This study addresses the burgeoning global shortage of healthcare workers and the consequential overburdening of medical professionals, a challenge that is anticipated to intensify by 2030 [1]. It explores the adoption and perceptions of AI-powered mobile medical applications (MMAs) by physicians in the Netherlands, investigating whether doctors discuss or recommend these applications to patients and the frequency of their use in clinical practice. The research reveals a cautious but growing acceptance of MMAs among healthcare providers. Medical mobile applications, with a substantial part of IA-driven applications, are being recognized for their potential to alleviate workload. The findings suggest an emergent trust in AI-driven health technologies, underscored by recommendations from peers, yet tempered by concerns over data security and patient mental health, indicating a need for ongoing assessment and validation of these applications
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ABSTRACT Objective: To evaluate the effectiveness of the WhiteTeeth mobile app, a theory-based mobile health (mHealth) program for promoting oral hygiene in adolescent orthodontic patients. Methods: In this parallel randomized controlled trial, the data of 132 adolescents were collected during three orthodontic check-ups: at baseline (T0), at 6-week follow-up (T1), and at 12-week follow-up (T2). The intervention group was given access to the WhiteTeeth app in addition to usual care (n=67). The control group received usual care only (n=65). The oral hygiene outcomes were the presence and the amount of dental plaque (Al-Anezi and Harradine plaque Index); and the total number of sites with gingival bleeding (Bleeding on Marginal Probing Index). Oral health behavior and its psychosocial factors were measured through a digital questionnaire. We performed linear mixed model analyses to determine the intervention effects. Results: At 6-week follow-up, the intervention led to a significant decrease in gingival bleeding (B=-3.74; 95%CI -6.84 to -0.65), and an increase in the use of fluoride mouth rinse (B=1.93; 95%CI 0.36 to 3.50). At 12-week follow-up, dental plaque accumulation (B=-11.32; 95%CI -20.57 to -2.07) and the number of sites covered. Conclusions: The results show that adolescents with fixed orthodontic appliances can be helped to improve their oral hygiene when usual care is combined with a mobile app that provides oral health education and automatic coaching. Netherlands Trial Registry Identifier: NTR6206: 20 February 2017.
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Background: Adequate self-management skills are of great importance for patients with chronic obstructive pulmonary disease (COPD) to reduce the impact of COPD exacerbations. Using mobile health (mHealth) to support exacerbation-related self-management could be promising in engaging patients in their own health and changing health behaviors. However, there is limited knowledge on how to design mHealth interventions that are effective, meet the needs of end users, and are perceived as useful. By following an iterative user-centered design (UCD) process, an evidence-driven and usable mHealth intervention was developed to enhance exacerbation-related self-management in patients with COPD. Objective: This study aimed to describe in detail the full UCD and development process of an evidence-driven and usable mHealth intervention to enhance exacerbation-related self-management in patients with COPD. Methods: The UCD process consisted of four iterative phases: (1) background analysis and design conceptualization, (2) alpha usability testing, (3) iterative software development, and (4) field usability testing. Patients with COPD, health care providers, COPD experts, designers, software developers, and a behavioral scientist were involved throughout the design and development process. The intervention was developed using the behavior change wheel (BCW), a theoretically based approach for designing behavior change interventions, and logic modeling was used to map out the potential working mechanism of the intervention. Furthermore, the principles of design thinking were used for the creative design of the intervention. Qualitative and quantitative research methods were used throughout the design and development process. Results: The background analysis and design conceptualization phase resulted in final guiding principles for the intervention, a logic model to underpin the working mechanism of the intervention, and design requirements. Usability requirements were obtained from the usability testing phases. The iterative software development resulted in an evidence-driven and usable mHealth intervention—Copilot, a mobile app consisting of a symptom-monitoring module, and a personalized COPD action plan. Conclusions: By following a UCD process, an mHealth intervention was developed that meets the needs and preferences of patients with COPD, is likely to be used by patients with COPD, and has a high potential to be effective in reducing exacerbation impact. This extensive report of the intervention development process contributes to more transparency in the development of complex interventions in health care and can be used by researchers and designers as guidance for the development of future mHealth interventions.
