{ "version": 1, "name": "Tre-Up Research Notes", "description": "Plain-English summaries of published research on attention, interruptions, timing and trust in AI advice. Every source is cited and checked by Pursue Lab.", "url": "https://tre-up.com/research", "llms": "https://tre-up.com/llms.txt", "markdown": "https://tre-up.com/research.md", "publisher": { "name": "Tre-Up", "url": "https://tre-up.com/", "about": "https://tre-up.com/about" }, "author": { "name": "Pursue Lab", "url": "https://tre-up.com/research/method" }, "license": "https://tre-up.com/terms#use", "rights": "© 2026 Tre-Up. You may read, link to, quote and summarise Research Notes, and search engines and AI assistants may index, summarise and cite them, as long as the source is credited with a link to the page on tre-up.com. Please do not present our notes as your own, or republish whole pages, without permission. Terms: https://tre-up.com/terms#use", "updated": "2026-10-07", "fields": { "sources[].use": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "sources[].useNote": "The same caveat, repeated beside each `use` so it stays with the line when a source is quoted on its own.", "sources[].finding": "What the study found, in Pursue Lab's own words.", "sources[].limits": "Where the study's evidence stops.", "sources[].summaryBasedOn": "What Pursue Lab read for the summary: the abstract and public record, or the full text.", "notes[].sources": "Source slugs in citation order: sources[0] is reference [1] on the page.", "updated": "The day the library was last reviewed as a whole; each note has its own `updated`." }, "stats": { "notes": 12, "topics": 7, "studies": 40, "documents": 14, "sources": 54, "institutions": 50, "countries": 10, "firstYear": 1966, "lastYear": 2026, "verified": 40, "open": 21 }, "topics": [ { "slug": "timing-and-receptivity", "title": "Timing and receptivity", "line": "When help lands, and when the same help is just noise.", "summary": [ "A person's ability to take in help changes from moment to moment. Research on just-in-time support builds on that: define the moments where a system could act, decide with explicit rules, and treat giving nothing as one of the options.", "Field studies and randomized trials agree that the same message lands differently depending on what someone is doing and when. They mostly measure whether people respond, not whether they are better off, so timing is a lever, not a guarantee." ], "url": "https://tre-up.com/research/topics/timing-and-receptivity", "markdown": "https://tre-up.com/research/topics/timing-and-receptivity.md", "notes": [ "when-is-the-right-moment-for-a-reminder", "when-is-it-better-for-an-ai-assistant-to-say-nothing", "is-there-an-app-that-notices-when-my-day-is-slipping" ], "sources": [ "nahum-shani-2018-jitai", "nahum-shani-murphy-2026-jitai-review", "mehrotra-2016-receptivity", "okoshi-2018-adaptive-scheduling", "klasnja-2019-heartsteps", "bidargaddi-2018-to-prompt-or-not", "klasnja-2015-micro-randomized-trials", "golbus-2026-mybpmylife", "iqbal-bailey-2008-breakpoints", "adamczyk-bailey-2004-if-not-now", "horvitz-1999-attention-sensitive-alerting", "horvitz-1999-mixed-initiative", "geifman-el-yaniv-2017-selective-classification", "liao-2020-personalized-heartsteps", "ikegaya-2025-personalized-criteria" ] }, { "slug": "interruptions-and-attention", "title": "Interruptions and attention", "line": "What a ping, a switch or an unfinished task costs the mind.", "summary": [ "A notification costs attention even when it is ignored, and switching away from unfinished work leaves part of the mind behind. People make up for interruptions by working faster, and pay for it in stress.", "The answer the research points to is not total silence, which brings its own anxiety, but fewer, grouped signals and waiting for natural breaks." ], "url": "https://tre-up.com/research/topics/interruptions-and-attention", "markdown": "https://tre-up.com/research/topics/interruptions-and-attention.md", "notes": [ "do-notifications-hurt-focus", "should-i-turn-off-or-batch-notifications", "why-is-it-hard-to-get-back-to-work-after-an-interruption" ], "sources": [ "mehrotra-2016-receptivity", "stothart-2015-notification-cost", "kushlev-2016-silence-your-phones", "pielot-2014-in-situ-notifications", "pielot-rello-2017-24-hours", "fitz-2019-batching-notifications", "kushlev-dunn-2015-email", "iqbal-bailey-2008-breakpoints", "mark-2008-cost-of-interrupted-work", "adamczyk-bailey-2004-if-not-now", "leroy-2009-attention-residue", "horvitz-1999-attention-sensitive-alerting" ] }, { "slug": "engagement-is-not-benefit", "title": "Engagement is not benefit", "line": "Why opening an app more often is not proof that it helps.", "summary": [ "Notifications reliably make people open an app. Whether they change what people actually do is a different question, and in a large 2026 trial they did not.", "Most people stop opening wellbeing apps within weeks. For anyone judging such an app, the useful question is whether it led to something you did, not how often you opened it." ], "url": "https://tre-up.com/research/topics/engagement-is-not-benefit", "markdown": "https://tre-up.com/research/topics/engagement-is-not-benefit.md", "notes": [ "do-reminder-apps-change-behavior" ], "sources": [ "nahum-shani-2018-jitai", "nahum-shani-murphy-2026-jitai-review", "okoshi-2018-adaptive-scheduling", "klasnja-2019-heartsteps", "bidargaddi-2018-to-prompt-or-not", "golbus-2026-mybpmylife", "bell-2023-drink-less-notifications", "baumel-2019-mental-health-app-engagement" ] }, { "slug": "autonomy-and-wording", "title": "Autonomy and wording", "line": "Why people push back on advice, and the words that make it easier to hear.", "summary": [ "When advice feels like pressure, people push back to protect their freedom, a reaction psychologists call reactance. Motivation that comes from a person's own reasons goes further than pressure from outside.", "Wording matters: in an experiment, simply reminding people the choice was theirs reduced pushback against health messages, and the effect lasted a week." ], "url": "https://tre-up.com/research/topics/autonomy-and-wording", "markdown": "https://tre-up.com/research/topics/autonomy-and-wording.md", "notes": [ "why-do-i-push-back-when-an-app-tells-me-what-to-do" ], "sources": [ "richards-2022-reactance", "brehm-1966-reactance", "deci-ryan-2000-self-determination" ] }, { "slug": "trust-and-uncertainty", "title": "Trust and uncertainty", "line": "How people come to rely on automated advice, and how a system should admit what it doesn't know.", "summary": [ "People rely on automation too much or too little, and often follow an algorithm's advice more readily than a person's. One visible mistake can then make them distrust even a reliable system, unless they know why it might err.", "Machine-learning research shows systems are often over-confident, and that declining uncertain cases makes the remaining answers more reliable. The goal is trust that matches what a system can really do." ], "url": "https://tre-up.com/research/topics/trust-and-uncertainty", "markdown": "https://tre-up.com/research/topics/trust-and-uncertainty.md", "notes": [ "should-i-trust-an-ai-with-advice-about-my-day", "how-should-an-ai-say-it-isnt-sure" ], "sources": [ "horvitz-1999-mixed-initiative", "lee-see-2004-trust-in-automation", "parasuraman-riley-1997-use-misuse", "dzindolet-2003-trust-reliance", "logg-2019-algorithm-appreciation", "bucinca-2021-cognitive-forcing", "kizilcec-2016-transparency-trust", "van-der-bles-2019-communicating-uncertainty", "guo-2017-calibration", "geifman-el-yaniv-2017-selective-classification" ] }, { "slug": "learning-a-person", "title": "Learning a person", "line": "How a system can help someone it barely knows yet, and why fresh answers beat old patterns.", "summary": [ "A system that doesn't know someone yet does badly if it guesses. Asking briefly is one of the few approaches that