# MindShift: Leveraging Large Language Models for Mental-States-Based
Problematic Smartphone Use Intervention

Ruolan Wu<EMAIL_ADDRESS>Tsinghua UniversityChina ,  Chun Yu
Tsinghua UniversityChina ,  Xiaole Pan  Tsinghua UniversityChina ,  Yujia Liu
Tsinghua UniversityChina ,  Ningning Zhang  Tsinghua UniversityChina ,  Yue Fu
University of WashingtonUSA ,  Yuhan Wang  Beijing University of Posts and
TelecommunicationsChina ,  Zhi Zheng  Tsinghua UniversityChina ,  Li Chen
Tsinghua UniversityChina ,  Qiaolei Jiang  Tsinghua UniversityChina ,  Xuhai
Xu<EMAIL_ADDRESS>Massachusetts Institute of TechnologyUSA and  Yuanchun Shi
Tsinghua UniversityChina

###### Abstract.

Problematic smartphone use negatively affects physical and mental health.
Despite the wide range of prior research, existing persuasive techniques are
not flexible enough to provide dynamic persuasion content based on users’
physical contexts and mental states. We first conduct a Wizard-of-Oz study
(N=12) and an interview study (N=10) to summarize the mental states behind
problematic smartphone use: boredom, stress, and inertia. This informs our
design of four persuasion strategies: understanding, comforting, evoking, and
scaffolding habits. We leverage large language models (LLMs) to enable the
automatic and dynamic generation of effective persuasion content. We develop
MindShift, a novel LLM-powered problematic smartphone use intervention
technique. MindShift takes users’ in-the-moment physical contexts, mental
states, app usage behaviors, users’ goals & habits as input, and generates
high-quality and flexible persuasive content with appropriate persuasion
strategies. We conduct a 5-week field experiment (N=25) to compare MindShift
with baseline techniques. The results show that MindShift significantly
improves intervention acceptance rates by 17.8-22.5% and reduces smartphone
use frequency by 12.1-14.4%. Moreover, users have a significant drop in
smartphone addiction scale scores and a rise in self-efficacy. Our study sheds
light on the potential of leveraging LLMs for context-aware persuasion in
other behavior change domains.

Problematic smartphone use, persuasion, large language model, mental model

††ccs: Human-centered computing Empirical studies in HCI

##  1\. Introduction

In recent years, the ubiquitous presence of smartphones has increased people’s
reliance on digital devices. This trend frequently leads to problematic
smartphone usage behaviors (Hwang et al., 2012), especially among adolescents
and young adults (Lee et al., 2014). Prior studies suggest that problematic
smartphone usage can detrimentally affect people in various areas such as
efficiency (leading to diminished academic or work performance (Arefin et al.,
2018; Duke and Montag, 2017)), physical well-being (resulting in decreased
sleep and activity levels (Clayton et al., 2015; Lapointe et al., 2013; Xu et
al., 2019, 2021)), and mental health (manifesting as anxiety and depression
(Hartanto and Yang, 2016; Harwood et al., 2014; Xu et al., 2022a)). Many
individuals have recognized their problematic smartphone usage and sought to
reduce their over-reliance on smartphones (Hiniker et al., 2016a; Ko et al.,
2015).

There has been a plethora of academic research and commercial products that
provide just-in-time (JIT) smartphone use interventions that intervene at the
moment when problematic use is taking place (Nahum-Shani et al., 2018).
Existing intervention techniques can be summarized into four categories based
on their degree of enforcement: (1) Feedback: offering insights on phone usage
patterns, enhancing user awareness about their smartphone habits (Whittaker et
al., 2016; Kim et al., 2016; Collins et al., 2014; Andone et al., 2016); (2)
Reminders: aiming to counter immediate phone indulgence and promote self-
reflection through pop-up notifications (Hiniker et al., 2016a; Park and
Gweon, 2015); (3) Interaction friction: raising the effort needed to use the
phone, thereby reducing its allure by introducing tasks like typing (Park et
al., 2018; Okeke et al., 2018; Xu et al., 2022b); (4) Lockout: disabling the
user’s phone access for a specified duration(Kim et al., 2019a; Ko et al.,
2015; Löchtefeld et al., 2013; Kim et al., 2019b).

Figure 1. Overview of MindShift. When users exhibit problematic phone usage,
MindShift actively collects data on phone usage behavior, physical contexts,
and mental states, and uses the customized persuasion strategies we designed
along with users’ goals and habits, to generate prompts. Then, the large
language model will generate a persuasion message. Finally, the persuasion
will show up in users’ phones to encourage more mindful usage.

However, we notice a few gaps among existing intervention techniques. One
major issue is the specific moment of intervention. Most existing intervention
techniques are time-based, such as simply upon app launch (Xu et al., 2022b;
Kim et al., 2019b), or after hitting the upper limit of usage duration
(Hiniker et al., 2016a; Kim et al., 2019a; Park et al., 2018). However, these
methods ignore an important factor: users’ smartphone use purpose. Users’
smartphone use purposes can be generally divided into instrumental use (i.e.,
for specific goals such as working or intentional relaxation) and habitual use
(i.e., ritualistic behavior, without a clear goal, such as passive social
media content consumption) (Rubin, 2009; Hiniker et al., 2016b). Recent
research suggests intervention should mainly focus on habitual use since it
often gives people a lower sense of meaningfulness (Lukoff et al., 2018).
Thus, an intervention technique should consider not only the use time but also
the smartphone usage purpose.

Furthermore, we identify a further gap in the intervention content. Existing
JIT intervention techniques rarely take users’ in-the-moment contexts (e.g.,
current environment, mental states) into account to achieve context-aware
intelligent intervention (Thomas Craig et al., 2021). This is particularly
important for smartphone persuasion intervention, one of the reminder-based
techniques that show messages designed to convince users to stop using
smartphones. The persuasion content is the key to the effectiveness of
intervention. Although some intervention techniques are designed to be
context-aware (e.g., an intervention is introduced when users are around a
specific location (Kim et al., 2017; Ko et al., 2015)), they are simply rule-
based and cannot adapt to the dynamic changing physical environment. In
addition, no prior work considers users’ mental state, which is another
important aspect of user contexts.

To address these gaps, we first conducted a Wizard-of-Oz study (N=12),
followed by an interview study (N=10) to better understand users’ mental
states during problematic phone usage. Focusing on habitual usage, we
summarized three major mental states to address: boredom, stress, and inertia.
Leveraging the Dual Systems Theory (Hofmann et al., 2009) and the ERG
(Existence, Relatedness, and Growth) Theory (Caulton, 2012), we proposed a set
of four persuasion strategies and built the mapping between these strategies
to the three mental states. These strategies include 1) understanding, 2)
comforting, 3) evoking, and 4) scaffolding habits.

Based on our persuasion strategies, we designed and implemented MindShift
(Figure 1), a new JIT intervention technique that can provide dynamic,
personalized persuasion content based on user contexts. MindShift leverages
Large Language Models’ (LLMs) strong capability in commonsense comprehension
and natural language generation (Chowdhery et al., 2022; Min et al., 2021).
Specifically, we employ LLMs to generate proper and effective persuasive
content based on phone usage behavior, physical contexts, mental states, goals
and habits with appropriate persuasion strategies we design.

To evaluate the effectiveness of MindShift, we conducted a 5-week field
experiment to deploy our intervention to 25 participants. We compared
MindShift against the basic notification-based intervention that asked users
to reflect and report their smartphone usage purpose. Moreover, to better
evaluate the effect of the mental states factor, we also compared MindShift
against a simplified version that excludes the mental states factor from the
LLM-based content generation.

