JMIR mHealth and uHealth

Although machine learning has increasingly been used to predict mental health symptoms and maladaptive behaviors, real-world prediction of addiction-related risk remains limited. Emotional and temperamental vulnerabilities are established correlates of alcohol-related problems, yet few studies have integrated these factors with wearable-derived biosignals in alcohol-risk prediction models.

Parents are increasingly using smartphone apps to help guide decision-making around aspects of caregiving, including safer sleep. However, it remains unclear whether the guidance provided aligns with best practice guidelines.

Digital health tools are increasingly used in mental health care to passively collect patient data and analyze health status outside of clinical settings. While technologies such as digital phenotyping, affective computing, and computational behavioral analysis offer new insights into symptom manifestation in daily life, they generate large volumes of potentially sensitive data that raise significant data privacy concerns, requiring high levels of patient awareness and consent. Empirical research is lacking on stakeholder understandings toward the sensitivity of these data and expectations for data stewardship, perspectives that are critical for developing robust informed consent and data protection policies for digital health data use.

Tobacco use remains a leading preventable cause of morbidity and mortality. Digital health tools and wearable technologies offer scalable opportunities for behavioral self-monitoring. However, real-world evidence characterizing long-term tobacco use trajectories and associated physiological changes during wearable adoption is limited.

Nurse burnout is a pervasive global problem. Cognitive behavioral therapy (CBT) has been shown to reduce burnout; however, most digital CBT programs use standardized approaches that overlook individual differences in burnout profiles. With advances in artificial intelligence (AI), algorithm-based recommendation systems now enable personalized intervention delivery by matching specific CBT modules to users.

Despite the high prevalence of depressive disorders, access to effective treatment remains limited due to financial, geographic, and social barriers. Online self-help groups offer a promising and scalable form of peer-based support beyond traditional clinical settings. Integrating cognitive behavioral therapy (CBT) techniques such as cognitive restructuring and behavioral activation into self-help groups may enhance their effectiveness.

As Canada’s population ages, accessible tools for chronic health monitoring are increasingly needed. Traditional and contact-based devices pose barriers for underserved populations due to cost, maintenance, and usability. Contactless sensing technologies offer a promising alternative, but equitable development requires inclusive engagement and diverse data collection.

Mobile health (mHealth) apps are widely used for noncommunicable disease prevention and self-management. However, their effectiveness and safety are undermined by substantial variation in content quality. Existing guiding frameworks primarily focus on user interface, functionality, and intervention delivery, with limited emphasis on content creation. In this viewpoint, we introduce Systematic and Collaborative Approach to Learning and Educational Content Development (SCALED), a conceptual framework designed to guide a systematic, collaborative, and evidence-based approach to mHealth educational content development, intended for app developers, researchers, health care providers, and the wider mHealth community. Developed and refined across 3 phases, the SCALED framework consists of 11 components organized into 3 sequential stages: planning and conceptualization, development of textual content, and finalization into delivery format. We discuss the rationale behind each component and illustrate its applicability through 2 mHealth use cases. The framework integrates real-world experience from the development of 3 mHealth apps, qualitative findings from 2 studies, and insights from key stakeholders. By offering a structured and replicable methodology for content development, SCALED addresses a critical gap in current mHealth frameworks and provides a practical guide to improve content veracity, with potential for adaptation across a range of medical conditions.

Non–small cell lung cancer (NSCLC) accounts for approximately 85% of primary pulmonary neoplasms. Complete surgical removal remains the cornerstone of curative therapy, yet it frequently diminishes residual lung function and exercise tolerance. Structured, center-based rehabilitation hastens physiological recovery, but conventional schemes rarely deliver continuous, patient-specific monitoring. Remote, digitally delivered exercise overcomes logistical obstacles; however, the lack of real-time quality assurance curtails effectiveness. Wearable motion capture platforms that provide millimeter-precise kinematic data and instantaneous biomechanical feedback can close this supervisory void by confirming movement accuracy and issuing immediate corrective prompts. Whether this technologically augmented telerehabilitation yields clinically relevant improvements in postoperative exercise capacity after NSCLC resection remains inadequately established.

Palliative care is a key component of comprehensive humanitarian health; yet, access and service capacity remain limited in displacement settings, where fragile health systems struggle to meet the complex needs of people living with advanced illness. Digital health technologies have the potential to enhance the reach and delivery of palliative care; yet, their feasibility and acceptability in humanitarian settings remain underexplored.

Unguided internet-based cognitive behavioral therapy (ICBT) is a low-cost and scalable treatment for major depressive disorder (MDD), but its long-term effects in Chinese populations remain unclear.

Patients with Parkinson disease (PD) along with subjective cognitive decline (PD-SCD) are considered an intermediate status between those with normal cognition (PD-NC) and those with mild cognitive impairment (PD-MCI). Wearable digital monitoring technologies and machine learning models offer significant potential for assessing cognitive impairment in patients with PD.