mHealth is a huge market that provides users the opportunity to have better health and healthcare quality. Health apps support citizen’s empowerment through self-management, health promotion, disease prevention, providing personalized health advice and care. However, the rapid development of the mHealth sector raises concerns about the potential risk of health functions apps providing transmission of health data, the capture of these data via sensors, self-diagnoses, disease management or diagnosis and appropriate processing of the data collected. Since mHealth solutions and devices can collect large quantities of personal information, including personal health information, they can process them as well.The mHONcode is a set of ethical, honesty, transparency, quality, and security standards covering various aspects of health apps, including the disclosure of the qualifications of the authors, the funding sources, references, when the content was created and last updated, what the privacy policy is, and how data is stored and transmitted over the internet. The mHONcode motivates health apps editors to be transparent in the production process and in the way to use user’s data. The commitment of a health information provider to implement or comply with the HON code of conduct for health apps is shown by the displaying of a quality label (logo or HONcode seal) on the website.As the adaptation of an already proven trustworthy code of conduct of health websites (the HONcode), the mHONcode is well placed to provide guidance for the next generation of health information providers—mobile apps in this case.KeywordsAppsmHealthTrustworthinessCertificationQuality

Introduction: Owning a smartphone is now almost a given, and with smartphone use comes the benefit of access to a large pool of apps on every topic conceivable, including health.So, it is not surprising that mHealth apps development is on the rise, as is the use of mHealth apps.However, unlike apps intended for other purposes, the use of mHealth apps carry, not only the advantage of improved health but also the burdens of potential misuse, misleading content and possible security breach of personal data.In this paper, we attempt to evaluate the possible hazards of some of the most popular mHealth apps in app stores from France.Objectives: To (1) examine the top 10 most downloaded health apps in term of security and transparency of content (2) identify the trends of the most downloaded apps (3) to assess the applicability of the mHONcode guidelines to identify main issues on health apps (4) to describe the still main risks of health apps and (5) to propose basic rules to overcome them.Results: As expected, the 10 apps displayed varying degrees of quality and trustworthiness or lack thereof.Only 20% of the apps disclose the editorial team and the funding source.80% of the apps use tracking tools such as analytics, crash reporting without prior consent or before the consent. 2 out of 10 apps did not used a https web address for health content and advertisement display.One app activates the location functionality without any justification in the app. ConclusionAs was the case for online health information more than 2 decades ago, the lack of uniformity of the trustworthiness of mHealth apps is worrying and could have some serious public health concerns.And just like the HONcode was required then, the mHONcode is required now to ensure the regulation of health apps, thus providing the end-user with trustworthy and quality tools to help in the management and maintenance of their healthcare.

Objective: Recent studies have shown that Apps are becoming a source of information and self-management for Pregnant and Postpartum women, but many users do not assess the validity of their content. To find out more, we investigated if App users perform Home Blood Pressure measurement (HBPM). Design and method: On March 2019 a notification was issued to 6627 women using Withings Health Mate app which includes a pregnancy monitoring program. This app, available in English, French and German provided these women with a questionnaire (8 questions with multiple choice answers) with the possibility of answering anonymously. No compensation or benefit whatsoever was provided; 420 women responded (response rate = 6,3%), with 46% in English, 39% in French, and 15% in German. Results: Of the respondents, 73% (n = 305) were still pregnant (term: first to ninth month, 38%: 8, 28, 33, 40, 19, 38, 41, 20) and 27% (n = 115) had already given birth; 53 % (221) were primiparous (1st pregnancy). Out of the rest who had already had a pregnancy (199), 10% (n = 19) reported having a blood pressure problem during a previous pregnancy; 5% (n = 20) receive anti-hypertensive medications, and 6 % (n = 7/115) had a history of treated gestational hypertension. Of the entire panel 26% (n = 109) still performed HBPM, and 34% (n = 143) did during pregnancy, among them 10% did so at the request of their physician or midwife. Before pregnancy, 30% (n = 116) reported having already measured their blood pressure from time to time to verify that they did not have hypertension, and 2.5% (n = 10) because of pre-existing hypertension or past history of gestational hypertension. Finally, 38% (n = 159) of the respondents wanted to receive medical information on the topic of gestational hypertension. Conclusions: A third of women using a mHealth app to manage their pregnancy performed HBPM during pregnancy, mostly (90 %) without medical incentive or instruction. Thus we need to provide pregnant women trustworthy apps, based on scientific research and established guidelines.

