# Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress
Coping: an In-The-Wild Study

Chaya Ben Yehuda<EMAIL_ADDRESS>Tel Aviv UniversityP.O. Box 39040Tel
Aviv Israel6139001 ,  Ran Gilad-Bachrach  Tel Aviv UniversityP.O. Box 39040Tel
AvivIsrael<EMAIL_ADDRESS>and  Yarin Udi<EMAIL_ADDRESS>Tel
Aviv UniversityP.O. Box 39040Tel Aviv Israel6139001

(2024; 14 September 2023)

###### Abstract.

Sustaining long-term user engagement with mobile health (mHealth)
interventions while preserving their high efficacy remains an ongoing
challenge in real-world well-being applications. To address this issue, we
introduce a new algorithm, the Personalized, Context-Aware Recommender (PCAR),
for intervention selection and evaluate its performance in a field experiment.

In a four-week, in-the-wild experiment involving 29 parents of young children,
we delivered personalized stress-reducing micro-interventions through a mobile
chatbot. We assessed their impact on stress reduction using momentary stress
level ecological momentary assessments (EMAs) before and after each
intervention.

Our findings demonstrate the superiority of PCAR intervention selection in
enhancing the engagement and efficacy of mHealth micro-interventions to stress
coping compared to random intervention selection and a control group that did
not receive any intervention. Furthermore, we show that even brief, one-minute
interventions can significantly reduce perceived stress levels (p=0.001). We
observe that individuals are most receptive to one-minute interventions during
transitional periods between activities, such as transitioning from afternoon
activities to bedtime routines.

Our study contributes to the literature by introducing a personalized context-
aware intervention selection algorithm that improves engagement and efficacy
of mHealth interventions, identifying key timing for stress interventions, and
offering insights into mechanisms to improve stress coping.

mobile micro-interventions, stress reduction, mental preparedness exercises,
parental stress, Reinforcement Learning, Digital Health, mHealth, just-in-time

††copyright: acmcopyright††journalyear: 2024††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation email; June 03–05, 2018; Woodstock, NY††ccs:
Human-centered computing Empirical studies in HCI††ccs: Applied computing
Health informatics

##  1\. Introduction

Stress is a pervasive issue with far-reaching implications for both physical
and mental well-being (Kalia, 2002). It is a complex psychological and
physiological response to external or internal stimuli, commonly referred to
as stressors (Chrousos and Gold, 1992). The body’s stress response is often
characterized by a cascade of hormonal changes that affect various cognitive
functions and overall health (Mendl, 1999; Arnsten, 2009). The duration of a
stressor is one of its most defining features and can be categorized into
three timescales: acute stressors, daily events, and chronic stressors. By
closely examining the response trajectory of a single acute stressor in real-
time, one can study an individual’s kinetics of stress response, including
anticipation, peak reactivity, recovery, and regulation processes (Epel et
al., 2018).

Recent advancements in ubiquitous technology, such as the proliferation of
smartphones and wearable devices, facilitate delivering timely and accessible
mobile health (mHealth) interventions for stress reduction anytime and
anywhere (Silva et al., 2015; Prescott and Rathbone, 2022). Despite the ease
of delivery, users engagement with these interventions displays variability
across different studies and generally proves lower in real-world applications
(Baumel et al., 2019a, b). Particularly, sustaining long-term engagement
remains an ongoing challenge. For example, Paredes et al. (2014) observed
reducing engagement during a four-week naturalistic study due to repetitive
interventions, leading to the introduction of the Last Switch Dependent (LSD)
setting to vary intervention content (Laforgue et al., 2022). A comprehensive
review by Borghouts et al. (2021) underscores the significance of contextual
factors as important determinants influencing engagement rates, endorsing the
use of machine learning algorithms to deliver context-aware, personalized
interventions to boost user engagement (Doherty et al., 2012; Liao et al.,
2020; Konrad et al., 2015; Schueller, 2010).

In this study, we introduce a novel algorithm for Personalized, Context-Aware
intervention Recommendation (PCAR). This algorithm selects interventions by
leveraging context variables, including personality traits and mobile sensor
data. It also accounts for intervention fatigue, the tendency for diminishing
returns from repeated use of the same intervention (Paredes et al., 2014;
Laforgue et al., 2022). Consequently, the algorithm aims to enhance the
efficacy and long-term engagement with mobile health (mHealth) micro-
interventions.

To investigate our algorithm’s performance, we conducted a four-week, in-the-
wild study with 29 parents of young children, for whom stress management is
especially critical. We developed a system that enables real-time interactions
with multiple participants simultaneously and delivered micro-interventions
via a WhatsApp chatbot. Through the chatbot, we delivered ecological momentary
assessments (EMAs) (Shiffman et al., 2008), surveys, and intervention content.

Our interactions were motivated by the emergence of Just-in-Time Adaptive
Interventions (JITAIs) aimed at addressing the challenge of effective stress
management (Schroeder et al., 2020; Sano et al., 2017) via notifications on
smartphones and wearables (Baumel et al., 2020). Existing JITAIs encounter
limitations in two primary domains: content personalization and intervention
timing (Howe et al., 2022). While technological advancements in JITAI
frameworks have aimed to enhance engagement through timely intervention
delivery (Rozet et al., 2019), the primary focus has been on peak stress
episodes (Konrad et al., 2015; Can et al., 2020; Hovsepian et al., 2015). This
narrow focus neglects the essential aspect of timing interventions across the
complete stress response cycle, including the anticipation and recovery
phases.

The main objective of this work is to evaluate PCAR’s ability to maintain
efficacy over time of mHealth micro-interventions (RQ1), which also serves as
an evaluation of the suitability of the LSD setting for addressing the
information fatigue problem. We initiated both random and PCAR micro-
interventions to examine their impacts on daily stress levels. Short-term
stress reduction was assessed through momentary stress level ratings before
and 10 minutes after each intervention via EMAs, while long-term effects were
measured using pre-, mid-, and post-experiment Perceived Stress Scale
questionnaire (PSS) (Cohen et al., 1983).

Our results demonstrate that even brief, one-minute interventions can
significantly reduce perceived stress levels (p=0.001), regardless of the time
of day. We also show the superiority of PCAR in intervention selection to
enhance the engagement and efficacy of mHealth micro-interventions compared to
random intervention selection.

Additionally, the study provides some information about how individuals engage
with stress-reducing micro-interventions across different stress stages and
time frames along the day (RQ2), as well as how they perceive and experience
these interventions integrated into their daily lives (RQ3). We observe that
individuals are most receptive during transitions between activities, serving
as both ”cool-down” and ”warm-up” exercises for subsequent engagements. Hence,
pinpointing the optimal timing (transitional periods) for these interventions
is essential for maximizing engagement and improving the effectiveness of
well-being applications.

Our study contributes to the literature by introducing a personalized context-
aware intervention selection algorithm that improves engagement and efficacy
of mHealth interventions, identifying key timing for stress interventions, and
offering insights into stress management and human-computer interaction.

The remainder of this paper is organized as follows: Section 2 provides a
literature review, Section 3 describes the methodology, Section 4 presents the
results, Section 5 presents limitation and Section 6 discusses the
implications of our findings.

##  2\. Related Work

Stress is a pervasive issue that became even more common during and after the
Covid-19 pandemic (Association et al., 2019; Jiang et al., 2020; Kalia, 2002).
Stress responses are characterized by the disruption of the body’s homeostasis
in response to real or perceived threats or challenges (hereafter ”stressors”
or ”stressor exposures”) (Chrousos and Gold, 1992), which can manifest in
various detrimental effects, including cognitive impairments (Mendl, 1999;
Arnsten, 2009).

###  2.1. Technological Solutions for Stress Management

Smartphone-based mental health applications have garnered considerable
attention due to their ubiquity and seamless integration into everyday life
(Fuller-Tyszkiewicz et al., 2019; Linardon et al., 2019). These consumer-
oriented apps offer a diverse array of services, ranging from self-guided
meditation platforms such as Headspaceaa _a_https://www.headspace.com/ and
Calmbb _b_https://www.calm.com/, to peer-support networks like Talklifecc
_c_https://www.talklife.com/, and online counseling services, for example,
Talkspacedd _d_https://www.talkspace.com/. However, the majority of the
existing stress management applications either necessitate user-initiated
activation for stress mitigation activities or depend on pre-configured,
context-agnostic notifications. These limitations highlight the absence of
personalization, a crucial element for the effectiveness of stress management
interventions (Neary and Schueller, 2018; Howe et al., 2022).

Research on personalization in the context of stress management has primarily
concentrated on two facets: delivery timing and content adaptation. The most
advanced form of such personalization is known as Just-In-Time Adaptive
Interventions (JITAI) (Nahum-Shani et al., 2018). These interventions leverage
ubiquitous sensing technologies to capture dynamic human behavior data,
thereby delivering personalized, contextualized, and adaptable interventions.
The objective is to improve efficacy by selecting the most appropriate
treatment for a specific moment (Nahum-Shani et al., 2018; Sano et al., 2017;
Smyth and Heron, 2016). In the following sections, we will explore the recent
advancements as well as the limitations associated with Just-in-time and its
adaptive intervention components.

####  2.1.1. Just-In-Time Technologies

Recent technological advancements have considerably enhanced the capabilities
of wearable biosensors, facilitating the collection of a wide range of
physiological signals. This technological progress serves as a foundational
element for the creation of personalized and real-time interventions in stress
management (Tazarv et al., 2021; Sarker et al., 2016; Battalio et al., 2021;
Burghardt et al., 2021). These interventions benefit from the real-time
collection of diverse data points, such as busyness, geographical location,
weather conditions, step count, and heart rate from users (Battalio et al.,
2021; Rehg et al., 2017; Sarker et al., 2016; Morshed et al., 2022). Operating
passively, these sensors continuously monitor individual stress levels,
thereby identifying optimal moments for intervention deployment.
Simultaneously, the domain has seen significant advancements in binary stress
detection algorithms and pattern mining techniques, which further enhance the
utility of sensor data in stress management frameworks (Zhang et al., 2022).

The data patterns identified by these sensors, often preceding stress
episodes, provide the ability to not only predict but also preemptively reduce
stress. This shift from a largely reactive to a proactive approach has
transformative implications for the field of stress management (Kramer et al.,
2019). Wearable devices like smartwatches and photo-plethysmography (PPG)
sensors are suitable for extracting crucial physiological metrics such as
heart rate (HR), heart rate variability (HRV), and skin conductance levels.
However, the financial burden associated with these wearable devices often
serves as an impediment to their broader adoption, thereby limiting the
scalability of these devices (Huarng et al., 2022). This highlights the
pressing need for a framework that can achieve similar results using only the
sensors integrated into mobile devices, thus democratizing access to effective
stress management solutions.

While JITAI offers considerable promise, empirical studies have primarily
focused on capturing moments of high stress (Konrad et al., 2015; Can et al.,
2020). Moreover, the implementation and efficacy of these interventions at
scale in real-world settings remain in their initial stages. This gap in the
existing methodologies accentuates the need for a more comprehensive approach
that can capture not just the onset but also the various subsequent stages of
stress as they manifest in real-world conditions.

