Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies

Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial

BACKGROUND

Early identification of an acute respiratory infection is important for reducing transmission and enabling earlier therapeutic intervention. We aimed to prospectively evaluate the feasibility of home-based diagnostic self-testing of viral pathogens in individuals prompted to do so on the basis of self-reported symptoms or individual changes in physiological parameters detected via a wearable sensor.

METHODS

DETECT-AHEAD was a prospective, decentralised, randomised controlled trial carried out in a subpopulation of an existing cohort (DETECT) of individuals enrolled in a digital-only observational study in the USA. Participants aged 18 years or older were randomly assigned (1:1:1) with a block randomisation scheme stratified by under-represented in biomedical research status. All participants were offered a wearable sensor (Fitbit Sense smartwatch). Participants in groups 1 and 2 received an at-home self-test kit (Alveo be.well) for two acute respiratory viral pathogens: SARS-CoV-2 and respiratory syncytial virus. Participants in group 1 could be alerted through the DETECT study app to take the at-home test on the basis of changes in their physiological data (as detected by our algorithm) or due to self-reported symptoms; those in group 2 were prompted via the app to self-test only due to symptoms. Group 3 served as the control group, without alerts or home testing capability. The primary endpoints, assessed on an intention-to-treat basis, were the number of acute respiratory infections presented (self-reported) and diagnosed (electronic health record), and the number of participants using at-home testing in groups 1 and 2. This trial is registered with ClinicalTrials.gov, NCT04336020.

FINDINGS

Between Sept 28 and Dec 30, 2021, 450 participants were recruited and randomly assigned to group 1 (n=149), group 2 (n=151), or group 3 (n=150). 179 (40%) participants were male, 264 (59%) were female, and seven (2%) identified as other. 232 (52%) were from populations historically under-represented in biomedical research. 118 (39%) of the 300 participants in groups 1 and 2 were prompted to self-test, with 61 (52%) successfully completing self-testing. Participants were prompted to home-test more frequently due to symptoms (41 [28%] in group 1 and 51 [34%] in group 2) than due to detected physiological changes (26 [17%] in group 1). Significantly more participants in group 1 received alerts to test than did those in group 2 (67 [45%] vs 51 [34%]; p=0·047). Of the 61 individuals who were prompted to test and successfully did so, 19 (31%) tested positive for a viral pathogen-all for SARS-CoV-2. The individuals diagnosed as positive for SARS-CoV-2 in the electronic health record were eight (5%) in group 1, four (3%) in group 2, and two (1%) in group 3, but it was difficult to confirm if they were tied to symptomatic episodes documented in the trial. There were no adverse events.

INTERPRETATION

In this direct-to-participant trial, we showed early feasibility of a decentralised programme to prompt individuals to use a viral pathogen diagnostic test based on symptoms tracked in the study app or physiological changes detected using a wearable sensor. Barriers to adequate participation and performance were also identified, which would need to be addressed before large-scale implementation.

FUNDING

Janssen Pharmaceuticals.

Experiences of children with bronchiectasis and their parents in a novel play-based therapeutic exercise programme: a qualitative analysis

OBJECTIVES

To explore the experiences and perceptions of children with bronchiectasis and their parents regarding an 8-week play-based therapeutic exercise programme.

DESIGN

Qualitative study with inductive content analysis.

SETTING

Individual semistructured interviews were conducted. Interview recordings were transcribed verbatim, and coding was guided by the content. Content categories were established via consensus moderation.

PARTICIPANTS

10 parents and 10 children with bronchiectasis aged 5-12 years.

RESULTS

From the perspective of children, the most important components of the programme were fun with friends and being active at home as a family. Parents valued the community-based sessions, perceived the programme to be engaging and motivating. Parents perceived improvements in their child's endurance, coordination and physical activity level. They described the home programme as fun but noted that finding time was difficult. Both parents and children thought that in-person exercise sessions would be better than exercise sessions delivered online.

CONCLUSIONS

Children who participated in the play-based exercise programme, found it fun, motivating and accessible. Parents perceived positive impacts on fitness, coordination and physical activity.

TRIAL REGISTRATION NUMBER

The trial was registered with, Australian and New Zealand Clinical Trials Register (ACTRN12619001008112).

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.

ChatGPT vs Medical Professional: Analyzing Responses to Laboratory Medicine Questions on Social Media

BACKGROUND

The integration of ChatGPT, a large language model (LLM) developed by OpenAI, into healthcare has sparked significant interest due to its potential to enhance patient care and medical education. With the increasing trend of patients accessing laboratory results online, there is a pressing need to evaluate the effectiveness of ChatGPT in providing accurate laboratory medicine information. Our study evaluates ChatGPT's effectiveness in addressing patient questions in this area, comparing its performance with that of medical professionals on social media.

METHODS

This study sourced patient questions and medical professional responses from Reddit and Quora, comparing them with responses generated by ChatGPT versions 3.5 and 4.0. Experienced laboratory medicine professionals evaluated the responses for quality and preference. Evaluation results were further analyzed using R software.

RESULTS

The study analyzed 49 questions, with evaluators reviewing responses from both medical professionals and ChatGPT. ChatGPT's responses were preferred by 75.9% of evaluators and generally received higher ratings for quality. They were noted for their comprehensive and accurate information, whereas responses from medical professionals were valued for their conciseness. The interrater agreement was fair, indicating some subjectivity but a consistent preference for ChatGPT's detailed responses.

CONCLUSIONS

ChatGPT demonstrates potential as an effective tool for addressing queries in laboratory medicine, often surpassing medical professionals in response quality. These results support the need for further research to confirm ChatGPT's utility and explore its integration into healthcare settings.

The next generation of healthcare ecosystem in the metaverse

The Metaverse has gained wide attention for being the application interface for the next generation of Internet. The potential of the Metaverse is growing, as Web 3·0 development and adoption continues to advance medicine and healthcare. We define the next generation of interoperable healthcare ecosystem in the Metaverse. We examine the existing literature regarding the Metaverse, explain the technology framework to deliver an immersive experience, along with a technical comparison of legacy and novel Metaverse platforms that are publicly released and in active use. The potential applications of different features of the Metaverse, including avatar-based meetings, immersive simulations, and social interactions are examined with different roles from patients to healthcare providers and healthcare organizations. Present challenges in the development of the Metaverse healthcare ecosystem are discussed, along with potential solutions including capabilities requiring technological innovation, use cases requiring regulatory supervision, and sound governance. This proposed concept and framework of the Metaverse could potentially redefine the traditional healthcare system and enhance digital transformation in healthcare. Similar to AI technology at the beginning of this decade, real-world development and implementation of these capabilities are relatively nascent. Further pragmatic research is needed for the development of an interoperable healthcare ecosystem in the Metaverse.

Efficient Use of Biological Data in the Web 3.0 Era by Applying Nonfungible Token Technology

CryptoKitties, a trendy game on Ethereum that is an open-source public blockchain platform with a smart contract function, brought nonfungible tokens (NFTs) into the public eye in 2017. NFTs are popular because of their nonfungible properties and their unique and irreplaceable nature in the real world. The embryonic form of NFTs can be traced back to a P2P network protocol improved based on Bitcoin in 2012 that can realize decentralized digital asset transactions. NFTs have recently gained much attention and have shown an unprecedented explosive growth trend. Herein, the concept of digital asset NFTs is introduced into the medical and health field to conduct a subversive discussion on biobank operations. By converting biomedical data into NFTs, the collection and circulation of samples can be accelerated, and the transformation of resources can be promoted. In conclusion, the biobank can achieve sustainable development through "decentralization."

Usage and limitations of medical consultation with patients' families using online video calls: a prospective cohort study

BACKGROUND

Few studies have been conducted on the usage of telehealth focusing on consultations between patients' families and physicians. This study aimed to identify the usage and limitations of online medical consultations with patients' families compared to the traditional in-person consultations.

