





























45

Graduate Student Journal of  Psychology 
2018, Vol. 17

Copyright 2018 by the Department of  Counseling and Clinical Psychology 
Teachers College, Columbia University

Remote Behavioral Sampling for Psychological Assessment: 
Using Interactive Technologies to Detect Depression

Jacob Edward Thomas, M.A. and Randall Richardson-Vejlgaard, Ph.D.
Teachers College, Columbia University

The US National Comorbidity Survey indicates that up to 47% of  Major Depressive Disorder (MDD) patients 
receive treatment predominately in a primary care setting. The screening methods primary care physicians 
use to monitor treatment response offer low time-sensitivity, and thus hinder the provider’s ability to adjust 
or augment treatment effectively. Contemporary technologies that directly capture behavioral data may offer 
objective insight into an individual’s mental health status, with little burden. The current study tracks 12 partici-
pants over two weeks to evaluate the convergent validity of  a clinician administered measure of  depression 
with physical activity and sleep measured by an actigraph, linguistic style present in Facebook content, and 
repeated brief  assessments of  mood delivered via a smartphone. Feasibility analysis showed that 91.7% of  
participants completed the study, with 83.6% of  targeted data captured. Correlations were found between 
depression symptoms and physical activity (r = -.501, p = .058), restful sleep (r = -.536, p = .045), ecologically 
assessed affect (r = -.582, p = .03), and 3 linguistic styles present in social media content (‘future focused’ 
(r = -.539, p = .043), ‘feeling’ (r = .532, p = .046), and ‘masculine’ (r = .590, p = .028)). The study suggests that 
data collected from interactive technologies offers an easily accessible inventory of  patient’s behaviors which 
is highly correlated with traditional MDD assessment. Depressed patients who are not seeing a specialist 
regularly are at high risk for nonadherence with medication during the latency period between antidepressant 
initiation and MDD remission. A monitoring platform utilizing interactive technologies would be useful in 
identifying patients who need treatment.

Introduction

Currently, 10% of  the American population is 
clinically depressed, and nearly 20% will experience a 
depressive episode at some point in their life (Kessler, 
Berglund, Demler, et al., 2005). Epidemiologists esti-
mate that Major Depressive Disorder (MDD) will be the 
leading cause of  life years lost due to ill-health, disability, 
or early death by 2030 (Mathers, Fat, & Boerma, 2008). 
This disorder is debilitating and recurrent. Longitudinal 
economic analyses indicate that being depressed leads to 
immense individual burden, including diminished educa-
tional accomplishments, annual family income reduction 
of  20%, seven fewer weeks worked per year, and an 
11% decrease in probability of  getting married (Smith, 
& Smith, 2010). Cumulatively, depression is estimated 
to cause an $83 billion annual American economic loss 
(Greenberg, Kessler, Birnbaum, et al., 2003); almost 
triple what the National Institutes of  Health (NIH) 

invests in all medical research each year (NIH, 2015). 
As evidenced by these staggering numbers, depression is 
one of  the most prevalent and destructive psychological 
forces in present society.

Up to 47% of  MDD patients receive treatment 
predominately in a primary care setting (Kessler, 
Berglund, Demler, et al., 2003). Healthcare provid-
ers often monitor antidepressant treatment response 
using self-report questionnaires such as the Beck 
Depression Inventory (BDI; Beck, Steer, & Brown, 
1996) or the Patient Health Questionnaire-9 (PHQ-9; 
Kroenke, Spitzer, & Williams, 2001). These screening 
tools are only validated to be sensitive to changes over 
two-week intervals (Lowe, Kroenke, Herzog, & Grafe, 
2004; Beck, et al., 1996; Kroenke, et al., 2001). With an 
average onset of  antidepressant action in pharmaco-
logical intervention of  20 days (Stassen, Delini-Stula, 
& Angst, 1993), systematically administered self-report 
screeners therefore fail to identify many patients’ antide-
pressant response trajectories until day 28 of  treatment. 
Clinical improvement during the first month is one of  

Override (Hidden running head text):
Thomas, Richardson-Vejlgaard
Detecting Depression Symptoms Using 
Interactive Technologies

Please address correspondence to: jet2173@tc.columbia.edu



46

THOMAS, RICHARDSON-VEJLGAARD

the strongest predictors of  long-term stability in MDD, 
and mounting evidence suggests that dosage modifica-
tion and treatment augmentation are key in achieving 
this (Trivedi, Fava, Wisniewski, et al., 2006). In order to 
more effectively modulate depressive symptoms during 
this critical window, primary care providers are in need 
of  a novel and more time-sensitive screening method 
that allows them to identify patients refractory to treat-
ment for earlier outreach (see Figure 1).

