A Technology Acceptance Model for Deploying Masks to Combat the COVID-19 Pandemic in Taiwan (My Health Bank): Web-Based Cross-sectional Survey Study

Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Scoping Review

BACKGROUND

The rapid progression and integration of digital technologies into public health have reshaped the global landscape of health care delivery and disease prevention. In pursuit of better population health and health care accessibility, many countries have integrated digital interventions into their health care systems, such as web-based consultations, electronic health records, and telemedicine. Despite the increasing prevalence and relevance of digital technologies in public health and their varying definitions, there has been a shortage of studies examining whether these technologies align with the established definition and core characteristics of digital public health (DiPH) interventions. Hence, the imperative need for a scoping review emerges to explore the breadth of literature dedicated to this subject.

OBJECTIVE

This scoping review aims to outline DiPH interventions from different implementation stages for health promotion, primary to tertiary prevention, including health care and disease surveillance and monitoring. In addition, we aim to map the reported intervention characteristics, including their technical features and nontechnical elements.

METHODS

Original studies or reports of DiPH intervention focused on population health were eligible for this review. PubMed, Web of Science, CENTRAL, IEEE Xplore, and the ACM Full-Text Collection were searched for relevant literature (last updated on October 5, 2022). Intervention characteristics of each identified DiPH intervention, such as target groups, level of prevention or health care, digital health functions, intervention types, and public health functions, were extracted and used to map DiPH interventions. MAXQDA 2022.7 (VERBI GmbH) was used for qualitative data analysis of such interventions' technical functions and nontechnical characteristics.

RESULTS

In total, we identified and screened 15,701 records, of which 1562 (9.94%) full texts were considered relevant and were assessed for eligibility. Finally, we included 185 (11.84%) publications, which reported 179 different DiPH interventions. Our analysis revealed a diverse landscape of interventions, with telemedical services, health apps, and electronic health records as dominant types. These interventions targeted a wide range of populations and settings, demonstrating their adaptability. The analysis highlighted the multifaceted nature of digital interventions, necessitating precise definitions and standardized terminologies for effective collaboration and evaluation.

CONCLUSIONS

Although this scoping review was able to map characteristics and technical functions among 13 intervention types in DiPH, emerging technologies such as artificial intelligence might have been underrepresented in our study. This review underscores the diversity of DiPH interventions among and within intervention groups. Moreover, it highlights the importance of precise terminology for effective planning and evaluation. This review promotes cross-disciplinary collaboration by emphasizing the need for clear definitions, distinct technological functions, and well-defined use cases. It lays the foundation for international benchmarks and comparability within DiPH systems. Further research is needed to map intervention characteristics in this still-evolving field continuously.

TRIAL REGISTRATION

PROSPERO CRD42021265562; https://tinyurl.com/43jksb3k.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/33404.

Psychological and psychosocial determinants of COVID Health Related Behaviours (COHeRe): An evidence and gap map

Background

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has resulted in illness, deaths and societal disruption on a global scale. Societies have implemented various control measures to reduce transmission of the virus and mitigate its impact. Individual behavioural changes are crucial to the successful implementation of these measures. Common recommended measures to limit risk of infection include frequent handwashing, reducing the frequency of social interactions and the use of face coverings. It is important to identify those factors that can predict the uptake and maintenance of these protective behaviours.

Objectives

We aimed to identify and map the existing evidence (published and unpublished) on psychological and psychosocial factors that determine uptake and adherence to behaviours aimed at reducing the risk of infection or transmission of COVID-19.

Search Methods

Our extensive search included electronic databases (n = 12), web searches, conference proceedings, government reports, other repositories including both published peer reviewed, pre-prints and grey literature. The search strategy was built around three concepts of interest including (1) context (terms relating to COVID-19), (2) behaviours of interest and (3) terms related to psychological and psychosocial determinants of COVID Health-Related Behaviours and adherence or compliance with recommended behaviours, to capture both malleable and non-malleable determinants (i.e. determinants that could be changed and those that could not).

Selection Criteria

This Evidence and Gap Map (EGM) includes all types of studies examining determinants of common recommended behaviours aimed at mitigating human-to-human spread of COVID-19. All potential malleable and non-malleable determinants of one or more behaviours are included in the map. As part of the mapping process, categories are used to group determinants. The mapping categories were based on a previous rapid review by Hanratty 2021. These include: 'behaviour', 'cognition', 'demographics', 'disease', 'emotions', 'health status', 'information', 'intervention', and 'knowledge'. Those not suitable for categorisation in any of these groups are included in the map as 'other' determinants.

