Data Science in Physical Medicine and Rehabilitation

Revolutionizing Sports Rehabilitation: Unleashing the Power of Tele-Rehabilitation for Optimal Physiotherapy Results

Background: Tele-rehabilitation programs have emerged as a promising approach to improve access to physiotherapy services for athletes with sports-related injuries. This randomized controlled trial aimed to compare the effectiveness of a tele-rehabilitation program with traditional in-person physiotherapy in improving outcomes for this population. Methods: This randomized controlled trial enrolled a large sample of 780 athletes with sports-related injuries to compare the effectiveness of tele-rehabilitation and traditional in-person physiotherapy. Blinding procedures were implemented to minimize bias. The intervention group received tele-rehabilitation physiotherapy, whereas the control group received traditional in-person physiotherapy. Pre- and post-intervention assessments were conducted to measure outcome measures, including range of motion, muscle strength, pain levels, and functional performance. Results: Significant improvements were observed in all outcome measures in both the tele-rehabilitation and in-person groups from baseline to postintervention. Independent t tests demonstrated no significant differences between the two groups in any of the outcome measures. These findings indicate that the tele-rehabilitation program was as effective as traditional in-person physiotherapy in improving the outcomes of athletes with sports-related injuries, even in a large sample size of 780 participants. Conclusion: This study provides robust evidence supporting the feasibility and effectiveness of tele-rehabilitation programs as viable alternatives to traditional in-person physiotherapy for athletes with sports-related injuries. These findings highlight the potential of tele-rehabilitation to significantly expand access to high-quality physiotherapy services for a large number of athletes. Further research should focus on evaluating the long-term effectiveness and cost-effectiveness of tele-rehabilitation programs in sports rehabilitation using larger sample sizes.

Perceptions of human movement researchers and clinicians on the barriers and facilitators to health research data sharing in Africa

INTRODUCTION

The benefits of research data sharing abound in the literature. However, some factors define how researchers and clinicians approach the challenges surrounding sharing human movement health research data.

PURPOSE

To describe the perceptions of human movement researchers and clinicians on the barriers and facilitators to research data sharing in Africa.

METHOD

A qualitative descriptive design with a purposive sampling method was used. In-depth interviews with human movement researchers and clinicians across Africa were conducted online via Microsoft Teams. Sixteen (n = 16) participants took part in this study. This sample size was representative of East, West, Northern, and Southern Africa. Efforts made to engage with participants in Central Africa were unsuccessful.

RESULT

Five themes emerged: 1) the researcher-clinician gap; 2) technological pros and cons in Africa; 3) cost matters; 4) bureaucracy and ethical factors; and 5) the unique African perspective. Mainly, barriers rather than facilitators to data sharing exist among African human movement researchers and clinicians.

CONCLUSION

There needs to be a societal and psychological shift through reorientation to encourage data sharing among African human movement researchers and clinicians.

STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient's progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings.

Leveraging real-world data to improve cochlear implant outcomes: Is the data available?

OBJECTIVES

A small but persistent proportion of individuals do not gain the expected benefit from cochlear implants(CI). A step-change in the understanding of factors affecting outcomes could come through data science. This study evaluates clinical data capture to assess the quality and utility of CI user's health records for data science, by assessing the recording of otitis media. Otitis media was selected as it is associated with the development of sensorineural hearing loss and may affect cochlear implant outcomes.

METHODS

A retrospective service improvement project evaluating the medical records of 594 people with a CI under the care of the University of Southampton Auditory Implant Service between 2014 and 2020.

RESULTS

The clinical records are suitable for data science research. Of the cohort studied 20% of Adults and more than 40% of the paediatric cases have a history of middle ear inflammation.

DISCUSSION

Data science has potential to improve cochlear implant outcomes and improve understanding of the mechanisms underlying poor performance, through retrospective secondary analysis of real-world data.

CONCLUSION

Implant centres and the British Cochlear Implant Group National Hearing Implant Registry are urged to consider the importance of consistently and accurate recording of patient data over time for each CI user. Data where links to hearing loss have been identified, such as middle ear inflammation, may be particularly valuable in future analyses and to inform clinical trials.