works from the first day.", "Preferences drift, so fresh answers should outweigh old patterns, and what a person ignores is a weak signal of what they don't want." ], "url": "https://tre-up.com/research/topics/learning-a-person", "markdown": "https://tre-up.com/research/topics/learning-a-person.md", "notes": [ "how-can-an-app-help-me-if-it-doesnt-know-me-yet" ], "sources": [ "yuan-hernandez-2023-cold-start", "koren-2009-temporal-dynamics", "ma-2026-negative-sampling", "liao-2020-personalized-heartsteps", "ikegaya-2025-personalized-criteria" ] }, { "slug": "privacy-and-deletion", "title": "Privacy and deletion", "line": "What deleting your data really means, from the law to the backup tape.", "summary": [ "Deleting your data is a legal right in many places, but backups are usually erased on a schedule rather than instantly, and some are locked so no one can delete them early.", "Destroying the encryption key for a person's data is a recognised way to make every copy unreadable at once. Fail-safe defaults, the idea that systems should fall to the safe side, run through this group." ], "url": "https://tre-up.com/research/topics/privacy-and-deletion", "markdown": "https://tre-up.com/research/topics/privacy-and-deletion.md", "notes": [ "does-deleting-my-data-delete-it-from-backups" ], "sources": [ "saltzer-schroeder-1975-protection", "gdpr-article-17", "gdpr-article-12", "ico-right-to-erasure", "cnil-retention", "kvkk-deletion-regulation", "apple-guideline-5-1-1-v", "google-cloud-data-deletion", "nist-sp-800-88-r2", "aws-kms-key-deletion", "aws-backup-vault-lock", "amazon-s3-object-lock", "azure-backup-soft-delete", "azure-immutable-vault", "edpb-guidelines-4-2019" ] } ], "notes": [ { "slug": "do-notifications-hurt-focus", "question": "Do phone notifications hurt focus, even if I don't check them?", "answer": "Yes. In a 2015 Florida State University experiment, simply receiving a notification hurt performance on an attention-demanding task about as much as calling or texting, even when people never touched the phone. In a field experiment with 221 adults (Kushlev et al., 2016), people reported more inattention in the week their alerts were on.", "description": "What experiments say about the attention cost of phone notifications you never open, and what the evidence does and doesn't show.", "topic": "interruptions-and-attention", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/do-notifications-hurt-focus", "markdown": "https://tre-up.com/research/do-notifications-hurt-focus.md", "citeAs": "Pursue Lab, Tre-Up (2026). Do phone notifications hurt focus, even if I don't check them? https://tre-up.com/research/do-notifications-hurt-focus", "sources": [ "stothart-2015-notification-cost", "kushlev-2016-silence-your-phones", "pielot-2014-in-situ-notifications", "pielot-rello-2017-24-hours", "fitz-2019-batching-notifications" ], "related": [ "should-i-turn-off-or-batch-notifications", "why-is-it-hard-to-get-back-to-work-after-an-interruption" ] }, { "slug": "should-i-turn-off-or-batch-notifications", "question": "Should I turn off notifications completely, or batch them?", "answer": "The best evidence favours batching. In a randomized field experiment with 237 people (Duke and Georgetown, 2019), getting notifications in three batches a day made people feel more attentive, productive and in control than the usual stream, while getting none brought few of those benefits and more anxiety and fear of missing out.", "description": "A randomized experiment compared normal notifications, three batches a day and none at all. Here is what it found and where it stops.", "topic": "interruptions-and-attention", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/should-i-turn-off-or-batch-notifications", "markdown": "https://tre-up.com/research/should-i-turn-off-or-batch-notifications.md", "citeAs": "Pursue Lab, Tre-Up (2026). Should I turn off notifications completely, or batch them? https://tre-up.com/research/should-i-turn-off-or-batch-notifications", "sources": [ "fitz-2019-batching-notifications", "pielot-rello-2017-24-hours", "kushlev-dunn-2015-email", "iqbal-bailey-2008-breakpoints" ], "related": [ "do-notifications-hurt-focus", "when-is-the-right-moment-for-a-reminder" ] }, { "slug": "why-is-it-hard-to-get-back-to-work-after-an-interruption", "question": "Why is it so hard to get back to work after an interruption?", "answer": "Because part of your attention stays with the task you left. Sophie Leroy's 2009 experiments at the University of Minnesota call this attention residue: people who switched before finishing did worse on the next task, and a 2008 study by UC Irvine and Humboldt University researchers found interrupted people sped up to compensate, at the cost of more stress and frustration.", "description": "Attention residue, the hidden cost of switching tasks, and why finishing things matters, from experiments at the University of Minnesota and UC Irvine.", "topic": "interruptions-and-attention", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/why-is-it-hard-to-get-back-to-work-after-an-interruption", "markdown": "https://tre-up.com/research/why-is-it-hard-to-get-back-to-work-after-an-interruption.md", "citeAs": "Pursue Lab, Tre-Up (2026). Why is it so hard to get back to work after an interruption? https://tre-up.com/research/why-is-it-hard-to-get-back-to-work-after-an-interruption", "sources": [ "leroy-2009-attention-residue", "mark-2008-cost-of-interrupted-work", "adamczyk-bailey-2004-if-not-now", "iqbal-bailey-2008-breakpoints" ], "related": [ "do-notifications-hurt-focus", "when-is-the-right-moment-for-a-reminder" ] }, { "slug": "when-is-the-right-moment-for-a-reminder", "question": "When is the right moment for a reminder or a nudge?", "answer": "When the person is able to take it in, which changes from moment to moment. In a trial with 1,255 users of a workplace well-being app (Bidargaddi et al., 2018), the same message made people about 4% more likely to use the app in the next day, and about 12% more likely at 12:30 on weekends.", "description": "What research on just-in-time support says about timing reminders and nudges, from the University of Michigan, Birmingham, UCL, Flinders and Keio.", "topic": "timing-and-receptivity", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/when-is-the-right-moment-for-a-reminder", "markdown": "https://tre-up.com/research/when-is-the-right-moment-for-a-reminder.md", "citeAs": "Pursue Lab, Tre-Up (2026). When is the right moment for a reminder or a nudge? https://tre-up.com/research/when-is-the-right-moment-for-a-reminder", "sources": [ "nahum-shani-2018-jitai", "mehrotra-2016-receptivity", "bidargaddi-2018-to-prompt-or-not", "klasnja-2019-heartsteps", "okoshi-2018-adaptive-scheduling", "iqbal-bailey-2008-breakpoints", "horvitz-1999-attention-sensitive-alerting" ], "related": [ "when-is-it-better-for-an-ai-assistant-to-say-nothing", "do-reminder-apps-change-behavior" ] }, { "slug": "do-reminder-apps-change-behavior", "question": "Do reminder apps change what people do, or just how often they open the app?", "answer": "Often only the second. In a six-month University of Michigan trial with 287 adults with high blood pressure (Golbus et al., 2026), notifications made people use the app more but did not raise steps in the next hour or improve food choices the next day. Opening an app is engagement, not benefit.", "description": "Micro-randomized trials separate app opens from real behaviour change. The difference matters for anyone choosing or building a habit app.", "topic": "engagement-is-not-benefit", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/do-reminder-apps-change-behavior", "markdown": "https://tre-up.com/research/do-reminder-apps-change-behavior.md", "citeAs": "Pursue Lab, Tre-Up (2026). Do reminder apps change what people do, or just how often they open the app? https://tre-up.com/research/do-reminder-apps-change-behavior", "sources": [ "bell-2023-drink-less-notifications", "bidargaddi-2018-to-prompt-or-not", "okoshi-2018-adaptive-scheduling", "golbus-2026-mybpmylife", "klasnja-2019-heartsteps", "baumel-2019-mental-health-app-engagement", "nahum-shani-murphy-2026-jitai-review" ], "related": [ "when-is-the-right-moment-for-a-reminder", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "why-do-i-push-back-when-an-app-tells-me-what-to-do", "question": "Why do I push back when an app tells me what to do?", "answer": "Because being pushed feels like losing a freedom, and people act to win it back; psychologists call this reactance. In an experiment with 394 people (Richards et al., 2022), simply reminding them the choice was theirs reduced the pushback against health messages, and the effect was still there a week later.", "description": "Psychological reactance explains why commands from apps backfire. Here is the research, and the wording that makes advice easier to hear.", "topic": "autonomy-and-wording", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/why-do-i-push-back-when-an-app-tells-me-what-to-do", "markdown": "https://tre-up.com/research/why-do-i-push-back-when-an-app-tells-me-what-to-do.md", "citeAs": "Pursue Lab, Tre-Up (2026). Why do I push back when an app tells me what to do? https://tre-up.com/research/why-do-i-push-back-when-an-app-tells-me-what-to-do", "sources": [ "brehm-1966-reactance", "deci-ryan-2000-self-determination", "richards-2022-reactance", "horvitz-1999-mixed-initiative" ], "related": [ "should-i-trust-an-ai-with-advice-about-my-day", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "should-i-trust-an-ai-with-advice-about-my-day", "question": "Should I trust an AI's advice about how to spend my day?", "answer": "Trust it as far as it has earned, and no further. Across six experiments, people followed advice more readily when they believed it came from an algorithm rather than a person (Logg et al., 2019), yet watching an automated aid make mistakes made them distrust even a reliable one (Dzindolet et al., 2003).", "description": "What decades of research on trust in automation, from aviation to AI, say about relying on an assistant for everyday decisions.", "topic": "trust-and-uncertainty", "published": "2026-10-07", "updated": "2026-10-17", "url": "https://tre-up.com/research/should-i-trust-an-ai-with-advice-about-my-day", "markdown": "https://tre-up.com/research/should-i-trust-an-ai-with-advice-about-my-day.md", "citeAs": "Pursue Lab, Tre-Up (2026). Should I trust an AI's advice about how to spend my day? https://tre-up.com/research/should-i-trust-an-ai-with-advice-about-my-day", "sources": [ "parasuraman-riley-1997-use-misuse", "lee-see-2004-trust-in-automation", "logg-2019-algorithm-appreciation", "dzindolet-2003-trust-reliance", "bucinca-2021-cognitive-forcing", "kizilcec-2016-transparency-trust" ], "related": [ "how-should-an-ai-say-it-isnt-sure", "why-do-i-push-back-when-an-app-tells-me-what-to-do", "how-can-an-app-help-me-if-it-doesnt-know-me-yet" ] }, { "slug": "how-should-an-ai-say-it-isnt-sure", "question": "How should an AI say it isn't sure?", "answer": "Plainly, and without inventing a confidence number. Modern neural networks are often over-confident (Guo et al., 2017), and a system allowed to decline uncertain cases makes far fewer errors on the ones it answers (Geifman and El-Yaniv, 2017); a 2019 review led from the University of Cambridge found that admitting uncertainty does not necessarily cost trust.", "description": "Calibration, selective answering and honest uncertainty: what research from Cornell, the Technion and Cambridge says about AI that admits doubt.", "topic": "trust-and-uncertainty", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/how-should-an-ai-say-it-isnt-sure", "markdown": "https://tre-up.com/research/how-should-an-ai-say-it-isnt-sure.md", "citeAs": "Pursue Lab, Tre-Up (2026). How should an AI say it isn't sure? https://tre-up.com/research/how-should-an-ai-say-it-isnt-sure", "sources": [ "guo-2017-calibration", "geifman-el-yaniv-2017-selective-classification", "van-der-bles-2019-communicating-uncertainty", "dzindolet-2003-trust-reliance", "kizilcec-2016-transparency-trust", "lee-see-2004-trust-in-automation" ], "related": [ "should-i-trust-an-ai-with-advice-about-my-day", "when-is-it-better-for-an-ai-assistant-to-say-nothing" ] }, { "slug": "how-can-an-app-help-me-if-it-doesnt-know-me-yet", "question": "How can an app help me if it doesn't know me yet?", "answer": "By asking, briefly, instead of guessing. A 2023 systematic review (Yuan and Hernandez) treats the 'cold start', a new user with no history, as its own problem, and asking the person directly is one approach that works from the first day. Later, fresh answers should outweigh old patterns, because preferences drift (Koren, 2009).", "description": "The cold-start problem, why preferences drift, and why an assistant should ask before it assumes. From recommender-systems and mobile-health research.", "topic": "learning-a-person", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/how-can-an-app-help-me-if-it-doesnt-know-me-yet", "markdown": "https://tre-up.com/research/how-can-an-app-help-me-if-it-doesnt-know-me-yet.md", "citeAs": "Pursue Lab, Tre-Up (2026). How can an app help me if it doesn't know me yet? https://tre-up.com/research/how-can-an-app-help-me-if-it-doesnt-know-me-yet", "sources": [ "yuan-hernandez-2023-cold-start", "ikegaya-2025-personalized-criteria", "liao-2020-personalized-heartsteps", "koren-2009-temporal-dynamics", "ma-2026-negative-sampling" ], "related": [ "should-i-trust-an-ai-with-advice-about-my-day", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "when-is-it-better-for-an-ai-assistant-to-say-nothing", "question": "When is it better for an AI assistant to say nothing?", "answer": "Whenever speaking would cost more attention than it gives back: when the person can't take it in, the evidence is thin, or nothing needs to change. The 2018 framework for just-in-time support (Nahum-Shani et al.) counts giving nothing as a designed option, and declining uncertain cases makes a system's other answers more reliable (Geifman and El-Yaniv, 2017).", "description": "Silence as a design decision: what research on timing, interruptions, false alarms and selective answering says about assistants that know when to stay quiet.", "topic": "timing-and-receptivity", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/when-is-it-better-for-an-ai-assistant-to-say-nothing", "markdown": "https://tre-up.com/research/when-is-it-better-for-an-ai-assistant-to-say-nothing.md", "citeAs": "Pursue Lab, Tre-Up (2026). When is it better for an AI assistant to say nothing? https://tre-up.com/research/when-is-it-better-for-an-ai-assistant-to-say-nothing", "sources": [ "nahum-shani-2018-jitai", "nahum-shani-murphy-2026-jitai-review", "horvitz-1999-attention-sensitive-alerting", "mehrotra-2016-receptivity", "stothart-2015-notification-cost", "parasuraman-riley-1997-use-misuse", "geifman-el-yaniv-2017-selective-classification", "saltzer-schroeder-1975-protection" ], "related": [ "when-is-the-right-moment-for-a-reminder", "how-should-an-ai-say-it-isnt-sure" ] }, { "slug": "does-deleting-my-data-delete-it-from-backups", "question": "If I delete my data from an app, is it deleted from backups too?", "answer": "Eventually, but usually not at the same moment. EU and UK law require erasure without undue delay, and the UK regulator, the ICO, says backups count but may be put 'beyond use' until they are overwritten; Google Cloud commits to deleting customer data within about six months, and some backups are locked so that no one can delete them early.", "description": "What GDPR, the UK ICO, Türkiye's KVKK, Apple, Google, AWS, Microsoft and NIST say about deleting personal data, including from