Our study results indicate that MindShift significantly outperforms the
baseline method by 17.8-22.5% on the intervention acceptance rate. MindShift
also reduces overall app opening frequency by 12.1-14.4% and app usage
duration by 2.4-9.8%. Specifically, MindShift leads to a significant reduction
in habitual proportions by 6.8%. Comparing the two versions of MindShift,
including the mental states factor in the persuasion content generation can
significantly enhance the acceptance rate by 8.1%. The subjective report data
show that participants have a significant decrease (25.8-34.7%) in the
smartphone addiction scale (SAS) score and a significant increase (10.4-10.7%)
in the self-efficacy scale score after using MindShift. Participants also
expressed a strong willingness to continue using this effective intervention
approach.

The contribution of our paper can be summarized as follows:

  1. (1)

We conducted a Wizard-of-Oz study and an interview study to investigate the
mental states that end-users experience during smartphone habitual use. Our
study highlights three major mental states (boredom, stress, and inertia),
leading us to design four persuasion strategies grounded in the Dual Systems
Theory and the ERG Theory: Understanding, Comforting, Evoking, and Scaffolding
Habits.

  2. (2)

We created MindShift, a novel persuasive intervention technique that leveraged
an LLM to generate dynamic and personalized persuasion content based on
appropriate persuasion strategies, users’ phone usage behavior, physical
contexts, mental states, goals and habits.

  3. (3)

We conducted a field experiment by deploying MindShift. Our results indicate
that our new technique could significantly improve intervention acceptance
rates and reduce smartphone use. Users’ subjective feedback also corroborates
these observations, demonstrating the effectiveness of MindShift.

##  2\. Related work

In this section, we first introduce the definition of smartphone problematic
use and habitual phone use (Sec. 2.1). We then briefly overview existing
intervention techniques (Sec. 2.2). Close to our work, we introduce the
behavior change persuasion techniques and their relationship with the boost of
LLMs (Sec. 2.3). Finally, we cover the theoretical foundations and mental
models of problematic smartphone use (Sec. 2.4).

###  2.1. Smartphone Problematic Use

Many studies have explored the definition of problematic smartphone use, which
can be broadly classified into two categories. The first category defines
whether users exhibit addictive behaviors toward their phones. Some studies
assessed addictive behavior by measuring the level of user dependence on
smartphones through questionnaires, such as the Smartphone Addiction Scale
(SAS) and the Smartphone Addiction Inventory (SPAI) (Lin et al., 2014; Kwon et
al., 2013). The second category defines whether a specific instance of phone
use is problematic. Recent research suggests that problematic smartphone use
is determined not only by excessive use but also by the purpose and content of
use in specific situations (Harwood et al., 2014; Prasad et al., 2021).
Studies have pointed out that users’ purposes for phone use can be categorized
into (1) habitual use that is performed unconsciously and ritualistically
usually without a specific goal (Pinder et al., 2018), and (2) instrumental
use with a specific task or goal in mind (Lukoff et al., 2018). Existing
research suggests that habitual use should be the primary target for
intervention (Morris et al., 2018; Lukoff et al., 2018; Pinder et al., 2018).
In this paper, we combine these two definitions. We use SAS to measure the
level of addictive smartphone usage behavior. We also distinguish users’ phone
use intention and focus on intervening habitual use.

###  2.2. Problematic Smartphone Use Intervention

Existing problematic smartphone use intervention techniques can be roughly
divided into four categories based on their level of enforcement: The first
category provides users with information about their behavior (Whittaker et
al., 2016; Kim et al., 2016; Hiniker et al., 2016a; Collins et al., 2014;
Apple, 2021; Lyngs et al., 2022; Nwagu, 2023), requiring users to view it
themselves to increase awareness of phone usage. The second actively sends
notifications and reminders to users to provoke their reflection such as
reminding users of their daily goals (Hiniker et al., 2016a), sending them the
results of previous decision-balancing exercises (Park and Gweon, 2015), or
informing their usage time (Apple, 2021). This category presents text to users
and, therefore, serves as a persuasion. The third involves increasing the
difficulty of using the phone and intentionally slowing down user interactions
to suppress the desire to use it. This can include requiring users to enter
random numbers or engage in self-reflection (Park et al., 2018; Xu et al.,
2022b), keeping the phone vibrating continuously (Okeke et al., 2018). The
fourth is particularly forceful by directly locking the users’ apps or phone
for a specific duration (APP, 2021; Kim et al., 2019a; Ko et al., 2015).

There are concerns about these methods’ ability to strike an optimal balance
between user experience and effectiveness. The first two intervention
categories (i.e., feedback-based self-awareness and notification reminders)
rely on the user’s self-control and can be easily ignored, leading to limited
effectiveness (Whittaker et al., 2016; Kim et al., 2016; Hiniker et al.,
2016a). While the other two intervention types (i.e., increasing usage
difficulty and locking out) are relatively more effective, research has shown
that users often struggle to maintain compliance due to a diminished user
experience (Kim et al., 2019a). Once the restrictions are removed, users often
relapse and revert to problematic use behavior in a short period of time (Kim
et al., 2019a; Xu et al., 2022b). To bridge the gap, we hope to develop a
method that ensures a good user experience while boosting intervention
effectiveness.

###  2.3. Persuasion for User Behavior Change and Large Language Models

Persuasion is a psychological approach designed to influence attitudes,
beliefs, or behaviors(Cialdini and Cialdini, 2007). Language is the most
commonly used means of persuasion, and this approach leverages facts,
emotional appeals, and so on in a message to achieve its goal. Its
effectiveness has been shown in multiple fields, such as advertising to
encourage consumers to buy a product(Bernritter et al., 2017), supporting
mental health such as coping with stress (Morris et al., 2018; Orji and
Moffatt, 2018), and managing physical health such as reducing snacking
behavior (Kaptein et al., 2012). For smartphone use intervention, persuasion
usually appears as reminders, such as leveraging the user’s usage time in a
template format (Apple, 2021; Inc, 2023), or some thought-provoking statements
(Xu et al., 2022b). Prior work has suggested that the personalization of
persuasive content can enhance the effectiveness of intervention (Choe et al.,
2017; Harrison et al., 2015; Kaptein et al., 2012). Some studies pointed out
that the effectiveness of static, unchanging intervention methods would
decrease over time, while dynamic intervention methods with diverse
intervention timings or contents (even appearing randomly) can improve
effectiveness (Kovacs et al., 2018).

The recent emergence of Large Language Models (LLMs), such as ChatGPT(OpenAI,
2022) and PaLM(Chowdhery et al., 2022), has made vast progress in the
generation of personalized and diverse content. Recent studies have explored
various health applications supported by LLMs, such as health information
seeking (Yunxiang et al., 2023), mental health support (Lamichhane, 2023;
Kumar et al., 2023), personal health coaching (Wei et al., 2023; Montagna et
al., 2023), and health education (Montagna et al., 2023; Kung et al., 2023).
Recent research in the HCI community has also explored LLMs’ potential in
public health interventions (Jo et al., 2023). However, there is a gap in
leveraging LLMs for a real-time, context-aware behavior intervention. In this
work, we aim to leverage LLMs’ ability to dynamically generate personalized
persuasion content to help users reduce their problematic phone usage.

