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.
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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.
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Background: The COVID-19 pandemic taught us how to rethink care delivery. It catalyzed creative solutions to amplify the potential of personnel and facilities. This paper presents and evaluates a promptly introduced triaging solution that evolved into a tool to tackle the ever-growing waiting lists at an academic ophthalmology department, the TeleTriageTeam (TTT). A team of undergraduate optometry students, tutor optometrists, and ophthalmologists collaborate to maintain continuity of eye care. In this ongoing project, we combine innovative interprofessional task allocation, teaching, and remote care delivery. Objective: In this paper, we described a novel approach, the TTT; reported its clinical effectiveness and impact on waiting lists; and discussed its transformation to a sustainable method for delivering remote eye care. Methods: Real-world clinical data of all patients assessed by the TTT between April 16, 2020, and December 31, 2021, are covered in this paper. Business data on waiting lists and patient portal access were collected from the capacity management team and IT department of our hospital. Interim analyses were performed at different time points during the project, and this study presents a synthesis of these analyses. Results: A total of 3658 cases were assessed by the TTT. For approximately half (1789/3658, 48.91%) of the assessed cases, an alternative to a conventional face-to-face consultation was found. The waiting lists that had built up during the first months of the pandemic diminished and have been stable since the end of 2020, even during periods of imposed lockdown restrictions and reduced capacity. Patient portal access decreased with age, and patients who were invited to perform a remote, web-based eye test at home were on average younger than patients who were not invited. Conclusions: Our promptly introduced approach to remotely review cases and prioritize urgency has been successful in maintaining continuity of care and education throughout the pandemic and has evolved into a telemedicine service that is of great interest for future purposes, especially in the routine follow-up of patients with chronic diseases. TTT appears to be a potentially preferred practice in other clinics and medical specialties. The paradox is that judicious clinical decision-making based on remotely collected data is possible, only if we as caregivers are willing to change our routines and cognitions regarding face-to-face care delivery.
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Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