METHODS

We conducted a prospective cohort study from April 1, 2020, to September 30, 2021, at an educational acute-care hospital in Japan. The study included hospitalized patients aged 20 years or older and their family members for whom an online or in-person medical consultation between the family member and physician was conducted during the hospitalization period. The primary endpoints assessed were three topics pertaining to medical consultation: medical conditions and treatment plans, policies for life-threatening events, and post-discharge support. The secondary endpoint was the number of consultations required.

RESULTS

Online consultations and traditional in-person consultations were provided to 58 and 53 patients' families, respectively. Of the patients in the online consultation group who underwent multiple consultations, 46 (79%) also underwent in-person consultations. Regarding the topics, all the patients' families in both consultation groups had consultations on medical conditions and treatment plans; regarding the policy for life-threatening events, 47% of patient families in the online consultation group were consulted compared to 53% of those in the in-person group. Regarding post-discharge support, 59% of patient families in the online group were consulted compared to 40% in the in-person group. In the online consultation group of 58 patients' families, 188 consultations were conducted, including 95 online and 93 in-person consultations. Consultations on policy for life-threatening events were significantly more frequent in in-person consultations than in online consultations (p < 0.05). Regarding post-discharge support, online consultations were significantly more frequent than in-person consultations (p < 0.05). The number of family members who attended online consultations was significantly higher than those who attended in-person consultations (p < 0.05).

CONCLUSIONS

Online consultation between the physician and patient's family may be an alternative to in-person consultation for explaining medical conditions and treatment plans. However, in-person consultation still plays an important role in sensitive topics, such as policy consultation for life-threatening events.

Making virtual health care accessible to the deaf community: Findings from the telehealth survey

INTRODUCTION

To reduce COVID-19 exposure risk, virtual visits became widely adopted as a common form of healthcare delivery for the general population. It is unknown how this affected the deaf population, a sociolinguistic minority group that continues to face communication and healthcare barriers. The survey's objective was to describe the deaf participants' experiences with telehealth visits.

METHODS

A 28-item online survey, available in American Sign Language and English, was developed and disseminated between November 2020 and January 2021. Ninety-nine deaf participants responded. Descriptive statistics were performed to assess the participant's virtual health care use, experiences, and communication approaches.

RESULTS

Seventy-five percent of respondents used telehealth at least once in the past 12 months (n = 74; age = 37.6 ± 14.5 years). Of those who used telehealth, nearly two-thirds experienced communication challenges (65.3%; n = 49). Half of the participants reported having to connect via a video relay service that employs interpreters who maintain general certification instead of a remote interpreter with specialized health care interpreting certifications for video visits with their health care providers (n = 37) and a third of participants reported needing to use their residual hearing to communicate with their providers (n = 25).

CONCLUSION

Standard protocols for health care systems and providers are needed to minimize the burden of access on deaf patients and ensure virtual visits are equitable. It is recommended these visits be offered on Health Insurance Portability and Accountability Act-compliant platforms and include multi-way video to allow for the inclusion of remote medical interpreters and/or real-time captionists to ensure effective communication between the provider and the deaf patient occurs.

New evidence on the real role of digital economy in influencing public health efficiency

In recent years, the rapid advancement of digital technology has supported the growth of the digital economy. The transformation towards digitization in the public health sector serves as a key indicator of this economic shift. Understanding how the digital economy continuously improves the efficiency of public health services and its various pathways of influence has become increasingly important. It is essential to clarify the impact mechanism of the digital economy on public health services to optimize health expenditures and advance digital economic construction. This study investigates the impact of digital economic development on the efficiency of public health services from a novel perspective, considering social media usage and urban-rural healthcare disparities while constructing a comprehensive index of digital economic development. The findings indicate that the digital economy reduces the efficiency of public health services primarily through two transmission mechanisms: the promotion of social media usage and the widening urban-rural healthcare gap. Moreover, these impacts and transmission pathways exhibit spatial heterogeneity. This study unveils the intrinsic connection and mechanisms of interaction between digital economic development and the efficiency of public health services, providing a theoretical basis and reference for government policy formulation. However, it also prompts further considerations on achieving synergy and interaction between the digital economy and public health services.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Continuity of care for patients with dementia during COVID-19 pandemic: flexibility and integration between in-person and remote visits

Introduction

During the pandemic, the Cognitive Disorders Unit of San Raffaele Hospital (Milan, Italy) offered patients the opportunity to undergo neuropsychological evaluations and cognitive training through telemedicine.

Method

We conducted an investigation to assess how patients responded to this option and to determine if telemedicine could ensure continuity of care.

Results

Between October 2019 and May 2022, a total of 5,768 telemedicine appointments and 8,190 in-person outpatient appointments were conducted, resulting in an increase in the rate of telemedicine activity from 16.81% in January 2020 to 23.21% in May 2022. Peaks in telemedicine activity reached 85.64% in May 2020 and 83.65% in February 2021, both representing a significant portion of the total activity. Interestingly, there was a notable positive correlation between telemedicine activity and the worsening of the Italian pandemic (r = 0.433, p = 0.027).

Discussion

During the peaks of contagion, the total number of visits remained stable, highlighting that telemedicine effectively served as a valuable and efficient tool to ensure continuity of care for vulnerable patients. This was evident from the integration of remote visits with in-person appointments.

AI in healthcare: navigating opportunities and challenges in digital communication

The landscape of healthcare communication is undergoing a profound transformation in the digital age, and at the heart of this evolution are AI-powered chatbots. This mini-review delves into the role of AI chatbots in digital health, providing a detailed exploration of their applications, benefits, challenges, and future prospects. Our focus is on their versatile applications within healthcare, encompassing health information dissemination, appointment scheduling, medication management, remote patient monitoring, and emotional support services. The review underscores the compelling advantages of AI chatbots. However, it also addresses the significant challenges posed by the integration of AI tools into healthcare communication.

A model of purchase intention of complementary and alternative medicines: the role of social media influencers' endorsements

BACKGROUND

Social Media Influencers (SMIs) are a fashionable way of marketing products by creating electronic word-of-mouth (e-WOM) on social media. The marketing of complementary and alternative medicines (CAMs) by SMIs is becoming increasingly popular and gaining credibility within consumers on social media platforms. Nonetheless, advising about healthcare products on social media should be examined as it is different from endorsing other kinds of commercial products. The aim of this study is to develop a model that provides the underlying mechanisms of the stimuli of SMIs on social media towards consumers' purchase intention of CAMs.

METHODS

This study used best fit framework synthesis methods to develop the model. A priori theory selection was conducted by identifying a BeHEMoTh strategy (Behavior of Interest, Health context, Exclusions and Models or Theories) to systematically approach identifying relevant models and theories relative to the research aim. Further evidence derived from primary research studies that describe the behavior identified is coded against selected a priori theory to develop the model.

RESULTS

This study presents a novel model for understanding the purchase behavior of CAMs using SMIs as a marketing strategy. The model included two well-known theories (theory of planned behaviour theory and source credibility theory) as well as extensive existing research from a multidisciplinary perspective. The model is exclusively designed to help identify elements affecting perceived source credibility and factors that have an influence over consumers' preferences to purchase CAMs by taking into consideration SMIs' endorsements.

CONCLUSIONS

This study provides unique insights introducing new research areas to health literature and offers, new roles for healthcare professionals in this digital era by gaining new skills and competencies required to provide more credible and accurate information about CAMs. The study also highlights the new marketing era of online health-related product endorsements and recommends that policymakers and researchers carefully evaluate the impact of SMI's on the use of CAMs, as well as to regulate the content of these promotional materials.

The Multidomain Metaverse Cancer Care Digital Platform: Development and Usability Study

BACKGROUND

As cancer treatment methods have diversified and the importance of self-management, which lowers the dependence rate on direct hospital visits, has increased, effective cancer care education and management for health professionals and patients have become necessary. The metaverse is in the spotlight as a means of digital health that allows users to engage in cancer care education and management beyond physical constraints. However, it is difficult to find a multipurpose medical metaverse that can not only be used in the field but also complements current cancer care.

OBJECTIVE

This study aimed to develop an integrated metaverse cancer care platform, Dr. Meta, and examine its usability.

