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Assessing the Availability of Users to Engage in Just-in-Time Intervention in the Natural Environment


Abstract and Figures

Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. We take a first step in modeling users' availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. We use delay in responding to a prompt to objectively measure availability. We compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. We find that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. We find that users are least available at work and during driving, and most available when walking outside. Our model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out.
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Assessing the Availability of Users to Engage in
Just-in-Time Intervention in the Natural Environment
Hillol Sarker, Moushumi Sharmin, Amin Ahsan Ali, Md. Mahbubur Rahman,
Rummana Bari, Syed Monowar Hossain, Santosh Kumar
Dept. of Computer Science, University of Memphis, TN 38152, USA
{hsarker, msharmin, aaali, mmrahman, rbari, smhssain, skumar4}
Wearable wireless sensors for health monitoring are enabling
the design and delivery of just-in-time interventions (JITI).
Critical to the success of JITI is to time its delivery so that the
user is available to be engaged. We take a first step in mod-
eling users’ availability by analyzing 2,064 hours of physio-
logical sensor data and 2,717 self-reports collected from 30
participants in a week-long field study. We use delay in re-
sponding to a prompt to objectively measure availability. We
compute 99 features and identify 30 as most discriminating to
train a machine learning model for predicting availability. We
find that location, affect, activity type, stress, time, and day of
the week, play significant roles in predicting availability. We
find that users are least available at work and during driving,
and most available when walking outside. Our model finally
achieves an accuracy of 74.7% in 10-fold cross-validation and
77.9% with leave-one-subject-out.
Author Keywords
Intervention; Interruption; EMA; Self-Report; Mobile
Application; Mobile Health
ACM Classification Keywords
H.1.2. Models and Principles: User/Machine Systems
Mobile technology has a potential to provide unprecedented
visibility into the health status of users in their natural en-
vironment [29]. Sensors embedded in smart phones (e.g.,
GPS, microphone), and wireless sensors worn on the body
(e.g., electrocardiography (ECG), accelerometers) can con-
tinuously monitor an individual’s health, behavior, and the
surrounding environment. Machine learning algorithms have
been developed to obtain measures of behavior and exposure
to the environment such as activity from accelerometers, geo-
exposure from GPS, stress from physiology, and social con-
text from microphone. These automated measures of behav-
ioral and environmental contexts enable the design of just-in-
time interventions (JITI) to support maintenance of healthy
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behaviors. However, the success of JITI depends on timing
the delivery of intervention so that users are available physi-
cally, cognitively, and socially to attend to the intervention.
We use smoking cessation to illustrate the potential of JITI
and the importance of timing the delivery of JITI. Smoking
is responsible for most deaths in the US, accounting for one
in five deaths [34]. Although majority of daily smokers want
to quit, less than 10% succeed according to Center for Dis-
ease Control. The highest lapse rate among newly abstinent
smokers is in the first week when over 50% of them lapse [1].
Smoking lapse is impulsive and the first lapse usually leads to
full relapse [47]. Hence, it is critical to help abstinent smok-
ers break their urge when and where it occurs (within first few
days of quitting). Although wearable sensors now provide us
an ability to detect the potential precipitants (e.g., stress [40]
or smoking cues detected via smart eyeglasses) and trigger
a JITI to break the urge, but it will succeed only if the user
is available to be engaged when the JITI is delivered. Oth-
erwise, we may lose the precious opportunity to prevent the
potent first lapse. Hence, timing a JITI is critical.
Considerable research have been conducted in a closely re-
lated topic of interruptibility [13, 23]. These works largely
aim to detect interruptibility of a user at workplace by analyz-
ing the user’s computer activity (e.g., key stokes), workplace
status via audio and/or video capture of the workplace, phone
status, and physical activity status via wearable sensors. Re-
search on interruptibility provides insights about tasks or so-
cial contexts where a person is more interruptible, however,
lessons from these studies cannot adequately guide the design
of JITIs. This is because, unlike the case of interruption that
may disrupt concentration of a task, JITI is aimed at improv-
ing the user’s health and require appropriate engagement of
the user. Further, these works asked users to rate their avail-
ability in-the-moment. Such reports are subjective, can be-
come an additional source of disruption, and do not assess
user’s capability to engage in a JITI.
In this paper, we develop a model to predict availability in
the natural environment. Our model is derived from data col-
lected from a week-long mobile health study with 30 partic-
ipants. During the study, participants wore a wireless physi-
ological sensor suite that collected ECG, respiration, and ac-
celerometry, and carried a smart phone that included GPS and
accelerometers. Participants were prompted by a smartphone
to complete Ecological Momentary Assessment (EMA) self-
reports consisting of 42 items, multiple times daily. Answer-
ing these 42-items required a level of engagement expected
in JITI. Each EMA was associated with micro-incentive to
encourage compliance [36].
To address the biases in human estimates of availability [4],
we use delay in responding to EMA as an objective metric
to measure the availability of a participant. To predict avail-
ability, we use GPS traces to identify participants’ location
and driving state, infer their physical activity states from on-
body accelerometers, and stress from ECG and RIP sensor
data. In addition, we use time of day, day of the week, and
self-reported affect, activity type, and conversation status. We
compute a total of 99 features.
We identify 30 most discriminating features and train a ma-
chine learning model to predict the availability of a user. We
find that several features derived from sensors such as lo-
cation, activity type, time, and day of the week, play sig-
nificant roles in predicting availability. In particular, fea-
tures derived from stress (inferred from physiological sen-
sors) play a significant role in predicting availability. We
find that the machine learning model can predict availability
with 74.7% accuracy (against a base accuracy of 50%). This
compares favorably against existing works on predicting in-
terruptibility, where the prediction accuracy was reported to
be 79.5% against a base accuracy of 70.1% in the office en-
vironment [14], and an accuracy of 77.85% against a base
accuracy of 77.08% in the natural environment [42]. We find
that users are usually available when walking outside of their
home or work, or even if just outside of their home or work lo-
cation. But, they are usually not available when driving or at
work. We also find that participants are more available when
they are happy or energetic versus when they are stressed.
In summary, our work makes the following contributions: 1.)
we propose a novel objective approach to determine user’s
availability to engage in a task which requires significant user
involvement (as compared to [14, 21, 42]), 2.) we propose a
model with 74.7% accuracy (over 50% base accuracy) and
0.494 kappa to predict availability in the natural environment
using data collected from a real-life field study with wear-
able sensors, and 3.) to the best of our knowledge this is
the first study related to interruptibility which uses micro-
incentives [36] to obtain a stronger indicator of unavailability.
We note that EMAs are widely used in scientific studies
on addictive behavior [33, 45, 46], pain [49], and mental
health [2, 30, 38, 50]. While EMAs have obvious benefits,
prompting EMAs at inopportune moments can be very dis-
ruptive for the recipients’ current task [44] or social situa-
tion [5, 44]. The work presented here can directly inform the
appropriate timing for delivering EMA prompts.
In an era of mobile computing and ubiquitous sensors, we
have unprecedented visibility into user’s contexts (e.g., phys-
ical, psychological, location, activity) and this awareness can
be used to guide the design of interventions.
A home reminder system for medication and healthcare was
reported in [24]. Smart home and wearable sensors were
used to identify a person’s contextual information for trig-
gering an intervention. A similar study was conducted us-
ing smart home sensors to remind patients about their med-
ications in [19], which considered availability of the patient
when triggering a prompt, e.g., the system did not trigger a
reminder when the patient was not at home, was in bed, or
was involved in a phone conversation. A context sensitive
mobile intervention for people suffering from depression was
developed in [10]. Data from phone sensors such as GPS and
ambient light, and self-reported mood were used to infer con-
textual information of the patient and predictions were made
about future mental health related state to trigger an appropri-
ate intervention. A system to assist diabetes patients was re-
ported in [43] to keep track of their glucose level, caloric food
intake, and insulin dosage by logging user contexts (e.g., lo-
cation from GSM cell tower, activity) and used these logged
data to learn trends and provide tailored advice to the user.
