Online dissemination of Cochrane reviews on digital health technologies: a cross-sectional study.

BACKGROUND: This cross-sectional study investigated the online dissemination of Cochrane reviews on digital health technologies. METHODS: We searched the Cochrane Database of Systematic Reviews from inception up to May 2023. Cochrane reviews with any population (P), intervention or concept supported by any digital technology (I), any or no comparison (C), and any health outcome (O) were included. Data on review characteristics (bibliographic information, PICO, and evidence quality) and dissemination strategies were extracted and processed. Dissemination was assessed using review information on the Cochrane website and Altmetric data that trace the mentions of academic publications in nonacademic online channels. Data were analysed using descriptive statistics and binary logistic regression analysis. RESULTS: Out of 170 records identified in the search, 100 Cochrane reviews, published between 2005 and 2023, were included. The reviews focused on consumers (e.g. patients, n = 86), people of any age (n = 44), and clinical populations (n = 68). All reviews addressed interventions or concepts supported by digital technologies with any devices (n = 73), mobile devices (n = 17), or computers (n = 10). The outcomes focused on disease treatment (n = 56), health promotion and disease prevention (n = 27), or management of care delivery (n = 17). All reviews included 1-132 studies, and half included 1-10 studies. Meta-analysis was performed in 69 reviews, and certainty of evidence was rated as high or moderate for at least one outcome in 46 reviews. In agreement with the Cochrane guidelines, all reviews had a plain language summary (PLS) that was available in 3-14 languages. The reviews were disseminated (i.e. mentioned online) predominantly via X/Twitter (n = 99) and Facebook (n = 69). Overall, 51 reviews were mentioned in up to 25% and 49 reviews in 5% of all research outputs traced by Altmetric data. Dissemination (i.e. higher Altmetric scores) was associated with bibliographic review characteristics (i.e. earlier publication year and PLS available in more languages), but not with evidence quality (i.e. certainty of evidence rating, number of studies, or meta-analysis performed in review). CONCLUSIONS: Online attention towards Cochrane reviews on digital health technologies is high. Dissemination is higher for older reviews and reviews with more PLS translations. Measures are required to improve dissemination of Cochrane reviews based on evidence quality. SYSTEMATIC REVIEW REGISTRATION: The study was prospectively registered at the Open Science Framework ( https://osf.io/mpw8u/ ).

Impact of Social Reference Cues on Misinformation Sharing on Social Media: Series of Experimental Studies.

BACKGROUND: Health-related misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information, but they have not yet targeted social processes. Current approaches focus on increasing scrutiny, providing corrections to misinformation (debunking), or alerting users prospectively about future misinformation (prebunking and inoculation). Here, we provide a test of a complementary strategy that focuses on the social processes inherent in social media use, in particular, social reinforcement, social identity, and injunctive norms. OBJECTIVE: This study aimed to examine whether providing balanced social reference cues (ie, cues that provide information on users sharing and, more importantly, not sharing specific content) in addition to flagging COVID-19-related misinformation leads to reductions in sharing behavior and improvement in overall sharing quality. METHODS: A total of 3 field experiments were conducted on Twitter's native social media feed (via a newly developed browser extension). Participants' feed was augmented to include misleading and control information, resulting in 4 groups: no-information control, Twitter's own misinformation warning (misinformation flag), social cue only, and combined misinformation flag and social cue. We tracked the content shared or liked by participants. Participants were provided with social information by referencing either their personal network on Twitter or all Twitter users. RESULTS: A total of 1424 Twitter users participated in 3 studies (n=824, n=322, and n=278). Across all 3 studies, we found that social cues that reference users' personal network combined with a misinformation flag reduced the sharing of misleading but not control information and improved overall sharing quality. We show that this improvement could be driven by a change in injunctive social norms (study 2) but not social identity (study 3). CONCLUSIONS: Social reference cues combined with misinformation flags can significantly and meaningfully reduce the amount of COVID-19-related misinformation shared and improve overall sharing quality. They are a feasible and scalable way to effectively curb the sharing of COVID-19-related misinformation on social media.

