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December | Issue 1&2 | Volume 34 | insna.org
Mobile Phone Assessment in Egocentric Networks: A Pilot Study on Gay Men and
Their Peers
Abstract
Mobile phone-based data collection encompasses the richness of social network research. Both individual-
level and network-level measures can be recorded. For example, health-related behaviors can be reported
via mobile assessment. Social interactions can be assessed by phone-log data. Yet the potential of mobile
phone data collection has largely been untapped. This is especially true of egocentric studies in public health
settings where mobile phones can enhance both data collection and intervention delivery, e.g. mobile users
can video chat with counselors. This is due in part to privacy issues and other barriers that are more difcult
to address outside of academic settings where most mobile research to date has taken place. In this article,
we aim to inform a broader discussion on mobile research. In particular, benets and challenges to mobile
phone-based data collection are highlighted through our mobile phone-based pilot study that was conducted
on egocentric networks of 12 gay men (n = 44 total participants). HIV-transmission and general health
behaviors were reported through a mobile phone-based daily assessment that was administered through
study participants’ own mobile phones. Phone log information was collected from gay men with Android
phones. Benets and challenges to mobile implementation are discussed, along with the application of
multi-level models to the type of longitudinal egocentric data that we collected.
Keywords: Gay men, HIV risk behaviors, mobile phone log, ecological momentary assessment, ohmage
Authors
W. Scott Comulada, is an assistant professor-in-residence in the Department of Psychiatry and Biobehavioral
Sciences at the University of California, Los Angeles, California. He is also a Methods Core scientist for the
Center for HIV Identication, Prevention, and Treatment Services (CHIPTS; P30MH058107)
Acknowledgements
This study was funded and supported by the National Institute of Mental Health (K01MH089270;
P30MH058107). Mobile phone-based data collection was supported by ohmage and the following centers at
the University of California, Los Angeles: the Center for Embedded Networked Sensing and Mobilize Labs.
In particular, I would like to thank Deborah Estrin and Nithya Ramanathan for input on the development of
the mobile assessment.
Please send all correspendence to W. Scott Comulada, UCLA Center for Community Health, 10920 Wilshire
Blvd, Suite 350, Los Angeles, CA 90024, USA. wcomulada@mednet.ucla.edu.
W. Scott Comulada
University of California
Los Angeles, California
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insna.org | Volume 34 | Issue 1 | December
Mobile Phone Assessment in Egocentric Networks
1. Introduction
Mobile phones are by nature social devices as highlighted
by numerous studies on the structure of mobile
communication networks (e.g., Onnela et al., 2007; Ye et
al., 2008) and individual tie strengths (Zhang & Dantu,
2010). Most studies have analyzed sociometric data where
large and bounded networks are observed. In contrast,
egocentric networks of individuals, i.e. egos, and their
peers, i.e. alters, have received less attention. In part, the
focus on sociometric data may be due to the availability
of “safe” data sets that are collected in laboratory settings,
often on faculty and students where privacy issues are
less critical and participants are technologically savvy. A
good example is the MIT Reality Mining data set (Zhang
& Dantu, 2010).
Egocentric networks are often assessed in
public health settings on marginalized populations, e.g.
drug-using networks (Yang, Latkin, Muth, & Rudolph,
2013). Privacy is critical and “tech-savvy” assumptions
may be unrealistic. Yet mobile assessment has been
successfully carried out in cocaine-addicted homeless
patients (Freedman et al., 2006) and other marginalized
populations. Furthermore, mobile technologies can
enhance data collection and intervention delivery
in public health settings, e.g. ecological momentary
interventions (Heron & Smyth, 2010). As we have found
in our own transition from more traditional modes of data
collection to mobile-based studies, an important part of
the implementation process is a clear understanding of
what mobile technologies can and cannot do. As noted by
Lazer et al. (2009), researchers and institutional review
boards (IRBs) alike need to be up to speed on the latest
technologies in order to design and evaluate proper
privacy and encryption protocols, respectively.
In this article, we highlight the benets and
limitations of mobile data collection through egocentric
data that was collected to test the implementation of a
mobile phone-based health assessment in a sample
of 12 gay men, i.e. egos, and their peers, i.e. alters.
