Related Experiment Video
Updated: May 7, 2026

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
Published on: April 23, 2015
Challenges and standardisation strategies for sensor-based data collection for digital phenotyping
Nadia Binte Alam1,2, Mohsin Surani3, Chayon Kumar Das4
1Warwick Medical School, University of Warwick, Coventry, England, UK. Nadia.Alam@warwick.ac.uk.
Abstract:
Sensor-based data collection of human behaviour (digital phenotyping) enables real-time monitoring of behavioural and physiological markers. This emerging approach offers immense potential to transform mental health research and care by identifying early signs of symptom exacerbation, supporting personalised interventions, and enhancing our understanding of daily lived experiences. However, despite its promise, technical and user-experience challenges limit its effectiveness. This Perspective critically examines these challenges and provides standardisation strategies, including universal protocols and cross-platform interoperability. We propose the development of universal frameworks, adoption of open-source APIs, enhanced cross-platform interoperability, and greater collaboration between academic researchers and industry stakeholders. We also highlight the need for culturally sensitive and user-centred designs to improve equity and engagement. By addressing these gaps, standardisation can enhance data reliability, promote scalability and maximise the potential of digital phenotyping in clinical and research mental health settings.
More Related Videos
11:21Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
15:30A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
Published on: August 5, 2020
Related Concept Videos
Uncertainty in Measurement: Reading Instruments
Rules for Significant Figures
It should be emphasized that when we represent measured values, the...
Range
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
Calculating Standard Deviation
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high variation.
Let us...
Data Validation
Key parameters for method validation include:
Electronic Distance Measuring Instruments