• Does not apply

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University students often spend excessive amounts of time on their phones, particularly on distracting applications. Although they are aware of their screen-time habits and may use existing tools such as app limits or blocking features, these strategies are often easy to bypass and difficult to maintain over time. Interviews also identified boredom and unstructured time as important triggers for excessive phone use, making it difficult for students to consistently reduce their screen time while still needing their phones for studying, communication, and entertainment.

*We propose an adaptive mobile application that helps university students develop greater control over their screen time by combining gamification, personalized goals, intentional access to distracting applications, focus sessions, and offline activity recommendations. Users can track their application usage, complete challenges before accessing selected distracting apps, participate in Pomodoro or focus sessions, and receive personalized offline activities based on their interests. Through features such as a virtual pet, points, streaks, and achievements, the solution aims to provide motivation while helping users replace unstructured screen use with more intentional behaviors.

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Camila is a 27-year-old data analyst on the product team who is comfortable working with analytics tools and relies on usage dashboards, logs, and reports rather than the app itself to understand how students behave. Unlike a prospective user, she doesn't experience the app's screen-time features firsthand. Instead, her relationship with the product is entirely data-driven, filtered through metrics like session frequency, feature engagement, and drop-off points.

Camila's primary goals are to understand how students actually use the app's focus and accountability features in practice and to identify which mechanisms genuinely drive behavior change versus which are underused or ignored. She wants access to reliable, well-structured data that lets her segment users by behavior pattern, distinguishing, for example, students who consistently engage with limits from those who frequently override them. At the same time, she knows that raw usage numbers don't always tell the full story, which pushes her to look for context beyond the dashboards.

Her main motivations come from wanting product decisions to be grounded in evidence rather than assumptions, and from the satisfaction of seeing a data insight translate into a concrete product improvement. She is particularly focused on catching shifts in usage patterns early, since spotting behavioral changes as they emerge lets the team intervene with design changes before those users disengage entirely. However, this creates a challenge, in that behavioral data can show what users are doing without explaining why, which means she often has to combine quantitative findings with qualitative research to get a complete picture.

Camila's main pain points stem from the limitations of the data itself rather than from using the app. Usage logs are sometimes incomplete or inconsistent across app versions, making trends harder to trust. It's often difficult to tell whether a feature is being ignored because it doesn't work or simply because users never discovered it. She also faces pressure to deliver insights quickly, sometimes before enough data has accumulated to draw statistically meaningful conclusions, and struggles to tell whether an observed improvement reflects lasting behavior change or just a short-term novelty effect that fades over the semester. On top of that, she finds it hard to separate meaningful trends from noise, since changes in usage could stem from external factors like holidays, exam periods, or app outages rather than the product itself.

Overall, Camila represents the internal, data-facing side of the product. Someone whose job is not to change her own habits, but to understand how well the app is changing habits for its actual users. Her problem is not a lack of technical skill or curiosity, but the difficulty of turning imperfect, partial usage data into confident, actionable recommendations for the team. This suggests an opportunity for the product to be designed with better instrumentation from the start, so that the events and signals Camila needs to evaluate engagement are captured reliably rather than reconstructed after the fact.

