<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.0 20120330//EN" "JATS-archivearticle1.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink">
  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Self-determined Behavior Change Goals are Dynamic, Diverse, and Intrinsically-Motivated</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Mina Khan</string-name>
          <email>minakhan01@gmail.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Pattie Maes</string-name>
          <email>pattie@media.mit.edu</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>MIT Media Lab</institution>
          ,
          <addr-line>Cambridge, MA</addr-line>
          ,
          <country country="US">USA</country>
        </aff>
      </contrib-group>
      <pub-date>
        <year>2021</year>
      </pub-date>
      <fpage>65</fpage>
      <lpage>72</lpage>
      <abstract>
        <p>Behavior change often involves externally-determined goals, including goals that are static over time, focused on specific domains, and/or externally-rewarded. We study self-determined behavior change goals over time, without external rewards or without focusing on a specific behavior change domain. We conducted a 4-week study, N=10 participants, and noted the participants' weekly behavior change goals. We recommended a goal, i.e., to 'stay hydrated', and the participants could report maximum 3 goals each week. The participants had dynamic goals - each week, each participant had an average of 1.725 total goals, added an average of 0.41 goals, and abandoned an average of 0.47 goals. Also, each participant chose diverse goals per week and over the weeks, e.g., related to sleep, emotions, etc. Finally, most participants chose intrinsically-motivated goals via self-reflection, not our recommended one. We recommend increased flexibility and self-reflection in behavior change goalsetting to facilitate self-determined diverse, dynamic, and intrinsicallymotivated goals.</p>
      </abstract>
      <kwd-group>
        <kwd>self-determined behavior change goals</kwd>
        <kwd>user study</kwd>
        <kwd>goal-setting</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>-</title>
      <p>Behavior change applications, both in research and industry, mostly focus on
externally-determined, domain-specific, and/or static behavior change goals over time.
While the goals may be guided by health recommendations, e.g., 10,000 steps a day, or
motivated by different theories, e.g., social rewards, they may not necessarily be aligned
with people’s intrinsic motivations and goals.</p>
      <p>
        Self-Determination Theory outlines that autonomy, competence, and relatedness are
important for user’s motivation, in particular, intrinsic motivation for behavior change
[
        <xref ref-type="bibr" rid="ref3">3</xref>
        ]. Research also suggests that sustained personal motivation is key for long-term
maintenance of behavior change [
        <xref ref-type="bibr" rid="ref5">5</xref>
        ] and that behavior change is an internal rather than
an external process [
        <xref ref-type="bibr" rid="ref14">14</xref>
        ]. Lastly, self-guided behavior change is the most common form
of long-term real-world behavior change [
        <xref ref-type="bibr" rid="ref2">2</xref>
        ].
      </p>
      <p>We investigate user’s real-world self-determined behavior change goals over time.
In particular, we had three research questions – RQ1: What are people’s free-living
behavior change goals? RQ2: How do those goals change over time?
RQ3: What drives the users to set and change their goals? We conducted a 4week study
with 10 participants to track free-living behavior change goals over time and allowed
each participant to list up to 3 behavior change goals per week.</p>
      <p>Our results show that the participants’ goals were dynamic, diverse, and
intrinsically-motivated – i. Dynamic: Each week, each participant had an average of
1.725 goals, out of which they added an average of 0.41 goals and abandoned an
average of 0.47 goals; ii. Diverse: Each participant chose diverse goals each week and
over time, e.g., goals related to sleep, diet, emotional well-being, work, chores, and
exercise; iii. Intrinsically-motivated: Most participants chose intrinsically-motivated
goals via self-reflection and not our recommended one.</p>
      <p>We propose adding more flexibility and periodic self-reflection to the behavior
change goals-setting, self-tracking, and rewards so that the users can reflect on their
rewards and motivations and set their goals accordingly.
2</p>
    </sec>
    <sec id="sec-2">
      <title>Related Work</title>
      <p>
        Self-Determination Theory suggests that intrinsic motivation is key for behavior change
[
        <xref ref-type="bibr" rid="ref3">3</xref>
        ], and intrinsic motivation is also considered helpful for long-term maintenance of
behavior change [
        <xref ref-type="bibr" rid="ref5">5</xref>
        ]. Rapp et al. conducted user interviews, which suggest that behavior
change is an internal rather than external process [
        <xref ref-type="bibr" rid="ref14">14</xref>
        ]. Finally, self-guided change is
the most common form of long-term and maintained health behavior change [
        <xref ref-type="bibr" rid="ref2">2</xref>
        ] and is
also important for environmental behavior change [
        <xref ref-type="bibr" rid="ref1">1</xref>
        ]. Thus, research highlights the
importance of self-guided behavior change [
        <xref ref-type="bibr" rid="ref2">2</xref>
        ].
