McDaniel, B. T. (2019). Passive sensing of mobile media use in children and families: A brief
commentary on the promises and pitfalls. Pediatric Research. doi: 10.1038/s41390-019-0483-8
Passive Sensing of Mobile Media Use in Children and Families:
A Brief Commentary on the Promises and Pitfalls
Brandon T. McDaniel
Parkview Research Center
Corresponding Author Information:
Brandon T. McDaniel, Ph.D., Parkview Research Center, Parkview Health, 10622 Parkview
Plaza Dr., Fort Wayne, IN 46845. Email: firstname.lastname@example.org. Phone: 260-266-5552.
Disclosures: No conflicts of interest to declare
Funding Support Statement: No support to declare
Passive Sensing of Mobile Media Use in Children and Families:
A Brief Commentary on the Promises and Pitfalls
In my work with families, screen use and the management of screen use is one of the
most frequent concerns I hear from parents and families. Additionally, for years I have fought to
help parents realize that their focus should not only be on their children’s screen use but also on
how their own use while around their children impacts their parenting and their children (e.g., 1,
2, 3). Many parents and families feel as if they are drowning in a flood of media use and devices
while others are unaware of the impact of their use. Anecdotally, many parents tell me they feel
they use their phone too much. Furthermore, in my research, I often utilize self-reports, which
for many years I have suspected result in the underreporting of use and technology distractions.
In fact, much of the research on media use relies on self-reports. Thus, there has been a need for
something better, something closer to the reality of individuals’ and families’ media use, and
passive sensing of device and media use—like was done by Yuan, Weeks, Ball, Newman,
Chang, and Radesky (4)—is a promising direction.
In line with my intuition, Yuan et al. (4) found that most parents are not accurate
reporters of their phone use. Not only could this prove to be a problem for research but also for
prevention and interventions. For instance, it could mean that many of the findings that rely on
self-reports of media use could be suspect, with effects being stronger (or weaker) than they
currently appear in the published research. Additionally, if individuals are asked to report on
their phone or other media use in therapy, medical, or other clinical settings, their answers may
unintentionally misrepresent what is occurring in their life, potentially leading to interventions
that are not well-fit to the individual’s or family’s actual media use. Yet, although passive
sensing could give us more accurate information on individuals’ actual use, only research can tell
us which type of data will be most useful for predicting outcomes. It would be important for
researchers to determine whether actual use or self-reports (individuals’ perceptions) best predict
outcomes over time for individuals, children, and families.
Below I outline a few exciting directions as well as limitations/struggles in regard to
passive sensing of media use. Please note this is not meant to be an exhaustive list.
Exciting Future Directions and Uses for Passive Sensing
The potential of passive sensing is exciting for examining individuals’ and families’ lived
experiences. We are often interested in understanding how children develop, family members
interact, positive and negative outcomes develop, and much more. However, “ideal longitudinal
research is characterized by the seamless integration of a well-articulated theoretical model of
change, an appropriate temporal design, and a statistical model that is an operationalization of
the theoretical model” (5; p. 507). In other words, our research designs and data collection efforts
must match our theories of family and developmental processes, and then the statistics we use to
analyze our data should also match our theories. Depending on the spacing of assessments, we
gain a very different view of the processes involved and may even miss the processes entirely—
if the spacing is too large between assessments (5). Passive sensing allows us to view media use
on a moment-to-moment basis across a day, getting us closer to real life. This is important
because we often make assertions about causation or theory but utilize data that is not the best
suited to answer our research questions. Let me present an example. It is likely that daily
technology interruptions in face-to-face interactions between parents and children may cause
children to act out. However, in our recent work, we utilized parent self-reports at a single point-
in-time or across months to examine this conceptualized causal process (2, 3). Yet, passive
sensing of media use linked with ecological momentary assessment data (such as multiple,
random self-reports across a day of child behavior, emotional states, stress, relationship feelings,
and so forth) would allow for an examination of the actual within-person, moment-to-moment
question at hand, getting us closer to understanding the causal process involved—instead of
inferring what is happening about causation from between-person, point-in-time surveys or
Another exciting avenue for passive sensing would be for use in interventions or clinical
settings. Researchers and intervention scientists have already begun working in this domain. For
example, Heron and Smyth wrote a review article in 2010 on ecological momentary
interventions (6), and their review concluded that interventions in individuals’ lives designed
around mobile technology can be effective. According to Google Scholar, their article has been
cited 904 times, and a search for “ecological momentary intervention” limited to 2018 and after
brings up 212 results (as of May 31, 2019), suggesting that this line of research on ecological
momentary interventions has continued to expand.