This thread of research highlights the tremendous capabilities
and utility of mobile sensor inferred context-sensitive inter-
ventions. However, research in this area focuses primarily on
determining the time of triggering the intervention. A timely
intervention may still not be effective if the receiver is not
available physiologically or cognitively to engage in that in-
tervention. Thus, assessing the cognitive, physical, and social
availability of a user in the natural environment will extend
and complement research in this area.
Research on interruption is closely related to availability of
an individual. A vast majority of research in this area fo-
cused on understanding the impact of interruption in work-
places. A feasibility study for detecting interruptibility in
work environment used features extracted from video capture
(a simulated sensor) [21]. Subjective probe of interruptibility
in Likert scale was converted to binary labels of interrupt-
ible and highly non-interruptible. A machine learning model
was able to classify these states with an accuracy of 78.1%
(base=68.0%). An extension of this research used sensors
(e.g., door magnetic sensor, keyboard/mouse logger, micro-
phone) installed in the office [14], which improved the accu-
racy to 79.5% (base=70.1%).
These studies provide insights on interruptibility in carefully
instrumented controlled environment (i.e., office), but may
not capture the user’s receptivity outside of these environ-
ments. For instance, a smoking urge may occur outside of
office setting, where most of the above used sensors (e.g.,
video, keystrokes, etc.) may not be available. In addition, the
approach of probing users at regular intervals to gauge their
interruptibility may not indicate their true availability due to
subjective biases as pointed out in [12].
Research on interruption in the natural environment has pri-
marily focused on determining the receptivity of a user to re-
ceive a phone call. In [20], a one-day study was conducted
with 25 users who wore accelerometers and responded to
prompted EMA’s on whether they are currently receptive to
receiving phone calls. Using accelerometers to detect transi-
tion, it is shown that people are more receptive during postu-
ral transition (e.g., between sitting, standing, and walking).
The first work to use an objective metric was [12] that con-
ducted a week-long study with 5 users. It collected the mo-
ments when users changed their ring tones themselves and
also in response to a prompt generated every 2 hours. By
using phone sensors (e.g., GPS, microphone, accelerometer,
proximity) to infer phone posture, voice activity, time, and
location, and training a person-specific model, it was able to
predict the ringer state with an average accuracy of 96.1%.
The accuracy dropped to 81% if no active queries were used.
We note that predicting the state of ringer is a broad measure
of the interruptibility of a user to receive calls and it does not
indicate the user’s availability to engage in a JITI.
The closest to our work is a recent work [42] that conducted a
large-scale study (with 79 users) to predict user’s availability
to rate their mood on 2 items when prompted by their smart-
phones. The prompt occurred every 3 hours, if the phone
was not muted. The notification is considered missed if not
answered in 1 minute. The users can also actively reject a no-
tification. A model is developed based on phone sensor data
(location provider, position accuracy, speed, roll, pitch, prox-
imity, time, and light level) to predict availability. It reports
an accuracy of 77.85% (base=77.08%, kappa=0.17), which is
only marginally better than chance.
The work presented here complements and improves upon
the work reported in [42] in several ways. First, [42] re-
cruited volunteers without any compensation. Other works
in the area of interruptibility also either used no compen-
sation [12, 42] or a fixed compensation [20, 22, 31, 32] for
participation. Micro-incentives are now being used in scien-
tific studies to achieve better compliance with protocols [36].
Ours is the first work to use micro-incentive to enhance par-
ticipant’s motivation. In [42], participants answered only
23% of the prompts (1508 out of 6581), whereas in our study
participants responded to 88% of the prompts (2394 out of
2717) within the same 1 minute cutoff used in [42]. This is
despite the fact that our EMA’s are more frequent (upto 20
per day) and require a deeper involvement (to complete 42
item questionnaires), which may be the case with JITI that re-
quire frequent and deeper engagement. Therefore, our work
complements all existing works by providing a stronger mea-
sure of unavailability, not considered before. Second, we use
wearable sensors in addition to a subset of smartphone sen-
sors used in [42]. Third, we report a significantly higher accu-
racy of 74.7% (over 50% base accuracy) and a kappa of 0.494
compared to [42]. Finally, to the best of our knowledge, this
is the first work to directly inform the timing of delivering
EMA prompts in scientific studies that use micro-incentives.
In this paper, we analyze data collected in a scientific user
study that aimed to investigate relationship among stress,
smoking, alcohol use, and their mediators (e.g., location, con-
versation, activity) in the natural environment when they are
all measured via wearable sensors, rather than via traditional
self-reports. The study was approved by the Institutional Re-
view Board (IRB), and all participants provided written in-
formed consents. In this section, we discuss participant de-
mographics, study setup, and data collection procedure.
Participants: Students from a large university (approximately
23,000 students) in the United States were recruited for the
study. Thirty participants (15 male, 15 female) with a mean
age of 24.25 years (range 18-37) were selected who self-
reported to be “daily smokers” and “social drinkers”.
Wearable Sensor Suite: Participants wore a wireless physio-
logical sensor suite underneath their clothes. The wearable
sensor suite consisted of two-lead electrocardiograph (ECG),
3-axis accelerometer, and respiration sensors.
Mobile Phone: Participants carried a smart phone that had
four roles. First, it robustly and reliably received and stored
data wirelessly transmitted by the sensor suite. Second,
it stored data from GPS and accelerometers sensors in the
phone. These measurements were synchronized to the mea-
surements received from wearable sensors. Third, partici-
pants used the phone to complete system-initiated self-reports
in the field. Fourth, participants self-reported the beginning
of drinking and smoking episodes by pressing a button.
Self-report Measures: The mobile phone initiated field
questionnaires based on a composite time and event based
scheduling algorithm. Our time-based prompt was uniformly
distributed to provide an unbiased experience to participants
throughout the day. However, using only time-based prompts
may not facilitate EMA collection about interesting events
such as smoking or drinking. To capture these, a prompt was
also generated around a random subset of self-reported smok-
ing and drinking events.
For availability modeling, we only use random EMAs that are
similar to sensor-triggered JITI in unanticipated appearance.
The 42-item EMA asked participants to rate their subjective
stress level on a 6-point scale. In addition, the EMA requested
contextual data on events of interest (stress, smoking, and
drinking episodes). For example, in case of a stress, users
were asked about social interactions, for smoking episodes
they were asked about presence of other smokers, and for
drinking, they were asked about the number of drinks con-
sumed. EMAs pose burden on the users [23] and we adopted
several measures to reduce this burden. First, the smart phone
software was programmed to deliver no more than 20 ques-
tionnaire prompts in a day. Second, two subsequent EMA
prompts were at least 18 minutes apart. Third, the anticipated
completion time of the EMA was designed to range between
1 and 3 minutes. As selection of different answers leads to
different paths, we report a time range considering the maxi-
mum and the minimum possible path length. Fourth, partic-
ipants had the option of delaying an EMA for up to 10 min-
utes. If the participant did not respond to the prompt at the
second opportunity, the prompt would disappear. Fifth, par-
ticipants were encouraged to specify time periods in advance
(every day before beginning the study procedure) when they
did not wish to receive prompts (e.g., during exams).
Participant Training: A training session was conducted to in-
struct participants on the proper use of the field study devices.
Participants were instructed on the proper procedures to re-
move the sensors before going to bed and put them back on
correctly the next morning. In addition, participants received
Classified as
Home Work Store Restaurant Other
Home 617 11 10 0 4
Work 12 708 1 0 1
Store 8 7 203 6 9
Restaurant 4 1 43 27 3
Other 62 14 40 196
Table 1. Confusion Matrix for the Semantic Labeling model [27].
an overview of the smart phone software’s user interface, in-
cluding the EMA questionnaires and the self-report interface.