Assessment of Patient Treatment and Rehabilitation Processes Using Electromyography Signals and Selected Industry 4.0 Solutions

Funding treatment and rehabilitation processes for patients with musculoskeletal conditions is an important part of public health insurance in European Union countries. By 2030, these processes will be planned in national health strategies (sequential process activities will be identified, care packages will be defined, service standards will be described, roles in the implementation of activities will be distinguished). Today, in many countries of the world (including the EU countries), these processes tend not to be very effective and to be expensive for both patients and insurance companies. This article aims to raise awareness of the need for process re-engineering and describes possible tools for assessing patient treatment and rehabilitation processes (using electromyographic signals-EMG and selected Industry 4.0 solutions). This article presents the research methodology prepared for the purpose of process evaluation. The use of this methodology will confirm the hypothesis that the use of EMG signals and selected Industry 4.0 solutions will improve the effectiveness and efficiency of treatment and rehabilitation processes for patients with musculoskeletal injuries.

Improving the Use of Blockchain Technology in Stroke Care Information Management Systems

Blockchain is a new and popular technology in the digital age. Blockchain technology is referred to as decentralised and distributed digital ledgers, which are called blocks. These blocks are linked together with the cryptographic hashes and are used to record transactions between many computers. No single block can be altered without altering the related blocks. Modification of individual block data is impossible because each block contains information from the previous block. This is the unique strength of blockchain. Timestamps and hashes are some of the important terms when blockchains are considered. Data security is guaranteed with this advanced technology. Blockchain technology finds its application in the healthcare industry with many advantages in a queue. Medical data can be transferred safely and securely for fool-proof management of the medicine supply chain, which helps in healthcare research. Blockchains are used to securely encrypt a patient's information in the event of an outbreak of a pandemic disease. A stroke is referred to as a brain attack, also called cerebral infarction. A cerebral infarction is a sudden stoppage of blood flow in the blood vessels connected to the brain. This study focused on evaluating the application of blockchain technology in Stroke Nursing Information Management Systems. This emerging technology is already in use in the healthcare industry. The patient's data is kept decentralized, transparent, and mainly incorruptible, thus keeping it secured and sharing of data is quick.

A Roadmap for Building Data Science Capacity for Health Discovery and Innovation in Africa

Technological advances now make it possible to generate diverse, complex and varying sizes of data in a wide range of applications from business to engineering to medicine. In the health sciences, in particular, data are being produced at an unprecedented rate across the full spectrum of scientific inquiry spanning basic biology, clinical medicine, public health and health care systems. Leveraging these data can accelerate scientific advances, health discovery and innovations. However, data are just the raw material required to generate new knowledge, not knowledge on its own, as a pile of bricks would not be mistaken for a building. In order to solve complex scientific problems, appropriate methods, tools and technologies must be integrated with domain knowledge expertise to generate and analyze big data. This integrated interdisciplinary approach is what has become to be widely known as data science. Although the discipline of data science has been rapidly evolving over the past couple of decades in resource-rich countries, the situation is bleak in resource-limited settings such as most countries in Africa primarily due to lack of well-trained data scientists. In this paper, we highlight a roadmap for building capacity in health data science in Africa to help spur health discovery and innovation, and propose a sustainable potential solution consisting of three key activities: a graduate-level training, faculty development, and stakeholder engagement. We also outline potential challenges and mitigating strategies.

Data sharing practices of health researchers in Africa: a scoping review protocol

OBJECTIVE

The aim of the review is to map the existing evidence regarding the data-sharing practices of health researchers in African countries. This review will also identify perceptions; barriers; facilitators; ethical-, legal-, and author-reported recommendations; institutional- and funding-related aspects that are being considered by African health researchers on data sharing in Africa and, as a result, identify areas for development and improvement in health care on the continent.

INTRODUCTION

The sharing of health-related data has been widely discussed in the literature. However, sharing health-related data has yet to become a common practice among health researchers in Africa, which bears a large burden of the global health diseases. The sharing of health research data could lead to greater development and improvement in health care in Africa.

INCLUSION CRITERIA

This review will incorporate studies that report on data sharing among health researchers in Africa. All primary, secondary, and gray literature that report on the practice of data sharing among health researchers in Africa will be included. Studies on data sharing on topics other than health-related data will be excluded. No language restrictions will be applied.