METHODS

We conducted a multicenter, cross-sectional survey between November and December 2021. A descriptive analysis was performed to examine users' experiences with Dr. Meta. In addition, a supplementary open-ended question was used to ask users for their suggestions and improvements regarding the platform.

RESULTS

Responses from 70 Korean participants (male: n=19, 27% and female: n=51, 73%) were analyzed. More than half (n=37, 54%) of the participants were satisfied with Dr. Meta; they responded that it was an interesting and immersive platform (n=50, 72%). Less than half perceived no discomfort when using Dr. Meta (n=34, 49%) and no difficulty in wearing and operating the device (n=30, 43%). Furthermore, more than half (n=50, 72%) of the participants reported that Dr. Meta would help provide non-face-to-face and noncontact services. More than half also wanted to continue using this platform in the future (n=41, 59%) and recommended it to others (n=42, 60%).

CONCLUSIONS

We developed a multidomain metaverse cancer care platform that can support both health professionals and patients in non-face-to-face cancer care. The platform was uniquely disseminated and implemented in multiple regional hospitals and showed the potential to perform successful cancer care.

Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine

This study conducted a comprehensive patent and bibliometric analysis to elucidate the evolving scientific landscape surrounding the development and application of pulse oximeters, including in the field of digital medicine. Utilizing data from the Lens database for the period of 2000-2023, we identified the United States, China, the Republic of Korea, Japan, Canada, Australia, Taiwan, and the United Kingdom as the predominant countries in patent issuance for pulse oximeter technology. Our bibliometric analysis revealed a consistent temporal trend in both the volume of publications and citations, underscoring the growing importance of pulse oximeters in digitally-enabled medical practice. Using the VOSviewer software(version 1.6.18), we discerned six primary research clusters: (1) measurement accuracy; (2) integration with the Internet of Things; (3) applicability across diverse pathologies; (4) telemedicine and mobile applications; (5) artificial intelligence and deep learning; and (6) utilization in anesthesiology, resuscitation, and intensive care departments. The findings of this study indicate the prospects for leveraging digital technologies in the use of pulse oximetry in various fields of medicine, with implications for advancing the understanding, diagnosis, prevention, and treatment of cardio-respiratory pathologies. The conducted patent and bibliometric analysis allowed the identification of technical solutions to reduce the risks associated with pulse oximetry: improving precision and validity, technically improved clinical diagnostic use, and the use of machine learning.

Digital health care service reform and health inequity for older people: a quasi-natural experiment in China

Objective

Addressing health inequity (HI) for older people is a pivotal global public health concern, as it impedes the process of healthy ageing. The digital health care service reform (DHSR) emerges as a progressive public health approach to enhance the health and well-being of older adults by providing comprehensive and equitable medical services. This study elucidates the association between DHSR and HI for older individuals to augment comprehension of DHSR implementation.

Methods

The initiation of the action plan for smart health and eldercare (SHE) in 2017 serves as a quasi-natural experiment. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) in 2015 and 2018, a propensity score matching (PSM) method was used to select samples, and a difference-in-differences (DID) regression was used to ascertain the net effect of DHSR on HI for older individuals in China. This methodology mitigates selection bias and segregates the DHSR effect from temporal shifts or other occurrences.

Results

The PSM-DID analysis reveals that DHSR reduced the HI index for older individuals by 0.301 (p < 0.01). Heterogeneity analyses indicate that the effect of DHSR was more pronounced in older males (-0.333, p < 0.01) than females (-0.251, p < 0.05). The impact of DHSR was notably higher for older population in the western (-0.557, p < 0.01) and central regions (-0.318, p < 0.05) compared to the eastern region, where the relationship was statistically non-significant.

Conclusion

The results demonstrate that DHSR plays a vital role in diminishing HI, fostering inclusive growth in public health. The study underscores the imperative of sustained DHSR endeavours and allocating resources to key older demographics to substantially mitigate HI.

Using artificial intelligence to improve public health: a narrative review

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.

Remote Perimetry in a Virtual Reality Metaverse Environment for Out-of-Hospital Functional Eye Screening Compared Against the Gold Standard Humphrey Visual Fields Perimeter: Proof-of-Concept Pilot Study

BACKGROUND

The growing global burden of visual impairment necessitates better population eye screening for early detection of eye diseases. However, accessibility to testing is often limited and centralized at in-hospital settings. Furthermore, many eye screening programs were disrupted by the COVID-19 pandemic, presenting an urgent need for out-of-hospital solutions.

OBJECTIVE

This study investigates the performance of a novel remote perimetry application designed in a virtual reality metaverse environment to enable functional testing in community-based and primary care settings.

METHODS

This was a prospective observational study investigating the performance of a novel remote perimetry solution in comparison with the gold standard Humphrey visual field (HVF) perimeter. Subjects received a comprehensive ophthalmologic assessment, HVF perimetry, and remote perimetry testing. The primary outcome measure was the agreement in the classification of overall perimetry result normality by the HVF (Swedish interactive threshold algorithm-fast) and testing with the novel algorithm. Secondary outcome measures included concordance of individual testing points and perimetry topographic maps.

RESULTS

We recruited 10 subjects with an average age of 59.6 (range 28-81) years. Of these, 7 (70%) were male and 3 (30%) were female. The agreement in the classification of overall perimetry results was high (9/10, 90%). The pointwise concordance in the automated classification of individual test points was 83.3% (8.2%; range 75%-100%). In addition, there was good perimetry topographic concordance with the HVF in all subjects.

CONCLUSIONS

Remote perimetry in a metaverse environment had good concordance with gold standard perimetry using the HVF and could potentially avail functional eye screening in out-of-hospital settings.

Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising

Distributed big data and digital healthcare technologies have great potential to promote medical services, but challenges arise when it comes to learning predictive model from diverse and complex e-health datasets. Federated Learning (FL), as a collaborative machine learning technique, aims to address the challenges by learning a joint predictive model across multi-site clients, especially for distributed medical institutions or hospitals. However, most existing FL methods assume that clients possess fully labeled data for training, which is often not the case in e-health datasets due to high labeling costs or expertise requirement. Therefore, this work proposes a novel and feasible approach to learn a Federated Semi-Supervised Learning (FSSL) model from distributed medical image domains, where a federated pseudo-labeling strategy for unlabeled clients is developed based on the embedded knowledge learned from labeled clients. This greatly mitigates the annotation deficiency at unlabeled clients and leads to a cost-effective and efficient medical image analysis tool. We demonstrated the effectiveness of our method by achieving significant improvements compared to the state-of-the-art in both fundus image and prostate MRI segmentation tasks, resulting in the highest Dice scores of 89.23% and 91.95% respectively even with only a few labeled clients participating in model training. This reveals the superiority of our method for practical deployment, ultimately facilitating the wider use of FL in healthcare and leading to better patient outcomes.

Clinical severity classes in COVID-19 pneumonia have distinct immunological profiles, facilitating risk stratification by machine learning

Objective

Clinical triage in coronavirus disease 2019 (COVID-19) places a heavy burden on senior clinicians during a pandemic situation. However, risk stratification based on serum biomarker bioprofiling could be implemented by a larger, nonspecialist workforce.

Method

Measures of Complement Activation and inflammation in patientS with CoronAvirus DisEase 2019 (CASCADE) patients (n = 72), (clinicaltrials.gov: NCT04453527), classified as mild, moderate, or severe (by support needed to maintain SpO2 > 93%), and healthy controls (HC, n = 20), were bioprofiled using 76 immunological biomarkers and compared using ANOVA. Spearman correlation analysis on biomarker pairs was visualised via heatmaps. Linear Discriminant Analysis (LDA) models were generated to identify patients likely to deteriorate. An X-Gradient-boost (XGB) model trained on CASCADE data to triage patients as mild, moderate, and severe was retrospectively employed to classify COROnavirus Nomacopan Emergency Treatment for covid 19 infected patients with early signs of respiratory distress (CORONET) patients (n = 7) treated with nomacopan.