Once the study coordinator felt that the participant under-
stood the technology, the participant left the lab and went
about their normal life for seven days. For all seven days,
the participant was asked to wear the sensors during working
hours, complete EMA questionnaires when prompted, and
self-report smoking and drinking episodes.
Incentives: We used micro-incentives to encourage compli-
ance with EMA’s [36]. Completing a self-report question-
naire was worth $1, if the sensors were worn for 60% of the
time since last EMA. An additional $0.25 bonus was awarded
if the questionnaire was completed within five minutes. A
maximum of 20 requests for self-reports occurred each day.
Thus, the participant could earn up to $25 per day ($1.25 x
20 self-report requests), adding up to $175 over seven days
of field study ($25 x 7). Since wearing physiological sensors
and answering 42-items questionnaire upto 20 times daily are
highly burdensome, level of compensation was derived from
the prevailing wage in similar behavioral science studies [36]
that involve wearable sensors. Most interruptibility studies
provided fixed incentive to participants for completing the
study [20, 22, 31, 32], while some studies were purely vol-
untary [42]. We believe that micro-incentive associated with
each EMA helps obtain a stronger measure of unavailability.
Data Collected: Average number of EMA prompts delivered
per day was 13.33, well below the upper limit of 20 per day.
This EMA frequency is consistent with prior work [14]. EMA
compliance rate was 94%. An average of 9.83 hours per day
of good quality sensor data was collected from physiological
sensors across all participants.
In this section, we describe the procedure we use to infer par-
ticipant’s semantic location from GPS traces, activity from
accelerometers, and stress from ECG and respiration. In each
case, we adapt existing inference algorithms.
Inference of Semantic Location
Locations of interest and their semantic labels are determined
from GPS traces that were collected on the phone. Figure 1
shows a typical GPS trace of a participant for one day. Places
of interest for a participant were places where the participant
spent a significant amount of time. We first apply a clustering
algorithm to the GPS data using the method proposed in [35].
Distance threshold of 100 meters and temporal threshold of 5
minutes are used to find the spatio-temporal clusters through-
out the day for each participant. These clusters represent the
Figure 1. A sample GPS trace for one day from a participant. The red
line shows the path commuted by the participant. The pinned locations
are the location at the time of EMA prompt.
locations of interest. Next, we assign semantic labels to these
locations using Semantic Context labeler from [27].
Label assignment is based on demographic, temporal and
business features. Demographic features include the age and
gender of the participant, which are obtained from recruit-
ment forms. The temporal features include the arrival time,
visit midpoint time, departure time, season, holiday, and the
duration of stay at that location. These features were com-
puted from the GPS traces and clusters. Lastly, the busi-
ness features include the count of different types of busi-
ness entities such as Arts/Entertainment, Food/Dining, Gov-
ernment/community, Education, etc. within different distance
thresholds from the current location (see [27] for details).
To compute the business features, we used Google Places
API. For this model, we obtain an accuracy of 85.8% (and
κ= 0.80). Table 1 presents the confusion matrix for this se-
mantic context labeler model where F-measure is 0.85 and
area under the curve is 0.97. We observe that Home, Work,
and Store are detected quite well. But, Restaurant is con-
fused with Store, because a Store and a Restaurant can be
co-located. We correct the labels (if necessary) by plotting
the GPS traces in Google earth and by visually inspecting it.
These location labels were considered as ground truth. But,
in some cases we could not reliably distinguish between a
store and a restaurant (due to inherent GPS inaccuracy). We
discard these data points by marking them unknown.
We also obtain a detailed level of semantic labeling. For
Home, detailed label can be Indoor Home, Dormitory, and
Backyard. Figure 2 shows a detailed breakdown of the labels.
Our labeling concept of these details evolved over time [28]
(e.g., by adding new levels). Hence, we made multiple itera-
tions to obtain consistent labels.
Driving Detection
Driving is detected from GPS-derived speed and by applying
a threshold for maximum gait speed of 2.533 meters/sec [9].
A driving session is composed of driving segments separated
by stops, e.g., due to a traffic light being red. Stops usually
are of short duration unless there is a congestion. The end of
a driving session is defined as a stop (speed=0) for more than
2 minutes. Otherwise, two driving segments sandwiched by
a less than 2 minute stop is considered to be part of the same
driving session. In case of loss of GPS signal for more than
Figure 2. Two level semantic labeling of GPS clusters.
Figure 3. Standard deviations <0.21384 are labeled as stationary and
others are labeled as non-stationary (i.e., walking or running).
30 seconds we also end the driving session at the timestamp
when we received the last GPS sample. In order to deter-
mine whether participant is driving or just riding a vehicle
we use the EMA question “If you commuted since the last
interview, what type?”, where possible responses are “Driv-
ing”, “Biking”, “Walking”, “Riding as a Passenger”, “Rid-
ing Public Transportation”, and “Did not commute”. Finally,
if an EMA prompting time is between start and end of a driv-
ing session, and the self-report response mentions “Driving”,
we mark that EMA to occur during driving.
Activity Inference
To infer whether a subject is in motion or not, we use a simple
threshold based activity detector using the 3-axis on-body ac-
celerometer (placed on chest). Phone accelerometer data was
not used because the phone may not be on the person and
thus may miss some physical activity. We adapt the physical
Figure 4. Using cut-off point 0.21384 we observe that subjects were phys-
ically active for around 20% of their total wearing time.
movement detection approach in [3,39]. As the placement of
the accelerometer and the participant population is different
from that presented in prior works, we collected training data
to determine an appropriate threshold for detecting activity.
We collected labeled data under walking and running (354.16
minutes), and stationary (1426.50 minutes) states from seven
pilot participants who wore the same sensor suite. Figure 3
shows the training data from seven pilot participants. We
filtered the raw signal, removed the drift, and extracted the
standard deviation of magnitude, which is independent of the
orientation of the accelerometers and recommended in liter-
ature [3, 39]. We find the distinguishing threshold for our
accelerometer to be 0.21384, which is able to distinguish sta-
tionary from non-stationary states with an accuracy of 97%
in 10-fold cross-validation. Figure 4 shows that subjects were
physically active for around 20% of their total wearing time.
Stress Inference
Measurements from the ECG and RIP sensors were used to
extract 13 features (including heart rate variability) for phys-
iological stress model as proposed in [40]. The model pro-
duces binary outputs on 30 second segments of measurements
that indicate whether a person is stressed or not. A correlation
of 0.71 between the stress model and the self-reported rating
of stress was reported in [40] in both lab and 2-day field study
with 21 participants. As proposed in [40], stress inference is
discarded when the participant is not stationary.
We define availability as a state of an individual in which
(s)he is capable of engaging in an incoming, unplanned ac-
tivity. For example, consider a software engineer who has
just quit smoking, is working on a project, when (s)he re-
ceives a JITI on the mobile phone, perhaps triggered by an
acute stress detection from sensors. In response, (s)he could
– 1) stop ongoing work and engage in the intervention (e.g.,
do a biofeedback exercise to relax), 2) continue working on
the project for a short time (pre-specified threshold) and then
stop the work to engage in JITI, 3) continue working on the
project but attend to JITI later, or 4) completely ignore the
JITI. In our proposed definition, for cases 1 and 2 the soft-
ware engineer will be considered as available while for cases
3 and 4 (s)he will be considered unavailable. We first con-
sider delay in starting to answer a randomly prompted EMA
as a metric for measuring availability.
Response Delay
Response delay for an EMA is the duration between the
prompt time and the time of completion of the first item in
the EMA. Figure 5 shows the probability distribution of re-
sponse delay across all participants. Delay distribution fits a
Gamma distribution with shape parameter κ= 1.2669 and
scale parameter θ= 35.5021. We use the p= 0.95 cutoff
(which occurs at 124 seconds) as the grace period to obtain a
good separation between the available and unavailable states.
