Background: Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, “off-the-shelf” devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. Objective: To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. Methods: The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. Results: The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. Conclusions: The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.

Background: Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. Objective: The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. Methods: In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. Results: We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users’ experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. Conclusions: When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not “one-size-fits-all,” and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.

Normal-weight obesity appears to be an extended diagnosis/syndrome associated with insufficient physical fitness levels and inadequate eating habits at least from school years. However, its relation to long term health parameters in pre-school children remains unknown, even though pre-school age is crucial for the determining healthy lifelong habits. Therefore, the aim of the current study was to investigate the differences in physical fitness level and basic eating habits between normal-weight obese, normal-weight non-obese, and overweight and obese preschoolers. The research sample consisted of 188 preschoolers aged 4.0–6.9 years (M_age = 5.52 ± 0.8 year), normal-weight obese = 25; normal-weight non-obese = 143, overweight and obese = 20. Body composition was measured using bio-impedance InBody230. Six tests assessed the physical fitness level: sit-ups; standing long jump; shuttle running 4 × 5 meters; throwing with a tennis ball; multistage fitness tests; sit and reach. A four-item eating habits questionnaire for parents focusing on breakfast regularity, consumption of sweet foods and drinks, selection of food and attitude towards eating was used. A non-parametric analysis of variance and Fisher’s exact test along with suitable effect sizes were used for data processing of physical fitness tests and the basic eating habits questionnaire, respectively. Normal-weight obese children performed significantly worse (from p = 0.03 to p < 0.001, ES ω2-G = low to medium) in muscular fitness, cardiorespiratory fitness and running agility compared to normal-weight non-obese counterparts and did not significantly differ in the majority of physical fitness performance tests from overweight and obese peers. In basic eating habits, normal-weight obese boys preferred significantly more sweet foods and drinks (p = 0.003 ES = 0.35, large), while normal-weight obese girls had significantly more negative attitude towards eating (p = 0.002 ES = 0.33, large) in comparison to their normal-weight non-obese peers. Normal-weight obesity seems to develop from early childhood and is associated with low physical fitness and deficits in eating habits which might inhibit the natural necessity for physically active life from pre-school age or sooner.

A chronotype is a designation for individual preference of times for different activities in humans. In chronobiological research, it can be measured in many ways, including subjective questionnaires. The most frequently used questionnaires for determining the chronotype are Morningness-Eveningness Questionnaire (MEQ) and Munich Chronotype Questionnaire (MCTQ). Many studies from around the world have already reported metric properties of the MEQ and MCTQ and their relationship in different languages. In this study, we created the Czech version of the MCTQ and examined its relationship with the Czech version of MEQ, including socio-demographic effects. We also examined the ability of the MCTQ to identify chronotypes and cutoffs for their determination. In total, 2703 people (1964 females, 739 males, 18–75 years of age) were screened by the MEQ, MCTQ and reported on age, sex and self-declared body mass index (BMI). We found a significant relationship (p < .001) between MEQ and MCTQ (MSFsc score, used as a chronotype indicator). No significant sex differences in MEQ and in MSFsc were found, but the relationship between age and MSFsc (mid-sleep on free days corrected for sleep debt on weekdays) (p < .001), MEQ (p < .001), social jet-lag (SJL, p < .001) and BMI (p < .001) were found. The SJL was related to MSFsc (p < .001), MEQ (p < .001) and BMI (p < .05). The optimal cutoff value of MSFsc to identify morning and evening chronotype was 3.35 and 4.6, respectively. The results of this study support the mutual substitutability of the Czech version of MEQ and MCTQ.

Background: Tracking and visualizing health data using mobile apps can be an effective self-management strategy for mental health conditions. However, little evidence is available to guide the design of mental health–tracking mechanisms. Objective: The aim of this study was to analyze the content of user reviews of depression self-management apps to guide the design of data tracking and visualization mechanisms for future apps. Methods: We systematically reviewed depression self-management apps on Google Play and iOS App stores. English-language reviews of eligible apps published between January 1, 2018, and December 31, 2021, were extracted from the app stores. Reviews that referenced health tracking and data visualization were included in sentiment and qualitative framework analyses. Results: The search identified 130 unique apps, 26 (20%) of which were eligible for inclusion. We included 783 reviews in the framework analysis, revealing 3 themes. Impact of app-based mental health tracking described how apps increased reviewers’ self-awareness and ultimately enabled condition self-management. The theme designing impactful mental health–tracking apps described reviewers’ feedback and requests for app features during data reporting, review, and visualization. It also described the desire for customization and contexts that moderated reviewer preference. Finally, implementing impactful mental health–tracking apps described considerations for integrating apps into a larger health ecosystem, as well as the influence of paywalls and technical issues on mental health tracking. Conclusions: App-based mental health tracking supports depression self-management when features align with users’ individual needs and goals. Heterogeneous needs and preferences raise the need for flexibility in app design, posing challenges for app developers. Further research should prioritize the features based on their importance and impact on users.

Objectives. Psychometric properties of the Czech version of the Pittsburgh Sleep Quality Index (PSQI‐CZ) have been evaluated only in patients with chronic insomnia, and thus, it is unclear whether PSQI‐CZ is suitable for use in other clinical and nonclinical populations. This study was aimed at examining the validity and reliability of the PSQI‐CZ and at assessing whether the unidimensional or multidimensional scoring of the instrument would be recommended. Methods. A total of 524 adult subjects from the Czech population participated in the study. The internal consistency of PSQI was evaluated using Cronbach’s alpha. The known‐group validity was tested using the Kruskal‐Wallis H test to verify the difference between patients with sleep disorders and healthy control sample. For testing the structural validity, a cross‐validation approach was used with both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). For EFA, the maximum likelihood method with direct oblimin rotation and parallel analysis was used. Results. The internal consistency of PSQI‐CZ items was moderate (α = 0.75). Receiver operating characteristic (ROC) curve analysis showed high specificity (0.79) and moderate sensitivity (0.64) using an optimal cut‐off score of 10. The EFA revealed a 3‐factor structure with factors labelled as “sleep duration and efficiency,” “sleep disturbances and quality,” and “sleep latency.” The CFA showed that the emerged 3‐factor model had a partly acceptable fit, which was better than other previously supported models. Conclusions. A high cut‐off score of 10 is recommended to define poor sleep quality. Given the inconsistency of structural analyses, alternative scoring was not recommended. However, the individual components in addition to a total score should be interpreted when assessing sleep quality. We recommend editing and verifying the PSQI‐CZ translation.