An Application for Pairing with Wearable Devices to Monitor Personal Health Status
February 3rd, 2022
The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.
This protocol facilitates the integration of a self-developed application and wearable devices to measure multiple dimensional states, including psychological, physiological, and environmental data. This is a proven technology to assess how landscape impacts human health and wellbeing. Flexible tasks and functions are provided in the smartphone and smartwatch application to measure different types of human states and reactions.
Now, thousands of people can be engaged in research, placing very little burden on them while producing a rich trove of data for landscape and urban-planning scholars. This method can be adopted in outdoor recreation and human health research. It can also be used in other fields that investigate relationships between human health and the environment, showing the flexibility of the application.
Users may struggle due to being unfamiliar with the app and devices. This can be solved by going through the protocol presented in this article. This protocol requires using several tools including webpages, smartphones, and smartwatches.
Therefore, visual demonstrations can help users and researchers get started quickly. To begin, log into the HealthCloud app backend website. Go to Admin management in the app database and add a new field name at the Set Field to mark the data collected during the experiment.
Then go to Configuration and click on the Select file button to upload the questionnaires. Set the time interval by choosing the number of minutes in the period of pop quiz. Download the HealthCloud app from the app store.
Ensure the tools are ready for use. Have good battery life, GPS functionality, and a stable internet connection on the site. Then log into the app on the phone by creating an account.
Ensure all realtime information, including heart rate, location, elevation, weather, the distance that the user has moved since starting, and duration since the app started are successfully collected on the app's main page on the smartphone. Press Set Measurements on the Settings page. Then select the Field Name and identify the participants according to their test number.
Before the walk, give the participants the following instructions. Verbally instruct the participants on how to use both the smartwatch and smartphone for proper assessment. Ask them to press start on the smartwatch to collect physiological and environmental information.
Once the tasks are received, the participants must follow the instructions on the smartwatch. For the pop quiz task, ask the participants to rate the statements using one to five stars to answer the psychological scale items and press send to complete this step. Next, the participants will receive a photo taking task on their smartwatch.
Instruct the participants to open the HealthCloud app on the phone and follow the instructions for the photo-taking task. The participants will also receive a Stroop Test followed by a voice task on their smartwatch. Ask the participants to follow the instructions on the phone to complete the Stroop Test and then the voice task.
For the HRV task, instruct the participants to open the breath app and click on start. The HRV data will be uploaded. Finally, for the SPO2 task, ask the participants to open the Blood Oxygen app on the smartwatch and click on Start.
The result will be uploaded. After the walk, collect the devices from the participants and hit Quit Calculate to end the experiment. Then log into the backend website.
Extract the research data from the data sheet by filtering the time, field, and tester ID.Press Export CSV to download the dataset and perform descriptive statistical analysis using statistical software. A dataset of 385 students from National Taiwan University were recruited for this study. The participants were between the ages of 20 and 36.
Regarding their psychological states, 514 ratings of preference, 548 ratings of being away, and 523 ratings of fascination were collected. For the physiological responses, that is, heart rate, 14, 253 data points were collected. The following environmental information was collected, 14, 253 data points related to GPS latitude and longitude, 14, 253 related to temperature, 14, 253 related to relative humidity, 14, 253 related to wind speed, and 12, 232 related to wind degree.
With these data, variables from different dimensions can be statistically analyzed to verify the relationships among them. Consider whether the activities intended to be included are suitable in the experiment since their intensity may affect the accuracy of the measurement. It is recommended that the researchers follow the protocol instructions to guide the participants in the experiment and to reduce the novelty effect of the devices and the application.
This method can be used to generate and collect additional information. For example, photographs, sound decibels, or GPS location can be used to analyze the quality of the physical environment. The main purpose of this method is to provide an efficient way to measure real-time psychological responses, physiological responses, and ambient environmental information.
Only a few previous studies have managed to provide frequent sectional data points. Therefore, the relationships among the dimensions can be discussed in greater detail.
