Background: Digital health represents an important strategy in the future of health care delivery. Over the past decade, mobile health has accelerated the agency of health care users. Despite prevailing excitement about the potential of digital health, questions remain on efficacy, uptake, usability, and patient outcome. This challenge is confounded by 2 industries, digital and health, which have vastly different approaches to research, design, testing, and implementation. In this regard, there is a need to examine prevailing design approaches, weigh their benefits and challenges toward implementation, and recommend a path forward that synthesizes the needs of this complex stakeholder group. Objective: In this review, we aimed to study prominent digital health intervention design approaches that mediate the digital health space. In doing so, we sought to examine the origins, perceived benefits, contrasting nuances, challenges, and typical use-case scenarios of each methodology. Methods: A narrative review of digital health design approaches was performed between September 2020 and April 2021 by referencing keywords such as “digital health design,” “mHealth design,” “e-Health design,” “agile health,” and “agile healthcare.” The studies selected after screening were those that discussed the design and implementation of digital health design approaches. A total of 120 studies were selected for full-text review, of which 62 (51.6%) were selected for inclusion in this review. Results: A review identifying the 5 overarching digital health design approaches was compiled: user-centered design, person-based design, human-centered design, patient-centered design, and patient-led design. The findings were synthesized in a narrative structure discussing the origins, advantages, disadvantages, challenges, and potential use-case scenarios. Conclusions: Digital health is experiencing the growing pains of rapid expansion. Currently, numerous design approaches are being implemented to harmonize the needs of a complex stakeholder group. Whether the end user is positioned as a person, patient, or user, the challenge to synthesize the constraints and affordances of both digital design and health care, built equally around user satisfaction and clinical efficacy, remains paramount. Further research that works toward a transdisciplinarity in digital health may help break down friction in this field. Until digital health is viewed as a hybridized industry with unique requirements rather than one with competing interests, the nuances that each design approach posits will be difficult to realize in a real-world context. We encourage the collaboration of digital and health experts within hybrid design teams, through all stages of intervention design, to create a better digital health culture and design ethos.

Mindfulness training (MT) programs represent an approach to attention training with well-validated mental health benefits. However, research supporting MT efficacy is based predominantly on weekly-meeting, facilitator-led, group-intervention formats. It is unknown whether participants might benefit from neurofeedback-assisted, technology-supported MT (N-tsMT), in which meditation is delivered individually, without the need for a facilitator, travel to a training site, or the presence of a supportive group environment. Mirroring the validation of group MT interventions, the first step in addressing this question requires identifying whether N-tsMT promotes measurable benefits. Here, we report on an initial investigation of a commercial N-tsMT system. In a randomized, active control trial, community-dwelling healthy adult participants carried out 6 weeks of daily practice, receiving either N-tsMT (n = 13), or a control condition of daily online math training (n = 13). Training effects were assessed on target measures of attention and well-being. Participants also completed daily post-training surveys assessing effects on mood, body awareness, calm, effort, and stress. Analysis revealed training effects specific to N-tsMT, with attentional improvements in overall reaction time on a Stroop task, and well-being improvements via reduced somatic symptoms on the Brief Symptom Inventory. Attention and well-being improvements were correlated, and effects were greatest for the most neurotic participants. However, secondary, exploratory measures of attention and well-being did not show training-specific effects. N-tsMT was associated with greater body awareness and calm, and initially greater effort that later converged with effort in the control condition. Preliminary findings indicate that N-tsMT promotes modest benefits for attention and subjective well-being in a healthy community sample relative to an active control condition. However, the findings would benefit from replication in a larger sample, and more intensive practice or more comprehensive MT instruction might be required to promote the broader benefits typically reported in group format, facilitated MT. Current Controlled Trials ISRCTN43629398 . Retrospectively registered on June 16, 2016.

