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EHealth Apps Replacing or Complementing Health Care Contacts: Scoping Review on Adverse Effects.

ABSTRACT: BACKGROUND:The use of eHealth has increased tremendously in recent years. eHealth is generally considered to have a positive effect on health care quality and to be a promising alternative to face-to-face health care contacts. Surprisingly little is known about possible adverse effects of eHealth apps. OBJECTIVE:We conducted a scoping review on empirical research into adverse effects of eHealth apps that aim to deliver health care at a distance. We investigated whether adverse effects were reported and the nature and quality of research into these possible adverse effects. METHODS:For this scoping review, we followed the five steps of Arksey and O'Malley's scoping review methodology. We searched specifically for studies into eHealth apps that replaced or complemented the face-to-face contact between a health professional and a patient in the context of treatment, health monitoring, or supporting self-management. Studies were included when eHealth and adverse effects were mentioned in the title or abstract and when empirical data on adverse effects were provided. All health conditions, with the exception of mental health conditions, all ages, and all sample sizes were included. We examined the literature published between December 2012 and August 2017 in the following databases: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and the Cochrane Library. The methodological quality of the studies was assessed using the Critical Appraisal Skills Programme (CASP) checklists. RESULTS:Our search identified 79 papers that were potentially relevant; 11 studies met our inclusion criteria after screening. These studies differed in many ways and the majority were characterized by small research populations and low study quality. Adverse effects are rarely subject to systematic scientific research. So far, information on real adverse effects is mainly limited to incidental reporting or as a bycatch from qualitative pilot studies. Despite the shortage of solid research, we found some indications of possible negative impact on patient-centeredness and efficiency, such as less transparency in the relationship between health professionals and patients and time-consuming work routines. CONCLUSIONS:There is a lack of high-quality empirical research on adverse effects of eHealth apps that replace or complement face-to-face care. While the development of eHealth apps is ongoing, the knowledge with regard to possible adverse effects is limited. The available research often focuses on efficacy, added value, implementation issues, use, and satisfaction, whereas adverse effects are underexplored. A better understanding of possible adverse effects could be a starting point in improving the positive impact of eHealth-based health care delivery.

SUBMITTER: Stevens WJM

PROVIDER: S-EPMC6421717 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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eHealth Apps Replacing or Complementing Health Care Contacts: Scoping Review on Adverse Effects.

Stevens Wilhelmina Josepha Maria WJM van der Sande Rob R Beijer Lilian J LJ Gerritsen Maarten Gm MG Assendelft Willem Jj WJ

Journal of medical Internet research 20190301 3

<h4>Background</h4>The use of eHealth has increased tremendously in recent years. eHealth is generally considered to have a positive effect on health care quality and to be a promising alternative to face-to-face health care contacts. Surprisingly little is known about possible adverse effects of eHealth apps.<h4>Objective</h4>We conducted a scoping review on empirical research into adverse effects of eHealth apps that aim to deliver health care at a distance. We investigated whether adverse eff ...[more]

PMID: 30821690

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Project description:BackgroundPrivacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments.ObjectiveThis scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed.MethodsWe followed the methodology defined by Arksey and O'Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy.ResultsThe database searches retrieved a total of 710 citations-24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps.ConclusionsThe privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.

Project description:BackgroundMobile devices can provide extendable learning environments in higher education and motivate students to engage in adaptive and collaborative learning. Developers must design mobile apps that are practical, effective, and easy to use, and usability testing is essential for understanding how mobile apps meet users' needs. No previous reviews have investigated the usability of mobile apps developed for health care education.ObjectiveThe aim of this scoping review is to identify usability methods and attributes in usability studies of mobile apps for health care education.MethodsA comprehensive search was carried out in 10 databases, reference lists, and gray literature. Studies were included if they dealt with health care students and usability of mobile apps for learning. Frequencies and percentages were used to present the nominal data, together with tables and graphical illustrations. Examples include a figure of the study selection process, an illustration of the frequency of inquiry usability evaluation and data collection methods, and an overview of the distribution of the identified usability attributes. We followed the Arksey and O'Malley framework for scoping reviews.ResultsOur scoping review collated 88 articles involving 98 studies, mainly related to medical and nursing students. The studies were conducted from 22 countries and were published between 2008 and 2021. Field testing was the main usability experiment used, and the usability evaluation methods were either inquiry-based or based on user testing. Inquiry methods were predominantly used: 1-group design (46/98, 47%), control group design (12/98, 12%), randomized controlled trials (12/98, 12%), mixed methods (12/98, 12%), and qualitative methods (11/98, 11%). User testing methods applied were all think aloud (5/98, 5%). A total of 17 usability attributes were identified; of these, satisfaction, usefulness, ease of use, learning performance, and learnability were reported most frequently. The most frequently used data collection method was questionnaires (83/98, 85%), but only 19% (19/98) of studies used a psychometrically tested usability questionnaire. Other data collection methods included focus group interviews, knowledge and task performance testing, and user data collected from apps, interviews, written qualitative reflections, and observations. Most of the included studies used more than one data collection method.ConclusionsExperimental designs were the most commonly used methods for evaluating usability, and most studies used field testing. Questionnaires were frequently used for data collection, although few studies used psychometrically tested questionnaires. The usability attributes identified most often were satisfaction, usefulness, and ease of use. The results indicate that combining different usability evaluation methods, incorporating both subjective and objective usability measures, and specifying which usability attributes to test seem advantageous. The results can support the planning and conduct of future usability studies for the advancement of mobile learning apps in health care education.International registered report identifier (irrid)RR2-10.2196/19072.

