Heuristics used for evaluating the usability of mobile health applications: A systematic literature review.

Objective: Mobile health applications hold immense potential for enhancing health outcomes. Usability is one of the main factors for the adoption and use of mobile health applications. However, despite the growing importance of mHealth applications, clear standards for their evaluation remain elusive. The present study aimed to determine heuristics for the usability evaluation of health-related applications. Methods: We systematically searched multiple databases for relevant papers published between January 2008 and April 2021. Articles were reviewed, and data were extracted and categorized from those meeting inclusion criteria by two authors independently. Heuristics were identified based on statements, words, and concepts expressed in the studies. These heuristics were first mapped to Nielsen's heuristics based on their differences or similarities. The remaining heuristics that were very important for mobile applications were categorized into new heuristics. Results: Seventeen studies met the eligibility criteria. Seventy-nine heuristics were extracted from the papers. After combining the items with the same concepts and removing irrelevant items based on the exclusion criteria, 20 heuristics remained. Common heuristics such as "Visibility of system status" and "Flexibility and efficiency of use" were categorized into 10 previously established heuristics and new heuristics like "Navigation" and "User engagement" were recognized as new ones. Conclusions: In our study, we have meticulously identified 20 heuristics that hold promise for evaluating and designing mHealth applications. These heuristics can be used by the researchers for the development of robust tools for heuristic evaluation. These tools, when adapted or tailored for health domain applications, have the potential to significantly enhance the quality of mHealth applications. Ultimately, this improvement in quality translates to enhanced patient safety. Protocol Registration: (10.17605/OSF.IO/PZJ7H).

Identifying the data elements and functionalities of clinical decision support systems to administer medication for neonates and pediatrics: a systematic literature review.

BACKGROUND: Patient safety is a central healthcare policy worldwide. Adverse drug events (ADE) are among the main threats to patient safety. Children are at a higher risk of ADE in each stage of medication management process. ADE rate is high in the administration stage, as the final stage of preventing medication errors in pediatrics and neonates. The most effective way to reduce ADE rate is using medication administration clinical decision support systems (MACDSSs). The present study reviewed the literature on MACDSS for neonates and pediatrics. It identified and classified the data elements that mapped onto the Fast Healthcare Interoperability Resources (FHIR) standard and the functionalities of these systems to guide future research. METHODS: PubMed/ MEDLINE, Embase, CINAHL, and ProQuest databases were searched from 1995 to June 31, 2021. Studies that addressed developing or applying medication administration software for neonates and pediatrics were included. Two authors reviewed the titles, abstracts, and full texts. The quality of eligible studies was assessed based on the level of evidence. The extracted data elements were mapped onto the FHIR standard. RESULTS: In the initial search, 4,856 papers were identified. After removing duplicates, 3,761 titles, and abstracts were screened. Finally, 56 full-text papers remained for evaluation. The full-text review of papers led to the retention of 10 papers which met the eligibility criteria. In addition, two papers from the reference lists were included. A total number of 12 papers were included for analysis. Six papers were categorized as high-level evidence. Only three papers evaluated their systems in a real environment. A variety of data elements and functionalities could be observed. Overall, 84 unique data elements were extracted from the included papers. The analysis of reported functionalities showed that 18 functionalities were implemented in these systems. CONCLUSION: Identifying the data elements and functionalities as a roadmap by developers can significantly improve MACDSS performance. Though many CDSSs have been developed for different medication processes in neonates and pediatrics, few have actually evaluated MACDSSs in reality. Therefore, further research is needed on the application and evaluation of MACDSSs in the real environment. PROTOCOL REGISTRATION: (dx.doi.org/10.17504/protocols.io.bwbwpape).

Exploring Common Symptoms in Patients with Respiratory Allergies Using K-Means Algorithm in the North-East of Iran in 2012-2015.