###  2.4. Theoretical Background and Mental Models of Smartphone Use

Understanding what leads to problematic phone use is essential for the design
of effective persuasion strategies. The Dual Systems Theory (Hofmann et al.,
2009) has been used to explain the phone usage patterns (Pinder et al., 2018).
This theory divides human cognitive activities into two types: System 1 (fast,
intuitive, unconscious) and System 2 (slow, analytical, conscious).
Problematic smartphone use is typically driven by System 1 (Hagger, 2016), as
it mainly involves unconscious, rapid responses and is easy to be guided by
instant gratification. Recent research suggests that two key factors
contribute to the failure of users to act on their goals: (i) limited ability
of System 2 control; and (ii) fluctuations caused by emotional states and
fatigue (Lyngs et al., 2019). These findings suggest that mental states play a
significant role in habitual smartphone usage(Pinder et al., 2018). While
previous research has identified external contexts that may trigger the
habitual use of smartphones (Tran et al., 2019), limited research has explored
users’ mental states as a factor. In our work, we pinpoint the major mental
states linked to habitual use and propose corresponding persuasion strategies
for these mental states.

##  3\. Mental States of Habitual Smartphone Use and Persuasion Strategies

To better understand the mental states of users’ smartphone use and guide the
design of our intervention system, we first conducted a Wizard-of-Oz (WoZ)
study, followed by a semi-structured interview study (Sec. 3.1). We summarize
the results in Sec. 3.2. Based on our initial findings, we devise four
persuasion strategies (Sec. 3.3).

###  3.1. Exploratory Wizard-of-Oz & Semi-structured Interview Studies

To identify particular smartphone usage behaviors requiring targeted
interventions and further develop persuasive intervention content material, we
first conducted a WoZ study in the wild, involving 12 end-users (6 females and
6 males, aged 18-28), followed by a semi-structured interview with another 10
users (5 females and 5 males, aged 18-29).

####  3.1.1. Wizard-of-Oz Study

We developed a chatbot system for smartphones that tracks user app activity.
First, we asked participants to select apps for intervention, adding them to a
blacklist. The chatbot then would send persuasive messages to the participants
upon opening a blacklisted app.

At the beginning of the study, participants were told that it was an automatic
chatbot instead of a human to reduce the observation effect (Kazdin, 1982). In
reality, when participants opened a blacklisted app, a human experimenter
would receive an email notification. Based on participants’ smartphone usage
data, the experimenter designed and delivered persuasive messages aiming to
reduce smartphone usage via the chatbot. Each evening, researchers carried out
a brief daily interview to probe (1) why participants use smartphones, (2)
factors affecting their acceptance of interventions, and (3) evaluation of the
effectiveness of the persuasive messages. Our WoZ study lasted five days for
each participant. At the study’s conclusion, we informed participants that the
chatbot was operated by a human experimenter at the back end.

####  3.1.2. Semi-structured Interview Study

We also carried out a semi-structured interview study with a different
participant group. During the semi-structured interview, we asked participants
to recollect instances of problematic smartphone use. Next, we asked them to
discuss their mental states during those instances. We followed the
participants’ lead during the interview. All interviews were audio recorded
and transcribed. Three researchers independently examined the transcriptions
and then collectively coded them through multiple iterations until they
reached a consensus.

###  3.2. Main Takeaways about Problematic Smartphone Use

First, interventions for problematic smartphone use should target habitual
usage. Our WoZ study delineates two primary types of smartphone usage:
instrumental and habitual, which corroborates the classification of prior
research (Lukoff et al., 2018). We find that only habitual smartphone use
warrants intervention. Providing interventions during instrumental use often
leads to user dissatisfaction. Notably, relaxation emerges as a crucial form
of instrumental use, where participants deliberately use their phones to
unwind or reward themselves after intense work or study. Intervening at such
times is considered intrusive and inappropriate. This classification aligns
with the Dual Systems Theory. In smartphone interactions, instrumental use
relies on conscious decision-making (System 2), while habitual use is more
instinctive (System 1). Hence, interventions should primarily target habitual
use, a conclusion also supported by earlier studies (Lukoff et al., 2018).

Second, the effectiveness of interventions depends on users’ mental states,
personal goals, and contextual information. This is consistent with the
literature, suggesting that a shift away from System 2 is due to emotional
fluctuations and the absence of defined goals and intentions (Lyngs et al.,
2019). We experimented with different persuasive message content during our
WoZ study. We find that when we incorporate users’ mental states factor, which
is inferred by their physical contexts and app usage patterns, into persuasive
message content, participants have an increased acceptance rate of the
intervention. In addition, we find emphasizing users’ personal goals can
enhance the interventions’ effectiveness. For instance, sending persuasive
messages to participants such as, “When you find yourself with idle time,
consider engaging in meaningful activities such as reading, writing, or
drawing” proves effective when users are in an idle state and aimed for self-
improvement. Our findings are supported by prior studies linking habitual
smartphone use to specific mental states (Anderson and Wood, 2021; Bayer and
LaRose, 2018).

Third, semi-structured interviews reveal three primary mental states connected
to habitual smartphone use: boredom, stress, and inertia.

  * •

Boredom is an affective state characterized by low arousal and dissatisfaction
due to insufficient stimulation (Fisherl, 1993; Mikulas and Vodanovich, 1993).
The WoZ and interview studies identify common scenarios leading to boredom:
(1) when the task at hand is too simple, lacking a balance between skill and
challenge, (2) lack of interest in the current activity, and (3) devoid of any
engaging activities during idle moments, which can trigger cognitive biases
like the fear of missing out and promote habitual use (Bozaci, 2020; Smith,
2015; Wolniewicz et al., 2020).

  * •

Stress refers to cognitive and behavioral reactions to unpredictable and
unmanageable stimuli (Koolhaas et al., 2011). Participants frequently use
smartphones because they experience (1) heightened anxiety when work demands
exceed their abilities and (2) uncertainty about whether something would have
a positive outcome. This aligns with increasing evidence links smartphone use
to perceived stress (Chiu, 2014; Samaha and Hawi, 2016; Vahedi and Saiphoo,
2018).

  * •

Inertia, in our context, refers to a psychological resistance that makes users
reluctant to change their current activity state. It is similar to the idea
illustrated by literature such as emotional inertia (Kuppens et al., 2010) or
decision-making inertia (Alós-Ferrer et al., 2016). Our research shows that
participants commonly use their phones habitually to avoid changing into a new
activity state from an idle state (e.g., checking the phone before starting to
work). Unlike stress or boredom, inertia does not elicit overt negative
emotions but impedes the shift to the next task.

Moreover, we find that users’ mental states are either triggered by specific
activities or have no specific reason. Therefore, we categorize scenarios
based on whether users are engaged in specific activities (“engaging in
activities”, “not engaging in activities”). The first category (“engaging in
activities”) denotes mental states triggered by ongoing or upcoming
activities. For instance, activities that lack challenge and engagement, or
activities that involve repetitive actions might induce boredom. In contrast,
the second category (“not engaging in activities”) has less clear causes,
often linked to broader life situations. For example, a lack of enthusiasm for
life may cause boredom, while challenges adapting to setbacks and
uncertainties can cause stress. Hence, combining the three mental state
factors with whether or not engaging in activities factor, we categorize
habitual phone use into six distinct scenarios (see the gray part of Figure
2).

Figure 2. Summary of Users’ Mental States and Corresponding Persuasion
Strategies Based on WoZ and Interview Studies. In the 3 x 2 table (three
mental states: boredom vs. stress vs. inertia, and two activities: engage in
vs. not engage in), each color denotes the persuasion strategy applicable in
this scenario.

###  3.3. Persuasion Strategies Design

Based on the Dual Systems Theory and the mental states we summarized in Sec
3.2, we propose four distinct persuasion strategies: Understanding,
Comforting, Evoking, and Scaffolding Habits (see the colorful part in Figure
2).

