JMIR mHealth and uHealth

Approximately one-third of university students are overweight or obese, and a similar proportion experience anxiety or depression. Despite the interrelated nature of weight and mental health, interventions rarely address these issues simultaneously in young adults. Digital peer support interventions have the potential to promote healthy lifestyles and mental well-being. However, evidence is limited on whether a digital peer-driven approach can concurrently improve weight management and mental health in pre-obese university populations.

Metamemory training (MMT) offers a potential nonpharmacological approach to enhance cognitive function in individuals with mild cognitive impairment (MCI). While digital cognitive training improves accessibility, the effectiveness of mobile app–based MMT has not been evaluated in a randomized clinical trial.

Insomnia is a prevalent sleep disorder characterized by difficulty initiating or maintaining sleep and is associated with substantial health and economic burdens. Although cognitive behavioral therapy (CBT) is recommended as the first-line treatment, pharmacotherapy remains widely used despite adverse effects and significant indirect costs related to impaired productivity and workplace safety. Digital therapeutics delivering CBT through mobile platforms have emerged as scalable alternatives to improve access and outcomes. Somzz is a commercially available, domestically developed digital therapeutic that delivers CBT-based interventions for insomnia via a mobile app.

Recently, mHealth and mobile applications (apps) have been proposed as a potential tool to improve different outcomes (e.g., daily steps, blood glucose) in both people with and without chronic conditions. In particular, healthy people could benefit from these tools by improving health variables and for prevention. Previous evidence investigated different types of health interventions adopting apps in various settings and populations, but evidence of their effectiveness is still unclear.

Limited empirical evidence exists on the effectiveness of a hybrid approach to heart-healthy lifestyle interventions that integrates mobile health (mHealth) technology with face-to-face counseling. Moreover, its superiority over exclusive mHealth use in promoting heart-healthy behavioral outcomes within a community setting remains unclear.

Intensive, repeated self-report measures are an important tool for behavioral and medical researchers and practitioners who are concerned with the dynamic interplay among variables at a granular level. Many mobile health applications rely on accurate measurement of immediate states and environments for both assessment and intervention delivery. Techniques for capturing repeated momentary assessments yield data with several salutary qualities: recall bias is minimized relative to assessments that rely on much longer recall periods; measurements are taken in individuals’ everyday environments; and dense, repeated measures allow a new window into the processes transpiring between individuals and their environments. In this paper, we highlight several features of repeatedly completing momentary assessments that may change the nature or quality of the data collected over time. Several lines of inquiry are discussed that call into question the presumption that there is invariance in how people complete repeated assessments over time. A result of this possibility could be a reduction in data quality. We present 4 phenomena, with selected results, that may induce noninvariance in repeated measures: the amount of time required to complete assessments, the rate of missing data, the degree of careless responding, and the presence of several components of reactivity. In each of these areas, we found evidence that changes could occur over time, and we consider how data might be affected by such changes. Our conclusion is that researchers should be aware that changes can occur over time and that these changes may affect data quality.

Health interventions delivered by digital platforms are gaining popularity and are evolving to address the needs of patients with chronic diseases. The heterogeneity of chronic diseases requires that digital health platforms vary in their approaches to chronic disease management.

Wearable sensor technologies such as inertial measurement units, smartwatches, and multisensor systems have emerged as valuable tools in clinical and real-world health monitoring. These devices enable continuous, noninvasive tracking of gait, mobility, and functional health across diverse populations. However, challenges remain in sensor placement standardization, data processing consistency, and real-world validation.

Mobile health (mHealth) apps are increasingly being used to promote physical activity (PA) and can support exercise uptake and maintenance. Despite their potential, these tools face high dropout rates and inconsistent adherence, posing a significant challenge. Understanding how users engage with fitness apps is essential for improving user experience and health outcomes.

Shoulder pain is prevalent in people living with spinal cord injury (pwSCI). Technology and digital rehabilitation tools are increasingly available, but this has not yet included provision of a self-guided exercise intervention focused on managing shoulder pain for pwSCI. We drew on the person-based approach (PBA) to intervention development to design a Shoulder Pain Intervention delivered over the interNet (SPIN) to address this gap. However, in preparation for the design process, we found few published examples of how the PBA had been operationalized. The aim of this paper is to provide a detailed explanation of our approach and how we operationalized the PBA in the design of SPIN to maximize relevance and engagement. Our design process followed the key PBA steps, combining additional evidence and theoretical components. Each step ensured guiding principles were formulated and followed to maximize the probability that SPIN would be fit for purpose. We followed three steps: 1) we drew on themes from preparatory research (existing and primary) to identify the key behavioral issues, needs and challenges, and existing features to form the basis of SPIN design; 2) we formatted guiding principles that included articulating specific design objectives to provide a framework to identify system requirements; and 3) we selected and refined intervention features using existing literature, behavioral theory, and tools such as the ‘Behaviour Change Wheel’. We have designed SPIN by incorporating a deep understanding of the users’ needs and best available evidence to maximize engagement and positive outcomes. In this paper, we have made clear how we operationalized the PBA phases, including how existing evidence, theory, tools, and methods were leveraged to support the PBA process. In explicating our process, we have provided a blueprint to guide future researchers using this approach.

While there is growing evidence demonstrating the usefulness of integrating social features within mHealth approaches, little research has explored how African American women use mobile platforms to facilitate physical activity within the context of a group-based physical activity intervention.

The progression of chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality, emphasizing the need for effective self-management. Challenges such as accessibility, cost, and patient engagement hinder self-management efforts, underscoring the need for evidence-based mobile health (mHealth) interventions.