Results

The LDA models distinctly discriminated between deteriorators, nondeteriorators, and HC, with IL-27, IP-10, MDC, ferritin, C5, and sC5b-9 among the key predictor variables during deterioration. C3a and C5 were elevated in all severity classes vs. HC (p < 0.05). sC5b-9 was elevated in the "moderate" and "severe" categories vs. HC (p < 0.001). Heatmap analysis shows a pairwise increase of negatively correlated pairs with IL-27. The XGB model indicated sC5b-9, IL-8, MCP1, and prothrombin F1 and F2 were key discriminators in nomacopan-treated patients (CORONET study).

Conclusion

Distinct immunological fingerprints from serum biomarkers exist within different severity classes of COVID-19, and harnessing them using machine learning enabled the development of clinically useful triage and prognostic tools. Complement-mediated lung injury plays a key role in COVID-19 pneumonia, and preliminary results hint at the usefulness of a C5 inhibitor in COVID-19 recovery.

Digital health funding for COVID-19 vaccine deployment across four major donor agencies

The international response to the COVID-19 pandemic provided opportunities for countries to use digital technologies for vaccine deployment and associated activities, but misaligned digital investments could weaken or fragment national systems. In this review of 311 funding applications from 120 country governments to four donor agencies (UNICEF; Gavi, the Vaccine Alliance; the Global Fund to Fight AIDS, Tuberculosis and Malaria; and the World Bank) up to May 1, 2022, we found that 272 (87%) of the applications included at least one digital aspect and that substantial funding has been dedicated towards digital aspects from donors. The majority of digital aspects concerned immunisation information systems, vaccine acceptance and uptake, and COVID-19 surveillance. As the global community sets its sights on a COVID-19-free world, continued coordinated investments in digital health and health information systems for pandemic preparedness and response will be key to strengthening the resilience of health systems.

The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai

Background

Chronic disease management (CDM) falls under production relations, and digital technology belongs to the realm of productivity. Production relations must adapt to the development of productivity. Simultaneously, the prevalence and burden of chronic diseases are becoming increasingly severe, leveraging digital technology to innovate chronic disease management model is essential.

Methods

The model was built to cover experts in a number of fields, including administrative officials, public health experts, information technology staff, clinical experts, general practitioners, nurses, metrologists. Integration of multiple big data platforms such as General Practitioner Contract Platform, Integrated Community Multimorbidity Management System and Municipal and District-Level Health Information Comprehensive Platform. This study fully analyzes the organizational structure, participants, service objects, facilities and equipment, digital technology, operation process, etc., required for new model in the era of big data.

Results

Based on information technology, we build Integrated Community Multimorbidity Care Model (ICMCM). This model is based on big data, is driven by "technology + mechanism," and uses digital technology as a tool to achieve the integration of services, technology integration, and data integration, thereby providing patients with comprehensive people-centered services. In order to promote the implementation of the ICMCM, Shanghai has established an integrated chronic disease management information system, clarified the role of each module and institution, and achieved horizontal and vertical integration of data and services. Moreover, we adopt standardized service processes and accurate blood pressure and blood glucose measurement equipment to provide services for patients and upload data in real time. On the basis of Integrated Community Multimorbidity Care Model, a platform and index system have been established, and the platform's multidimensional cross-evaluation and indicators are used for management and visual display.

Conclusions

The Integrated Community Multimorbidity Care Model guides chronic disease management in other countries and regions. We have utilized models to achieve a combination of services and management that provide a grip on chronic disease management.

Awareness and attitudes of elderly Southeast Asian adults towards telehealth during the COVID-19 pandemic: a qualitative study

Introduction

We aimed to understand the awareness and attitudes of elderly Southeast Asians towards telehealth services during the coronavirus disease 2019 (COVID-19) pandemic in this study.

Methods

In this qualitative study, 78 individuals from Singapore (51.3% female, mean age 73.0 ± 7.6 years) were interviewed via telephone between 13 May 2020 and 9 June 2020 during Singapore's first COVID-19 'circuit breaker'. Participants were asked to describe their understanding of telehealth, their experience of and willingness to utilise these services, and the barriers and facilitators underlying their decision. Transcripts were analysed using thematic analysis, guided by the United Theory of Acceptance Use of Technology framework.

Results

Of the 78 participants, 24 (30.8%) were able to describe the range of telehealth services available and 15 (19.2%) had previously utilised these services. Conversely, 14 (17.9%) participants thought that telehealth comprised solely home medication delivery and 50 (51.3%) participants did not know about telehealth. Despite the advantages offered by telehealth services, participants preferred in-person consultations due to a perceived lack of human interaction and accuracy of diagnoses, poor digital literacy and a lack of access to telehealth-capable devices.

Conclusion

Our results showed poor overall awareness of the range of telehealth services available among elderly Asian individuals, with many harbouring erroneous views regarding their use. These data suggest that public health education campaigns are needed to improve awareness of and correct negative perceptions towards telehealth services in elderly Asians.

Investigating Individuals' Preferences in Determining the Functions of Smartphone Apps for Fighting Pandemics: Best-Worst Scaling Survey Study

BACKGROUND

Smartphone apps have been beneficial in controlling and preventing the COVID-19 pandemic. However, there is a gap in research surrounding the importance of smartphone app functions from a user's perspective. Although the insights and opinions of different stakeholders, such as policymakers and medical professionals, can influence the success of a public health policy, any strategy will face difficulty in achieving the expected effect if it is not based on a method that users can accept.

OBJECTIVE

This study aimed to assess the importance of a hypothetical smartphone app's functions for managing health during a pandemic based on the perspective of user preferences.

METHODS

A cross-sectional and web-based survey using the best-worst scaling (BWS) method was used to investigate the general population's preferences for important smartphone app functions. Participants were recruited from a professional surveying company's web-based surveying panel. The attributes of the BWS questionnaire were developed based on a robust process, including literature review, interviews, and expert discussion. A balanced incomplete block design was used to construct the choice task to ensure the effectiveness of the research design. Count analysis, conditional logit model analysis, and mixed logit analysis were used to estimate preference heterogeneity among respondents.

RESULTS

The responses of 2153 participants were eligible for analysis. Nearly 55% (1192/2153) were female, and the mean age was 31.4 years. Most participants (1765/2153, 81.9%) had completed tertiary or higher education, and approximately 70% (1523/2153) were urban residents. The 3 most vital functions according to their selection were "surveillance and monitoring of infected cases," "quick self-screening," and "early detection of infected cases." The mixed logit regression model identified significant heterogeneity in preferences among respondents, and stratified analysis showed that some heterogeneities varied in respondents by demographics and COVID-19-related characteristics. Participants who preferred to use the app were more likely to assign a high weight to the preventive functions than those who did not prefer to use it. Conversely, participants who showed lower willingness to use the app tended to indicate a higher preference for supportive functions than those who preferred to use it.

CONCLUSIONS

This study ranks the importance of smartphone app features that provide health care services during a pandemic based on the general population's preferences in China. It provides empirical evidence for decision-makers to develop eHealth policies and strategies that address future public health crises from a person-centered care perspective. Continued use of apps and smart investment in digital health can help improve health outcomes and reduce the burden of disease on individuals and communities.

Economic evaluations of artificial intelligence-based healthcare interventions: a systematic literature review of best practices in their conduct and reporting

Objectives: Health economic evaluations (HEEs) help healthcare decision makers understand the value of new technologies. Artificial intelligence (AI) is increasingly being used in healthcare interventions. We sought to review the conduct and reporting of published HEEs for AI-based health interventions. Methods: We conducted a systematic literature review with a 15-month search window (April 2021 to June 2022) on 17th June 2022 to identify HEEs of AI health interventions and update a previous review. Records were identified from 3 databases (Medline, Embase, and Cochrane Central). Two reviewers screened papers against predefined study selection criteria. Data were extracted from included studies using prespecified data extraction tables. Included studies were quality assessed using the National Institute for Health and Care Excellence (NICE) checklist. Results were synthesized narratively. Results: A total of 21 studies were included. The most common type of AI intervention was automated image analysis (9/21, 43%) mainly used for screening or diagnosis in general medicine and oncology. Nearly all were cost-utility (10/21, 48%) or cost-effectiveness analyses (8/21, 38%) that took a healthcare system or payer perspective. Decision-analytic models were used in 16/21 (76%) studies, mostly Markov models and decision trees. Three (3/16, 19%) used a short-term decision tree followed by a longer-term Markov component. Thirteen studies (13/21, 62%) reported the AI intervention to be cost effective or dominant. Limitations tended to result from the input data, authorship conflicts of interest, and a lack of transparent reporting, especially regarding the AI nature of the intervention. Conclusion: Published HEEs of AI-based health interventions are rapidly increasing in number. Despite the potentially innovative nature of AI, most have used traditional methods like Markov models or decision trees. Most attempted to assess the impact on quality of life to present the cost per QALY gained. However, studies have not been comprehensively reported. Specific reporting standards for the economic evaluation of AI interventions would help improve transparency and promote their usefulness for decision making. This is fundamental for reimbursement decisions, which in turn will generate the necessary data to develop flexible models better suited to capturing the potentially dynamic nature of AI interventions.