Our strategy design is inspired by the theory of Existence, Relatedness, and
Growth (ERG). Developing from Maslow’s Hierarchy of Needs, ERG theory further
summarizes human motivation into three levels: (i) the physiological and
safety basic needs for existence, (ii) the social needs for feeling related
and accepted, and (iii) the need to grow and self-actualize. The theory has
been applied to workplaces to increase productivity and job satisfaction
(Arnolds and Boshoff, 2002; Caulton, 2012).

Maintaining System 2 relies on cultivating motivation for meaningful
activities. Thus, we map strategies to the relatedness level and growth level
of the ERG theory in Figure 3. At the level of relatedness, Understanding and
Comforting aim to empathize with users’ emotions, offering support and
empowering users to manage System 2. At the growth level, Evoking reminds
users of their personal development goals, and Scaffolding Habits guides them
in replacing habitual smartphone use with activities conducive to self-
fulfillment, thereby turning awareness into action.

Figure 3. Mapping of Persuasion Strategies to the ERG Theory (Existence,
Relatedness, and Growth) and the Dual Systems Theory. According to the Dual
Systems Theory, there is a competitive relationship between System 1 (habitual
smartphone use) and System 2 (meaningful activity), much like being placed on
a scale. To make System 2 heavier than System 1, weights are added—Relatedness
needs and Growth needs (the second and third levels of ERG Theory). To support
users’ Relatedness needs, we design two persuasion strategies: Understanding
and Comforting. To motivate Growth needs, we design two other persuasion
strategies: Evoking and Scaffolding Habits.

####  3.3.1. Understanding

Understanding is a critical strategy to motivate users at the Relatedness
level. Past literature suggests that seeking understanding is a coping
mechanism (Compas et al., 2001), and chatbots’ empathetic expressions are
favored over emotionally neutral advice (Liu and Sundar, 2018). Therefore,
Understanding is one of our strategies and covers all mental states in Figure
2. For instance, this example shows how we integrate understanding into the
persuasive content intervention: ”Hi, I know that sometimes you may feel bored
and lacking interest. It’s okay, this is a very normal feeling. Everyone goes
through such times.”

####  3.3.2. Comforting

Comforting aims to comfort users who are experiencing emotional fluctuations,
which cause them to shift away from System 2. Prior studies suggest that
coping with boredom should focus on meaningfulness (Nett et al., 2010). For
example, we design a persuasive message to say ”Hey, I know some things might
seem a bit boring, but sometimes we need to find the fun in them. Have you
ever thought that completing this task would bring you closer to your goals?”
In addition, encouragement, humor, acceptance, and wishful thinking (Compas et
al., 2001) can be used to cope with stress by lightening the uncontrollable
and unpredictable nature of stressors. For example, ”Don’t worry, you have the
capability to complete the task! Believe in yourself, and the outcome will
pleasantly surprise you.” In the state of inertia without explicit negative
emotions, Comforting is not employed.

####  3.3.3. Evoking

We find evoking specific work-related goals is a compelling, persuasive
technique based on the WoZ study. Literature suggests that goals and values
held in working memory are important for people to sustain System 2 (Lyngs et
al., 2019) and are closely related to growth motivation. Thus, Evoking
considers users’ goals (e.g., maintaining physical health, achieving academic
success) for designing persuasive strategies. For example, ”Hi! I know you
want to play with your phone, but completing tasks is crucial for your IELTS!
Keep going, and you’re one step closer to a high score!” This strategy is
applied only to scenarios that which users are engaged in activities, because
they may lack work-related goals when are not engaged in activities.

####  3.3.4. Scaffolding Habits

Last, we encourage users to develop alternative beneficial habits to excessive
smartphone use, aligning with their personal value and growth need (Lyngs et
al., 2019; Pinder et al., 2018). By pre-identifying users’ preferred habits
and considering variables like location and time of habitual smartphone use,
we suggest appropriate substitutes to assist users in Scaffolding Habits. For
example, ”Hi, why not use this moment to memorize vocabulary instead of using
your phone? It can help you learn a language and achieve your goals faster!”

Through our Wizard of Oz (WoZ) and interview studies, we target habitual use
for intervention and pinpoint crucial mental states that drive habitual usage.
We then apply the Dual Systems Theory and ERG Theory to develop four targeted
persuasion strategies. This informs the design of our MindShift system.

##  4\. MindShift DESIGN

Building on top of the persuasion strategies we propose in the previous
section, we introduce the design of our intervention system, including
persuasive content generation (what content to intervene, Sec. 4.1), the
interaction flow (how to intervene, Sec. 4.2), and the intervention moment
(when to intervene, Sec. 4.3).

###  4.1. What Content to Intervene: LLM-Powered Persuasive Content Generation

Figure 4. Prompt Templates Used to Persuasive Generate Content for Smartphone
Usage Intervention with GPT-3.5. The word slots (in the top box) represent
different categories of information to be filled in based on the user’s
current situation. The color indicates the mapping between the slots and the
input prompt. The small words under each slot explain the source of the
content. The input prompt consists of four parts: (1) Task Setup; (2)
Description of the Current Contexts; (3) Prompt Optimization (to improve
language quality and reduce harmful content); and (4) Description of
Persuasion Strategy (as introduced in Section 3 and Figure 2). The LLM output
shows the persuasion example of four strategies when the user’s mental state
is “stressful, engaging in an activity”.

One of the key aspects that influence the effectiveness of our intervention is
the persuasion content design. We propose a set of important content factors
and couple them with an LLM for high-quality and intelligent content
generation. We test both GPT-3.5 and GPT-4 and choose to adopt GPT-3.5 as our
target LLM to strike the balance between the content generation quality and
the speed. 111GPT-4 introduces a long lag and negatively impacts user
experience. Moreover, as we introduce below, the prompts we use as the input
for GPT-3.5 include general information that is not individually identifiable.
However, we do acknowledge the privacy risk of our method. We will have more
discussion in Section 8 Our prompt design incorporates four factors, arranged
in a sequence from 1 to 4, as illustrated by purple text in Figure 4. The
combined prompt subsequently serves as input to LLMs to generate persuasive
content for tailored intervention.

(1) Task Setup: As shown in the left top box in the ”Input Prompt to LLM” in
Figure 4, Task Setup includes ¡Background¿ module, which provides GPT-3.5 with
the global instructions.

(2) Description of the Current Contexts: To make each generated content
contextually relevant and personalized, it is necessary to describe the user’s
current contexts (see the left bottom box in the Input prompt in Figure 4).
This part includes three modules: the user’s physical context (¡User Data¿
module), mental state (¡User Mental State¿ module), and their goals (¡User
Goals¿ module). (a) The ¡User Data¿ module describes the user’s real-time
physical context, including the current time, location, habitual phone usage
duration, and the time elapsed since the last habitual phone check. (b) The
¡User Mental State¿ module includes users’ input from their devices regarding
activities and negative emotions. The prompt for this module is selected from
the mental state definition (the gray part of Figure 2). (c) Finally, the
¡User Goals¿ module describes what the user values and plays a crucial role in
the generation of Evoking strategies. Specific use goals are collected during
the initialization (Step 1 in Figure 5). The elements mentioned above are
represented as word slots (enclosed in brackets in the input prompt), where
the actual users’ context information can be inserted.

(3) Prompt Optimization: To improve the quality and effectiveness of the
content generated by GPT, we carefully crafted the prompt according to
OpenAI’s official guidelines (OpenAI, 2023). We acknowledge sometimes LLMs can
generate harmful, offensive, or biased texts (Zhuo et al., 2023; Hartvigsen et
al., 2022). To address the ethical concerns, we employed an iterative prompt
design process to ensure that the persuasive content is appropriate.
Initially, one researcher conducted a preliminary design and invoked the
GPT-3.5 API to generate 30 sets of content using artificial data.
Subsequently, two other researchers rated the satisfaction level of the
generated content (scoring from 1 to 5). This process is iterated multiple
rounds until the average satisfaction score is above 4. This process strives
to create content that is both appropriate and engaging, aligning with our
persuasive strategy. We note that this process cannot fully address the
ethical concerns. We discuss the ethical concerns and LLM’s use risk in
Section 8. The middle top box in Figure 4 shows our final optimization prompt.
Prompt Optimization includes two modules: (a) The ¡Notes¿ module clarifies
restrictions; (b) The ¡Output Format¿ module guarantees the correct format of
the output.