The Present Study

Recent technological advances have ushered in a 
new era of  data availability and access. Americans are 
densely interconnected with online social networks and 
carry a powerful computer capable of  internet access 
in their pockets. Polls indicate that 65% of  American 
adults use social networking sites (Pew Research Center, 
2015A), and nearly 70% own a smartphone (Pew 
Research Center, 2015B). Wearable devices, such as 
fitness trackers and smart watches, provide users with 
real-time behavioral data. Ten million of  such devices 
are sold annually, and this industry is trending rapidly 
(Wei, 2014). Hidden within the way we interact with our 
technology is a map of  our lives. Our watches track the 
way we move, our phones geo-tag our locations, our 
cameras record what we see, and our online habits offer 
an abstract narrative of  our thoughts. All of  this data is 
recorded, stored, time stamped, and easily transmitted.

The present study seeks to use data captured by 
interactive technologies to make inferences about 
depression symptoms. First, discrete behavioral domains 
that are passively monitored by existing technology are 
identified, and then the evidence linking these domains 
to depression is reviewed. Next, a platform that inte-
grates these domains into a data collection system 
intended to remotely monitor the presence of  depres-
sion-related behaviors is deployed in a two-week study 
of  12 participants to test (1) the platform’s feasibility 
for implementation, and (2) the convergent validity of  
data generated in each domain with a clinician admin-
istered assessment of  MDD. In closing, discussion is 
presented on how the platform can impact antidepres-
sant treatment response monitoring in primary care, as 
well as limitations to the present study, and directions 
for future research.

Measurable Behavioral Domains and 
Their Association with Depression

Population-based studies assessing physical activ-
ity levels indicate that people who are more active are 
less likely to be depressed (Stathopoulou, Powers, Berry, 
Smits, & Otto, 2006). Meta-analyses of  intervention 
studies using exercise as a treatment for depression 
show moderate to large effect sizes consistent across 
multiple participant variables and exercise types (Cooney, 
Dawn, Greg, et al., 2013). Furthermore, MDD patients 

Figure 1. The Current Model of Antidepressant Treatment Response Monitoring in Primary Care.



47

DETECTING DEPRESSION SYMPTOMS USING INTERACTIVE TECHNOLOGIES

often report increases in desire and ability to exercise 
as symptoms remit (Otto, Church, Craft, et al, 2007). 
Actigraphs present in wrist-worn technology provide 
accurate reports of  physical activity, often reporting step 
counts and distance traversed (Tudor-Locke, Williams, 
Reis, Pluto, 2002). Discreet by nature, these devices 
automatically pair with user’s smartphone to upload 
data to a target server, which is accessible remotely.

Up to 90% of  depressed individuals report sleep 
disturbances, commonly including insomnia, frequent 
nighttime wakefulness, hypersomnia, and recurrent daily 
fatigue despite sleeping throughout the night (Franzen, 
& Buysse, 2008). A good indicator of  MDD symptom 
improvement is a reduction in these issues (Keller, 2003). 
Wrist-worn actigraphs provide accurate assessments of  
sleep duration and quality, with outputs highly corre-
lated to metrics of  traditional laboratory sleep studies 
(Ferguson, Rowlands, Olds, & Maher, 2015; Slater, 
Botsis, Walsh, et al., 2015). Similar to physical activ-
ity monitoring, sleep data is passively collected and 
remotely accessible after syncing with a smartphone.