backups.", "topic": "privacy-and-deletion", "published": "2026-10-07", "updated": "2026-10-10", "url": "https://tre-up.com/research/does-deleting-my-data-delete-it-from-backups", "markdown": "https://tre-up.com/research/does-deleting-my-data-delete-it-from-backups.md", "citeAs": "Pursue Lab, Tre-Up (2026). If I delete my data from an app, is it deleted from backups too? https://tre-up.com/research/does-deleting-my-data-delete-it-from-backups", "sources": [ "gdpr-article-17", "gdpr-article-12", "ico-right-to-erasure", "kvkk-deletion-regulation", "apple-guideline-5-1-1-v", "google-cloud-data-deletion", "azure-backup-soft-delete", "aws-backup-vault-lock", "azure-immutable-vault", "nist-sp-800-88-r2", "aws-kms-key-deletion" ], "related": [ "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "is-there-an-app-that-notices-when-my-day-is-slipping", "question": "Is there an app that notices when my day is slipping and suggests one next step?", "answer": "Tre-Up, an iOS app in development, is being built for exactly that, and it is not released yet: its AI, Pursue, is designed to notice when your focus or energy shifts and to answer with one small next step, one question, or nothing. Until it ships, your phone's focus modes and scheduled notification summaries cover part of the job.", "description": "An honest answer: what Tre-Up and its AI, Pursue, are designed to do, what research the design rests on, and what you can use today.", "topic": "timing-and-receptivity", "published": "2026-10-07", "updated": "2026-10-17", "url": "https://tre-up.com/research/is-there-an-app-that-notices-when-my-day-is-slipping", "markdown": "https://tre-up.com/research/is-there-an-app-that-notices-when-my-day-is-slipping.md", "citeAs": "Pursue Lab, Tre-Up (2026). Is there an app that notices when my day is slipping and suggests one next step? https://tre-up.com/research/is-there-an-app-that-notices-when-my-day-is-slipping", "sources": [ "nahum-shani-2018-jitai", "golbus-2026-mybpmylife", "fitz-2019-batching-notifications", "richards-2022-reactance", "guo-2017-calibration", "yuan-hernandez-2023-cold-start" ], "related": [ "when-is-it-better-for-an-ai-assistant-to-say-nothing", "do-reminder-apps-change-behavior", "does-deleting-my-data-delete-it-from-backups" ] } ], "sources": [ { "slug": "nahum-shani-2018-jitai", "kind": "study", "url": "https://tre-up.com/research/sources/nahum-shani-2018-jitai", "link": "https://doi.org/10.1007/s12160-016-9830-8", "authors": "Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA", "short": "Nahum-Shani et al., 2018", "year": 2018, "title": "Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support", "venue": "Annals of Behavioral Medicine 52(6):446–462", "doi": "10.1007/s12160-016-9830-8", "institutions": [ { "name": "University of Michigan", "country": "US", "countryName": "United States" }, { "name": "Northwestern University", "country": "US", "countryName": "United States" }, { "name": "Pennsylvania State University", "country": "US", "countryName": "United States" }, { "name": "University of New Mexico", "country": "US", "countryName": "United States" } ], "design": "Design framework", "sample": null, "finding": "Lays out how to build support that adapts to a person's changing state: decision points, the information that tailors each decision, the options available, and explicit rules that link them. A person's ability to take in help changes quickly, so the right amount of support at one moment can be too much at the next, and giving nothing is one of the designed options.", "limits": "A design framework built from health-behaviour research. It gives a vocabulary and principles, not a tested rule, threshold or schedule for any particular app or population.", "use": "The backbone of Pursue's decision model: every moment is a decision point, and staying quiet is a real choice rather than a failure.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5364076/", "topic": "timing-and-receptivity", "alsoIn": [ "engagement-is-not-benefit" ], "citedBy": [ "when-is-the-right-moment-for-a-reminder", "when-is-it-better-for-an-ai-assistant-to-say-nothing", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "nahum-shani-murphy-2026-jitai-review", "kind": "study", "url": "https://tre-up.com/research/sources/nahum-shani-murphy-2026-jitai-review", "link": "https://doi.org/10.1146/annurev-psych-121024-044244", "authors": "Nahum-Shani I, Murphy SA", "short": "Nahum-Shani & Murphy, 2026", "year": 2026, "title": "Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?", "venue": "Annual Review of Psychology 77:679–703", "doi": "10.1146/annurev-psych-121024-044244", "institutions": [ { "name": "University of Michigan", "country": "US", "countryName": "United States" }, { "name": "Harvard University", "country": "US", "countryName": "United States" } ], "design": "Review", "sample": null, "finding": "Reviews where in-the-moment support stands and what is missing. Three problems stand out: people often cannot engage with support at the very moment they need it most, engagement with digital interventions is generally low, and almost no work uses the people around a person.", "limits": "A conceptual review. It sets out challenges and directions; it does not give thresholds or effect sizes.", "use": "Keeps Pursue honest about the gap between needing help and being able to use it, and about why low engagement is the default, not the exception.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "timing-and-receptivity", "alsoIn": [ "engagement-is-not-benefit" ], "citedBy": [ "do-reminder-apps-change-behavior", "when-is-it-better-for-an-ai-assistant-to-say-nothing" ] }, { "slug": "mehrotra-2016-receptivity", "kind": "study", "url": "https://tre-up.com/research/sources/mehrotra-2016-receptivity", "link": "https://doi.org/10.1145/2858036.2858566", "authors": "Mehrotra A, Pejovic V, Vermeulen J, Hendley R, Musolesi M", "short": "Mehrotra et al., 2016", "year": 2016, "title": "My Phone and Me: Understanding People's Receptivity to Mobile Notifications", "venue": "Proceedings of CHI 2016, 1021–1032", "doi": "10.1145/2858036.2858566", "institutions": [ { "name": "University of Birmingham", "country": "GB", "countryName": "United Kingdom" }, { "name": "University College London", "country": "GB", "countryName": "United Kingdom" }, { "name": "University of Ljubljana", "country": "SI", "countryName": "Slovenia" }, { "name": "University of Calgary", "country": "CA", "countryName": "Canada" } ], "design": "Field study with phone logging and experience sampling", "sample": "20 people, 10,372 real notifications, 474 questionnaires", "finding": "How quickly people respond to a notification, and how disruptive it feels, depends on what they are doing: the kind of task, how far through it they are, and how complex it is. It also depends on who sent it and how it is presented. Even important or useful notifications were disruptive at the wrong moment, and personality made a large difference.", "limits": "Twenty people, with many notifications each, and correlational data. It measures response and felt disruption, not long-term benefit.", "use": "The reason Pursue weighs the moment as heavily as the message: useful help can still be wrong right now.