Some potentially promising uses for passive sensing interventions could include nudges
or notifications centered around screen limits and other recommendations given by the American
Academy of Pediatrics (7, 8). This could be done in a variety of ways, but as one example
imagine working with a parent, and they inform you that they typically are with their children
from 5:30pm until 9pm. They have also decided to follow the AAP’s recommendations that you
taught them and implement this as a screen free time. Passive sensing apps could be designed to
monitor phone use and if the phone is used during this predetermined time the phone could give
a gentle nudge or reminder to be fully present with their family. (Note that some phone tracking
apps already on the market—such as QualityTime—allow individuals to set phone use limits,
such as locking the device during a certain time period). There are also many more advanced
ways to do this and more, where one could design apps or devices that interact with one another
such that if a device is used in the presence of a family member the device would recognize this
and would therefore nudge them to be present with their family. One could also use location data
such that the device recognizes when the individual is at home, school, or some other
predetermined location and could give nudges when the phone is used in that specific location.
The possibilities are exciting and endless for interventions.
In terms of therapy/clinical settings, one exciting and incredibly simple way passive
sensing apps could be used would be to determine what a patient’s media use habits are, times
they tend to get on the device most, apps they use the most, and much more. This information
could then be used by the clinician to teach the patient about their use and the potential for this
use to impact their well-being and family well-being. For example, the clinician and patient may
notice together—as they examine the patient’s use via the app—that the patient tends to use their
phone often from 11pm to 1am. A discussion could then be had about the possible effects of this
use on sleep/depression and a plan could be put in place to reduce this use and improve their
well-being. Although I do not discuss it here, there are also much more complex ways to utilize
passive sensing to track or intervene in patients’ lives and improve their well-being (for an
example, see 9; for a review, see 10).
A Few Limitations and Struggles with Passive Sensing
Individuals live media saturated lives where use does not occur only on one device. For
instance, one might switch between their phone, computer, tablet, TV, and more across a day (or
use multiple devices simultaneously). Passive sensing on smartphones is a good first step, but we
need passive sensing data that can integrate media use across devices to truly obtain the best
picture of an individual’s, child’s, or family’s media use. As one example, according to self-
report surveys, children and teenagers clearly utilize multiple devices per day and also fit into
categories of use—some engage in more passive TV viewing, others engage in more mobile
gaming, and many more categories (11). Only having data concerning phone use would miss the
complex ways individuals and families engage in media use, and one could mistakenly believe
that an individual or child is a light user, when in reality they are a light phone user but engage in
much more media use on the TV, tablet, or other devices. Additionally, without data combined
across multiple devices, we might successfully help individuals or families to manage phone use,
but unbeknownst to us they have simultaneously expanded their TV or tablet use.
Another problem with passive sensing is that researchers do not know what participants
are doing while on specific apps. For example, an individual could do any variety of things while
on social media—e.g., posting photos, expressing love, providing support, engaging in infidelity,
criticizing someone’s beliefs, comparing oneself to others—all of which could have different
effects on well-being. Unfortunately, passive sensing would only tell us that the individual had
used social media for a certain amount of minutes/seconds at this specific time in the day.
An additional limitation or difficulty is how best to obtain the passive sensing data in an
easy to research format. There are a variety of apps that can easily be downloaded on phones
(e.g., Moment, QualityTime). However, these apps often will not allow researchers to access the
data, meaning participants will need to export (or screenshot) their use and send this data to
researchers. Then researchers may need to do extra steps to prepare the data for storage and
analysis. Alternatively, researchers could create their own passive sensing apps or code—like
was done by Yuan et al. (4). However, phones and operating systems change frequently, which
would likely require updating or creating new apps or code. It is also possible that the passive
sensing app does not always continue working in the background on some phones—as was seen
by Yuan et al. (4) and has also been reported in user reviews in the app stores for the various
phone use tracking apps.
Finally, passive sensing data requires a new set of management and analysis tools that
many researchers may not currently have mastered. For example, depending on the data that is
being collecting there could be privacy, HIPPA, or other concerns, meaning the data must be
protected and properly stored. Datasets could also become quite cumbersome with each
individual having thousands (or even more) of data points per day (e.g., big data). To utilize
passive sensing data to its fullest extent, researchers must also collect and manage other moment-
to-moment data streams (e.g., passive sensing of physiology, self-reports of momentary stress or
mood) and then link these multiple data streams together. This requires complex data
management and also complex statistics that can model the within-person and moment-to-
moment processes (for example, see dynamical systems modeling, data mining, machine
learning). Additionally, researchers must not allow themselves to become solely data driven, but
must maintain their connections to theory and theory building.
Although the tracking or passive sensing of mobile device use is not new, passive sensing
applied specifically to parents, children, and families is a frontier not yet fully explored. Passive
sensing data has the potential to expand our views of individual, child, and family media use and
the moment-to-moment processes involved, lending itself useful for finding and better
understanding the causal mechanisms and potential links between digital habits and well-being.
There are many potential pitfalls and limitations, such as managing apps and multiple phone
operating systems, data management, and complex modeling. However, we can and should work
together (within and across our various fields) to solve these potential issues and realize the
potential of passive sensing for researching and improving the lives of individuals, children, and
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