      </p>
      <p>
        Personalization is a common theme in behavior change, and self-determined and
personalized goals have been explored for specific behavior change domains, e.g.,
personalized step goals [
        <xref ref-type="bibr" rid="ref15">15</xref>
        ]. Goal-setting is a key part of behavior change support
systems (BCSSs) and BCSSs “emphasize autogenous approaches” by building on “own
motivation or goal” [
        <xref ref-type="bibr" rid="ref12">12</xref>
        ]. Our research explores self-determined behavior change goals,
not confined to specific behavior change domains.
      </p>
      <p>
        Self-experimentation has also been explored [
        <xref ref-type="bibr" rid="ref6">6</xref>
        ], e.g., for irritable bowel syndrome
support [
        <xref ref-type="bibr" rid="ref4">4</xref>
        ] and for behavior change plans using just-in-time support [
        <xref ref-type="bibr" rid="ref9">9</xref>
        ]. There are also
guidelines for supporting personalized behavior change goalsetting and plans [
        <xref ref-type="bibr" rid="ref7 ref8 ref9">9,8,7</xref>
        ].
However, there are no investigations like ours, which focus purely on self-determined
behavior change goals, not behavior change plans or implementations, over time. We
investigate users’ free-living behavior change goals over time, without confining them
to specific plans or implementations.
3
      </p>
    </sec>
    <sec id="sec-3">
      <title>Study Design</title>
      <p>We conducted a 4-week study with 10 participants and inquired the participants about
their free-living behavior change goals each week for the 4-week study.</p>
      <sec id="sec-3-1">
        <title>Procedure</title>
        <p>We allowed the participants to list up to three behavior change goals per week. The
participants could choose each week’s goals independently of the previous weeks. We
conducted the study in a free-living real-world setting.</p>
        <p>Each week, we sent the participants a weekly survey form via Google Forms. The
participants could add up to 3 behavior change goals each week and the survey had the
following open-ended questions for each goal: Q1. What is your behavior change goal?
Q2. What do you plan to do to/how would you implement the goal? Q3. Why is the goal
helpful or important to you?</p>
        <p>We suggested one behavior change goal, i.e., “stay hydrated, e.g., drink a glass of
water in the morning”. After the first week, we also included two additional survey
questions to learn more about the participants’ behavior change experience: Q4. How
was your behavior change experience last week? Did you learn something? Did
something change? Q5. Anything you would like to add?
3.2</p>
      </sec>
      <sec id="sec-3-2">
        <title>Participants</title>
        <p>We sent a recruitment email to our university’s department mailing list. We received
10 responses (N=10; µ= 23 yrs, σ= 2.36 yrs; 7 males, 3 females; all students), and
included the participants without any inclusion or exclusion criteria. The participants
did not receive any financial or other compensation. The participation was voluntary
and the participants could drop out of the study at any point. None of the participants
dropped out of the study.
3.3</p>
      </sec>
      <sec id="sec-3-3">
        <title>Data Analysis</title>
        <p>We coded the qualitative responses and performed thematic analysis. Additionally, we
calculated the total number of goals listed by each participant each week and also
computed the number of goals added and abandoned by each participant each week.
We considered modifications in goal implementations (Q2), e.g., changes in the
frequency, time, or duration as the same goal, not a new goal.
4</p>
      </sec>
    </sec>
    <sec id="sec-4">
      <title>Results</title>
      <p>We conducted a 4-week study, N=10 participants, to investigate the participants’
behavior change goals over time. The participants could add up to 3 open-ended goals
each week and we recommended a goal of staying hydrated. We share below (i) weekly
goal statistics for total, added, and abandoned goals, (ii) weekly goals categories, and
(iii) qualitative participant feedback. Our results show that the participants chose
dynamic, diverse, and intrinsically-motivated goals over time.