The Definitions of Health Apps and Medical Apps From the Perspective of Public Health and Law: Qualitative Analysis of an Interdisciplinary Literature Overview.

BACKGROUND: The terms health app and medical app are often used interchangeably but do not necessarily mean the same thing. To better understand these terms and better regulate such technologies, we need distinct definitions of health and medical apps. OBJECTIVE: This study aimed to provide an overview of the definitions of health and medical apps from an interdisciplinary perspective. We summarized the core elements of the identified definitions for their holistic understanding in the context of digital public health. METHODS: The legal frameworks for medical device regulation in the United States, the European Union, and Germany formed the basis of this study. We then searched 6 databases for articles defining health or medical apps from an interdisciplinary perspective. The narrative literature review was supported by a forward and backward snowball search for more original definitions of health and medical apps. A qualitative analysis was conducted on the identified relevant aspects and core elements of each definition. On the basis of these findings, we developed a holistic definition of health and medical apps and created a decision flowchart to highlight the differences between the 2 types. RESULTS: The legal framework showed that medical apps could be regulated as mobile medical devices, whereas there is no legal term for health apps. Our narrative literature review identified 204 peer-reviewed publications that offered a definition of health and medical apps. After screening for original definitions and applying the snowball method, 11.8% (24/204) of the publications were included in the qualitative analysis. Of these 24 publications, 22 (88%) provided an original definition of health apps and 11 (44%) described medical apps. The literature suggests that medical apps are a part of health apps. To describe health or medical apps, most definitions used the user group, a description of health, the device, the legal regulation, collected data, or technological functions. However, the regulation should not be a distinction criterion as it requires legal knowledge, which is neither suitable nor practical. An app's intended medical or health use enables a clear differentiation between health and medical apps. Ultimately, the health aim of an app and its main target group are the only distinction criteria. CONCLUSIONS: Health apps are software programs on mobile devices that process health-related data on or for their users. They can be used by every health-conscious person to maintain, improve, or manage the health of an individual or the community. As an umbrella term, health apps include medical apps. Medical apps share the same technological functions and devices. Health professionals, patients, and family caregivers are the main user groups. Medical apps are intended for clinical and medical purposes and can be legally regulated as mobile medical devices.

The digital rainbow: Digital determinants of health inequities.

The widely used socioecological rainbow model from Dahlgren and Whitehead specifies determinants of health inequity on multiple hierarchical levels and suggests that these determinants may interact both within and between levels. At the time of its inception, digital determinants only played a minor role in tackling inequities in public health and were therefore not specifically considered. This has dramatically changed: From today's perspective, health inequities increasingly depend on digital determinants. In this article, we suggest adapting the Dahlgren-Whitehead model to reflect these developments. We propose a model that allows formulating testable hypotheses, interpreting research findings, and developing policy implications against the background of the global spread of digital technologies. This may facilitate the development of a new line of research and logic models for public health interventions in the digital age. Using the COVID-19 pandemic as a case study, we illustrate how the digitization of all aspects of life affects the different levels of determinants of health inequities in the Dahlgren-Whitehead model. In doing so, we deliberately argue for not introducing a separate digital sphere in its own right, but for understanding digitization as a phenomenon that permeates all levels of determinants of health inequities. As a result, we present a digital rainbow model that integrates Dahlgren and Whitehead's 1991 model with digital environments to identify current health promotion and research issues without changing the rainbow model's initial structure.

What are Digital Public Health Interventions? First Steps Toward a Definition and an Intervention Classification Framework.

Digital public health is an emerging field in population-based research and practice. The fast development of digital technologies provides a fundamentally new understanding of improving public health by using digitalization, especially in prevention and health promotion. The first step toward a better understanding of digital public health is to conceptualize the subject of the assessment by defining what digital public health interventions are. This is important, as one cannot evaluate tools if one does not know what precisely an intervention in this field can be. Therefore, this study aims to provide the first definition of digital public health interventions. We will merge leading models for public health functions by the World Health Organization, a framework for digital health technologies by the National Institute for Health and Care Excellence, and a user-centered approach to intervention development. Together, they provide an overview of the functions and areas of use for digital public health interventions. Nevertheless, one must keep in mind that public health functions can differ among different health care systems, limiting our new framework's universal validity. We conclude that a digital public health intervention should address essential public health functions through digital means. Furthermore, it should include members of the target group in the development process to improve social acceptance and achieve a population health impact.

