Hatem H. Alsaqqa
Abdallah Alwawi- 1Deanship of Scientific Research, Al-Quds University, Jerusalem, Palestine
- 2Ministry of Health, Gaza Strip, Palestine
- 3Anesthesia and Resuscitation Technology, Health Professions Faculty, Al Quds University, Jerusalem, Palestine
Studying the impact of digital interventions on public health can help ensure that the offered services produce the desired results. In order to address these factors, the subsequent study uses a scope review to evaluate the state of the field while concentrating on ideas and suggestions that represent factors that have been crucial in the management of digital intervention for public health. To shed light on the traits, ideas and suggestions related to public health digital intervention, a scoping review was carried out. Five electronic databases were used to locate pertinent research that were published before February 2022. All texts were examined, and study abstracts were scrutinized to determine their eligibility. The last analysis of this study included fifteen publications; five reviews, four qualitative studies, two quantitative studies, one viewpoint study, one mixed-method study, one perspective study, and one interventional study. The key ideas for digital interventions in population management and health studies are presented in this overview. Many concepts, implementation characteristics and recommendations have been raised which highlight the future role of these interventions to enhance public engagement and health equity.
Introduction
The choice of an appropriate theory to administrator the implementation course and the technique selection, assuring that proper consideration is paid to planning implementation, and having a flexible tactic that allows for response to recently evolving obstacles are all of the utmost importance.
Public health professionals and academics have proposed a variety of responses in their search for solutions, including stepping up current initiatives to promote information and health literacy, coming up with plans for widely refuting distortion, and educating clinicians and public health professionals on how to discourse misinformation one-on-one (1).
Better data science ought to lead to healthier behaviors and wiser health decisions. In this way, technologically mediated health data processing might support patient empowerment and individual sovereignty (2, 3). However, because human decision-making is complex and influenced by environment and intellectual biases, mixing emotion and rational, the embracing of healthy habits does not occur linearly as a result of better health knowledge. In addition to being the area of health care that is focused with promoting healthy behavior, health promotion is viewed as crucial to attempts to avoid diseases (4).
However, methods from all these fields are needed since these studies sit at the nexus of biological, behavioral, computational, and engineering research. Related research answers encompass identifying the issue and the expected assistance of the digital health intervention (DHI), which in flip necessitates determining the intervention’s likely reach and uptake, the causal model outlining how the intervention will produce the optimally selected, key elements and how they socialize with one another, and assessing the actual advantage in terms of effectiveness, cost effectiveness, and harms (5).
Distinguishing the implication of digital technology in this expanse and in pandemic preparation planning has become crucial since the future of public health is anticipated to be more and more digital. Technology productions and other noteworthy players in the digital area ought to work together as longstanding allies in readiness rather than only through crises times (6).
DHIs, which are therapies given through digital technology like smartphones or websites, have a huge potential to provide efficient, affordable, safe, and scaled interventions to promote healthcare. DHIs can be used to optimize outcomes for those with longstanding conditions, such as cardiovascular disease, diabetes, and mental health issues, as well as to provide remote access to effective treatments. They are frequently intricate interventions with numerous parts, and many of them have multiple goals, such as empowering users to learn more about their health, connect with others in a similar situation, alter perceptions and beliefs about it, monitor certain health conditions or behaviors, titrate prescription, identify treatment priorities, and enhance patient-provider interactions (5).
This review aims to examine the series of digital inventions used universally to address public health challenges, as well as their restrictions and implementation footraces, such as those associated to the law, ethics, and privacy as well as legislative and personnel issues. The objective of the paper is to identify mechanism-based explanations for how and in what contexts digital intervention for public health achieved its effects.
Appraising digital health interventions
Integrating assessment from the start of the DHI progress process allows for the development of evaluation thinking, abilities, and tools. The resulting evaluation service gives non-academics and digital developers the ability to use evaluation approaches and thinking when designing, developing, and implementing their DHI. By doing this, it demystifies evaluation, which has historically been the purview of academia, and uses people’s motivations to make sure that their DHI is as effective as it can be while enhancing the health and wellbeing of end users (7).
Diagnostic or population health interventions, digital product design, product and service design, as well as communication and health promotion, are all components of the interdisciplinary endeavor known as DHIs. Therefore, interdisciplinary approaches to evaluation are best for understanding the effectiveness of DHIs as well as their usableness and attractiveness, with success criteria that consider the various parties involved in the hiring, design, and growth of a DHI as well as its end users (8). To validate the appraisal enterprise path for a DHI, seven key ideas for evaluating DHIs have been identified; evaluation thought, review image, contract assistant, testing tools, progress history, data hub, and published health results.
Argue the structural and epistemic aspects
Along with the crucial problems of safety, data privacy, and the value of human caring touch, structural unfairness raises concerns. DHIs for “reporting and evidence building” urge users to actively recount their involvements and join with other survivors’ tales, creating a shared epistemic space for people who have come into contact with violence. Users are encouraged to apply their epistemic capacity, are recognized as epistemic subjects, and are able to communicate and possibly advance their knowledge within the user community by allowing consumers to express their opinions, even if only digitally through a digital application.
These DHIs can thus stress the need to respond not only on an interpersonal basis but also on a structural level and may aid in better comprehending patterns and clusters of violence (e.g., societies, regimes). These DHIs might also make people impress less alone in their involvements, teach them coping mechanisms, and help them locate guidance and support. Even if applications are created in a way that considers and reflects systemic factors of violence, their impact would be dubious if not everyone can use them. By endorsing digital intervention tactics that can only be opened by users with specific advantages, this runs the risk of highlighting and strengthening structural and epistemic unfairness.