The participants could choose up to 3 goals per week, independently of their goals last
week. Each week, each participant chose an average of 1.725 goals (σ = 0.14), added
an average of 0.41 goals, and abandoned an average of 0.47 goals, indicating a ~50%
change in weekly goals (0.41+0.47)/(1.725) = 0.51).</p>
      <p>After the first week, when the average number of goals per participant was 2.3, the
average number of goals declined to around 1.6 or 1.5 in weeks 2-4. In week 2-4, the
participants added around 0.4 or 0.5 goals per participant per week, meaning that around
4 or 5 participants in a group of 10 added a new behavior change goal per week.
Similarly, in weeks 2-4, the participants abandoned around 0.4 or 0.5 goals per
participant per week, meaning that around 4 or 5 participants in a group of 10
abandoned one behavior change goal from last week. Two participants went back to
their previously abandoned goals when adding new goals whereas the rest of the
participants added new goals.</p>
      <p>The results for the total chosen, added, and abandoned goals for each week are in
Figure 1 and the results for the total chosen, added, and abandoned goals for each
participant are in Figure 2.
The participants chose diverse and personalized goals (Q1). Even when the goal was
similar (Q1), e.g., exercise, different participants chose different implementations for
the same goal (Q2), e.g., stretching versus running versus gyming for exercise. Also,
not only did different participants choose different goals, but also, the participants, who
chose multiple goals in a week or changed goals over the weeks, chose different and
diverse goals in a week and over time. We categorized the goals into seven categories
and share the list of goal categories (Q1) chosen by each of the participants over 4
weeks in Figure 3.
We recommended a goal to stay hydrated but only two participants chose it as their goal
- one participant chose “staying hydrated” for only one out of the four weeks, and the
other chose it for three out of the four weeks. Even when the participants chose staying
hydrated as their goal (Q1), they entered different ways to implement their goals (Q2).
The other goals were all personalized.</p>
      <p>Moreover, the participants had personalized reasons for choosing their goals (Q3).
For example, “Smiling signifies happiness. I really want to learn how to appreciate life
better. Notice all the great details. Care for the people around me. Even perhaps turning
my happiness into meaningful experiences. If I can successfully wrap my mentality
around this mindset, then I can be happier.”, “It is important because water = health!
And I don’t always get enough water, especially during the school weeks.”, “digital
devices are distractions, need high level of dedication and need to prioritize”, and
“(sleeping early) Helps the next day to be more productive and proactive”.</p>
      <p>Finally, the reasons for changing or continuing goals were related to the participants’
changing self-knowledge (Q4,5), e.g., “I realized that sometimes I don’t put achievable
goals”, “I’ve yet again surprised myself at how quickly my emotions can come and go.
I need to try harder to focus on the good stuff”, “That it takes effort, and in my case, I
do need to work extra hard to get into a rhythm of self-care.”, “I need to form a fixed
schedule to be successful with my habits.”, and “I learned that I like working hard in
bursts and taking breaks in between. I just need to incorporate stretching and exercising
into those breaks!”.
5</p>
    </sec>
    <sec id="sec-5">
      <title>Discussion</title>
      <p>5.1</p>
      <sec id="sec-5-1">
        <title>Findings</title>
        <p>We conducted a 4-week study to investigate the free-living behavior change goals of
everyday users over time. We discuss the key findings, implications, and limitations of
our study below.</p>
        <p>Our results show that in a free-living real-world setting, behavior change goals were
dynamic, diverse, and intrinsically-motivated: i. Dynamic as the behavior change goals
changed over time, i.e., the participants added or abandoned their goals based on
changing external circumstances and changing self-knowledge; ii. Diverse as the
behavior change goals for different participants and for each participant over time were
related to diverse behavior change categories; iii. Intrinsically-motivated as even
though we suggested a goal, the participants picked personalized goals via
selfreflection, i.e., had personalized reasons for choosing, changing, and continuing their
goals.
5.2</p>
      </sec>
      <sec id="sec-5-2">
        <title>Implications and Recommendations</title>
        <p>
          Our research investigated self-determined behavior change goals and we found that
people have dynamic, diverse, and intrinsically-motivated self-determined behavior
change goals. We believe that self-determined goals are important because is ethically
important to give agency to the users to allow them to set, update, and evaluate their
behavior change goals according to their behavior change personalized needs and
preferences. Also, previous research has shown that users prefer personalization,
diversity, and choice in behavior change applications [
          <xref ref-type="bibr" rid="ref13">13</xref>
          ]. Thus, diverse, dynamic, and
intrinsically-motivated self-determined behavior change goals are important ethically
and for personalized support.