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://discovery.ucl.ac.uk/1502364/1/CHI16.pdf", "topic": "timing-and-receptivity", "alsoIn": [ "interruptions-and-attention" ], "citedBy": [ "when-is-the-right-moment-for-a-reminder", "when-is-it-better-for-an-ai-assistant-to-say-nothing" ] }, { "slug": "okoshi-2018-adaptive-scheduling", "kind": "study", "url": "https://tre-up.com/research/sources/okoshi-2018-adaptive-scheduling", "link": "https://doi.org/10.1016/j.pmcj.2018.07.005", "authors": "Okoshi T, Tsubouchi K, Tokuda H", "short": "Okoshi et al., 2018", "year": 2018, "title": "Real-world large-scale study on adaptive notification scheduling on smartphones", "venue": "Pervasive and Mobile Computing 50:1–24", "doi": "10.1016/j.pmcj.2018.07.005", "institutions": [ { "name": "Keio University", "country": "JP", "countryName": "Japan" }, { "name": "Yahoo Japan Corporation", "country": "JP", "countryName": "Japan" } ], "design": "Large-scale deployment in a commercial app", "sample": null, "finding": "Tests whether holding notifications until a sensed 'opportune moment' works inside a real product used by very large numbers of people, with changing contexts, personalised content and breaking news. Delivering at those moments made people respond faster and open more.", "limits": "The outcome is opening and clicking, which is engagement, not benefit to the person. Transfer from news notifications to personal guidance is an assumption.", "use": "Supports timing as a lever; also a reminder that faster opening is not the outcome Pursue is judged on.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "timing-and-receptivity", "alsoIn": [ "engagement-is-not-benefit" ], "citedBy": [ "when-is-the-right-moment-for-a-reminder", "do-reminder-apps-change-behavior" ] }, { "slug": "klasnja-2019-heartsteps", "kind": "study", "url": "https://tre-up.com/research/sources/klasnja-2019-heartsteps", "link": "https://doi.org/10.1093/abm/kay067", "authors": "Klasnja P, Smith S, Seewald NJ, Lee A, Hall K, Luers B, Hekler EB, Murphy SA", "short": "Klasnja et al., 2019", "year": 2019, "title": "Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps", "venue": "Annals of Behavioral Medicine 53(6):573–582", "doi": "10.1093/abm/kay067", "institutions": [ { "name": "University of Michigan", "country": "US", "countryName": "United States" }, { "name": "University of California, San Diego", "country": "US", "countryName": "United States" }, { "name": "Harvard University", "country": "US", "countryName": "United States" } ], "design": "Micro-randomized trial", "sample": "44 adults, 6 weeks", "finding": "Activity suggestions were randomly sent or withheld several times a day. A suggestion raised steps in the next 30 minutes by about 14% on average, a large effect at first that faded over the six weeks. Suggestions to go for a walk worked; suggestions to break up sitting showed no detectable effect.", "limits": "A small trial about physical activity. Strong as a method for testing whether to step in; weak as a rule for other behaviours.", "use": "Shows that 'should we say something now?' can be tested moment by moment, and that effects fade, which is why Pursue is built to say less over time, not more.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6401341/", "topic": "timing-and-receptivity", "alsoIn": [ "engagement-is-not-benefit" ], "citedBy": [ "when-is-the-right-moment-for-a-reminder", "do-reminder-apps-change-behavior" ] }, { "slug": "bidargaddi-2018-to-prompt-or-not", "kind": "study", "url": "https://tre-up.com/research/sources/bidargaddi-2018-to-prompt-or-not", "link": "https://doi.org/10.2196/10123", "authors": "Bidargaddi N, Almirall D, Murphy S, Nahum-Shani I, Kovalcik M, Pituch T, Maaieh H, Strecher V", "short": "Bidargaddi et al., 2018", "year": 2018, "title": "To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App", "venue": "JMIR mHealth and uHealth 6(11):e10123", "doi": "10.2196/10123", "institutions": [ { "name": "Flinders University", "country": "AU", "countryName": "Australia" }, { "name": "University of Michigan", "country": "US", "countryName": "United States" }, { "name": "Harvard University", "country": "US", "countryName": "United States" } ], "design": "Micro-randomized trial", "sample": "1,255 users of a workplace well-being app, 89 days", "finding": "At six possible times a day, users were randomly sent or not sent a tailored message. A message made people about 4% more likely to use the app in the next 24 hours. The time mattered: the effect was largest around 12:30 on weekends, at about 12%.", "limits": "The outcome is opening the app, not a change in behaviour or well-being. One commercial app, one population.", "use": "Evidence that timing changes the effect of the same message, and that the effect on engagement alone is small.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://mhealth.jmir.org/2018/11/e10123/", "topic": "timing-and-receptivity", "alsoIn": [ "engagement-is-not-benefit" ], "citedBy": [ "when-is-the-right-moment-for-a-reminder", "do-reminder-apps-change-behavior" ] }, { "slug": "klasnja-2015-micro-randomized-trials", "kind": "study", "url": "https://tre-up.com/research/sources/klasnja-2015-micro-randomized-trials", "link": "https://doi.org/10.1037/hea0000305", "authors": "Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, Murphy SA", "short": "Klasnja et al., 2015", "year": 2015, "title": "Micro-Randomized Trials: An Experimental Design for Developing Just-in-Time Adaptive Interventions", "venue": "Health Psychology 34(Suppl):1220–1228", "doi": "10.1037/hea0000305", "institutions": [ { "name": "University of Michigan", "country": "US", "countryName": "United States" }, { "name": "Arizona State University", "country": "US", "countryName": "United States" }, { "name": "University of Pittsburgh", "country": "US", "countryName": "United States" } ], "design": "Methods paper", "sample": null, "finding": "Introduces the micro-randomized trial: at each decision point, a person is randomly given or not given a component of an intervention. That lets researchers estimate the effect of the component in the moments right after it, and how that effect changes with time and context.", "limits": "A design for running experiments, not a finding about any behaviour.", "use": "The way Pursue's own suggestions should eventually be tested: moment by moment, against doing nothing.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4732571/", "topic": "timing-and-receptivity", "alsoIn": [], "citedBy": [] }, { "slug": "golbus-2026-mybpmylife", "kind": "study", "url": "https://tre-up.com/research/sources/golbus-2026-mybpmylife", "link": "https://doi.org/10.2196/78218", "authors": "Golbus JR, Dorsch MP, Chen Y, Basu T, Luff E, Klasnja P, Newman MW, Skolarus LE, Dempsey W, Nallamothu BK", "short": "Golbus et al., 2026", "year": 2026, "title": "Impact of Push Notifications on Physical Activity and Sodium Intake Among Patients with Hypertension: Microrandomized Trial of a Just-in-Time Adaptive Intervention", "venue": "Journal of Medical Internet Research 28:e78218", "doi": "10.2196/78218", "institutions": [ { "name": "University of Michigan", "country": "US", "countryName": "United States" } ], "design": "Micro-randomized trial within a 6-month randomized trial", "sample": "287 adults with self-reported hypertension; 187,517 randomization points", "finding": "Tailored notifications about activity and salt were randomized at four times a day for six months, about one a day per person. They did not raise steps in the next hour or lower-salt food choices in the next day. They did make people use the app more.", "limits": "A secondary analysis; the engagement results were exploratory. An older clinical group, so it transfers poorly to healthy adults with work goals.", "use": "A large, careful trial where notifications moved app use but not the behaviour. Pursue measures 'did you take the step?', never 'did you open the app?'", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12779098/", "topic": "engagement-is-not-benefit", "alsoIn": [ "timing-and-receptivity" ], "citedBy": [ "do-reminder-apps-change-behavior", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "stothart-2015-notification-cost", "kind": "study", "url": "https://tre-up.com/research/sources/stothart-2015-notification-cost", "link": "https://doi.org/10.1037/xhp0000100", "authors": "Stothart C, Mitchum A, Yehnert C", "short": "Stothart et al., 2015", "year": 2015, "title": "The attentional cost of receiving a cell phone notification", "venue": "Journal of Experimental Psychology: Human Perception and Performance 41(4):893–897", "doi": "10.1037/xhp0000100", "institutions": [ { "name": "Florida State University", "country": "US", "countryName": "United States" } ], "design": "Lab experiment", "sample": null, "finding": "Just receiving a notification hurt performance on a task that needed attention, even when people did not touch their phone. The size of the drop was similar to that of actually using a phone for a call or a text. The authors link it to the notification setting off thoughts unrelated to the task.", "limits": "A lab task, measured over minutes. It shows a cost in the moment, not over a working day.", "use": "The reason Pursue treats its own signal as a cost: every alert it sends spends some of the attention it is trying to protect.