####  2.1.2. Adaptive Interventions Technologies

The concept of personalization extends beyond the timing of interventions to
include content that is either tailored for personal needs or selected to
match personality and other context factors. Factors such as personality
traits, demographic characteristics, and past experiences are instrumental in
shaping an individual’s vulnerability to stressors and their perception of
stress intensity (Bonanno and Burton, 2013; Sur and Ng, 2014).

Research has demonstrated that participants who received interventions
personalized to these individual characteristics exhibited a marked
improvement in their coping skills compared to those who received non-
specific, random interventions (Paredes et al., 2014; Mishra et al., 2021).
This underscores the importance of adaptive interventions that are not only
contextually relevant but also intrinsically aligned with the individual’s
unique profile for more effective stress management.

One challenge encountered in previous implementations is the tendency of
recommendation algorithms to generate repetitive recommendations, leading to
user disengagement and boredom (Paredes et al., 2014; Liao et al., 2020; Park
and Lee, 2023). Addressing this issue without resorting to computationally
intensive algorithms, such as full-fledged reinforcement learning approaches
that necessitate large datasets and exhibit slow convergence rates (Geva and
Sitte, 1993), remains a significant obstacle.

The Last Switch Dependent (LSD) model offers a viable solution to this
challenge by incorporating dynamic personalization. It monitors the ”state” of
each action (intervention), denoted by $\tau$, to gauge the time elapsed since
that particular action was last selected. Positive values of $\tau$ signify
that the action has been dormant for some time, while negative values indicate
frequent recent selection. This enables the model to adapt its recommendations
in real-time based on the user’s most recent behavior. However, finding the
optimal policy in the LSD setting is NP-hard, and the proposed approximation
algorithm requires solving an integer linear programming problem in every
round (Laforgue et al., 2022). The LSD setting involves both a learning
problem, that is, studying the expected reward of different actions given
different states, and a planning problem of finding an optimal policy given
these expected rewards. Papadigenopoulos et al. (2022) and Foussoul et al.
(2023) introduced improved approximation algorithm for the LSD planning
problem with some additional constrains on the reward function. Moreover,
while Laforgue et al. (2022) introduces the LSD setting and provides a
theoretical analysis of the model, it was not tested in real-life intervention
delivery settings.

In this paper, we propose an economical algorithm for the LSD setting and
evaluate it in the wild. More information can be found in Section 3.1.

###  2.2. The Role of Timing in Coping Mechanisms and Interventions

One of the most defining characteristics of a stressor is duration, which can
be described in three timescales: acute stressors, daily events, and chronic
stressors. Examining the response trajectory of one acute stressor using a
magnifying glass in real time over minutes allows examination of an
individual’s stress response kinetics, anticipation, peak reactivity,
recovery, and regulation processes. The Anticipation stage is marked by pre-
stressor rumination and elevated physiological arousal (Niedenthal et al.,
2005; van Paridon et al., 2017). At the Peak juncture, the body’s stress
response mechanisms are fully activated. Subsequently, in the Recovery stage,
the body returns to its baseline physiological condition. Each stage presents
its unique challenges and opportunities for coping (Epel et al., 2018).

Healthy coping mechanisms can reduce the negative impact of stress, while
maladaptive strategies can exacerbate it, prolong each stage, and contribute
to adverse long-term health outcomes (Epel et al., 2018). In the Anticipation
stage, emotional regulation strategies such as attentional deployment are
crucial for managing pre-stressor ruminations and physiological arousal (Cohen
et al., 2016; Koole et al., 2011; Gross, 2015). Without effective coping,
maladaptive strategies like excessive worrying can exacerbate stress at this
stage. During the Peak stage, adaptive coping strategies like cognitive change
are particularly effective for managing the fully activated stress response
(Koole et al., 2011; Gross, 2015). In the Recovery stage, response modulation
strategies, including relaxation techniques and positive reframing, are
essential for expediting the body’s return to its baseline physiological state
(Koole et al., 2011; Gross, 2015). Maladaptive coping strategies, such as
substance abuse, can delay recovery and contribute to sustained stress and
associated health risks.

Specific interventions are effective at different stress stages. In the
Anticipation stage, mindfulness, yoga (Toussaint et al., 2021; John et al.,
2011), visualization (Felix et al., 2018),rhythmic activities (Cairney et al.,
2014), and to-do lists (Jeremy Sutton, 2023), have been identified as
effective mechanisms for reducing anticipatory stress (Vadoa et al., 1997;
Koole et al., 2011). In high-performance domains, such as actors and athletes,
these exercises not only mitigate anticipatory stress but also strongly
correlate with their success (Gould et al., 2009; Van Raalte et al., 2019;
Filaire et al., 2007; Arvinen-Barrow et al., 2007; Taylor et al., 2008; Zaza,
1994). During the Peak stage, cognitive-behavioral interventions (CBT) and
Distracting activities shown to be effective (Gross, 1998; Sheppes and Meiran,
2007; Koole et al., 2011; Nakao et al., 2021). In the Recovery stage,
relaxation techniques and scribbling aid in returning to baseline (Morin,
2023; Toussaint et al., 2021; Simmons, 2016; Gilad et al., 2017). Within these
stages, special interest is directed towards the ”about-to-happen” temporal
window as a key moment for preemptive interventions for predictable stressors
like public speaking (Bassett et al., 1987), school tests (Putwain, 2008), or
bedtime routines with young children (De Stasio et al., 2018). Various studies
corroborate this timing concept (Rahman et al., 2016; Battalio et al., 2021;
Sarker et al., 2016; Mark et al., 2016; Howe et al., 2022) suggesting that
proactive approaches could significantly enhance intervention outcomes (Tazarv
et al., 2021; Sarker et al., 2016). Complementing these findings, research by
Gross underscores the importance of timing in emotional regulation strategies
(Gross, 1998). Sheppes and Meiran (2007) suggests that intervening before the
onset of stress can prevent the activation of the stress response, leading to
more favorable outcomes in both acute and chronic stress scenarios. This
preemptive approach is especially valuable in situations where stress is
predictable, such as public speaking or challenges related to parenting such
as morning and bedtime routines. Thus, the efficacy of various interventions
is tied to the timing of their application.

Understanding the timing and peaks of stress is undeniably vital; however,
theoretical knowledge about when to intervene constitutes only half of the
equation. The other half resides in the efficacious delivery and sustained
engagement with these interventions, particularly in real-world contexts where
stressors are less predictable and more varied. Without robust engagement,
even the most well-timed interventions risk becoming ineffectual. Therefore,
attention is increasingly being channeled towards technological solutions that
facilitate not only timely delivery but also foster effective engagement in
stress management strategies (Nahum-Shani et al., 2018; Howe et al., 2022;
Doherty et al., 2012).

###  2.3. Stress Management in Parents of Young Children

Our research is specifically focused on parents of young children, a
demographic for which three primary transitional periods have been identified
as particularly stress-inducing: the transition from morning routines to work,
the shift from work to afternoon activities, and the transition into bedtime
routines (Schneider et al., 2004; De Stasio et al., 2018). This section offers
a literature review centered on stress management strategies pertinent to this
demographic.

Parenting stress is characterized by psychologists as the emotional tension
experienced when one feels inundated by the responsibilities of parenthood,
often attributed to a perceived insufficiency of resources to meet these
demands (Deater-Deckard, 1998; Holly et al., 2019). Amidst the daily balancing
act of managing multiple child-care tasks, parents frequently encounter
stressful situations. Notably, parental stress levels have surged during the
COVID-19 pandemic and have yet to revert to pre-pandemic norms (Adams et al.,
2021), underscoring the necessity for augmented mental health resources and
support mechanisms.

Three primary transitional periods have been pinpointed as especially stress-
inducing: the transition from morning routines to occupational activities, the
shift from work to afternoon engagements, and the transition into bedtime
routines (Schneider et al., 2004; De Stasio et al., 2018). The morning
separation from children is particularly highlighted as a potential stressor
(Drugli and Undheim, 2012). A survey conducted by OnePoll, on behalf of Amazon
Devices, polled 2,000 parents to ascertain the most stressful segments of
their day. A majority of respondents identified morning preparations as the
principal source of daily anxietyee _e_https://www.independent.co.uk/life-
style/parents-mornings-stressful-childcare-poll.

Comprehending these stress patterns is imperative for ascertaining the optimal
timing for interventions. Such understanding also facilitates a nuanced
analysis of the efficacy of interventions deployed before, during, and
subsequent to these identified stress episodes.

##  3\. Methods

The primary objective of this research is to explore the short- and long-term
effects of mHealth micro-interventions on daily life stress coping, and how
personalization influences these effects, focusing on parents of young
children, utilizing a comprehensive framework we have developed for this
purpose (Figure 1). We also aim to enhance user engagement through
personalized, context-aware recommendation (PCAR) (Nahum-Shani et al., 2018;
Christensen et al., 2009; Paredes et al., 2014).

The study employed a four-week, real-time, in-the-wild design with 29 parents,
focusing on parents of young children. We used WhatsApp as the intervention
delivery method, given its familiarity and popularity messaging platform, to
deliver timely and accessible interventions. We compare PCAR to a random-based
intervention recommender and a control one. The experiment was approved by
institutional ethics committee.

###  3.1. Personalized Context Aware Recommender (PCAR)

Here, we introduce the PCAR algorithm for personalizing intervention selection
which dynamically tailors interventions to the specific needs and contextual
factors of each participant.

The primary objective is to identify the most effective intervention for
reducing stress levels. While previous research has modeled this problem as a
multi-armed bandit issue, it was observed that such models tend to converge to
selecting the same interventions repeatedly, leading to diminishing
performance (Paredes et al., 2014). Following Laforgue et al. (2022) we argue
for the necessity of a more sophisticated reinforcement learning model, yet,
due to scarcity of feedback, since studies run for short periods with a
limited number of participants, the model has to have limited capacity.
Therefore, we adopt the Last Switch Dependent model (Laforgue et al., 2022).
In this setting, it is assumed that the reward of using an intervention
depends only on the time since this arm (intervention) was switched in or out
denoted by $\tau_{a}$ for the arm $a$. If $a$ was used consistently during the
last $r$ rounds then $\tau_{a}=-r$ whereas if $a$ was not used in the last $r$
rounds then $\tau_{a}=r$.

In this setting, the state $\tau$ is fully observed. Moreover, the transition
between states is known, however, the reward function itself is unknown and
has to be learned. Towards this goal we introduce PCAR , a variant of the
SARSA($\lambda$) algorithm (Sutton and Barto, 2018). Unlike traditional SARSA,
PCAR uses the structure of the Last Switch Dependent model to evaluate not
just the current trajectory but also all potential trajectories that lead to
the current state. This accelerates the learning process since every feedback
received from a participant is used to update the $Q$ function multiple times.
PCAR assumes that there are environmental factors (context) that are
independent of the current state and encodes them in the policy as well. The
details of PCAR are presented in Algorithm 1.