        </p>
        <p>
          Moreover, previous research shows that self-determination and intrinsic motivation
are key to behavior change [
          <xref ref-type="bibr" rid="ref3 ref5">3,5</xref>
          ], especially since behavior change is an internal process
[
          <xref ref-type="bibr" rid="ref14">14</xref>
          ]. Also, self-guided behavior change is the most common form of long-term
realworld behavior change [
          <xref ref-type="bibr" rid="ref2">2</xref>
          ]. Short-term behavior change adherence and long-term
behavior change retention are key challenges in behavior change [
          <xref ref-type="bibr" rid="ref11">11</xref>
          ]. Based on
previous work in self-determined and self-guided work in behavior, we believe that it
may be worth investigating dynamic, diverse, and intrinsically-motivated
selfdetermined goals to improve short-term behavior change adherence and/or long-term
behavior change retention.
        </p>
        <p>
          Lastly, self-reflection has been key in behavior change, especially via
selfexperimentation [
          <xref ref-type="bibr" rid="ref7 ref9">9,7</xref>
          ] and self-tracking [
          <xref ref-type="bibr" rid="ref10">10</xref>
          ]. Our study shows that users set
intrinsically-motivated behavior change goals via self-reflection.
        </p>
        <p>We recommend increased support for self-determination and self-reflection to allow
for dynamic, diverse, and intrinsically-motivated behavior change goals, which may
boost behavior change autonomy, personalization, and even efficacy.
5.3</p>
      </sec>
      <sec id="sec-5-3">
        <title>Limitations</title>
        <p>
          We note three key limitations of our work. First, our work focuses on goalsetting and
we do not track actual behavior change. In addition to goal-setting, successful
realworld behavior change may need more support techniques like self-tracking and
interventions. Second, what users desire may not be necessarily efficacious or optimal
for them but it is, nonetheless, important to consider the free-living self-determined
behavior change goals of users, especially since self-determined behavior change has
been shown to be helpful [
          <xref ref-type="bibr" rid="ref2">2</xref>
          ]. Third, our study is a preliminary study, done with a
convenience sample from a college population. Larger and more diverse studies may
be needed to understand the needs of different user groups and populations over time.
6
        </p>
      </sec>
    </sec>
    <sec id="sec-6">
      <title>Conclusion</title>
      <p>
        Previous research suggests that intrinsic motivation is important for behavior change
[
        <xref ref-type="bibr" rid="ref5">5</xref>
        ], that behavior change is an internal process [
        <xref ref-type="bibr" rid="ref14">14</xref>
        ], that users prefer personalized
behavior change support [
        <xref ref-type="bibr" rid="ref13">13</xref>
        ], and that self-guided change is the most common form of
real-world long-term behavior change [
        <xref ref-type="bibr" rid="ref2">2</xref>
        ]. However, most behavior change applications
focus on externally-decided narrow, unchanging, and externally-rewarded behavior
change goals. We investigated people’s real world behavior change goals over time.
      </p>
      <p>
        Our study highlights that people have dynamic, diverse, and intrinsically-motivated
goals, supported by self-determination and self-reflection, over time. We believe that
self-determination is not only important ethically and for personalization, but may also
improve behavior change efficacy as self-determination and intrinsic motivation are
helpful for behavior change [
        <xref ref-type="bibr" rid="ref2 ref3 ref5">5,2,3</xref>
        ]. We, thus, recommend more flexibility and
selfreflection in terms of behavior change goal-setting to support diverse, dynamic, and
intrinsically-motivated behavior change goals.
      </p>
      <p>In particular, we recommend the following to enable users to align their goals with
their changing self-knowledge, personal motivations, and external circumstances: i.
Enable the users to add and abandon goals over time; ii. Allow users to set multiple,
diverse, and personalized goals; iii. Facilitate self-reflection on goals and their intrinsic
rewards.</p>
    </sec>
  </body>
  <back>
    <ref-list>
      <ref id="ref1">
        <mixed-citation>
          1.
          <string-name>
            <surname>Aitken</surname>
            ,
            <given-names>N.M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Pelletier</surname>
            ,
            <given-names>L.G.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Baxter</surname>
            ,
            <given-names>D.E.</given-names>
          </string-name>
          :
          <article-title>Doing the difficult stuff: Influence of selfdetermined motivation toward the environment on transportation proenvironmental behavior</article-title>
          .