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Overview
This protocol facilitates the integration of a self-developed application and wearable devices to measure multiple dimensional states, including psychological, physiological, and environmental data. It demonstrates a proven technology to assess how landscape impacts human health and wellbeing.
Key Study Components
Area of Science
- Neuroscience
- Psychology
- Environmental Health
Background
- The application collects real-time data on psychological scales, GPS location, heart rate, and blood-oxygen saturation.
- It was tested in an empirical study conducted in Taiwan in 2020.
- The method allows for minimal burden on participants while gathering extensive data.
- It can be applied in various fields, including outdoor recreation and human health research.
Purpose of Study
- To assess the impact of landscape on human health and wellbeing.
- To engage thousands of participants in research.
- To provide a flexible tool for researchers in related fields.
Methods Used
- Integration of a smartphone and smartwatch application.
- Measurement of psychological, physiological, and environmental data.
- Use of wearable devices for real-time data collection.
- Empirical study as a demonstration of the application.
Main Results
- Successful collection of diverse data types from participants.
- Demonstrated the flexibility of the application for various research fields.
- Engaged a large number of participants with minimal burden.
- Provided insights into the relationship between landscape and health.
Conclusions
- The application is a valuable tool for researchers studying human health and the environment.
- It can be adapted for various research contexts.
- Future studies can leverage this technology for broader applications.
Frequently Asked Questions
Executive Industry Relevance
Integrating wearable devices with mobile applications enables scalable, real-time collection of multidimensional human health data in naturalistic settings. This approach enhances predictive confidence in linking environmental exposures to physiological and psychological responses, supporting early discovery and mechanistic de-risking in biopharma R&D. The protocol's flexibility and participant scalability position it as a reusable capability for portfolio-wide human-environment interaction studies.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables hypothesis-driven interrogation of environmental impacts on human physiological and psychological states.
- Supports biological de-risking by capturing real-world, multidimensional health data.
- Facilitates functional target validation through synchronized measurement of environmental and human response variables.
Screening & Assay Development
- Prepares validated, real-time data streams for downstream digital biomarker development.
- Standardizes data collection across large participant cohorts, improving reproducibility.
- Generates quantitative outputs suitable for scalable screening of environmental or behavioral interventions.
Translational & Preclinical Research
- Aligns real-world physiological and psychological data with disease-relevant environmental exposures.
- Enables continuity from discovery through preclinical validation by supporting longitudinal, in situ data capture.
- Provides predictive de-risking for interventions targeting human-environment interactions.
Pipeline & Workflow Integration
This protocol bridges early discovery and translational research by enabling continuous, real-world data capture from wearable devices and mobile applications.
- Discovery Biology: Supports hypothesis testing and pathway clarification by correlating environmental metrics with human health responses.
- Screening: Delivers reproducible, quantitative data streams for digital biomarker and intervention screening.
- Analytics: Provides high-frequency, multidimensional datasets for robust statistical analysis and condition comparison.
- Translational Research: Facilitates alignment of environmental exposures with preclinical and clinical endpoints.
- Enterprise Reuse: Offers a scalable, adaptable platform for diverse human-environment studies across the R&D portfolio.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in human-environment health studies.
- Operational Value: Standardizes and scales real-time data collection with minimal participant burden.
- Strategic Value: Enables data-driven go/no-go decisions and capital-efficient study designs.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of environment-linked health interventions.
Implementation Considerations
- Requires expertise in wearable device integration and mobile app deployment.
- Needs robust data infrastructure for real-time collection and backend management.
- Demands cross-team standardization of protocols and participant instructions.
- Must consider adaptation for different environmental contexts and participant populations.
- Measurement accuracy may be affected by activity intensity and user familiarity with devices.
Biopharma FAQs
Chapters in this video
0:05
Introduction
1:32
Preparing the Psychological and Physiological Experiment
2:02
Preparing the Wearable Devices and the App
4:11
Data Collection and Data Analysis
4:40
Results: Descriptive Statistics for the Psychological, Physiological, and Environmental Data
5:47
Conclusion