We define the state $s$ as comprising two components: $\tau$ and _env_. Here,
$\tau$ captures the historical sequence of actions undertaken by the agent,
akin to the Last Switch Dependent model $\tau$. The _env_ component represents
environmental factors independent of the agent’s actions. A ghost state
$\hat{\tau}$ is defined for $\tau$ if the expected rewards for actions are
identical for both $\tau$ and $\hat{\tau}$, implying that their corresponding
actions $\hat{a}^{(i)}$ and $a^{(i)}$ are identical. Using a known transition
function $\delta_{a}$ (Laforgue et al., 2022), we then determine the
subsequent ghost state $\hat{\tau}_{a(t+1)}$ and the next ghost action
$\hat{a}^{(i)}$, updating the ghost state’s weight as if it had been
encountered. Despite its prowess, PCAR grapples with computational and sample
complexities. To elucidate, computational complexity concerns the resources
(time and memory) an algorithm demands, whereas sample complexity relates to
the interactions needed for satisfactory performance. In its raw form, PCAR
demands exhaustive traversal of the state-action space, resulting in an
impractical computational complexity of $O(k^{h})$ and high sample complexity.

The TensorAgent method deconstructs each action into an attribute dictionary,
streamlining both complexities. An action $a$ transforms from a solitary
intervention to an attribute vector:

$a=[a_{1},a_{2},\ldots,a_{P}]$

where each $a_{i}$ corresponds to an action attribute’s possible value and $P$
represents the number of attributes. We ran independent copies of PCAR for
selecting appropriate values for each attribute, we call these copies agents.
Every copy (agent) received the same reward but had different actions to
select from and hence leading to independent models.

As we bound the number of possible values an attribute can take by a constant
$C$, the complexity becomes $O\left((P\cdot\widetilde{C})^{h}\right)$. Given
that $P\ll k$, it offers a significant reduction in complexity. In practical
terms, interventions like yoga are decomposed to attributes such as ’emotional
regulation type’: ’response modulation’, ’therapy group’: ’somatic’,
’location’: ’indoor’.

###  3.2. System Design

To address our research aims, we have developed a system that enables real-
time interactions with multiple participants simultaneously. The system can
run autonomously 24/7 and utilizes sensor data from mobile devices, along with
contextual features, to infer the appropriate time to initiate stress coping
micro-interventions. The system comprises four primary components, as depicted
in Figure 2. In the following sections, we provide detailed descriptions of
each component:

  1. (1)

Data Collection Component: Tasked with gathering sensor data from
participants’ mobile devices via a dedicated application. To reduce power
consumption on the phone, data was uploaded at approximately fifteen-minute
intervals to our database for subsequent use in the timing detection algorithm
and for offline analysis.

  2. (2)

Just-A-Minute Component: Utilizes the sensor data from participants’ mobile
devices to infer appropriate intervention timings. It aims to maximize the
likelihood of user acceptance of interventions while adhering to constraints
such as a maximum of three interventions per day and a minimum of two hours
between consecutive interventions. More information can be found in Section
3.2.2.

  3. (3)

Personalized, Context-Aware Recommender Component: Customizes the selection of
interventions for each participant, taking into account factors like
personality traits, current location, and past responses. This component is
designed to mitigate the issue of repetitive recommendations, thereby reducing
user disengagement (Paredes et al., 2014; Liao et al., 2020). It is based on
the implementation of our algorithm PCAR described in Section 3.1.

  4. (4)

User Interface (UI) Component: Comprises a WhatsApp chatbot engineered to
enable user-friendly interactions. This serves as the main conduit for human-
computer communication within our system.

Figure 1. The framework developed to improve engagement with the stress-
reducing micro-interventions: Mobile sensor data from the user is gathered via
a specialized application and uploaded to a centralized database. A dedicated
process analyzes this data to infer the most opportune moment for
intervention, aiming to maximize user engagement. When the timing is deemed
appropriate, a specific intervention is chosen, triggering a chatbot
interaction with the user. User feedback is subsequently stored in the cloud
for future analysis and refinement.

####  3.2.1. Data Collection

The primary input to our system consists of raw sensor data collected from
mobile devices, including accelerometers, gyroscopes, and location sensors (a
detailed list of sensors is provided in Appendix 7.1). These sensors were
sampled continuously and concurrently using the AWARE-Light application
(Ferreira et al., 2015), which was specifically configured for this research.
We selected this application after evaluating various other platforms, such as
the mCerebrum application (Hossain et al., 2017) and the LAMP-platform (Bilden
et al., 2023). AWARE-Light allowed for simultaneous data collection from an
unlimited number of participants, offered customizable sampling
configurations, and supports low sampling rates. However, the use of this
application restricted our study to Android phone users exclusively.

Data was transmitted and uploaded to our databases at intervals ranging from
10 to 15 minutes, depending on the brand of the mobile device. We classified
raw sensor data into two categories, which were subsequently utilized by the
Just-A-Minute inference component (Figure 3.2.2):

  * •

Spatial Sensor Data: These are static and non-motion sensors that provide
insights into the device’s current state or environmental conditions. Sensors
in this category offer a snapshot of the present situation without necessarily
monitoring time-based changes. They include the location, battery, Bluetooth,
proximity, screen, and light sensors.

  * •

Temporal Sensor Data: These sensors are dynamic and track changes over time.
They capture motion, orientation, or other time-sensitive phenomena. The
sensors in this category include the linear accelerometer, gyroscope,
rotation, gravity, and magnetometer sensors.

####  3.2.2. Just-A-Minute Component

This component was designed to infer the time for interventions for every
active participant in the study independently. It adheres to a daily limit of
three interventions, mandates a two-hour gap between each, and restricts
delivery to between 8 AM and 9 PM. Two models are employed for this purpose: a
neural network-based encoder and a classification head.

  * •

Encoder: Utilizes a TS2Vec model (Yue et al., 2022) to learn a representation
space that better describes the raw temporal sensor data for timing inference,
this model harnesses data collected from all the participants. It employs
hierarchical contrastive learning over augmented context views, enabling
robust contextual representation for each timestamp.

  * •

Classification Head: This part of the component uses both temporal and spatial
raw data from a specific participant to infer the likelihood of intervention
acceptance. It also leverages contextual features to adjust the prediction for
different types of participants.

These models operate in two distinct modes — offline and real-time. The
offline mode is scheduled to execute nightly at 2 AM, while the real-time mode
runs at 5-minute intervals throughout the day. The offline process is tasked
with training our models using the past day’s streaming data, sensor readings,
and participant feedback (binary label indicating whether the intervention’s
timing was good or not) from all active study participants. While, the real-
time process utilizes these trained models to make on-the-fly inferences about
whether an intervention should be initiated for a specific participant. Both
processes utilize the same pre-processing layer.

Preprocessing Layer. This layer is responsible for preprocessing both spatial
and temporal raw data to fit the models’ inputs. Timestamp alignment is
performed to ensure all sensor readings are synchronized. Data is segmented
into fifteen-minute batches, from which statistical features such as median,
mean, mode, and variance are extracted. These features are then standardized
along the feature axis to guarantee that batches from different time intervals
throughout the day have consistent mean values and standard deviations.

Offline Process. Each night, two threads are initiated in parallel to update
our models — one for each model. The preprocessing layer is employed to
extract features in both threads. The encoder is updated using the previous
day’s temporal raw data, while the classification head is retrained from
scratch using all available feedback on interventions since the beginning of
the study, along with the corresponding sensor data (both temporal and
spatial). A custom loss function, comprising a classification loss (MSE) and a
budget loss, is used for training. The MSE loss aims to identify the ideal
intervention timing to maximize user engagement, whereas the budget loss
ensures that interventions adhere to the allocated budget constraints. Upon
completion of the retraining, both models are saved to the cloud and
integrated into the real-time process for subsequent inference.

Real-Time Process. This process infers the appropriate time to intervene,
aiming to maximize the likelihood of user acceptance. It is scheduled to run
at 5-minute intervals for all active participants . The process comprises four
sequential modules, detailed below.

  1. (1)

Ingestion: The pre-processing layer loads and processes the streaming temporal
and spatial sensor data from each participant .

  2. (2)

Encoding: The trained encoder is utilized on the processed temporal data to
generate embeddings.

  3. (3)

Enrichment: These embeddings are enriched by concatenating them with the
following features:

     * •

Features extracted from spatial sensors to capture static changes in the
participant ’s condition.

     * •

Timestamp-related features, such as the day of the week, hour, and relative
time of day, are also included. These features offer context to the temporal
data and help identify patterns related to the user’s daily routine.

     * •

Budget-related features, including the remaining time to intervene and the
number of interventions left to deliver.

  4. (4)

Model: A fully connected neural network serves as the classification head,
outputting the likelihood that a proposed intervention will be accepted at
that moment. A post-processor then evaluates these odds against the current
budget constraints to decide if it’s the appropriate time to intervene.

Figure 2. Just-A-Minute Component: Timing Inference Pipeline. The pipeline is
divided into two main processes: the Offline Process, which involves dual jobs
for model retraining using aggregated user data, and the Real-Time Process,
which ingests and encodes data. A decision to initiate a stress intervention
is made based on the calculated likelihood of user acceptance and current
budget constraints.

####  3.2.3. User Interface (UI) Component

Recognizing that users often prefer multi-functional apps over specialized
ones (Torous and Vaidyam, 2020) and that social media platforms significantly
influence app choices (Al-Shamaileh and Sutcliffe, 2023), we have chosen
WhatsApp, a widely-used communication app, as the platform for delivering our
interventions. This strategy aims to maximize user engagement and minimize the
time a participant needs to adapt to using a new application on a daily
routine (up to eliminate it at all).

###  3.3. Stress Intervention Design

The stress intervention encompasses common coping strategies like Relaxation
Techniques and Cognitive Strategies. Relaxation techniques, including deep
breathing and mindfulness meditation, have been proven effective in reducing
anxiety and stress (Morin, 2023; Filaire et al., 2007; John et al., 2011).
Rhythmic activities like walking and yoga are also interventions aiding in
stress reduction (Cairney et al., 2014).

Cognitive Strategies involve techniques such as cognitive restructuring and
goal-setting, aimed at altering stress perceptions (Jeremy Sutton, 2023;
Morin, 2023). The interventions are organized into four categories based on
Gross’s emotional regulation types (Gross, 2015) and further classified into
four therapy groups: positive psychology, cognitive behavioral, meta-
cognitive, and somatic practices (Paredes et al., 2014). We developed micro-
intervention, quick exercises lasting up to 60 seconds, designed to integrate
seamlessly into daily routines. These micro-interventions consist of short
videos or text prompts. More details are provided in Appendix 7.3.

###  3.4. Study Design

We employed a four-week, real-time, in-the-wild design, focusing on parents of
young children. During the study, the participants engaged with our WhatsApp
chatbot which delivered stress-coping micro-interventions.

####  3.4.1. Participants

We recruited participants through social media, specifically targeting parents
of children aged up to 12 years who use Android devices, to participate in a
four-week study. An initial screening for demographic information and mental
health was conducted, leading to the enrollment of 103 participants. Of these,
72 (70%) provided consent to participate. A total of 37 participants
successfully installed the data collection application, while eight
encountered compatibility issues, leaving us with a final sample of 29
participants. These individuals were then randomly assigned to one of two
conditions, taking into account gender balance due to its known influence on
individual stress perception (Association et al., 2010, 2019). Participants
were reimbursed with gift cards of the equivalent of $\sim$15 USD for every
phase of the study in which they participated. We refer to phase as a period
of two weeks in the study (a total of two phases).

