Dataset Information

Development and evaluation of a mobile-optimized daily self-rating depression screening app: A preliminary study.

ABSTRACT: The aims of this study were to design a mobile app that would record daily self-reported Korean version of the Center for Epidemiologic Studies Depression Scale-Revised (K-CESD-R) ratings in a "Yes" or "No" format, develop two different algorithms for converting mobile K-CESD-R scores in a binary format into scores in a 5-point response format, and determine which algorithm would be more appropriately applied to the newly developed app. Algorithm (A) was designed to improve the scoring system of the 2-week delayed retrospective recall-based original K-CESD-R scale, and algorithm (B) was designed to further refine the scoring of the 24-hour delayed prospective recall-based mobile K-CESD-R scale applied with algorithm (A). To calculate total mobile K-CESD-R scores, each algorithm applied certain cut-off criteria for a 5-point scale with different inter-point intervals, defined by the ratio of the total number of times that users responded "Yes" to each item to the number of days that users reported daily depressive symptom ratings during the 2-week study period. Twenty participants were asked to complete a K-CESD-R Mobile assessment daily for 2 weeks and an original K-CESD-R assessment delivered to their e-mail accounts at the end of the 2-week study period. There was a significant difference between original and mobile algorithm (B) scores but not between original and mobile algorithm (A) scores. Of the 20 participants, 4 scored at or above the cut-off criterion (?13) on either the original K-CESD-R (n = 4) or the mobile K-CESD-R converted with algorithm (A) (n = 3) or algorithm (B) (n = 1). However, all participants were assessed as being below threshold for a diagnosis of a mental disorder during a clinician-administered diagnostic interview. Therefore, the K-CESD-R Mobile app using algorithm (B) could be a more potential candidate for a depression screening tool than the K-CESD-R Mobile app using algorithm (A).

SUBMITTER: Chung K

PROVIDER: S-EPMC6019749 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Development and evaluation of a mobile-optimized daily self-rating depression screening app: A preliminary study.

Chung Kyungmi K Jeon Min-Jeong MJ Park Jaesub J Lee San S Kim Chang Oh CO Park Jin Young JY

PloS one 20180626 6

The aims of this study were to design a mobile app that would record daily self-reported Korean version of the Center for Epidemiologic Studies Depression Scale-Revised (K-CESD-R) ratings in a "Yes" or "No" format, develop two different algorithms for converting mobile K-CESD-R scores in a binary format into scores in a 5-point response format, and determine which algorithm would be more appropriately applied to the newly developed app. Algorithm (A) was designed to improve the scoring system of ...[more]

PMID: 29944663

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Project description:BackgroundOver 11 million care partners in the United States who provide care to people living with Alzheimer disease and related dementias (ADRD) cite persistent and pervasive unmet needs related to their caregiving role. The proliferation of mobile apps for care partners has the potential to meet care partners' needs, but the quality of apps is unknown.ObjectiveThis study aims to evaluate the quality of publicly available apps for care partners of people living with ADRD and identify design features of low- and high-quality apps to guide future research and user-centered app development.MethodsWe searched the US Apple App and Google Play stores with the criteria that included apps needed to be available in the US Google Play or Apple App stores, accessible to users out of the box, and primarily intended for use by an informal (family or friend) care partner of a person living with ADRD. We classified and tabulated app functionalities. The included apps were then evaluated using the Mobile App Rating Scale (MARS) using 23 items across 5 dimensions: engagement, functionality, aesthetics, information, and subjective quality. We computed descriptive statistics for each rating. To identify recommendations for future research and app development, we categorized rater comments on score-driving factors for each MARS rating item and what the app could have done to improve the item score.ResultsWe evaluated 17 apps. We found that, on average, apps are of minimally acceptable quality. Functionalities supported by apps included education (12/17, 71%), interactive training (3/17, 18%), documentation (3/17, 18%), tracking symptoms (2/17, 12%), care partner community (3/17, 18%), interaction with clinical experts (1/17, 6%), care coordination (2/17, 12%), and activities for the person living with ADRD (2/17, 12%). Of the 17 apps, 8 (47%) had only 1 feature, 6 (35%) had 2 features, and 3 (18%) had 3 features. The MARS quality mean score across apps was 3.08 (SD 0.83) on the 5-point rating scale (1=inadequate to 5=excellent), with apps scoring highest on average on functionality (mean 3.37, SD 0.99) and aesthetics (mean 3.24, SD 0.92) and lowest on average on information (mean 2.95, SD 0.95) and engagement (mean 2.76, SD 0.89). The MARS subjective quality mean score across apps was 2.26 (SD 1.02).ConclusionsWe identified apps whose mean scores were more than 1 point below minimally acceptable quality, whereas some were more than 1 point above. Many apps had broken features and were rated as below acceptable for engagement and information. Minimally acceptable quality is likely to be insufficient to meet care partner needs. Future research should establish minimum quality standards across dimensions for care partner mobile apps. Design features of high-quality apps identified in this study can provide the foundation for benchmarking these standards.

