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Developer | Sahha

Data Analysis


Table of contents
  1. Terms & Definitions
  2. How does Sahha work?
  3. Current Status
  4. Types of Analysis Sahha provides
    1. Binary Classification
    2. Binary classification use cases:
  5. Multi-class Classification (Coming soon)
    1. Multi-class classification use cases:

Terms & Definitions

  • Inferences: An array of inferences (analyses) in relation to a profile

  • Prediction State: The predicted condition state ie: depressed or not-depressed

  • Prediction Sub-state: The severity of the condition assessed ie: moderate.

  • Prediction Similarity: The similarity the prediction has to a condition ie: 0.71 means, 71% similarity with depressed or not-depressed conditions

  • Data source: The data types that have been used to predict the state ie: sleep time, age

  • Data source summary: A summary of data used in an inference such as sleep logs, phone screen time or steps


How does Sahha work?

Sahha works by analyzing step, sleep and phone lock data collected through the Sahha API using Sahha ML models that have been trained to predict depressed, or note depressed classes using such data. Our models have been trained on over 37,500 PHQ9 samples from over 3,500 participants. More information can be found here: Sahha Research.

The resulting “analysis” is JSON formatted and can be used in a variety of ways.

Our example apps showcase our technology, they can be downloaded from the Google Play Store and Apple App Store (Coming soon!)

Android App


Current Status

Model status will be regularly updated here: Developer Blog


Types of Analysis Sahha provides

Currently Sahha provides two levels of analysis, a “simple” version and a more data rich version. More detailed and immediately available analysis types will be available soon.

Binary Classification

A “simple” analysis providing a binary output of either depressed or not depressed as well as a probability score indicating the likelihood the analysis is correct.

Binary Classification output:

{
    "inferences": [
        {
            "createdAt": "2022-05-20T00:30:00+00:00",
            "predictionState": "depressed",
            "predictionSubState": "",
            "predictionRange": -1,
            "predictionSimilarity": 0.8,
            "dataSource": ["sleep", "screenTime"],
            "dataSourceSummary": [
                {
                    "type":"sleep",
                    "amount":"0"
                },
                {
                    "type":"screenTime",
                    "amount":"0"
                }
            ]
        }
    ]
}

Binary classification use cases:

  • Provide a simple indication of mental state of a user
  • Use to enrich data for better feature delivery

Multi-class Classification (Coming soon)

A more feature rich classification output providing more detail and sub-state analysis.

Multi class classification output:

{
    "inferences": [
        {
            "createdAt": "2022-05-20T00:30:00+00:00",
            "predictionState": "depressed",
            "predictionSubState": "moderate",
            "predictionRange": 17,
            "predictionSimilarity": 0.8,
            "dataSource": ["sleep", "screenTime"],
            "dataSourceSummary": [
                {
                    "type":"sleep",
                    "amount":"0"
                },
                {
                    "type":"screenTime",
                    "amount":"0"
                }
            ]
        }
    ]
}

Multi-class classification use cases:

  • Provide a detailed indication of mental state of a user
  • Use to enrich data for better feature delivery
  • Use to deliver specific interventions with more confidence