 39. Ecological Sampling Methods  813
tively. Mobile sensing —the use of mobile 
phone data for making scientific inferences 
about human behavior—  can produce com -
prehensive, real-world observational data 
by downloading a smartphone’s sensor and 
interaction logs. For example, location and 
mobility are tracked with high resolution via 
WiFi and Global Positioning System (GPS) 
information. In addition, a person’s social 
interaction behavior is increasingly encod -
ed in the phone’s communication metadata 
(e.g., call, text, and social media logs; Chit -
taranjan, Blom, & Gatica-  Perez, 2013; Ha -
rari et al., 2020; Montag et al., 2015).
In many ways, it appears that the pre -
dicted smartphone revolution (Miller, 2012) 
has arrived (Harari et al., 2016, 2020; 
Sandstrom, Lathia, Mascolo, & Rentfrow, 
2016; Schmid Mast, Gatica-  Perez, Frauen -
dorfer, Nguyen, & Choudhury, 2015; 
Stachl et al., 2017). Yet researchers’ excite -
ment about what smartphones can do may 
currently exceed what is practical for even 
an experienced, interdisciplinary research 
group—any smartphone sensing study cur -
rently needs computer science or engineer -
ing experts on the team. Until recently, most 
mobile sensing applications were proof-of- 
concept apps developed to show technical 
feasibility.
For example, Lu et al. (2012) developed a 
smartphone application for stress detection. 
StressSense detects the user’s voice, extracts 
stress- relevant parameters (e.g., speech rate, 
pitch variability, jitter), and integrates them 
into a stress estimate using algorithms that 
are trained against skin conductance as 
stress “ground truth.” Lane et al. (2014) de -
veloped BeWell as an app to provide feedback 
on three health behaviors: physical activity 
(via the embedded accelerometer), sleep ac -
tivity (via accelerometer), and social activity 
(via privacy-  sensitive ambient sound moni -
toring). And Wang et al. (2014) developed 
StudentLife,  an app that “senses” social and 
activity behaviors to predict mental health 
and academic performance over the course 
of a term (Harari, Gosling, et al., 2017; Ha -
rari et al., 2020). Importantly, these were 
all proof-of-  concept apps, not designed for 
broad implementation, and validated only 
in small pilot studies, often with “internal” 
participants (e.g., staff members of a labora -
tory; students in a programming class).In one of the first studies aimed at col -
lecting a large, mobile sensing dataset, 
Gatica-  Perez and his colleagues provided 
mobile phones to more than 100 partici -
pants. The participants completed a set of 
questionnaires and used the phones for 17 
months (October 2009 to February 2011). A 
custom-  built sensing application extracted 
usage data (e.g., text, call, Bluetooth logs, 
app usage). The data were then anonymized 
and made available for open analysis (Lauri -
la et al., 2013). Similar to a small study by de 
Montjoye, Quoidbach, Robic, and Pentland 
(2013), the researchers found links between 
smartphone usage and personality. For ex -
ample, extraversion was positively related to 
the number and length of received calls and 
conscientiousness was negatively related to 
the use of video, audio, and music apps such 
as YouTube (Chittaranjan et al., 2013; see 
also Stachl et al., 2017).
An example of a research smartphone 
app that was explicitly designed for broad 
dissemination and at-scale use is Emotion -
Sense (Rachuri et al., 2010). The Android 
app tracked emotion and behavior through 
combined experience sampling (e.g., emo -
tional experience, self-  reported activity) and 
mobile sensing (e.g., accelerometer, GPS, in -
teraction metadata). Over the course of its 
life on the Google Play store (2011–2016), 
more than 13,000 individuals downloaded 
the app and used it for varying amounts of 
time. Because the sensing part of the app ran 
in the background (until the app was closed 
out), the researchers gathered a very large 
database that, for the first time, really al -
lowed realization of some of the promised 
potentials of mobile sensing. For example, 
using longitudinal EmotionSense data from 
more than 10,000 individuals, Lathia, Sand -
strom, Mascolo, and Rentfrow (2017) found 
that participants who were objectively more 
physically active (determined via accelerom -
etry) were subjectively happier (determined 
via experience sampling). Furthermore, this 
effect also held at the within-  person level; 
participants were happier in (or shortly after) 
the moments when they were more physi -
cally active. Importantly, in addition to their 
empirical studies, the EmotionSense team 
has also conducted psychometric validations 
of their sensor data collected “in the wild” 
(Rachuri, Efstratiou, Leontiadis, Mascolo, 