ArticleCureus2024
Aiding Clinical Decision-Making at the Individual and Community Level Using Mobile Sensor Data: Study Protocol for an Experimental Design.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Mobile sensor data can provide vital information about patients' behavior and physical activity. In particular, the mobile accelerometer sensor data, if monitored effectively, can be used further for keeping watch on the physical activity of an individual and planning an intervention to improve it. These sensor data can also be used for improving clinical decision-making in sedentary lifestyle-related diseases. However, despite the availability of smartphone devices to the majority of the population, the sensor statistics captured by those devices are underused because of the lack of awareness about sensor metadata and the absence of clear-cut guidelines for using them. Objective The objective of this study is to study and encourage the use of the accelerometer sensor metadata of the mobile device in facilitating the delivery of interventions to enhance physical activity. At the end of the study, it should be able to lay down the basic guidelines for using the accelerometer sensor data of the mobile device for monitoring patients' activity and aiding the clinical decision monitoring. Methods A sequential explanatory mixed-method research design will be used between July 2023 and December 2024. Firstly, individuals between the ages of 30 and 45 visiting the clinic of healthcare workers and having lifestyle-related diseases will be screened for a sedentary lifestyle with the pre-tested sedentary lifestyle measuring tool. After enrolling patients with a sedentary lifestyle in the study, physical activity-related accelerometer sensor data will be collected from them. Based on the initial data recorded, an individualized activity plan will be created. Periodic monitoring of physical activity levels will be conducted over a three-month period. Participants will receive a message from clinicians based on their current physical activity levels to encourage them to adhere to the prescribed regimen suggested by clinicians. The improvement in physical activity over the period will be monitored, and feedback from clinicians to improve the mechanism will be recorded. Results The proposed research will help in exploring the role of remote monitoring of sensor data in improving adherence to physical activity plans as prescribed by their clinicians. It will also help in studying the variables that influence the monitoring of remote data collection. Conclusions This study will help create a protocol to bring improvement in the physical activity levels of patients with a sedentary lifestyle and with confirmed hypertension and type 2 diabetes through remote capturing and monitoring of mobile sensor data, ongoing messaging, and facilitation through clinical decision-making.
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