METHODS

The JBI scoping review methodological framework will be adopted. An initial search of databases such as MEDLINE (PubMed), Scopus, LILAC, and Web of Science will be conducted. All search results will be screened and relevant data extracted by two independent reviewers. Data extracted will be exported into JBI SUMARI. The findings will be presented in the final scoping review report and illustrated in a PRISMA flow diagram.

Design and Application of Electronic Rehabilitation Medical Record (ERMR) Sharing Scheme Based on Blockchain Technology

As the value of blockchain has been widely recognized, more and more industries are proposing their blockchain solutions, including the rehabilitation medical industry. Blockchain can play a powerful role in the field of rehabilitation medicine, bringing a new research idea to the management of rehabilitation medical data. The electronic rehabilitation medical record (ERMR) contains rich data dimensions, which can provide comprehensive and accurate information for assessing the health of patients, thereby enhancing the effect of rehabilitation treatment. This paper analyzed the data characteristics of ERMR and the application requirements of blockchain in rehabilitation medicine. Based on the basic principles of blockchain, the technical advantages of blockchain used in ERMR sharing have been studied. In addition, this paper designed a blockchain-based ERMR sharing scheme in detail, using the specific technologies of blockchain such as hybrid P2P network, block-chain data structure, asymmetric encryption algorithm, digital signature, and Raft consensus algorithm to achieve distributed storage, data security, privacy protection, data consistency, data traceability, and data ownership in the process of ERMR sharing. The research results of this paper have important practical significance for realizing the safe and efficient sharing of ERMR, and can provide important technical references for the management of rehabilitation medical data with broad application prospects.

A Mobility Measure for Inpatient Rehabilitation Using Multigroup, Multidimensional Methods

BACKGROUND AND PURPOSE

Inpatient rehabilitation facilities (IRFs) report patient functional status to Medicare and other payers using Quality Indicators (QI). While the QI is useful for payment purposes, its measurement properties are limited for monitoring patient progress. A mobility measure based on QI items and additional standardized assessments may enhance clinicians' ability to track patient improvement. Thus, we developed the Mobility Ability Quotient (Mobility AQ) to assess mobility during inpatient rehabilitation.

METHODS

For 10 036 IRF inpatients, we extracted assessments from electronic health records, used confirmatory factor analysis to define subdimensions of mobility, and then applied multidimensional item response theory (MIRT) methods to develop a unidimensional construct. Assessments included the QI items and standardized measures of mobility, motor performance, and wheelchair and transfer skills.

RESULTS

Confirmatory factor analysis resulted in good-fitting models (root-mean-square errors of approximation ≤0.08, comparative fit indices, and nonnormed fit indices ≥0.95) for 3 groups defined by anticipated primary mode of locomotion at discharge-walking, wheelchair propulsion, or both. Reestimation as a multigroup, MIRT model yielded scores more sensitive to change compared with QI mobility items (dlast-first = 1.08 vs 0.60 for the QI; dmax-min = 1.16 vs 1.05 for the QI). True score equating analysis demonstrated a higher ceiling and lower floor for the Mobility AQ than the QI.

DISCUSSION AND CONCLUSIONS

The Mobility AQ demonstrates improved sensitivity over the QI mobility items. This MIRT-based mobility measure describes patient function and progress for patients served by IRFs and has the potential to reduce assessment burden and improve communication regarding patient functional status.Video Abstract available for more insights from authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A341).

Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation

Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantages to outpatient rehabilitation (e.g., lower cost, more relevant to home and community function), there are also challenges including lack of information about how patient progress observed in the outpatient clinic translates into improved functional performance at home. At present, outpatient providers must rely on patient-reported information about functional progress (or lack thereof) at home and in the community. Information and communication technologies (ICT) offer another option—data collected about the patient’s adherence, performance and progress made on home exercises could be used to help guide course corrections between clinic visits, enhancing effectiveness and efficiency of outpatient care. In this article, we describe our efforts to explore use of sensor-enhanced home exercise and big data analytics in medical rehabilitation. The goal of this work is to demonstrate how sensor-enhanced exercise can improve rehabilitation outcomes for patients with significant neurological impairment (e.g., from stroke, traumatic brain injury, and spinal cord injury). We provide an overview of big data analysis and explain how it may be used to optimize outpatient rehabilitation, creating a more efficient model of care. We describe our planned development efforts to build advanced analytic tools to guide home-based rehabilitation and our proposed randomized trial to evaluate effectiveness and implementation of this approach.