As social media usage has grown in recent years, 
social scientists have utilized the forum to generate new 
findings. Researchers at Stanford University have shown 
that status updates accurately reflect dimensions of  life 
satisfaction (Lui, Tov, Kosinski, Stillwell, & Qui, 2015). 
An Italian team has demonstrated that emotionally posi-
tive or negative linguistic styles in Facebook content 
is closely related to emotional well-being (Settanni, 
& Marengo, 2015). Positive psychosocial dimensions 
such as these have a long history in depression research, 
universally having strong discriminant validity with 
depression (Pavot, & Diener, 1993). Users generate 
their social media content naturalistically, curating it for 
remote access on their social media profile.

The majority of  screening and diagnostic traditions 
conceptualize depression as the persistent experience 
of  symptoms (e.g., American Psychiatric Association, 
2013). A proposed way to record the experience of  
symptoms as they occur is by repeatedly sampling 
subjects’ current behaviors and experiences in real 
time, in their natural environment—a strategy known 
as Ecological Momentary Assessment (EMA; Shiffman, 
Stone, & Hufford, 2008). EMA mitigates recall biases, 
which greatly affect the accuracy of  retrospective 
reports (Porta, Greenland, Hernan, dos Santon, & 
Last, 2014; Hunt, Auriemma, & Cashaw, 2003). With 

the exponential growth of  smartphone use, EMA 
becomes highly feasible, as assessments can be sent to 
subjects’ cell phones instantaneously. If  the assessment 
is designed in an extremely brief  and intuitive manner, 
capturing this data can become an unintrusive part of  
one’s day, similar to sliding a finger across a screen to 
unlock a smartphone or replying to a short text message.

The Proposed Platform & Research Objectives

Physical activity, sleep, social media interaction, and 
moment-to-moment experiences are all intricately linked 
to depression. These data are captured with minimal 
burden using existing technologies. We propose a data 
collection system that combines physical activity and 
sleep variables from an actigraph, linguistic style present 
in Facebook posts, and repeated brief  assessments of  
mood delivered via a smartphone into a single, multi-
modal diagnostic tool. The study that follows compares 
outputs from the platform to a clinician administered 
MDD assessment.

We have two primary aims. First, to assess the 
feasibility of  implementing the platform by monitor-
ing adherence and attrition. Adherence reports will be 
based on percentage of  targeted data collected: daily 
outputs of  physical activity, nightly outputs of  sleep 
duration, presence of  Facebook content, and number of  
responses to EMA. Second, to test the convergent valid-
ity of  each behavioral output generated by the platform 
with MDD symptom ratings using bivariate Pearson 
Correlation tests.

We hypothesize that lower physical activity levels, 
less sleep, and lower EMA reported mood will be associ-
ated with higher MDD symptom rating. Additionally, we 
hypothesize that by mining Facebook content, signifi-
cant correlations between the content’s linguistic styles 
and MDD symptom rating can be extrapolated.

Methods

Participants
Participants (N = 12) were four men (mean 

age = 29.5; range = 24–34) and eight women 
(mean age = 24.4; range = 22–28) who were recruited 
through advertisements posted online and on the 
Teachers College, Columbia University campus. Nine of  
the participants were students, four were professionals. 



48

THOMAS, RICHARDSON-VEJLGAARD

Of  the 12 participants, one was lost to follow-up, and 
thus, excluded from the final analysis.

Inclusion criteria were as follows: (1) men and 
women aged 18–65; (2) has a Facebook profile and is 
a self-reported “active” user, defined as making at least 
1–2 interactions every few days; (3) possesses a smart-
phone that is activated on a cellular network; (4) willing 
and able to provide informed consent; (5) willing and 
able to complete a follow-up assessment two weeks after 
consent.

Exclusion criteria were as follows: (1) substantial 
physical injury or disability that prevents the partici-
pant from exercising should they wish to; (2) English 
illiteracy; (3) prior diagnosis of  mental retardation or 
acute psychosis.

