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Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity

Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.

Personalized aftermovies

Impressions, messages and recommendations

Over the past year and a half, the pandemic has posed serious challenges to many professions, including education in general and teacher education in particular. COVID19 created an unprecedented and unimagined disruption without preparation. As in schools, in teacher education daily routines were challenged and we needed to rethink both our understanding of what teaching in schools looks like and what teachers need, and our understanding of TE pedagogy which is traditionally based on close interpersonal interaction and classroom teaching practice.In this turbulent period and given the circumstances and the need for ‘emergency distance learning’, many teacher educators did their very best with full dedication to their responsibilities towards students, schools and society. At the same time, the question can be raised whether we can do better next time. The answer to that question depends on the extent in which we are able to learn from our past experiences. Research and exchange of our experiences can support this learning process.The Teacher Education Policy in Europe (TEPE) network has been actively involved in efforts to learn from our past experiences and to strengthen knowledge to meet the new challenges. In doing so, we are aware of the importance of bringing together experts from different fields in a broad international forum. Among others, on 15 April 2021, with the help of colleagues from Tallinn University, we organized a webinar on the dilemmas and challenges teachers and schools face during the pandemic to ensure quality education for all, and how teacher education can support this. A month later, on 20-22 May, we held our fifteenth annual conference entitled Challenges of distance teaching in teacher education and education, organized by colleagues from the University of Ljubljana. It was attended by over 200 participants from 34 countries. In this document we summarize the main findings and recommendations that emerged from the presentations and discussions at both events.

Handling consumer messages on social networking sites

Firms increasingly use social network sites to reach out to customers and proactively intervene with observed consumer messages. Despite intentions to enhance customer satisfaction by extending customer service, sometimes these interventions are received negatively by consumers. We draw on privacy regulation theory to theorize how proactive customer service interventions with consumer messages on social network sites may evoke feelings of privacy infringement. Subsequently we use privacy calculus theory to propose how these perceptions of privacy infringement, together with the perceived usefulness of the intervention, in turn drive customer satisfaction. In two experiments, we find that feelings of privacy infringement associated with proactive interventions may explain why only reactive interventions enhance customer satisfaction. Moreover, we find that customer satisfaction can be modeled through the calculus of the perceived usefulness and feelings of privacy infringement associated with an intervention. These findings contribute to a better understanding of the impact of privacy concerns on consumer behavior in the context of firm–consumer interactions on social network sites, extend the applicability of privacy calculus theory, and contribute to complaint and compliment management literature. To practitioners, our findings demonstrate that feelings of privacy are an element to consider when handling consumer messages on social media, but also that privacy concerns may be overcome if an intervention is perceived as useful enough.

An Artificial Intelligence–Based, Personalized Smartphone App to Improve Childhood Immunization Coverage and Timelines Among Children in Pakistan

Background: The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI. Objective: The primary objectives of this study are to evaluate whether a personalized mobile app can improve children’s on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone–based app on vaccination improvement. Methods: A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children’s 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to no-show study findings, which will explore caregivers’ perceptions about RCI and a mobile phone–based app in improving RCI coverage. Results: Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age. Conclusions: This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs.

How location-based messages influence customers’ store visit attitudes

PurposeThis study aims to develop an understanding of how customers of a physical retail store valuate receiving location-based mobile phone messages when they are in proximity of the store. It proposes and tests a model relating two benefits (personalization and location congruency) and two sacrifices (privacy concern and intrusiveness) to message value perceptions and store visit attitudes.Design/methodology/approachThe study uses a vignette-based survey to collect data from a sample of 1,225 customers of a fashion retailer. The postulated research model is estimated using SmartPLS 3.0 with the consistent-PLS algorithm and further validated via a post-hoc test.FindingsThe empirical testing confirms the predictive validity and robustness of the model and reveals that location congruency and intrusiveness are the location-based message characteristics with the strongest effects on message value and store visit attitude.Originality/valueThe paper adds to the underexplored field of store entry research and extends previous location-based messaging studies by integrating personalization, location congruency, privacy concern and intrusiveness into one validated model.