The main epistemic circumstances and traits are recognized under the categorization: data and information structures are related to psychological effects in four ways: (1) they are caused by psychological properties, (2) they are caused by information features, (3) they are related to psychological properties, and (4) information features (co-)constitute psychological properties (9).
Additionally, as DHIs become more prevalent as an intervention strategy, problems like the loss of private contact in intervention settings (such as social workforces providing resident counseling) as a result of a change to digital technology, the possibilities for mistranslation of the details given owed to the absence of non-verbal cues, as well as matters with language and comprehension and access to technology, may crop up (10).
Meanwhile, social media usage for purposes related to health has the ability to create interpersonal networks that support specific epistemic positions on medical matters, which could have a negative impact on public health. Another illustration is the impact of technology-mediated interaction on the connection between the patient and the healthcare professional (11).
Personal agency and motivation
As patients and the general public tended to engross with and enroll in DHIs because they wanted to be healthy or have more influence over how they managed their welfare, the first topic that arose was personal agency and motivation. Information technology was believed that using technology could help people stay motivated to engage in physical activity, reduce their weight, and stay healthy (12, 13). As a result of having the freedom to obtain health information whenever and wherever they pleased, many people joined a DHI, which in some circumstances helped lower anxiety (14, 15). The level of regulating knowledge provided for tracking and comprehending health-related behaviors, such as food and exercise, or for managing chronic diseases on one’s own, was also well-liked by users, which prompted registration (12).
Personal life and values
The recurring topic was how patients’ and the general public’s capacity to participate in and participate in DHIs was impacted by a busy personal life with many conflicting demands. People tended to sign up for new technology if they felt it was useful, could be customized to meet their needs, and was simple to integrate into their daily lives (15, 16). Additionally, individuals who were digitally literate (14, 16) and had experience with or were already familiar with utilizing technology (14) found it simpler to enroll since they possessed the necessary knowledge and abilities. Some people registered because they valued the privacy that online health services offered, being secure and protected from the discrimination and disgrace they occasionally encountered in the actual life (12, 16).
Perceived fit perceived
In contrast to a one-size-fits-all approach, perceived fit describes how much users handled the intervention was acceptable, applicable to their culture and values, and/or oriented at others who were similar to them. For instance, the information’s applicability to their current circumstance (17, 18) and the ability to adapt or tailor the intervention (19, 20) made it more likely that it would fit. Users’ ability to relate to the intervention’s presenters, who may be coaches, teachers, or samples of people with comparable situations, was a facilitating element (21). Culturally appropriate material (22), level of literacy (23), and content given with little use of technical terminology (24) are all examples of elements that make the information pertinent and in a vocabulary suited for the user.
Perceived usefulness
The term “perceived usefulness” describes how a user feels about an intervention and how they judge if it will be helpful to them. Users’ ability to comprehend the information presented to them (104,117,170), the clarity of the action they should take (17, 25), and the perception that the intervention offered a distinct advantage over previous or ongoing care received (17, 26) all contributed to this perception. Facilitators were identified as making it simpler for users to get services they would not otherwise have access to (26, 27) and removing the need for them to travel far to a health center (28).
Level of guidance
The amount of assistance a user receives when using an intervention—for instance, through reminders or a web-based supporter—determines how much accountability they receive to frequently engage with the information. If the intervention raised the level of control, leading to users perceived more responsibility over their own health, it would be a facilitating factor for utilizing DHIs (29, 30). Participants had trouble interacting with interventions that were entirely self-guided, and they occasionally failed to use the intervention (31). The demand for more structured use was voiced by the participants, for examples of this structured use include app alerts or routine human coaching checks (32, 33).
Social connectedness
User engagement was revealed to be facilitated by an intervention’s impact on participants’ feelings of social connectivity. Another facilitating element was if the intervention (34, 35) helped to mainstream lived perspectives by giving instances of others who had comparable experiences. Additional beneficial outcomes that could promote engagement included enhanced abilities (36, 37), a greater understanding of users’ health (38, 39), and a sense of empowerment over having control over their wellness (40).
Iterative methods to adjust an intervention
Regular stakeholder involvement, new scientific information, evolving government directives, quick qualitative research (telephone interviews and open-text questionnaires), and usage data analysis all influenced the optimizations. All comments were quickly compiled, and potential improvements were prioritized according to their likelihood of having an influence on behavior change (41).
In order to improve a health intervention and/or its execution to achieve stakeholder-specified public health benefits within supply limits, optimization can be defined as a purposeful, iterative, and data-driven procedure. This study sought to characterize the core ideas, procedures, or processes of selected frameworks to maximize the efficacy of health interventions and/or their administration (42).
The optimization step’s goal was to test and perfect the intervention’s reasoning and program theory in order to comprehend intervention mechanisms and increase its effectiveness. This frequently happened by doing numerous or repeated “little experiments.” If the “experimentation” step was unsuccessful, the intervention may then go back to the preparation or “theoretical/literature base” phase (42).
An intentional, iterative, and data-driven effort to enhance a health intervention is what is meant by optimization (43). In-depth qualitative research pinpoints obstacles to behavior and intervention adoption and iteratively improves the intervention to get around them (44, 45). The theory- and evidence-based behavioral analysis is integrated into this strategy to choose the most suitable set of efficient behavior change strategies (46).
A live intervention’s effectiveness was improved quickly and iteratively to keep pace with the terrifying and continuously changing environment of an international crisis. Making sure the intervention’s contents is inspiring, credible, and convincing may be more crucial for fostering involvement than making changes to the intervention’s design (41).