Project description:ObjectiveThe objective of this study is to describe the algorithm and technical implementation of a mobile app that uses adaptive testing to assess an efficient mobile app for the diagnosis of delirium.Materials and methodsThe app was used as part of a NIH-funded project to assess the feasibility, effectiveness, administration time, and costs of the 2-step delirium identification protocol when performed by physicians and nurses, and certified nursing assistants (CNA). The cohort included 535 hospitalized patients aged 79.7 (SD = 6.6) years enrolled at 2 different sites. Each patient was assessed on 2 consecutive days by the research associate who performed the reference delirium assessment. Thereafter, physicians, nurses, and CNAs performed adaptive delirium assessments using the app. Qualitative data to assess the experience of administering the 2-step protocol, and the app usability were also collected and analyzed from 50 physicians, 189 nurses, and 83 CNAs. We used extensible hypertext markup language (XHTML) and JavaScript to develop the app for the iOS-based iPad. The App was linked to Research Electronic Data Capture (REDCap), a relational database system, via a REDCap application programming interface (API) that sent and received data from/to the app. The data from REDCap were sent to the Statistical Analysis System for statistical analysis.ResultsThe app graphical interface was successfully implemented by XHTML and JavaScript. The API facilitated the instant updating and retrieval of delirium status data between REDCap and the app. Clinicians performed 881 delirium assessments using the app for 535 patients. The transmission of data between the app and the REDCap system showed no errors. Qualitative data indicated that the users were enthusiastic about using the app with no negative comments, 82% positive comments, and 18% suggestions of improvement. Delirium administration time for the 2-step protocol showed similar total time between nurses and physicians (103.9 vs 106.5 seconds). Weekly enrollment reports of the app data were generated for study tracking purposes, and the data are being used for statistical analyses for publications.DiscussionThe app developed using iOS could be easily converted to other operating systems such as Android and could be linked to other relational databases beside REDCap, such as electronic health records to facilitate better data retrieval and updating of patient's delirium status.ConclusionOur app operationalizes an adaptive 2-step delirium screening protocol. Its algorithm and cross-plat formed code of XHTML and JavaScript can be easily exported to other operating systems and hardware platforms, thus enabling wider use of the efficient delirium screening protocol that we have developed. The app is currently implemented as a research tool, but with adaptation could be implemented in the clinical setting to facilitate widespread delirium screening in hospitalized older adults.