Since each EMA is associated with a micro-incentive, it is
plausible that some participants may be financially sensitive
and fill out each EMA in a timely fashion, even when not fully
available. In such cases, they may complete some EMAs
Figure 5. Delay distribution is fitted with a Gamma distribution with
shape parameter κ=1.2669 and scale parameter θ=35.5021. We use the
cutoff of p= 0.95 that occurs at 124.1 seconds, as the grace period. A
response delay beyond this grace period is marked as unavailable.
quickly without sufficient care. We, therefore, consider com-
pletion time as another metric to complement response delay.
Completion Time
Completion Time for an EMA is the ratio of total comple-
tion time to the number of items answered. However, time
to answer the first item includes the time to take the phone
out. Therefore, we compute completion time, starting from
the second item. Finally, there is between person difference
in completion time due to participant’s cognitive processing,
typing variations, and affinity to micro-incentive. To remove
these biases, we compute the z-score of completion time for
each participant and then use this z-score in further analysis.
To investigate if there is a threshold such that a completion
time of lower than this threshold indicates urgency and lack
of care in answering an EMA, we measure the consistency of
response to the EMA. For this purpose, we use a measure of
consistency that is used widely in psychometrics. It is called
Cronbach’s alpha [8]. For a given set of items in an EMA
(with numerical responses) that measure the same psycholog-
ical construct, Cronbach’s alpha is given by
k1(1 Ps2
where kis the number of items in the set, s2
iis the variance
in response to the ith item, and s2
Tis the variance of the total
scores formed by summing up the responses to all the items
in the set. We observe that if all the items in the set have equal
variance and thus were perfectly correlated, we obtain α= 1.
On the other hand, if all the items in the set are independent,
α= 0. An α0.7is regarded as acceptable [8].
In our 42-item EMA, there are several affect items that
measure the same psychological construct. These items
are Cheerful?,Happy?,Energetic?,Frustrated/Angry?,Ner-
vous/Stressed?, and Sad?, where participants respond on
a Likert scale of 1–6. To compute alpha, items that as-
sess positive affect (Cheerful,Happy, and Energetic) are re-
tained as scored and items that assess negative affect (Frus-
trated/Angry,Nervous/Stressed, and Sad) are reverse coded
(e.g., 1 becomes 6). To test whether these six items indeed
measure the same psychological construct, we compute the
Figure 6. We plot Cronbach’s alpha value for various thresholds of
z-score of completion times. We observe that the alpha is always accept-
able (i.e., 0.7). This holds even when we consider various subsets of
items that require recollection or multiple choice selection.
overall alpha score for all responses from all participants. The
overall α= 0.88 indicates a good agreement [16].
We next compute the Cronbach’s alpha score for various
thresholds of (z-scores of) completion times. We observe
that the 42-item EMA questionnaire contains different types
of item. First, there are single choice items which partici-
pant can answer right away. Second, there are multiple choice
items which requires going through various possible answers,
which may take more time. Third, there are recall based items
where participants need to remember about past actions. An
example of such an item is “How long ago was your last con-
versation?”. Such items may require longer to complete. We
consider subsets of EMA items in each of the above three
categories and compute their corresponding z-scores. Fig-
ure 6 plots the alpha values for various thresholds on com-
pletion times for four cases — when the completion time for
all items is considered and when the completion times for
each of the above three subset of EMA items is considered.
Since our goal is to find a lower threshold such that com-
pleting EMA items quicker than this threshold may indicate
lack of availability, we only plot completion times lower than
average (i.e., z-score of 0). We observe that in each case,
α0.7, which implies that completing EMA items quickly
does not indicate inconsistent response. Hence, completion
time is not a robust estimator of unavailability and we retain
only the response delay as our metric of unavailability.
Labeling of Available and Unavailable States
When an EMA prompt occurs, the phone beeps for 4 minutes.
If the participant begins answering or presses the delay but-
ton, this sound goes away. There are 4 possible outcomes for
each such prompt — i) Missing: Participant neither answers
the EMA nor presses delay, i.e., just ignores it, ii) Delayed:
Participant explicitly delays the EMA, and plans to answer it
when (s)he becomes available, iii) After Grace: Participant
answers after a grace period, which is defined in Figure 5, iv)
Before Grace: Participant answers within the grace period.
We mark the first three scenarios as Unavailable.
To identify available EMAs, we use two different approaches.
In the first, we take nquickest answered EMA’s from each
participant, where nis the number of EMA prompts when this
participant was found to be unavailable. We mark each such
EMA as available. We call this a Representative dataset, be-
cause it gives more weight to those participants’ data, who
sometimes forego micro-incentives by missing or delaying
EMA’s when they are not available. This may be similar to the
situation in a class where several students may have a ques-
tion, but only a few speak up, thus helping others who may
be shy. This dataset gives less weight to data from those par-
ticipants who are always prompt in answering EMA’s, due to
their sincerity, scientific devotion to the study, or affinity to
micro-incentives. This dataset thus recognizes and respects
wide between person variability inherent in people.
Counting missed, delayed, or delayed above grace period
(124.1s), we label 170 EMA’s as triggered when participants
were unavailable. Number of instances when a participant
was unavailable ranges from 0 to 15. By marking nquickest
answered EMA from each participant as available, where n
is the number of EMA prompts for which that particular par-
ticipant was unavailable, we obtain a total of 340 EMAs for
training data. This dataset provides a robust separation of de-
lay between the available and unavailable class (with a mean
of 141.4s±51.7sand a minimum separation of 107.7s). This
kind of wide separation helps us mitigate the effect of delay
in taking out the phone to answer an EMA.
Due to the definition of Representative dataset, 3 participants
are completely ignored due to always being compliant, re-
sponding within grace period, and never delaying an EMA.
Hence, we construct a Democratic dataset, where we con-
sider equal number of EMA’s from each participant. To ob-
tain a similar size of training data as in the Representative
dataset, we use 6 quickest EMA from each participant as
available and 6 slowest (including delayed or missed) as un-
available. We thus obtain 12 samples from each participant,
making for a total of 360 samples. The delay separation be-
tween available and unavailable class in this dataset has a
similar mean of 169.8s, but a higher standard deviation of
193.8s, and a smaller minimum separation of 5.2s.
Before presenting our model for predicting availability, we
conduct a preliminary analysis of various factors in this sec-
tion to understand their role in predicting availability. We in-
vestigate various contextual factors (e.g., location, time, etc.),
temporal factors (e.g., weekend vs. weekdays, time of transi-
tion, etc.), mental state (e.g., happy, stressed, etc.), and activ-
ity state (e.g., walking, driving, etc.).
Figures 7 and 8 present the probability of participants being
available and the mean response delay across different con-
texts (e.g., location, activity, mental state, and time) respec-
tively. In these figures, outside refers to outside of home,
work, store, restaurant, and vehicle. We observe in Fig-
ure 8 that the response delay has high variance (range 23.2-
137.5) across different contexts, which can be attributed to
the Gamma distribution of response delay (see Figure 5).
Location: From Figure 7, we observe that participants are
more likely to be available (p(A)=0.711) when they are
outside and they are most likely to be unavailable at work
(p(A)=0.34). When participants are outside, their response
1We use p(A)to denote the probability of being available.
Figure 7. Probability of participants being available across different
contexts. Here morning is defined as before 9 AM and evening as after 5
PM. Arrived at a location means arrival within 30 minute, while leaving
means 30 minute prior to leaving. Red line is drawn for p(A)=0.5.
