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Related Concept Videos

Data Collection I01:30

Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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Data Collection III01:05

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Updated: Aug 26, 2025

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
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Enhancing mHealth data collection applications with sensing capabilities.

Maximilian Karthan1,2, Robin Martin3, Felix Holl1,4

  • 1DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany.

Frontiers in Public Health
|October 3, 2022
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Summary

Mobile devices enhance health data collection by integrating sensors. A new framework simplifies adding sensor capabilities to apps, aiding researchers in gathering richer participant and environmental data for deeper insights.

Keywords:
mHealthmobile data collectionsensorssmart mobile devicessoftware architecture (SA)

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Area of Science:

  • Digital Health
  • Mobile Health (mHealth)
  • Biomedical Informatics

Background:

  • Smart mobile devices are increasingly used for health data collection, replacing traditional methods.
  • Current mobile health applications often do not leverage built-in sensor technologies (e.g., GPS, microphone).
  • Integrating diverse sensor data into health studies presents significant development challenges due to platform variations and data formats.

Purpose of the Study:

  • To develop a cross-platform mobile data collection framework.
  • To enable the integration of sensor capabilities into existing data collection applications.
  • To reduce development effort and facilitate richer data acquisition for researchers.

Main Methods:

  • Development of a novel cross-platform framework for mobile data collection.
  • Extension of existing data collection applications with mobile sensing functionalities.
  • Addressing platform-specific challenges (Android vs. iOS) and proprietary sensor data formats.

Main Results:

  • A framework enabling seamless integration of sensor data into mobile health applications.
  • Facilitation of collection of additional participant and environmental data.
  • Potential for increased data volume and novel insights from health studies.

Conclusions:

  • The developed framework simplifies the incorporation of mobile sensing capabilities into health research.
  • It overcomes technical hurdles, enabling researchers to gather more comprehensive data.
  • This advancement supports deeper data analysis and discovery in complex health scenarios.