During the study, one participant didn’t complete any phase and two
participants withdrew after the first two weeks (first phase) and did not
continue to the subsequent phase. The fluctuations in participant numbers
throughout the study are illustrated in Figure 3.

Figure 3. Phase-wise User Cluster Differentiation: Initial allocation included
7 users in the control group and 21 in the random intervention group (Phase
1). After two weeks, 10 users remained in the random intervention group, while
16 were transitioned to PCAR intervention (Phase 2). 1 user didn’t complete
any phase and 2 users completed the first phase, totalling to 3 dropouts in
the study.

The demographic breakdown is as follows: 17.2% male and 82.8% female; age
ranges were 10% (20-29), 80% (30-39), and 10% (40-49); 16% had one child, 47%
two, 17% three, and 20% four or more; 90% were married or cohabiting;
educational levels were 13.3% high school, 16.7% undergraduate, and 50%
graduate or higher.

####  3.4.2. Procedure and Interactions

The study flow was structured as follows:

  * •

Onboarding Upon joining the study, participants completed pre-study surveys,
including demographic information and psychological scales: the Perceived
Stress Scale (PSS) (Cohen et al., 1983) and Big Five Inventory (BFI-10)
(Rammstedt and John, 2007). They were guided through the data collection
application installation online, running in the background throughout the
study only showing a notification ”active data collection” (see Section 3.2).

  * •

Study Phases The study was divided into two phases, each lasting two weeks.
Interventions were restricted to be delivered on weekdays only from 8AM to
9PM. During the first two weeks (defined as phase 1), participants interacted
with our WhatsApp chatbot, which prompted them with either random micro-
intervention suggestions or only asked to fill in momentary stress levels if
they were assigned to the control group.

After two weeks, the participants Post-phase PSS questionnaires were
administered and participants were divided into two new groups, one receiving
personalized context-aware recommendations (PCAR) interventions while the
other group receiving random interventions (Random group) when the right time
to intervene was detected.

  * •

Feedback and Adaptation We collected two types of feedback during the study: A
binary label on whether the intervention’s timing was good or not and pre- and
post-intervention stress level on a 7-point Likert scale (1=Not at all
stressed; 4=Moderately stressed; 7=Extremely stressed) (Likert, 1932). Every
interaction began with participants receiving a message asking if the current
moment was suitable for an intervention. If it was, they were asked to rate
their pre-intervention stress level, engage in the suggested activity, and
then rate their post-intervention stress level 10 minutes after engaging in a
micro-intervention. An example of a full micro-intervention dialog is shown in
Figure 4. Participants also had the option to initiate micro-interventions by
messaging the chatbot but this option was never used.

  * •

Offboarding At the end of the four-week study, participants were guided
through the process of uninstalling the data collection application. They then
completed a post-study usability questionnaire and a post-experiment PSS
questionnaire.

Figure 4. Stress Reduction Micro-Intervention Example Dialog: (a) Our system
inferred an ideal time for a stress reduction micro-intervention and initiated
a WhatsApp conversation. (b) If the user decides to accept the intervention
and engage with our chatbot, he is asked whether it’s a good time to
intervene. (c) Once the user replies ”Yes”, he is asked for the current stress
level. (d) Our chatbot suggests a stress-coping activity. (e) 10 minutes
later, our chatbot asks again for the current stress level.

###  3.5. Data Processing & Analysis

Our analysis was divided into two parts, the first was to understand the
engagement of the participants during the study, and the second was to
evaluate the effectiveness of the interventions and their ability to help
participants reduce their short- and long-term stress levels.

Overall, 1207 unique interventions were initiated by our chatbot during the
study. A conversation with the chatbot remained active for an hour, that is,
if a participant didn’t respond to the chatbot, the session would be closed
after an hour and the conversation couldn’t be completed, resulting in
labeling it as not a good timing for intervention. With that policy in mind,
51.2% (618/1207) of the interventions were initiated, accepted, and engaged to
some extent. 91.6% (566/618) of the accepted interventions were fully
completed and collected both pre- and post-intervention momentary stress
levels via EMAs. In total, We collected 1179 stress level ratings, 613 pre-
intervention and 566 post-intervention. Study-long stress reduction was
calculated using intake, middle (after two weeks), and final PSS scores. All
PSS forms during the study were filled appropriately by 26 participants ,
totaling with 78 PSS scores measured. The other 3 participants filled in 1 PSS
score at the beginning of the study.

Momentary stress reduction (reward) was computed by subtracting the pre-
intervention stress levels from the post-intervention stress levels. A higher
value indicates a higher stress reduction. Study-long stress reduction was
computed by calculating the gradient of the calculated PSS scores along the
study for each participant . Negative values of the gradient indicate higher
stress reduction.

###  3.6. Statistical Tests

For comparing the means of two conditions, two examined groups (Control,
Random, Personalized), we used the Welch Two Sample t-test. Wherever
applicable. We used one-way analysis of variance (ANOVA) tests to examine
differences in outcome variables, such as stress reduction and intervention
acceptance rate, considering multi-level factors like intervention activities.
If the test p-value was below a significance level, indicating a difference in
at least one group mean, a post hoc test, Tukey’s Test in our case, was used
for pairwise comparisons while managing the family-wise error rate. Pearson’s
correlation was used for correlation analyses.

We employed the Generalized Estimating Equations (GEE) approach to analyze
correlated data with repeated measures, focusing on stress reduction and
intervention acceptance rate from the participant perspective. Our GEE model,
which accounted for intrinsic correlations, incorporated multi-level
attributes like patient day and gender. Due to the time-related nature of our
data, we considered correlations to be highest between adjacent time points,
with a systematically decreasing correlation as the distance between time
points increased. Therefore, we used the first-order autoregressive (AR(1))
working dependence structure as the covariance structure. For the distribution
families, the Binomial distribution was used to investigate the acceptance
rate, whereas the Gaussian distribution for the stress reduction. The logit
function was used as the model’s link function. A comprehensive list of
features is available in Appendix 7.2. This method provided insight into
predominant trends while considering individual variations.

All the mentioned procedures for processing the data and for statistical
analyses in this study were performed using Python 3.8.8. (statsmodels 0.13.2
was used to establish the GEE models (Seabold and Perktold, 2010)).

##  4\. Results

In this section, we present our key findings. The main metrics we follow are
the acceptance rate, measuring the willingness of participants to engage when
the system triggers a conversation, and the stress reduction, measuring the
efficacy of the interventions. When a metric is displayed as an average across
samples, it is calculated as the mean of means, that is, we initially
calculated the average for each participant , followed by an overall average
across users. The corresponding standard deviation, in that case, is the
standard deviation of the mean.

###  4.1. Impact of Stress Interventions (RQ1)

Here, we present data and analysis on the short- and long-term impact of
micro-interventions on self-reported stress levels. The results are displayed
across different intervention types, comparing no intervention group
(control), random interventions, and personalized, context-aware interventions
selected by PCAR to evaluate the impact of personalization.

####  4.1.1. Short-Term Stress Reduction

The key finding in this section is that with PCAR, we observe the maintenance
of efficacy over time and even a slight increase, as illustrated by the
positive $\Delta$ in Figure 5(b), whereas for the other intervention types, we
observe a decrease.

Figure 5(a) illustrates the average weekly stress levels reported by
participants, segmented into the two phases as outlined in Section • ‣ 3.4.2.
The term ”reward” is used to denote the processed feedback, calculated as the
difference between initial and subsequent stress levels. A positive reward
indicates a reduction in stress levels, as quantified on a 7-point Likert
scale (1=Not at all stressed; 4=Moderately stressed; 7=Extremely stressed). We
see that the interventions groups had a positive impact on stress reduction in
both phases, while the control group exhibited a marginal increase in stress
levels. Figure 5(b) summarizes these changes within the phases to demonstrate
the changes over time.

A Generalized Estimating Equations (GEE) model was employed to assess the
effects of the random interventions in comparison to a control group during
the initial phase of the study (see Section 3.6). The analysis revealed a
statistically significant difference in self-reported stress levels pre- and
post-intervention across the two conditions (p=0.001) with a coefficient of
0.32, favoring the random group. This result substantiates the immediate,
short-term efficacy of the stress interventions.

Although existing literature posits that ”intervention fatigue” may arise from
impersonal or repetitive content, thereby reducing the effectiveness of
interventions (Paredes et al., 2014; Laforgue et al., 2022), our study did not
find a significant difference in stress reduction between the random groups
across different phases. Similarly, no significant difference was observed
between the first phase random group and PCAR group in the second phase.

Figure 5. Average Weekly Stress Reduction Across Phases and Groups: (a) This
figure is divided into two sections, each corresponding to a different study
phase as outlined in Section • ‣ 3.4.2. The y-axis shows the ”reward”,
calculated as the difference between initial and subsequent stress levels on a
7-point Likert scale. The x-axis is divided into weeks. Error bars denote 95%
confidence intervals. We see that for PCAR, the efficacy is maintained week
over week while for the other settings, there is a decrease, especially in
phase 2. We also observe a significant stress reduction within intervention
groups. (b) This figure shows the change in the average reward of each group
over the corresponding weeks within the phase.

Figure 6 presents a further analysis of the Control group participants
($n=7$). This group of participants was not exposed to interventions during
the first phase. The analysis reveals a significant increase in the average
reward over time for the PCAR intervention group compared to the random
interventions. That is, not only did PCAR succeed in reducing stress and
maintaining the positive efficacy of interventions, but its impact was
increased over time. We also notice a significant difference ($p=1.1\cdot
10^{-}3$) in the average reward across the two phases.

These results demonstrate the advantage of using PCAR intervention selection
algorithm to improve efficacy over time and highlight the significant impact
of even a one-minute intervention for stress coping.

Figure 6. Average Weekly Stress Reduction Across Phases of Control Group: (a)
This figure is divided into two sections, each corresponding to a different
study phase as outlined in Section • ‣ 3.4.2. The y-axis shows the ’reward,’
calculated as the difference between initial and subsequent stress levels on a
7-point Likert scale. The x-axis is divided into weeks. In phase 1, the
Control intervention group had a mean reward of -0.14 ($\sigma=0.03\%$),
indicating increasing stress. In phase 2, the average reward was 0.39
($\sigma=0.01\%$) for the random interventions group and 0.64
($\sigma=0.30\%$) for the PCAR group, showing stress reduction. Error bars
denote 95% confidence intervals. The graph reveals a significant stress
reduction within intervention groups and suggests a preference for PCAR over
random interventions. (b) This figure illustrates the change in the average
reward for each group over the corresponding weeks within the phase. It
underscores the advantage of using personalized, context-aware interventions
to enhance participants’ gained value over time.