Figure 8. Mean response delay across different contexts. Morning,
evening, arrival, and leaving are defined as in Figure 7. Red line rep-
resents the overall mean of 49.5s (±116.0s).
delay is also lower than any other location (mean=41.0s;
p= 0.074 on Wilcoxon rank-sum) (see Figure 8). This lower
response delay may be because when participants are outside,
they are unlikely to be engaged in a time-sensitive activity
(e.g., deadline, driving a vehicle) and thus can attend to the in-
coming prompt relatively quickly. As expected, during driv-
ing participants are usually unavailable (p(A)=0.33) and
the delay in response during driving is significantly higher
than other times (p= 0.019 on Wilcoxon rank-sum).
Walking: In contrast to [20], which found posture change as
an indication of being interruptible, we find that in daily life,
walking by itself does not indicate availability (p(A)=0.44).
Interestingly though, walking outside indicates a highly avail-
able state (p(A) = 0.92), while walking at work indicates
a highly unavailable state (p(A)=0.16). We observe a
mean response delay of 29.7s when participants are walk-
ing outside, which is not significantly lower than stationary
(p= 0.318 on Wilcoxon rank-sum), but significantly lower
(p= 0.008 on Wilcoxon rank-sum) when compared with
other locations (e.g., home, work, etc.).
Mental State: When participants are in a happier state, they
are more likely to be available (p(A) = 0.82) and we observe
a lower response delay (mean=41.3s; p= 0.008 on Wilcoxon
rank-sum). Similarly, when participants are feeling energetic,
they are more available (p(A) = 0.6). But, unlike happy, in
the energetic state the delay (45.2s) decrease is not signifi-
cant (p= 0.144 on Wilcoxon rank-sum). On the other hand,
participants being stressed reduces the probability of being
available (p(A)=0.43). A good news for JITI that may be
triggered upon detection of stress is that participants are not
Event occurred in every past window
within N second of corresponding sensor
prior to random EMA prompt
Event occurred in any past window within
N second of corresponding sensor prior to
random EMA prompt
Duration-N Duration of occurrence of event within past
N second prior to EMA prompt
Number of change where event occurred in
one window followed by non-event within
N second prior to EMA prompt
Table 2. For 6 values of N(30 seconds, 1 min, 2 min, 3 min, 4 min, and
5 min), the above four derivative features are computed for stress and
activity, producing 24 features for each.
found to be highly unavailable when stressed as is the case at
work. Such JITI, therefore, may still be attended to by users.
Investigation of the receptivity of stress-triggered JITI may
represent an interesting future research opportunity.
Home: Since being at home indicates only a marginally
available state (p(A) = 0.54), we investigate whether time
of day makes a difference. We find that availability at home
is lower during morning (p(A) = 0.5), and higher in the
evening (p(A)=0.56), but not by much. However, response
delay in the morning (54.5s) is higher than that in the evening
(41.8s) (p= 0.052 on Wilcoxon rank-sum). This indicates
that participants are more pressed for time in the morning.
Transition: To further investigate the effect of location on
availability, we analyze the availability of participants when
they are about to leave a place (within 30 minutes of de-
parture) or have just arrived at a place (30 minute since ar-
rival). We find that the availability of participants when leav-
ing home (p(A) = 0.55) is similar to when in home gener-
ally. But, their availability is higher when they have just ar-
rived home (p(A) = 0.67). The scenario is reversed at work.
The availability at work upon arrival (p(A)=0.33) is similar
to the overall availability at work. But, their availability is
higher when about to leave work (p(A)=0.58).
Day: Finally, we analyze the effect of weekday vs. weekend.
We find that participants are more likely to be available on
weekend (p(A)=0.56) than on weekdays (p(A)=0.48).
Interestingly, the response delay on weekends is higher than
that during weekdays (p= 0.061 on Wilcoxon rank-sum).
Although one could investigate several combinations of fac-
tors, we next develop a model that uses several features de-
rived from these factors to predict availability of participants.
In this section, we develop a model to predict availability. We
first discuss the features we compute, feature selection meth-
ods to find the most discriminating features, and then the ma-
chine learning model to predict availability. We conclude this
section by reporting the evaluation of our availability model.
Feature Computation
To predict availability, we compute a variety of features. Ma-
jority of them come from sensors, but we also obtain several
from self-reported EMA responses because the sensor models
for their detection is not mature enough today to detect them
with reasonable confidence. We expect that these features
will also become reliably detectable from sensors in near fu-
ture. In total, we compute 99 features.
Clock: Time and Day (6 features): We compute several
time related features. We include “day of the week” since
there may be a day-specific influence, “elapsed hour” in a
day to identify work vs. non-work hours, and “Time since
last EMA” to capture the cumulative fatigue caused by fre-
quent EMA prompts. We also include binary features such as
“Working Hour?”, which is defined to be between 9 AM and
5 PM, “Weekend?”, and “Holiday?”.
Sensor: Stress (4+24 features): As discussed earlier, we in-
fer stress level for each 30 second window. Since sometimes
EMA prompts itself may cause stress, we used binary stress
levels in the 30 second windows prior to the generation of an
EMA prompt. From these windows, 24 derived features are
computed (see Table 2), similar to that in [13]. We note that
if the participant is physically active during a 30-second win-
dow, we mark the stress feature as undefined for this window
(due to stress being confounded by physical activity). Hence,
stress level in each of the 30 second windows for derived fea-
tures may not be available. Consequently, we compute four
other features. The first two of these come from the first win-
dow preceding the prompt where stress inference is available
(i.e., unaffected by physical activity). Binary stress state and
probability of being stressed are used as features from this
first window. The remaining two features are the number
of windows where the participant is stressed over the prior
3 (and 5) windows preceding the prompt, for which stress
inference is available. These windows must occur within 5
minutes prior to the prompt.
Sensor: Location, Place, Commute Status (7 features):
We compute several location related features. This includes
coarse level location such as Home, Work, Store, Restaurant,
Vehicle, and Other and detailed location such as inside home,
dormitory, backyard, etc. We also include “Previous Loca-
tion” and “time spent in current location” because it is likely
that after immediate arrival at home from work or from other
locations people are less likely to be available. We include
a binary feature for “driving” because driving requires unin-
terrupted attention and distraction during driving can result in
injury, loss of life, and/or property. It is also illegal in several
parts of the world to engage a driver in a secondary task such
as texting. Since EMA prompts are generated randomly (as
per the norm in behavioral science [48]), some EMA prompts
did occur during driving. Participants were instructed to find
a safe place to park the car in such cases before answering.
A binary feature outdoor is also included since we observe
participants being more available when they are outdoors and
Sensor: Physical Activity (3+24 features): Since physical
activity can also indicate availability, we use physical activity
data from the chest accelerometer sensor as a binary feature,
and intensity of activity as a numeric feature. EMA question-
naire contains items such as “Describe physical movement”
with possible answers “Limited (writing)”, “Light (walk-
ing)”, “Moderate (jogging)”, and “Heavy (running)”. We
include features such as writing as a categorical feature be-
cause writing state may affect availability and we are unable
to infer it activities from our sensors with reasonable confi-
dence today.
EMA: Activity Context (13 features): EMA questionnaire
asked participants to describe their ongoing activity using the
following items: “How would you describe your current ac-
tivity?”, with possible responses as “Leisure/Recreation” or
“Work/Task/Errand”, a multiple choice item “What’s going
on?” with possible responses as Meeting, E-mail, Reading,
Phone Call, Writing, Sports, Video Game, Surfing the In-
ternet, Watching Movies/TV/Video, Listening to Music, and
Other. Each possible response is used as a binary feature. We
also use binary response to the “Taken alcohol?” item.
EMA: Social Interaction (6 features): Research on inter-
ruption has revealed that situations involving social engage-
ment are considered less interruptible [6, 15, 18]. To model
availability, we used participants’ responses for the social in-
teraction related EMA queries that includes “In social inter-
action?”, “Talking?”, “If talking, on the phone?”, “If talk-
ing, with whom?”, “If not talking, how long ago was your
last conversation?”, and “Who was it with?”.