Key principles of intervention development
Important guidelines for developing interventions should follow certain key tenets, including being dynamic, iterative, innovative, adaptable, and forward-looking in terms of evaluation and application in the future. When developing an intervention, developers are likely to switch back and forth between redundant tasks including examining the available data, using preexisting theory, and interacting with stakeholders. Iterative cycles will also be used to establish a rendition of the intervention, with feedback from stakeholders used to identify issues, possible alternatives put into action, their appropriateness evaluated, and the cycle repeated until evaluation of subsequent iterations of the intervention shows rare variations.
The need for the intervention, its style, substance, or method of delivery may be strongly held beliefs when the intervention is first being developed. Throughout the design process, keeping open to various alternatives may result in abandoning the project or moving both backward and forward. Being adaptable is a good idea since it could lessen the likelihood that you produce an intervention that fails in a later evaluation or is never used in practice. In order to prepare for this and highlight lessons learned and significant uncertainties that need to be addressed in future evaluations, developers may also gain from anticipating how the intervention will be appraised (47).
Monitoring and iterative evaluation should be prioritized to the greatest extent possible, and results should be regularly discussed and understood in collaboration with stakeholders, as well as thoughtfully and continually implemented in any system redesign or anticipated adaptations/modifications (48). The iterative method of data collection, assessment, evaluation, review, and change responds to the dynamic nature of evidence and the requirement for learning from and with stakeholders, such as populations and field workers (49).
Patient and public involvement
By raising disease understanding and recognizing patients as active participants in their own conditions, patient and public involvement (PPI) can support patient empowerment (50). However, PPI varies significantly between nations and research organizations, and even today, many patients and the general public do not participate in or have access to study protocols (51). Cohort studies are increasingly including digital resources like websites, social media, and connected gadgets, which could be used to boost PPI (52). Digital tools can also help PPI by facilitating feedback and communication between study collaborators and patients (53). PPI is an effective strategy for raising the relevance of research efforts. We have demonstrated that PPI must be designed from the early stages of the construction of a original epidemiological study and then deliberated as the research project progresses. The most successful technique for raising the caliber of research appears to be combining various PPI approaches (54).
Contextual indicators
The contextual indicators basis was created to offer direction on the interdependent context, implementation, and setting characteristics that may have an impact on the efficient delivery of complex interventions (55). What is crucial is that the framework explains how ambitious contextual factors outside of the administrative environment may affect how a complicated intervention with community-facing components is implemented. The seven dimensions that contextual indicators consider are; geographical, epidemiological, sociocultural, socioeconomic, ethical, legal, and political setting. Here, researchers look into the interactions between the political (healthcare infrastructure), epidemiological (blood pressure, body mass index, and older population) and geographical (region, urbanicity) domains of the contextual indicators (56).
Share viewpoints and knowledge from public health experts
The deeper understandings into participants’ involvements, opinions, views, and tips helped the researchers produce more detailed data and also helped others. Focus group involvement provided community members with new perspectives on issues they were discussing as well as a sense of insertion and community development, according to their reports (57).
Future studies should concentrate on a few unanswered problems about the use of digital forms in community-based health promotion interventions. If digital forms potentially take the place of outdated setups for health promotion and prevention actions, notably in vulnerable groups, this should be seriously evaluated. One of the most pressing unknowns, in our opinion, is whether the use of digital health promotion interventions results in an extra enlargement of a selection bias or whether such interventions combat this bias and are utilized and recognized by vulnerable groups and environments where inclusion struggle (58).
The researchers came to the conclusion that emphasizing participation in DHIs and utilizing standardized metrics to describe DHIs will aid future research and potentially open up more possibilities for meta-analyses of DHI results. This is further confirmed by Zanaboni et al. (59), who state that more emphasis should be given to clinical research in the form of high-quality randomized controlled trials in order to run a credible evidence base about the use of digital health and health results. According to Blandford et al. (8), established health research methodologies need to be flexible and modified in order to evaluate DHIs in study.
Methodology
By using scoping review approach, this study investigates DHIs for public health. A review of the literature known as a “scoping study” or “scoping review” has the goal of “fastly mapping the major concepts driving a research topic and the main sources and forms of evidence available, notably where a subject is intricate or has not been studied thoroughly before” (60).
This kind of scoping review may not go into individual study findings, but rather maps and visualizes the body of knowledge that exists within the confines of the research field (61). Data were gathered and evaluated throughout five stages, as per the scoping review process delineated by Arksey and O’Malley (61), which is described below.
According to the stated rules for writing systematic reviews, peer reviews, and research articles, a systematic review was planned and carried out. The literature on digital interventions for public health has undergone a thorough assessment. The articles’ quality was not evaluated because it is not a part of the typical scoping review technique.
The main review interrogation was; “what are the implementing characteristics, concepts and recommendations of the digital interventions for public health considered.” In addition to updates in five databases, an electronic search of digital interventions for public health was conducted. We searched databases from EBSCO, PubMed, ScienceDirect, Scopus and the Cochrane Library. We looked for the words “digital intervention” and “public health” in the article titles. In relation to the objective of the study, it was determined that these terms should be used the most. Duplicate articles were removed, and articles had to have been released in English before February 2022. All scholarly investigations underwent a thorough search of the peer-reviewed literature.
Four main inclusion criteria were defined (Figure 1):
• Published papers as peer-reviewed.
• Original research articles.
• Papers with full access possibility.
• Not targeted mental, sexual or productive health research.
• Papers written in the English language.
• Published before February/2022.
Figure 1. PRISMA flow chart of the literature review search.
Studies that did not match the aforementioned requirements were excluded, while those that did were listed and subjected to further evaluation. Studies were assessed and given a critical review. Extraction of the key conclusions from each repossessed study and literature screening (a three-stage technique involving exclusion by reading the title, the abstract, and the full text). The following details were taken from each of the studies that were included (Table 1): title, authors, country, study design, research objective, and key findings.
Table 1. A summary of reviewed studies.
