Building habit forming app with implementation intention

Adhi Wicaksono axw412@cs.bham.ac.uk

Why changing habits is difficult?

Behaviour change

Theory of Reasoned Action

Attitude

Subjective norm

Intention Behaviour

Fishbein and Ajzen, 1975

Theory of Planned Behaviour

Attitude

Subjective norm Intention Behaviour

Ajzen, 1985

Perceived behavioural

control

Theory of Planned Behaviour

Attitude

Subjective norm Intention Behaviour

Ajzen, 1985

Perceived behavioural

control

I will do …

Theory of Planned Behaviour

Attitude

Subjective norm Intention Behaviour

Ajzen, 1985

Perceived behavioural

control

I will do …

???

Implementation Intention

• Plan to execute your intentions

• “IF situation X happens, THEN I will do Y”

• Strengthen cue-response link

• Simple plans, strong effects

• Found to give positive impact on medium-to-large goal attainment

if this then that

The habit loop

The habit loop

image source: https://www.slideshare.net/AshDonaldson/behaviour-design-predicting-irrational-decisions

Motivation

• Changing habit is difficult, intentions are not enough!

• Understanding cue-response link is very important in changing habit

• Implementation intentions could promote changes by creating a strategic plan and modifying cue-response link.

• How can the smartphone app be used to do this?

Source: everydaypeoplecartoons.com

Identifying the cues

Source: dreamstime.com

Context as cues

“Context is any information that can be used to characterise the situation of an entity. An entity is a

person, place, or object that is considered relevant to the interaction between a user and application, including the

user and applications themselves” (Dey, 2001)

Capturing context using a smartphone

The forms of context in smartphone

• Physical context: location, movement, calendar, activity, time → captured using the sensors on the smartphone

• Affective state: mood, attitude → captured using self-report and infer smartphone’s data

Why context matters?

• Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change (Bentley et al., 2013)

• Need for a Context-Aware Personalized Health Intervention System to Ensure Long-Term Behavior Change to Prevent Obesity (Khan and Lee, 2013)

Context helps people to understand their behaviour, therefore, incorporating context in a mobile app can

improve user’s engagement and promoting a personalised intervention

Related works

• Reviewed 115 habit formation apps, majority of the apps focus on task tracking & reminders, lacking on the contextual cues

• Run 4-weeks study to investigate the impact of event-based trigger vs reminders on the automaticity of behaviour

Beyond Self-Tracking and Reminders: Designing Smartphone Apps That Support Habit Formation

(Stawarz et al., 2015)

Related works

• Investigating nonconscious behaviour change interventions based on habit and dual process theories

• Strategy: subliminal priming, implementation intention, cognitive bias modification

Nonconscious Behaviour Change Technology: Targeting The Automatic (Pinder, 2017)

Related works

• Developed an app to target depression

• Promising accuracy rates (60% to 91%) on contextual states (e.g. location), but poor results on mood detection

Harnessing Context Sensing to Develop a Mobile Intervention for Depression (Burns et al., 2011)

Related works

• Investigating habit formation apps, found mismatch between the theory and apps usage

• Habit formation apps with reminders and streaks tend to create a dependency towards the apps

Don’t Kick the Habit: The Role of Dependency in Habit Formation Apps (Renfree et al., 2016)

Related works

• Developed an app that sets adaptive goals by modifying the difficulty based on the user’s performance

• The app improved user’s compliance and achievement

Finding the Adaptive Sweet Spot: Balancing Compliance and Achievement in Automated Stress

Reduction (Konrad et al., 2015)

Related works

• Developed a library to sense smartphone sensors data

• Used the library in an experiment that captures mood

EmotionSense: A Mobile Phones based Adaptive Platform for Experimental Social Psychology Research

(Rachuri et al., 2010)

• Investigated how SRBAI changes on implementation intention app with reminder.

• Users formed their own implementation intention.

• Users received reminders when the IF condition is detected: location, movement, time, calendar, device battery and orientation.

• 10 participants use the app for one week. We then measured the SRBAI scores pre & post intervention.

If this, then habit: exploring context-aware implementation intentions on smartphones

(Pinder et al., 2016)

SRBAI scores betweenpre & post-test

Problems

• Majority of habit formation apps focus on the task tracking & reminders, whereas a habit is often triggered by situational cues

• Implementation intention to support habit formation on the smartphone remains underused

Proposed solution

• Using technologically-mediated implementation intention to support habit formation

• Create IF-THEN plan (implementation intention) with existing routine events as the cue for the intended task

• Reinforce the implementation intention to strengthen cue-response’s link, so people can build on automaticity instead of relying on the reminders

Experiment design

• 4-week study, ask people to form implementation intention of reporting their mood every day, specify their existing routine event as the cue and mood report as the response.

• Using between-subject with two different groups: without reinforcement & reinforcement

• In the reinforcement group, remind them about the cue and its response

Proposed experiment

Using reinforcement to strengthen implementation intentions and cue-response’s link

Cue Response

! 🙂

Reinforcement

📩

Lunch time Evening

Mood Journal app

What we are the interested in?

• Automaticity as the strength of implementation intention, measured using Self-Report Behavioural Automaticity Index (SRBAI)

• Adherence to the routines/response when the cue occurs, measured by daily mood report data

Hypotheses

• Participants who receive reinforcement will have a higher automaticity score compared to participants without reinforcement

• Participants who receive reinforcement will be more adhered in reporting their mood every day compared to participants without reinforcement

Want to join in this study?

bit.ly/joinmoodstudy