Online Interventions for the Selective Prevention of Illicit Drug Use in Young Drug Users: Exploratory Study

Health information needs and feedback of users in the online TCM community

To improve the information service quality of the online Traditional Chinese Medicine (TCM) community, this study investigated users' information needs, feedback and the relationship between them. Using qualitative content analysis, the basic characteristics of users' needs were obtained. Logistic regression was used to explore the impact of different need characteristics of feedback. The main findings are as follows: 1) Disease consultation, health preservation, professional discussion, knowledge sharing and experience description are the major 5 types of information needs in the online TCM community; 2) Some users provided TCM-related information, such as the tongue image and the TCM four diagnosis; 3) A total of 78.8% of the posts received effective feedback, and the main types of feedback were answering, discussing, inquiring and emotional supporting; 4) Providing enough information can significantly and positively affect whether needs receive effective feedback, suggesting that users can present information about their condition in as many different formats as possible when articulating their needs.

Evaluation of Mobile Applications for Patients with Diabetes Mellitus: A Scoping Review

There has been increasing interest in mobile healthcare for diabetes management. However, there remains limited evidence regarding the effectiveness of these mobile applications (apps). This scoping review aimed to evaluate the clinical effectiveness of mobile diabetes management apps. We used the following search terms: "mobile app", "mobile application", and "diabetes". We included only articles written in English and published between January 2016 and August 2021. We identified two, six, and four articles focused on type 1 diabetes, type 2 diabetes, and both diabetes types, respectively. Five, four, and three of these studies reported on the apps' functionality, usability, and both, respectively. Our findings indicated that diabetes mobile apps allowed for convenient user experience and improved blood sugar levels in patients with diabetes. Considering these findings, usability must be comprehensively evaluated by using definitions such as the ISO9241-11 usability definition or the mobile application rating scale (MARS) when developing diabetes-related apps. For the feasibility of diabetes mobile apps, we recommend that HbA1C and self-management be included as evaluation variables. Given the increasing importance of continuous management for patients with diabetes, interventions using mobile apps are bound to become effective tools for patient-led self-management.

Digital interventions for substance use disorders in young people: rapid review

BACKGROUND

Young people are disproportionately more likely than other age groups to use substances. The rise in substance use and related harms, including overdose, during the Covid-19 pandemic has created a critical need for more innovative and accessible substance use interventions. Digital interventions have shown effectiveness and can provide more engaging, less stigmatizing, and accessible interventions that meet the needs of young people. This review provides an overview of recent literature on the nature of recently published digital interventions for young people in terms of technologies used, substances targeted, intended outcomes and theoretical or therapeutic models employed.

METHODS

Rapid review methodology was used to identify and assess the literature on digital interventions for young people. An initial keyword search was conducted using MEDLINE the Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects (DARE), Health Technology Assessment Database (HTA) and PROSPERO for the years 2015-2020, and later updated to December 2021. Following a title/abstract and full-text screening of articles, and consensus decision on study inclusion, the data extraction process proceeded using an extraction grid developed for the study. Data synthesis relied on an adapted conceptual framework by Stockings, et al. that involved a three-level treatment spectrum for youth substance use (prevention, early intervention, and treatment) for any type of substance.

RESULTS

In total, the review identified 43 articles describing 39 different digital interventions. Most were early interventions (n = 28), followed by prevention interventions (n = 6) and treatment interventions (n = 5). The identified digital technologies included web-based (n = 14), game-based (n = 10), mobile-based (n = 7), and computer-based (n = 5) technologies, and virtual reality (n = 3). Most interventions targeted alcohol use (n = 20) followed by tobacco/nicotine (n = 5), cannabis (n = 2), opioids (n = 2), ketamine (1) and multiple, or any substances (n = 9). Most interventions used a personalized or normative feedback approach and aimed to effect behaviour change as the intended outcome. Interestingly, a harm reduction approach guided only one of the 39 interventions.