Materials
Actigraph. Participants wore a Misfit Flash™ (by 

Misfit Inc., Burlingame, CA) for the duration of  the 
study. This device utilizes an accelerometer to continu-
ously monitor wrist movement. A six-month battery life 
and an open-source application program interface make 
this device ideal for use in research. The device is inex-
pensive, costing less than $25. Data is output into five 
interval time-stamped variables: steps per day, distance 
traversed, total sleep duration, restful sleep duration, 
and light sleep duration.

Facebook web application (Facebook, 2016). 
Participants granted the investigators access to all public 
activity conducted on their Facebook account for the 
duration of  the study. All content generated by the user 
that was viewable by their social network was recorded. 
Thus, private messages were not included. When web 
articles, videos, songs, or other content not explicitly 
composed by the participant were posted, the material’s 
title and subtitle was captured as relevant data. This deci-
sion was made because the title and subtitle is viewable 
directly by the participant’s social network without being 
redirected to a different webpage. That is, the title and 
subtitle are expressed in full and may capture something 
about what the participant was doing or thinking at 
the time of  the post. All content was compiled into a 
single text document for each participant, which was 
then analyzed for linguistic style.

Linguistic Inquiry and Word Count 2015 (LIWC) 
software. LIWC is a text analysis program that outputs 
percentages of  words in a given document which fall 

into over 80 linguistic categories (Pennebaker, Francis, & 
Booth, 2001). The program allows for users to develop 
custom dictionaries tailored to their interest, or use the 
internal dictionary which was built from decades of  
the developer, Dr. James Pennebaker’s, own work on 
psycholinguistics. Linguistic style has been investigated 
extensively in social science research. It can be utilized 
to explore a vast array of  constructs from emotional 
tone to intelligence (Kess, 1992). Pennebaker’s internal 
dictionary was utilized for the present analysis.

Google Voice web application. All EMA corre-
spondence was conducted through the Google Voice 
text messaging service. Google Voice is a secure web-
based telephone service offered to Google users (About 
Google Voice, 2016). Transmissions are composed on 
the user’s computer and forwarded to a target cellular 
number. Responses are received by the user’s Google 
account.

Measures
Beck Depression Inventory-II (BDI). The BDI 

(Beck, et al., 1996) was used to provide a stratified 
depression rating, wherein higher scores indicate more 
severe depression symptoms. This scale has been exten-
sively used in research and repeatedly shown to have 
strong validity and reliability in multiple populations, 
with a recent comprehensive review reporting concur-
rent validity to other depression scales ranging from 
r = 0.66–0.86, and reliability ranging from α = 0.83–0.96 
(Wang, & Gorenstein, 2013). The BDI was administered 
during the follow-up assessment.

The Affect Grid. The Affect Grid is a single item 
measurement of  current affective state, constructed 
on two continuous dimensions of  hedonic valence 
(level of  pleasure) and arousal (see Figure 2; Russell, 
Weiss, & Mendelsohn, 1989). From these two dimen-
sions, higher-order emotional states can be inferred. For 
example, high arousal with pleasant feelings is often 
conceptualized as happy or excited. The measure has 
been independently validated for accuracy on the 
hedonic valence arousal dimensions (Killgore, 1998). 
The Affect Grid was utilized as the EMA variable in 
the present study. Preliminary analysis has shown the 
Affect Grid to be sensitive to emotionally salient events, 
and thus a prime measure for use in EMA (Renaud, & 
Thomas 2016). The assessment was administered twice 
daily via text message. Participants received a picture 



49

DETECTING DEPRESSION SYMPTOMS USING INTERACTIVE TECHNOLOGIES

of  the grid with numbers 1–9 plotted on the X and Y 
axis. Coordinates (X,Y) of  the response were recorded. 
Times of  assessments were determined randomly, 
between 9am and 9pm.

