Qualitative data will be gathered via semi-structured interviews post-intervention. Data analysis will involve descriptive analyses, generalized linear mixed models (quantitative) and the Framework Approach (qualitative), integrating quantitative and qualitative data during interpretation.
Conclusions
This study will provide novel insight into the potential of interventions like Perfect Fit, as a multiple health behavior change strategy. Findings will inform further intervention development and help identify methods to foster feasibility and acceptability. Successful mHealth interventions with virtual coaches will prevent chronic diseases and promote public health.
Adopting healthy behavior is vital for preventing chronic diseases. Mobile health (mHealth) interventions utilizing virtual coaches (i.e., artificial intelligence conversational agents) can offer scalable and cost-effective solutions. Additionally, targeting multiple unhealthy behaviors, like low physical activity and smoking, simultaneously seems beneficial. We developed Perfect Fit, an mHealth intervention with a virtual coach providing personalized feedback to simultaneously promote smoking cessation and physical activity. Through innovative methods (e.g., sensor technology) and iterative development involving end-users, we strive to overcome challenges encountered by mHealth interventions, such as shortage of evidence-based interventions and insufficient personalization. This paper outlines the content of Perfect Fit and the protocol for evaluating its feasibility, acceptability, and preliminary effectiveness, the role of participant characteristics, and the study's feasibility.
Methods
A single-arm, mixed-method, real-world evaluation study will be conducted in the Netherlands. We aim to recruit 100 adult daily smokers intending to quit within 6 weeks. The personalized intervention will last approximately 16 weeks. Primary outcomes include Perfect Fit's feasibility and acceptability. Secondary outcomes are preliminary effectiveness and study feasibility, and we will measure participant characteristics. Quantitative data will be collected through questionnaires administered at baseline, post-intervention and 2, 6, and 12 months post-intervention. Qualitative data will be gathered via semi-structured interviews post-intervention. Data analysis will involve descriptive analyses, generalized linear mixed models (quantitative) and the Framework Approach (qualitative), integrating quantitative and qualitative data during interpretation.
Conclusions
This study will provide novel insight into the potential of interventions like Perfect Fit, as a multiple health behavior change strategy. Findings will inform further intervention development and help identify methods to foster feasibility and acceptability. Successful mHealth interventions with virtual coaches will prevent chronic diseases and promote public health.
Abstract
(2023)
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Kristell M. Penfornis, Milon van Vliet, Eline Meijer, Anke Versluis, N. Albers, Bouke Scheltinga, Sven van der Burg, Walter Baccinelli
Background: Smoking and physical inactivity are two key preventable risk factors of cardiovascular disease. Yet, as with most health behaviors, they are difficult to change. In the interdisciplinary Perfect Fit project, scientists from different fields join forces to develop an evidence-based virtual coach that supports smokers in quitting smoking and increasing their physical activity. Intervention content, design and implementation as well as lessons learnt are presented in the hopes of guiding other research groups working on similar projects.
Methods: Numerous approaches were used and combined to support the development of the Perfect Fit virtual coach. Approaches include literature reviews, empirical studies, collaboration with end-users, content and technical development sprints, interdisciplinary collaboration and iterative proof-of-concept implementation. Findings: The Perfect Fit intervention integrates evidence-based behavioral change techniques as well as new techniques focused on identity change, big data science, sensor technology and personalized real-time coaching. Intervention content of the virtual coaching matches communication preferences and individual needs of end users. Lessons learnt include ways to optimally implement and tailor interactions from the virtual coach (e.g., ‘explain why user is asked for input’, ‘tailor timing and frequency of intervention components’). With regards to the development process, lessons learnt include strategies for effective interdisciplinary collaboration and technical development (e.g., ‘Find a good balance between wishes of end-users and legal possibilities’).
Discussion: The Perfect Fit development process was interactive, iterative and challenging at times. We hope
that our experiences and lessons learnt can inspire and benefit others.
...