Evaluation of Digital Interventions for Physical Activity Promotion: Scoping Review.

BACKGROUND: Digital interventions are interventions supported by digital tools or technologies, such as mobile apps, wearables, or web-based software. Digital interventions in the context of public health are specifically designed to promote and improve health. Recent reviews have shown that many digital interventions target physical activity promotion; however, it is unclear how such digital interventions are evaluated. OBJECTIVE: We aimed to investigate evaluation strategies in the context of digital interventions for physical activity promotion using a scoping review of published reviews. We focused on the target (ie, user outcomes or tool performance), methods (ie, tool data or self-reported data), and theoretical frameworks of the evaluation strategies. METHODS: A protocol for this study was preregistered and published. From among 300 reviews published up to March 19, 2021 in Medline, PsycINFO, and CINAHL databases, 40 reviews (1 rapid, 9 scoping, and 30 systematic) were included in this scoping review. Two authors independently performed study selection and data coding. Consensus was reached by discussion. If applicable, data were coded quantitatively into predefined categories or qualitatively using definitions or author statements from the included reviews. Data were analyzed using either descriptive statistics, for quantitative data (relative frequencies out of all studies), or narrative synthesis focusing on common themes, for qualitative data. RESULTS: Most reviews that were included in our scoping review were published in the period from 2019 to 2021 and originated from Europe or Australia. Most primary studies cited in the reviews included adult populations in clinical or nonclinical settings, and focused on mobile apps or wearables for physical activity promotion. The evaluation target was a user outcome (efficacy, acceptability, usability, feasibility, or engagement) in 38 of the 40 reviews or tool performance in 24 of the 40 reviews. Evaluation methods relied upon objective tool data (in 35/40 reviews) or other data from self-reports or assessments (in 28/40 reviews). Evaluation frameworks based on behavior change theory, including goal setting, self-monitoring, feedback on behavior, and educational or motivational content, were mentioned in 22 out of 40 reviews. Behavior change theory was included in the development phases of digital interventions according to the findings of 20 out of 22 reviews. CONCLUSIONS: The evaluation of digital interventions is a high priority according to the reviews included in this scoping review. Evaluations of digital interventions, including mobile apps or wearables for physical activity promotion, typically target user outcomes and rely upon objective tool data. Behavior change theory may provide useful guidance not only for development of digital interventions but also for the evaluation of user outcomes in the context of physical activity promotion. Future research should investigate factors that could improve the efficacy of digital interventions and the standardization of terminology and reporting in this field. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/35332.

Evaluation of Digital Interventions for Physical Activity Promotion: Protocol for a Scoping Review.

BACKGROUND: Digital interventions (DIs) could support physical activity (PA) promotion, according to recent reviews. However, it remains unclear if and how DIs for PA promotion are evaluated; thus, it is unclear if they support behavior change in real-world settings. A mapping of evidence from published reviews is required to focus on the evaluation of DIs for PA promotion. OBJECTIVE: The aim of our study is to investigate evaluation strategies for any outcome in the context of DIs for PA promotion by conducting a scoping review of published reviews. METHODS: Our scoping review adheres to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The information sources include bibliographic databases (MEDLINE, PsycINFO, and CINAHL) and the bibliographies of the selected studies. The electronic search strategy was developed and conducted in collaboration with an experienced database specialist. The electronic search was conducted in English with no limits up to March 19, 2021, for sources with the terms digital intervention AND evaluation AND physical activity in titles or abstracts. After deduplication, 300 reviews selected from 4912 search results were assessed for eligibility by 2 authors working independently. The inclusion criteria were (1) healthy or clinical samples (population), (2) DIs for PA promotion (intervention), (3) comparisons to any other intervention or no intervention (comparison), (4) evaluation strategies (methods, results, or frameworks) for any outcome in the context of DIs for PA promotion (outcome), and (5) any published review (study type). According to the consensus reached during a discussion, 40 reviews met the inclusion criteria-36 from the electronic search and 4 from the manual search of the bibliographies of the 36 reviews. All reviews reported the evaluation strategies for any outcomes in the context of DIs for PA promotion in healthy or clinical samples. Data coding and the quality appraisal of systematic reviews are currently being performed independently by 2 authors. RESULTS: Our scoping review includes data from 40 published reviews (1 rapid review, 9 scoping reviews, and 30 systematic reviews). The focus of data coding is on evaluation strategies in the context of DIs for PA promotion and on the critical appraisal of the included systematic reviews. The final consensus regarding all data is expected in early 2022. CONCLUSIONS: Interventions for PA promotion that are supported by digital technologies require evaluation to ensure their efficacy in real-world settings. Our scoping review is needed because it addresses novel objectives that focus on such evaluations and are not answered in the published reviews identified in our search. The evaluation strategies addressing DIs for PA promotion will be mapped to synthesize the results that have been reported in published reviews so far. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35332.

