Assessing the Influence of Patient Empowerment Gained Through Mental Health Apps on Patient Trust in the Health Care Provider and Patient Compliance With the Recommended Treatment: Cross-sectional Study.

BACKGROUND: In chronic mental illness, noncompliance with treatment significantly worsens the illness course and outcomes for patients. Considering that nearly 1 billion people worldwide experience mental health issues, including 1 of 5 Canadians in any given year, finding tools to lower noncompliance in these populations is critical for health care systems. A promising avenue is apps that make mental health services more accessible to patients. However, little is known regarding the impact of the empowerment gained from mental health apps on patient compliance with recommended treatment. OBJECTIVE: This study aimed to investigate the impact of patient empowerment gained through mental health apps on patient trust in the health care provider and patient compliance with the recommended treatment. METHODS: A cross-sectional web-based survey was conducted in Canada. Eligible participants were Canadian adults diagnosed with chronic mental health disorders who were using at least one of the following apps: Dialogue, MindBeacon, Deprexis, Ginger, Talkspace, BetterHelp, MindStrong, Mindshift, Bloom, Headspace, and Calm. A total of 347 valid questionnaires were collected and analyzed using partial least-squares structural equation modeling. Trust in the health care provider and patient compliance were measured with multiple-item scales adapted from existing scales. Patient empowerment was conceived and measured as a higher-order construct encompassing the following 2 dimensions: patient process and patient outcome. All the items contributing to the constructs in the model were measured with 7-point Likert scales. The reliability and validity of the measurement model were assessed, and the path coefficients of the structural model were estimated. RESULTS: The results clearly show that patient empowerment gained through mental health apps positively influenced patient trust in the health care provider (ß=.306; P<.001). Patient trust in the health care provider had a positive effect on patient compliance (ß=.725; P<.001). The direct relationship between patient empowerment and patient compliance was not significant (ß=.061, P=.23). Interestingly, the data highlight that the effect of patient empowerment on patient compliance was fully mediated by trust in the health care provider (ß=.222; P<.001). The results show that patient empowerment gained through the mental health app involves 2 dimensions: a process and an outcome. CONCLUSIONS: This study shows that for individuals living with mental health disorders, empowerment gained through mental health apps enhances trust in the health care provider. It reveals that patient empowerment impacts patient compliance but only through the full mediating effect of patient trust in the health care provider, indicating that patient trust is a critical variable to enhance patient compliance. Hence, our results confirm that health care systems could encourage the use of mental health apps to favor a climate that facilitates patients' trust in health care provider recommendations, possibly leading to better compliance with the recommended treatment.

Developing A Mobile App With a Human-Centered Design Lens to Improve Access to Mental Health Care (Mentallys Project): Protocol for an Initial Co-Design Process.

BACKGROUND: Co-design is one of the human-centered design approaches that allows end users to significantly and positively impact the design of mental health technologies. It is a promising approach to foster user acceptance and engagement in digital mental health solutions. Surprisingly, there is a lack of understanding of what co-design is in this field. In this paper, co-design is approached as a cocreation process involving persons with a lived experience of mental health problems, health professionals, and design experts who lead and facilitate the overall creative process. OBJECTIVE: This paper describes an initial co-design research protocol for the development of a mobile app that aims to improve access to mental health care. It highlights the characteristics of a co-design approach in e-mental health rooted in human-centered design and led by design experts alongside health experts. The paper focuses on the first steps (phase 1) of the co-design process of the ongoing Mentallys project. METHODS: This Mentallys project will be located in Montréal (Quebec, Canada). The method approach will be based on the "method stories," depicting the "making of" this project and reflecting adjustments needed to the protocol throughout the project in specific situations. Phase 1 of the process will focus on the desirability of the app. Targeted participants will include people with a lived experience of mental health problems, peer support workers and clinicians, and 3 facilitators (all design experts or researchers). Web-based sessions will be organized because of the COVID-19 pandemic, using Miro (RealtimeBoard Inc) and Zoom (Zoom Video Communications, Inc). Data collection will be based on the comments, thoughts, and new ideas of participants around the imaginary prototypes. Thematic analysis will be carried out after each session to inform a new version of the prototype. RESULTS: We conducted 2 stages in phase 1 of the process. During stage 1, we explored ideas through group co-design workshops (divergent thinking). Six co-design workshops were held: 2 with only clinicians (n=7), 2 with peer support workers (n=5) and people with a lived experience of mental health problems (n=2), and 2 with all of them (n=14). A total of 6 facilitators participated in conducting activities in subgroups. During stage 2, ideas were refined through 10 dyad co-design sessions (convergent thinking). Stage 2 involved 3 participants (n=3) and 1 facilitator. Thematic analysis was performed after stage 1, while analytic questioning is being performed for stage 2. Both stages allowed several iterations of the prototypes. CONCLUSIONS: The design of the co-design process, the leadership of the design expertise throughout the process, and the different forms of co-design activities are key elements in this project. We highly recommend that health researchers partner with professional designers or design researchers who are familiar with co-design. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47220.