(4) Description of Persuasion Strategy: To ensure each generated content
aligned with our proposed strategies, we provide a short description of each
strategy, as indicated by the middle bottom box in Figure 4. This description
is based on our design in Section 3 (also see Figure 2). Based on users’
different in-the-moment mental states, we will select the corresponding
strategies. Note that for the scaffolding habits strategy, we also input a
habit selected from users’ initialization. The way we obtain the mental states
and specific habits is mentioned in Section 4.2.

Figure 5. Interaction Flow. Users first set their value and app list (Step 1).
When opening a black-listed app, users need to first self-report their phone
use intent (Step 2). If the intent is habitual use, the app asks them to
report their current mental state (Step 3). After that, a corresponding
persuasion shows (Step 4).

###  4.2. How to Intervene: Interaction Flow

We design a four-step user interaction flow for MindShift, as depicted in
Figure 5.

Step 1: Initialization. When initiating MindShift, we ask users to set their
values in four categories: (Step 1 in Figure 5) career, health, life, and
hobbies. For each category, users are asked to fill in both their goals and
actions. This process serves two purposes: first, it provides the necessary
user-specific information for our Evoking strategy; second, it enables the
creation of personalized habits in the Scaffolding Habits persuasion strategy.
After that, users are asked to set a blacklist of apps. Launching apps from
this list will trigger intervention.

Step 2: Intention report. Based on our findings in Section 3, interventions
are only necessary when users are habitually using their phones. To collect
users’ intentions, we employ a self-reporting approach (Step 2 in Figure 5).
When an intervention is triggered, users are presented with three options to
choose from: habitual use, instrumental use, and relaxation. Only when users
select ’habitual use’, the intervention will proceed. Since reporting intent
itself is an intervention, driving users to reflect on their behavior, we add
an exit button at the bottom of the interface to offer an exit from the app.
It is noteworthy that detecting habitual use is beyond the scope of this
paper. We envision future work on contextual sensing and user intent detection
that can automate this process. We have more discussion in Section 8.

Step 3: Current mental state report. The mental state is a key factor that
triggers users’ habitual phone use. Unlike physical context, which can be
automatically detected, current mental state detection techniques are not
mature and robust enough. Therefore, we ask users to self-report (Step 3 in
Figure 5). Based on the mental states listed in Section 3.2, we propose two
single-choice questions for users to report their mental state: (1) whether
they are engaged in activities (”Yes” or ”No”), and (2) whether they currently
have any negative feelings, including options of ”Stress”, ”Boredom”, ”None”
(i.e., inertia), or ”Other Negative Feelings”. The ”Other Negative Feelings”
option is used to capture other cases that offer choices that do not cover.
Users can input in a text box next to the radio button.

Step 4: Persuasion. Based on our intervention content design in Figure 4 and
our persuasion strategy, we leverage the power of GPT-3.5 to generate
persuasive messages. The messages are displayed in a pop-up window. Below the
message, users can choose to either quit the app or continue using it (the
bottom of Step 4 in Figure 5). Additionally, users can also provide optional
feedback by giving a thumbs up or down to the intervention content.

Furthermore, for the Scaffolding Habits strategy, it is essential to link
between users’ own habits and their specific use contexts. Therefore, we
implement a user participation mechanism. The interface for scaffolding habits
intervention has an additional habit item along with an edit button (the upper
interface of Step 4 in Figure 5). The habit item represents a system-generated
suggestion based on the user’s current context and initial settings. Users
have the control to edit and define their desired habits. Once submitted, the
modified habit will be recommended the next time when users are in the same
context.

###  4.3. When to Intervene: Intervention Trigger Mechanism

As in Figure 2, each mental state allows for multiple persuasion strategies.
Therefore, we devise a simple procedure. After determining the mental state
and narrowing down the specific strategies, we first pick one strategy and
generate an intervention message. Then, we loop over other different
strategies and show new strategy messages every two minutes until the users
leave the app. After looping over all appropriate strategies under this mental
state, the intervention will stop. Users only need to report (i.e., Step 2 and
3 in Figure 5) the first time they open a specific app after each screen
unlock. This design is based on two considerations: 1. We assume that the
user’s initial opening usually reflects their subsequent usage intentions. 2.
Requiring users to report multiple times when switching between apps can lead
to a poor user experience.

##  5\. System Implementation

We built an Android application to instantiate our design. Our system consists
of a client and a server.

###  5.1. Client-Side Implementation

The client-side is an Android app that implements all the features of our
design. We use accessibility services to detect the opening and closing of
apps on the phone. The client is also responsible for collecting and uploading
data to the server, including the screen’s off and unlock status, the name,
opening and closing times of the application, and location data obtained
through the Amap API (Amap, 2023). To prevent the accidental killing of the
accessibility service and ensure compliance, we also add a background service
in the app to check the status of the accessibility service every 5 minutes.
If it detects that the accessibility service is terminated, the client will
notify the server to send an email to a researcher. The researcher can remind
the user to turn on the accessibility service again to ensure the integrity of
the data.

###  5.2. Server-Side Implementation

The server side is responsible for generating persuasive content tailored to
the user’s context through four key tasks. By performing these tasks, the
server side ensures that the intervention is personalized, contextually
relevant, and delivered in a timely manner.

(1) User Data Computation: The server first computes the user data that needs
to be used in the word slots from the app data uploaded by the client,
including the total habitual usage time of the phone and the time of the last
habitual opening.

(2) Habit Selection: Next, the server selects a habit mostly matched with the
current user’s mental state, location (i.e., the specific building), and time
(i.e., the hour of the day) from users’ initialization (i.e., Step 1 in Figure
5). To create a strong association between habit and context, as long as users
do not thumb down or modify it, the same habit will be recommended in the same
context. More details can be seen in Step 4 of 4.2.

(3) Strategy Counterbalance: To ensure that the number of times each strategy
appears in each mental state is roughly the same, the server then
counterbalances the strategy order in the prompt mentioned in Section 4.2.

(4) Content Generation: After obtaining the user contexts, habits, and
persuasion strategy, the server uses the OpenAI GPT-3.5 API to generate
appropriate intervention content. We adopted a streaming, character-by-
character generation approach, allowing the persuasive content to start being
displayed within 2 seconds.

##  6\. Field Experiment

To evaluate the effectiveness of MindShift, we conducted a 5-week field
experiment. In this section, we introduce experimental design, participant
recruitment, and experiment procedure.

###  6.1. Baseline Intervention Methods

As MindShift is one of the first persuasion intervention systems leveraging an
LLM to generate dynamic persuasion content, there are no comparable systems
other than the traditional persuasion techniques. We compare MindShift against
a persuasive reminder baseline (also as a pop-up window), one of the most
commonly adopted intervention methods in commercial apps (Inc, 2023; Apple,
2021). To ensure the fairness of the comparison, the persuasion baseline is
designed to be the same as the intent report step, as illustrated in Step 2 in
Figure 5. We name this intervention as Baseline.