Figure 7 shows the average reward across the phases for the participants who
were assigned random interventions during Phase 1 (n=21). The average reward
of the random intervention groups was positive in both phases 0.38
$(\sigma=0.01)$ and 0.42 $(\sigma=0.14)$ respectively. The average reward for
the PCAR interventions was also positive 0.57 $(\sigma=0.09)$. Despite the
participants’ feedback on the interventions being positive, we have observed a
decrease in the impact of the interventions over time. Nevertheless, the
decrease, for the PCAR group, is smaller than for the group of participants
who continued to receive random interventions.

Figure 7. Average Weekly Stress Reduction Across Phases of Random Phase 1
Group: (a) This figure is divided into two sections, each corresponding to a
different study phase as outlined in Section • ‣ 3.4.2. The y-axis shows the
’reward,’ calculated as the difference between initial and subsequent stress
levels on a 7-point Likert scale. The x-axis is divided into weeks. In phase
1, the Random interventions group had a mean reward of 0.38 ($\sigma=0.01\%$),
indicating stress reduction. In phase 2, the average reward was 0.42
($\sigma=0.14\%$) for the random interventions group and 0.57
($\sigma=0.09\%$) for the PCAR group, showing stress reduction again. Error
bars denote 95% confidence intervals. (b) This figure shows the change in the
average reward of each group over the corresponding weeks within the phase.

####  4.1.2. Long-Term Stress Reduction

Figure 8(a) illustrates the average PSS scores across the different study
groups. The upper graph represents the participants assigned to the control
group in the first phase, while the bottom graph represents those assigned to
the random group in the first phase.

For the bottom graph, a decreasing trend is observed after two weeks for
participants assigned to the PCAR group, whereas an increasing trend in
perceived stress is noted for those receiving more random interventions in the
second phase. in both subgroups the initial average PSS scores and those
calculated after the first two weeks exhibit similar values, as both groups
received random interventions during this period. This finding highlights the
advantage of utilizing PCAR for reducing percieved stress in the long-term.

Pairwise comparisons revealed a significant decrease ($p\leq 0.0002$) in
perceived stress levels after four weeks of study (PSS scores) with their
initial scores. The average PSS score of all the participants dropped from
18.3 to 16.0. PSS scores of 14-26 would be considered moderate stress and
below that considered low stress further highlighting the enduring impact of
the stress interventions. This decrease, shown in Figure 8(b), shows the
potential for persistent engagement with those interventions in the long term.

Figure 8. Self-reported stress levels throughout the study: This figure shows
the average perceived stress scale from intake, after 2 weeks, and exit
surveys. PSS scores of 14-26 would be considered moderate stress. The error
bars indicate 95% confidence intervals. (a) illustrates the average PSS scores
across the different study groups. The upper graph represents the participants
assigned to the control group in the first phase, while the bottom graph
represents those assigned to the random group in the first phase. A decreasing
trend is observed after two weeks for participants assigned to the PCAR group,
whereas an increasing trend in perceived stress is noted for those receiving
more random interventions in the second phase. (b) illustrates The average PSS
score of all the participants in the study.

###  4.2. Timing and Engagement (RQ2)

In the following section, we will discuss the aspects of timing and engagement
in which participants engaged with the proposed micro-interventions.

####  4.2.1. Weekly Acceptance Rates Across Phases

To understand the temporal impact of interventions on participants’ retention,
we refer to Figure 9. The average acceptance rate for the control group in
phase 1 was 0.77 ($\sigma=0.09$). The average acceptance rate for the random
group in both phase 1 and phase 2 was 0.49 ($\sigma=0.04$). The average
acceptance rate for the PCAR group in phase 2 was 0.44 ($\sigma=0.00$). The
control group achieved the highest acceptance rate among all the tested
groups, with a significant difference ($p=1.31e-15$). The PCAR group was the
only group that maintained a non-decreasing trend in the acceptance rates over
the corresponding weeks within the phase. This observation suggests that
personalized interventions are more effective in maintaining engagement with
stress-reduction micro-interventions than random interventions.

Figure 9. Weekly Acceptance Rates Across Phases and Groups: (a) This figure is
divided into two sections, each corresponding to a different study phase as
outlined in Section • ‣ 3.4.2. The y-axis shows the acceptance rate calculated
as the average number of interventions accepted during the corresponding time
frame. The x-axis is divided into weeks. In phase 1, for both the random
interventions group and the control group, a decrease in the acceptance rate
is observed. In phase 2, the random interventions group’s acceptance rate is
again decreasing, while the PCAR interventions group’s acceptance rate is not
affected by the temporal dynamics. Error bars denote 95% confidence intervals.
(b) This figure shows the change in the acceptance rates of each group over
the corresponding weeks within the phase. A higher change means a higher
retention. The graphs suggests a slight preference for PCAR over random
interventions to overcome users retention over time.

####  4.2.2. Weekly Acceptance Rates (All Participants Altogether)

Maintaining user engagement over time is essential for the effectiveness of a
stress management framework. Sustained engagement allows for a more nuanced
understanding of user behavior patterns and contributes to the long-term
impact of the interventions. Throughout the four-week study period,
participants engaged in an average of 22.1 interventions ($\sigma$=2.31%,
min=4, max=50).

As depicted in Figure 10, a noticeable decline in the acceptance rate of
micro-interventions is observed across all groups during the main four-week
period. Intriguingly, this downward trend is followed by a pronounced increase
in the fifth week, which specifically represents participants who voluntarily
opted to extend their participation in the experiment beyond the main four-
week duration.

Figure 10. Weekly Acceptance Rates of Micro-Interventions: The figure
delineates the trend of acceptance rates over a five-week period, emphasizing
a decline in the initial weeks followed by a significant uptick in the fifth
week.

We hypothesize that the initial decrease may be symptomatic of ”intervention
fatigue” or boredom. For the surge observed in the fifth week, we propose that
it may be attributed to the specific characteristics of the remaining
participants (n=3). These individuals may be more predisposed to accept
interventions due to particular personality traits.

This assertion is supported by the observed high variability in participant
collaboration. Such variability is manifested in a broad spectrum of
acceptance rates with an average rate of 51.6%
$(\sigma=23.7\%,\mbox{min}=0.08,\mbox{max}=1,n=29)$ and engagement levels
among different users. Some participants were highly collaborative,
consistently accepting interventions and actively engaging with our chatbot
throughout the study answering more than 75% of interventions (n=6). In
contrast, others displayed lower levels of collaboration, characterized by
intermittent acceptance of interventions and variable engagement levels
answering less than 25% of interventions (n=6). This divergence in behavior
could offer an alternative explanation for the sharp increase in acceptance
rates observed in the fifth week, a subject we will explore further in the
following paragraph.

####  4.2.3. Hourly Acceptance Rates

To elucidate the temporal dynamics of stress and intervention acceptance
within and between groups, Figure 11 presents a histogram that delineates the
distribution of intervention opportunities across the day. Each bin signifies
the average acceptance rate for a given hour, shedding light on the diurnal
patterns of participant engagement with the chatbot. This histogram is
particularly focused on behavior observed during Phase 1. i.e., the first two
weeks of the experiment. In this phase, the treatment group— hereafter
referred to as the ’random’ group — received arbitrary recommendations for
engagement, whereas the control group was merely prompted to rate their
current stress level. For an in-depth discussion on the study phases, refer to
Section • ‣ 3.4.2.

The data reveals dual peaks in acceptance rates at 4 PM and 7 PM for both the
control and random groups. Noteworthy declines in acceptance rates manifest at
6 PM. Additionally, the control group experiences a secondary dip at 9 AM,
while the random group shows a minor yet pronounced dip at 1 PM. These
observed patterns are congruent with extant research on parents of young
children, which pinpoints three primary stress-inducing transitional periods:
morning, afternoon, and bedtime. Further insights into these periods are
elaborated in Section 2.3.

Figure 11. Hourly Acceptance Rates of Micro-Interventions: The figure
delineates the average hourly acceptance rates for both control and random
groups. Notable peaks and dips correspond with recognized stress-inducing
transitional periods for parents of young children.

While the patterns between the groups are similar, the control group exhibits
higher overall acceptance rates, with a rate of 79.7% ($\sigma$=3.43%) (n=180
initiated interventions). Conversely, the random group had an acceptance rate
of 50.5% ($\sigma$=4.77%) (n=480 initiated interventions). The most pronounced
divergence between the groups occurred at 1 PM, with the control group showing
an acceptance rate of 85.7% ($\sigma$=14.3%) compared to 25.6%
($\sigma$=12.0%) in the random group. This suggests that even though the
intervention takes just a minute, during lunchtime, a minute is highly
valuable.

Another significant divergence between the groups occurred at 8 AM. The
control group exhibited an acceptance rate of 85.7% ($\sigma$=9.22%), in
contrast to the random group’s rate of 25.6% ($\sigma$=8.35%). Given that our
system was operational solely on weekdays from 8 AM to 9 PM, this suggests
that at 8 AM participants may have already been too busy to engage with an
intervention, even for a minute. We hypothesize that earlier interventions
could function as mental preparation for the day, potentially leading to
higher adherence.

In light of these findings, which demonstrate a significant effect of
engagement duration even for one-minute interventions, the optimal timing for
interventions should consider two factors: the peak efficacy of the
intervention and its alignment with the most receptive moments in the
participant’s daily routine.

###  4.3. Qualitative Assessment (RQ3)

Here We will delve into analyzing the user’s personal experience through
quantitative and qualitative feedback. Upon concluding the study, we collected
both open and closed feedback from participants. A total of $n=26$ (89%)
participants contributed to the feedback analysis.

Approximately 61% of participants expressed satisfaction with the exercises,
attributing a positive impact to their daily routine. The content received an
average rating of 3.64 on a 5-point scale. Notably, the duration of the
exercises garnered the highest mean rating, with nearly half of the
participants rating it above 4, indicating strong satisfaction.

Participants generally appreciated the choice of communication platform and
found the duration and content of the micro-interventions to be both pleasing
and relaxing. These aspects are recommended for retention in future iterations
of the study.

On a 5-point agreement scale $(1=\text{Strongly disagree};5=\text{Strongly
agree})$, participants overwhelmingly agreed that the intervention system
facilitated engagement due to an average rating of 4.19 $(\sigma=0.75)$ and
found the WhatsApp platform to be convenient with an average rating of 4.29
$(\sigma=0.29)$. The system was not perceived as frustrating, with an average
rating of 1.47 $(\sigma=0.23)$.

However, the timing of the interventions received a mixed response, with an
average rating of 3 $(\sigma=1.0)$.

No statistically significant differences were observed between groups across
all usability metrics.

Open feedback generally reflected satisfaction with the exercises,
particularly their utility as mindfulness reminders. A subset of participants
(n=4) expressed the expectation for more personalized and timely
interventions, highlighting the need for further research in this area.

A point of concern raised in both open and closed feedback was the cumbersome
installation process of the AWARE Light application(Section 3.2.1). The app’s
absence from the Google Play Store led to a protracted installation process,
which included watching an instructional video and granting multiple
permissions. Users also reported a 15% increase in battery consumption,
affecting the overall user experience and resulting in a ”good for daily use”
an average score of 3 $(\sigma=1.2)$.

Figure 12. Average agreement on six usability statements across n=25
participants, measured on a 5-point Likert scale. The error bars represent 95%
confidence intervals.