We are well into the 21st century and the Internet has been around long enough that there are adults who have not known a world without this wonderful tool. And just as time has gone since the beginnings of the Internet, so too has it developed, probably above and beyond the wildest dreams of its founders. These developments, though mostly positive, also have their share of the not so positive. One of these challenges is the difficulty in maintaining accuracy and quality of all the information, data gathered, aggregated or automatically generated being displayed on the Internet on Web websites or via mobile application, and this is a concern in the health domain. In this paper, we attempt to discuss in detail, some of the latest developments along with the challenges each of them entail and proposed Code of Conduct for health apps and connected objects.

Objective: Hy-Result is a web-based rule management software designed to help patients to comply with the home blood pressure measurement (HBPM) protocol and to self-interpret their results. The study Explore patients’ experience using the Hy-Result® system. Design and method: Three focus groups with 24 hypertensives patients, 5 general practitioners and 1 hypertension specialist were proposed to hypertensive patients who possess a blood pressure monitor at home and an internet access to use Hy-Result® for home blood pressure monitoring. A maximum variation sampling was performed and the analysis was thematic in a grounded theory approach. The researcher clustered patients’ responses into sub-themes and themes which were compared to highlighted concepts and issues which had been checked by the hypertension expert Results: 1) Functionality. Hy-Result® is easy to use for all patients. The main drawback is the need to transcribe blood pressure values in absence of automatic data transfer. 2) Medical content. Hy-Result® contains essential information on arterial hypertension and home blood pressure monitoring. According to user interpretation, Hy-Result® generated appropriate reactions: alert, reassurance, delay before going to doctor's office. For some patients information was obvious. 3) Feelings and expectations. Half of the patients trust Hy-Result®. They all agree that the application gives suggestions and not a diagnosis. Hy-Result® did not cause anxiety and the risk of exaggerated measurements have been discussed. 4) Physician-patient relationship. For patients, using Hy-Result® need to be a doctor's request. They are aware that Hy-Result® does not replace the judgement of the doctor. Physician- patient relationship did not change, doctor still have the main role in arterial hypertension management. Conclusions: Hy-Result® is a validated, easy to use, e-health tool for hypertensive patients undergoing HBPM. It can be considered for hypertensive patients of all ages. Most of the patients welcomed it as a complementary tool to facilitate discussion with their physician. Some patients expressed their doubts about Hy-Result® considering that the system is only for people comfortable with technology. Patients are ready to use Hy-Result® on their doctors’ requests. We still need to evaluate the opinion of medical professionals concerning the system.

Accessing online health content of high quality and reliability presents challenges. Laypersons cannot easily differentiate trustworthy content from misinformed or manipulated content. This article describes complementary approaches for members of the general public and health professionals to find trustworthy content with as little bias as possible. These include the Khresmoi health search engine (K4E), the Health On the Net Code of Conduct (HONcode) and health trust indicator Web browser extensions.

When searching on the Web, laypersons and professionals have difficulty in determining the quality or trustworthiness of health websites. No simple approach can differentiate among trustworthy and unreliable health websites in the results provided by major search engines such as Google, Yahoo or Bing. The European project Kconnect proposes to classify the reliability and readability levels of health-related Web sites and pages, tools according to the detection of HONcode criteria, the Health On the Net Foundation (HON) Code of Conduct using automated tools that examine how technical the health information contained in each document is. This article focuses on enhancement of automated detection of HONcode criteria in real-time settings. Applications of the approach include integration into the HONcode certification process, and embedding it as generic filtering tool into user-centered health domain search engines as well as into major general search engines such as a Web browser extension.

The Health On the Net Foundation (HON) was born in 1996, during the beginning of the World Wide Web, from a collective decision by health specialists, led by the late Jean-Raoul Scherrer, who anticipated the need for online trustworthy health information. Because the Internet is a free space that everyone shares, a search for quality information is like a shot in the dark: neither will reliably hit their target. Thus, HON was created to promote deployment of useful and reliable online health information, and to enable its appropriate and efficient use. Two decades on, HON is the oldest and most valued quality marker for online health information. The organization has maintained its reputation through dynamic measures, innovative endeavors and dedication to upholding key values and goals. This paper provides an overview of the HON Foundation, and its activities, challenges, and achievements over the years.