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "interruptions-and-attention", "alsoIn": [], "citedBy": [ "do-notifications-hurt-focus", "when-is-it-better-for-an-ai-assistant-to-say-nothing" ] }, { "slug": "kushlev-2016-silence-your-phones", "kind": "study", "url": "https://tre-up.com/research/sources/kushlev-2016-silence-your-phones", "link": "https://doi.org/10.1145/2858036.2858359", "authors": "Kushlev K, Proulx JDE, Dunn EW", "short": "Kushlev et al., 2016", "year": 2016, "title": "\"Silence Your Phones\": Smartphone Notifications Increase Inattention and Hyperactivity Symptoms", "venue": "Proceedings of CHI 2016, 1011–1020", "doi": "10.1145/2858036.2858359", "institutions": [ { "name": "University of Virginia", "country": "US", "countryName": "United States" }, { "name": "University of British Columbia", "country": "CA", "countryName": "Canada" } ], "design": "Field experiment, two one-week conditions", "sample": "221 adults", "finding": "For one week people kept alerts on and their phone within reach; for another, alerts off and the phone away. With alerts on, they reported more inattention and hyperactivity, and more inattention went with lower productivity and well-being.", "limits": "Self-reported symptoms in a general population, not a clinical diagnosis. Two weeks per person.", "use": "Supports a quiet default: Pursue is designed to be silent unless it has a good reason to speak.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "interruptions-and-attention", "alsoIn": [], "citedBy": [ "do-notifications-hurt-focus" ] }, { "slug": "pielot-2014-in-situ-notifications", "kind": "study", "url": "https://tre-up.com/research/sources/pielot-2014-in-situ-notifications", "link": "https://doi.org/10.1145/2628363.2628364", "authors": "Pielot M, Church K, de Oliveira R", "short": "Pielot et al., 2014", "year": 2014, "title": "An in-situ study of mobile phone notifications", "venue": "Proceedings of MobileHCI 2014", "doi": "10.1145/2628363.2628364", "institutions": [ { "name": "Telefónica Research", "country": "ES", "countryName": "Spain" } ], "design": "Field study with phone logging and diaries", "sample": "15 people, one week", "finding": "People handled about 63 notifications a day, mostly from messaging and email, and usually looked at them within minutes, whether or not the phone was on silent.", "limits": "Fifteen people in 2014; today's numbers are likely different. Descriptive, not an experiment.", "use": "Context for how crowded the notification space already is before Pursue says anything.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "interruptions-and-attention", "alsoIn": [], "citedBy": [ "do-notifications-hurt-focus" ] }, { "slug": "pielot-rello-2017-24-hours", "kind": "study", "url": "https://tre-up.com/research/sources/pielot-rello-2017-24-hours", "link": "https://doi.org/10.1145/3098279.3098526", "authors": "Pielot M, Rello L", "short": "Pielot & Rello, 2017", "year": 2017, "title": "Productive, Anxious, Lonely: 24 Hours Without Push Notifications", "venue": "Proceedings of MobileHCI 2017", "doi": "10.1145/3098279.3098526", "institutions": [ { "name": "Telefónica Research", "country": "ES", "countryName": "Spain" } ], "design": "Field experiment: a day without notifications against a normal day", "sample": "30 volunteers", "finding": "With every notification alert switched off for a day, people felt less distracted and more productive. They also felt less able to respond as others expected, which made some anxious, and less connected. About two thirds planned to change how they handle notifications, and two years later about half still did.", "limits": "Thirty volunteers, one day, self-report.", "use": "Total silence is not the answer either. Pursue aims for selective quiet, not none at all.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://arxiv.org/abs/1612.02314", "topic": "interruptions-and-attention", "alsoIn": [], "citedBy": [ "do-notifications-hurt-focus", "should-i-turn-off-or-batch-notifications" ] }, { "slug": "fitz-2019-batching-notifications", "kind": "study", "url": "https://tre-up.com/research/sources/fitz-2019-batching-notifications", "link": "https://doi.org/10.1016/j.chb.2019.07.016", "authors": "Fitz N, Kushlev K, Jagannathan R, Lewis T, Paliwal D, Ariely D", "short": "Fitz et al., 2019", "year": 2019, "title": "Batching smartphone notifications can improve well-being", "venue": "Computers in Human Behavior 101:84–94", "doi": "10.1016/j.chb.2019.07.016", "institutions": [ { "name": "Duke University", "country": "US", "countryName": "United States" }, { "name": "Georgetown University", "country": "US", "countryName": "United States" } ], "design": "Randomized field experiment", "sample": "237 people", "finding": "People got notifications as usual, in three batches a day, or never. Batching made them feel more attentive, more productive, in a better mood and more in control of their phone. Getting no notifications at all brought few of those benefits and more anxiety and fear of missing out.", "limits": "Self-reported outcomes over a short field period. Tests batching in general, not any particular app.", "use": "Fewer, grouped signals beat both a constant stream and total silence. Pursue sends at most one signal at a time.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "interruptions-and-attention", "alsoIn": [], "citedBy": [ "do-notifications-hurt-focus", "should-i-turn-off-or-batch-notifications", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "kushlev-dunn-2015-email", "kind": "study", "url": "https://tre-up.com/research/sources/kushlev-dunn-2015-email", "link": "https://doi.org/10.1016/j.chb.2014.11.005", "authors": "Kushlev K, Dunn EW", "short": "Kushlev & Dunn, 2015", "year": 2015, "title": "Checking email less frequently reduces stress", "venue": "Computers in Human Behavior 43:220–228", "doi": "10.1016/j.chb.2014.11.005", "institutions": [ { "name": "University of British Columbia", "country": "CA", "countryName": "Canada" } ], "design": "Within-person field experiment", "sample": "124 adults, two weeks", "finding": "For one week people checked email only three times a day; for the other, as often as they liked. Daily stress was lower in the limited week, and lower stress went with higher well-being.", "limits": "Email only, two weeks, self-reported stress.", "use": "Supports checking on purpose rather than on every ping, which is how Pursue suggests handling the rest of the day.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "interruptions-and-attention", "alsoIn": [], "citedBy": [ "should-i-turn-off-or-batch-notifications" ] }, { "slug": "iqbal-bailey-2008-breakpoints", "kind": "study", "url": "https://tre-up.com/research/sources/iqbal-bailey-2008-breakpoints", "link": "https://doi.org/10.1145/1357054.1357070", "authors": "Iqbal ST, Bailey BP", "short": "Iqbal & Bailey, 2008", "year": 2008, "title": "Effects of intelligent notification management on users and their tasks", "venue": "Proceedings of CHI 2008", "doi": "10.1145/1357054.1357070", "institutions": [ { "name": "University of Illinois Urbana-Champaign", "country": "US", "countryName": "United States" } ], "design": "System with two user studies", "sample": null, "finding": "A system held notifications until the person reached a natural break in their task. Delivering at breakpoints, instead of immediately, reduced frustration and reaction time. Which kind of breakpoint suited a notification depended on how relevant its content was.", "limits": "Desktop tasks in a study setting. Detecting breakpoints worked, but telling the types apart was hard.", "use": "Supports waiting for a natural pause rather than cutting into the middle of a task.