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#	Problems (P)	Alternatives (A)	Solutions (S)
1	She already knows her screen time is high and has actively tried built-in tools such as app timers, screen-time reports, and application limits.	She sets app timers and screen-time limits through her phone's built-in tools, but when she hits the limit she overrides it in a single tap or ignores the warning entirely. She has also tried deleting distracting apps outright, only to reinstall them later, and she checks screen-time reports, but only after the time is already lost.	A system that inserts a real decision point before granting extra time, rather than a single dismissible prompt. • Challenge Gate: Require completing a short task (a quiz, a breathing exercise, a brief physical activity) before unlocking a flagged app once the limit is reached. • Escalating Friction: Increase the difficulty or length of the challenge each additional time the same limit is bypassed within a day.
2	She reaches for her phone specifically during boredom or unstructured time (between classes, during breaks, before bed) not because she consciously chooses entertainment over rest, but because nothing else is readily available to fill that gap.	She defaults to picking up her phone whenever she has idle time, since it is the lowest-effort option within reach. She has occasionally tried planning offline activities on her own, but without any reminder or support system these attempts rarely last more than a few days, and she otherwise relies on willpower alone, with no external prompt at the moment boredom actually occurs.	A recommendation system that offers something to do instead of the phone, right at the moment idle time appears. • Offline Break Suggestion: Once she reaches her daily limit for a flagged app, offer a short, personalized offline activity as an alternative before letting her continue. • Interest Profile: Let her select her interests upfront so suggestions actually match what she enjoys instead of generic filler activities. • Activity Streaks: Track consecutive times she chooses a suggested offline activity over her phone, reinforcing the alternative the same way a habit streak would.
3	She needs a way to stop feeling like her phone is vibrating or receiving notifications when it actually isn't	She checks her phone repeatedly to make sure she hasn't received anything. Sometimes she turns notifications off or puts her phone away, but she still feels the urge to check.	• A system that helps users recognize and manage phantom notification sensations, providing reassurance when there are no new notifications and helping them gradually reduce compulsive checking. • Check Counter: Track how many times users check their phone without receiving a notification. • Delayed Notifications: Group notifications together and deliver them at specific times.
4	She needs a way to disconnect from social media without feeling like she is missing something important.	She keeps checking social media throughout the day, even when she doesn't have anything specific to look for. She sometimes leaves notifications on or checks apps before going to sleep because she doesn't want to miss anything.	• A tool that summarizes important social updates after a period of disconnection, allowing users to stay informed without constantly checking their apps. • FOMO Journal: Ask users what they thought they were missing and compare it with what actually happened.
5	She needs a way to reduce her screen time without immediately reinstalling the apps she deleted.	She deletes social media apps when she realizes she is spending too much time on them, but after a few hours or days she feels the need to use them again and reinstall them.	• A gradual disconnection system that helps users manage the urge to reinstall an app by providing alternative activities, tracking their progress, and creating a temporary barrier before reinstalling. • Reinstallation Cooldown: Make users wait a certain amount of time before reinstalling an app.
6	She needs a way to stay focused during study sessions without losing access to important communication.	She puts her phone away, turns on Do Not Disturb, silences notifications, or checks her phone between study periods to make sure she hasn't missed anything important.	A focused communication mode that filters non-urgent notifications while allowing important contacts or messages to reach her. • Priority Contacts: Allow notifications from selected people during study sessions. • Focus Summary: Show non-urgent notifications in a summary after the session.
7	She needs a way to stay motivated to reduce her screen time over the long term.	She sets screen-time limits, checks her daily screen-time reports, deletes apps, or tries to manually reduce the amount of time she spends on her phone.	A long-term progress system that helps her build sustainable screen-time habits instead of relying only on daily limits. • Progress Streaks: Track consecutive days meeting personalized screen-time goals. • Weekly Goals: Set gradual goals based on her previous usage and adjust them over time.
8	She feels sad after uninstalling apps because she loses her dopamine dose	She reinstalls the apps, replaces them with another app, watches videos, listens to music, or looks for another quick source of entertainment.	A transition system that provides rewarding alternatives when she removes an app, helping her replace the habit instead of simply taking it away. • Replacement Activities: Suggest short activities based on her interests and available time. • Reward System: Give progress points or achievements for choosing offline or intentional activities instead of reinstalling the app.
9	She needs a way to use her phone for studying without being exposed to distracting content along the way.	She searches for academic information on her phone but encounters social media, entertainment, recommendations, or unrelated content. She uses a laptop instead, uses printed or previously downloaded material, or creates a separate academic environment on her phone.	A distraction free academic mode that allows access to academic resources while hiding or removing non-academic content. • Study Only Mode: Temporarily restrict the phone to selected academic apps, websites, files, and tools. • Clean Search: Open academic searches in an interface that removes distracting recommendations, feeds, and unrelated content. • Academic Whitelist: Let the student select the specific resources they need before beginning a study session.
10	She needs a way to quickly regain focus after becoming distracted by her phone instead of losing a large portion of her study session.	She tries to return to studying on her own, restarts her study timer, takes a break, or simply tells herself to focus again. However, after switching to stimulating content, returning to a demanding academic task can be difficult.	A focus recovery system that helps the student transition back into studying immediately after a distraction. • After leaving a distracting activity, immediately show the student the task they were working on and their next planned action. • Save the exact study activity the student was doing before the distraction and reopen it when they return. • Quizzes the student on what they were doing before leaving.
11	She needs a way to protect her sleep routine from late-night phone use.	She uses her phone in bed, often intending to scroll for only a few minutes before sleeping. She may set a bedtime reminder or use Night Mode, but these features do not prevent her from continuing to use the phone.	A bedtime routine that gradually encourages the user to disconnect rather than simply notifying them that it is time to sleep. • Wind-Down Mode: Begin reducing stimulating interactions before the user's target bedtime. • Bedtime Goal: Reward completing a phone-free period before sleeping. • Wind-Down Activities: Suggest offline activities such as reading, preparing for the next day, or listening to relaxing audio.
12	She needs a way to become aware of when she is picking up her phone out of habit rather than because she has a specific reason to use it.	She usually picks up her phone automatically when it is nearby or when she experiences a moment of boredom, without first deciding what she wants to do. She could rely on manually tracking these moments, keeping her phone out of reach, or reminding herself to question why she is using it, but these approaches require her to notice the behavior herself.	A system that introduces a brief moment of awareness before habitual phone use, helping her distinguish intentional use from automatic checking. • Intent Prompt: Before opening frequently used distracting apps, ask the user to briefly select why they are opening the app (specific task, checking messages, just browsing). • Habit Pattern Feedback: Identify repeated unintentional checks and show the user when and under what circumstances they tend to occur. • Usage Intent History: Let the user compare the reasons they intended to use an app with how long they actually spent using it.