Data Collection and Analysis

Results were imported to a bibliographic reference manager where duplications of identical studies gathered from multiple sources were removed. Data extraction procedures were managed in EPPI-Reviewer software. Information on study type, population, behaviours measured and determinants measured were extracted. We appraised the methodological quality of systematic reviews with AMSTAR-2. We did not appraise the quality of primary studies in this map.

Main Results

As of 1 June 2022 the EGM includes 1034 records reporting on 860 cross-sectional, 68 longitudinal, 78 qualitative, 25 reviews, 62 interventional, and 39 other studies (e.g., mixed-methods approaches). The map includes studies that measured social distancing (n = 487), masks and face coverings (n = 382), handwashing (n = 308), physical distancing (n = 177), isolation/quarantine (n = 157), respiratory hygiene/etiquette (n = 75), cleaning surfaces (n = 59), and avoiding touching the T-zone (n = 48). There were 333 studies that assessed composite measures of two or more behaviours. The largest cluster of determinants was 'demographics' (n = 730 studies), followed by 'cognition' (n = 496 studies) and determinants categorised as 'other' (n = 447). These included factors such as 'beliefs', 'culture' and 'access to resources'. Less evidence is available for some determinants such as 'interventions' (n = 99 studies), 'information' (n = 101 studies), and 'behaviour' (149 studies).

Authors' Conclusions

This EGM provides a valuable resource for researchers, policy-makers and the public to access the available evidence on the determinants of various COVID-19 health-related behaviours. The map can also be used to help guide research commissioning, by evidence synthesis teams and evidence intermediaries to inform policy during the ongoing pandemic and potential future outbreaks of COVID-19 or other respiratory infections. Evidence included in the map will be explored further through a series of systematic reviews examining the strength of the associations between malleable determinants and the uptake and maintenance of individual protective behaviours.

Understanding the Drivers of Ghanaian Citizens' Adoption Intentions of Mobile Health Services

Mobile health (m-health) application development and diffusion in developing countries have always been a challenge; therefore, research that seeks to provide an elucidation of the drivers of m-Health adoption is vital. Mobile health information systems and applications can contribute to the delivery of a good healthcare system. This study examined the factors influencing citizens' adoption of mobile health services. The Technology Acceptance Model (TAM) was used as the research underpinning for this study, while the data gathered were analyzed with SmartPLS through the use of the structural equation modeling technique. The results showed that perceived usefulness and ease of use were both significant predictors of the behavioral intention to use and recommend the adoption of mobile health services. Also, perceived risk was negative but significant in predicting the intention to use and recommend adoption. Mobile self-efficacy was found to significantly determine the behavioral intention to use, intention to recommend, perceived usefulness, and perceived ease of use of mobile health services. Besides, word-of-mouth showed a positive impact on both the intention to use and recommend. Contrary to expectations, the intention to use had no significant impact on the recommendation intention. The theoretical and practical implications of these findings are thoroughly examined.

Telehealth adoption during the COVID-19 pandemic: A social media textual and network analysis

Objective

The telemedicine industry has rapidly grown during the COVID-19 pandemic, and telemedicine has become a common form of care. The present study looks at the online conversation regarding telemedicine at the beginning of the pandemic and one year later. The Technology Acceptance Model is utilized to explain the findings.

Methods

Brandwatch and NUVI software captured social mentions on Twitter regarding telemedicine during the beginning of the pandemic (March 15, 2020–April 20, 2020) and one year later (March 12, 2021–April 19, 2021). SAS text-mining software analyzed the social mentions and organized them into ten unique topics for each time period. The research team analyzed the topics and organized them into themes. A network analysis was also performed to examine structure and influence within the network.

Results

In March–April 2020, the themes focused on the use of telehealth in general, telehealth for mental health applications, and Medicare covering telehealth services. In March–April 2021, the themes focused on news events regarding telehealth and the rise in prominence of telehealth services. The network analysis shows a shift in the distribution of telehealth information among influential accounts and reveals that the network became more connected, with a change in the control of information spread.

Conclusions

Technology Acceptance Model explains the social acceptance and spread of telemedicine. The transition in the conversation about telemedicine suggests a pattern of greater system use consistent with the Technology Acceptance Model. Telemedicine may have greatly increased in use because of the pandemic, but data suggests that its use may persist after the pandemic subsides.