Polarimetric imaging for the detection of synthetic models of SARS-CoV-2: A proof of concept

OBJECTIVE

To conduct a proof-of-concept study of the detection of two synthetic models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using polarimetric imaging.

APPROACH

Two SARS-CoV-2 models were prepared as engineered lentiviruses pseudotyped with the G protein of the vesicular stomatitis virus, and with the characteristic Spike protein of SARS-CoV-2. Samples were prepared in two biofluids (saline solution and artificial saliva), in four concentrations, and deposited as 5-µL droplets on a supporting plate. The angles of maximal degree of linear polarization (DLP) of light diffusely scattered from dry residues were determined using Mueller polarimetry from87 samples at 405 nm and 514 nm. A polarimetric camera was used for imaging several samples under 380-420 nm illumination at angles similar to those of maximal DLP. Per-pixel image analysis included quantification and combination of polarization feature descriptors in 475 samples.

MAIN RESULTS

The angles (from sample surface) of maximal DLP were 3° for 405 nm and 6° for 514 nm. Similar viral particles that differed only in the characteristic spike protein of the SARS-CoV-2, their corresponding negative controls, fluids, and the sample holder were discerned at 10-degree and 15-degree configurations.

SIGNIFICANCE

Polarimetric imaging in the visible spectrum may help improve fast, non-contact detection and identification of viral particles, and/or other microbes such as tuberculosis, in multiple dry fluid samples simultaneously, particularly when combined with other imaging modalities. Further analysis including realistic concentrations of real SARS-CoV-2 viral particles in relevant human fluids is required. Polarimetric imaging under visible light may contribute to a fast, cost-effective screening of SARS-CoV-2 and other pathogens when combined with other imaging modalities.

Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19

Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss' Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications.

Expanding the Evidence on the Safety and Efficiency of 2-Way Text Messaging-Based Telehealth for Voluntary Medical Male Circumcision Follow-up Compared With In-Person Reviews: Randomized Controlled Trial in Rural and Urban South Africa

BACKGROUND

There is a dearth of high-quality evidence from digital health interventions in routine program settings in low- and middle-income countries. We previously conducted a randomized controlled trial (RCT) in Zimbabwe, demonstrating that 2-way texting (2wT) was safe and effective for follow-up after adult voluntary medical male circumcision (VMMC).

OBJECTIVE

To demonstrate the replicability of 2wT, we conducted a larger RCT in both urban and rural VMMC settings in South Africa to determine whether 2wT improves adverse event (AE) ascertainment and, therefore, the quality of follow-up after VMMC while reducing health care workers' workload.

METHODS

A prospective, unblinded, noninferiority RCT was conducted among adult participants who underwent VMMC with cell phones randomized in a 1:1 ratio between 2wT and control (routine care) in North West and Gauteng provinces. The 2wT participants responded to a daily SMS text message with in-person follow-up only if desired or an AE was suspected. The control group was requested to make in-person visits on postoperative days 2 and 7 as per national VMMC guidelines. All participants were asked to return on postoperative day 14 for study-specific review. Safety (cumulative AEs ≤day 14 visit) and workload (number of in-person follow-up visits) were compared. Differences in cumulative AEs were calculated between groups. Noninferiority was prespecified with a margin of -0.25%. The Manning score method was used to calculate 95% CIs.

RESULTS

The study was conducted between June 7, 2021, and February 21, 2022. In total, 1084 men were enrolled (2wT: n=547, 50.5%, control: n=537, 49.5%), with near-equal proportions of rural and urban participants. Cumulative AEs were identified in 2.3% (95% CI 1.3-4.1) of 2wT participants and 1.0% (95% CI 0.4-2.3) of control participants, demonstrating noninferiority (1-sided 95% CI -0.09 to ∞). Among the 2wT participants, 11 AEs (9 moderate and 2 severe) were identified, compared with 5 AEs (all moderate) among the control participants-a nonsignificant difference in AE rates (P=.13). The 2wT participants attended 0.22 visits, and the control participants attended 1.34 visits-a significant reduction in follow-up visit workload (P<.001). The 2wT approach reduced unnecessary postoperative visits by 84.8%. Daily response rates ranged from 86% on day 3 to 74% on day 13. Among the 2wT participants, 94% (514/547) responded to ≥1 daily SMS text messages over 13 days.

CONCLUSIONS

Across rural and urban contexts in South Africa, 2wT was noninferior to routine in-person visits for AE ascertainment, demonstrating 2wT safety. The 2wT approach also significantly reduced the follow-up visit workload, improving efficiency. These results strongly suggest that 2wT provides quality VMMC follow-up and should be adopted at scale. Adaptation of the 2wT telehealth approach to other acute follow-up care contexts could extend these gains beyond VMMC.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04327271; https://www.clinicaltrials.gov/ct2/show/NCT04327271.

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.

Changes in digital healthcare search behavior during the early months of the COVID-19 pandemic: A study of six English-speaking countries

Public interest is an important component influencing the likelihood of successfully implementing digital healthcare. The onset of the COVID-19 pandemic allowed us to assess how public interest in digital health changed in response to disruptions in traditional health services. In this study, we used a difference-in-differences approach to determine how digital healthcare search behavior shifted during the early months of the COVID-19 pandemic compared to the same period in 2019 across six English-speaking countries: the United States, Canada, the United Kingdom, New Zealand, Australia, and Ireland. In most cases, we observed that the official declaration of the COVID-19 pandemic on 11 March 2020 was associated with a significant overall increase in the volume of digital healthcare searches. We also found notable heterogeneity between countries in terms of the keywords that were used to search for digital healthcare, which could be explained by linguistic differences across countries or the different national digital health landscapes. Since online searches could be an initial step in the pathway to accessing health services, future studies should investigate under what circumstances increased public interest translates into demand for and utilization of digital healthcare.

Digital health in fragile states in the Middle East and North Africa (MENA) region: A scoping review of the literature

INTRODUCTION

Conflict, fragility and political violence, that are taking place in many countries in the Middle East and North Africa (MENA) region have devastating effects on health. Digital health technologies can contribute to enhancing the quality, accessibility and availability of health care services in fragile and conflict-affected states of the MENA region. To inform future research, investments and policy processes, this scoping review aims to map out the evidence on digital health in fragile states in the MENA region.

METHOD

We conducted a scoping review following the Joanna Briggs Institute (JBI) guidelines. We conducted descriptive analysis of the general characteristics of the included papers and thematic analysis of the key findings of included studies categorized by targeted primary users of different digital health intervention.

RESULTS

Out of the 10,724 articles identified, we included 93 studies. The included studies mainly focused on digital health interventions targeting healthcare providers, clients and data services, while few studies focused on health systems or organizations managers. Most of the included studies were observational studies (49%). We identified no systematic reviews. Most of the studies were conducted in Lebanon (32%) followed by Afghanistan (13%) and Palestine (12%). The first authors were mainly affiliated with institutions from countries outside the MENA region (57%), mainly United Kingdom and United States. Digital health interventions provided a platform for training, supervision, and consultation for health care providers, continuing education for medical students, and disease self-management. The review also highlighted some implementation considerations for the adoption of digital health such as computer literacy, weak technological infrastructure, and privacy concerns.

