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Each record comprises 4 main tabs: medical information, statistics, questionnaires, and follow-up. The medical information tab allows the study leader and the clinician to view study-specific patient parameters or comorbidities. Study parameters include the study-specific information needed to interpret the vital parameters.
The statistics tab displays a graphical overview of each vital parameter. The questions tab provides an overview of the questionnaires that were sent to and completed by the patient. The follow-up tab allows caregivers to send text messages among multiple disciplines. Each patient contact is logged in this tab. The remote monitoring study platform was built between February and July Table 1 provides an overview of the main studies in which DHARMA was tested, together with the numbers of patients and the vital parameters.
Each vital parameter displays the number of individual measurements that were uploaded during the study.
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Briefly, this prospective cohort study enrolled pregnant women at high risk of developing pre-eclampsia. The participating women were asked to measure their blood pressure twice daily, their weight once daily, and wear an activity tracker for 24 hours per day.
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All of the information recorded by the devices was sent wirelessly to the smartphone, which then transmitted the data to the Web-based platform for aggregation. The sensors collected up to 12 different signals as functions of time, enabling multiparametric longitudinal research. A midwife reviewed all incoming remote monitoring data via the dashboard. Alarm events were sent to the obstetrician to discuss possible interventions. If the data triggered a specific alert, the dashboard prioritized the alerts based on the predefined thresholds and displayed them to the person responsible for reviewing the data.
This enables the platform to triage patient alerts and facilitate patient handling and follow-up. Figure 2 shows an overview of the dashboard. Screenshot of the alert representation upon login of the dashboard. The received parameters are individually plotted as functions of time to identify specific trends that could trigger an alert by crossing specified thresholds. For the patient shown in Figure 3 , systolic blood pressure showed a trend toward crossing the predefined thresholds mmHg for systolic blood pressure and 90 mmHg for diastolic blood pressure , triggering a high-risk alert.
On the basis of these results, the patient was admitted to hospital where early symptoms of pre-eclampsia were identified, and appropriate treatment was started.
The challenges of working with different types of data include how to handle, analyze, and store the data appropriately and transparently. In the normal workflow, only the summarized values of larger datasets are used because granular detail is not required for daily patient management. However, if required, the data are available for Web-based data processing or can be exported for offline scientific research, such as the development of novel algorithms or data-processing techniques.
Figure 4 shows the average step count over a period of 12 weeks. Granular, minute-by-minute data are also shown for a single day in Figure 5. This paper outlines the development of a digital health research platform for remote monitoring. By combining advanced wearable sensors with smartphone technologies for remote monitoring, it is possible to monitor the health of patients in their home environment, an approach that may reduce the number of health care visits.
Remote monitoring requires multiple hardware and infrastructure tools.
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Each vendor provides dedicated infrastructure and data review platforms specific to their own devices. Accordingly, data aggregation is impossible when collating data from medical devices and tools from different manufacturers, creating a barrier to clinical practice and academic research. This fragmentation is also very inconvenient for the user. Therefore, a one size fits all solution ie, one platform for all devices is highly desirable [ 23 ]. This will improve efficiencies in cost and time. In addition, the platform was designed to enable rapid and cost-effective scalability.
Privacy is a fundamental right in the public health care domain, especially following the recently implemented GDPRs. Health care practitioners and patients are becoming increasingly aware of this important aspect. Confidential handling and storage of private patient data have also become a critical aspect of study design. Therefore, all personal data in our platform are deidentified and every unique identification number, characteristic, or name is removed.
Moreover, all participants need to provide signed written informed consent.
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The development of a centralized visualization platform has been described in earlier reports, for example, for monitoring arrhythmias [ 24 ], nonmotor symptoms of Parkinson disease [ 25 ], and pressure ulcers [ 26 ]. However, most of these studies monitored a specific disease, and thus the platforms have limited applicability to studies of other diseases.
Zens et al [ 18 ] developed a modular smartphone app that could be used in different medical studies without the need for advanced programming skills. Another example of an open-source framework is ResearchKit Apple Inc. However, the initial studies revealed that technical programming skills, such as Object C or Swift, are needed to develop a functional app [ 27 - 29 ]. Another limitation is that ResearchKit only supports iOS devices.