Project description:BackgroundSelecting and integrating health-related apps into patient care is impeded by the absence of objective guidelines for identifying high-quality apps from the many thousands now available.ObjectiveThis study aimed to evaluate the App Rating Inventory, which was developed by the Defense Health Agency's Connected Health branch, to support clinical decisions regarding app selection and evaluate medical and behavioral apps.MethodsTo enhance the tool's performance, eliminate item redundancy, reduce scoring system subjectivity, and ensure a broad application of App Rating Inventory-derived results, inventory development included 3 rounds of validation testing and 2 trial periods conducted over a 6-month interval. The development focused on content validity testing, dimensionality (ie, whether the tool's criteria performed as operationalized), factor and commonality analysis, and interrater reliability (reliability scores improved from 0.62 to 0.95 over the course of development).ResultsThe development phase culminated in a review of 248 apps for a total of 6944 data points and a final 28-item, 3-category app rating system. The App Rating Inventory produces scores for the following three categories: evidence (6 items), content (11 items), and customizability (11 items). The final (fourth) metric is the total score, which constitutes the sum of the 3 categories. All 28 items are weighted equally; no item is considered more (or less) important than any other item. As the scoring system is binary (either the app contains the feature or it does not), the ratings' results are not dependent on a rater's nuanced assessments.ConclusionsUsing predetermined search criteria, app ratings begin with an environmental scan of the App Store and Google Play. This first step in market research funnels hundreds of apps in a given disease category down to a manageable top 10 apps that are, thereafter, rated using the App Rating Inventory. The category and final scores derived from the rating system inform the clinician about whether an app is evidence informed and easy to use. Although a rating allows a clinician to make focused decisions about app selection in a context where thousands of apps are available, clinicians must weigh the following factors before integrating apps into a treatment plan: clinical presentation, patient engagement and preferences, available resources, and technology expertise.

Project description:BackgroundMultiple sclerosis (MS) is one of the most common neurological disorders worldwide, and self-management is considered an essential dimension in its control. This study aimed to develop an evidence-based mobile application for MS self-management and evaluate it.MethodsThis study was undertaken in three phases: content preparation, design, and evaluation. In the content preparation phase, the researchers extracted MS self-management needs based on related guidelines and guides, existing apps on the self-management of MS, and the field experts' views and confirmation. The design phase was conducted in five steps: defining app functionalities, depicting the wireframe, preparing the media, coding the app, and testing the app's performance. The app was developed using the Android Studio environment and Java programming language for the Android operating system. The performance of the developed app was tested separately in several turns, and existing defects were corrected in each turn. Finally, after using the app for three weeks, the app was evaluated for its short-term impact on MS management and user-friendliness using a researcher-constructed questionnaire from participants' (N = 20) perspectives.ResultsThe IDoThis app is an offline app for people with MS that includes five main modules: three modules for training or informing users about different aspects of MS, one module for monitoring the user's MS condition, and a reporting module. In the initial evaluation of the app, 75% (n = 15) of participants mentioned that using this app improved MS self-management status at intermediate and higher levels, but 25% (n = 5) of the participants mentioned that the effect of using the app on the self-management tasks was low or was very low. The majority of users rated the user-friendliness of the app as high. The users found the sections "exercises in MS" and "monitoring of MS status" beneficial to their self-management. Still, the fatigue and sleep management sections are needed to meet users' expectations.ConclusionUsing IDoThis app as a self-management tool for individuals with MS appears feasible, that can meet the need for a free and accessible self-management tool for individuals with MS. Future directions should consider the users' fatigue and sleep management expectations.

Project description:BackgroundMobile apps are becoming increasingly popular, with 5.70 million apps available in early 2021. Smartphones can provide portable and convenient access to health apps. Here, we consider apps for people with one of the estimated 7000 rare conditions, which are defined as having an incidence of <1 in 2000. The needs of people with rare conditions are known to be different from those of people with more common conditions. The former may be socially isolated (not knowing anyone else who has the condition) and may not be able to find reliable information about the disorder.ObjectiveThe aim of this review is to search for apps developed specifically for people diagnosed with a rare disease and to assess them for quality using the Mobile App Rating Scale (MARS). We examine features that address 6 identified needs of people with a rare disorder and make recommendations for future developers.MethodsGoogle Play Store (Android) and Apple App Store (iOS) were searched for relevant health-related apps specifically for rare diseases. The search included the names of 10 rare disease groups. App quality was determined using MARS, assessing app engagement, functionality, aesthetics, and information.ResultsWe found 29 relevant apps (from a total of 2272) addressing 14 rare diseases or disease groups. The most common rare conditions addressed were cystic fibrosis (n=6), hemophilia (n=5), and thalassemia (n=5). The most common app features were web-based information and symptom trackers. The mean MARS score was 3.44 (SD 0.84). Lowest scores were for engagement.ConclusionsMost apps provided factual and visual information, providing tools for self-monitoring and resources to help improve interactions during health consultations. App origin and quality varied greatly. Developers are recommended to consider ways to make appropriate apps more easily identifiable to consumers, to always include high-quality information, improve engagement, provide qualitative evaluations of the app, and include consumers and clinicians in the design.