          <source>Ecopsychology</source>
          <volume>8</volume>
          (
          <issue>2</issue>
          ),
          <fpage>153</fpage>
          -
          <lpage>162</lpage>
          (
          <year>2016</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation>
          2.
          <string-name>
            <surname>Bishop</surname>
            ,
            <given-names>F.M.</given-names>
          </string-name>
          :
          <article-title>Self-guided change: The most common form of long-term, maintained health behavior change</article-title>
          .
          <source>Health Psychol Open</source>
          <volume>5</volume>
          (
          <issue>1</issue>
          ),
          <volume>2055102917751576</volume>
          (Jan
          <year>2018</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation>
          3.
          <string-name>
            <surname>Flannery</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          :
          <article-title>Self-determination theory: Intrinsic motivation and behavioral change</article-title>
          . In:
          <article-title>Oncology nursing forum</article-title>
          . vol.
          <volume>44</volume>
          (
          <year>2017</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation>
          4.
          <string-name>
            <surname>Karkar</surname>
            ,
            <given-names>R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Schroeder</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Epstein</surname>
            ,
            <given-names>D.A.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Pina</surname>
            ,
            <given-names>L.R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Scofield</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Fogarty</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Kientz</surname>
            ,
            <given-names>J.A.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Munson</surname>
            ,
            <given-names>S.A.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Vilardaga</surname>
            ,
            <given-names>R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Zia</surname>
          </string-name>
          , J.:
          <article-title>Tummytrials: a feasibility study of using selfexperimentation to detect individualized food triggers</article-title>
          .
          <source>In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems</source>
          . pp.
          <fpage>6850</fpage>
          -
          <lpage>6863</lpage>
          (
          <year>2017</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref5">
        <mixed-citation>
          5.
          <string-name>
            <surname>Kwasnicka</surname>
            ,
            <given-names>D.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Dombrowski</surname>
            ,
            <given-names>S.U.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>White</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Sniehotta</surname>
            ,
            <given-names>F.</given-names>
          </string-name>
          :
          <article-title>Theoretical explanations for maintenance of behaviour change: a systematic review of behaviour theories</article-title>
          .
          <source>Health Psychol. Rev</source>
          .
          <volume>10</volume>
          (
          <issue>3</issue>
          ),
          <fpage>277</fpage>
          -
          <lpage>296</lpage>
          (
          <year>Sep 2016</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref6">
        <mixed-citation>
          6.
          <string-name>
            <surname>Lee</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          :
          <article-title>Supporting self-experimentation of behavior change strategies</article-title>
          .
          <source>In: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication</source>
          . pp.
          <fpage>361</fpage>
          -
          <lpage>366</lpage>
          (
          <year>2013</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref7">
        <mixed-citation>
          7.
          <string-name>
            <surname>Lee</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Walker</surname>
            ,
            <given-names>E.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Burleson</surname>
            ,
            <given-names>W.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Hekler</surname>
            ,
            <given-names>E.B.</given-names>
          </string-name>
          :
          <article-title>Exploring users' creation of personalized behavioral plans</article-title>
          .
          <source>In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication</source>
          . pp.
          <fpage>703</fpage>
          -
          <lpage>706</lpage>
          (
          <year>2014</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref8">
        <mixed-citation>
          8.
          <string-name>
            <surname>Lee</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Walker</surname>
            ,
            <given-names>E.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Burleson</surname>
            ,
            <given-names>W.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Hekler</surname>
            ,
            <given-names>E.B.</given-names>
          </string-name>
          :
          <article-title>Understanding users' creation of behavior change plans with theory-based support</article-title>
          .
          <source>In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems</source>
          . pp.
          <fpage>2301</fpage>
          -
          <lpage>2306</lpage>
          (
          <year>2015</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref9">
        <mixed-citation>
          9.
          <string-name>
            <surname>Lee</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Walker</surname>
            ,
            <given-names>E.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Burleson</surname>
            ,
            <given-names>W.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Kay</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Buman</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Hekler</surname>
            ,
            <given-names>E.B.</given-names>
          </string-name>
          :
          <article-title>Self-experimentation for behavior change: Design and formative evaluation of two approaches</article-title>
          .
