Jove
Visualize
Contact Us

Related Concept Videos

Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
Rules for Significant Figures01:44

Rules for Significant Figures

In any measurement, the precision of the measuring tool is an essential factor. An ordinary ruler, for example, can measure length to the closest millimeter; a caliper, on the other hand, can measure length to the nearest 0.01 mm. As a result, the caliper is a more precise measurement tool because it can measure extremely minute changes in length. The measurements will be more accurate if the measuring tool is more precise.
It should be emphasized that when we represent measured values, the...
Range00:59

Range

The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
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 Deviation01:08

Calculating Standard Deviation

The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
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 Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short distances...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Eating Together, Eating Alone: A Cross-Sectional Survey of Associations Between Social Eating Contexts, Mealtime Emotions, Technology Use, and Loneliness in UK University Students.

International journal of environmental research and public health·2026
Same author

Prevalence of mental disorders among young people living in urban slums of low- and middle- income countries: A systematic review and Meta-analysis.

Social psychiatry and psychiatric epidemiology·2026
Same author

Real-world comprehensive care of people living with schizophrenia: recommendations across different settings and clinical stages.

World psychiatry : official journal of the World Psychiatric Association (WPA)·2026
Same author

Temporal dynamics of mental defeat in chronic pain: a longitudinal network analysis of ecological momentary assessment data.

Pain·2026
Same author

Transitions from child to adult mental health care: the evidence-base for ESCAP guidance for clinicians.

European child & adolescent psychiatry·2026
Same author

The effect of managed transition on the proportion of young people transitioning from CAMHS to AMHS: Analysis of the Milestone Cluster Randomised Clinical Trial.

Psychological medicine·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 7, 2026

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
12:18

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

10.1K

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.

Communications Medicine
|August 19, 2025
PubMed
Summary

Digital phenotyping uses sensors to monitor behavior for mental health research. Standardization is crucial to overcome technical and user-experience challenges, improving data reliability and personalized care.

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K
A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

11.8K

Related Experiment Videos

Last Updated: May 7, 2026

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
12:18

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST

Published on: April 23, 2015

10.1K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K
A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

11.8K

Area of Science:

  • Digital phenotyping
  • Mental health research
  • Behavioral and physiological monitoring

Background:

  • Digital phenotyping offers real-time monitoring of behavioral and physiological markers.
  • It has significant potential for transforming mental health research and care.
  • Current technical and user-experience challenges limit its effectiveness.

Purpose of the Study:

  • To critically examine the challenges in digital phenotyping.
  • To propose standardization strategies for digital phenotyping.
  • To enhance the reliability, scalability, and application of digital phenotyping in mental health.

Main Methods:

  • Critical examination of existing technical and user-experience challenges.
  • Proposal of standardization strategies including universal protocols and cross-platform interoperability.
  • Emphasis on open-source APIs, universal frameworks, and industry-academia collaboration.

Main Results:

  • Identified technical and user-experience challenges hindering digital phenotyping effectiveness.
  • Proposed standardization strategies to address these challenges.
  • Highlighted the need for culturally sensitive and user-centered designs.

Conclusions:

  • Standardization is essential to maximize the potential of digital phenotyping.
  • Addressing challenges through universal frameworks and interoperability will enhance data reliability and scalability.
  • Improved digital phenotyping can significantly advance mental health research and clinical care.