Background: As a common disease among people of almost any age, allergic rhinitis has many adverse effects such as lowering the quality of life and efficiency at work or school. Considering these conditions and the collection of large amounts of data, the present research was conducted on allergic rhinitis and asthma patients' data to extract the common symptoms of these diseases using cluster analysis and the k-means algorithm. Materials and Methods: The present cross-sectional research was conducted in Mashhad city. The inclusion criteria were affliction with one or two respiratory allergy diseases diagnosed by an allergy specialist through clinical history taking and physical examination. A researcher-made checklist was used in the present study for data collection. Then, the K-means algorithm's cluster analysis model was conducted to extract clusters (WEKA software (3, 6, 9)). Results: Overall, 1,231 patients met the inclusion criteria. The result of the Cluster analysis consisted of Cluster 1 in allergic rhinitis consisted of 702 patients, and cluster 2 consisted of 382 patients.46 asthma patients were assigned to cluster 1 and 23 to cluster 2.Also, 60 asthma and allergic rhinitis patients were assigned to cluster 1 and 19 to cluster 2. The most common symptoms in all patients were rhinorrhea, sneezing, nasal congestion, and itchy nose. Conclusion: Overall, Salsola kali was the most common allergen in allergic rhinitis and asthma patients. Also, the most common symptoms in patients are rhinorrhea, sneezing, itchy nose, and nasal congestion. This study can help physicians diagnose allergic rhinitis and asthma in geographical areas with a high prevalence of Salsola kali.

A pragmatically before-after trial of tele-visits vs face-to-face visits for chronic patients during the COVID-19 pandemic: patient-reported adherence.

BACKGROUND: During the COVID-19 pandemic, chronic patients are at a higher risk of contamination with the virus. Specific strategies are required to restrict these patients' exposure to contaminated areas and improve medication adherence. One suggested strategy is tele-visiting, which is effective for the continuity of care and medication adherence during the pandemic. OBJECTIVE: The present study aimed to explore the effect of tele-visiting services via telephone on chronic patients' medication adherence before and after implementing a tele-visit program during the COVID-19 pandemic. METHODS: All patients received the tele-visit twice on the phone during the study. To compare patients' medication adherence in face-to-face visits and tele-visit, an adapted version of the Morisky Medication Adherence Scale-8 was used. Paired-samples T-test was run to measure participants' medication adherence before and after the tele-visit program. RESULTS: The tele-visit was run for 314 patients. The participants' adherence score before the intervention was 60.02, and after the intervention was 59.9. As the paired-sample T-test results showed, the difference between these two was not statistically significant. Moreover, medication adherence was not significantly associated with any of these variables: BMI, occupation, comorbidities, duration of disease, age, gender, marital status, and education level. CONCLUSIONS: The present findings showed that chronic patients' medication adherence did not differ significantly in face-to-face visits and tele-visiting. During the Covid-19 pandemic, due to the effectiveness of tele-visiting services, they can be used effectively to lower the transmission rate of the disease and reduce healthcare providers' burden.

A Mobile Application for Managing Diabetic Patients' Nutrition: A Food Recommender System.

BACKGROUND: As a prevalent metabolic disease, diabetes has different side effects and causes a wide range of co morbidity with a high rate of mortality. There is a need for certain interventions to manage this disease. Iranians usually have three main meals a day. Considering the special needs of diabetic patients and the possibility of hypoglycemia between the main meals, it is essential for these patients to eat something as a snack. Considering these conditions and the society's orientation towards modern technologies such as smart phones, designing mobile-based nutrition recommender systems can be helpful. METHODS: The snack recommender system is a knowledge-based smart phone application. This study has focused on the development of a recommender system that combines artificial intelligence techniques and makes up a knowledge base according to the guidelines posed by the American Diabetes Association (ADA). The snack menu was recommended in accordance with the patient's favorites and conditions. The accuracy of the recommended menu was assessed in 2 steps. First, it was compared with the diet prescribed by three nutrition specialists. In the second step, system's suggested menu was evaluated by the data from 30 diabetic patients using a valid questionnaire. RESULTS: The results of evaluating the snack recommender system by nutritionists showed that this system is capable of recommending various snacks according to the season (accuracy of 100%) and personal interests (accuracy of 90%) to diabetic patients. According to health nutritionists, the snacks suggested by this system are matched with Iranian culture. Moreover, the results revealed that a higher body mass index (BMI) makes the recommender system less sensitive to personal interests to suggest what is basically beneficial for one's health. CONCLUSION: This study was a pioneering research to develop a more comprehensive dietary recommender system for diabetic patients which includes main meals as well. Patients found the system useful and were satisfied with the application. This system is believed to be able to help diabetic patients to take more healthy diet which leads to a better lifestyle.