I Need to Know: Using the CeHRes Roadmap to Develop a Treatment Feedback Tool for Youngsters with Mental Health Problems.

Patient-Reported Outcome Measures (PROMs) are often used to monitor treatment outcomes in youth mental health care. Unfortunately, youngsters are rarely informed about the results of their PROMs or, when they are, it is in an insufficient manner. Therefore, a web application was developed-together with youngsters-aimed at giving them feedback about their PROMs. The aim of this study is to describe the development process of the application. An expert panel consisting of youngsters, web designers and researchers, as well as a representative from a client organisation, developed the e-health application INK (short for 'I Need to Know') in an iterative process based on the Centre for eHealth Research roadmap (CeHRes roadmap). Youngsters prefer, among other aspects, a simple, easy-to-use e-health application with a colourful appearance and want to be able to compare their results across different time points and informants. The INK tool provides youngsters with insight into their PROM results. Based on the youngsters' preferences, INK users can choose which feedback information is visible. INK facilitates youngsters' active participation in their treatment as well as shared decision-making with their professional caregivers.

Psychometric Properties of the QoL-ME: A Visual and Personalized Quality of Life Assessment App for People With Severe Mental Health Problems.

Background: Quality of Life (QoL) assessment in people with severe mental health problems may benefit from improved personalization and accessibility. Therefore, an innovative, digital, visual, and personalized QoL assessment app for people with severe mental health problems was recently developed: the QoL-ME. The aim of this study was to evaluate the psychometric quality of the QoL-ME by assessing its reliability, validity, and responsiveness. Methods: To examine the reliability of the QoL-ME, the internal consistency of its subscales was assessed using Cronbach's Alpha. Correlations between the QoL-ME and the MANSA were computed to appraise the construct validity of the QoL-ME. Internal responsiveness was evaluated using the standardized response mean and external responsiveness was investigated using hierarchical regression. Results: Cronbach's Alpha's of the subscales of the QoL-ME ranged between 0.5 and 0.84. In accordance with expectations, the language-based core version of the QoL-ME correlated strongly (r = between 0.55 and 0.76) with the MANSA, whilst the picture-based additional modules of the QoL-ME correlated moderately (r = 0.3) with the MANSA. The standardized response mean was 0.23 and the regression model revealed a coefficient ß of -0.01. Conclusions: The QoL-ME has adequate psychometric properties. In comparison with similar pictorial instruments, both the QoL-ME's reliability and validity can be considered as sufficient. The results indicate that the responsiveness of the QoL-ME is insufficient. Additional research is needed to evaluate and potentially modify the instrument to improve its responsiveness.

Cocreative Development of the QoL-ME: A Visual and Personalized Quality of Life Assessment App for People With Severe Mental Health Problems.

BACKGROUND: Quality of life (QoL) is a prominent outcome measure in mental health. However, conventional methods for QoL assessment rely heavily on language-based communication and therefore may not be optimal for all individuals with severe mental health problems. In addition, QoL assessment is usually based on a fixed number of life domains. This approach conflicts with the notion that QoL is influenced by individual values and preferences. A digital assessment app facilitates both the accessibility and personalization of QoL assessment and may, therefore, help to further advance QoL assessment among individuals with severe mental health problems. OBJECTIVE: This study focused on the development of an innovative, visual, and personalized QoL assessment app for people with severe mental health problems: the QoL-ME. METHODS: This study targeted 3 groups of individuals with severe mental health problems: (1) people with psychiatric problems, (2) people treated in forensic psychiatry, and (3) people who are homeless. A group of 59 participants contributed to the 6 iterations of the cocreative development of the QoL-ME. In the brainstorming stage, consisting of the first iteration, participants' previous experiences with questionnaires and mobile apps were explored. Participants gave their feedback on initial designs and wireframes in the second to fourth iterations that made up the design stage. In the usability stage that comprised the final 2 iterations, the usability of the QoL-ME was evaluated. RESULTS: In the brainstorming stage, participants stressed the importance of privacy and data security and of receiving feedback when answering questionnaires. Participants in the design stage indicated a preference for paging over scrolling, linear navigation, a clean and minimalist layout, the use of touchscreen functionality in various modes of interaction, and the use of visual analog scales. The usability evaluation in the usability stage revealed good to excellent usability. CONCLUSIONS: The cocreative development of the QoL-ME resulted in an app that corresponds to the preferences of participants and has strong usability. Further research is needed to evaluate the psychometric quality of the QoL-ME and to investigate its usefulness in practice.

Subjectively different but objectively the same? Three profiles of QoL in people with severe mental health problems.

PURPOSE: Quality of life (QoL) is a broad outcome that is often used to assess the impact of treatment and care interventions in mental health services. QoL, however, is known to be influenced by individual values and preferences. To investigate this heterogeneity on the individual level, this study aimed to distinguish classes with distinct QoL profiles in a broad group of people with severe mental health problems and to identify the QoL domains that are most strongly related to the classes. METHODS: QoL data of seven studies that used the Lancashire quality of life profile (LQoLP) were used in a latent class analysis. Sociodemographic variables, health-related variables, and measures of well-being were used to characterise the classes. Additionally, univariate entropy scores were used to assess the strength of the association between the ten LQoLP domains and the latent classes. RESULTS: Two of the three indices of fit pointed towards a three-class model. The three classes differed significantly on all of the LQoLP domains, on well-being, and on 'being in an intimate relationship'. No differences were found for the majority of the health-related and sociodemographic variables. The LQoLP domains 'family relations', 'positive self-esteem', and 'negative self-esteem' were most strongly related to the latent classes. CONCLUSIONS: The identification of three distinct classes of QoL scores re-emphasises the heterogenic nature of QoL. The lack of differences in sociodemographic or health-related characteristics between the three classes suggests that QoL is primarily determined by subjective, personal evaluations, rather than by objective characteristics and circumstances.

Worth a thousand words? Visual concept mapping of the quality of life of people with severe mental health problems.

OBJECTIVES: Conventional approaches to quality of life (QoL) measurement rely heavily on verbal, language-based communication. They require respondents to have significant cognitive and verbal ability, making them potentially unsuitable for people with severe mental health problems. To facilitate an alternative approach to QoL assessment, the current study aims to develop an alternative, visual representation of QoL for people with severe mental health problems. METHODS: An alternative, visual adaptation of the concept mapping method was used to construct this visual representation of QoL. Eighty-two participants (i.e., patients, care professionals, and family members) contributed to this study. Results were processed statistically to construct the concept map. RESULTS: The concept map contains 160 unique visual statements, grouped into 8 clusters labelled (1) Support and Attention, (2) Social Contacts, (3) Happiness and Love, (4) Relaxation and Harmony, (5) Leisure, (6) Lifestyle, (7) Finances, and (8) Health and Living. Examples of visual statements are pictures of family silhouettes, romantic couples, natural scenes, houses, sports activities, wallets and coins, smiley faces, and heart shapes. The clusters were interpreted and labelled by participants. CONCLUSIONS: Almost all of the statements correspond to clusters found in previous (non-visual) QoL research. Hence, QoL domains can also be presented visually.