JMIR mHealth and uHealth

Good motor performance skills (MPS) are relevant in all stages of life. Higher MPS are associated with enhanced cognitive abilities and physical and mental health. The assessment of MPS is important to identify deficits in MPS at an early stage and to implement interventions to address these deficits. One method to assess MPS is through marker-based motion capture in a laboratory setting with multiple cameras. However, this approach is expensive and time-consuming, making it impractical, for example, in large-scale studies for MPS assessment. Recent advancements (eg, artificial intelligence) in technology (eg, smartphone cameras) have opened up innovative solutions for various challenges (eg, testing large sample sizes). A potential solution is using video-based smartphone apps to assess MPS through markerless motion capture with a single camera.

Goal personalization features integrated into mobile health apps have the potential to enhance physical activity, as some evidence shows that the personalized goals generated by algorithms are more effective than default or fixed goals. However, it remains unclear whether goals set by users are more effective than fixed goals and which personalization strategy is more effective for different user segments.

Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder that poses substantial challenges to global health care systems and patient management. Telemedicine, defined as the use of information and communication technologies to enhance health care delivery, has emerged as a potential tool to improve access to care and facilitate the management of T2DM.

As the world’s population ages, the prevalence of chronic low back pain (CLBP) is increasing, placing a substantial burden on individuals and healthcare systems. Mobile health (mHealth) apps offer a potentially scalable solution to support self-management, but little is known about how, why, for whom, and under what circumstances such tools work in real-world settings.

E-health is an increasingly utilized method of healthcare in the field of psycho-oncology. While many reports highlight the positive impact of psychological e-health tools, there are patients who refuse to use them.

Mobile health (mHealth) apps have shown promise to support recovery from substance use disorders. However, evidence on engagement and efficacy is still inconclusive.

The use of mobile apps in oncology has been expanding rapidly, encompassing prevention, treatment, and patient support. These technologies hold significant potential to improve care delivery and enhance the efficiency of health care services. However, their integration into clinical practice faces important challenges. A key issue lies in the difficulties health care professionals (HCPs) encounter when selecting apps that adequately meet their specific needs and comply with appropriate standards of quality and clinical effectiveness. This lack of robust evidence on the availability, adoption, and evaluation of mobile apps designed for cancer care professionals not only hinders their wider adoption but also restricts their potential to serve as reliable tools in oncology practice.

Driven by technological advancements, the proliferation of mobile sports and health applications (apps) has revolutionized health management by improving efficiency, cost-effectiveness, and accessibility. While the widespread adoption of these platforms has transformed public health practices and social well-being in China, emerging evidence suggests that inadequacies in their privacy policies may compromise personal information (PI) protection.

Wearable devices enable the remote collection of health parameters, supporting the outpatient plans recommended by the World Health Organization (WHO) to manage chronic diseases. While disease-specific monitoring is accurate, a comprehensive analysis of wearables across various chronic diseases helps to standardize remote patient monitoring (RPM) systems.

Smoking is a leading cause of mortality and morbidity across the global. Efforts to reduce smoking prevalence have used text-message based interventions, which typically send participants a series of short, informational, motivational and practical messages over a set period. Evidence highlights the efficacy of using this approach to support smoking cessation, with such trials typically reporting the average treatment effects, in which causal inference is made regarding the average effect of a treatment on a heterogeneous sample. Nonetheless, using this approach to assessing treatment effects means we are unable to account for individual factors that impact the effectiveness of a treatment on outcomes, such as age, gender, and genetics.

Previous research has attempted to determine the minimum number of days of accelerometry required to reliably reflect an individual’s physical activity. However, human behaviors on a day-to-day basis can be highly variable. As a consequence, the number of days required to reliably predict habitual physical activity is dependent on the variability that exists within an individual. There is a concern that adopting generic recommendations from previous research could provide unreliable estimates by failing to represent individuals with specific physical activity patterns.

Mobile health (mHealth) interventions show promise in supporting tobacco cessation. However, Black adults who use tobacco products are not well represented in mHealth studies for tobacco cessation and their preferred features of mHealth apps are not well known. Identifying types of mHealth app features for tobacco cessation preferred by Black adults is critical to developing a culturally adapted app, with increased uptake by the target population