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ContextCanvasFin.pdf

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• BQ1: What percentage of active users have devices without an accelerometer, and which device models and operating systems are affected?
  • Justification: This is related to device-specific statistics and helps the development team understand the compatibility and availability of the Check Counter feature across the user base.

• BQ2: How much does users' weekly screen time decrease after using the app's interventions?

  • Justification: This directly measures whether the app helps users achieve its primary goal of reducing excessive screen time.

• BQ3: How much does Focus Mode help users maintain uninterrupted study or work sessions?

  • Justification: This measures the direct benefit of the focus feature by evaluating whether users are able to remain focused for longer periods.

• BQ4: How much does using Pomodoro sessions help users complete their intended study or work periods?

  • Justification: This evaluates the benefit users obtain from the Pomodoro feature in terms of completing planned activities.

• BQ5: How much do personalized offline activity recommendations reduce screen use during periods of boredom or unstructured time?

  • Justification: This measures whether the recommendation feature provides users with an effective alternative to using their phones when they have free or unstructured time.

• BQ6: Do users who have used Check Counter for more than four weeks show a greater reduction in weekly phone pickups than users who have used it for less than one week?

  • Justification: This question helps determine whether the Check Counter feature should be maintained, modified, or further developed by comparing user behavior before and after extended use.

• BQ7: Do users with an active virtual pet complete longer uninterrupted Focus Mode/Pomodoro sessions than users without an active virtual pet?

  • Justification: This question evaluates whether the virtual pet contributes to the intended behavior change. The results can help the business decide whether the gamification feature should be maintained or modified.

• BQ8: How does unlock-challenge difficulty affect the rate at which users successfully access flagged applications?

  • Justification: This question helps determine the appropriate difficulty level for the Challenge Gate feature and whether its current implementation should be adjusted to better achieve the desired behavior.

• BQ9: Which categories of suggested offline activities have the highest acceptance rate, and could aggregated insights support partnerships with businesses in those categories?
  • Justification: This question explores how the business could take advantage of aggregated activity-preference data by identifying categories that could be relevant to potential third-party partners.

• BQ10: Do users who accept offline activity suggestions maintain longer streaks of meeting their screen-time goals than users who do not accept them?
  • Justification: This question combines user behavior, feature usage, and behavioral outcomes. It therefore requires information from more than one of the other question types rather than focusing on a single aspect of the application.

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