Results
The following research question was developed:
What implementing characteristics, concepts and recommendations that encourage digital intervention in public health? The terms “digital health intervention” were recognized as the use of digital, mobile, and wireless technologies to support the achievement of health objectives (71), encompassing both mHealth and eHealth. Arksey and O’Malley (61) advise using a broad definitional approach and propose that search words can be modified and reduced later to manage bibliographic references after the entire breadth of information within a given field has been attained. Given that it applies a uniform analytical framework to all studies, which is considered as a standard practice in scoping reviews, this methodology reflects a “descriptive-analytical” approach to charting.
From the publications, this study obtained both qualitative and quantitative data. This study’s major objective was to conceptually clarify the characteristics of digital intervention for public health.
The results of this study may have also been exaggerated by other search parameters, such as restricting results to English-language articles. The current study’s goal was to regulate the existing status of digital intervention for public health and make recommendations. The methodology was suitable for a policy analysis topic like this one. The limitations found in the literature highlight the need for public health practice information and more rigorous study approaches.
Concepts of digital intervention for public health in the context of the reviewed articles
The articles focused on diverse concepts for the digital intervention for public health and also on different methods on the topic. Article focus on commercial corporations to adopt cutting-edge technologies (62), advance information and health literacy (63), framework for evaluation that integrates biological and digital methods (7), multidimensional, global public based on an awareness of structural, societal, and individual extents of violence against women and girls (10), factors that influence recruitment and involvement in DHIs (64), DHI’s impact on rural communities’ (66), range of technological developments for the global public health (65), collection of iterative techniques (41), theoretically based implementation strategy (67), share information and thoughts from public health academics, healthcare system’s ability to support digital health treatments (68), encourage community engagement (56), digital health support on hemoglobin (46), circumstances and important players in China’s quick deployment of digital health solutions (69) and teaching hospitals that make use of social media (70).
Discussion
This scoping review only discovered uncommon studies that used a digital platform to empower substantial community involvement in health promotion and prevention, highlighting a research gap in this area. Digital formats were discovered to be appropriate for situations where obscurity is advantageous. This was evident in the included studies’ qualitative participatory research investigations, notably in the virtual focus groups where contributors had to discuss difficult topics. Additionally, it indicated that anonymity and ease of access were helpful in assisting marginalized and disadvantaged communities, such as through interaction with peers and social exchange programs (58).
With the help of this scoping study, we were able to map the body of research on digital platforms that encourage community involvement in the field of health promotion and prevention. In addition, we obtained a deeper awareness of the fundamental ideas in this field in terms of the sorts of involvement that can be facilitated, the ways to use digital forms, and the advantages and drawbacks associated with them (58).
DHIs are provided through digital channels, such as websites and mobile applications, with the goal of providing care or promoting health (5). Such DHIs are anticipated to combine the effectiveness of individualized therapies with the influence of large-scale population campaigns. DHIs are also meant to expand access and capacity for public health efforts by offering services in places where face-to-face choices are absent or inadequate to satisfy demand (7).
However, it is also important to analyze how institutional inequality, particularly epistemic injustice, affects the content and purposes of the DHIs utilized in public health treatments. They outline and emphasize the significance of violence against women and girls as a global public health concern and briefly evaluate its multifaceted character on structural, societal, and personal levels (10). According to the author, technical solutions like DHIs may be a useful tool in the battle against violence against women or gender inequality, but they must be placed within the larger context of public health that recognizes and the structural components of such fierceness.
The vast amounts of personal information gathered are highly illuminating, and the means for using that information to target digital communications are strong and easily accessible. It is simple to comprehend why academics and professionals in public health are keen to investigate the potential good they may do with them. Such technologies run the risk of discriminatory message targeting against disadvantaged groups. Targeted digital public health interventions pose a risk of interfering with our autonomy by influencing our decision-making. Each situation should be evaluated individually to determine whether the advantages of these interventions exceed the disadvantages. Practitioners should weigh the seriousness of the health risks they are targeting (e.g., promoting a healthy diet as opposed to intervening in suicide cases or eradicating health misinformation during a pandemic) as well as their ability to lessen probable harms (e.g., whether messaging can be clear and collected data respect entities’ privacy) (63).
Collecting the quantitative and qualitative results will produce a strong set of data that can be used to adapt the intervention’s execution with access to the digital health platform as well as to evaluate the DHI. The study illustrates a participatory and community-based component that has the opportunity to have an improved, context-specific influence on local communities’ digital health education by leveraging upon conclusions from both research techniques to enhance the intervention (65).
In addition to a list of the hurdles and implementors that patients and the general public encounter while appealing with and enrolling in DHIs, this review gives an overview of reported engagement and recruitment tactics. In line with the findings of our review, literacy abilities (72) and financial resources (73) do have an impact on people’s capacity to interact with and use DHIs.
Digital technologies must be integrated into the current public healthcare systems since they cannot function alone (74). For instance, as one of many approaches, South Korea and Singapore effectively implemented contact-tracing DHIs to support massive teams of manual contact tracers (66). The digital infrastructure and public health systems’ readiness, which include secondary, primary, and social care systems, will be key factors in the analysis and utilization of these data. With multiple symptom-reporting sites in a single nation, coordination of therapies is especially difficult and runs the danger of fragmentation (66).
The intervention, however, was clinically correct since tight collaboration with clinical stakeholders guaranteed that the information concerning transmission and exposure was compatible with the available data, for instance. Contributors to patient and public involvement (PPI) identified crucial justifications to the intervention’s content, such as whether epidemics can spread through the air as well as surfaces and made sure that evidence about challenging behaviors (like self-isolation) was encouraging and practical (41).