CONCLUSION

This review showed that digital health technologies can provide promising solutions in addressing health needs in fragile and conflict-affected states. However, rigorous evaluation of digital technologies in fragile settings and humanitarian crises are needed to inform their design and deployment.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.

Centering Frontline Health Care Workers in Digital Innovation Design to Inform the Optimization of an App for Improved Male Circumcision Follow-up in South Africa: Qualitative Usability Study

BACKGROUND

Voluntary medical male circumcision (VMMC) is a safe and effective HIV prevention strategy. However, adherence to recommended in-person, postoperative follow-up is inefficient for procedures with few adverse events. Two-way texting (2wT)-based follow-up appears to be a safe and an efficient alternative to scheduled clinic visits for low-risk patients who underwent VMMC. To ensure that 2wT responds to the needs of health care workers (HCWs) and patients, HCWs were closely involved in app design using a human-centered design (HCD) approach.

OBJECTIVE

Embedded within an ongoing randomized controlled trial of 2wT in South Africa and complementary HCD processes of 2wT app optimization, this qualitative study aimed to use key informant interviews (KIIs) to explore the thoughts, suggestions, and opinions on and perceptions of 2wT's usability and acceptability among HCWs involved in 2wT implementation in both urban and rural South Africa.

METHODS

A total of 7 HCWs using 2wT in Gauteng and the North West province participated in KIIs regarding the usability and acceptability of 2wT. HCWs were asked for their opinions on 2wT as a viable, useful, appropriate, and accessible method of postoperative VMMC care. They were also asked about 2wT-related working, exploring areas where 2wT could add to or reduce their daily tasks. The KII data were explored, coded, and analyzed by 3 qualitative researchers using thematic content analysis and the ATLAS.ti (ATLAS.ti Scientific Software Development GmbH) software.

RESULTS

Most HCWs felt confident, comfortable, satisfied, and well supported using a 2wT-based follow-up as an alternative to in-person clinical reviews. They felt that 2wT was easy to use and required little technical support after initial mentoring on how to use the 2wT system. Few noted safety concerns, as men can receive clinical guidance, reassurance, and referral via 2wT. Although fewer in-person visits reduced the in-person review workload and eased clinical flow, HCWs noted the added burden of having to interact with clients via SMS text messages on evenings or weekends. HCWs reinforced the need for enhanced postoperative counseling to ensure that 2wT patients could recognize and understood how to respond to early signs of complications. HCWs suggested a rotation to spread the evening and weekend workload and ensure swift patient responses.

CONCLUSIONS

In this formative qualitative study focused on HCWs, 2wT was a highly usable alternative to in-person postoperative reviews for patients who underwent VMMC in South Africa. The HCD processes likely improved the usability and acceptability of 2wT for HCWs. HCWs supported the scale-up of 2wT given the distance from the clinic to the men's homes and the potential for reducing workload. To ensure success, providers urged sensitizing patients to the fact that 2wT augments, but does not replace, the existing after-hours and emergency care services.

How Can Sport-Based Interventions Improve Health among Women and Girls? A Scoping Review

Sport has been identified by the World Health Organization as an underutilized yet important contributor to global physical activity, by UNESCO as a fundamental right, and by the United Nations as a promising driver for gender equity through improved long-term health of women and girls. Although sport-based interventions have been popularized to advance educational, social, and political development globally, little attention has been given to its impacts on health outcomes among women and girls. We undertook a scoping review of research on sport-based interventions for health among women and girls to summarize current research approaches and findings. PRISMA scoping review guidelines were observed. Online databases (PubMed, PsycINFO, Web of Science) were used to identify peer-reviewed records published through August 2022. The interventions identified (n = 4) targeted health outcomes such as gender-based violence, HIV prevention, reproductive health, and child marriage. Based on our review, we recommend four key opportunities to advance the field of sport-based interventions in addressing health equity among women and girls. In addition, we highlight promising future research directions to broaden sport engagement of women and girls, improve long-term health, and build capacity toward health equity.

Lessons Learned from Natural Disasters around Digital Health Technologies and Delivering Quality Healthcare

As climate change drives increased intensity, duration and severity of weather-related events that can lead to natural disasters and mass casualties, innovative approaches are needed to develop climate-resilient healthcare systems that can deliver safe, quality healthcare under non-optimal conditions, especially in remote or underserved areas. Digital health technologies are touted as a potential contributor to healthcare climate change adaptation and mitigation, through improved access to healthcare, reduced inefficiencies, reduced costs, and increased portability of patient information. Under normal operating conditions, these systems are employed to deliver personalised healthcare and better patient and consumer involvement in their health and well-being. During the COVID-19 pandemic, digital health technologies were rapidly implemented on a mass scale in many settings to deliver healthcare in compliance with public health interventions, including lockdowns. However, the resilience and effectiveness of digital health technologies in the face of the increasing frequency and severity of natural disasters remain to be determined. In this review, using the mixed-methods review methodology, we seek to map what is known about digital health resilience in the context of natural disasters using case studies to demonstrate what works and what does not and to propose future directions to build climate-resilient digital health interventions.

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

BACKGROUND

Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

OBJECTIVE

We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

METHODS

We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

RESULTS

A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization.

CONCLUSIONS

The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

BACKGROUND

Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities.

OBJECTIVE

This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them.

METHODS

We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies.

RESULTS

In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance.

CONCLUSIONS

Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.

Voice-Based Screening for SARS-CoV-2 Exposure in Cardiovascular Clinics (VOICE-COVID-19-II): Protocol for a Randomized Controlled Trial

BACKGROUND

The COVID-19 pandemic has disrupted the health care system, limiting health care resources such as the availability of health care professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools have become an important asset in increasing the efficiency of patient care delivery. Digital tools can help support health care institutions by tracking transmission of the virus, aiding in the screening process, and providing telemedicine support. However, digital health tools face challenges associated with barriers to accessibility, efficiency, and privacy-related ethical issues.

OBJECTIVE

This paper describes the study design of an open-label, noninterventional, crossover, randomized controlled trial aimed at assessing whether interactive voice response systems can screen for SARS-CoV-2 in patients as accurately as standard screening done by people. The study aims to assess the concordance and interrater reliability of symptom screening done by Amazon Alexa compared to manual screening done by research coordinators. The perceived level of comfort of patients when interacting with voice response systems and their personal experience will also be evaluated.

METHODS

A total of 52 patients visiting the heart failure clinic at the Royal Victoria Hospital of the McGill University Health Center, in Montreal, Quebec, will be recruited. Patients will be randomly assigned to first be screened for symptoms of SARS-CoV-2 either digitally, by Amazon Alexa, or manually, by the research coordinator. Participants will subsequently be crossed over and screened either digitally or manually. The clinical setup includes an Amazon Echo Show, a tablet, and an uninterrupted power supply mounted on a mobile cart. The primary end point will be the interrater reliability on the accuracy of randomized screening data performed by Amazon Alexa versus research coordinators. The secondary end point will be the perceived level of comfort and app engagement of patients as assessed using 5-point Likert scales and binary mode responses.

RESULTS

Data collection started in May 2021 and is expected to be completed in fall 2022. Data analysis is expected to be completed in early 2023.

CONCLUSIONS

The use of voice-based assistants could improve the provision of health services and reduce the burden on health care personnel. Demonstrating a high interrater reliability between Amazon Alexa and health care coordinators may serve future digital tools to streamline the screening and delivery of care in the context of other conditions and clinical settings. The COVID-19 pandemic occurs during the first digital era using digital tools such as Amazon Alexa for disease screening, and it represents an opportunity to implement such technology in health care institutions in the long term.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04508972; https://clinicaltrials.gov/ct2/show/NCT04508972.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/41209.