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For many EU citizens, working across the border is the only way to make a living in the EU. The battle for cheap labour has now become a well-oiled machine, in which almost all Western European countries participate. Nevertheless, the employment situation of EU Mobile Citizens, workers of low-skilled and -paid jobs, is often substandard. Challenges are housing, health care and working conditions. In addition, due to the lack of registration in municipalities, it is impossible to have an overview of the numbers and to offer effective help. This is a problem in small to medium-sized cities, where many workers live to work in agriculture, transport, construction, meat industry and logistics. For this study, 32 interviews were conducted in eleven small to medium-sized towns (SMSTs) in Sweden, Germany, the Netherlands, Ireland, Poland, and Spain. The study uses three different perspectives: EU representatives of participating regions, municipalities, and employers. The outcomes show that most SMSTs deal with a shortage of housing, and a lack of grip on the registration process of EU citizens. Although there are some success stories, most SMSTs are not in touch with each other to share these. The paper concludes with proposals for further action-research and collaborations to impact local policies.
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Background: There is an emergence of mobile health (mHealth) interventions to support self-management in patients with chronic obstructive pulmonary disease (COPD). Recently, an evidence-driven mHealth intervention has been developed to support patients with COPD in exacerbation-related self-management: the Copilot app. Health care providers (HCPs) are important stakeholders as they are the ones who have to provide the app to patients, personalize the app, and review the app. It is, therefore, important to investigate at an early stage whether the app is feasible in the daily practice of the HCPs. Objective: The aim of this study is to evaluate the perceived feasibility of the Copilot app in the daily practice of HCPs. Methods: A multimethods design was used to investigate how HCPs experience working with the app and how they perceive the feasibility of the app in their daily practice. The feasibility areas described by Bowen et al were used for guidance. HCPs were observed while performing tasks in the app and asked to think aloud. The System Usability Scale was used to investigate the usability of the app, and semistructured interviews were conducted to explore the feasibility of the app. The study was conducted in primary, secondary, and tertiary care settings in the Netherlands from February 2019 to September 2019. Results: In total, 14 HCPs participated in this study—8 nurses, 5 physicians, and 1 physician assistant. The HCPs found the app acceptable to use. The expected key benefits of the app were an increased insight into patient symptoms, more structured patient conversations, and more tailored self-management support. The app especially fits within the available time and workflow of nurses. The use of the app will be influenced by the autonomy of the professional, the focus of the organization on eHealth, costs associated with the app, and compatibility with the current systems used. Most HCPs expressed that there are conditions that must be met to be able to use the app. The app can be integrated into the existing care paths of primary, secondary, and tertiary health care settings. Individual organizational factors must be taken into account when integrating the app into daily practice. Conclusions: This early-stage feasibility study shows that the Copilot app is feasible to use in the daily practice of HCPs and can be integrated into primary, secondary, and tertiary health care settings in the Netherlands. The app was considered to best fit the role of the nurses. The app will be less feasible for those organizations in which many conditions need to be met to use the app. This study provides a new approach to evaluate the perceived feasibility of mHealth interventions at an early stage and provides valuable insights for further feasibility testing.
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Background: To facilitate adherence to adaptive pain management behaviors after interdisciplinary multimodal pain treatment, we developed a mobile health app (AGRIPPA app) that contains two behavior regulation strategies. Objective: The aims of this project are (1) to test the effectiveness of the AGRIPPA app on pain disability; (2) to determine the cost-effectiveness; and (3) to explore the levels of engagement and usability of app users. Methods: We will perform a multicenter randomized controlled trial with two parallel groups. Within the 12-month inclusion period, we plan to recruit 158 adult patients with chronic pain during the initial stage of their interdisciplinary treatment program in one of the 6 participating centers. Participants will be randomly assigned to the standard treatment condition or to the enhanced treatment condition in which they will receive the AGRIPPA app. Patients will be monitored from the start of the treatment program until 12 months posttreatment. In our primary analysis, we will evaluate the difference over time of pain-related disability between the two conditions. Other outcome measures will include health-related quality of life, illness perceptions, pain self-efficacy, app system usage data, productivity loss, and health care expenses. Results: The study was approved by the local Medical Research Ethics Committee in October 2019. As of March 20, 2020, we have recruited 88 patients. Conclusions: This study will be the first step in systematically evaluating the effectiveness and efficiency of the AGRIPPA app. After 3 years of development and feasibility testing, this formal evaluation will help determine to what extent the app will influence the maintenance of treatment gains over time. The outcomes of this trial will guide future decisions regarding uptake in clinical practice.
