Wearable Devices and the Human Factors of Behavior Change

Over the past year, many companies, coaches and clinicians have contacted me at SyncStrength for help in making sense of new wearable technologies. The conversations focus mostly on activity trackers, typically beginning with “Which device is best for my (enter one of the following) athlete, team, aging parent, significant other, patient?”  With a vast experience in health data analytics as a behavioral scientist and product manager, I am uniquely positioned to answer these questions and navigate the growing trend of health and fitness devices.

Wearable Devices, Apple Store Wall, Boston

Wearable Devices Wall at Apple Store – Choice Paralysis?

With dozens of devices to choose from (see picture above) which are the most likely to help users reach their health goals?  In essence, this is really a question about which product will best integrate into daily life, influence current habits and create a lasting change in behavior. Through my experiences as a former college athlete, in clinical psychology and neuroscience, and professional work with iBELIEVE and SyncStrength, I have developed a deep understanding of the numerous mechanisms at work that ultimately create sustainable behavior change. But these wearable devices have opened a new frontier for behavior change including goal setting, feedback, social dynamics, and others. We put wearable’s on our bodies to make ourselves better, ushering in a new era for the potential for technology to enhance behavior change.

Over the next few weeks I will be evaluating which of the popular devices (listed below) has the most potential for long-term engagement. This is the first activity tracker review to focus exclusively on long-term product engagement using an understanding of the human factors of behavior change. I have chosen this particular lens of research, for I argue that success is most greatly defined by the degree to which these devices and services make a long-term impact on their users’ health and happiness. For mass adoption of these devices to occur, they will need more then just lifestyle integration, affordability and relevancy.

Over several blog posts, the key questions I will explore are:

  • Which devices design becomes invisible to my lifestyle after one week?
  • Which device promotes long-term, lasting sustainable behavior change?
  • Which device surprises me with additional features and services?
  • Which device is the most accurate in its collection of activity data?
  • Why are these devices failing to achieve long-term utilization?

I’ve teamed up with Endeavor Partners and Barry’s Bootcamp to help me in this process. Endeavor Partners is a consulting boutique based in Cambridge, MA with deep expertise in mobile, digital business and technologies. I’ve co-authored a report, “How the Science of Human Behavior Change Offers the Secret to Long-Term Engagement,” with Endeavor Partners, please email me for more info. Barry’s Bootcamp is a boutique fitness studio that recently opened in downtown Boston. After attending the majority of Boston’s fitness centers over the past six years, Barry’s Bootcamp is far and away the best combination of intense interval cardiovascular training and strength training within a structured one-hour class session. This type of class structure is the ideal training type to test the durability, utility, experience and accuracy of these devices.

Devices to be tested:

Nike FuelBandSE (second edition)

Fitbit Force

Jawbone UP24

Skechers Go Walk

Under Armour 39 + HR Monitor

Withings Pulse

Nike FuelBand

MyBasis B1

Polar Loop + HR Monitor

Pear Fitness Tracker

Comparison Criteria:

1. Selectability

2. Design

3. Out of the Box Experience

4. Fit/Comfort

5. Durability

6. User Experience

7. API

8. Lifestyle Compatibility

9. Overall Utility

10. Habit Formation

11. Social Motivation

12. Goal Reinforcement

(More detail regarding comparison criteria: visit here)

14 Responses to Wearable Devices and the Human Factors of Behavior Change

  1. Maneesh Sethi January 8, 2014 at 9:25 PM

    This is great! I can’t wait to see what results and data you gather!

  2. Joanne Rohde January 14, 2014 at 9:43 AM

    Can’t wait. Guidance is needed

  3. Wayne Caswell January 14, 2014 at 10:22 AM

    I’m very interested in the results of your research, and if you’ve not yet started, I ask that you also consider collecting the following information.

    What types of sensors are used (motion, altitude, temperature, moisture, etc.) and what specifically is sensed: steps, stairs, pedals, pushups, sleep, pulse, heart rate variability, etc.

    How the device is worn: wrist, earphone, jewelry, attached, carried (including smartphone).

    What is required to collect, display and analyze the data (smartphone (iOS, Android), PC, cloud service) and how easy and natural is it to use.

    • daniel January 15, 2014 at 8:33 AM

      Thanks Wayne. I will try to include as much of this as possible.

  4. Liam Ryan January 14, 2014 at 8:51 PM

    Great post Daniel! This TED talk might be of interest: http://www.youtube.com/watch?v=lleX1tIEG-M&t=7.2s “Changing Behaviour – We Are More Than Rational Robots!”

    • daniel January 15, 2014 at 8:33 AM

      Thanks Liam. I will be sure to check out your TED talk.

  5. Rex Stock January 16, 2014 at 1:41 PM

    I hope you’re able to do this in some sort of Quantified Self format…

    • daniel January 16, 2014 at 2:46 PM

      Hi Rex. What do you mean, “Quantified Self format”?

  6. Mary Beth Schoening January 16, 2014 at 2:43 PM

    Great project. I love your ranking criteria and your approach. Can’t wait to see the results.

    • daniel January 16, 2014 at 2:46 PM

      Thanks Mary Beth

  7. Mark Spohr January 17, 2014 at 12:49 PM

    This looks like a very interesting project and I look forward to your future reporting.
    I would like to ask you to consider the data that is produced by each device and the factors which will allow the user access and control of that data.
    Unfortunately, many of these devices send their data to a walled garden with limited accessibility. Users often have a hard time in getting access to all of their data. In addition, the data is usually in an odd format which makes it difficult to use for long term analysis.
    These devices produce valuable data and that is the point of using them. It is shortsighted to limit users access to the data.

  8. Rex Stock January 19, 2014 at 10:07 PM

    Daniel, Quantified Self: http://quantifiedself.com/

    Can’t improve without measuring… QS folks take that mantra to the extreme. Then they repeat it…

  9. Roel Smolders January 20, 2014 at 3:56 AM

    Hello Daniel,

    Would it be possible to tell me how to get access to the publication “How the Science of Human Behavior Change Offers the Secret to Long-Term Engagement”?

    And also, please keep me posted on the outcome of the experiment. I’ very eager to find out the results…

    Kind regards,



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