Procedure
Each participant provided informed consent, as 

approved by the Institutional Review Board at Teachers 
College, Columbia University. Participants completed a 
basic contact/demographic information sheet and were 
fitted with a Misfit Flash™ device. They were instructed 
to keep the device on for the remainder of  the study, 
including while they sleep. If  the participant needed 
to remove the device (e.g., for comfort while bathing), 
they were instructed to replace it on their wrist as soon 
as possible. They were then trained to complete the 
Affect Grid assessment (see Appendix A). Participants 
were told that they would receive two text messages 
per day that contained a picture of  the Affect Grid 
with numbers 1–9 plotted on the X and Y axis. They 
were instructed to respond to each text message with 
coordinates of  their square’s location (e.g., Feelings-5, 
Arousal-6) as soon as they safely could. A two-week 
follow-up appointment was then scheduled. For the 

subsequent two weeks, participant’s physical activity 
and sleep was continuously sampled, and they received 
two text message EMA’s each day. When the participant 
returned to the lab, the Misfit Flash data was collected, 
and the BDI assessment was administered. Participants 
were then asked to temporarily log onto their Facebook 
profile on a study computer in order to extract their 
Facebook data.

Data Analysis
Descriptive statistics were used to generate adher-

ence reports. Daily physical activity and sleep variables 
were averaged over two-weeks to provide discrete steps-
per-day and sleep-duration values. Hedonic valence and 
arousal ratings on the affect grid were each averaged over 
26 assessments to generate two discrete global ratings. 
LIWC generated content percentages of  44 linguistic 
variables (see Appendix B). For tests comparing these 
behavioral domain outputs to BDI score Pearson prod-
uct-moment correlation coefficients were calculated. All 
analyses were carried out on IBM SPSS (2011), and 
G*Power 3 (Faul, Erdfelder, Lang, & Buchner, 2007).

Figure 2. The Affect Grid (Russel, et al., 1989). The x-axis is hedonic valence. 
The y-axis is Level of arousal. The four corners display emotional landmarks.



50

THOMAS, RICHARDSON-VEJLGAARD

Results

Feasibility
The observation period lasted an average of  14 

days (range = 13–15). Attrition was low, as 91% of  the 
participants completed the study protocol through to 
the follow-up. Physical activity data was captured for 
86.4% of  all days observed; sleep data was captured 
79.3% of  all nights. All participants received one EMA 
on intake and follow-up days, and two EMAs on all 
other days of  observation, up to 14 days total. On aver-
age, 70.6% of  the EMAs were responded to. Facebook 
content composed during the observation period 
consisted of  an average of  189 words (range = 14–468, 
SD = 142.47). Adherence was high—cumulatively 83.6% 
of  targeted data was collected. Participants reported 
minimal interference with daily life and had generally 
positive commentary about the design. A systematic 
review of  previous studies using experience sampling 
methods comparable to ours reported similar partici-
pant compliance rates, with an average of  73–90% of  
targeted data acquired, and an 82–86% completion rate 
(Csikszentmihalyi, & Larson, 2014). The platform has 
shown to be highly feasible.

Convergent Validity
Indeed, evidence suggests that physical activity, 

sleep, social media activity, and momentary affect 
change concurrently with MDD status. Theoretically, as 
depression severity increases, physical activity levels and 
sleep quality should decrease. As psychologically positive 
social media content dissolves, and psychologically 
negative social-media content surfaces, depression 
severity is expected to increase. As momentary negative 
affect is more frequently reported, depression severity 
is anticipated to increase as well. Here we report if  the 
behavioral data collected by the platform is reflective 
of  these theories.

Physical activity. A bivariate correlation was 
conducted comparing average steps per day and BDI 
score. A moderate inverse correlation was very near 
significant (r = -.501, p = .058), suggesting a trend of  
higher levels of  physical activity being associated with 
lower levels of  depression. In agreement with the antici-
pated association, physical activity levels measured by 
actigraph, and BDI score validly converge.

Sleep. Bivariate correlations were conducted inde-
pendently comparing minutes of  total sleep, minutes of  
light sleep, and minutes of  restful sleep to BDI score. 
Restful sleep duration and BDI score were found to 
be moderately inversely correlated (r = -.536, p = .045). 
Getting more restful sleep was associated with lower 
levels of  depression. In agreement with the anticipated 
association, sleep quality measured by actigraph, and 
BDI score validly converge.