Project description:BackgroundGermany is the first country worldwide that has introduced a digital care act as an incentive system to enhance the use of digital health devices, namely health apps and wearables, among its population. The act allows physicians to prescribe statutory financed and previously certified health apps and wearables to patients. This initiative has the potential to improve treatment quality through better disease management and monitoring.ObjectiveThe aim of this paper was to outline the key concepts related to the potential risks and benefits discussed in the current literature about health apps and wearables. Furthermore, this study aimed to answer the research question: Which risks and benefits may result from the implementation of the digital care act in Germany?MethodsWe conducted the scoping study by searching the databases PubMed, Google Scholar, and JMIR using the keywords health apps and wearables. We discussed 55 of 136 identified articles published in the English language from 2015 to March 2019 in this paper using a qualitative thematic analysis approach.ResultsWe identified four key themes within the articles: Effectivity of health apps and wearables to improve health; users of health apps and wearables; the potential of bring-your-own, self-tracked data; and concerns and data privacy risks. Within these themes, we identified three main stages of benefits for the German health care system: Usage of health apps and wearables; continuing to use health apps and wearables; and sharing bring-your-own; self-tracked data with different agents in the health care sector.ConclusionsThe digital care act could lead to an improvement in treatment quality through better patient monitoring, disease management, personalized therapy, and better health education. However, physicians should play an active role in recommending and supervising health app use to reach digital-illiterate or health-illiterate people. Age must not be an exclusion criterion. Yet, concerns about data privacy and security are very strong in Germany. Transparency about data processing should be provided at all times for continuing success of the digital care act in Germany.

Project description:BackgroundDigital health interventions (DHIs) are increasingly being adopted globally to address various public health issues. DHIs can be categorized according to four main types of technology: mobile based, web based, telehealth, and electronic health records. In 2006, Norman and Skinner introduced the eHealth literacy model, encompassing six domains of skills and abilities (basic, health, information, scientific, media, and computer) needed to effectively understand, process, and act on health-related information. Little is known about whether these domains are assessed or accounted for in DHIs.ObjectiveThis study aims to explore how DHIs assess and evaluate the eHealth literacy model, describe which health conditions are addressed, and which technologies are used.MethodsWe conducted a scoping review of the literature on DHIs, based on randomized controlled trial design and reporting the assessment of any domain of the eHealth literacy model. MEDLINE, CINAHL, Embase, and Cochrane Library were searched. A duplicate selection and data extraction process was performed; we charted the results according to the country of origin, health condition, technology used, and eHealth literacy domain.ResultsWe identified 131 unique DHIs conducted in 26 different countries between 2001 and 2020. Most DHIs were conducted in English-speaking countries (n=81, 61.8%), delivered via the web (n=68, 51.9%), and addressed issues related to noncommunicable diseases (n=57, 43.5%) or mental health (n=26, 19.8%). None of the interventions assessed all six domains of the eHealth literacy model. Most studies focused on the domain of health literacy (n=96, 73.2%), followed by digital (n=19, 14.5%), basic and media (n=4, 3%), and information and scientific literacy (n=1, 0.7%). Of the 131 studies, 7 (5.3%) studies covered both health and digital literacy.ConclusionsAlthough many selected DHIs assessed health or digital literacy, no studies comprehensively evaluated all domains of the eHealth literacy model; this evidence might be overlooking important factors that can mediate or moderate the effects of these interventions. Future DHIs should comprehensively assess the eHealth literacy model while developing or evaluating interventions to understand how and why interventions can be effective.

Project description:ObjectivesDespite an estimated 300 000 mobile health apps on the market, there remains no consensus around helping patients and clinicians select safe and effective apps. In 2018, our team drew on existing evaluation frameworks to identify salient categories and create a new framework endorsed by the American Psychiatric Association (APA). We have since created a more expanded and operational framework Mhealth Index and Navigation Database (MIND) that aligns with the APA categories but includes objective and auditable questions (105). We sought to survey the existing space, conducting a review of all mobile health app evaluation frameworks published since 2018, and demonstrate the comprehensiveness of this new model by comparing it to existing and emerging frameworks.DesignWe conducted a scoping review of mobile health app evaluation frameworks.Data sourcesReferences were identified through searches of PubMed, EMBASE and PsychINFO with publication date between January 2018 and October 2020.Eligibility criteriaPapers were selected for inclusion if they meet the predetermined eligibility criteria-presenting an evaluation framework for mobile health apps with patient, clinician or end user-facing questions.Data extraction and synthesisTwo reviewers screened the literature separately and applied the inclusion criteria. The data extracted from the papers included: author and dates of publication, source affiliation, country of origin, name of framework, study design, description of framework, intended audience/user and framework scoring system. We then compiled a collection of more than 1701 questions across 79 frameworks. We compared and grouped these questions using the MIND framework as a reference. We sought to identify the most common domains of evaluation while assessing the comprehensiveness and flexibility-as well as any potential gaps-of MIND.ResultsNew app evaluation frameworks continue to emerge and expand. Since our 2019 review of the app evaluation framework space, more frameworks include questions around privacy (43) and clinical foundation (57), reflecting an increased focus on issues of app security and evidence base. The majority of mapped frameworks overlapped with at least half of the MIND categories. The results of this search have informed a database (apps.digitalpsych.org) that users can access today.ConclusionAs the number of app evaluation frameworks continues to rise, it is becoming difficult for users to select both an appropriate evaluation tool and to find an appropriate health app. This review provides a comparison of what different app evaluation frameworks are offering, where the field is converging and new priorities for improving clinical guidance.

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EHealth Apps Replacing or Complementing Health Care Contacts: Scoping Review on Adverse Effects.

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eHealth Apps Replacing or Complementing Health Care Contacts: Scoping Review on Adverse Effects.

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