Both egos and alters used their own phones to ll out
a health assessment and enter sensitive information on
HIV-transmission behaviors. We collected phone-log
data from a subset of the egos with Android phones in
order to compare mobile communications with alters in
the study and with individuals who did not enroll in the
study. Therefore, our study provides a good opportunity
to discuss privacy and ethical issues that are central to
public health settings.
We also give examples of research questions
and analytic strategies that are afforded by the collection
of mobile data in an egocentric study. A key feature of
our data is the three levels of hierarchy. Egocentric data
normally contains two levels where individuals (both egos
and alters) are nested within egocentric networks. Multi-
level models are applied and contain random effects for
each network to allow mean levels of the outcome to
differ across networks (e.g., Hall, 2010; Rice et al., 2009;
Snijders, Spreen, & Zwaagstra, 1995; Valente, 2010). In
our study, participants lled out an end-of-the-day mobile
assessment over a month; repeated observations are nested
within individuals. We discuss extensions to the basic
multi-level model to analyze longitudinal egocentric data.
It is important to note that longitudinal data in our study
resulted from daily reporting which is a course version of
ecological momentary assessment (EMA) where events
are recorded as they occur in situ. EMA also involves
a large number of repeated measurements and depends
on careful timing, e.g. several times a day, to capture
variations in behavior within days (See Shiffman, Stone,
& Hufford, 2008, and Stone and Shiffman, 1994, for
overviews). In contrast, standard assessment methods rely
on retrospective recall where study participants are asked
to report on behaviors over a period of time and are often
interviewed in a clinical setting. EMA minimizes recall
biases that are intensied as individuals reconstruct and
retrieve events from their memory over longer periods of
time. By self-administering assessments, EMA may also
reduce interview bias, e.g. in giving socially desirable
responses to sexual behavior questions (Kissinger et al.,
1999).
2. Data and Methods
2.1 Participants
Recruitment was conducted online (Figure 1). From April
to August, 2013, 455 egos were recruited through pop-
up messages on Grindr, a dating website for gay men,
and postings on Craigslist that directed them to a study
webpage. Craigslist is an online forum for classied ads.
The study webpage directed egos to online
screening and consent forms that were hosted by
SurveyMonkey (http://www.surveymonkey.com/). Study
eligibility required egos to 1) self-identify as a gay or
bisexual man; 2) be at least 18 years old; 3) live in Los
Angeles County; 4) use a web-enabled Android phone,
version 2.3 or higher (issued after November 2010), or
an iPhone; 5) use their mobile phone to participate in the
study; and 6) recruit at least 3 alters who had an Android
or iPhone they could use to participate in the study.
Out of 455 egos who started the online forms,
19% were not eligible (n = 85) and 37% did not nish
lling out the forms (n = 167). It is hard to know why
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December | Issue 1 | Volume 34 | insna.org
Mobile Phone Assessment in Egocentric Networks
so many individuals did not nish lling out forms. In at
least one instance, a peer started to ll out the forms on
their mobile phone, lost internet connection, and did not
attempt to re-initiate the forms.
Eligible egos (45%; n = 203 of 455) were
e-mailed to set up a one-time telephone interview and
also received instructions on how to install and use the
study mobile apps; calls were scheduled with 64 egos.
During the call, we administered a demographic and
social network assessment. Grindr banner ads were
the primary recruitment source (n = 53 of 64 telephone
interviews).
After the telephone interview, egos were sent
an e-mail template they could send to alters they wished
to invite into the study. The e-mail template contained
a link that directed interested alters to a separate study
webpage and in turn, online screener and consent forms.
The online form asked alters to enter the rst name and
phone number of the ego who recruited them so we could
construct ego-alter links. Eligible alters fullling 2), 4),
and 5) were contacted and administered a demographic
assessment. We relaxed the requirement for egos to
recruit 3 alters and allowed egos and alters to participate
if at least 2 alters per egocentric network were recruited.