Project description:BACKGROUND:The spread of technology and dissemination of knowledge across the World Wide Web has prompted the development of apps for American Sign Language (ASL) translation, interpretation, and syntax recognition. There is limited literature regarding the quality, effectiveness, and appropriateness of mobile health (mHealth) apps for the deaf and hard-of-hearing (DHOH) that pose to aid the DHOH in their everyday communication and activities. Other than the star-rating system with minimal comments regarding quality, the evaluation metrics used to rate mobile apps are commonly subjective. OBJECTIVE:This study aimed to evaluate the quality and effectiveness of DHOH apps using a standardized scale. In addition, it also aimed to identify content-specific criteria to improve the evaluation process by using a content expert, and to use the content expert to more accurately evaluate apps and features supporting the DHOH. METHODS:A list of potential apps for evaluation was generated after a preliminary screening for apps related to the DHOH. Inclusion and exclusion criteria were developed to refine the master list of apps. The study modified a standardized rating scale with additional content-specific criteria applicable to the DHOH population for app evaluation. This was accomplished by including a DHOH content expert in the design of content-specific criteria. RESULTS:The results indicate a clear distinction in Mobile App Rating Scale (MARS) scores among apps within the study's three app categories: ASL translators (highest score=3.72), speech-to-text (highest score=3.6), and hard-of-hearing assistants (highest score=3.90). Of the 217 apps obtained from the search criteria, 21 apps met the inclusion and exclusion criteria. Furthermore, the limited consideration for measures specific to the target population along with a high app turnover rate suggests opportunities for improved app effectiveness and evaluation. CONCLUSIONS:As more mHealth apps enter the market for the DHOH population, more criteria-based evaluation is needed to ensure the safety and appropriateness of the apps for the intended users. Evaluation of population-specific mHealth apps can benefit from content-specific measurement criteria developed by a content expert in the field.

Project description:BackgroundWith thousands of health apps in app stores globally, it is crucial to systemically and thoroughly evaluate the quality of these apps due to their potential influence on health decisions and outcomes. The Mobile App Rating Scale (MARS) is the only currently available tool that provides a comprehensive, multidimensional evaluation of app quality, which has been used to compare medical apps from American and European app stores in various areas, available in English, Italian, Spanish, and German. However, this tool is not available in Arabic.ObjectiveThis study aimed to translate and adapt MARS to Arabic and validate the tool with a sample of health apps aimed at managing or preventing obesity and associated disorders.MethodsWe followed a well-established and defined "universalist" process of cross-cultural adaptation using a mixed methods approach. Early translations of the tool, accompanied by confirmation of the contents by two rounds of separate discussions, were included and culminated in a final version, which was then back-translated into English. Two trained researchers piloted the MARS in Arabic (MARS-Ar) with a sample of 10 weight management apps obtained from Google Play and the App Store. Interrater reliability was established using intraclass correlation coefficients (ICCs). After reliability was ascertained, the two researchers independently evaluated a set of additional 56 apps.ResultsMARS-Ar was highly aligned with the original English version. The ICCs for MARS-Ar (0.836, 95% CI 0.817-0.853) and MARS English (0.838, 95% CI 0.819-0.855) were good. The MARS-Ar subscales were highly correlated with the original counterparts (P<.001). The lowest correlation was observed in the area of usability (r=0.685), followed by aesthetics (r=0.827), information quality (r=0.854), engagement (r=0.894), and total app quality (r=0.897). Subjective quality was also highly correlated (r=0.820).ConclusionsMARS-Ar is a valid instrument to assess app quality among trained Arabic-speaking users of health and fitness apps. Researchers and public health professionals in the Arab world can use the overall MARS score and its subscales to reliably evaluate the quality of weight management apps. Further research is necessary to test the MARS-Ar on apps addressing various health issues, such as attention or anxiety prevention, or sexual and reproductive health.

Dataset Information

Development and evaluation of a mobile-optimized daily self-rating depression screening app: A preliminary study.

Publications

Development and evaluation of a mobile-optimized daily self-rating depression screening app: A preliminary study.

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