Facebook content. Bivariate correlations were 
conducted independently comparing each of  the 
44 linguistic variables mined from LIWC with BDI 
score. A moderate correlation with BDI was found in 
the percentage of  masculine linguistic content (e.g., boy, 
man, he) (r = .590, p = .028). Composing more mascu-
line words during the observation period was associated 
with higher levels of  depression. A moderate inverse 
correlation with BDI was also found in the percent-
age of  future-tense linguistics (e.g., will, soon, going to) 
(r = -.539, p = .043). Composing more future focused 
words during the observation period was associated with 
lower levels of  depression. A third moderate correlation 
with BDI was found in the presence of  perceptual-
processing through ‘feeling’ linguistics, including 
emotion-focused words (e.g., feel, sad, angry) (r = .532, 
p = .046). Composing more words in the ‘feeling’ cate-
gory during the observation period was associated with 
higher levels of  depression.

Surprisingly, explicitly positive or negative linguis-
tic content (e.g., positive emotional tone and negative 
emotional tone) was unrelated to BDI score. Instead, 
more dynamic associations were found. Masculine 
content being associated with depression is perplex-
ing. It may be an artifact that emerged, in part, because 
of  the 2016 Presidential Election. These data were 
collected from February–May of  2016, during which 
a blitz of  negative content circulated social media 
concerning “him”—Donald Trump—which may be 
a confounding variable. The relationships between 
future-tense linguistics and depression, and feeling 
linguists and depression are not surprising. Rumination, 
particularly about the past, is a common attribute in the 
Self-Regulatory Executive Function (S-REF) model of  
depression (Wells, & Matthews, 1996). Participants with 
more symptoms of  depression may have used Facebook 
as a vehicle to express their ruminative or retrospec-
tive thoughts. The correlation we found is reflective 



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DETECTING DEPRESSION SYMPTOMS USING INTERACTIVE TECHNOLOGIES

of  the S-REF model. Furthermore, Billings, & Moos 
(1984) demonstrated that recruiting social resources for 
emotional-discharge—that is, expressing your emotional 
problems to others—is a common coping strategy 
used by individuals with MDD. Participants may have 
utilized their social networks on Facebook to engage in 
emotional-discharge. In line with these theories, two of  
the linguistic variables that emerged from the behavioral 
data that the platform collected validly converge with 
BDI score.

EMA Affect Grid. Bivariate correlations were 
conducted independently comparing global hedonic 
valence, and arousal to BDI score. A moderate inverse 
correlation was found between global hedonic valence 
and BDI score (r = -.582, p = .03). Lower average ratings 
of  hedonic valence—that is, repeatedly reporting 
unpleasant feelings—was associated with higher BDI 
scores. In agreement with the anticipated association, 
ecologically assessed negative affect and BDI score 
validly converge.

Ancillary Analysis
Given the good convergent validity of  each of  

our behavioral domains with depression rating, we 
attempted a multiple linear regression to predict BDI 
score based on average daily steps, average nightly 
minutes of  restful sleep, global hedonic valence, and 
percentage of  future-focused, feeling, and masculine 
linguistics in Facebook content. The underpowered 
model (β-1 = .46) was unable to achieve significance 
(F(6,4) = 1.450, p = .375, R2 = .685). With R2 = .685, 
α = .05, and six predictor variables, the model needs 
n = 18 to achieve >95% power.

Discussion

Using data generated by interactive technologies, 
the platform was able to identify six discrete behav-
ioral measures that have good convergent validity with 
the BDI for assessing depression symptoms (five were 
significant, one was trending towards significance). If  
deployed in a primary care setting, providers would be 
able to remotely monitor patients from the onset of  
their prescribed antidepressant regimen. The presented 
evidence suggests that the platform is capable of  
detecting behaviors reminiscent of  remission, or more 
importantly, lack thereof, in real time. Thus, the platform 

can supply healthcare providers with valuable informa-
tion that helps identify patients who require medication 
adjustment or augmentation. With further research to 
achieve more statistical power, the regression model 
tested in the ancillary analysis has the potential to have 
unprecedented predictive ability of  depression rating, 
explaining 68.5% of  felt symptom variance.