Measuring Food-Related Attentional Bias.

Objective: Food-related attentional bias has been defined as the tendency to give preferential attention to food-related stimuli. Attentional bias is of interest as studies have found that increased attentional bias is associated with obesity; others, however, have not. A possible reason for mixed results may be that there is no agreed upon measure of attentional bias: studies differ in both measurement and scoring of attentional bias. Additionally, little is known about the stability of attentional bias over time. The present study aims to compare attentional bias measures generated from commonly used attentional bias tasks and scoring protocols, and to test re-test reliability. Methods: As part of a larger study, 69 participants (67% female) completed two food-related visual probe tasks at baseline: lexical (words as stimuli), and pictorial (pictures as stimuli). Reaction time bias scores (attentional bias scores) for each task were calculated in three different ways: by subtracting the reaction times for the trials where probes replaced (1) neutral stimuli from the trials where the probes replaced all food stimuli, (2) neutral stimuli from the trials where probes replaced high caloric food stimuli, and (3) neutral stimuli from low caloric food stimuli. This resulted in three separate attentional bias scores for each task. These reaction time results were then correlated. The pictorial visual probe task was administered a second time 14-days later to assess test-retest reliability. Results: Regardless of the scoring use, lexical attentional bias scores were minimal, suggesting minimal attentional bias. Pictorial task attentional bias scores were larger, suggesting greater attentional bias. The correlation between the various scores was relatively small (r = 0.13-0.20). Similarly, test-retest reliability for the pictorial task was poor regardless of how the test was scored (r = 0.20-0.41). Conclusion: These results suggest that at least some of the variation in findings across attentional bias studies could be due to differences in the way that attentional bias is measured. Future research may benefit from either combining eye-tracking measurements in addition to reaction times.

Digitization and Health in Germany: Cross-sectional Nationwide Survey.