Furthermore, the author has created knowledge about some of the enablers and barriers to putting DHIs into reality. In a system with limited resources, we discovered that requiring personnel to assist patients in registering to use a DHI was a barrier (67). A live intervention’s effectiveness was improved quickly and iteratively to keep pace with the terrifying and continuously changing environment of an international crisis. A rich approach for swift stakeholder assignation was crucial for apprising decisions about how to discourse these obstacles, and the variety of methods assisted in developing a thorough grasp of the potential hurdles to the target behaviors (41).
Conclusion
Understanding the variables connected to digital interventions for public health begins with this scoping review of the literature. The review has given ideas about the factors that contribute to success and insight into some of the techniques used to identify high achievers, but it has also highlighted the need for new approaches to understanding what counts as high impact and how to enhance elements that are crucial to population health. As, the public health is likely to become more and more digital in the future, the author examines the requirement for the synchronization of global approaches for the regulation, assessment and use of digital technologies in order to improve population health supervision and imminent alertness for diseases.
The author contends that elements that go beyond the inter-individual level must be considered for any intervention technique to be successful and long-lasting. There is little research on the function and importance of DHIs as tactics for addressing the structural and epistemological components. The participants and those around them will gain more awareness about health issues by receiving health messages in a digital format, which may change how they seek out health care. More work is required to develop effective engagement tactics, significantly greater, individualized digital solutions, and to obtain clinical accreditation and support where necessary.
The choice of an appropriate theory to direct the course of implementation and strategy selection is essential. The reporting of implementation strategies using terms that are clear and defined, and using a flexible approach are all important considerations. In addition, physical infrastructure gaps were typically indicating that beyond information technology infrastructure, shortages of indispensable staff enforce significant barriers to the adoption of DHIs.
To sum up, the author’s work outlines an iterative, cross-disciplinary, participatory progression for creating, implementing, and appraising DHI, emphasizing the adjacent collaboration between behavior scientists, designers, data engineers, software developers, and data scientists as well as on a constant reaction circle from end users. A defined approach for swift stakeholder involvement was crucial for guiding decisions about how to discourse these obstacles, and the variety of ways contributed to the development of a deep consideration of the potential barriers to the target behaviors. Making sure the intervention’s content is inspiring, reliable, and convincing may be more crucial for fostering engagement than making changes to the intervention’s design (66).
DHI offers a viewpoint that emphasizes a considerable larger series of issues related to the sociotechnical system involved by a specific digital health technology and the health of the numerous communities. This study could be used in other areas of public health policy and practice and will attend as a source for enduring discussion in this area.
Author contributions
HA held the main parts of the research, writing, collecting the data, and results and discussion. AA helped HA in reviewing the paper and gave notes. All authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Abbreviations
DHI, Digital Health Intervention; DHIs, Digital Health Interventions; PPI, Patient and Public Involvement.
References
1. Chou, WYS, Oh, A, and Klein, WM. Addressing health-related misinformation on social media. JAMA. (2018) 320:2417–8. doi: 10.1001/jama.2018.16865
2. Human Rights Watch (2020). Myanmar: end world’s longest internet shutdown. Available at: https://www.hrw.org/news/2020/06/19/myanmar-end-worlds-longestinternetshutdown.
3. Rimmer, A. COVID-19: disproportionate impact on ethnic minority healthcare workers will be explored by government. Br Med J. (2020) 369:m1562. doi: 10.1136/bmj.m1562
4. World Health Organization. Education for health: a manual on health education in primary health care World Health Organization (1988) Available at: https://apps.who.int/iris/handle/10665/77769.
5. Murray, E, Hekler, EB, Andersson, G, Collins, LM, Doherty, A, Hollis, C, et al. Evaluating digital health interventions: key questions and approaches. Am J Prev Med. (2016) 51:843–51. doi: 10.1016/j.amepre.2016.06.008
6. Tognotti, E. Lessons from the history of quarantine, from plague to influenza A. Emerg Infect Dis. (2013) 19:254. doi: 10.3201/eid1902.120312
7. Karpathakis, K, Libow, G, Potts, HW, Dixon, S, Greaves, F, and Murray, E. An evaluation service for digital public health interventions: user-centered design approach. J Med Internet Res. (2021) 23:e28356. doi: 10.2196/28356
8. Blandford, A, Gibbs, J, Newhouse, N, Perski, O, Singh, A, and Murray, E. Seven lessons for interdisciplinary research on interactive digital health interventions. Digit Health. (2018) 4:2055207618770325. doi: 10.1177/2055207618770325
9. Coghlan, S, and D’Alfonso, S. Digital phenotyping: an epistemic and methodological analysis. Philos Technol. (2021) 34:1905–28. doi: 10.1007/s13347-021-00492-1
10. Sauerborn, E, Eisenhut, K, Ganguli-Mitra, A, and Wild, V. Digitally supported public health interventions through the lens of structural injustice: the case of mobile apps responding to violence against women and girls. Bioethics. (2022) 36:71–6. doi: 10.1111/bioe.12965
11. Oudshoorn, N. Telecare technologies and the transformation of healthcare. Houndmills, UK: Palgrave Macmillan. (2011).
12. Greenhalgh, T, Wood, GW, Bratan, T, Stramer, K, and Hinder, S. Patients’ attitudes to the summary care record and HealthSpace: qualitative study. BMJ. (2008) 336:1290–5. doi: 10.1136/bmj.a114
13. Bardus, M, Blake, H, Lloyd, S, and Suzanne Suggs, L. Reasons for participating and not participating in a e-health workplace physical activity intervention: A qualitative analysis. Int. J. Workplace Health Manag. (2014) 7:229–46.