These findings affirm the efficacy of separating the communication platform
from the sensing app and underscore the importance of seamless user
interaction. The decision to limit micro-interventions to a duration of up to
one minute appears to be a significant factor in their positive impact,
motivating further research into optimizing timing and content
personalization.

##  5\. Limitations

Conducting our study in a naturalistic setting presented both opportunities
and challenges. While this approach enhances the ecological validity of our
findings, it also imposes constraints. Specifically, we were limited to
intervening only on weekdays, between the hours of 8 a.m. and 9 p.m., and with
a maximum of three interventions per day. Additionally, we had limited
visibility into other life events affecting our participants. Despite these
limitations, we posit that the real-world context of our study lends greater
translational value to the results compared to controlled lab experiments.

For a more nuanced understanding and validation of these insights, future
research should consider a larger sample size and an extended study duration.
Such an approach would provide a more robust assessment of the influence of
timing on the effectiveness of stress intervention strategies.

Our study predominantly relied on self-reported measures of stress, which,
although valuable, have their own set of limitations. These subjective
measures may not always align with physiological indicators of stress (Epel et
al., 2018). They are also susceptible to individual biases, such as reluctance
to disclose vulnerability or cultural influences. However, self-reported
measures remain a practical choice for in-the-wild studies, as they do not
necessitate specialized equipment for data collection and analysis.

Lastly, our data collection was dependent on the AWARE Lite App, which is
restricted to Android devices. During the study, we encountered compatibility
issues with certain phone models, notably Xiaomi, leading to a significant
dropout rate. Of the initial 78 participants who consented to participate and
began the installation process, only 30 successfully completed it.

##  6\. Discussion & Conclusion

The prevalence of reported stress has been on the rise, a trend further
exacerbated by the Covid-19 pandemic (Association et al., 2019; Jiang et al.,
2020). Mobile health applications have the potential to play an important role
in coping with the stress crisis due to the ubiquitous use of smartphones
which allows the delivery of interventions, particularly micro-interventions
(Baumel et al., 2020). However, sustaining long-term user engagement with
these interventions while preserving their high efficacy remains a challenge.

In this work we introduce a new algorithm, the Personalized, Context-Aware
Recommender (PCAR), for intervention selection to mitigate this issue. We
conducted a real-time, field experiment to evaluate PCAR in the wild. Our
findings shows that PCAR outperformed random selection in reducing stress and
maintaining long-term engagement.

By investigating the Control group participants, which was not exposed to
interventions during the first phase, we noticed that even though the average
stress of the Control group participants wasn’t reduced in the first phase,
once the system integrated micro-interventions within the initiated
conversations, the impact was positive and the average stress declined, most
noticeably for the PCAR group.

We also observe that while these interventions may require just a minute to
complete, there exist specific moments during the day when individuals are
more receptive to engaging in them. These moments often coincide with
transitions between activities. In our study focusing on parents of young
children, we observed higher engagement rates during transitions from work
responsibilities to afternoon activities with their children, as well as
transitions from afternoon activities to bedtime routines. These insights have
implications for the design of interventions, suggesting that they could serve
dual purposes: aiding in the ”cool-down” process from one activity and acting
as mental warm-up exercises for the next engagement. Future work should test
intervention delivery timing regarding the stress evolution steps.