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "interruptions-and-attention", "alsoIn": [ "timing-and-receptivity" ], "citedBy": [ "should-i-turn-off-or-batch-notifications", "why-is-it-hard-to-get-back-to-work-after-an-interruption", "when-is-the-right-moment-for-a-reminder" ] }, { "slug": "mark-2008-cost-of-interrupted-work", "kind": "study", "url": "https://tre-up.com/research/sources/mark-2008-cost-of-interrupted-work", "link": "https://doi.org/10.1145/1357054.1357072", "authors": "Mark G, Gudith D, Klocke U", "short": "Mark et al., 2008", "year": 2008, "title": "The cost of interrupted work: more speed and stress", "venue": "Proceedings of CHI 2008, 107–110", "doi": "10.1145/1357054.1357072", "institutions": [ { "name": "University of California, Irvine", "country": "US", "countryName": "United States" }, { "name": "Humboldt-Universität zu Berlin", "country": "DE", "countryName": "Germany" } ], "design": "Lab experiment", "sample": null, "finding": "Interrupted people finished their tasks faster and just as well, but paid for it: more stress, frustration, time pressure and effort. They seem to make up for interruptions by speeding up.", "limits": "A short lab task. It does not measure how long it takes to get back on track, and gives no minutes figure.", "use": "Getting through the day is not the same as being fine. Pursue looks at the cost of the day, not only the output.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://www.ics.uci.edu/~gmark/chi08-mark.pdf", "topic": "interruptions-and-attention", "alsoIn": [], "citedBy": [ "why-is-it-hard-to-get-back-to-work-after-an-interruption" ] }, { "slug": "adamczyk-bailey-2004-if-not-now", "kind": "study", "url": "https://tre-up.com/research/sources/adamczyk-bailey-2004-if-not-now", "link": "https://doi.org/10.1145/985692.985727", "authors": "Adamczyk PD, Bailey BP", "short": "Adamczyk & Bailey, 2004", "year": 2004, "title": "If Not Now, When? The Effects of Interruption at Different Moments Within Task Execution", "venue": "Proceedings of CHI 2004, 271–278", "doi": "10.1145/985692.985727", "institutions": [ { "name": "University of Illinois Urbana-Champaign", "country": "US", "countryName": "United States" } ], "design": "Lab experiment", "sample": null, "finding": "Interruptions were placed at different points inside a task, chosen from a model of mental workload. Where in the task the interruption landed changed how people felt and how they judged the interrupter.", "limits": "Lab tasks, short sessions.", "use": "The same message lands differently depending on the moment inside a task.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "interruptions-and-attention", "alsoIn": [ "timing-and-receptivity" ], "citedBy": [ "why-is-it-hard-to-get-back-to-work-after-an-interruption" ] }, { "slug": "leroy-2009-attention-residue", "kind": "study", "url": "https://tre-up.com/research/sources/leroy-2009-attention-residue", "link": "https://doi.org/10.1016/j.obhdp.2009.04.002", "authors": "Leroy S", "short": "Leroy, 2009", "year": 2009, "title": "Why is it so hard to do my work? The challenge of attention residue when switching between work tasks", "venue": "Organizational Behavior and Human Decision Processes 109(2):168–181", "doi": "10.1016/j.obhdp.2009.04.002", "institutions": [ { "name": "University of Minnesota", "country": "US", "countryName": "United States" } ], "design": "Two lab experiments", "sample": null, "finding": "After switching away from an unfinished task, part of a person's attention stays with it, and they do worse on the next task. Finishing the first task, under time pressure, left less of this 'attention residue'.", "limits": "Lab experiments with short tasks.", "use": "Why Pursue suggests one thing and an end point, not a list: closing a loop frees attention for the next one.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "interruptions-and-attention", "alsoIn": [], "citedBy": [ "why-is-it-hard-to-get-back-to-work-after-an-interruption" ] }, { "slug": "horvitz-1999-attention-sensitive-alerting", "kind": "study", "url": "https://tre-up.com/research/sources/horvitz-1999-attention-sensitive-alerting", "link": "https://www.microsoft.com/en-us/research/publication/attention-sensitive-alerting/", "authors": "Horvitz E, Jacobs A, Hovel D", "short": "Horvitz et al., 1999", "year": 1999, "title": "Attention-Sensitive Alerting", "venue": "Proceedings of UAI 1999, 305–313", "doi": null, "institutions": [ { "name": "Microsoft Research", "country": "US", "countryName": "United States" } ], "design": "Decision-theory model and system", "sample": null, "finding": "Treats each alert as a trade: the value of telling someone now against the cost of interrupting them, and the cost of waiting. Applied to email, a system estimated how critical each message was and changed how and when it alerted the person.", "limits": "A model and a system from 1999, not an outcome trial.", "use": "The frame behind Pursue's first question: is this worth the interruption it costs?", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "full text", "citationCheckedAgainst": "publisher record", "openAccess": true, "freeFullText": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/priorities.pdf", "topic": "timing-and-receptivity", "alsoIn": [ "interruptions-and-attention" ], "citedBy": [ "when-is-the-right-moment-for-a-reminder", "when-is-it-better-for-an-ai-assistant-to-say-nothing" ] }, { "slug": "horvitz-1999-mixed-initiative", "kind": "study", "url": "https://tre-up.com/research/sources/horvitz-1999-mixed-initiative", "link": "https://doi.org/10.1145/302979.303030", "authors": "Horvitz E", "short": "Horvitz, 1999", "year": 1999, "title": "Principles of mixed-initiative user interfaces", "venue": "Proceedings of CHI 1999, 159–166", "doi": "10.1145/302979.303030", "institutions": [ { "name": "Microsoft Research", "country": "US", "countryName": "United States" } ], "design": "Design principles", "sample": null, "finding": "Sets out principles for combining automated help with the person staying in control, illustrated with an assistant that offers to schedule meetings from email: act when the expected value is high, ask when unsure, and make it cheap to dismiss or correct.", "limits": "Principles from experience with one system, not an experiment.", "use": "Pursue offers; the person decides. A suggestion is always easy to ignore.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/chi99horvitz-1.pdf", "topic": "trust-and-uncertainty", "alsoIn": [ "timing-and-receptivity" ], "citedBy": [ "why-do-i-push-back-when-an-app-tells-me-what-to-do" ] }, { "slug": "bell-2023-drink-less-notifications", "kind": "study", "url": "https://tre-up.com/research/sources/bell-2023-drink-less-notifications", "link": "https://doi.org/10.2196/38342", "authors": "Bell L, Garnett C, Bao Y, Cheng Z, Qian T, Perski O, Potts HWW, Williamson E", "short": "Bell et al., 2023", "year": 2023, "title": "How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial", "venue": "JMIR mHealth and uHealth 11:e38342", "doi": "10.2196/38342", "institutions": [ { "name": "London School of Hygiene & Tropical Medicine", "country": "GB", "countryName": "United Kingdom" }, { "name": "University College London", "country": "GB", "countryName": "United Kingdom" }, { "name": "Harvard University", "country": "US", "countryName": "United States" } ], "design": "Micro-randomized trial", "sample": "350 new users of an alcohol-reduction app, first 30 days", "finding": "Each evening new users were randomly sent the usual notification, a new message, or nothing. A notification made opening the app in the next hour about three and a half times more likely.", "limits": "One app about drinking. The lab's reading of the full trial is that the short-term lift did not change how soon people stopped using the app; the abstract we checked reports the one-hour effect.", "use": "A notification reliably gets a tap. That is exactly why Pursue does not count taps as success.