Human-Centered Design Approaches in Digital Mental Health Interventions: Exploratory Mapping Review.

BACKGROUND: Digital mental health interventions have a great potential to alleviate mental illness and increase access to care. However, these technologies face significant challenges, especially in terms of user engagement and adoption. It has been suggested that this issue stems from a lack of user perspective in the development process; accordingly, several human-centered design approaches have been developed over the years to consider this important aspect. Yet, few human-centered design approaches to digital solutions exist in the field of mental health, and rarely are end users involved in their development. OBJECTIVE: The main objective of this literature review is to understand how human-centered design is considered in e-mental health intervention research. METHODS: An exploratory mapping review was conducted of mental health journals with the explicit scope of covering e-mental health technology. The human-centered design approaches reported and the core elements of design activity (ie, object, context, design process, and actors involved) were examined among the eligible studies. RESULTS: A total of 30 studies met the inclusion criteria, of which 22 mentioned using human-centered design approaches or specific design methods in the development of an e-mental health solution. Reported approaches were classified as participatory design (11/27, 41%), codesign (6/27, 22%), user-centered design (5/27, 19%), or a specific design method (5/27, 19%). Just over half (15/27, 56%) of the approaches mentioned were supported by references. End users were involved in each study to some extent but not necessarily in designing. About 27% (8/30) of all the included studies explicitly mentioned the presence of designers on their team. CONCLUSIONS: Our results show that some attempts have indeed been made to integrate human-centered design approaches into digital mental health technology development. However, these attempts rely very little on designers and design research. Researchers from other domains and technology developers would be wise to learn the underpinnings of human-centered design methods before selecting one over another. Inviting designers for assistance when implementing a particular approach would also be beneficial. To further motivate interest in and use of human-centered design principles in the world of e-mental health, we make nine suggestions for better reporting of human-centered design approaches in future research.

MRI definition of target volumes using fuzzy logic method for three-dimensional conformal radiation therapy.

PURPOSE: Three-dimensional (3D) volume determination is one of the most important problems in conformal radiation therapy. Techniques of volume determination from tomographic medical imaging are usually based on two-dimensional (2D) contour definition with the result dependent on the segmentation method used, as well as on the user's manual procedure. The goal of this work is to describe and evaluate a new method that reduces the inaccuracies generally observed in the 2D contour definition and 3D volume reconstruction process. METHODS AND MATERIALS: This new method has been developed by integrating the fuzziness in the 3D volume definition. It first defines semiautomatically a minimal 2D contour on each slice that definitely contains the volume and a maximal 2D contour that definitely does not contain the volume. The fuzziness region in between is processed using possibility functions in possibility theory. A volume of voxels, including the membership degree to the target volume, is then created on each slice axis, taking into account the slice position and slice profile. A resulting fuzzy volume is obtained after data fusion between multiorientation slices. Different studies have been designed to evaluate and compare this new method of target volume reconstruction and a classical reconstruction method. First, target definition accuracy and robustness were studied on phantom targets. Second, intra- and interobserver variations were studied on radiosurgery clinical cases. RESULTS: The absolute volume errors are less than or equal to 1.5% for phantom volumes calculated by the fuzzy logic method, whereas the values obtained with the classical method are much larger than the actual volumes (absolute volume errors up to 72%). With increasing MRI slice thickness (1 mm to 8 mm), the phantom volumes calculated by the classical method are increasing exponentially with a maximum absolute error up to 300%. In contrast, the absolute volume errors are less than 12% for phantom volumes calculated by the fuzzy logic method. On radiosurgery clinical cases, target volumes defined by the fuzzy logic method are about half of the size of volumes defined by the classical method. Also, intra- and interobserver variations slightly decrease with the fuzzy logic method, resulting in the definition of a better common volume fraction. CONCLUSION: Our fuzzy logic method shows accurate, robust, and reproducible results on phantoms and clinical targets imaged on MRI.