BACKGROUND: Digital technologies are shaping medicine and public health. OBJECTIVE: The aim of this study was to investigate the attitudes toward and the use of digital technologies for health-related purposes using a nationwide survey. METHODS: We performed a cross-sectional study using a panel sample of internet users selected from the general population living in Germany. Responses to a survey with 28 items were collected using computer-assisted telephone interviews conducted in October 2020. The items were divided into four topics: (1) general attitudes toward digitization, (2) COVID-19 pandemic, (3) physical activity, and (4) perceived digital health (eHealth) literacy measured with the eHealth Literacy Scale (eHEALS; sum score of 8=lowest to 40=highest perceived eHealth literacy). The data were analyzed in IBM-SPSS24 using relative frequencies. Three univariate multiple regression analyses (linear or binary logistic) were performed to investigate the associations among the sociodemographic factors (age, gender, education, and household income) and digital technology use. RESULTS: The participants included 1014 internet users (n=528, 52.07% women) aged 14 to 93 years (mean 54, SD 17). Among all participants, 66.47% (674/1014) completed up to tertiary (primary and secondary) education and 45.07% (457/1017) reported a household income of up to 3500 Euro/month (1 Euro=US $1.18). Over half (579/1014, 57.10%) reported having used digital technologies for health-related purposes. The majority (898/1014, 88.56%) noted that digitization will be important for therapy and health care, in the future. Only 25.64% (260/1014) reported interest in smartphone apps for health promotion/prevention and 42.70% (433/1014) downloaded the COVID-19 contact-tracing app. Although 52.47% (532/1014) reported that they come across inaccurate digital information on the COVID-19 pandemic, 78.01% (791/1014) were confident in their ability to recognize such inaccurate information. Among those who use digital technologies for moderate physical activity (n=220), 187 (85.0%) found such technologies easy to use and 140 (63.6%) reported using them regularly (at least once a week). Although the perceived eHealth literacy was high (eHEALS mean score 31 points, SD 6), less than half (43.10%, 400/928) were confident in using digital information for health decisions. The use of digital technologies for health was associated with higher household income (odds ratio [OR] 1.28, 95% CI 1.11-1.47). The use of digital technologies for physical activity was associated with younger age (OR 0.95, 95% CI 0.94-0.96) and more education (OR 1.22, 95% CI 1.01-1.46). A higher perceived eHealth literacy score was associated with younger age (ß=-.22, P<.001), higher household income (ß=.21, P<.001), and more education (ß=.14, P<.001). CONCLUSIONS: Internet users in Germany expect that digitization will affect preventive and therapeutic health care in the future. The facilitators and barriers associated with the use of digital technologies for health warrant further research. A gap exists between high confidence in the perceived ability to evaluate digital information and low trust in internet-based information on the COVID-19 pandemic and health decisions.

[Contact-Tracing Apps in Contact Tracing of COVID-19]. / Contact-Tracing-Apps als unterstützende Maßnahme bei der Kontaktpersonennachverfolgung von COVID-19.

Contact tracing is currently one of the most effective measures to contain the COVID-19 pandemic. In order to identify persons that would otherwise not be known or remembered and to keep the time delay when reporting an infection and when contacting people as short as possible, digital contact tracing using smartphones seems to be a reasonable measure additional to manual contact tracing. Although first modelling studies predicted a positive effect in terms of prompt contact tracing, no empirically reliable data are as yet available, neither on the population-wide benefit nor on the potential risks of contact tracing apps. Risk-benefit assessment of such an app includes investigating whether such an app fulfils its purpose, as also research on the effectiveness, risks and side effects, and implementation processes (e. g. planning and inclusion of different participants). The aim of this article was to give an overview of possible public health benefits as well as technical, social, legal and ethical aspects of a contact-tracing app in the context of the COVID-19 pandemic. Furthermore, conditions for the widest possible use of the app are presented.

Socioeconomic differences in the motivation to stop using e-cigarettes and attempts to do so.

INTRODUCTION: In England, the use of electronic cigarettes as a smoking cessation aid has become more popular than any other aid. Previous research suggests that ex-smokers from lower social groups are more likely to use e-cigarettes compared to ex-smokers from more socially advantaged groups. The present study aimed to assess the association between baseline education, income and employment status and (1) baseline motivation to stop using e-cigarettes (2) attempts to stop using e-cigarettes during follow-up among current smokers, recent ex-smokers and long-term ex-smokers who use e-cigarettes. METHODS: UK online longitudinal survey of smokers, ex-smokers and e-cigarette users, May/June 2016 (baseline) and September 2017 (follow-up). In logistic regression models, motivation to stop using e-cigarettes at baseline (n = 994) and attempts to stop using e-cigarettes at follow-up (n = 416) among current smokers and ex-smokers were regressed onto baseline educational attainment, income, employment status while adjusting for baseline demographics, vaping status, smoking and e-cigarette dependence. RESULTS: (1) Respondents with higher education (OR = 1.36; 95% CI: 1.06-1.74) or higher income (OR = 1.52; 95% CI: 1.17-1.98) were more likely to be motivated to stop using e-cigarettes, but only in unadjusted analysis. (2) Again, in unadjusted analysis only, employment was associated with reduced odds of attempting to stop using e-cigarette (OR = 0.50; 95% CI: 0.32-0.79). CONCLUSION: Higher socio-economic status may be associated with higher motivation to stop vaping but with lower likelihood of trying to do so.