Moreover, in order to evaluate the effectiveness of the mental states and
persuasion strategies proposed in Section 3, we further design a simplified
version of MindShift by removing the mental states and persuasion strategies
from the prompt design. The content generation will solely be based on users’
physical contexts and their app usage behavior. We name this group as
MindShift-Simple. In total, we have three intervention methods to compare:
Baseline, MindShift-Simple, and MindShift. Table 1 shows the comparison of
these three intervention techniques.

Table 1. Comparison of Three Intervention Techniques Intervention Methods | Characteristics
---|---
Intent Report |  LLM-powered Persuasion |  Mental-State-based Persuasion Strategies
Baseline | $\checkmark$ |  |
MindShift-Simple | $\checkmark$ | $\checkmark$ |
MindShift | $\checkmark$ | $\checkmark$ | $\checkmark$

###  6.2. Experiment Design

We adopt a within-subject design, with intervention techniques as independent
variables (Baseline, MindShift-Simple, and MindShift). We design a 5-week
field experiment. To measure users’ everyday phone usage behavior, the first
week is set as the Baseline stage, followed by two weeks of one MindShift
version and another two weeks of the other version. We counter-balance the
order of MindShift-Simple and MindShift.

Our evaluation metrics include various aspects: (1) Intervention acceptance
rate. We measure the percentage of times users accept the intervention and
quit the blacklist app use when interventions are shown. (2) Intervention
thumb-up rate. For MindShift-Simple, and MindShift that show persuasion
content, users can provide feedback by pressing buttons (thumb-up or thumb-
down, see Figure5 step 4 interface). (3) App usage behavior, which includes
both app opening frequency and usage duration. (4) Subjective reports. At the
beginning of the study and at the end of each intervention session, we
distributed the Smartphone Addiction Scale (SAS) (Kwon et al., 2013) and the
self-efficacy Scale (Schwarzer and Jerusalem, 1995). In addition, at the end
of the study, we also conducted a brief semi-structured interview to gather
user experiences and feedback on our intervention techniques. These metrics
cover both the objective and subjective measures of the interventions.

###  6.3. Participants

We sent out recruitment material on social media platforms. We included a
screening survey aiming to identify potential participants who showed signs of
smartphone addiction and the willing to reduce their smartphone use.
Specifically, besides basic demographics, we included four questions selected
from the SAS and self-efficacy questionnaires. We also included questions
about the willingness to reduce smartphone use, the extent of habitual phone
use, future plans for the next five weeks, and a screenshot of phone usage
time of the last week. We excluded users who (1) did not show signs of
smartphone addiction (SAS sub-score ¡ 15), or (2) were unwilling to reduce
smartphone use, or (3) had a total weekly usage time of less than 20 hours, or
(4) those who had special plans such as long-term travel in the next five
weeks (which may shift their phone usage patterns).

We received a total of 42 responses. We recruited 31 participants after the
screening process. 6 participants voluntarily dropped out during the study.
For the remaining participants, we divided them into groups according to
counterbalanced intervention orders. We conducted a Kruskal-Wallis test
analysis to ensure that groups had no significant difference in the SAS scores
and self-efficacy scores. In the end, 25 participants completed the entire
study (females=13, males=12, age=22±2 years). Among them, 17 participants were
undergraduates, 5 were graduate students, and 3 were professionals.

###  6.4. Experiment Procedure

After all the participants signed the consent form, we held a 20-minute
onboarding session online to familiarize participants with the research
process and introduce the Android application. We explained in detail the
meanings of each selection in the report interfaces (Step 2 and 3 in Figure
5). After the meeting, participants filled out the first SAS and self-efficacy
questionnaires. The app was then deployed for a 5-week field experiment.

Before users started using one of two versions of MindShift (MindShift and
MindShift-Simple), we provided participants with a tutorial explaining the
three mental states (i.e., boredom, stress, and inertia) that they need to
report. This ensured that they had an accurate and consistent understanding of
the mental states, which in turn improved the accuracy and reliability of the
reported data. At the end of the experiment, participants received a
compensation of $50 for their time.

##  7\. RESULTS

During the five-week study, we collect 50,815 minutes of restricted app usage
duration, and 54,467 restricted app opening events (7539, 23,769, 21,994, and
835 for habitual use, instrumental use, relax, and quit). We analyze the
quantitative data collected via app and the qualitative data collected via
questionnaires and interviews.

###  7.1. Intervention Acceptance Rate

The effectiveness of a persuasion strategy is directly measured by the rate of
successful prevention of user engagement with the targeted application (i.e.,
intervention acceptance rate). We compare the overall acceptance rate (Sec.
7.1.1), acceptance rate for generated persuasion content (Sec. 7.1.2), and
thumb-up rate (Sec. 7.1.3). Moreover, we also conduct a detailed analysis of
the acceptance rate across strategies (Sec. 7.1.4), mental states and
activities (Sec. 7.1.5).

####  7.1.1. Overall Acceptance Rate

Figure 6. Overall Acceptance Rate

MindShift and MindShift-Simple increase the overall acceptance rate
significantly and MindShift achieve best. We first investigate the overall
acceptance rate during the three intervention sessions, the total number of
quit times divided by the total number of app visits (excluding instrumental
uses and relaxation). Figure 6 shows the comparison, MindShift (35.2±24.1%)
has a higher acceptance than MindShift-Simple (30.5±23.6%) and Baseline
(12.7±17.4%). These differences are statistically significant according to a
Friedman test ($\chi^{2}$(2) = 13.69, p ¡ .01). Three post hoc Wilcoxon
signed-rank tests, corrected with Holm’s sequential Bonferroni procedure,
indicate that MindShift vs. Baseline (V = 52, p ¡ .01) and MindShift-Simple
vs. Baseline (V = 38, p ¡ .001) are statistically significantly different, but
that MindShift vs. MindShift-Simple is not significant (V = 129, n.s.).

####  7.1.2. Acceptance Rate for Generated Persuasion Content

(a) Overall Persuasion Acceptance Rate

(b) Persuasion Acceptance Rate Grouped by Round

Figure 7. Persuasion Acceptance Rate

MindShift has a significantly higher persuasion acceptance rate than
MindShift-Simple. The overall acceptance rate includes two parts: exiting when
reporting intention and exiting after seeing the persuasion content. To narrow
down the comparison between MindShift and MindShift-Simple, we focus on the
intervention acceptance rate during the persuasion stage, as the two
intervention techniques’ differences lie only in the persuasive content. As
shown in Figure 7(a), MindShift achieves higher persuasion acceptance
(20.1±20.2%) than MindShift-Simple (12.0±15.0%) and a paired-samples t-test
shows that MindShift was statistically significantly higher (t = -2.21,
p¡0.05).

Moreover, as we introduce in Section 4.3, every persuasion intervention could
consist of 1 to 4 rounds, depending on which mental state participants are in
and which stage participants leave the app. Therefore, we further compare the
persuasion acceptance rates between MindShift and MindShift-Simple across
different rounds. The results show that MindShift outperforms MindShift-Simple
at each round ($\Delta_{\textit{Round1}}$=6%,
$\Delta_{\textit{Round2}}$=14.7%, $\Delta_{\textit{Round3}}$=10.2%,
$\Delta_{\textit{Round4}}$=11%) as indicated in Figure 7(b). Moreover, we
observe a decreasing trend of the acceptance rate of MindShift-Simple, while
MindShift does not show such a trend. We conduct a paired-samples t-test
between the two intervention techniques in each round. The results indicate
that, except for the first round, rounds 2 (p¡0.05), 3 (p¡0.001), and 4
(p¡0.001) all exhibit that MindShift achieves significantly higher acceptance
rate.

