Recent Articles
Mobile visual acuity (VA) applications have emerged as valuable tools in both clinical and home settings, particularly in the context of expanding teleophthalmology. Despite the growing number of apps available to measure visual acuity, studies evaluating their overall quality, functionality, and clinical relevance are limited.
Approximately 1 out of 5 pregnant women develops depression. Internet-based cognitive behavioral therapy (iCBT) is an effective way to treat not only depression but also mild depressive symptoms or subthreshold depression. While numerous iCBT programs have been developed and tested through randomized controlled trials for various mental health conditions and specific populations, research on their effectiveness and application in the real world remains limited.
Background: Latina adolescents report low levels of moderate-vigorous physical activity (MVPA) and high lifetime risk of lifestyle-related diseases. There is a lack of MVPA interventions targeted at this demographic despite documented health disparities. Given their high rates of using mobile technology, interventions delivered through mobile devices may be effective for this population.
Adverse events (AEs) related to cancer treatment represent a valuable source of information that can be used to adjust therapy for individual patients. The NIH developed the Common Terminology Criteria for Adverse Events (CTCAE), a comprehensive standardized terminology for healthcare providers to consistently report AEs during patient visits. mHealth technologies, in principle, also allow AEs to be self-reported by patients in-between visits; however, the terminology poses challenges for them, both in selecting the correct symptom to report and in rating its severity. NIH developed the Patient-Reported Outcomes (PRO)-CTACE as the patient-oriented companion of the CTCAE. However, it shows some weaknesses in completeness and precision when used for continuous home patient monitoring and for decision support.
Sleep is essential for overall health and plays a critical role in the diagnosis of psychiatric disorders. Although polysomnography remains the gold standard for measuring sleep, its reliance on laboratory settings limits its feasibility for long-term, naturalistic monitoring, particularly for patients with mental disorders.
Regular physical activity offers extensive health benefits, yet current consumer wearables struggle to accurately quantify these effects at an individualized level. Sensor performance often falls short due to susceptibility to interferences, nonstandardized validation, and reliance on indirect estimations. Further, sensors often cannot capture or account for disparities in measurement types, populations, and physiological or anatomical characteristics, nor can they account for how different exercise modalities affect results on a personalized scale. There is a drive for developers to refine the impact of how we measure the benefits of exercise, improving the usefulness of data through advanced optical modeling and spectroscopic applications. This review critically examines the shortcomings of prevailing noninvasive measurements and techniques used in common, commercially available fitness trackers and describes why it is difficult to quantify the effects of exercise as an individualized, quality-based metric. Next, we discuss newer sensing applications that attempt to curtail known limitations, some of which may unveil novel biometric insights through differentiated approaches, bridging gaps not only in technological advancement but also in physiological metrology. In conclusion, we believe that new sensing techniques should explore solutions beyond population-based statistics and aim to provide an individualized understanding of a person’s response to exercise, while also reducing disparities in personalized health monitoring. The results could lead to a more effective understanding of exercise efficacy and its impact on performance management and clinical outcomes.
Chronic noncommunicable diseases (NCDs) remain a leading health challenge worldwide, and reducing modifiable lifestyle risk factors is a key prevention strategy. Digital health interventions (DHIs) offer scalable, cost-effective tools to support healthy behaviors, but concerns persist about their equitable reach and uptake across population groups.
Keystroke dynamics on smartphones have emerged as a promising form of passive digital biomarker. While previous studies have explored their utility in several diseases and disorders, relatively few have examined how these dynamics change systematically with chronological age in the general population.
Digital health tools, such as mobile apps and wearable devices, have been widely adopted to support self-management of health behaviors. However, user engagement remains inconsistent, particularly among populations with varying BMI. While digital health technologies have the potential to promote healthier behaviors, little is known about how psychological and behavioral factors interact with BMI to influence use patterns.
Many individuals in urban low-income settings face barriers with engaging in primary care and experience systemic challenges such as homelessness and discrimination in the healthcare system. This study takes places in the Downtown Eastside (DTES) of Vancouver, Canada, a low-income neighbourhood with intersecting structural vulnerabilities and disproportionate rates of substance use disorders. Advancements in mobile health expands options for facilitating communication between primary care providers and clients.
Various mental disorders are associated with impaired cognitive control, which is crucial for effective emotion regulation. Cognitive control training has demonstrated promise in enhancing emotion regulation and alleviating distress in disorders characterized by repetitive negative thinking, such as depression and anxiety.