14. Lorimer, K, Martin, S, and McDaid, LM. The views of general practitioners and practice nurses towards the barriers and facilitators of proactive, internet-based chlamydia screening for reaching young heterosexual men. BMC Fam Pract. (2014) 15:1–10. doi: 10.1186/1471-2296-15-127
15. Trujillo Gómez, JM, Díaz-Gete, L, Martín-Cantera, C, Fábregas Escurriola, M, Lozano Moreno, M, Burón Leandro, R, et al. Intervention for smokers through new communication technologies: what perceptions do patients and healthcare professionals have? A qualitative study. PLoS One. (2015) 10:e0137415. doi: 10.1371/journal.pone.0137415
16. Winkelman, WJ, Leonard, KJ, and Rossos, PG. Patient-perceived usefulness of online electronic medical records: employing grounded theory in the development of information and communication technologies for use by patients living with chronic illness. J Am Med Inform Assoc. (2005) 12:306–14. doi: 10.1197/jamia.M1712
17. Lundgren, J, Johansson, P, Jaarsma, T, Andersson, G, and Köhler, AK. Patient experiences of web-based cognitive behavioral therapy for heart failure and depression: qualitative study. J Med Internet Res. (2018) 20:e10302. doi: 10.2196/10302
18. Wilhelmsen, M, Lillevoll, K, Risør, MB, Høifødt, R, Johansen, ML, Eisemann, M, et al. Motivation to persist with internet-based cognitive behavioural treatment using blended care: a qualitative study. BMC Psychiatry. (2013) 13:1–9. doi: 10.1186/1471-244X-13-296
19. Wachtler, C, Coe, A, Davidson, S, Fletcher, S, Mendoza, A, Sterling, L, et al. Development of a mobile clinical prediction tool to estimate future depression severity and guide treatment in primary care: user-centered design. JMIR Mhealth Uhealth. (2018) 6:e9502. doi: 10.2196/mhealth.9502
20. Pagliari, C, Burton, C, McKinstry, B, Szentatotai, A, David, D, Ferrini, L, et al. Psychosocial implications of avatar use in supporting therapy for depression. Annu Rev Cyberther Telemed. (2012) 2012:329–333. doi: 10.1111/nyas.13336
21. Gonsalves, PP, Hodgson, ES, Kumar, A, Aurora, T, Chandak, Y, Sharma, R, et al. Design and development of the “POD adventures” smartphone game: a blended problem-solving intervention for adolescent mental health in India. Front Public Health. (2019) 7:238. doi: 10.3389/fpubh.2019.00238
22. Povey, J, Mills, PPJR, Dingwall, KM, Lowell, A, Singer, J, Rotumah, D, et al. Acceptability of mental health apps for aboriginal and Torres Strait islander Australians: a qualitative study. J Med Internet Res. (2016) 18:e5314. doi: 10.2196/jmir.5314
23. Hunter-Jones, JJ, Gilliam, SM, Carswell, AL, and Hansen, NB. Assessing the acceptability of a mindfulness-based cognitive therapy intervention for African-American women living with HIV/AIDS. J Racial Ethn Health Disparities. (2019) 6:1157–66. doi: 10.1007/s40615-019-00617-5
24. Henshall, C, Marzano, L, Smith, K, Attenburrow, MJ, Puntis, S, Zlodre, J, et al. A web-based clinical decision tool to support treatment decision-making in psychiatry: a pilot focus group study with clinicians, patients and carers. BMC Psychiatry. (2017) 17:1–10. doi: 10.1186/s12888-017-1406-z
25. Walsh, DM, Moran, K, Cornelissen, V, Buys, R, Cornelis, N, and Woods, C. Electronic health physical activity behavior change intervention to self-manage cardiovascular disease: qualitative exploration of patient and health professional requirements. JMIR. (2018) 20:e163.
26. Feijt, MA, de Kort, YA, Bongers, IM, and WA, IJ. Perceived drivers and barriers to the adoption of eMental health by psychologists: the construction of the levels of adoption of eMental health model. J Med Internet Res. (2018) 20:e9485. doi: 10.2196/jmir.9485
27. Apolinário-Hagen, J, Vehreschild, V, and Alkoudmani, RM. Current views and perspectives on e-mental health: an exploratory survey study for understanding public attitudes toward internet-based psychotherapy in Germany. JMIR Ment health. (2017) 4:e6375. doi: 10.2196/mental.6375
28. Jordan, SE, and Shearer, EM. An exploration of supervision delivered via clinical video telehealth (CVT). Train Educ Prof Psychol. (2019) 13:323. doi: 10.1037/tep0000245
29. Kumarasiri, J, and Jubb, C. Framing of climate change impacts and use of management accounting practices. Asian Acad Manag J Account Finance. (2017) 13. doi: 10.21315/aamjaf2017.13.2.3
30. Huerta-Ramos, E, Escobar-Villegas, MS, Rubinstein, K, Unoka, ZS, Grasa, E, Hospedales, M, et al. Measuring users’ receptivity toward an integral intervention model based on mHealth solutions for patients with treatment-resistant schizophrenia (m-RESIST): A qualitative study. JMIR Mhealth Uhealth. (2016) 4:e5716. doi: 10.2196/mhealth.5716
31. Donkin, L, and Glozier, N. Motivators and motivations to persist with online psychological interventions: a qualitative study of treatment completers. J Med Internet Res. (2012) 14:e2100. doi: 10.2196/jmir.2100
32. Wallin, E, Norlund, F, Olsson, EMG, Burell, G, Held, C, and Carlsson, T. Treatment activity, user satisfaction, and experienced usability of internet-based cognitive behavioral therapy for adults with depression and anxiety after a myocardial infarction: mixed-methods study. J Med Internet Res. (2018) 20:e9690. doi: 10.2196/jmir.9690
33. Anderson, AP, Fellows, AM, Binsted, KA, Hegel, MT, and Buckey, JC. Autonomous, computer-based behavioral health countermeasure evaluation at HI-SEAS Mars analog. Aerosp Med Hum Perform. (2016) 87:912–20. doi: 10.3357/AMHP.4676.2016
34. Jonathan, G, Carpenter-Song, EA, Brian, RM, and Ben-Zeev, D. Life with FOCUS: a qualitative evaluation of the impact of a smartphone intervention on people with serious mental illness. Psychiatr Rehabil J. (2019) 42:182. doi: 10.1037/prj0000337