####  7.1.3. Thumb-up Rate of Interventions

(a) Feedback Proportion

(b) Acceptance Rate Grouped by Persuasion Strategies

Figure 8. Feedback and Strategy Acceptance Rate

MindShift has a significantly higher thumb-up rate. Users can give feedback in
the persuasion interface. As depicted in Figure 8(a), 6.8% of interventions in
MindShift receives thumb-up while MindShift-Simple receives only 2.2%. A
paired-samples t-test shows that significant differences (p¡0.05) are observed
for the thumb-up rate, but no significant differences (p=0.22) for the thumb-
down rate between the two techniques. This indicates that MindShift aligns
better with users’ preferences.

####  7.1.4. Acceptance Rate across Different Strategies

We design four persuasion strategies in MindShift whereas MindShift-Simple
does not incorporate any specific strategies, so we further compare the
persuasion acceptance rates across different strategies. Figure 8(b) shows
that all strategies we design outperform MindShift-Simple
($\Delta_{\textit{Understanding}}$=8.9%, $\Delta_{\textit{Comforting}}$=3.3%,
$\Delta_{\textit{Evoking}}$=10.3%, $\Delta_{\textit{Scaffolding
Habits}}$=4.9%) but the differences are not statistically significant.

(a) Strategy Acceptance Rate Grouped by Mental State. Significant differences
compared to no strategy (green bar) are indicated above the X coordinate axis,
while differences among strategies are below.

(b) Strategy Acceptance Rate Grouped by Activity. The same annotation method
as grouped by mental state.

Figure 9. Strategy Acceptance Rate across Different Mental States and
Activities

####  7.1.5. Strategy Acceptance Rate across Different Mental States and
Activities

As we show in Figure 2, each mental state has a different strategy mapping.
Therefore, we also sought to derive insights regarding which strategies are
most effective for users under different mental states (Figure 9(a)) and
activities (Figure 9(b)). Friedman test and post hoc Wilcoxon signed-rank
tests are employed to investigate the influence of different strategies on
acceptance rate.

For mental states: Under the mental state of ”Stress”, Comforting and Evoking
show a trend toward significance compared with MindShift-Simple
($\Delta_{\textit{}}$ = 10.8%, p¡0.1 and $\Delta_{\textit{}}$ = 3.8%, p¡0.1
respectively). Under the mental state of ”Boredom” and ”Inertia”, Evoking is
significantly more effective ($\Delta_{\textit{Boredom}}$=3.6%,
$\Delta_{\textit{Inertia}}$=5.8% ) than MindShift-Simple (ps ¡ 0.01).

For activity levels: When not engaging in activity, Comforting is
significantly more effective than MindShift-Simple ($\Delta_{\textit{}}$=5.6%,
p ¡ 0.05). When engaging in activity, there are no significant differences in
all the strategies compared to MindShift-Simple.

###  7.2. App Usage Behavior

We then investigate the influence of the intervention on participants’ app
usage behavior. Overall, participants have less app usage frequency and
duration when using MindShift and MindShift-Simple, especially in habitual
usage.

####  7.2.1. Overall Usage Behavior

We count the number of app opening attempts for restricted apps. Figure 10(a)
presents the opening frequency under three intervention techniques. MindShift-
Simple (63.6±9.2) and MindShift (65.3±9.5) have lower opening frequency than
Baseline (74.3±9.7). Compared to Baseline, MindShift reduces by 12.1% usage
duration while MindShift-Simple reduces by 14.4%. However, a Friedman test
does not show significance.

We also measure restricted app usage duration which is another important
factor for phone overuse. As can be seen from Figure 10(b), participants have
the lowest app usage duration in MindShift (1.11 ±0.7 hours) compared to the
Baseline (1.23±0.7 hours) and MindShift-Simple (1.20 ± 0.8 hours). Compared to
Baseline, MindShift reduces by 9.8% usage duration while MindShift-Simple
reduces by 2.4%. We conduct a Friedman test and find a significant difference
among different techniques (p¡0.05). A post-hoc Wilcoxon test shows that
MindShift has a trend of declining compared to Baseline, with marginal
significance (p¡0.1).

(a) Total App Opening Frequency

(b) Total Phone Usage (Hours) per Day

Figure 10. App Usage Behavior

####  7.2.2. Habitual Usage Behavior

MindShift and MindShift-Simple significantly reduce habitual app usage
duration and frequency. The focus of our intervention is habitual use, so we
investigate the changes in habitual usage behavior across each intervention
technique. Overall, our results show that MindShift and MindShift-Simple can
both significantly reduce habitual use. The app visit frequency and duration
of habitual usage cases also decrease significantly during the two versions of
MindShift compared to Baseline ($\Delta_{\textit{{MindShift-Simple}}}$ equaled
80.6% for visit frequency, 84.4% for usage duration, and 6.8% for habitual use
proportion, $ps$ ¡ 0.0001; $\Delta_{\textit{MindShift}}$ equaled 77.3% for
visit frequency, 80% for usage duration and 6.8% for habitual use proportion,
$p$s ¡ 0.0001).

###  7.3. Subjective Report

We further analyze on user-reported SAS and self-efficacy scale results. When
using MindShift and MindShift-Simple, participants experience a significant
decrease in SAS Score and a significant increase in self-efficacy score, but
they see no change when using Baseline. We summarize the results as follows.

####  7.3.1. Decrease of SAS Score

Both MindShift and MindShift-Simple decrease SAS Score significantly and
MindShift achieves best. Figure 11(a) shows the results of the SAS scores
during the intervention stages. MindShift exhibits the lowest SAS scores
(35.2±10.2), followed by MindShift-Simple in the second position (37.6±10.1),
with the Baseline intervention ranking the third (44.5±8.7) and the initial
ranking the last (47.5±6.7). We conduct a Friedman test and the results show a
significant difference(p¡0.001). Post-hoc Wilcoxon tests show that both
MindShift and MindShift-Simple are significantly lower than the Baseline and
initial scores (all ps¡0.01). This suggests that the two versions of MindShift
have the potential to fundamentally alter individuals’ mobile phone usage
behavior.

####  7.3.2. Increase of Self-efficacy

MindShift and MindShift-Simple increase Self-efficacy Score significantly.
Figure 11(b) shows the results of the self-efficacy scores during the
intervention stages. MindShift (26.7±6.1) and MindShift-Simple (26.6±6.8)
exhibit higher scores compared to the Baseline intervention (24.1±5.6) and the
initial (24.1±5.6). We conduct a Friedman test, and the results show a
significant difference (p¡0.001). The post-hoc Wilcoxon test shows that both
MindShift and MindShift-Simple are significantly higher than the Baseline and
initial scores (all ps¡0.05). This indicates that persuasive techniques have
the potential to significantly enhance individuals’ self-efficacy, which can
result in overcoming excessive mobile phone usage. In contrast, conventional
reminders may not achieve this goal.

(a) SAS Score

(b) Self-efficacy Score

Figure 11. Subjective Scales Report

####  7.3.3. Subjective Comments

During the exit interview, participants generally had a positive experience.
One participant said, “I can actually feel that my recent dependency on the
phone has decreased” (P17). Another participant felt using the app shifted
them to self-improvement tasks instead of mindlessly using the smartphone,
“Now, when I have nothing to do, I tend to do other things, like learning
vocabulary, instead of aimlessly browsing my phone” (P10). Additionally,
participants had positive comments on our design which includes the mental
states factor during the intervention, “I feel that its suggestions align well
with my emotional state at that time.” (P25). They also valued the
“Understanding” and “Comforting” strategies, saying “It tells me that I’m not
the only one experiencing these painful emotions, which helps me feel better”
(P22).

