A Scoping Review on Mobile Health Technology for Assessment and Intervention of Upper Limb Motor Function in Children with Motor Impairments.

Upper limb (UL) motor dysfunctions impact residual movement in hands/shoulders and limit participation in play, sports, and leisure activities. Clinical and laboratory assessments of UL movement can be time-intensive, subjective, and/or require specialized equipment and may not optimally capture a child's motor abilities. The restrictions to in-person research experienced during the COVID-19 pandemic have inspired investigators to design inclusive at-home studies with child participants and their families. Relying on the ubiquity of mobile devices, mobile health (mHealth) applications offer solutions for various clinical and research problems. This scoping review article aimed to aggregate and synthesize existing research that used health technology and mHealth approaches to evaluate and assess the hand function and UL movement in children with UL motor impairment. A scoping review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) model was conducted in March 2023 yielding 25 articles (0.32% of 7891 studies). Assessment characteristics included game or task-based tests (13/25, 52%), primarily for neurological disorders (e.g., autism spectrum disorder [ASD], dystonia, dysgraphia) or children with cerebral palsy (CP). Although several mHealth studies were conducted in the clinical environment (10/25, 40%), studies conducted at home or in nonclinical settings (15/25, 60%) reported acceptable and highly satisfactory to the patients as minimizing the potential risks in participation. Moreover, the remaining barriers to clinical translation included object manipulation on a touch screen, offline data analysis, real-world usability, and age-appropriate application design for the wider population. However, the results emphasize the exploration of mHealth over traditional approaches, enabling user-centered study design, family-oriented methods, and large-scale sampling in future research.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity.

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

A comparative study on HIPAA technical safeguards assessment of android mHealth applications.

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate. The Health Insurance Portability and Accountability Act (HIPAA) provides guidelines to the app developers so that the apps must be compliant with required and addressable Technical Safeguards. However, most mobile app developers, including mHealth apps are not aware of HIPAA security and privacy regulations. Therefore, a research opportunity has emerged to develop an analytical framework to assist the developer to maintain a secure and HIPAA-compliant source code and raise awareness among consumers about the privacy and security of sensitive and personal health information. We proposed an Android source code analysis framework that evaluates twelve HIPAA Technical Safeguards to check whether a mHealth application is HIPAA compliant or not. The implemented meta-analysis and data-flow analysis algorithms efficiently identify the risk and safety features of mHealth apps that violate HIPAA regulations. Furthermore, we addressed API level checking for secure data communication mandated by recent CMS guidelines between third-party mobile health apps and EHR systems. Experimentally, a web-based tool has been developed for evaluating the efficacy of analysis techniques and algorithms. We have investigated 200 top popular Medical and Health & Fitness category Android apps collected from Google Play Store. We identified from the comparative analysis of the HIPAA rules assessment results that authorization to access sensitive resources, data encryption-decryption, and data transmission security is the most vulnerable features of the investigated apps. We provided recommendations to app developers about the most common mistake made at the time of app development and how to avoid these mistakes to implement secure and HIPAA-compliant apps. The proposed framework enables us to develop an IDE plugin for mHealth app developers and a web-based interface for mHealth app consumers.