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil

BACKGROUND

Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. It safely provides access to health care services and expands assistance to remote, rural areas and underserved communities in situations of shortage of specialized health professionals. Qualified data are systematically collected by health care workers containing information on suspected cases and can be used as a proxy of disease spread for surveillance purposes. However, the use of this approach for syndromic surveillance has yet to be explored. Besides, the mathematical modeling of epidemics is a well-established field that has been successfully used for tracking the spread of SARS-CoV-2 infection, supporting the decision-making process on diverse aspects of public health response to the COVID-19 pandemic. The response of the current models depends on the quality of input data, particularly the transmission rate, initial conditions, and other parameters present in compartmental models. Telehealth systems may feed numerical models developed to model virus spread in a specific region.

OBJECTIVE

Herein, we evaluated whether a high-quality data set obtained from a state-based telehealth service could be used to forecast the geographical spread of new cases of COVID-19 and to feed computational models of disease spread.

METHODS

We analyzed structured data obtained from a statewide toll-free telehealth service during 4 months following the first notification of COVID-19 in the Bahia state, Brazil. Structured data were collected during teletriage by a health team of medical students supervised by physicians. Data were registered in a responsive web application for planning and surveillance purposes. The data set was designed to quickly identify users, city, residence neighborhood, date, sex, age, and COVID-19-like symptoms. We performed a temporal-spatial comparison of calls reporting COVID-19-like symptoms and notification of COVID-19 cases. The number of calls was used as a proxy of exposed individuals to feed a mathematical model called "susceptible, exposed, infected, recovered, deceased."

RESULTS

For 181 (43%) out of 417 municipalities of Bahia, the first call to the telehealth service reporting COVID-19-like symptoms preceded the first notification of the disease. The calls preceded, on average, 30 days of the notification of COVID-19 in the municipalities of the state of Bahia, Brazil. Additionally, data obtained by the telehealth service were used to effectively reproduce the spread of COVID-19 in Salvador, the capital of the state, using the "susceptible, exposed, infected, recovered, deceased" model to simulate the spatiotemporal spread of the disease.

CONCLUSIONS

Data from telehealth services confer high effectiveness in anticipating new waves of COVID-19 and may help understand the epidemic dynamics.

The Effect of the COVID-19 Pandemic on Digital Health-Seeking Behavior: Big Data Interrupted Time-Series Analysis of Google Trends

BACKGROUND

Due to the emergency responses early in the COVID-19 pandemic, the use of digital health in health care increased abruptly. However, it remains unclear whether this introduction was sustained in the long term, especially with patients being able to decide between digital and traditional health services once the latter regained their functionality throughout the COVID-19 pandemic.

OBJECTIVE

We aim to understand how the public interest in digital health changed as proxy for digital health-seeking behavior and to what extent this change was sustainable over time.

METHODS

We used an interrupted time-series analysis of Google Trends data with break points on March 11, 2020 (declaration of COVID-19 as a pandemic by the World Health Organization), and December 20, 2020 (the announcement of the first COVID-19 vaccines). Nationally representative time-series data from February 2019 to August 2021 were extracted from Google Trends for 6 countries with English as their dominant language: Canada, the United States, the United Kingdom, New Zealand, Australia, and Ireland. We measured the changes in relative search volumes of the keywords online doctor, telehealth, online health, telemedicine, and health app. In doing so, we capture the prepandemic trend, the immediate change due to the announcement of COVID-19 being a pandemic, and the gradual change after the announcement.

RESULTS

Digital health search volumes immediately increased in all countries under study after the announcement of COVID-19 being a pandemic. There was some variation in what keywords were used per country. However, searches declined after this immediate spike, sometimes reverting to prepandemic levels. The announcement of COVID-19 vaccines did not consistently impact digital health search volumes in the countries under study. The exception is the search volume of health app, which was observed as either being stable or gradually increasing during the pandemic.

CONCLUSIONS

Our findings suggest that the increased public interest in digital health associated with the pandemic did not sustain, alluding to remaining structural barriers. Further building of digital health capacity and developing robust digital health governance frameworks remain crucial to facilitating sustainable digital health transformation.

Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis

Background

The frequent occurrence of major public health emergencies globally poses a threat to people's life, health, and safety, and the convergence development of digital technology is very effective and necessary to cope with the outbreak and transmission control of public epidemics such as COVID-19, which is essential to improve the emergency management capability of global public health emergencies.

Methods

The published literatures in the Web of Science Core Collection database from 2003 to 2022 were utilized to analyze the contribution and collaboration of the authors, institutions, and countries, keyword co-occurrence analysis, and research frontier identification using the CiteSpace, VOSviewer, and COOC software.

Results

The results are shown as follows: (1) Relevant research can be divided into growth and development period and rapid development period, and the total publications show exponential growth, among which the USA, China, and the United Kingdom are the most occupied countries, but the global authorship cooperation is not close; (2) clustering analysis of high-frequency keyword, all kinds of digital technologies are utilized, ranging from artificial intelligence (AI)-driven machine learning (ML) or deep learning (DL), and focused application big data analytics and blockchain technology enabled the internet of things (IoT) to identify, and diagnose major unexpected public diseases are hot spots for future research; (3) Research frontier identification indicates that data analysis in social media is a frontier issue that must continue to be focused on to advance digital and smart governance of public health events.

Conclusion

This bibliometric study provides unique insights into the role of digital technologies in the emergency management of public health. It provides research guidance for smart emergency management of global public health emergencies.

Precision public-health intervention for care coordination: a real-world study

BACKGROUND

Health emergencies disproportionally affect vulnerable populations. Digital tools can help primary care providers find, and reach, the right patients.

AIM

To evaluate whether digital interventions delivered directly to GPs' clinical software were more effective at promoting primary care appointments during the COVID-19 pandemic than interventions delivered by post.

DESIGN AND SETTING

Real-world, non-randomised, interventional study involving GP practices in all Australian states.

METHOD

Intervention material was developed to promote care coordination for vulnerable older veterans during the COVID-19 pandemic, and sent to GPs either digitally to the clinical practice software system or in the post. The intervention material included patient-specific information sent to GPs to support care coordination, and education material sent via post to veterans identified in the administrative claims database. To evaluate the impact of intervention delivery modalities on outcomes, the time to first appointment with the primary GP was measured; a Cox proportional hazards model was used, adjusting for differences and accounting for pre-intervention appointment numbers.

RESULTS

The intervention took place in April 2020, during the first weeks of COVID-19 social distancing restrictions in Australia. GPs received digital messaging for 51 052 veterans and postal messaging for 26 859 veterans. The digital group was associated with earlier appointments (adjusted hazard ratio 1.38 [1.34 to 1.41]).

CONCLUSION

Data-driven digital solutions can promote care coordination at scale during national emergencies, opening up new perspectives for precision public-health initiatives.

Assessing the Acceptability of Home Blood Monitoring for Patients With Cancer Who Are Receiving Systemic Anticancer Therapy From a Patient, Caregiver, and Clinician Perspective: Focus Group and Interview Study

BACKGROUND

Regular blood testing is an integral part of systemic anticancer therapy delivery. Blood tests are required before every administration of treatment to ensure that a patient is sufficiently well to receive it. Blood testing is burdensome for patients as they require either an extra visit within 48 hours of planned administration of treatment or a significantly long visit if performed on the day of treatment. The additional time for appointments can have a significant impact on the quality of life of someone who is living with cancer. In the United Kingdom, the COVID-19 pandemic created unprecedented disruption to the delivery of cancer care. Face-to-face hospital visits were reduced, resulting in the need to develop more innovative ways of working to minimize treatment interruptions. This led to significant uptake of digital technologies, with new models of care rapidly deployed across the UK health service to meet these challenges.

OBJECTIVE

This study aimed to explore the acceptability of a point-of-care home blood monitoring device for people with cancer who are receiving systemic anticancer therapy, which is being developed in response to the increased need for remote care for patients with cancer.

METHODS

Qualitative focus groups and semistructured interviews were conducted with patients (23/47, 49%), caregivers (6/47, 13%), and health care professionals (18/47, 38%) over a 19-month time frame from May 2019 to December 2020. Data were analyzed using framework analysis guided by the Unified Theory of Acceptance and Use of Technology model.

RESULTS

Analysis identified 4 overarching themes: performance expectancy, effort expectancy, social influence, and facilitating conditions.