          <source>In: Proceedings of the 2017 CHI conference on human factors in computing systems</source>
          . pp.
          <fpage>6837</fpage>
          -
          <lpage>6849</lpage>
          (
          <year>2017</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref10">
        <mixed-citation>
          10.
          <string-name>
            <surname>Li</surname>
            ,
            <given-names>I.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Dey</surname>
            ,
            <given-names>A.K.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Forlizzi</surname>
          </string-name>
          , J.:
          <article-title>Understanding my data, myself: supporting self-reflection with ubicomp technologies</article-title>
          .
          <source>In: Proceedings of the 13th international conference on Ubiquitous computing</source>
          . pp.
          <fpage>405</fpage>
          -
          <lpage>414</lpage>
          (
          <year>2011</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref11">
        <mixed-citation>
          11.
          <string-name>
            <surname>Nahum-Shani</surname>
            ,
            <given-names>I.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Smith</surname>
            ,
            <given-names>S.N.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Spring</surname>
            ,
            <given-names>B.J.</given-names>
          </string-name>
          , Collins,
          <string-name>
            <given-names>L.M.</given-names>
            ,
            <surname>Witkiewitz</surname>
          </string-name>
          ,
          <string-name>
            <given-names>K.</given-names>
            ,
            <surname>Tewari</surname>
          </string-name>
          ,
          <string-name>
            <given-names>A.</given-names>
            ,
            <surname>Murphy</surname>
          </string-name>
          ,
          <string-name>
            <surname>S.A.</surname>
          </string-name>
          :
          <article-title>Just-in-Time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support</article-title>
          .
          <source>Ann. Behav. Med</source>
          .
          <volume>52</volume>
          (
          <issue>6</issue>
          ),
          <fpage>446</fpage>
          -
          <lpage>462</lpage>
          (May
          <year>2018</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref12">
        <mixed-citation>
          12.
          <string-name>
            <surname>Oinas-Kukkonen</surname>
          </string-name>
          , H.:
          <article-title>A foundation for the study of behavior change support systems</article-title>
          .
          <source>Personal and ubiquitous computing 17(6)</source>
          ,
          <fpage>1223</fpage>
          -
          <lpage>1235</lpage>
          (
          <year>2013</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref13">
        <mixed-citation>
          13.
          <string-name>
            <surname>Oyebode</surname>
            ,
            <given-names>O.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Ndulue</surname>
            ,
            <given-names>C.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Alhasani</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Orji</surname>
          </string-name>
          , R.:
          <article-title>Persuasive mobile apps for health and wellness: A comparative systematic review</article-title>
          .
          <source>In: International Conference on Persuasive Technology</source>
          . pp.
          <fpage>163</fpage>
          -
          <lpage>181</lpage>
          . Springer (
          <year>2020</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref14">
        <mixed-citation>
          14.
          <string-name>
            <surname>Rapp</surname>
            ,
            <given-names>A.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Tirassa</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Tirabeni</surname>
            ,
            <given-names>L.</given-names>
          </string-name>
          :
          <article-title>Rethinking technologies for behavior change: A view from the inside of human change</article-title>
          .
          <source>ACM Trans. Comput. -Hum. Interact</source>
          .
          <volume>26</volume>
          (
          <issue>4</issue>
          ),
          <volume>22</volume>
          :
          <fpage>1</fpage>
          -
          <lpage>22</lpage>
          :
          <fpage>30</fpage>
          (Jun
          <year>2019</year>
          )
        </mixed-citation>
      </ref>
      <ref id="ref15">
        <mixed-citation>
          15.
          <string-name>
            <surname>Zhou</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Fukuoka</surname>
            ,
            <given-names>Y.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Mintz</surname>
            ,
            <given-names>Y.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Goldberg</surname>
            ,
            <given-names>K.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Kaminsky</surname>
            ,
            <given-names>P.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Flowers</surname>
            ,
            <given-names>E.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Aswani</surname>
            ,
            <given-names>A.:</given-names>
          </string-name>
          <article-title>Evaluating machine learning-based automated personalized daily step goals delivered through a mobile phone app: Randomized controlled trial</article-title>
          .
          <source>JMIR mHealth and uHealth 6</source>
          (
          <issue>1</issue>
          ),
          <year>e28</year>
          (
          <year>2018</year>
          )
        </mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>