Overview

In PHA ProHealth Analytics, datasources, metrics, and user groups are fundamental components that work together to provide insightful reports and analyses. This guide will help you understand each component and how they interact, using practical examples.

Datasources

A datasource is a collection of data retrieved from various assessments and user profiles. On its own, a datasource is not very useful but becomes powerful when used by user groups or metrics.

Key Points:

• Purpose: Gather and filter data based on specific criteria.
• Usage: Can be configured with procedures and connectors to refine the data.
• Examples:
  • User Group Example: A datasource might collect users who have completed a fitness assessment within a specific timeframe.
  • Metric Example: A datasource could retrieve answers from assessments where users reported a "pass" status.

Metrics

Metrics are used to analyze data based on the answers in the assessments. They provide insights into trends, performance, and other key indicators.

Key Points:

• Purpose: Analyze and report on specific data points.
• Usage: Metrics focus on the answered forms and return lists of answers meeting the criteria.
• Examples:
  • A metric can analyze the average age of users passing a fitness assessment.
  • Another metric might show the distribution of users' scores in a specific assessment.

User Groups

User groups are collections of users that meet certain criteria defined by datasources. They are essential for segmenting users based on specific attributes or assessment results.

Key Points:

• Purpose: Segment users for targeted analysis.
• Usage: Focuses on users and returns a list of users meeting the criteria.
• Examples:
  • A user group could include users who have completed a health assessment and reported smoking.
  • Another user group might consist of users who failed a fitness test within the last year.

Practical Examples

Example 1: Fitness Assessment Passes

1. Datasource: Collect assessments that resulted in a "pass" using procedures for assessment_code and pass_fail.
2. Metric: Analyze the average vo2max result of the passed assessments.
3. User Group: Show those who passed the fitness assessment.

Example 2: Users with Specific Health Attributes

1. Datasource: Retrieve user profiles with attributes such as "smoker" or "non-smoker".
2. Metric: Report on the average cholesterol among smokers.
3. User Group: Show those users who have answered the question about smoking.