Background: Digital health interventions (DHIs) are changing the dynamic of health care by providing personalized, private, and instantaneous solutions to end users. However, the explosion of digital health has been fraught with challenges. The approach to co-design with end users varies across a diverse domain of stakeholders, often resulting in siloed approaches with no clear consensus. The concept of validating user experiences contrasts greatly between digital stakeholders (ie, user experience and retention) and health stakeholders (ie, safety and efficacy). Several methodologies and frameworks are being implemented to address this challenge to varying degrees of success. Objective: We aimed to broadly examine the advancements and challenges to co-design DHIs with end users over the last decade. This task was undertaken to identify the key problem areas at the domain level, with the ultimate goal of creating recommendations for better approaches to co-design DHIs with end users. Methods: We conducted a systematic search of key databases for co-design studies involving end users in DHIs. Searches were divided into 3 relevant streams: health behavior, user experience, and digital methodologies and frameworks. The eligibility criteria were guided by the PerSPEcTiF framework and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. In line with this framework, studies were included in this review that (1) address research on DHIs; (2) focus on interaction and co-design with end users; (3) explain results such that uptake, effectiveness, satisfaction, and health outcomes are discernible, positively or negatively; and (4) describe actionable procedures for better DHI design. The search was conducted in a diverse group of 6 bibliographical databases from January 2015 to May 2024: PsycINFO, PubMed (MEDLINE), Web of Science, CINAHL, Institute of Electrical and Electronics Engineers Xplore, and Scopus. From the 13,961 studies initially screened for titles and abstracts, 489 (3.6%) were eligible for a full-text screening, of which 171 (1.2%) studies matched the inclusion criteria and were included in a qualitative synthesis. Results: Of the 171 studies analyzed across 52 journals, we found 5 different research approaches, spanning 8 different digital health solution types and 5 different design methodologies. These studies identified several core themes when co-designing with end users: advancements, which included participatory co-design; challenges, which included participatory co-design, environment and context, testing, and cost and scale; and gaps, which included a pragmatic hybridized framework and industry implementability. Conclusions: This research supports a pragmatic shift toward using mixed methods approaches at scale, methods that are primed to take advantage of the emerging big data era of digital health co-design. This organic outlook should blend the vision of digital health co-designers with the pragmatism of Agile design methodology and the rigor of health care metrics. Trial Registration: PROSPERO CRD42021238164; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021238164 International Registered Report Identifier (IRRID): RR2-10.2196/28083

The number of patients suffering from dementia is expected to more than triple by the year 2040, and this represents a major challenge to publicly-funded healthcare systems throughout the world. One of the most effective prevention mechanisms against dementia lies in increasing brain- and cognitive-reserve capacity, which has been found to reduce the behavioral severity of dementia symptoms as neurological degeneration progresses. To date though, most of the factors known to enhance this reserve stem from largely immutable history factors, such as level of education and occupational attainment. Here, we review the potential for basic lifestyle activities, including physical exercise, meditation and musical experience, to contribute to reserve capacity and thus reduce the incidence of dementia in older adults. Relative to other therapies, these activities are low cost, are easily scalable and can be brought to market quickly and easily. Overall, although preliminary evidence is promising at the level of randomized control trials, the state of research on this topic remains underdeveloped. As a result, several important questions remain unanswered, including the amount of training required to receive any cognitive benefit from these activities and the extent to which this benefit continues following cessation. Future research directions are discussed for each lifestyle activity, as well as the potential for these and other lifestyle activities to serve as both a prophylactic and a therapeutic treatment for dementia.

Large survey databases for aging-related analysis are often examined to discover key factors that affect a dependent variable of interest. Typically, this analysis is performed with methods assuming linear dependencies between variables. Such assumptions however do not hold in many cases, wherein data are linked by way of non-linear dependencies. This in turn requires applications of analytic methods, which are more accurate in identifying potentially non-linear dependencies. Here, we objectively compared the feature selection performance of several frequently-used linear selection methods and three non-linear selection methods in the context of large survey data. These methods were assessed using both synthetic and real-world datasets, wherein relationships between the features and dependent variables were known in advance. In contrast to linear methods, we found that the non-linear methods offered better overall feature selection performance than linear methods in all usage conditions. Moreover, the performance of the non-linear methods was more stable, being unaffected by the inclusion or exclusion of variables from the datasets. These properties make non-linear feature selection methods a potentially preferable tool for both hypothesis-driven and exploratory analyses for aging-related datasets.

Background: This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia. Methods: We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines. Results: Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia. Conclusion: Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews– CRD42019133402 and published in the Systematic Reviews journal.