## References

  * (1)
  * Adams et al. (2021) Elizabeth L Adams, Danyel Smith, Laura J Caccavale, and Melanie K Bean. 2021.  Parents are stressed! Patterns of parent stress across COVID-19.  _Frontiers in psychiatry_ 12 (2021), 626456.
  * Al-Shamaileh and Sutcliffe (2023) Ons Al-Shamaileh and Alistair Sutcliffe. 2023.  Why people choose Apps: An evaluation of the ecology and user experience of mobile applications.  _International Journal of Human-Computer Studies_ 170 (2023), 102965\.
  * Arnsten (2009) Amy FT Arnsten. 2009\.  Stress signalling pathways that impair prefrontal cortex structure and function.  _Nature reviews neuroscience_ 10, 6 (2009), 410–422.
  * Arvinen-Barrow et al. (2007) Monna Arvinen-Barrow, Daniel A Weigand, Scott Thomas, Brian Hemmings, and Malcolm Walley. 2007\.  Elite and novice athletes’ imagery use in open and closed sports.  _Journal of Applied Sport Psychology_ 19, 1 (2007), 93–104.
  * Association et al. (2010) American Psychological Association et al. 2010\.  Stress in America: Gender and stress.  _American Psychological Association. Recuperado de http://www. apa. org/news/press/releases/stress/2010/gender-stress. aspx_ (2010).
  * Association et al. (2019) American Psychological Association et al. 2019\.  Stress in America™ 2018: Stress and Generation Z Interactive Graphics.  _Recuperado de: https://www. apa. org/news/press/releases/stress/2018/interactive-graphics_ (2019).
  * Bassett et al. (1987) JR Bassett, PM Marshall, and R Spillane. 1987.  The physiological measurement of acute stress (public speaking) in bank employees.  _International Journal of Psychophysiology_ 5, 4 (1987), 265–273.
  * Battalio et al. (2021) Samuel L Battalio, David E Conroy, Walter Dempsey, Peng Liao, Marianne Menictas, Susan Murphy, Inbal Nahum-Shani, Tianchen Qian, Santosh Kumar, and Bonnie Spring. 2021\.  Sense2Stop: a micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention.  _Contemporary Clinical Trials_ 109 (2021), 106534.
  * Baumel et al. (2019a) Amit Baumel, Stav Edan, and John M Kane. 2019a.  Is there a trial bias impacting user engagement with unguided e-mental health interventions? A systematic comparison of published reports and real-world usage of the same programs.  _Translational behavioral medicine_ 9, 6 (2019), 1020–1033.
  * Baumel et al. (2020) Amit Baumel, Theresa Fleming, and Stephen M Schueller. 2020\.  Digital micro interventions for behavioral and mental health gains: core components and conceptualization of digital micro intervention care.  _Journal of medical Internet research_ 22, 10 (2020), e20631.
  * Baumel et al. (2019b) Amit Baumel, Frederick Muench, Stav Edan, and John M Kane. 2019b.  Objective user engagement with mental health apps: systematic search and panel-based usage analysis.  _Journal of medical Internet research_ 21, 9 (2019), e14567.
  * Bilden et al. (2023) Rebecca Bilden, dcurrey88, Aditya Vaidyam, Suraj Patel, Ashley Meyer, Luke Scheuer, ertjlane, Tanvi Lakhtakia, Lucy Gray, Ryan D’Mello, carlan1, Ryan Hays, jtorous, Sarah Chang, Mauro Curbelo, Hunter Lester, and toujames. 2023\.  _BIDMCDigitalPsychiatry/LAMP-platform: Release 2023.2.15_.  https://doi.org/10.5281/zenodo.7643628
  * Bonanno and Burton (2013) George A Bonanno and Charles L Burton. 2013.  Regulatory flexibility: An individual differences perspective on coping and emotion regulation.  _Perspectives on psychological science_ 8, 6 (2013), 591–612.
  * Borghouts et al. (2021) Judith Borghouts, Elizabeth Eikey, Gloria Mark, Cinthia De Leon, Stephen M Schueller, Margaret Schneider, Nicole Stadnick, Kai Zheng, Dana Mukamel, and Dara H Sorkin. 2021.  Barriers to and facilitators of user engagement with digital mental health interventions: systematic review.  _Journal of medical Internet research_ 23, 3 (2021), e24387.
  * Burghardt et al. (2021) Keith Burghardt, Nazgol Tavabi, Emilio Ferrara, Shrikanth Narayanan, and Kristina Lerman. 2021\.  Having a bad day? detecting the impact of atypical events using wearable sensors. In _Social, Cultural, and Behavioral Modeling: 14th International Conference, SBP-BRiMS 2021, Virtual Event, July 6–9, 2021, Proceedings 14_. Springer, 257–267.
  * Cairney et al. (2014) John Cairney, Matthew YW Kwan, Scott Veldhuizen, and Guy EJ Faulkner. 2014.  Who uses exercise as a coping strategy for stress? Results from a national survey of Canadians.  _Journal of physical activity and health_ 11, 5 (2014), 908–916.
  * Can et al. (2020) Yekta Said Can, Heather Iles-Smith, Niaz Chalabianloo, Deniz Ekiz, Javier Fernández-Álvarez, Claudia Repetto, Giuseppe Riva, and Cem Ersoy. 2020.  How to relax in stressful situations: a smart stress reduction system. In _Healthcare_ , Vol. 8. MDPI, 100.
  * Christensen et al. (2009) Helen Christensen, Kathleen M Griffiths, Louise Farrer, et al. 2009\.  Adherence in internet interventions for anxiety and depression: systematic review.  _Journal of medical Internet research_ 11, 2 (2009), e1194.
  * Chrousos and Gold (1992) George P Chrousos and Philip W Gold. 1992.  The concepts of stress and stress system disorders: overview of physical and behavioral homeostasis.  _Jama_ 267, 9 (1992), 1244–1252.
  * Cohen et al. (2016) Sheldon Cohen, Peter J Gianaros, and Stephen B Manuck. 2016\.  A stage model of stress and disease.  _Perspectives on Psychological Science_ 11, 4 (2016), 456–463.
  * Cohen et al. (1983) Sheldon Cohen, Tom Kamarck, and Robin Mermelstein. 1983\.  A global measure of perceived stress.  _Journal of health and social behavior_ (1983), 385–396.
  * De Stasio et al. (2018) Simona De Stasio, Benedetta Ragni, Francesca Boldrini, Francesca Bevilacqua, and Simonetta Gentile. 2018.  Parental stress and bedtime routines in toddlerhood.  _Anthropological Researches and Studies_ 1, 8 (2018), 75–84.
  * Deater-Deckard (1998) Kirby Deater-Deckard. 1998\.  Parenting stress and child adjustment: Some old hypotheses and new questions.  _Clinical psychology: Science and practice_ 5, 3 (1998), 314\.
  * Doherty et al. (2012) Gavin Doherty, David Coyle, and John Sharry. 2012\.  Engagement with online mental health interventions: an exploratory clinical study of a treatment for depression. In _Proceedings of the SIGCHI conference on human factors in computing systems_. 1421–1430.
  * Drugli and Undheim (2012) May Britt Drugli and Anne Mari Undheim. 2012.  Partnership between parents and caregivers of young children in full-time daycare.  _Child care in practice_ 18, 1 (2012), 51–65.
  * Epel et al. (2018) Elissa S Epel, Alexandra D Crosswell, Stefanie E Mayer, Aric A Prather, George M Slavich, Eli Puterman, and Wendy Berry Mendes. 2018.  More than a feeling: A unified view of stress measurement for population science.  _Frontiers in neuroendocrinology_ 49 (2018), 146–169.
  * Felix et al. (2018) Márcia Marques dos Santos Felix, Maria Beatriz Guimarães Ferreira, Lucas Felix de Oliveira, Elizabeth Barichello, Patricia da Silva Pires, and Maria Helena Barbosa. 2018.  Guided imagery relaxation therapy on preoperative anxiety: a randomized clinical trial.  _Revista latino-americana de enfermagem_ 26 (2018).
  * Ferreira et al. (2015) Denzil Ferreira, Vassilis Kostakos, and Anind K Dey. 2015\.  AWARE: mobile context instrumentation framework.  _Frontiers in ICT_ 2 (2015), 6.
  * Filaire et al. (2007) Edith Filaire, Matthieu Rouveix, Deborah Alix, and Christine Le Scanff. 2007.  Motivation, stress, anxiety, and cortisol responses in elite paragliders.  _Perceptual and Motor Skills_ 104, 3_suppl (2007), 1271–1281.
  * Foussoul et al. (2023) Ayoub Foussoul, Vineet Goyal, Orestis Papadigenopoulos, and Assaf Zeevi. 2023.  Last switch dependent bandits with monotone payoff functions. In _International Conference on Machine Learning_. PMLR, 10265–10284.
  * Fuller-Tyszkiewicz et al. (2019) Matthew Fuller-Tyszkiewicz, Ben Richardson, Vivienne Lewis, Jake Linardon, Jacqueline Mills, Kerry Juknaitis, Charlotte Lewis, Kim Coulson, Renee O’Donnell, Lilani Arulkadacham, et al. 2019\.  A randomized trial exploring mindfulness and gratitude exercises as eHealth-based micro-interventions for improving body satisfaction.  _Computers in Human Behavior_ 95 (2019), 58–65.
  * Geva and Sitte (1993) Shlomo Geva and Joaquin Sitte. 1993.  A cartpole experiment benchmark for trainable controllers.  _IEEE Control Systems Magazine_ 13, 5 (1993), 40–51.
  * Gilad et al. (2017) Efrat Gilad, Ran Gilad-Bachrach, Daniel McDuff, and Mary Czerwinski. 2017.  _Scribbling Intervention for Depression, Anxiety and Stress_.  Technical Report MSR-TR-2017-19. Microsoft.  https://www.microsoft.com/en-us/research/publication/scribbling-intervention-depression-anxiety-stress/ Presented at the 2nd Symposium on Computing and mental health.
  * Gould et al. (2009) Daniel Gould, M Ryan Flett, and Eric Bean. 2009.  Mental preparation for training and competition.  _Handbook of sports medicine and science: Sport psychology_ (2009), 53–63.
  * Gross (1998) James J Gross. 1998\.  Antecedent-and response-focused emotion regulation: divergent consequences for experience, expression, and physiology.  _Journal of personality and social psychology_ 74, 1 (1998), 224.
  * Gross (2015) James J Gross. 2015\.  Emotion regulation: Current status and future prospects.  _Psychological inquiry_ 26, 1 (2015), 1–26.
  * Holly et al. (2019) Lindsay E Holly, Alicia R Fenley, Tessa K Kritikos, Rachel A Merson, Richard R Abidin, and David A Langer. 2019.  Evidence-base update for parenting stress measures in clinical samples.  _Journal of Clinical Child & Adolescent Psychology_ 48, 5 (2019), 685–705.
  * Hossain et al. (2017) Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017\.  mCerebrum: a mobile sensing software platform for development and validation of digital biomarkers and interventions. In _Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems_. 1–14.
  * Hovsepian et al. (2015) Karen Hovsepian, Mustafa Al’Absi, Emre Ertin, Thomas Kamarck, Motohiro Nakajima, and Santosh Kumar. 2015\.  cStress: towards a gold standard for continuous stress assessment in the mobile environment. In _Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing_. 493–504.
  * Howe et al. (2022) Esther Howe, Jina Suh, Mehrab Bin Morshed, Daniel McDuff, Kael Rowan, Javier Hernandez, Marah Ihab Abdin, Gonzalo Ramos, Tracy Tran, and Mary P Czerwinski. 2022\.  Design of digital workplace stress-reduction intervention systems: Effects of intervention type and timing. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_. 1–16.
  * Huarng et al. (2022) Kun-Huang Huarng, Tiffany Hui-Kuang Yu, and Cheng fang Lee. 2022\.  Adoption model of healthcare wearable devices.  _Technological Forecasting and Social Change_ 174 (2022), 121286.
  * Jeremy Sutton (2023) Ph.D Jeremy Sutton. 2023\.  16 Effective Stress-Management Activities and Worksheets.  https://positivepsychology.com/stress-management-activities-worksheets/ [Accessed April 26, 2023].
  * Jiang et al. (2020) Hong-juan Jiang, Jiang Nan, Zhi-yue Lv, and Juan Yang. 2020\.  Psychological impacts of the COVID-19 epidemic on Chinese people: Exposure, post-traumatic stress symptom, and emotion regulation.  _Asian Pacific Journal of Tropical Medicine_ 13, 6 (2020), 252–259.
  * John et al. (2011) Shaji John, Satish Kumar Verma, and Gulshan L Khanna. 2011\.  The effect of mindfulness meditation on HPA-Axis in pre-competition stress in sports performance of elite shooters.  _National Journal of Integrated Research in Medicine_ 2, 3 (2011), 15–21.
  * Kalia (2002) Madhu Kalia. 2002\.  Assessing the economic impact of stress [mdash] The modern day hidden epidemic.  _Metabolism-clinical and experimental_ 51, 6 (2002), 49–53.
  * Konrad et al. (2015) Artie Konrad, Victoria Bellotti, Nicole Crenshaw, Simon Tucker, Les Nelson, Honglu Du, Peter Pirolli, and Steve Whittaker. 2015\.  Finding the adaptive sweet spot: Balancing compliance and achievement in automated stress reduction. In _Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems_. 3829–3838.
  * Koole et al. (2011) Sander L Koole, Lotte F Van Dillen, and Gal Sheppes. 2011\.  The self-regulation of emotion.  _Handbook of self-regulation: Research, theory, and applications_ 2 (2011), 22–40.
  * Kramer et al. (2019) Andrea C Kramer, Andreas B Neubauer, Martin Stoffel, Andreas Voss, and Beate Ditzen. 2019.  Tomorrow’s gonna suck: Today’s stress anticipation predicts tomorrow’s post-awakening cortisol increase.  _Psychoneuroendocrinology_ 106 (2019), 38–46.
  * Laforgue et al. (2022) Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, and Ran Gilad-Bachrach. 2022.  A last switch dependent analysis of satiation and seasonality in bandits. In _International Conference on Artificial Intelligence and Statistics_. PMLR, 971–990.
  * Liao et al. (2020) Peng Liao, Kristjan Greenewald, Predrag Klasnja, and Susan Murphy. 2020.  Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity.  _Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies_ 4, 1 (2020), 1–22.
  * Likert (1932) Rensis Likert. 1932\.  A technique for the measurement of attitudes.  _Archives of psychology_ (1932).
  * Linardon et al. (2019) Jake Linardon, Pim Cuijpers, Per Carlbring, Mariel Messer, and Matthew Fuller-Tyszkiewicz. 2019\.  The efficacy of app-supported smartphone interventions for mental health problems: A meta-analysis of randomized controlled trials.  _World Psychiatry_ 18, 3 (2019), 325–336.
  * Mark et al. (2016) Gloria Mark, Shamsi T Iqbal, Mary Czerwinski, Paul Johns, Akane Sano, and Yuliya Lutchyn. 2016\.  Email duration, batching and self-interruption: Patterns of email use on productivity and stress. In _Proceedings of the 2016 CHI conference on human factors in computing systems_. 1717–1728.
  * Mendl (1999) Michael Mendl. 1999\.  Performing under pressure: stress and cognitive function.  _Applied Animal Behaviour Science_ 65, 3 (1999), 221–244.
  * Mishra et al. (2021) Varun Mishra, Florian Künzler, Jan-Niklas Kramer, Elgar Fleisch, Tobias Kowatsch, and David Kotz. 2021.  Detecting receptivity for mHealth interventions in the natural environment.  _Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies_ 5, 2 (2021), 1–24.
  * Morin (2023) Amy Morin. 2023\.  Healthy Coping Skills for Uncomfortable Emotions.  https://www.verywellmind.com/forty-healthy-coping-skills-4586742 [Accessed Feb 16, 2023].
  * Morshed et al. (2022) Mehrab Bin Morshed, Javier Hernandez, Daniel McDuff, Jina Suh, Esther Howe, Kael Rowan, Marah Abdin, Gonzalo Ramos, Tracy Tran, and Mary Czerwinski. 2022\.  Advancing the Understanding and Measurement of Workplace Stress in Remote Information Workers from Passive Sensors and Behavioral Data. In _2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII)_. IEEE, 1–8.
  * Nahum-Shani et al. (2018) Inbal Nahum-Shani, Shawna N Smith, Bonnie J Spring, Linda M Collins, Katie Witkiewitz, Ambuj Tewari, and Susan A Murphy. 2018\.  Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support.  _Annals of Behavioral Medicine_ 52, 6 (2018), 446–462.
  * Nakao et al. (2021) Mutsuhiro Nakao, Kentaro Shirotsuki, and Nagisa Sugaya. 2021\.  Cognitive–behavioral therapy for management of mental health and stress-related disorders: Recent advances in techniques and technologies.  _BioPsychoSocial medicine_ 15, 1 (2021), 1–4.
  * Neary and Schueller (2018) Martha Neary and Stephen M Schueller. 2018.  State of the field of mental health apps.  _Cognitive and Behavioral Practice_ 25, 4 (2018), 531–537.
  * Niedenthal et al. (2005) Paula M Niedenthal, Lawrence W Barsalou, Piotr Winkielman, Silvia Krauth-Gruber, and François Ric. 2005.  Embodiment in attitudes, social perception, and emotion.  _Personality and social psychology review_ 9, 3 (2005), 184–211.
  * Papadigenopoulos et al. (2022) Orestis Papadigenopoulos, Constantine Caramanis, and Sanjay Shakkottai. 2022.  Non-stationary bandits under recharging payoffs: Improved planning with sublinear regret.  _Advances in Neural Information Processing Systems_ 35 (2022), 20325–20337.
  * Paredes et al. (2014) Pablo Paredes, Ran Gilad-Bachrach, Mary Czerwinski, Asta Roseway, Kael Rowan, and Javier Hernandez. 2014.  PopTherapy: Coping with stress through pop-culture. In _Proceedings of the 8th international conference on pervasive computing technologies for healthcare_. 109–117.
  * Park and Lee (2023) Joonyoung Park and Uichin Lee. 2023.  Understanding Disengagement in Just-in-Time Mobile Health Interventions.  _Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies_ 7, 2 (2023), 1–27.
  * Prescott and Rathbone (2022) Julie Prescott and Amy Rathbone. 2022.  The efficacy of mHealth for stress and burnout in clinical settings and supervisions.  _Mental Health and Social Inclusion_ 26, 4 (2022), 347–362.
  * Putwain (2008) DW Putwain. 2008\.  Examination stress and test anxiety.  _Psychologist_ 21, 12 (2008), 1026–1029.
  * Rahman et al. (2016) Tauhidur Rahman, Mary Czerwinski, Ran Gilad-Bachrach, and Paul Johns. 2016.  Predicting” about-to-eat” moments for just-in-time eating intervention. In _Proceedings of the 6th International Conference on Digital Health Conference_. 141–150.
  * Rammstedt and John (2007) Beatrice Rammstedt and Oliver P John. 2007.  Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German.  _Journal of research in Personality_ 41, 1 (2007), 203–212.
  * Rehg et al. (2017) James M Rehg, Susan A Murphy, and Santosh Kumar. 2017\.  Mobile health.  _Cham: Springer International Publishing_ (2017).
  * Rozet et al. (2019) Alan Rozet, Ian M Kronish, Joseph E Schwartz, and Karina W Davidson. 2019.  Using machine learning to derive just-in-time and personalized predictors of stress: observational study bridging the gap between nomothetic and ideographic approaches.  _Journal of medical Internet research_ 21, 4 (2019), e12910.
  * Sano et al. (2017) Akane Sano, Paul Johns, and Mary Czerwinski. 2017.  Designing opportune stress intervention delivery timing using multi-modal data. In _2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)_. IEEE, 346–353.
  * Sarker et al. (2016) Hillol Sarker, Matthew Tyburski, Md Mahbubur Rahman, Karen Hovsepian, Moushumi Sharmin, David H Epstein, Kenzie L Preston, C Debra Furr-Holden, Adam Milam, Inbal Nahum-Shani, et al. 2016\.  Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. In _Proceedings of the 2016 CHI conference on human factors in computing systems_. 4489–4501.
  * Schneider et al. (2004) Barbara Schneider, Alisa M Ainbinder, and Mihaly Csikszentmihalyi. 2004\.  Stress and working parents.  _Work and leisure_ (2004), 145–167.
  * Schroeder et al. (2020) Jessica Schroeder, Jina Suh, Chelsey Wilks, Mary Czerwinski, Sean A Munson, James Fogarty, and Tim Althoff. 2020.  Data-driven implications for translating evidence-based psychotherapies into technology-delivered interventions. In _Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare_. 274–287.
  * Schueller (2010) Stephen M Schueller. 2010\.  Preferences for positive psychology exercises.  _The Journal of Positive Psychology_ 5, 3 (2010), 192–203.
  * Seabold and Perktold (2010) Skipper Seabold and Josef Perktold. 2010.  statsmodels: Econometric and statistical modeling with python. In _9th Python in Science Conference_.
  * Sheppes and Meiran (2007) Gal Sheppes and Nachshon Meiran. 2007.  Better late than never? On the dynamics of online regulation of sadness using distraction and cognitive reappraisal.  _Personality and Social Psychology Bulletin_ 33, 11 (2007), 1518–1532.
  * Shiffman et al. (2008) Saul Shiffman, Arthur A Stone, and Michael R Hufford. 2008\.  Ecological momentary assessment.  _Annu. Rev. Clin. Psychol._ 4 (2008), 1–32.
  * Silva et al. (2015) Bruno MC Silva, Joel JPC Rodrigues, Isabel de la Torre Díez, Miguel López-Coronado, and Kashif Saleem. 2015.  Mobile-health: A review of current state in 2015.  _Journal of biomedical informatics_ 56 (2015), 265–272.
  * Simmons (2016) Megan Simmons. 2016\.  _Keep calm and doodle on: A relationship between doodling and stress reduction_.  Notre Dame de Namur University.
  * Smyth and Heron (2016) Joshua M Smyth and Kristin E Heron. 2016.  Is providing mobile interventions” just-in-time” helpful? An experimental proof of concept study of just-in-time intervention for stress management. In _2016 IEEE Wireless Health (WH)_. IEEE, 1–7.
  * Sur and Ng (2014) Sujit Sur and Eddy S Ng. 2014.  Extending theory on job stress: The interaction between the “other 3” and “big 5” personality traits on job stress.  _Human Resource Development Review_ 13, 1 (2014), 79–101.
  * Sutton and Barto (2018) Richard S Sutton and Andrew G Barto. 2018.  _Reinforcement learning: An introduction_.  MIT press.
  * Taylor et al. (2008) Marcus K Taylor, Daniel Gould, and Cristina Rolo. 2008\.  Performance strategies of US Olympians in practice and competition.  _High Ability Studies_ 19, 1 (2008), 19–36.
  * Tazarv et al. (2021) Ali Tazarv, Sina Labbaf, Stephanie M Reich, Nikil Dutt, Amir M Rahmani, and Marco Levorato. 2021\.  Personalized stress monitoring using wearable sensors in everyday settings. In _2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)_. IEEE, 7332–7335.
  * Torous and Vaidyam (2020) John Torous and Aditya Vaidyam. 2020.  Multiple uses of app instead of using multiple apps–a case for rethinking the digital health technology toolbox.  _Epidemiology and psychiatric sciences_ 29 (2020), e100.
  * Toussaint et al. (2021) Loren Toussaint, Quang Anh Nguyen, Claire Roettger, Kiara Dixon, Martin Offenbächer, Niko Kohls, Jameson Hirsch, and Fuschia Sirois. 2021\.  Effectiveness of progressive muscle relaxation, deep breathing, and guided imagery in promoting psychological and physiological states of relaxation.  _Evidence-Based Complementary and Alternative Medicine_ 2021 (2021).
  * Vadoa et al. (1997) Eva A Vadoa, Craig R Hall, and Sandra E Moritz. 1997\.  The relationship between competitive anxiety and imagery use.  _Journal of Applied Sport Psychology_ 9, 2 (1997), 241–253.
  * van Paridon et al. (2017) Kjell N van Paridon, Matthew A Timmis, Charlotte M Nevison, and Matt Bristow. 2017.  The anticipatory stress response to sport competition; a systematic review with meta-analysis of cortisol reactivity.  _BMJ open sport & exercise medicine_ 3, 1 (2017), e000261.
  * Van Raalte et al. (2019) Judy L Van Raalte, Britton W Brewer, Allen E Cornelius, Mary Keeler, and Christyan Gudjenov. 2019\.  Effects of a Mental Warmup on the Workout Readiness and Stress of College Student Exercisers.  _Journal of Functional Morphology and Kinesiology_ 4, 3 (2019), 42.
  * Yue et al. (2022) Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu. 2022.  TS2Vec: Towards Universal Representation of Time Series.  _Proceedings of the AAAI Conference on Artificial Intelligence_ 36, 8 (Jun. 2022), 8980–8987.  https://doi.org/10.1609/aaai.v36i8.20881
  * Zaza (1994) Christine Zaza. 1994\.  based prevention for musicians.  _Medical Problems of Performing Artists_ 9, 1 (1994), 3\.
  * Zhang et al. (2022) Yihan Zhang, Yubing Hu, Nan Jiang, and Ali K Yetisen. 2022\.  Wearable artificial intelligence biosensor networks.  _Biosensors and Bioelectronics_ (2022), 114825.