Out of 64 egos who completed a telephone interview,
roughly 1 in 5 recruited at least 2 alters and enrolled in
the study (n = 12 of 64; Figure 1). We did not follow-up
with unenrolled egos to nd out the reason. One ego let
us know that his friends did not want to join and “share
private information”.
Out of 68 alters who started the online screener,
75% (n=51) completed the screener and provided contact
information to schedule an interview. Thirty two of 36
alters who were interviewed by telephone enrolled in the
study.
Egos and alters were e-mailed Amazon gift card
activation codes worth $60 and $50, respectively, at
the end of the study as incentives. Egos and alters who
were the most compliant in lling out the daily health
assessment were entered into a drawing to also receive an
Amazon gift card activation code worth $100. All study
procedures were approved by the Institutional Review
Board at the University of California, Los Angeles.
2.2 Data collection
Telephone interviews were conducted at the beginning of
the study prior to the start of the mobile phone health
assessment. Egos were queried on where they heard about
the study, the model of the mobile phone they would be
using during the study, age, and ethnicity. Alters were
queried on their relationship to the ego who recruited
them, gender, age, ethnicity, whether or not they lived in
Los Angeles County, and their sexual orientation. During
Figure 1: Recruitment and enrollment of egos (gay men) and alters and initiation of mobile phone-based data collection.
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Mobile Phone Assessment in Egocentric Networks
the telephone interview, egos were also administered a
9-item adapted version of the Arizona Social Support
Inventory (Barrera & Gottlieb, 1981) to elicit names of
people with whom the respondent socializes, lives, eats
meals, has sex, does alcohol and drugs, receives health
advice, calls upon for material and emotional support,
or any other people who were important to them that
had not been prompted by the prior name-generator
questions. After the 9-item inventory, we asked for names
of alters the ego was planning to ask to join them in the
study. Almost all of the egos who enrolled in the study
recruited at least one alter who had not been prompted
by the 9-item inventory (n = 10 of 12). We calculated
the size of each egocentric network based on the number
of names generated by the 9-item inventory. Seven of
the 64 egos gave “other” responses that encompassed
multiple people, e.g. “family”, and were excluded from
the network-size calculation.
Phone logs were recorded through SystemSens
(http://systemsens.ohmage.org), an Android application
that was designed to collect passive system data and
developed through the UCLA Center for Embedded
Networked Sensing. Egos with Android phones were
asked to download SystemSens to their mobile phone
through an e-mail link. Once installed, SystemSens
automatically encrypted and uploaded phone-log data
(including phone numbers, the duration, and date /
time stamp of incoming and outgoing calls and text
messages) to servers at UCLA whenever the user charged
their phone. To protect the identity of phone numbers
belonging to individuals who were not enrolled in the
study, all phone numbers in the phone log were scrambled
using SHA-256, a cryptographic hash function published
by the National Institute of Standards and Technology.
There are several notable features of SHA-256.
Hashed numbers appear as unique 256-bit values, e.g.
“b8475260a8bdd4af2984d7d7d8eb9b5a”. As a result,
one is able to identify if two hashed numbers are of the
same phone number. However, it is nearly impossible to
recover the original phone number from a hashed number
alone. The original phone number is necessary to act as a
key that unscrambles the hashed number and veries the
original number.
Mobile phone assessment. All participants
(both egos and alters) were asked to ll out the same
daily assessment on their mobile phone for a month.
Assessments were launched using the ohmage application
(http://www.ohmage.org), an open-source application
that is compatible with Android and iPhones. Ohmage
allows for assessments to be rapidly authored using the
Extensible Markup Language (XML), and allows data
to ow from participants’ mobile phones to a centralized
database. In this study, ohmage was launched with an
HTML5 application implemented using the Mobile
Web Framework (MWF). The application runs on both
Android and iPhones and is available for download from
the Google Play and Apple app stores, respectively.
A version of ohmage that is native to Android phones
has been implemented in prior studies (Swendeman et
al., 2014); feedback from focus groups on prior mobile
studies informed the design of the mobile assessment
(Ramanathan et al., 2012). Once installed, participants
accessed the mobile asssessment through the ohmage
dashboard shown in Figure 2A. At the end of each
assessment, responses were encrypted, uploaded to
servers at UCLA, and removed from the user’s mobile
phone, as long as there was network connectivity and
the phone battery was not low. Responses could also be
manually uploaded at a later time.
