Aim: Informal caregivers have an important role in bridging the gap between the assistance care recipients need and what can be provided by the health care systems across Europe. The burden of the caregiving role places a significant threat to caregiver health, and the vast majority of caregiver's report stress and emotional strain, depression, and increased rates of chronic diseases. In line with this, strengthening the caregiver's mental health is one of the main goals for optimal caregiving. Caregivers already struggle with the demand of their role while coping with health problems, social, family, and work obligations. The solution for the caregiver's mental health needs to be accessible, low cost, and time-effective. This scoping review investigates digital mental health tools available as a mean of supporting the mental health of caregivers. Method: Databases searched include Summon search box, the Cochrane Library, and PubMed. Three groups of keywords were combined: relating to digital mental health interventions for caregivers, digital mental health interventions and stress in elderly care, and digital mental health interventions and burden in elderly care. Results: Caregivers reported that digital mental health tools have an overall positive role in their health. Coping skills, emotion regulation, skill building, and education are found to be important aspects of digital mental health tools. There was a noted lack of digital mental health apps available specifically for the caregiver of older adults. Furthermore, the digital mental health tools, divided into three categories in this review, focused either on building skills or educating caregivers and assisting with the duties rather than the mental health of the caregiver itself. As repeatedly suggested in the reviewed studies, digital mental health interventions overall contribute to reducing the caregiver burden with a limitation of addressing one aspect of caregiver needs –i.e., specific coping skills or education regarding illnesses such as Alzheimer's disease and Dementia. The lack of all-encompassing, data and theory-driven digital mental health tools for addressing and supporting the caregiver's mental health is evident.

Background: Depression is highly recurrent and heterogeneous. The unobtrusive, continuous collection of mobile sensing data via smartphones and wearable devices offers a promising approach to monitor and predict individual depression trajectories, distinguish illness states, and anticipate changes in symptom severity. Objective: This systematic review evaluates whether objective data from wearable devices and smartphones can (1) monitor and distinguish different states of depression, (2) predict changes in symptom severity, and (3) identify clinically relevant objective features for tracking and forecasting depression within diagnosed individuals. Methods: We searched PubMed and Web of Science databases for English-language studies (published 2012-2022) that used smartphone or wearable device data, included participants aged ≥14 years with a depression diagnosis, and collected continuous data for at least 12 weeks. Results: Out of 12,997 peer-reviewed articles, 9 original studies met the inclusion criteria, with sample sizes ranging from 45 to 2200 and durations of 12-52 weeks. Of the 9 studies, 3 used smartphone data, 1 used wearable device data, and 5 used both data types. Commonly collected variables were step count, distance moved, smartphone usage, call logs, sleep, heart rate, light exposure, and speech patterns. One study (11%) successfully differentiated between depressive states (worsening, relapse, or recovery). Six studies (67%) showed that mobile sensing data could predict depressive episodes or symptom severity. Four studies reported the predictive accuracy for depression using mobile sensing data from smartphones and wearable devices, ranging from 81% to 91%. Higher accuracy was achieved with personalized models or multimodal data. Conclusions: Real-time passive monitoring via wearable devices and smartphones holds promise for personalized self-management, but key gaps remain, such as a lack of longitudinal and long-term studies with data collection for 1 year or longer, studies with confirmatory parameters on an individual level, and studies with a strong correlation between parameters in individual patients to support clinical decision-making. Improvements in reporting standards are highly recommended to provide better-informed insights for clinicians. Throughout this process, there is a clear need to address various other issues, such as limited types of collected data, reliability, user adherence, and privacy concerns. Trial Registration: PROSPERO CRD42022355696; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022355696

