Last updated on Mar 13, 2024

What are the best practices for collecting and analyzing data from wearables?

Wearables are devices that can measure and track various aspects of human health, behavior, and activity, such as heart rate, sleep, steps, stress, and location. They can provide valuable insights for market research, but also pose some challenges in terms of data quality, privacy, and analysis. In this article, you will learn some of the best practices for collecting and analyzing data from wearables, and how to use them to gain a deeper understanding of your target audience.

1 Define your research objectives

Before you start collecting data from wearables, you need to have a clear idea of what you want to achieve with your research. What are the main questions you want to answer? How will you use the data to inform your decisions? What are the key metrics and indicators you want to measure? Having a well-defined research objective will help you choose the most suitable wearables, design the data collection process, and select the appropriate analytical methods.

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Noureldin Metwally

GP registrar interested in Digital health and Wellbeing
One project I think would be helpful would be in the realm of holistic medicine. I am thinking of guided wellbeing and guided stress reduction with a feedback mechanism. The question I want answered is whether my meditation/exercise helping me promote my well-being or not. Data to be used would be brain waves, HRV, sleep data, and breathing data. Personalised correlation to be built between activity and different metrics.
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2 Choose the right wearables

There are many types of wearables available in the market, such as smartwatches, fitness trackers, smart glasses, and wearable cameras. Each one has its own advantages and limitations, depending on the data you want to collect. For example, if you want to measure physical activity, you might opt for a device that can track steps, calories, and distance. If you want to measure emotional responses, you might prefer a device that can monitor heart rate variability, skin conductance, and facial expressions. You should also consider the usability, reliability, and compatibility of the wearables, and how they fit with your target audience's preferences and habits.

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Aashil Rahman Vallanchira

Co-Founder & Chief of Technology at Infoline Technologies | ResTech Innovator | Gamified Surveys Expert | Custom Software Development | Market research
When choosing the device it’s important to make sure all the markers we’re looking for is accessible easily . Since manufacturers typically ship devices with their own SDKs and API’s. Its important to make sure the tech is in place for data collection purposes. This typically involves the user downloading a mobile app that can track data from the device and upload it to the cloud, where researchers can access the data for analysis.
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Noureldin Metwally

GP registrar interested in Digital health and Wellbeing
I believe that when answering a question such as wellbeing that involves many organs beyond the brain, a single wearable will not be enough to answer the question. I would suggest an ecosystem of syncing wearables that provide realtime data and closed-loop feedback.
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3 Ensure data quality and privacy

One of the biggest challenges of collecting data from wearables is ensuring its quality and privacy. Wearables can generate large amounts of data, but not all of it is accurate, relevant, or useful. You need to have a robust data quality management system that can filter out noise, errors, and outliers, and validate and verify the data. You also need to comply with the ethical and legal standards of data protection and privacy, and obtain the informed consent of your participants. You should explain to them how you will collect, store, use, and share their data, and what are the risks and benefits of participating in your research.

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Noureldin Metwally

GP registrar interested in Digital health and Wellbeing
Actually, cryptography has made leaps forward in terms of securing data and now quantum cryptography is available to big companies who can afford it. LLM should be trained and retrained to be more effective in filtering out noise and errors. Data should be anonymised when stored and encrypted. The benefits of such research would be huge in terms of improving human wellbeing. the downside would be data breaches which can happen to anyone wearing wearable devices but there should be measures to mitigate the risk.
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4 Analyze the data with the right tools and techniques

Another challenge of collecting data from wearables is analyzing it in a meaningful and actionable way. Wearables can produce complex and multidimensional data, that requires advanced tools and techniques to process, visualize, and interpret. You need to have a clear analytical framework that aligns with your research objectives, and use the appropriate statistical methods, algorithms, and software to analyze the data. You should also look for patterns, trends, and correlations in the data, and compare it with other sources of information, such as surveys, interviews, or observations.

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5 Communicate the insights effectively

The final step of collecting and analyzing data from wearables is communicating the insights effectively to your stakeholders. You need to present the data in a clear, concise, and compelling way, that highlights the main findings, implications, and recommendations. You should use visual aids, such as graphs, charts, and dashboards, to illustrate the data, and tell a story that engages and persuades your audience. You should also provide the context, limitations, and assumptions of your analysis, and invite feedback and questions from your audience.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Aashil Rahman Vallanchira

Co-Founder & Chief of Technology at Infoline Technologies | ResTech Innovator | Gamified Surveys Expert | Custom Software Development | Market research
Data collection from wearables is especially useful for Pharma research and Lifestyle research. It involves periodically collecting data on specific markers that are relevant for the research study. A few a great examples I’ve seen include: - Making the user take a cognitive puzzle after exercises as part of brain function research - Measuring recovery heart rate - Monitoring exercise activity and stress levels
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What are the best practices for collecting and analyzing data from wearables?

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1 Define your research objectives

2 Choose the right wearables

3 Ensure data quality and privacy

4 Analyze the data with the right tools and techniques

5 Communicate the insights effectively

6 Here’s what else to consider

Market Research

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What are the best practices for collecting and analyzing data from wearables?

1

2

3

4

5

6

1 Define your research objectives

2 Choose the right wearables

3 Ensure data quality and privacy

4 Analyze the data with the right tools and techniques

5 Communicate the insights effectively

6 Here’s what else to consider

Market Research

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