Mobile phone assessment consisted of 14
questions that participants were asked to ll out at the
end of the day for a month. Questions encompassed the
Table 1: Frequency of communication with alters based on ego reports (includes face-to-face, telephone, and social media contact)
and based on the number of days between phone log calls/ text messages between egos and alters
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Mobile Phone Assessment in Egocentric Networks
following domains in the following order: (a) An adapted
version of the Healthy Days Symptoms Module from
the Health-Related Quality of Life instrument (HRQOL;
Centers for Disease Control and Prevention, 1995),
including 5 questions on mood, worry, sleep, energy level,
and impairment; (b) Daily minutes of exercise and type of
exercise, e.g. “Jogging”; (c) Rating of one’s daily eating,
e.g. “Less healthy than usual”; (d) A food inventory that
was constructed from multiple food inventories (e.g.,
Fulkerson et al., 2008; Kaiser et al., 2003; Sisk, Sharkey,
McIntosh, & Anding, 2010) and designed to t across
two cell phone screens; (e) Sexual behavior, including the
number of sexual encounters involving anal or vaginal
sex, the number of encounters with “casual (including
one-time and rst-time) partners”, and condom usage;
and (g) Alcohol and substance use.
All questions included a “Refuse to answer”
response option so that participants were not forced to
answer any questions they did not want to. However, we
did not want participants to repetitively select refusal
responses in order to get through the daily assessment more
quickly. We placed additional “speed bump” questions
that required participants to specify why they refused to
answer the prior question in two places. The rst speed-
bump question was placed after minutes of exercise were
queried, and the second was placed after the number
of sexual encounters was queried at approximately the
halfway point and end of the assessment. No refusals
were entered, except for the impairment question (1
refusal) and substance use (3 refusals).
3. Analytic Strategies and Results
3.1. Sample characteristics
Among egos who were interviewed over the telephone (n
= 64), the average age was 30.8 years old (range = 18 to
58). Ethnicity was reported as African American (12.5%),
Latino (34.4%), White (37.5%), or Other (15.6%). Egos
reported a network size of 8.4 members, on average
(range = 2 to 30). Networks were fairly homogenous
with respect to age and ethnicity. For example, most of
the White egos (n = 5 of 6) only recruited White alters.
Half of the Latino egos (n = 2 of 4) only recruited Latino
alters.
3.2. Call logs
Phone logs were recorded for four egos with Android
phones. Logs began recording as soon as SystemSens
was installed and continued until the end of the study
that included the 30-day health assessment time period
(range = 20 to 45 days). One ego was only able to
recruit one peer and dropped out of the study after 20
days. Similar to Onnela et al. (2007), we excluded
one-way communications where calls or text from
an ego to a phone number occurred, or vice versa, but
were not reciprocated. By focusing on reciprocated
communications, we eliminated communications related
to single events where egos did not personally know
individuals they were communicating with. Two phone
log analyses are discussed.
Agreement between self-reported contact and
phone logs. The frequency of contact with network
members is typically self-reported by egos. Given the
additional contact information provided by mobile
communication (both calls and text messages), a natural
question arises. Do phone logs provide overlapping
information to self-reported contact or do phone logs
provide additional information? Table 1 demonstrates
a way to address this question by showing egos’ self-
reported frequency of contact with alters that was reported
during the telephone interview and the median number of
days between mobile communications with alters. Phone
logs corroborate the self-reported frequencies fairly well.
For example, four of ve “daily” reports matched up with
call logs where half of the communications occurred
within a day of each other.
Alter closeness. There is a general understanding
in social network research that observed networks in a
study are incomplete. Social ties with individuals outside
Figure 2: ohmage MWF screenshots showing (A) dashboard
for accessing daily health survey and sample questions from
the daily health survey, including a (B) multiple-response item
and (C) an item requiring numeric entry.