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Abstract Background: With the growing shortage of nurses, labor-saving technology has become more important. In health care practice, however, the fit with innovations is not easy. The aim of this study is to analyze the development of a mobile input device for electronic medical records (MEMR), a potentially labor-saving application supported by nurses, that failed to meet the needs of nurses after development. Method: In a case study, we used an axiomatic design framework as an evaluation tool to visualize the mismatches between customer needs and the design parameters of the MEMR, and trace these mismatches back to (preliminary) decisions in the development process. We applied a mixed-method research design that consisted of analyzing of 118 external and internal files and working documents, 29 interviews and shorter inquiries, a user test, and an observation of use. By factoring and grouping the findings, we analyzed the relevant categories of mismatches. Results: The involvement of nurses during the development was extensive, but not all feedback was, or could not be, used effectively to improve the MEMR. The mismatches with the most impact were found to be: (1) suboptimal supportive technology, (2) limited functionality of the app and input device, and (3) disruption of nurses’ workflow. Most mismatches were known by the IT department when the MEMR was offered to the units as a product. Development of the MEMR came to a halt because of limited use. Conclusion: Choices for design parameters, made during the development of labor-saving technology for nurses, may conflict with the customer needs of nurses. Even though the causes of mismatches were mentioned by the IT department, the nurse managers acquired the MEMR based on the idea behind the app. The effects of the chosen design parameters should not only be compared to the customer needs, but also be assessed with nurses and nurse managers for the expected effect on the workflow.
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This study provides an in-depth understanding of the perceptions of patients with T2DM before use (acceptability) and after use (acceptance) regarding 4 different mobile health apps for diabetes control and self-management. This study was part of the TOPFIT Citizenlab project. TOPFIT Citizenlab is a 3-year research and innovation program in the eastern part of the Netherlands. Citizens, health care professionals (HCPs), and companies have joined forces with researchers to develop and implement technology for health and well-being.
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Background:It is unclear why some physical activity (PA) mobile health (mHealth) interventions successfully promote PA whereas others do not. One possible explanation is the variety in PA mHealth interventions—not only do interventions differ in the selection of persuasive strategies but also the design and implementation of persuasive strategies can vary. However, limited studies have examined the different designs and technical implementations of strategies or explored if they indeed influenced the effectiveness of the intervention.Objective:This scoping review sets out to explore the different technical implementations and design characteristics of common and likely most effective persuasive strategies, namely, goal setting, monitoring, reminders, rewards, sharing, and social comparison. Furthermore, this review aims to explore whether previous mHealth studies examined the influence of the different design characteristics and technical operationalizations of common persuasive strategies on the effectiveness of the intervention to persuade the user to engage in PA.Methods:An unsystematic snowball and gray literature search was performed to identify the literature that evaluated the persuasive strategies in experimental trials (eg, randomized controlled trial, pre-post test). Studies were included if they targeted adults, if they were (partly) delivered by a mobile system, if they reported PA outcomes, if they used an experimental trial, and when they specifically compared the effect of different designs or implementations of persuasive strategies. The study methods, implementations, and designs of persuasive strategies, and the study results were systematically extracted from the literature by the reviewers.Results:A total of 29 experimental trials were identified. We found a heterogeneity in how the strategies are being implemented and designed. Moreover, the findings indicated that the implementation and design of the strategy has an influence on the effectiveness of the PA intervention. For instance, the effectiveness of rewarding was shown to vary between types of rewards; rewarding goal achievement seems to be more effective than rewarding each step taken. Furthermore, studies comparing different ways of goal setting suggested that assigning a goal to users might appear to be more effective than letting the user set their own goal, similar to using adaptively tailored goals as opposed to static generic goals. This study further demonstrates that only a few studies have examined the influence of different technical implementations on PA behavior.Conclusions:The different implementations and designs of persuasive strategies in mHealth interventions should be critically considered when developing such interventions and before drawing conclusions on the effectiveness of the strategy as a whole. Future efforts are needed to examine which implementations and designs are most effective to improve the translation of theory-based persuasive strategies into practical delivery forms.
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Background: Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Objective: The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Methods: This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization’s (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy. Results: Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations’ inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects.
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