35. Williams, A, Fossey, E, Farhall, J, Foley, F, and Thomas, N. Recovery after psychosis: qualitative study of service user experiences of lived experience videos on a recovery-oriented website. JMIR Ment Health. (2018) 5:e9934. doi: 10.2196/mental.9934
36. Görges, F, Oehler, C, von Hirschhausen, E, Hegerl, U, and Rummel-Kluge, C. GET. HAPPY2—user perspectives on an internet-based self-management positive psychology intervention among persons with and without depression: results from a retrospective survey. J Clin Psychol. (2020) 76:1030–46. doi: 10.1002/jclp.22886
37. Lawal, AM, Idemudia, ES, and Senyatsi, T. Emotional intelligence and mental health: an exploratory study with south African university students. J Psychol Afr. (2018) 28:492–7. doi: 10.1080/14330237.2018.1540229
38. Eisner, E, Drake, RJ, Berry, N, Barrowclough, C, Emsley, R, Machin, M, et al. Development and long-term acceptability of ExPRESS, a mobile phone app to monitor basic symptoms and early signs of psychosis relapse. JMIR Mhealth Uhealth. (2019) 7:e11568. doi: 10.2196/11568
39. Edbrooke-Childs, J, Edridge, C, Averill, P, Delane, L, Hollis, C, Craven, MP, et al. A feasibility trial of power up: smartphone app to support patient activation and shared decision making for mental health in young people. JMIR Mhealth Uhealth. (2019) 7:e11677. doi: 10.2196/11677
40. Borghouts, J, Eikey, E, Mark, G, De Leon, C, Schueller, SM, Schneider, M, et al. Barriers to and facilitators of user engagement with digital mental health interventions: systematic review. J Med Internet Res. (2021) 23:e24387. doi: 10.2196/24387
41. Morton, K, Ainsworth, B, Miller, S, Rice, C, Bostock, J, Denison-Day, J, et al. Adapting behavioral interventions for a changing public health context: a worked example of implementing a digital intervention during a global pandemic using rapid optimisation methods. Front Public Health. (2021) 9:668197. doi: 10.3389/fpubh.2021.668197
42. McCrabb, S, Mooney, K, Elton, B, Grady, A, Yoong, SL, and Wolfenden, L. How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks. BMC Public Health. (2020) 20:1–12. doi: 10.1186/s12889-020-09950-5
43. Long, JA, Jahnle, EC, Richardson, DM, Loewenstein, G, and Volpp, KG. Peer mentoring and financial incentives to improve glucose control in African American veterans: a randomized trial. Ann Intern Med. (2012) 156:416–24. doi: 10.7326/0003-4819-156-6-201203200-00004
44. Palmas, W, March, D, Darakjy, S, Findley, SE, Teresi, J, Carrasquillo, O, et al. Community health worker interventions to improve glycemic control in people with diabetes: a systematic review and meta-analysis. J Gen Intern Med. (2015) 30:1004–12. doi: 10.1007/s11606-015-3247-0
45. Heisler, M, Vijan, S, Makki, F, and Piette, JD. Diabetes control with reciprocal peer support versus nurse care management: a randomized trial. Ann Intern Med. (2010) 153:507–15. doi: 10.7326/0003-4819-153-8-201010190-00007
46. Harte, R, Norton, L, Whitehouse, C, Lorincz, I, Jones, D, Gerald, N, et al. Design of a randomized controlled trial of digital health and community health worker support for diabetes management among low-income patients. Contemp Clin Trials Commun. (2022) 25:100878. doi: 10.1016/j.conctc.2021.100878
47. O’Cathain, A, Croot, L, Duncan, E, Rousseau, N, Sworn, K, Turner, KM, et al. Guidance on how to develop complex interventions to improve health and healthcare. BMJ Open. (2019) 9:e029954. doi: 10.1136/bmjopen-2019-029954
48. Chambers, DA. Considering the intersection between implementation science and COVID-19. Implement Res Pract. (2020) 1:0020764020925994. doi: 10.1177/0020764020925994
49. Yousefi Nooraie, R, Shelton, RC, Fiscella, K, Kwan, BM, and McMahon, JM. The pragmatic, rapid, and iterative dissemination and implementation (PRIDI) cycle: adapting to the dynamic nature of public health emergencies (and beyond). Health Res Policy Syst. (2021) 19:1–10. doi: 10.1186/s12961-021-00764-4
50. Bereczky, T. Patient advocacy-with a feeling patient citizenship-the description of an affective model of patient advocacy In: Doctoral dissertation. Hungary: ELTE University (2019) Available at: https://www.academia.edu/42773922/Patient_Advocacy_With_a_Feeling_Patient_citizenship_The_description_of_an_affective_model_of_patient_advocacy
51. Biddle, MS, Gibson, A, and Evans, D. Attitudes and approaches to patient and public involvement across Europe: a systematic review. Health Soc Care Community. (2021) 29:18–27. doi: 10.1111/hsc.13111
52. Huckvale, K, Venkatesh, S, and Christensen, H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. NPJ Digi Med. (2019) 2:1–11. doi: 10.1038/s41746-019-0166-1
53. Algeo, N, Hunter, D, Cahill, A, Dickson, C, and Adams, J. Usability of a digital self-management website for people with osteoarthritis: a UK patient and public involvement study. Int J Ther Rehabil. (2017) 24:78–82. doi: 10.12968/ijtr.2017.24.2.78
54. Aguayo, GA, Goetzinger, C, Scibilia, R, Fischer, A, Seuring, T, Tran, VT, et al. Methods to generate innovative research ideas and improve patient and public involvement in modern epidemiological research: review, patient viewpoint, and guidelines for implementation of a digital cohort study. J Med Internet Res. (2021) 23:e25743. doi: 10.2196/25743
55. Pfadenhauer, LM, Gerhardus, A, Mozygemba, K, Lysdahl, KB, Booth, A, Hofmann, B, et al. Making sense of complexity in context and implementation: the context and implementation of complex interventions (CICI) framework. Implement Sci. (2017) 12:1–17. doi: 10.1186/s13012-017-0552-5
56. Patel, SA, Vashist, K, Jarhyan, P, Sharma, H, Gupta, P, Jindal, D, et al. A model for national assessment of barriers for implementing digital technology interventions to improve hypertension management in the public health care system in India. BMC Health Serv Res. (2021) 21:1–11. doi: 10.1186/s12913-021-06999-9