Limitations
We believe the platform is in its infancy. We 

have presented evidence that suggests it can validly 
monitor depression symptoms; however, we have not 
yet developed the software infrastructure needed to 
deploy the platform on a large scale. To achieve large 
scale deployment, we must collaborate with software 
developers to automate the platform’s back-end data 
aggregation methods.

Our analyses were inherently limited by our sample 
size, and BDI range (0–15). In order to strengthen the 
validity of  the platform, our findings must be repli-
cated with a larger sample size, and an expanded BDI 
range, which requires studying more severely depressed 
individuals. As this range and the sample size grows, 
it is imperative to be iterative in re-mining LIWC and 
re-testing other available variables for new correlations.

Ethical considerations are important when provid-
ing clinicians with access to personal information that 
is not traditionally considered related to healthcare. 
Some patients may be uncomfortable with having their 
behaviors tracked in the manner that the platform does. 
As we further develop the platform, all back-end soft-
ware development will need to be secured to federally 
mandated privacy standards for patient data, and all 
identifiable data will need to be classified as protected 
health information. Additionally, patients will need to 
provide consent prior to enrolling in the platform.

Cost of  implementation is another potential limit-
ing factor. Although designed to consider cost, some 
patients will inevitably have economic restrictions that 
limit their enrollment in the platform. The actigraph we 
utilized retails for $19.99. It may be possible to mitigate 
this expense by capitalizing on select health insurance 
benefits (e.g., Humana, 2016) which provide activity 
trackers to customers at no cost; however, this option is 
not available to everyone. Perhaps the most substantial 
limiting economic factor is that enrolment requires easy 
access to a computer or smartphone for social media 



52

THOMAS, RICHARDSON-VEJLGAARD

participation, and the EMA requires a smartphone. A 
shrinking minority of  Americans either don’t use social 
media, or don’t possess a smartphone and hence, the 
platform will be unavailable to these individuals.

The presented study only establishes preliminary 
validity of  the constructs that the platform monitors. 
This does not necessarily mean that the platform will 
assist real-life identification of  patients in need of  treat-
ment adjustment/augmentation. Testing this requires 
a randomized controlled field implementation study 
comparing the platform versus a sham monitoring 
system. All of  the presented findings must be consid-
ered alongside these limitations. Substantial further 
research and development is needed.

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Appendix A

Affect Grid Training

With the grid in front of  them, participants were read the following passage:

This is the Affect Grid. It is intended to capture how you feel in a given 
moment. First you orient yourself  with the horizontal axis. On the left side 
of  the grid represents unpleasant feelings, on the right are pleasant feelings, 
and in the middle is neutral. After you have located your place on this axis, 
you then adjust either up or down on the vertical axis according to your 
current energy level. At the top represents high arousal or high energy, 
and the bottom represents sleepiness or low arousal. Again, the middle is 
neutral. In the four corners you will find landmarks to help you navigate the 
grid. These landmarks are emotions that many people conceptualize certain 
couplings of  feelings and arousal. For example, someone with unpleasant 
feelings and high arousal may view that state as stress. Inversely, pleasant 
feelings with low arousal may feel like relaxation. These landmarks are 
there to help you navigate the grid; however, you may conceptualize the 
feelings differently than other people. That is ok. Please more so focus on 
your location along the horizontal and vertical axis. When you are ready, 
please indicate so by touching the center of  the square associated with 
where you are located.



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DETECTING DEPRESSION SYMPTOMS USING INTERACTIVE TECHNOLOGIES

Appendix B

List of  LIWC Variables Analyzed

Summary Dimensions

Word count
Analytic
Clout
Authentic
Tone
Words per sentence
Words longer than six letters
Dictionary word count

Perceptual Processes

See
Hear
Feel

Biological Processes

Body
Health
Sexual
Ingest

Drives

Affiliation
Achieve
Power
Reward
Risk

Time Orientation

Past Focus
Present Focus
Future Focus

Affect

Positive Emotional Tone
Negative Emotional Tone
Relativity:
Motion
Space
Time

Social

Family
Friend
Feminine
Masculine

Cognitive Processes

Insight
Casual
Discrepancies
Tentative
Certainty
Differentiation

Personal Concerns

Work
Leisure
Home
Money
Religion
Death