##  7\. Appendix: Experimental Details

###  7.1. Raw Sensors Data

In the table below, mentioned all the raw sensors data we obtained from
participants ’ mobile device during the experimental study.

Sensor | Data | Type
---|---|---
Battery | battery information (e.g. power, status) | int
Screen | screen status (e.g. turning on, turning off, lock, and unlock) | int
Gravity | provides a three dimensional vector indicating the direction and magnitude of gravity (x-, y-, and z-axis) | float
Gyroscope | Rate or rotation in rad/s around a device (x-, y-, and z-axis) | float
Light | Level of ambient light | int
Linear Accelerometer | Acceleration applied to the device, excluding the force of gravity (x-, y-, and z-axis) | float
Location | location estimate of the users’ current location | float
Magnetometer | Geomagnetic field strength around the device (x-, y-, and z-axis) | float
Rotation | Orientation of the device as a combination of an angle and an axis (x-, y-, and z-axis) | float
Proximity | Distance to an object in front of the device | float

Table 1. Raw Sensors Description

###  7.2. GEE Features Description

  1. (1)

Daily intervention index - a categorical feature where each sample get the
value 1 if the sample was given in the corresponding index along the day.

  2. (2)

Intervention counter - counts the number of interventions a user got till the
current sample.

  3. (3)

Intervention type - a categorical feature denotes the type of intervention a
sample got (control, random or sarsa).

  4. (4)

Patient day - a number corresponds to the absolute number of days passed since
a user joined the study.

  5. (5)

Patient week - a number correspond to the number of weeks passed since a user
joined the study.

  6. (6)

Phase - a Boolean feature equals 1 if the user is in the second phase of the
experiment.

  7. (7)

PID (patient id) - user’s (cluster) identifier.

  8. (8)

Relative time along the day.

  9. (9)

Study day - a number corresponds to the absolute number of days passed since
the beginning of the study till a sample was given.

  10. (10)

Study week - a number corresponds to the absolute number of weeks passed since
the beginning of the study till a sample was given.

###  7.3. Examples of Interventions and Structure

Due to the constraints of blind review, a comprehensive list of interventions
(n = 116) cannot be disclosed here. However, a link to the full pool of
interventions will be made available following the review process.

Presented here sample of interventions: Each node represents a line in the bot
conversation as depicted in Figure 13.

Figure 13. Structure of Stress Intervention, each node represents a
configurable text while others remain static.

text | node | intervention_type | emotional_regulation | therapy_group | location
---|---|---|---|---|---
It feels like a good time to take a break | node_id_1 | meditation | response_modulation | meta_cognitive | both
May I suggest you relax and disconnect with a minute of meditation? | node_id_2 | meditation | response_modulation | meta_cognitive | both
Excellent! Here is a short and easy mindfulness exercise to apply anywhere | node_id_3 | meditation | response_modulation | meta_cognitive | both
One-minute meditation: close your eyes, and focus only on your breathing. | node_id_4 | meditation | response_modulation | meta_cognitive | both
A wise man said that the best time to take a break is when you do not have time for it | node_id_1 | breathing | response_modulation | somatic | both
| Did you know that even a minute of sports really helps release stress? Sport
releases a hormone
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called endorphin into the body that immediately improves the feeling.
node_id_1 | exercise | response_modulation | somatic | indoor
So may I suggest a short stretching exercise? You don’t even have to get up from your chair! | node_id_2 | exercise_short | response_modulation | somatic | indoor
Excellent! Here is a short and easy movement exercise that can be applied anywhere | node_id_3 | exercise_short | response_modulation | somatic | indoor
Neck stretches: gently tilt your head from side to side, releasing tension in the neck. | node_id_4 | exercise_short | response_modulation | somatic | indoor
There are moments when distraction is what is most needed. | node_id_1 | scribbling | attention_deployment | meta_cognitive | both
Is now a good time for a fun diversion? | node_id_2 | scribbling | attention_deployment | meta_cognitive | both
Thanks here is a link | node_id_3 | scribbling_long | attention_deployment | meta_cognitive | both
https://quickdraw.withgoogle.com/ | node_id_4 | scribbling_long | attention_deployment | meta_cognitive | both
Did you know that music is a powerful healing tool with no side effects and it’s also free? | node_id_1 | music | attention_deployment | meta_cognitive | both
Can I suggest you a relaxing song? | node_id_2 | music | attention_deployment | meta_cognitive | both

Table 2. Examples of the types of interventions that participants received
during the study