EMA: Mental State (9 features): We also include emo-
tional state due to their wide acceptability as a factor in Hu-
man Computer Interaction [7, 26]. Although stress is de-
tectable from sensors, affect is not yet detectable reliably
in the field setting from physiological sensors. Hence, we
use EMA responses. We include response to our EMA
items, “Cheerful?”, “Happy?”, “Frustrated/Angry?”, “Ner-
vous/Stressed?”, “Sad?”, “Facing a problem?”, “Thinking
about things that upset you?”, “Difficulties seem to be piling
up?”, and “Able to control important things?”. Response in
Likert scale 1-6 is used as feature.
EMA: Fatigue (3 features): Each EMA prompt resulted in
some level of fatigue on the recipient [23]. We find that re-
sponses to the first half of the EMA’s are more consistent than
the second half EMA for the day (p= 0.056,n= 30, paired
t-test on Cronbach’s alpha). Therefore, we add EMA index of
the day as a feature. Our EMA questionnaire contained items
such as “Energetic?” and “Sleepy?”. Subjective responses
of these items in 1-6 Likert scale are also used as features.
Feature Selection
As reported in the preceding, a total of 99 features were com-
puted. But, to avoid overfitting of the model, we select a sub-
set of the features for modeling availability. We base our fea-
ture selection on two complementary methods.
Correlation based Feature Subset Selection: Our goal is to
find features that are highly correlated to the class available
vs. unavailable, and not correlated with each other. We used
Hall’s [17] method to find the optimal non-correlated feature
set regardless of the underlying machine learning algorithm.
Wrapper for Feature Subset Selection: Correlation based
feature selection may discard some features that are useful for
R Feature R Feature
1 Detailed Location 16 Stress probability
2 Coarse Location 17 Stress count in 5 pre-
vious window
3 Weekday 18 StressChange-300
4 Outdoor? 19 StressChange-240
5 Sleepy? 20 ActivityAll-120
6 Happy? 21 StressAny-180
7 Energetic? 22 StressChange-180
8 Commute Mode? 23 StressAny-240
9 Recreation? 24 StressAny-60
10 Activity type 25 StressChange-30
11 Weekend? 26 StressDuration-30
12 Talking on phone? 27 ActivityAll-180
13 Taken Alcohol? 28 ActivityAll-240
14 Elapsed hour of day 29 ActivityAny-300
15 Time spent in current
location 30 EMA Index
Table 3. Selected 30 features ranked (R) according to information gain.
Detailed location offers the highest information gain.
a particular machine learning algorithm. Therefore, we also
use Wrapper [25] based feature selection to find an optimal
feature subset for the SVM machine learning algorithm [41].
By taking a union over the features selected by correlation
based feature selection and Wrapper applied to SVM, we ob-
tain a total of 30 features. Table 3 lists these features ordered
according to their information gain [11]. We make several
observations. First, we observe that most of the features se-
lected are either already detectable from sensors (1-4, 8, 11-
30) or are potentially detectable in near future from sensors
(9-10). But, three features (5-7) are hard to detect automat-
ically today. An inward looking camera in smart eyeglasses
could potentially detect some of these in near future as well.
Second, we observe that stress features (16-19, 21-26) fig-
ure quite prominently in this list, indicating a significant role
of stress in predicting availability. Finally, we observe that
driving is not included in the list of selected features, though
intuitively it appears relevant. We hypothesize that features
ranked 1, 2, 4, and 8 contain information about driving and as
such driving may not be needed as a separate feature.
Due to its well-accepted robustness, we train a Support Vec-
tor Machine (SVM) [41] model with RBF kernel to predict
availability of users. To evaluate the model, we use both the
standard 10 fold cross-validation and leave-one-subject-out to
evaluate between subject generalizability. As described ear-
lier, we use two diverse methods to label EMA’s as available
and unavailable to generate training data. We present the per-
formance of the model on each of these labeling methods.
Representative Dataset: Based on the missed, explicitly de-
layed, or delayed above grace period (124.1s) we mark 170
EMA’s as triggered when participants were unavailable. We
mark the nquickest answered EMA from each participant as
available, where nis the number of EMA prompts for which
that particular participant was unavailable. This provides us
SVM Classified as
Available Unavailable
Available 134 (78.8%) 36 (21.2%)
Unavailable 50 (29.4%) 120 (70.6%)
Table 4. Confusion Matrix for predicting availability using SVM model
on RBF kernel built on Representative Dataset. Overall Accuracy is
74.7% against a base accuracy of 50%, with a kappa of 0.494.
SVM Classified as
Available Unavailable
Available 135 (75.0%) 45 (25.0%)
Unavailable 66 (36.7%) 114 (63.3%)
Table 5. Confusion Matrix for predicting availability using SVM model
on RBF kernel built on Democratic Dataset. Overall Accuracy is 69.2%
against a base accuracy of 50%, with a kappa of 0.383.
with 340 instances as training data for modeling with 170 in-
stances coming from each class2.
Using this dataset we get an overall accuracy of 74.7%
(against a base accuracy of 50%) with kappa of 0.494 for 10-
fold cross-validation. From the confusion matrix in Table 4,
we find that for 78.8% cases, the classifier is able to predict
availability versus 70.6% in the case of unavailability. We get
a precision of 0.749, a recall of 0.747, an F-measure of 0.747,
and area under the curve of 0.747. For leave-one-subject-out,
we get a weighted average accuracy of 77.9%.
Democratic Dataset: In this dataset, we take 12 samples
from each participant, which leads to similar 360 samples
from 30 participants. The 6 quickest responded EMA’s are
considered available and 6 slowest responded ones (includ-
ing explicitly delayed ones) are considered as unavailable.
For this labeling, the SVM model achieves an accuracy of
69.2% with a kappa of 0.383, slightly lower than the Repre-
sentative model. However, from the confusion matrix in Ta-
ble 5, we find that for 75.0% cases, classifier is able to predict
availability. We get a precision of 0.694, a recall of 0.692, an
F-measure of 0.691, and area under the curve of 0.692. For
leave-one-subject-out, we get an accuracy of 76.4%.
Being the first work to inform the timing of sensor-triggered
just-in-time intervention (JITI), this work has several limita-
tions that open up interesting opportunities for future works.
First, several features used to predict availability are not yet
reliably detectable via sensors today. For a model to be au-
tomated in informing the timing of JITI, all features need to
be inferred from sensors. Second, this work used data from
wearable sensors. Since wearing sensors involves user bur-
den, it is more desirable to use only those sensors that are
available on the phone. But, some features are not feasible
to obtain today from phone sensors (e.g., stress) and hence
represents interesting future works. Third, the type of sen-
sors available on the phone is growing richer rapidly. Several
sensors such as proximity sensor, acoustic sensor, and phone
2We note that although only a small subset of EMA’s (340 out of
2717) is used in model development, SVM can produce posterior
probability of availability for any EMA. Hence, the applicability of
the model is not limited to the data used in training.
orientation and other data in the phone (e.g., calendar, task
being performed on the phone, etc.) that may inform the cur-
rent context of a user were not used in this work. Using these
and other sensors emerging in phone may further improve
the prediction accuracy. Similarly, using additional sensors
on the body and those in instrumented spaces such as office,
home, and vehicle (e.g., cameras) can also be used wherever
available to further improve the prediction accuracy.
Fourth, this work used micro-incentive to improve compli-
ance in responding to EMA prompts and used it to accom-
plish a high level of motivation. Although the work presented
in this paper can inform the timing of delivering randomly
prompted self-reports in scientific studies, it remains an open
question how well the micro-incentive captures the motiva-
tion level expected in users who choose to use JITI due to
certain health condition or due to a wellness or fitness moti-
Fifth, given that filling out a 42-item EMA requires signifi-
cant user involvement (i.e., 2.4 minutes to complete), the re-
sults of this work may be more applicable to JITI that involve
similar engagement. Its applicability to lighter JITI may need
further investigation. We note, however, that if the user is
found to be unavailable for a more involved active JITI (e.g.,
when driving), passive intervention could be delivered in the
meantime (e.g., by playing music [37]).
