Researchers Guide to Wearables

Apps for Ecological Momentary Assessment are rapidly changing the manner in which psychology and behavioral health research is conducted. Close behind this wave of development is the use of wearable technologies in the same fields. This is a guide for researchers wishing to augment self-report EMA data with objective data collected from wearable devices and engage in some form of ambulatory assessment.

There are five main health metrics that can be reliably captured by current wearable devices, all of which can be integrated with mobile EMA (mEMA) measures: Activity, Blood pressure, Heart Rate, Sleep, Skin Conductance and EEG (almost!).

There are three problems you will encounter with commercial wearables regardless of which metric you are attempting to capture:

  1. No raw data: you will find it next to impossible to get raw data from many commercial companies, you will instead get data processed through their algorithms (e.g. “steps”, “calories burnt” or “hours of sleep”) without gaining access to the actual measures that went into these calculations and certainly not to the algorithms themselves. The accuracy of many of these processed metric is questionable and devices can vary widely on metrics with the same label.
  2. Coarse temporal granularity of the data: most companies will only give you access to the summary data per day so you may know how many steps your participant took that day but have no idea how these “steps” were distributed throughout the day so you can not synchronize these data with the self-report mEMA data they are simultaneously providing.
  3. No aggregated data sets: again the revenue model of the companies providing these devices gives them no incentive to provide an API (an expensive undertaking) that would allow you to capture aggregated data sets from a group of participants. If you are providing devices to your participants and can wait until you collect them all back to access the data then no problem. If you are hoping for real-time insight into participants activity or want to use these data to trigger mobile EMA (mEMA) surveys then it’s not going to work.
Activity tracking

Physical activity can be tracked both by accelerometers already in the Smartphone or with a wearable activity / fitness tracker. One study suggests that phone apps measuring “steps” are more accurate than wearables. For many studies the data captured directly from the phone is sufficient, the obvious drawback is that it requires making the assumption that the phone is being carried by the participant. We can get enough fine grained data from the 3-axis accelerometers in the phones to make a fairly accurate assumption about when the phone is actually been worn on the body (i.e. in a pocket) as opposed to laying on a table but cannot determine if it is in a bag sitting next to a person, or if that person has walked away from the bag!

Thus, many researchers prefer to use a wearable activity tracker to capture physical movement. An ever growing plethora of commercial activity / fitness trackers are on the market and the choice for researchers can seem overwhelming at first. However, rest assured most of them are not going to give you the data you want. Companies providing commercial fitness devices are interested in selling devices to consumers and processing their data –  not aiding research efforts.

Over the years of being involved in ambulatory assessment and mobile EMA (mEMA) we have found some solutions to these problems, and are aware that these solutions keep changing as new devices become available.

If synchronization between the activity and the EMA data are not necessary then you have aActiGraphsome options. The ActiGraph ( will give you fine-grained (30 – 100Hz), high-quality, raw, timestamped activity data. They have a researcher dashboard from were you can administer your study, assign subject ID#s to participants and download the aggregated dataset. You can then compare these data post hoc with those from the mEMA system, which are also timestamped (to the millisecond). Actigraph currently do not have an API or SDK that will allow for real-time integration with other apps such as mEMA.

If this post hoc method of data integration will work for your study and you don’t need activity readings multiple times a second then another option is the FitBit. The regular FitBit fitbit-charge-and-surgeAPI only gives you daily summary data but through a company called FitaBase ( you can get minute-to-minute granularity on your aggregated data set. You are still left with FitBit’s processed data (e.g. “steps”) not the raw accelerometer data. But if this is sufficient for your project then this data set could be compared with your mEMA data by the timestamps.

If you have some tech savvy people on your team then the Basis Band MyBasis( will let you capture aggregated raw data (except for skin conductance which is really the reason to use the Basis Band). However, Basis don’t have their own API, instead you have to do some fancy footwork via Google Fit to access the data. But the whole thing relies on the relationship between Basis and Google being maintained (and in this climate who knows), it also means your data collection will only run on Android (not Apple).

ms_band_detailsIf you want real-time integration, high quality, raw data and the ability to trigger real-time mobile surveys (mEMA) based on your participants’ individual levels of activity then the best current solution is the Microsoft band ( Microsoft has an SDK which allows us to communicate directly (via Bluetooth) from both Android and Apple phones. So we are building an app that receives raw data directly from the MS Band, sends to it our server and does on-phone computation to communicate with the mEMA app allowing you to not only receive all the raw data but also to trigger mEMA surveys based on activity levels (and other metrics).

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