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Mobile Phone Assessment in Egocentric Networks
the study network can sometimes be constructed by
self-report (e.g., Fowler & Christakis, 2008), though
this is typically not the case. Therefore, phone log
communication data can ll in gaps on self-reported
network compositions. In particular, we focus on
the frequency of egos’ mobile communications with
recruited alters and individuals outside the study as a
proxy for ego-alter closeness. Information on closeness
with alters who are likely to be recruited into a study has
the potential to inform the design of both social network-
based interventions (see Valente, 2012 for a review) and
recruitment strategies (e.g., respondent-driven sampling;
Heckathorn, 1997, 2002). Figure 3 shows the percentage
of communications with each alter and with the remaining
telephone numbers in the phone logs. Among these four
egos, we note that they recruited at least one alter they
were in fairly frequent contact with, e.g. partners for Egos
1 and 3 (15.3% and 20.0% of the total communications,
respectively).
3.3. Mobile health assessment
We discuss two types of multi-level regression models
that address research questions specic to each level of
hierarchy in a longitudinal egocentric data set.
Network-level questions. Holistic health
approaches often track multiple and disparate measures
of health. For example, le Roux et al. (2013) examined
mental health, general health, and HIV-transmission
behaviors. In this vein, we examined how multiple health
behaviors and HRQOL cluster within networks. Due
to the small sample size, an ad hoc approach was used.
Responses for each individual were aggregated over their
30-day study period. We then t separate multi-level
models to each HRQOL or behavioral measure. Pearson
product-moment correlations were then examined within
the 12 pairs of network-level random effects between
all possible pairs of HRQOL and behavioral outcomes.
Correlations were in expected directions. For example,
at the network level, there were negative correlations
between numbers of alcoholic beverages and both mean
levels of healthy feelings (r = -.42) and days of exercise
(r = -.50).
A more formal modeling approach uses a
bivariate-outcome multi-level model similar to Comulada
et al. (2010, 2012). Here we consider longitudinal
egocentric data with two continuous outcome measures,
e.g. levels of mood and sleep. For individual i in network
n at time point t and outcome k (= 1,2), a bivariate
random-intercept linear model on continuous outcome
ynitk is expressed as
ynitk = xnitk´βk + λnk + ηnik + εnitk, (1)
where βk is a vector of regression coefcients for covariate
vector xnitk on outcome k. Correlations for each outcome
within networks and across repeated observations within
individuals are accounted for by random effects λnk
and ηnik, respectively. Residual error term εnitk accounts
for variance that is unexplained by the random effects.
A key feature of the model is that correlations between
outcomes are modeled through a variance-covariance
matrix that is shared by random effects and residual terms
Figure 3: Percentage of (n) reciprocated calls or text messages:
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Mobile Phone Assessment in Egocentric Networks
across outcomes. In particular, cross-correlations can be
examined between outcomes at different time points,
e.g. the relationship between drug use and trust between
egos and alters over several time points (Comulada et al.,
2012).
Individual-level questions. Longitudinal studies
typically entail a few time points. Analyses focus on
mean changes over time, e.g. decreases in drug use. EMA
in our study resulted in numerous time points (intensive
longitudinal data; Walls & Shafer, 2006). In larger
samples, changes in variability, as well as mean levels,
can be examined using location scale models (Hedeker,
Mermelstein, & Demirtas, 2008; Hedeker, Demirtas, &
Mermelstein, 2009). For example, Hedeker, Demirtas,
& Mermelstein (2009) examined mood uctuations in
smokers over time.
4. Discussion
Our mobile phone-based pilot study on egocentric
networks of gay men and their peers highlights a number
of benets that are scalable to larger studies and other
populations.
First, recruitment and implementation of the
study was carried out without in-person visits with study
participants. Second, participants used their own mobile
phones, which alleviated the need to carry another
electronic data-entry device. Both features served to
reduce participant burden and study costs that are
associated with traditional studies, e.g. interviewers were
not needed. A degree of anonymity was also provided
for participants, which may be an important issue for
marginalized populations.