Sixth, the analysis in this work used only the unanticipated
(i.e., randomly prompted) EMA’s to simulate the triggering
of a sensor-triggered JITI, but the participants also filled out
EMA’s that resulted from their self-initiation. Although these
self-initiated EMA’s were voluntary, they may add to the bur-
den and fatigue of participants. It remains open whether the
results of a future study that only uses randomly prompted
EMA’s may be any different than the one reported here.
Sensor-triggered just-in-time-interventions (JITI) promise to
promote and maintain healthy behavior. But, critical to the
success of JITI is determining the availability of the user to
engage in the triggered JITI. This paper takes a first step to
inform the timing of delivering JITI. We propose a novel ob-
jective metric to measure a user’s availability to engage in a
JITI and propose a model to predict availability in the natural
environment based on data collected in real-life. Our findings
indicate that availability of a user depends not only on user’s
ongoing activity or physical state, but also on user’s psycho-
logical state. Our results can inform the design of JITIs and
opens up numerous opportunities for future works to improve
the accuracy, utility, and generalizability of our model.
We would like to thank Karen Hovsepian from Troy Univer-
sity and J. Gayle Beck, Satish Kedia, Jeremy Luno, Lucas
Salazar, Sudip Vhaduri, and Kenneth D. Ward from Univer-
sity of Memphis for their contributions. This work was sup-
ported in part by NSF grants CNS-0910878 (funded under
the American Recovery and Reinvestment Act of 2009 (Pub-
lic Law 111-5)), CNS-1212901, IIS-1231754, and by NIH
Grants U01DA023812 and R01DA035502, from NIDA.
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... Finally, the impact of location appears to have an impact. People are more responsive in outdoor locations (Sarker et al., 2014) but are less responsive in shopping malls, in the workplace (Yuan et al., 2017), and in social settings (Choi et al., 2019). Okoshi et al. (2015aOkoshi et al. ( , 2015b proposed a system to defer delivery of notifications until a user's natural breakpoint to minimize disruption and cognitive overload. ...
... Research has shown that people react differently depending on their personality type, for example, extroverts are more inclined to react to notifications (Mehrotra, Pejovic, Vermeulen, Hendley, & Musolesi, 2016;Yuan et al., 2017). Individuals are also more interruptable when in a pleasant or happier mood state than when stressed (Sarker et al., 2014;Yuan et al., 2017) and are more receptive when bored (Pielot, Dingler, San Pedro, & Oliver, 2015). ...
... 3 Location Choi et al., 2019;Künzler et al., 2019;Pielot et al., 2017;Sarker et al., 2014;Yuan et al., 2017 There is mixed evidence that location tends to be related to receptivity (e.g., being at home/at work, traveling, driving, shopping). ...
Intervention authors can likely increase the effectiveness of mobile health interventions (MHIs) by determining conditions under which individuals are able to receive, process, and use support. In this chapter, we will therefore first introduce and motivate the relevance of receptivity to MHIs. Second, we will describe the anatomy of an “ideal” MHI before key processes involved in the detection and prediction of receptivity to MHIs are discussed. Thereafter, we will review research on receptivity and summarize factors that may carry relevant signals for determining receptive states and thus, offer guidance for the design of receptivity-capable MHIs. We will conclude with challenges intervention authors and engineers face and offer opportunities for future work.
... According to Sarker et al. (2014), a well-timed reminder may not be effective if the user is not cognitively or physiologically available to engage in the reminded action. Their study showed that users were more attentive to notifications when they were happy or energetic than when they were stressed. ...
... Participants were unlikely to start training sessions due to prompting during HCL (about 25% chance). This was consistent with a previous study that showed that stress reduced the probability of being available (Sarker et al., 2014). As seen in Figure 4.8b for HCL condition, the average count of responses and ignores were approximately the same value of two. ...
... As gathered from our interviews, we also need to take into account users' preferences on suitable timings for prompting. I note that although multiple contexts have to be used for a more robust and accurate system (Sarker et al., 2014), this work first focused on examining the physiological context on its own which could then be added to relevant multi-context models of receptivity (Choi et al., 2019). ...
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Memory is a cognitive ability used in our everyday lives. However, it eventually declines and its adaptive nature results in memory troubles. We most often face prospective memory troubles related to forgetting to perform future intended tasks, for example, forgetting to take medication or bring items when leaving the house. With the advancement of technologies and the growth of the ageing population, new requirements and opportunities arise. Many technologies available today lack the means to understand our complex everyday situations to support prospective memory. There is a need to provide support through enhancing our memory and assisting with memory tasks, as well as to encourage our receptivity to these interventions. This thesis aims to investigate the ways to augment prospective memory through technologies that are aware of our cognitive contexts and integrate with our perception and behaviours. The original contributions of my work are as follows. 1. An overview of memory augmentation technologies that introduces a new taxonomy and reveals research challenges and opportunities. 2. The design and implementation of artefacts that provide technical details for novel technologies that augment prospective memory. 3. Empirical findings from user and evaluation studies with the artefacts that show user needs and experience. 4. A collection of design insights for realising envisioned future memory augmentation technologies. I introduce how we can enhance memory through memory training by digitally mediating a memory strategy. The user studies show that this improves users’ performance on prospective memory tasks and self-reported memory. Technologies can encourage receptivity to memory training by sensing cognitive contexts through physiological signals (biosignals) and suggesting training sessions during moments of low emotional arousal and cognitive load (calm moments). I unveil user perceptions of voice reminders which use the voices of friends and family, and discuss how this might benefit user receptivity. I discuss opportunities for memory assistance through technologies that use biosignals to understand cognitive contexts such as our visual recognition processes. This work offers a new perspective on designing technologies that support our memory abilities and experiences.
... To mitigate the disruption caused by the interruptions, deferring them to opportune moments, such as task breakpoints [34,80] or rough boundaries during task execution [4] could largely mitigate the cost of interruption. In addition to fnding factors which contribute to the impact of interruption, many recent studies have sought to predict opportune and interruptible moments for receiving notifcations that leverage features including sensor data [39,76] and contextual information such as the current activity [65,95] and engagement [20,64] of users. As VR is not yet prevalent in people's daily lives, only a few attempts to investigate the sending of real-life notifcations in VR have been made (e.g. ...
... For the sensor data, we focused on the features that captured users' movement in VR. Previous research found that interruptibility could be predicted through velocity [27,39,41,76] and the rotation of the device users wear [70,88]; as such, we used the velocity and angular velocity of the HMD and the controllers, as well as the rotation of the HMD, as features. We also used the angle between the HMD and gaze, as well as gaze-shift speed over time, as features since previous research has found that diferent tasks afect eye-head dynamics [13] and gaze-shift dynamics [14]. ...
... Overall, using all the features-sensor, activity, and summarized engagement-to predict opportune moments in VR achieved an accuracy of 85% and an AUROC of 0.93 for a personalized model and an accuracy of 75% and an AUROC of 0.81 for a general model, comparable to the interruptibility prediction results on desktop computers (e.g., [20,31,79,81,100]) and mobile phones (e.g. [57,70,76,88,90,95]). ...
Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via wearable and mobile sensors has created new opportunities to improve the effectiveness of behavioral interventions. A key goal is to find opportune moments for intervention by passively detecting rising risk of an imminent adverse behavior. But, it has been difficult due to substantial noise in the data collected by sensors in the natural environment and a lack of reliable label assignment of low- and high-risk states to the continuous stream of sensor data. In this paper, we propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of an adverse behavior. Next, to circumvent the lack of any confirmed negative labels (i.e., time periods with no high-risk moment), and only a few positive labels (i.e., detected adverse behavior), we propose a new loss function. We use 1,012 days of sensor and self-report data collected from 92 participants in a smoking cessation field study to train deep learning models to produce a continuous risk estimate for the likelihood of an impending smoking lapse. The risk dynamics produced by the model show that risk peaks an average of 44 minutes before a lapse. Simulations on field study data show that using our model can create intervention opportunities for 85% of lapses with 5.5 interventions per day.
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Craving for alcohol and other drugs is often described as a momentary hyperarousal state that interferes with one's ability to use top-down strategies. As such, it may be best interrupted ‘in the moment' through bottom-up modulation. We recently reported that episodic resonance paced breathing (eRPB) delivered via mobile phone app as an add-on to outpatient treatment for substance use disorder (SUD) was effective at dampening craving over the course of an 8-week intervention (NCT#02579317). However, not all participants engaged with the eRPB app and there was high intra- and inter-individual variability in weekly ratings of usefulness. Here we examined baseline demographic, physiological, and psychiatric measures as well as time-varying exposure to positive, negative, and temptation craving triggers as predictors of frequency of eRPB app use and ratings of usefulness. Seventy-seven outpatient women were randomized to an eRPB (0.1 Hz) or a faster paced breathing sham (0.23 Hz) condition. Baseline measures were assessed within the first 3 weeks of treatment entry prior to randomization. App use frequency, ratings of usefulness, and trigger exposure were measured weekly throughout the intervention. Variables were entered into marginal means models with forward stepwise model selection and examined as predictors of use and usefulness. Frequent app use was associated with a lifetime alcohol use disorder (AUD) diagnosis ( p = 0.026), higher ratings of usefulness ( p < 0.001), and fewer exposures to positive triggers (e.g., celebration, socialization; p < 0.001). There was a trend-level association between frequency of app use and greater cardiovascular capacity at baseline ( p = 0.088). Higher ratings of usefulness were associated with greater exposure to negative triggers (e.g,. loneliness, frustration; p < 0.001) and parasympathetic dysregulation at baseline ( p = 0.05). A positive relationship between app use frequency and ratings of usefulness was present only in the eRPB group ( p = 0.045). Matching ideal candidates and moments to an arousal modulation anti-craving intervention can help streamline screening and implementation of eRPB in the treatment of SUD. Clinical Trial Registration , identifier NCT02579317.
Peer support is a powerful tool in improving the digital literacy of older adults. However, while existing literature investigated reactive support, this paper examines proactive support for mobile safety. To predict moments that users need support, we conducted a user study to measure the severity of mobile scenarios (n=300) and users' attitudes toward receiving support in a specific interaction around safety on a mobile device (n=150). We compared classification methods and showed that the random forest method produces better performance than other regression models. We show that user anxiety, openness to social support, self-efficacy, and security awareness are important factors to predict willingness to receive support. We also explore various age variations in the training sample on moments users need support prediction. We find that training on the youngest population produces inferior results for older adults, and training on the aging population produces poor outcomes for young adults. We illustrate that the composition of age can affect how the sample impacts model performance. We conclude the paper by discussing how our findings can be used to design feasible proactive support applications to provide support at the right moment.
In the previous chapter, we have presented an approach for discovering time-dependent rules of individual mobile phone users based on their unique behavioral patterns. In this chapter, we focus on discovering behavioral rules of individual mobile phone users by taking into account multi-dimensional contexts—for example temporal, spatial, or social context. Association rule mining is the most prominent rule-based machine learning method for generating rules for a particular constraint preference utilizing a given dataset. However, it generates various uninteresting contextual associations which lead to output vast number of redundant rules that become ineffective in making context-aware decisions. This redundant generation not only makes the rule set unnecessarily large in size, but also complicates the decision-making process in a context-aware system. In this chapter, we present an effective rule-based machine learning method that minimizes the issue and generates a set of non-redundant behavioral rules by taking into account the precedence of relevant contexts. Finally, the effectiveness of the technique presented in this chapter, has been provided through experimental results.
Voice assistants, such as Amazon's Alexa and Google Home, increasingly find their way into consumer homes. Their functionality, however, is currently limited to being passive answer machines rather than proactively engaging users in conversations. Speakers' proactivity would open up a range of important application scenarios, including health services, such as checking in on patient states and triggering medication reminders. It remains unclear how passive speakers should implement proactivity. To better understand user perceptions, we ran a 3-week field study with 13 participants where we modified the off-the-shelf Google Home to become proactive. During the study, our speaker proactively triggered conversations that were essentially Experience Sampling probes allowing us to identify when to engage users. Applying machine-learning, we are able to predict user responsiveness with a 71.6% accuracy and find predictive features. We also identify self-reported factors, such as boredom and mood, that are significantly correlated with users' perceived availability. Our prototype and findings inform the design of proactive speakers that verbally engage users at opportune moments and contribute to the design of proactive application scenarios and voice-based experience sampling studies.
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Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects' current behaviors and experiences in real time, in subjects' natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects' lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.
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In recent decades technology-induced interruptions emerged as a key object of study in HCI and CSCW research, but until recently the social dimension of interruptions has been relatively neglected. The focus of existing research on interruptions has been mostly on their direct effects on the persons whose activities are interrupted. Arguably, however, it is also necessary to take into account the "ripple effect" of interruptions, that is, indirect consequences of interruptions within the social context of an activity, to properly understand interrupting behavior and provide advanced technological support for handling interruptions. This paper reports an empirical study, in which we examine a set of facets of the social context of interruptions, which we identified in a previous conceptual analysis. The results suggest that people do take into account various facets of the social context when making decisions about whether or not it is appropriate to interrupt another person.
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Scholars emphasize the importance of rich communication media for the development of Teleworkers' organizational identification, but tests of these relationships have produced inconsistent results. The connectivity paradox helps explain these findings. The paradox suggests that Teleworkers' connectivity to others through communication media facilitates remote work by affording greater social presence, while also negating the benefits of telework by enabling stressful interruptions. These outcomes of connectivity may benefit and detract from identification. We propose a model linking the core features of the connectivity paradox to organizational identification. Teleworkers and office workers were surveyed, and a multigroup path analysis was utilized. Results indicate that connectivity increases stress from interruptions and indirectly diminishes Teleworkers' identification.
Conference Paper
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Labeling data is a seemingly simple task required for training many machine learning systems, but is actually fraught with problems. This paper introduces the notion of concept evolution, the changing nature of a person's underlying concept (the abstract notion of the target class a person is labeling for, e.g., spam email, travel related web pages) which can result in inconsistent labels and thus be detrimental to machine learning. We introduce two structured labeling solutions, a novel technique we propose for helping people define and refine their concept in a consistent manner as they label. Through a series of five experiments, including a controlled lab study, we illustrate the impact and dynamics of concept evolution in practice and show that structured labeling helps people label more consistently in the presence of concept evolution than traditional labeling.
Semantic place labels are labels like "home", "work", and "school" given to geographic locations where a person spends time. Such labels are important both for giving understandable location information to people and for automatically inferring activities. Deployed products often compute semantic labels with heuristics, which are difficult to program reliably. In this paper, we develop Placer, an algorithm to infer semantic places labels. It uses data from two large, government diary studies to create a principled algorithm for labeling places based on machine learning. Our labeling reduces to a classification problem, where we classify locations into different label categories based on individual demographics, the timing of visits, and nearby businesses. Using these government studies gives us an unprecedented amount of training and test data. For instance, one of our experiments used training data from 87,600 place visits (from 10,372 distinct people) evaluated on 1,135,053 visits (from 124,517 distinct people). We show labeling accuracy for a number of experiments, including one that gives a 14 percentage point increase in accuracy when labeling is a function of nearby businesses in addition to demographic and time features. We also test on GPS data from 28 subjects.