Exploring the COVID-19 Pandemic as a Catalyst for PHR App User Behavior Change in Taiwan: A Development and Usability Study

Background

During the COVID-19 pandemic, personal health records (PHRs) have enabled patients to monitor and manage their medical data without visiting hospitals and, consequently, minimize their infection risk. Taiwan’s National Health Insurance Administration (NHIA) launched the My Health Bank (MHB) service, a national PHR system through which insured individuals to access their cross-hospital medical data. Furthermore, in 2019, the NHIA released the MHB software development kit (SDK), which enables development of mobile apps with which insured individuals can retrieve their MHB data. However, the NHIA MHB service has its limitations, and the participation rate among insured individuals is low.

Objective

We aimed to integrate the MHB SDK with our developed blockchain-enabled PHR mobile app, which enables patients to access, store, and manage their cross-hospital PHR data. We also collected and analyzed the app’s log data to examine patients’ MHB use during the COVID-19 pandemic.

Methods

We integrated our existing blockchain-enabled mobile app with the MHB SDK to enable NHIA MHB data retrieval. The app utilizes blockchain technology to encrypt the downloaded NHIA MHB data. Existing and new indexes can be synchronized between the app and blockchain nodes, and high security can be achieved for PHR management. Finally, we analyzed the app’s access logs to compare patients’ activities during high and low COVID-19 infection periods.

Results

We successfully integrated the MHB SDK into our mobile app, thereby enabling patients to retrieve their cross-hospital medical data, particularly those related to COVID-19 rapid and polymerase chain reaction testing and vaccination information and progress. We retrospectively collected the app’s log data for the period of July 2019 to June 2021. From January 2020, the preliminary results revealed a steady increase in the number of people who applied to create a blockchain account for access to their medical data and the number of app subscribers among patients who visited the outpatient department (OPD) and emergency department (ED). Notably, for patients who visited the OPD and ED, the peak proportions with respect to the use of the app for OPD and ED notes and laboratory test results also increased year by year. The highest proportions were 52.40% for ED notes in June 2021, 88.10% for ED laboratory test reports in May 2021, 34.61% for OPD notes in June 2021, and 41.87% for OPD laboratory test reports in June 2021. These peaks coincided with Taiwan’s local COVID-19 outbreak lasting from May to June 2021.

Conclusions

This study developed a blockchain-enabled mobile app, which can periodically retrieve and integrate PHRs from the NHIA MHB's cross-hospital data and the investigated hospital's self-pay medical data. Analysis of users’ access logs revealed that the COVID-19 pandemic substantially increased individuals’ use of PHRs and their health awareness with respect to COVID-19 prevention.

Technology Acceptance Among Patients With Hemophilia in Hong Kong and Their Expectations of a Mobile Health App to Promote Self-management: Survey Study

Background

The lifelong management of hemophilia is demanding and complex. In July 2019, we published a review in the Journal of Medical Internet Research, summarizing telehealth interventions that facilitate monitoring of bleeding events and promoting the appropriate use of clotting factors among patients with hemophilia. This work has led to the development of a community program that aims to harness technology to promote self-management among patients with hemophilia in Hong Kong.

Objective

Before the inception of this program, we conducted a cross-sectional survey to evaluate the patients’ level of technology acceptance and identify their expectations of the use of mobile technology for self-management of hemophilia.

Methods

In total, 56 participants (75% adult patients and 25% parents of pediatric patients; 87.5% with moderate to severe disease) were recruited from a local nongovernmental organization that serves patients with hemophilia. They rated their perceived confidence and acceptance in using the new mobile technology (score 1 to 5 for each item, with a higher score indicating better acceptance) using a structured questionnaire (adapted from the Technology Acceptance Model). They also identified the top features that they perceived to be the most important components of a mobile app for the self-management of hemophilia. The Mann–Whitney U test was used to compare technology acceptance scores across subgroups of different clinical and socioeconomic characteristics.

Results

In general, the participants considered themselves skilled in using mobile apps (mean 4.3, 95% CI 4.1-4.5). They were willing to learn to use the new mobile app to organize their bleeding records (mean 4.0, 95% CI 3.7-4.3) and to manage their health (mean 4.2, 95% CI 4.1-4.5). Participants who lived in public housing (a surrogate marker for lower socioeconomic status in Hong Kong) reported lower technology acceptance than those who lived in private housing (P=.04). The most important features identified by the participants concerned documenting of infusion logs (n=49, 87.5%), bleeding events (n=48, 85.7%), and the secure delivery of the bleeding information to health care professionals (n=40, 71.4%).

Conclusions

It is encouraging to infer that patients with hemophilia in Hong Kong are receptive to the use of mobile health technology. The findings of this survey are applicable in designing the key features of a patient-centered, multimodal program harnessing mobile technology to promote self-management among patients with hemophilia. Future studies should evaluate participants’ acceptability and perceived usability of the mobile app via user metrics and assess clinical and humanistic outcomes of this program.