CONCLUSIONS

While web-based interventions represented the most common type of technology, more recently developed immersive and interactive technologies such as virtual reality and game-based interventions call for further exploration. Digital interventions focused mainly on alcohol use, reflecting less concern for tobacco, cannabis, co-occurring substance use, and illicit drug use. Specifically, the recent exacerbation in the opioid crisis throughout North American underlines the urgent need for more prevention-oriented digital interventions for opioid use. The uptake of digital interventions among youth also depends on the incorporation of harm reduction approaches.

Online Health Information Seeking: An Italian Case Study for Analyzing Citizens' Behavior and Perception

This study aims to understand people's behavior when searching for online health information (and COVID-19 information) and their perception of the trustworthiness and credibility of the searched information, the actors, and sources used to obtain it. A questionnaire addressed to people who permanently live in Italy between ages 19 and 60 has been used to collect data. Data extracted from the analysis are reassuring from the point of view of trust and credibility both in the actors and in the sources used to obtain information on health and COVID-19. A correlation between the analyzed individual features, the online health information-seeking behavior, and perception resulted from the analysis. The study also underlined a positive correlation between the perception of the influence of information on the knowledge of health problems and the ability to identify false online health information, and between the experience in detecting false health online information and the ability to detect it. Finally, a positive correlation also resulted between the experience in finding online health information and the experience in finding false COVID-19 information.

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

Effects of Changes in Multiple Chronic Conditions on Medical Costs among Older Adults in South Korea

This study aims to analyze the relationship between cognitive function and out-of-pocket cost of the state change of multiple chronic conditions in individuals aged 60 or older. Data from the 2014 to 2018 Korean Longitudinal Study of Aging were used for 2202 older adults who were cognitively “normal” at the start of the survey. Four status change groups were established (“Good → Good,” “Good → Bad,” “Bad → Good,” and “Bad → Bad”) according to the change in the number of chronic diseases. Generalized estimating equation modeling analyzed the association between these changes and out-of-pocket medical cost. Out-of-pocket cost was significantly higher among older adults with multiple chronic conditions (p < 0.0001). Total out-of-pocket medical cost and out-of-pocket cost for outpatient care and prescription drugs were significantly higher for Bad→ Bad or Good → Bad changes. Older adults with cognitive decline had significantly higher total out-of-pocket medical cost and out-of-pocket cost for prescription drugs. This study demonstrates the need to improve the multiple chronic conditions management construction model to enhance the health of older adults in Korea and secure national health care finances long-term. It provides a foundation for related medical and medical expenses-related systems.

Strengthening the Trialability for the Intention to Use of mHealth Apps Amidst Pandemic: A Cross-Sectional Study

Recent advancements in mHealth apps and services have played a vital role in strengthening healthcare services and enabling their accessibility to marginalized people. With the alarming rise in COVID-19 infection rates around the world, there appears to be an urgent call to modernize traditional medical practices to combat the pandemic. This study aims to investigate the key factors influencing the trialability of mHealth apps/services and behavioral intention to adopt mobile health applications. The study also examines the moderating effects of self-discipline motivation, knowledge, and attitude on the relationship between trialability and behavioral intention to use. The deductive reasoning approach was followed in a positivism paradigm. The study used convenience sampling and collected responses from 280 Generation Y participants in Bangladesh. Partial least square-based structural equation modeling was employed. The results revealed that relative advantage (β = 0.229, p < 0.05), compatibility (β = 0.232, p < 0.05), complexity (β = −0.411, p < 0.05), and observability (β = 0.235, p < 0.05) of mHealth apps influence the trialability of mHealth apps and services among users. Trialability compatibility (β = 0.425, p < 0.05) of mHealth was positively related to the behavioral intention to use these mobile apps. The study found no moderating effects of attitude (β = 0.043, p > 0.05) or self-discipline motivation (β = −0.007, p > 0.05) on the hypothesized relationships. The empirical findings of this study may facilitate the development, design process, and implementation of mHealth applications with improved features that can lead to high user acceptance among Generation Y during future health crises.

Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.