JMIR Human Factors

Strengthening shared decision-making in mental healthcare may improve the quality of services and treatment outcomes, but its implementation in services for severe mental disorders is currently lacking.

Patient-reported outcome measures (PROMs) are tools for assessing symptoms and the quality of care. Despite their growing use, conventional data collection methods limit widespread PROM implementation. In orthopaedics, pain is a frequent patient complaint and a common PROM, especially following total knee arthroplasty (TKA). Although TKA is generally successful, some patients still report post-operative pain, potentially due to tourniquet use. Using an improved PROM data-gathering technique may help to address tourniquet use during a TKA procedure and its impact on post-operative pain. The PainPad, an automated self-logging device, was developed to capture patient pain levels accurately.

With the increasing prevalence of type 1 diabetes alongside limited healthcare resources, the need for more sustainable healthcare services is apparent. Central to ensuring the standard of diabetes care while simultaneously optimizing resource utilization is improved patient-clinician communication and the provision of individualized care. Digital outpatient solutions incorporating patient-reported outcome measures (PROMs) have been introduced in diabetes outpatient care over recent years; however, features and delivery methods differ, and existing studies on their use and perceived clinical value are limited. Furthermore, clinicians’ acceptance has been suggested as a key factor in the sustainability of digital solutions. Thus, to support the implementation of digital outpatient solutions perceived as valuable by clinicians and patients, we need more knowledge about how they are accepted and utilized in clinical practice.

Mental health (MH) difficulties are increasing among Canadian postsecondary students, and many face barriers to accessing MH care. Mobile health smartphone applications for MH (MH apps) reduce common barriers to care and improve student MH outcomes. However, students’ engagement and use of MH apps is low. Evaluating the usability and quality of MH apps is essential not only for user engagement, but also for safety and overall utility. Few MH apps have undergone usability and quality evaluations, especially with measures explicitly designed for these apps. The JoyPopTM app is a resilience-building MH app with evidence supporting its effectiveness for student MH. It has yet to be evaluated using standardized measures of MH app usability and quality, and the influence of usability and quality on use is unknown.

Background: Although e-health has shown promise in managing chronic diseases, there remains a substantial digital divide among older adults. The concept of a growth mindset, based on psychological theory, offers a new direction and potential breakthrough for addressing this dilemma.

Digital health interventions (DHIs) are widely used to manage users’ health in everyday life through digital devices. The use of DHIs generates various data, such as records of intervention use and the status of target symptoms, providing researchers with data-driven insights for improving these interventions even after deployment. Although DHI researchers have investigated these data, existing analysis practices have been fragmented, limiting a comprehensive understanding of the data.

The rapid increase in the number of health Apps and their volatility in (Austrian) App stores for Android and Apple are signs of a flourishing business sector in the wellness industry.

The utilisation of the Internet of Things (IoT) can significantly enhance healthcare. However, successful implementation of IoT requires a holistic approach including factors beyond technology alone.

Falls are the leading cause of injury-related death among adults aged 65 years and older. Falls and fear of falling in turn threaten older adults’ ability to live independently and often lead to self-imposed activity restriction, social isolation, decreased cognitive and physical function, and, paradoxically, an increased risk of serious falls. There has been extensive research on interventions that can reduce the risk of fall-related injury and mortality in older adults. One tool, home safety assessments, has been shown to significantly decrease the rate and risk of falling by up to 36%, along with reducing serious fall-related complications, including hip fractures and death. Despite their efficacy, there has been very low adoption and poor implementation of home safety assessments. Understanding barriers to implementation is critical for improving uptake of this evidence-based intervention.

Ireland is ranked among the most disadvantageous European countries in terms of mental health challenges. Contrary to general health services that primarily focus on diagnosis and treatment, the mental-health sector in Ireland deals with highly sensitive psychiatric case notes based on patient-doctor conversations. Such data, therefore, must be collected, analyzed, and stored with an approach customized specifically for psychiatry.

Depression is a severe and prevalent mental disorder among youth that requires professional care; however, various barriers hinder access to effective treatments. Chatbots, one of the latest innovations in the research on digital mental health interventions (DMHIs), have shown potential in addressing these barriers. However, most studies on how to design chatbots to treat depression have focused on adult populations or prevention in the general population.

The use of individual wearable devices or online applications to collect biometric data from research participants is popular, but several devices may be needed to replace a full set of research measurements.