The majority of the adult population in both Europe and North America have access to the internet. Over 70% state that they have used the internet to look for health information and the majority started their search at a search engine. Given that search engines list sites according to popularity and not quality, it is imperative that users have a means of discerning trustworthy and honest information from non-reliable health information. The HONcode, a set of eight quality guidelines, ensures access to standardized trustworthy health information which can be used as a tool to guide consumers.

The Health On the Net’s Foundation (HON) Code of Conduct, HONcode, is the oldest and the most used ethical and trustworthy code for medical and health related information available on the Internet. Until recently, websites voluntarily applying for the HONcode seal were evaluated manually by an expert medical team according to 8 principles, referred to as criteria, and associated published guidelines. In the scope of the European project Kconnect, HON is developing an automated system to identify the 8 HONcode criteria within health webpages. When the research on the development of such a system evolved from simple algorithmic testing to a real full-content setting, it revealed a number of issues. The preceding study consisted in taking a set of 27 health-related websites and having them assessed for their compliance to each of the 8 HONcode criterion, first manually by senior HONcode experts, and then through supervised machine learning by the automated system. The results showed discrepancies mainly for two criteria: “submerged content” under the Complementarity criterion and “extremely low recall” under the Date Attribution criterion. In this article, the authors investigate different approaches to solve the problems related to each of these criteria, namely a customized Named Entity Recognition Model instead of a machine learning component for Date Attribution, and a sliding window instead of the whole document as a unit of detection for Complementarity. The results obtained show that the newly adapted automated system greatly improves accuracy: 74% vs. 41% for the Date Attribution criterion and 74% vs. 22% for the Complementarity criterion.

The HONcode of conduct is composed of eight ethical and quality criteria (www.hon.ch/Conduct.html) This study evaluates supervised automatic classification algorithms capability to determinate whether a health related web page is in compliance with any of those criteria. Various length character ngram vectors were used to represent health web page documents. Classification performance of the 5-grams was compared to that obtained by words or stems. The study attempts to determine whether the language-independent approach might result in similar classification performance as wordbased classification for both English and French languages. The training/testing collection for both languages were created from web page fragments extracted by HONcode experts during the manual certification process as the basis for individual HONcode compliance. Naive Bayes classifier and DF (document frequency) dimensionality reduction metrics were used. The overall results of this study indicate that the n-gram tokenization provides a potentially viable alternative to document word stemming.

Purpose: This article reports the user-oriented evaluation of a text-and content-based medical image retrieval system. User tests with radiologists using a search system for images in the medical literature are presented. The goal of the tests is to assess the usability of the system, identify system and interface aspects that need improvement and useful additions. Another objective is to investigate the system's added value to radiology information retrieval. The study provides an insight into required specifications and potential shortcomings of medical image retrieval systems through a concrete methodology for conducting user tests.Methods: User tests with a working image retrieval system of images from the biomedical literature were performed in an iterative manner, where each iteration had the participants perform radiology information seeking tasks and then refining the system as well as the user study design itself. During these tasks the interaction of the users with the system was monitored, usability aspects were measured, retrieval success rates recorded and feedback was collected through survey forms.Results: In total, 16 radiologists participated in the user tests. The success rates in finding relevant information were on average 87% and 78% for image and case retrieval tasks, respectively. The average time for a successful search was below 3 min in both cases. Users felt quickly comfortable with the novel techniques and tools (after 5 to 15 min), such as content-based image retrieval and relevance feedback. User satisfaction measures show a very positive attitude toward the system's functionalities while the user feedback helped identifying the system's weak points. The participants proposed several potentially useful new functionalities, such as filtering by imaging modality and search for articles using image examples.Conclusion: The iterative character of the evaluation helped to obtain diverse and detailed feedback on all system aspects. Radiologists are quickly familiar with the functionalities but have several comments on desired functionalities. The analysis of the results can potentially assist system refinement for future medical information retrieval systems. Moreover, the methodology presented as well as the discussion on the limitations and challenges of such studies can be useful for user-oriented medical image retrieval evaluation, as user-oriented evaluation of interactive system is still only rarely performed. Such interactive evaluations can be limited in effort if done iteratively and can give many insights for developing better systems. (C) 2015 Published by Elsevier Ireland Ltd.

