Milon H.M. van Vliet
Please Note
9 records found
1
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.
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.
Harmful communication behaviors in cancer care
A systematic review of patients and family caregivers perspectives
Objective: Issues regarding clinician communication remain an important source of complaints within healthcare. This systematic review aims to determine cancer patients' and their family caregivers' views on which clinicians' communication behaviors can harm (i.e. eliciting negative feelings/consequences for patients/family caregivers). Methods: We searched for all types of peer-reviewed studies that determined adult (≥18 years) cancer patients' and/or family caregivers' perspectives on which clinicians' communication behaviors can harm in several databases (PubMed, Embase, Web of Science, Cochrane Library, Emcare, PsycINFO and Academic Search Premier), supplemented by expert-consultation. Studies were screened using the Artificial intelligence screening tool of ASReview and data was analyzed using Thematic Analysis. To assess the quality of the studies the Qualsyst critical appraisal tool was used. Results: A total of 47 studies were included. Four main themes of harmful communication behaviors were identified: (1) Lack of tailored information provision (e.g. giving too little or too much/specific information) (2) Lack of tailored decision making (ranging from; patient exclusion, to the patients' responsibility, and/or haste) (3) Lack of feeling seen and heard (seen as a disease, not as a human being; not listened to concerns and emotions) (4) Lack of feeling held and remembered (forgotten agreements; lack of care continuity). Conclusions: Our results reveal an overview of patients' and family caregivers' perspectives on which clinicians' communication behaviors can harm. Harm could be prevented when information and decision involvement are tailored and patients' and family caregivers' needs to feel seen, heard, held and remembered are met.