Other platforms, including ResearchStack and ResearchDroid, have been developed for use in research projects. ResearchStack is a functional software development kit with a framework comparable to that of ResearchKit for developing research apps for Android devices [ 30 ]. ResearchDroid is an Android library developed to automate survey forms and the information-building process [ 31 ].
Appbakery integrates ResearchKit and ResearchDroid, enabling researchers to create apps without requiring programming skills [ 32 ]. Patient data can be exported to a comma-separated values file or viewed in the Google Cloud platform [ 33 ]. As examples of PC-based software, PsychoPy [ 34 ] and Labview National Instruments enable users to create individual software solutions with a graphical user interface in a process that does not require programming skills. The platform most similar to DHARMA was developed by Validic and can provide continuous access to personal health data obtained by over in-home medical devices and wearables.
Companies, such as Philips and IBM, also provide health platforms for remote monitoring. Although these commercially available platforms could have worked for the Premom research project, there were 3 main reasons why we chose to develop our own platform. First, because of the limited budget and the need for a vendor-independent research platform, we created our own solution that had the minimum number of components and required minimum development. Second, the platform needed to be flexible and customizable for use with new study invalidated thresholds.
Third, we used integrated components and functions that could differ from the development roadmap for commercial platforms. Although DHARMA provides exciting opportunities to improve remote monitoring services, it is not free from limitations. This approach could be improved by creating a third-party app that connects directly to the medical devices; however, not all vendors allow direct access to their medical device via an open API.
This process would bypass data transfer to an external company. A second limitation is the applicability of remote monitoring studies among technophobic individuals and people with limited cognition or ability to express consent, such as neonates, elderly, and sedated patients in an intensive care unit [ 35 ]. Related to this limitation, some people may not comply with manual entry of daily information, especially in long-term monitoring settings [ 36 ]. Automated invisible wearables, such as smartwatches or smart clothing, could help address noncompliance issues. Finally, it is difficult to keep pace with the rapidly evolving smartphone and wearable sensor technologies.
Our platform was initially developed using PHP version 5. An advantage of DHARMA is its flexible architecture that enables rapid integration of new smartphone and wearable sensor technologies as they become available. Currently, participants in DHARMA remote monitoring studies need to provide written informed consent to be enrolled in each study, as previously reported by Eysenbach et al [ 37 ].
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Zens et al [ 18 ] described an alternative approach that uses an eligibility module to check the inclusion criteria and an integrated electronic informed consent component to obtain consent via a customized app. Another step to improve the platform will involve embracing the definitions of standard information models and information technology communication standards, such as Health Level 7 fast healthcare interoperability resources, together with clinical terminologies, such as systematized nomenclature of medicine—clinical terms, to ensure interoperability with hospital electronic medical record EMR systems [ 38 ].
Smartphone health apps and medical devices collect large amounts of vendor-specific data. There are currently very few tools to collate and handle the data generated by multiple medical devices.
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We developed a component-based digital research platform to integrate the data in different formats from different sensors into a single integrated system. The platform performed well in a health care setting in real-time circumstances for the follow-up of pregnant women at risk of developing pre-eclampsia. The next stage in its development will involve integrating the platform with existing EMR systems to create a closed-loop information system.
Scientists or companies willing to contribute to this study are welcome to contact the authors. Read article at publisher's site DOI : To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation. Cited by: 36 articles PMID: J Med Internet Res , 21 8 :e, 19 Aug Coronavirus: Find the latest articles and preprints. Europe PMC requires Javascript to function effectively. Recent Activity.
Recent history Saved searches. Vandenberk T 1 ,. Storms V 1 ,. Lanssens D 1 ,. Smeets CJ 1 ,. Thijs IM 1 ,. Batool T 2 ,. Vanrompay Y 2 ,. Vandervoort PM 1 ,. Grieten L 1. Affiliations 8 authors 1. Share this article Share with email Share with twitter Share with linkedin Share with facebook. OBJECTIVE:The aim of this study was to develop and implement the digital health research platform for mobile health DHARMA that combines data saved in different formats from a variety of sources into a single integrated digital platform suitable for mobile remote monitoring studies.
The patients' blood pressure, weight, and activity were semi-automatically captured at home using different devices.