CONCLUSIONS

This study found that patients with cancer, their caregivers, and health care professionals had positive perceptions about home blood monitoring. Although they are often considered synonymously, self-testing and self-management are not mutually exclusive, and this study illustrated some disparity in opinions regarding patient self-management. Home blood monitoring has the potential to provide patients with cancer with a convenient option for blood monitoring. It would minimize hospital attendances, decrease late treatment deferrals, and provide prompt recognition of cancer treatment toxicities, thus enhancing the existing nurse-led protocols and clinical pathways. Home blood monitoring would create a long-term sustainable transformation for the delivery of cancer care, using digital health to act as a facilitator to address a pertinent issue regarding improving the efficiency of hospital resources and increasing the delivery of personalized patient care. Further studies are needed to determine how and where home blood monitoring would fit within clinical pathways, in a way that is robust and equitable.

A digital health ecosystem ontology from the perspective of Australian consumers: a mixed-method literature analysis

This study presents an ontology that scopes the digital health ecosystem from a consumer-centered perspective. We used a mixed-method analysis on a set of papers collected for a comprehensive review to identify common themes, components, and patterns that repeatedly emerge within Australian-based digital health studies. Three major and four child themes were identified as the foundational aspects of the proposed ontology. The child themes have more precise concept definitions, inherited and distinguishing attributes. Out of 179 recognized concepts, 33 were related to the Healthcare theme; 23 concepts formed a cluster of employed devices under the Technology theme; 40 concepts were associated with Use and Usability factors. 60 other concepts formed the cluster of the consumer-user theme. The theme of Digital Health was seen as being connected to 2 independent clusters. The main cluster embodied 21 extracted concepts, semantically related to "data, information, and knowledge," whilst the second cluster embodied concepts related to "healthcare." Different stakeholders can utilize this ontology to define their landscape of digitally enabled healthcare. The novelty of this work resides in capturing a consumer-centered perspective and the method we used in deriving the ontology - formalizing the results of a systematic review based on data-driven analysis methods.

Embedding digital sleep health into primary care practice: A triangulation of perspectives from general practitioners, nurses, and pharmacists

Introduction

While digital health interventions (DHIs) can potentially address the unmet needs for sleep health services, little is known about their implementation in practice. The current study aimed to explore primary care health providers' attitudes and beliefs towards DHIs for sleep and implementation into practice.

Methods

A cross-sectional online survey was administered to Australian primary care health professionals: general practitioners (GPs), community nurses, and community pharmacists. Semi-structured interviews were conducted within a sub-sample of participants exploring their experiences with DHIs and perceived barriers/facilitators for embedding DHIs into primary care. Semi-structured interviews were thematically analysed using the framework approach to contextualise survey findings.

Results

Ninety-six surveys were returned (GPs  =  36, nurses = 30, and pharmacists = 30) and 45 interviews conducted (GPs  =  17, nurses = 14, and pharmacists  =  14). From the survey, GPs were more likely to endorse familiarity (p  =  0.009) and use (p < 0.001) of sleep DHIs in clinical practice than pharmacists and nurses. GPs were more interested in utilising the diagnostic features within a sleep DHI (p  =  0.009) compared to other professionals. Thematic analysis of the interviews revealed three major themes, contextualised by profession: (1) Scope for DHIs in Current Practice, (2) Practice Gaps and Training Needs, and (3) Envisioning a Model of Care Using Sleep DHIs. While DHIs can potentially improve care, greater clarity of care pathways and reimbursement structures are needed for integration into practice.

Conclusion

Primary care health professionals highlighted the training, care pathway and financial models required to realise the potential for translating findings from efficacy studies for DHIs into primary care to optimise sleep health.

Advances in artificial intelligence models and algorithms in the field of optometry

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.

Digital health literacy and well-being among university students: Mediating roles of fear of COVID-19, information satisfaction, and internet information search

Background

Digital health literacy (DHL) enables healthy decisions, improves protective behaviors and adherence to COVID-19 measures, especially during the era of the “infodemic”, and enhances psychological well-being.

Objective

We aimed to explore the mediating roles of fear of COVID-19, information satisfaction, and the importance of online information searching on the association between DHL and well-being.

Methods

A cross-sectional web-based survey was conducted among 1631 Taiwanese university students, aged 18 years and above, from June 2021 to March 2022. The collected data include sociodemographic characteristics (sex, age, social status, and financial satisfaction), the importance of online information searching, information satisfaction, fear of COVID-19, DHL, and well-being. A linear regression model was utilized to investigate factors associated with well-being, followed by a pathway analysis to assess the direct and indirect relationship between DHL and well-being.

Results

The scores of DHL and overall well-being were 3.1 ± 0.4 and 74.4 ± 19.7, respectively. Social status (B = 2.40, 95% confidence interval (CI) 1.73–3.07, p < 0.001), DHL (B 0.29, 95% CI 0.10–0.49, p < 0.001), importance of online information searching (B = 0.78, 95% CI 0.38–1.17, p < 0.001), and information satisfaction (B = 3.59, 95% CI 2.22–4.94, p < 0.001) were positively associated with well-being, whereas higher fear of COVID-19 scores (B = −0.38, 95% CI −0.55-(−0.21), p < 0.001) and female (B = −2.99, 95% CI −5.02–0.6, p = 0.004) were associated with lower well-being, when compared with lower fear scores and male, respectively. Fear of COVID-19 (B = 0.03, 95% CI 0.016–0.04, p < 0.001), importance of online information searching (B = 0.03, 95% CI 0.01–0.05, p = 0.005), and information satisfaction (B = 0.05, 95% CI 0.023–0.067, p < 0.001) were significantly mediated the relationship between DHL and well-being.

Conclusion

Higher DHL scores show direct and indirect associations with higher well-being scores. Fear, importance of online information searching, and information satisfaction significantly contributed to the association.

Similarity Analysis in Understanding Online News in Response to Public Health Crisis

BACKGROUND

The "Syphilis No!" campaign the Brazilian Ministry of Health (MoH) launched between November 2018 and March 2019, brought forward the concept "Test, Treat and Cure" to remind the population of the importance of syphilis prevention. In this context, this study aims to analyze the similarity of syphilis online news to comprehend how public health communication interventions influence media coverage of the syphilis issue.

METHODS

This paper presented a computational approach to assess the effectiveness of communication actions on a public health problem. Data were collected between January 2015 and December 2019 and processed using the Hermes ecosystem, which utilizes text mining and machine learning algorithms to cluster similar content.

RESULTS

Hermes identified 1049 google-indexed web pages containing the term 'syphilis' in Brazil. Of these, 619 were categorized as news stories. In total, 157 were grouped into clusters of at least two similar news items and a single cluster with 462 news classified as "single" for not featuring similar news items. From these, 19 clusters were identified in the pre-campaign period, 23 during the campaign, and 115 in the post-campaign.

CONCLUSIONS

The findings presented in this study show that the volume of syphilis-related news reports has increased in recent years and gained popularity after the SNP started, having been boosted during the campaign and escalating even after its completion.

White Paper: Open Digital Health - accelerating transparent and scalable health promotion and treatment

In this White Paper, we outline recommendations from the perspective of health psychology and behavioural science, addressing three research gaps: (1) What methods in the health psychology research toolkit can be best used for developing and evaluating digital health tools? (2) What are the most feasible strategies to reuse digital health tools across populations and settings? (3) What are the main advantages and challenges of sharing (openly publishing) data, code, intervention content and design features of digital health tools? We provide actionable suggestions for researchers joining the continuously growing Open Digital Health movement, poised to revolutionise health psychology research and practice in the coming years. This White Paper is positioned in the current context of the COVID-19 pandemic, exploring how digital health tools have rapidly gained popularity in 2020-2022, when world-wide health promotion and treatment efforts rapidly shifted from face-to-face to remote delivery. This statement is written by the Directors of the not-for-profit Open Digital Health initiative (n = 6), Experts attending the European Health Psychology Society Synergy Expert Meeting (n = 17), and the initiative consultant, following a two-day meeting (19-20th August 2021).