[Participatory development of digital public health: tension between user perspectives and evidence]. / Partizipative Entwicklung von Digital-Public-Health-Anwendungen: Spannungsfeld zwischen Nutzer*innenperspektive und Evidenzbasierung.

Digital public health promises not only more comprehensive medical care, but also individual health promotion and support for positive lifestyle changes. Mobile digital health devices and services, also called mobile health (mHealth), play a key role in this. They include health-specific hardware and software applications such as smartphone apps and wearable technology for recording, monitoring, and evaluating specific health parameters. Although there is scientific evidence for the effectiveness of individual applications, most often applications are used for a relatively short amount of time. In order to achieve a higher acceptance and utilization rate, evidence is needed that is more practice oriented.This paper explains how participatory development approaches take into account the individual needs and preferences of users and can improve the quality and effectiveness of mHealth services. The sociodemographic characteristics of the target group as well as individual, social, linguistic, and cultural barriers should be considered. The wishes of users, for example personalization, transmission of real-time information, and transparency in terms of privacy should also be considered. In the co-design approach, users are therefore included directly in the product concept. However, the study situation is still limited and there are no methodical approaches.In order to increase the use of mHealth services in the future, participation processes should be systematized. In addition, a framework for classification and certification as well as procedures for promoting effective applications should be developed.

Daily stress as link between disadvantage and smoking: an ecological momentary assessment study.

BACKGROUND: There is a well-established social gradient in smoking, but little is known about the underlying behavioral mechanisms. Here, we take a social-ecological perspective by examining daily stress experience as a process linking social disadvantage to smoking behavior. METHOD: A sample of 194 daily smokers, who were not attempting to quit, recorded their smoking and information about situational and contextual factors for three weeks using an electronic diary. We tested whether socioeconomic disadvantage (indicated by educational attainment, income and race) exerts indirect effects on smoking (cigarettes per day) via daily stress. Stress experience was assessed at the end of each day using Ecological Momentary Assessment methods. Data were analyzed using random effects regression with a lower-level (2-1-1) mediation model. RESULTS: On the within-person level lower educated and African American smokers reported significantly more daily stress across the monitoring period, which in turn was associated with more smoking. This resulted in a small significant indirect effect of daily stress experience on social disadvantage and smoking when using education and race as indicator for social disadvantage. No such effects were found when for income as indicator for social disadvantage. CONCLUSION: These findings highlight the potential for future studies investigating behavioral mechanisms underlying smoking disparities. Such information would aid in the development and improvement of interventions to reduce social inequality in smoking rates and smoking rates in general.

Momentary smoking context as a mediator of the relationship between SES and smoking.

There is a well-established socioeconomic gradient in smoking behavior: those with lower socioeconomic status smoke more. However, much less is known about the mechanisms explaining how SES is linked to smoking. This study takes a social-ecological perspective by examining whether socioeconomic status affects smoking behavior by differential exposure to places where smoking is allowed. Exposure to smoking restrictions was assessed in real-time using Ecological Momentary Assessment methods. A sample of 194 daily smokers, who were not attempting to quit, recorded their smoking and information about situational and contextual factors for three weeks using an electronic diary. We tested whether a smoker's momentary context mediated the relationship between socioeconomic status (educational attainment) and cigarettes smoked per day (CPD). Momentary context was operationalized as the proportion of random assessments answered in locations where smoking was allowed versus where smoking was not allowed. Data were analysed using multilevel regression (measurements nested within participants) with a lower level mediation model (2-1-1 mediation). Although no significant direct effect of SES on CPD were observed, there was a significant indirect effect of SES on CPD via the momentary context. Compared to participants with higher education, lower educated participants were more likely to encounter places where smoking was allowed, and this in turn, was associated with a higher number of CPD. These findings suggest that SES is associated with smoking at least partially via differential exposure to smoking-friendly environments, with smokers from lower SES backgrounds accessing more places where smoking is allowed. Implications for current smoke-free legislation are discussed.