Past EMA studies have typically relied on paper
diaries that are prone to backlling (Stone et al., 2002,
2003). Palm top computers address this issue, but still
introduce a degree of user burden that can be attenuated
by making use of an individual’s own mobile phone. An
important feature of our study, in terms of data quality, was
the ability to visualize uploaded mobile assessment data
through a website portal in near real time. Research staff
checked the data every few days. In one instance, no data
was observed for an alter after initial study enrollment.
Telephone contact with the study participant revealed that
they were accidentally preventing their assessment data
from being uploaded to the study team. The problem was
easily corrected, and only a few days of data were lost.
The strength of our technologically-driven study
design is also an obvious limitation for implementation in
other populations. In studying egocentric networks of gay
men who use Grindr and live in Los Angeles, we focused
on a fairly tech-savvy population. Furthermore, gay men
in Los Angeles are often targeted for HIV-related studies,
especially through Grindr (e.g., Rendina et al., 2014).
At enrollment, a number of our study participants were
already familiar with standard study protocols. These
characteristics facilitated the use of online recruitment
and mobile assessment. Using these tools would be more
difcult in other populations where study details are
better explained in person and where study participants
may be more reluctant to enter sensitive information
during a survey, especially on an electronic device.
Few concerns were voiced by participants in our study.
Online recruitment may be unethical in populations were
a language barrier is present, and online consent forms
may be easy to click through without understanding the
content.
Despite the technological savvy of our population,
three main limitations remained with our study design.
Approximately half of the eligible gay men who clicked
through our Grindr banner ad and initiated the online
forms, completed the study participation forms (55%;
n = 203 / 370; Figure 1). This percentage is similar to
initial participation rates that were found in another study
that recruited gay men through Grindr and asked them to
ll out a one-time online survey (43%; n = 2175 / 5026;
Rendina et al., 2014). A big difference between Rendina
et al. (2014) and our study is that they retained 27% of
the initial gay men in their analysis sample. We retained
12 egos (3%) in our study. Increasing rates of online
recruitment offers potential participants a smorgasbord
of studies to select from. Moreover, there is less buy-in
when shopping amongst online studies.
For example, rapport may be established with a
recruiter during recruitment in a clinic. Online recruitment
may be best suited for studies offering instant participation.
Recruitment through Grindr reached gay men with risky
sexual behavior proles as intended; Nineteen percent of
interviewed gay men reported anonymous / one-time sex
partners in their network during the telephone interview
(n = 12 of 64). Yet only one of the 12 (8%) enrolled
gay men had reported anonymous partners. Though not
statistically signicant, this percentage drop suggests
that an online forum that attracts users with a targeted
behavior is not necessarily a good recruitment source.
Another limitation was our restriction of phone-
log data collection to Android users. In our study, the
majority of participants were iPhone users, e.g. 64%
of egos and 70% of alters who lled out online forms.
iPhone users tend to have other iPhone users as friends
(Canright, 2013). Android and iPhone users also tend
to have different demographic and social characteristics
(Albanesius, 2011). Phone log-based inference that is
based on one type of mobile phone is likely to miss a
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Mobile Phone Assessment in Egocentric Networks
segment of the population and be biased. Text message-
based assessment that that does not require a smartphone
may be a better option in other populations.
Lastly, lengthy assessments may call for larger
computer screens and human interaction to encourage
compliance. Our mobile assessment could be taken
in a few minutes. Questions contained a few response
categories and mostly t on one screen. This may partly
explain high compliance in our study (a median of 24
days of reporting).
The benets and challenges in our study support
a marriage of traditional and new data collection methods
that is likely to remain in social network research. Visual
web interfaces that allow participants to construct their
own personal networks through a self-administered
social network inventory have met with limited success;
an interviewer may still be necessary (Matzat & Snijders,
2013). Moreover, one mode of electronic communication
may not adequately capture social interaction (Quintane
& Kleinbaum, 2011). That is why we assessed the
frequency of ego-alter contacts through self-report
and mobile communication. Despite the challenges of
incorporating new technologies into research, the social
dynamics of mobile devices and social media are difcult
to ignore. It is hard to fully understand the dynamics of
health-related behaviors without them.
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