57. Nared, J, and Bole, D. Participatory research and planning in practice. Berlin, Germany: Springer. (2020). 227 p.
58. Schroeer, C, Voss, S, Jung-Sievers, C, and Coenen, M. Digital formats for community participation in health promotion and prevention activities: a scoping review. Front Public Health. (2021) 9:713159. doi: 10.3389/fpubh.2021.713159
59. Zanaboni, P, Ngangue, P, Mbemba, GIC, Schopf, TR, Bergmo, TS, and Gagnon, MP. Methods to evaluate the effects of internet-based digital health interventions for citizens: systematic review of reviews. J Med Internet Res. (2018) 20:e10202. doi: 10.2196/10202
60. Mays, N, Roberts, E, and Popay, J. Synthesising research evidence In: N Fulop, P Allen, A Clarke, and N Black, editors. Studying the organization and delivery of health services: research methods. London: Routledge. (2001)
61. Arksey, H, and O'Malley, L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. (2005) 8:19–32. doi: 10.1080/1364557032000119616
62. Burke, C, and Bloss, C. Social media surveillance in schools: rethinking public health interventions in the digital age. J Med Internet Res. (2020) 22:e22612. doi: 10.2196/22612
63. Susser, D. Ethical considerations for digitally targeted public health interventions. Am J Public Health. (2020) 110:S290–1. doi: 10.2105/AJPH.2020.305758
64. O’Connor, S, Hanlon, P, O’Donnell, CA, Garcia, S, Glanville, J, and Mair, FS. Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies. BMC Med Inform Decis Mak. (2016) 16:1–15. doi: 10.1186/s12911-016-0359-3
65. Holst, C, Sukums, F, Ngowi, B, Diep, LM, Kebede, TA, Noll, J, et al. Digital health intervention to increase health knowledge related to diseases of high public health concern in Iringa, Tanzania: protocol for a mixed methods study. JMIR Res Protoc. (2021) 10:e25128. doi: 10.2196/25128
66. Budd, J, Miller, BS, Manning, EM, Lampos, V, Zhuang, M, Edelstein, M, et al. Digital technologies in the public-health response to COVID-19. Nat Med. (2020) 26:1183–92. doi: 10.1038/s41591-020-1011-4
67. Ross, J, Stevenson, F, Dack, C, Pal, K, May, C, Michie, S, et al. Developing an implementation strategy for a digital health intervention: an example in routine healthcare. BMC Health Serv Res. (2018) 18:1–13. doi: 10.1186/s12913-018-3615-7
68. Merolli, M. Insights from public health researchers into the digital transformation of an educational lifestyle course. Healthier Lives, Digitally Enabled: Selected Papers from the Digital Health Institute Summit 2020, vol. 276. (2021) p. 14.
69. Chen, M, Xu, S, Husain, L, and Galea, G. Digital health interventions for COVID-19 in China: a retrospective analysis. Intell Med. (2021) 1:29–36. doi: 10.1016/j.imed.2021.03.001
70. Batta, HE, and Iwokwagh, NS. Optimising the digital age health-wise: utilisation of new/social media by Nigerian teaching hospitals. Procedia Soc Behav Sci. (2015) 176:175–85. doi: 10.1016/j.sbspro.2015.01.459
71. World Health Organization. Monitoring and evaluating digital health interventions: a practical guide to conducting research and assessment. Geneva, Switzerland: World Health Organization (2016).
72. Kontos, E, Blake, KD, Chou, WYS, and Prestin, A. Predictors of eHealth usage: insights on the digital divide from the health information National Trends Survey 2012. J Med Internet Res. (2014) 16:e3117. doi: 10.2196/jmir.3117
73. Neter, E, and Brainin, E. eHealth literacy: extending the digital divide to the realm of health information. J Med Internet Res. (2012) 14:e1619. doi: 10.2196/jmir.1619
Keywords: characteristics, concepts, digital interventions, implementation, public health
Citation: Alsaqqa HH and Alwawi A (2023) Digital intervention for public health: searching for implementing characteristics, concepts and recommendations: scoping review. Front. Public Health. 11:1142443. doi: 10.3389/fpubh.2023.1142443
Edited by:
Ajaya Bhattarai, Tribhuvan University, NepalReviewed by:
Victoria Ramos Gonzalez, Carlos III Health Institute (ISCIII), SpainGraciela Rojas, University of Chile, Chile
Copyright © 2023 Alsaqqa and Alwawi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hatem H. Alsaqqa, aHMtbWNoQGhvdG1haWwuY29t