Background: Smoking and physical inactivity are two key preventable risk factors of cardiovascular disease. Yet, as with most health behaviors, they are difficult to change. In the interdisciplinary Perfect Fit project, scientists from different fields join forces to develop an evidence-based virtual coach that supports smokers in quitting smoking and increasing their physical activity. Intervention content, design and implementation as well as lessons learnt are presented in the hopes of guiding other research groups working on similar projects.
Methods: Numerous approaches were used and combined to support the development of the Perfect Fit virtual coach. Approaches include literature reviews, empirical studies, collaboration with end-users, content and technical development sprints, interdisciplinary collaboration and iterative proof-of-concept implementation. Findings: The Perfect Fit intervention integrates evidence-based behavioral change techniques as well as new techniques focused on identity change, big data science, sensor technology and personalized real-time coaching. Intervention content of the virtual coaching matches communication preferences and individual needs of end users. Lessons learnt include ways to optimally implement and tailor interactions from the virtual coach (e.g., ‘explain why user is asked for input’, ‘tailor timing and frequency of intervention components’). With regards to the development process, lessons learnt include strategies for effective interdisciplinary collaboration and technical development (e.g., ‘Find a good balance between wishes of end-users and legal possibilities’).
Discussion: The Perfect Fit development process was interactive, iterative and challenging at times. We hope
that our experiences and lessons learnt can inspire and benefit others.
Abstract
(2023)
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Anke Versluis, Kristell M. Penfornis, Milon van Vliet, N. Albers, Bouke Scheltinga, Sven van der Burg, Walter Baccinelli, Eline Meijer
Conference paper
(2022)
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Walter Baccinelli, Sven van der Burg, Robin Richardson, Bouke Scheltinga, N. Albers, Djura Smits, Cunliang Geng, W.P. Brinkman, Jasper Reenalda, More authors...
Smoking tobacco and physical inactivity are key preventable behavioural risk factors of cardiovascular disease (CVD). Computerised coaching systems can help individuals to modify risky behaviours, thereby preventing CVD. However, most reported eHealth or computerized coaching systems are hard to reuse in slightly different settings. To provide an open-source, reusable computer coaching system, we developed Perfect Fit. The reusability is manifested by building around the open-source text- and voice-based contextual assistant framework Rasa. Rasa provides a simple, standard interface to many popular messaging and voice channels, and custom connectors are easily implemented. A set of algorithms have been developed and connected to Rasa to drive and personalize the conversation flow and the coaching process. Such algorithms make use of data stored in a devoted database. Furthermore, Perfect Fit adheres to best practices and standards in software engineering. The modular design of Perfect Fit will allow researchers to connect the virtual coach to any messaging or voice channel with only modest modification. Perfect Fit is available under open-source license in GitHub and is currently in prototype-phase. Concluding, Perfect Fit will deliver a virtual coach that can easily be adapted and reused in different settings. The coach helps individuals to achieve and maintain abstinence from smoking and sufficient physical activity (PA).
...
Smoking tobacco and physical inactivity are key preventable behavioural risk factors of cardiovascular disease (CVD). Computerised coaching systems can help individuals to modify risky behaviours, thereby preventing CVD. However, most reported eHealth or computerized coaching systems are hard to reuse in slightly different settings. To provide an open-source, reusable computer coaching system, we developed Perfect Fit. The reusability is manifested by building around the open-source text- and voice-based contextual assistant framework Rasa. Rasa provides a simple, standard interface to many popular messaging and voice channels, and custom connectors are easily implemented. A set of algorithms have been developed and connected to Rasa to drive and personalize the conversation flow and the coaching process. Such algorithms make use of data stored in a devoted database. Furthermore, Perfect Fit adheres to best practices and standards in software engineering. The modular design of Perfect Fit will allow researchers to connect the virtual coach to any messaging or voice channel with only modest modification. Perfect Fit is available under open-source license in GitHub and is currently in prototype-phase. Concluding, Perfect Fit will deliver a virtual coach that can easily be adapted and reused in different settings. The coach helps individuals to achieve and maintain abstinence from smoking and sufficient physical activity (PA).