Background: The transformative storytelling technique is an innovative top-down approach to narrative therapy that aims to provide building blocks for creating flourishing narratives for target groups or populations. This approach acts as a facilitator for implementing the human-centered design in developing digital self-help tools for larger samples or target groups. Objective: This study applied the transformative storytelling technique, as a new approach in mental health, to develop empowering audio narratives for informal caregivers. Methods: A narrative inquiry was conducted with 17 informal caregivers (16 women and 1 man) who completed a semistructured interview, “Caregiver Life Story,” acquiring information about the beginning of the role, rising action, and critical point of the role. The participants’ ages ranged from 41 to 84 years, with all participants providing care for at least a 6-month period. This inquiry was guided by the transformative storytelling technique, and aimed to collect data relevant to creating fictional stories based on real-life themes. Results: Twenty-five overall themes were distinguished across three a priori–set categories, providing narrative building blocks for the informal caregiver life stories. The final empowering caregiver life story was created as an example for this study, demonstrating the application of the transformative storytelling technique in an informal care context. Conclusions: The creation of empowering stories for populations or target groups in mental health care requires a unified and guided approach that will follow clear guidelines and storytelling principles. The transformative storytelling technique is a first of its kind in the mental health context, representing an initial step in enabling and supporting the creation of meaningful stories and the development of relatable, but productive, narratives. Such narratives have the potential to serve across media and digital platforms for supporting and improving well-being, and potentially triggering self-change in the target group or population.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: A popular trend in depression forecasting research is the development of machine learning (ML) models trained with various types of smartphone sensor data and periodic self-ratings to derive early indications of changes in depression severity. While most works focus on model performance, there is little concern about the universal usability and reliable operation of such systems across smartphone platforms. This review serves as foundational work for the MENTINA clinical trial, which investigates smartphone-based health self-management for depression. The usability and reliability of mobile applications for depression is commonly perceived through the lens of the approaches and interventions offered rather than the reliability of the built-in mobile phone functions to support effortless and exact delivery of intended interventions. Objective: This work aimed to synthesize existing systematic reviews to identify smartphone sensor modalities used in mental health monitoring and, building on this foundation, assess the cross-platform availability of these data streams using PhoneDB to inform the design and implementation of digital depression indication systems. Methods: To identify the already used hard- and software sensors and their purposes in mental health monitoring, an umbrella review was conducted. Three electronic databases including PubMed, Web of Science Core Collection, and Scopus were searched using smartphone, sensor data, and depression keyword combination to retrieve relevant literature reviews published within the past five years (2019-2024). Once the initial search was completed, the extracted hardware sensors were checked for availability on Android and iOS smartphones by analyzing device specifications in the PhoneDB over the past ten years. Results: The extracted data streams observed across nine included studies covered sixteen hardware and three software data streams. Hardware data streams include accelerometer, barometer, battery level, Bluetooth, camera, cell, GPS, gyroscope, humidity, light, magnetometer, proximity, sound, step count, temperature, and Wi-Fi. Software data streams include app usage, call and message logs, and screen status. Hardware component availability on Android and iOS systems shows the changes in component trends from 2014 to 2024 as of September 2024 with the accelerometer, battery, camera, and GPS being consistent on Android and iOS while components such as gyroscope, step counter, and barometer were gradually increasing over the years, particularly on Android. Conclusions: Multiple data streams identified in literature reviews have shown a consistent increase in availability over time, enabling improved use of these outputs for depression forecasting and the training of machine learning models with diverse smartphone data, including sensor-derived information. For more precise and reliable data to be utilized in the mental health field, particularly in critical areas such as tracking and predicting changes in depression severity, further research is required to streamline smartphone data across varying mobile hardware and software configurations to provide reliable output for digital mental health purposes.

The existing interventions for informal caregivers assist with managing health outcomes of the role burden. However, the deeper meaning-making needs of informal caregivers have been generally neglected. This paper reflects on the meaning-making needs of informal caregivers, through the theory of narrative identity, and proposes a new approach – the Transformative Video Design technique delivered via video storytelling. Transformative Video Design assists informal caregivers to re-create a cohesive caregiving story and incorporate it into the narrative identity. The technique is used as a stimulus for triggering the self-re-structure within the narrative identity and facilitating role transformation.

Background: Digital health interventions incorporating self-management strategies are increasingly used to support individuals in managing depression. These interventions often leverage self-monitoring and passive sensor-based data collection to provide personalized feedback, guiding behavioral change. With the proliferation of smartphones and wearable devices, there is growing potential to continuously collect behavioral and physiological data. However, a major limitation in the field is the lack of consolidated evidence on which specific parameters are most useful for monitoring and predicting depression-related outcomes. Objective: This scoping review aims to identify and synthesize common digital phenotyping and self-report parameters for monitoring and predicting depression. Specifically, it addresses the methodological and knowledge gap concerning which types of sensor-based and self-reported data are most frequently used and which demonstrate predictive value in tracking changes in depressive symptoms across digital platforms. Methods: A literature search was conducted across 4 databases, including PubMed, Embase, Cochrane Library, and the Web of Science Core Collection. Articles published between January 1, 2021, and November 26, 2025, were included. Eligible studies included adults (≥18 years) with depression confirmed through validated clinical measures and using digital approaches that collected passive sensor data, self-reports, or both. Studies focusing on comorbid disorders, nondigital interventions, or not reporting depression-related outcomes were excluded. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines were followed, and a 5-stage methodological framework for scoping reviews was implemented. Quality assessment was performed using the Downs and Black Instrument and the Mixed Methods Appraisal Tool (MMAT). Results: Nineteen studies were included, comprising a total of 85,193 participants. Most studies used smartphone- or wearable-based tools, with passive sensing as the predominant data source and Patient Health Questionnaire-9 (PHQ-9) as the most commonly used depression measure. Five overarching parameter categories were identified: (1) physical activity and location, (2) behavioral patterns, (3) physiological signals, (4) sleep indicators, and (5) sociability and self-reported assessments. Within these categories, 11 metrics, including step count, heart rate variability, sleep duration and mood self-ratings, were most frequently reported. Most studies used a multimodal digital phenotyping approach, integrating passive sensor-derived data with active user-reported input, enabling more individualized symptom monitoring over time. Conclusions: This scoping review provides a novel synthesis of common digital parameters used across diverse tools for monitoring and predicting depression, moving beyond tool- or modality-specific perspectives adopted in prior reviews. Unlike existing reviews focusing on individual sensing modalities, prediction methods, or intervention effectiveness, this review maps shared parameters across observational, predictive, and interventional studies. By identifying convergent digital markers, the review supports comparability across studies and informs future model development. These findings have practical implications for the design of scalable digital mental health tools and for translating digital phenotyping into real-world clinical and self-management contexts.