Background To earn HONcode certification, a website must conform to the 8 principles of the HONcode of Conduct In the current manual process of certification, a HONcode expert assesses the candidate website using precise guidelines for each principle. In the scope of the European project KHRESMOI, the Health on the Net (HON) Foundation has developed an automated system to assist in detecting a website’s HONcode conformity. Automated assistance in conducting HONcode reviews can expedite the current time-consuming tasks of HONcode certification and ongoing surveillance. Additionally, an automated tool used as a plugin to a general search engine might help to detect health websites that respect HONcode principles but have not yet been certified. Objective The goal of this study was to determine whether the automated system is capable of performing as good as human experts for the task of identifying HONcode principles on health websites. Methods Using manual evaluation by HONcode senior experts as a baseline, this study compared the capability of the automated HONcode detection system to that of the HONcode senior experts. A set of 27 health-related websites were manually assessed for compliance to each of the 8 HONcode principles by senior HONcode experts. The same set of websites were processed by the automated system for HONcode compliance detection based on supervised machine learning. The results obtained by these two methods were then compared. Results For the privacy criterion, the automated system obtained the same results as the human expert for 17 of 27 sites (14 true positives and 3 true negatives) without noise (0 false positives). The remaining 10 false negative instances for the privacy criterion represented tolerable behavior because it is important that all automatically detected principle conformities are accurate (ie, specificity [100%] is preferred over sensitivity [58%] for the privacy criterion). In addition, the automated system had precision of at least 75%, with a recall of more than 50% for contact details (100% precision, 69% recall), authority (85% precision, 52% recall), and reference (75% precision, 56% recall). The results also revealed issues for some criteria such as date. Changing the “document” definition (ie, using the sentence instead of whole document as a unit of classification) within the automated system resolved some but not all of them. Conclusions Study results indicate concordance between automated and expert manual compliance detection for authority, privacy, reference, and contact details. Results also indicate that using the same general parameters for automated detection of each criterion produces suboptimal results. Future work to configure optimal system parameters for each HONcode principle would improve results. The potential utility of integrating automated detection of HONcode conformity into future search engines is also discussed.

Authors evaluated supervised automatic classification algorithms for determination of health related web-page compliance with individual HONcode criteria of conduct ( www.hon.ch/Conduct.html ). The current study used varying length character n-gram vectors to represent healthcare web page documents – not the traditional approach of using word vectors. The training/testing collection comprised web page fragments that HONcode experts had cited as the basis for individual HONcode compliance during the manual certification process (described below). The authors compared automated classification performance of n-gram tokenization to the automated classification performance of document words and Porter-stemmed document words using a Naive Bayes classifier and DF (document frequency) dimensionality reduction metrics. The study attempted to determine whether the automated, language-independent approach might safely replace single word-based classification. Using 5-grams as document features, authors also compared the baseline DF reduction function to Chi-square and Z-score dimensionality reductions. While the Z-score approach statistically significantly improved precision for some HONcode compliance components, the Chi-square performance was unreliable, performing very well for some criteria and poorly for others. Overall study results indicate that n-gram tokenization provide a potentially viable alternative to document word stemming.

Authors evaluated supervised automatic classification algorithms for determination of health related web-page compliance with individual HONcode criteria of conduct using varying length character n-gram vectors to represent healthcare web page documents. The training/testing collection comprised web page fragments extracted by HONcode experts during the manual certification process. The authors compared automated classification performance of n-gram tokenization to the automated classification performance of document words and Porter-stemmed document words using a Naive Bayes classifier and DF (document frequency) dimensionality reduction metrics. The study attempted to determine whether the automated, language-independent approach might safely replace word-based classification. Using 5-grams as document features, authors also compared the baseline DF reduction function to Chi-square and Z-score dimensionality reductions. Overall study results indicate that n-gram tokenization provided a potentially viable alternative to document word stemming.

The HONcode provided by Health On the Net (HON) foundation is the most successful third party certification initiative. In nearly 20 years, it has acquired a database of over 8000 trustworthy health websites, has been translated into over 30 languages and become not only a well-respected name amongst health information providers but also an increasingly well-recognised brand amongst health information end-users. The HONcode, having begun in the mid-1990s has managed to stay current and relevant two decades on, because of its ability to change and remain relevant to the present times. The past few years have been no different, and HON has been heavily involved in bringing out certain updates to further its mission of unrestricted trustworthy health information online. In this paper we present the HONcode certification process and the main changes taking place in the certification process to enable its continued success and sustainability.