Despite the majority positive comments, a small number of participants
expressed their dissatisfaction with MindShift. Some participants found the
persuasive message to be “a bit stiff and templated” (P4, P6), and they
believed that “they would develop tolerance as they repeatedly use” (P20).
Some participants also mentioned privacy concerns. For example, P15 mentioned
that MindShift-Simple’s ability to capture time and location made her
uncomfortable.

###  7.4. Summary of Results

Overall, two versions of MindShift show significantly higher acceptance rates
compared to the Baseline. MindShift has the highest acceptance (35.2%) and
thumb-up rates (6.8%), and it significantly outperforms MindShift-Simple in
both acceptance rates of generated persuasion content (8.1%) and on a per-
round basis (6-14.7%). Furthermore, there exist strategies that significantly
outperform MindShift-Simple (3.6-10.8%) in every mental state, suggesting that
the strategies we design are meaningful. Regarding app usage behavior, two
versions of MindShift lead to a decrease in overall app opening frequency
(12.1-14.4% ) and usage duration (2.4-9.8%) and are significantly effective in
reducing habitual use. MindShift achieves the best results in reducing SAS
scores (34.7%) and increasing self-efficacy scores (10.7%). Statistical
analysis indicates that two versions of MindShift reduce SAS scores and
increase self-efficacy scores significantly while Baseline (traditional pop-up
reflections) does not. This suggests that MindShift has the potential to
profoundly transform human behavior with enduring effects. Finally, users’
subjective comments also confirm a perceived reduction in smartphone
dependency and an inclination to continue using MindShift.

##  8\. DISCUSSION

In this section, we discuss the MindShift’s novelty in contrast to previous
intervention techniques and discuss the limitations and future work.

###  8.1. The Roles of Users’ Phone Use Purpose and Mental States in
Smartphone Intervention

Most previous intervention techniques are designed to initiate interventions
based on the amount of time and frequency of smartphone usage. However,
quantifying smartphone use just by considering time oversimplifies and ignores
the underlying causes. People use smartphones for work, study, and relaxation.
MindShift initiates persuasion only when users agree the current usage belongs
to habitual use. Our intervention enhances users’ self-awareness of their
habitual phone use behavior.

Additionally, certain mental states are linked to habitual smartphone use as a
form of self-distraction. Although smartphone use serves as a coping mechanism
for emotion fluctuations, studies show that habitually using them for escapism
fails to effectively mitigate emotions (Duvenage et al., 2020). Using
smartphones for emotional regulation can lead to problematic smartphone use
behavior, potentially leading to severe psychological issues such as
depression (Coyne et al., 2021; Zsido et al., 2021). MindShift aims to
intervene in habitual smartphone usage triggered by specific mental states.
Our goal is to reduce users’ problematic smartphone use and help individuals
transition from avoidance-oriented coping to approach-oriented coping (Compas
et al., 2001). Future work can also explore automatic detection of users’
phone use intention and mental states, simplifying the intervention process
and improving user experience. However, it remains an open question on how
skipping the self-reflection may impact the effectiveness of such a persuasion
technique.

###  8.2. Towards Adaptive Persuasion Intervention

MindShift generates dynamic and personalized persuasion content by combining
information such as users’ physical contexts, mental states, and other
behaviors. However, several participants still mentioned that the generated
content sometimes could be “stiff and templated”. This may be attributed to
the limited prompt templates. Although the content generated by the LLM
varies, the main theme is guided by our prompts, which could limit the
variation of persuasion content. To improve, we suggest to integrate user
feedback into the system. Currently, MindShift supports a simple thumb-up and
thumb-down feedback mechanism. Even with such simple information, we could
establish a human-in-the-loop setup to fine-tune content, aligning better with
user preferences. Moreover, future work can also consider collecting more
comprehensive feedback from users. Users could customize the language style
generated by an LLM, which can be coupled with adaptive algorithms such as
reinforcement learning to achieve a more intelligent just-in-time adaptive
intervention (JITAI) system that evolves with users (Goldstein et al., 2017).

###  8.3. Ethical Concerns and Risk of Using LLM for Persuasion

Although MindShift performs well in reducing smartphone use frequency, there
are important ethical concerns we want to highlight, especially about the
application of LLMs for persuasion. Despite our intentionally devised prompts
given to the LLM, it is not possible for us to guarantee that the generated
persuasive messages are always safe. For example, if users access a
blacklisted shopping app, LLM might include unwelcome commercial ideas in the
message, causing user aversion and rejection. Meanwhile, because of LLM’s
limited capability in understanding human psychology and society (Xu et al.,
2023), it may evoke goals or recommend habits at the wrong time. For instance,
if users are already stressed due to their life objectives (e.g., struggling
with academic stress), additional reminders could exacerbate the stress or
even cause harm. Recent research in the field of AI alignment aims to ensure
that AI systems behave in ways that are safe, beneficial, and aligned with
human values and objectives (Gabriel, 2020). Real-world deployment is needed
to integrate these techniques into the intervention.

Meanwhile, privacy is another critical concern since our system detects users’
location (as general categories) and captures their smartphone usage data.
MindShift employs a commercial API, transmitting users’ data to a third party.
Although we intentionally designed the prompt to avoid including any
identifiable information, there is still the risk of revealing information
about their behavior and mental states. One solution for future study is to
leverage open-sourced LLMs (such as LLaMA2 (Touvron et al., 2023) or PaLM2
(Anil et al., 2023)) so that user’s data can be appropriately handled and
encrypted by ourselves instead of a third party.

###  8.4. Limitations

There are a few limitations in our study. First, our experimental user group
is limited to college students, which limits the applicability of the results.
Future studies can expand the sample size and involve more diverse user
groups. Second, our field experiment is short. A five-week deployment cannot
reveal the longitudinal effect of such an intervention technique. Moreover, if
our time and monetary budget allow, our experiment design can be improved by
making the Baseline another two-week intervention session for a more fair
comparison. Third, our current method for detecting habitual use and mental
states relies on self-reporting, which increases users’ burden. We only
consider initial habitual use and do not consider the shifts in user purposes
during app usage. As we mentioned in Section 8.1, future work can explore
automatic intent and mental state detection, and the data collected in this
study can serve as a starting point for machine learning training models.
Finally, our study utilizes GPT-3.5 as a large language model, and its
performance is still unstable. Future work can explore more lighted weighted
and robust LLMs for local deployment, which can address the concerns mentioned
in Section 8.3 to some extent.

##  9\. CONCLUSION

In this paper, we propose MindShift, a mental-based persuasion intervention
technique powered by LLM that aims to reduce problematic smartphone use. We
conduct a Wizard-of-Oz study and an interview study to explore the mental
states behind problematic smartphone use: stress, boredom, inertia, and design
four persuasion strategies: understanding, comforting, evoking, and
scaffolding habits. MindShift (1) collects users’ purpose, usage behavior,
physical context, mental states, goals&habits, (2) uses the persuasion
strategy we design, (3) leverages LLMs to generate dynamic, personalized
persuasion messages. Through a five-week within-subjects user experiment
(N=25), we compare three intervention techniques (MindShift, MindShift-Simple,
Baseline). Our results show that MindShift significantly improves the
acceptance rate of generated persuasion content (8.1%) compared with
MindShift-Simple. Two versions of MindShift decrease overall app opening
frequency (12.1-14.4%) and usage duration (2.4-9.8%) and are particularly
effective in reducing habitual use. Moreover, two versions of MindShift
significantly reduced SAS scores (25.8-34.7%) and increased self-efficacy
scores (10.4-10.7%). Finally, users’ subjective comments also confirm a
perceived reduction in smartphone dependency and a willingness to continue to
use MindShift. Our work provides valuable insights into the mental states
behind problematic smartphone use and the effectiveness of LLMs-powered
persuasion for smartphone intervention.

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