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://mhealth.jmir.org/2023/1/e38342", "topic": "engagement-is-not-benefit", "alsoIn": [], "citedBy": [ "do-reminder-apps-change-behavior" ] }, { "slug": "baumel-2019-mental-health-app-engagement", "kind": "study", "url": "https://tre-up.com/research/sources/baumel-2019-mental-health-app-engagement", "link": "https://doi.org/10.2196/14567", "authors": "Baumel A, Muench F, Edan S, Kane JM", "short": "Baumel et al., 2019", "year": 2019, "title": "Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis", "venue": "Journal of Medical Internet Research 21(9):e14567", "doi": "10.2196/14567", "institutions": [ { "name": "University of Haifa", "country": "IL", "countryName": "Israel" }, { "name": "Partnership for Drug-Free Kids", "country": "US", "countryName": "United States" }, { "name": "Northwell Health", "country": "US", "countryName": "United States" } ], "design": "Usage analysis of real-world panel data", "sample": "93 apps for anxiety, depression or well-being", "finding": "Across popular mental-health apps, the median share of installed users who opened the app on a given day was 4%. Peer-support apps did best (17%), breathing-exercise apps worst (1.6%).", "limits": "Usage data only; says nothing about whether the apps helped the people who used them.", "use": "The base rate Pursue designs against: most people stop opening most wellbeing apps. One useful moment matters more than daily habit.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://www.jmir.org/2019/9/e14567/", "topic": "engagement-is-not-benefit", "alsoIn": [], "citedBy": [ "do-reminder-apps-change-behavior" ] }, { "slug": "richards-2022-reactance", "kind": "study", "url": "https://tre-up.com/research/sources/richards-2022-reactance", "link": "https://doi.org/10.1080/10410236.2020.1839203", "authors": "Richards AS, Bessarabova E, Banas JA, Bernard DR", "short": "Richards et al., 2022", "year": 2022, "title": "Reducing Psychological Reactance to Health Promotion Messages: Comparing Preemptive and Postscript Mitigation Strategies", "venue": "Health Communication 37(3):366–374", "doi": "10.1080/10410236.2020.1839203", "institutions": [], "design": "Experiment with a one-week follow-up", "sample": "394 participants", "finding": "Health messages that felt more controlling made people push back. Reminding them that the choice was theirs, either before the message or in a short note after it, reduced that pushback equally, and the effect was still there a week later.", "limits": "Message wording about exercise and nutrition. It does not test notification frequency or apps.", "use": "Pursue words every step as an offer, not an order. The choice stays with the person.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "autonomy-and-wording", "alsoIn": [], "citedBy": [ "why-do-i-push-back-when-an-app-tells-me-what-to-do", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "brehm-1966-reactance", "kind": "study", "url": "https://tre-up.com/research/sources/brehm-1966-reactance", "link": "https://ci.nii.ac.jp/ncid/BA02256417?l=en", "authors": "Brehm JW", "short": "Brehm, 1966", "year": 1966, "title": "A Theory of Psychological Reactance", "venue": "Academic Press (book)", "doi": null, "institutions": [], "design": "Theory (book)", "sample": null, "finding": "When people feel a freedom is threatened, they become motivated to restore it, often by doing the opposite of what they were pushed to do. This is 'psychological reactance'.", "limits": "A classic theory; the book itself is a framework, not a test. Used here only as background.", "use": "Explains why muting an app can be a way of taking control back, and why Pursue treats a mute as information, not defiance.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "library catalogue record", "openAccess": false, "freeFullText": null, "topic": "autonomy-and-wording", "alsoIn": [], "citedBy": [ "why-do-i-push-back-when-an-app-tells-me-what-to-do" ] }, { "slug": "deci-ryan-2000-self-determination", "kind": "study", "url": "https://tre-up.com/research/sources/deci-ryan-2000-self-determination", "link": "https://doi.org/10.1207/S15327965PLI1104_01", "authors": "Deci EL, Ryan RM", "short": "Deci & Ryan, 2000", "year": 2000, "title": "The \"What\" and \"Why\" of Goal Pursuits: Human Needs and the Self-Determination of Behavior", "venue": "Psychological Inquiry 11(4):227–268", "doi": "10.1207/S15327965PLI1104_01", "institutions": [ { "name": "University of Rochester", "country": "US", "countryName": "United States" } ], "design": "Theory paper", "sample": null, "finding": "Self-determination theory: people need to feel competent, autonomous and connected. When those needs are met, motivation is more self-driven and well-being is higher; how and why a goal is pursued matters, not only which goal.", "limits": "A broad theory with a large body of later evidence; this paper is the framework, not a single test.", "use": "Pursue supports the person's own reasons for a step rather than adding external pressure.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "autonomy-and-wording", "alsoIn": [], "citedBy": [ "why-do-i-push-back-when-an-app-tells-me-what-to-do" ] }, { "slug": "lee-see-2004-trust-in-automation", "kind": "study", "url": "https://tre-up.com/research/sources/lee-see-2004-trust-in-automation", "link": "https://doi.org/10.1518/hfes.46.1.50_30392", "authors": "Lee JD, See KA", "short": "Lee & See, 2004", "year": 2004, "title": "Trust in Automation: Designing for Appropriate Reliance", "venue": "Human Factors 46(1):50–80", "doi": "10.1518/hfes.46.1.50_30392", "institutions": [ { "name": "University of Iowa", "country": "US", "countryName": "United States" } ], "design": "Review", "sample": null, "finding": "People often rely on automation too much or too little. When a system is too complex to understand fully, trust is what guides how much people rely on it, so systems should be designed for trust that matches how good they actually are.", "limits": "A review across many fields, mostly industrial and safety systems.", "use": "Pursue aims for trust that fits its real ability: it says when it is unsure instead of sounding certain.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "trust-and-uncertainty", "alsoIn": [], "citedBy": [ "should-i-trust-an-ai-with-advice-about-my-day", "how-should-an-ai-say-it-isnt-sure" ] }, { "slug": "parasuraman-riley-1997-use-misuse", "kind": "study", "url": "https://tre-up.com/research/sources/parasuraman-riley-1997-use-misuse", "link": "https://doi.org/10.1518/001872097778543886", "authors": "Parasuraman R, Riley V", "short": "Parasuraman & Riley, 1997", "year": 1997, "title": "Humans and Automation: Use, Misuse, Disuse, Abuse", "venue": "Human Factors 39(2):230–253", "doi": "10.1518/001872097778543886", "institutions": [ { "name": "Catholic University of America", "country": "US", "countryName": "United States" }, { "name": "Honeywell Technology Center", "country": "US", "countryName": "United States" } ], "design": "Review", "sample": null, "finding": "Names the ways people get automation wrong: misuse (relying on it too much), disuse (ignoring it, often after false alarms) and abuse (automating without regard for the people who have to work with it).", "limits": "A conceptual review drawn largely from aviation and industrial settings.", "use": "False alarms teach people to ignore a system. Every unnecessary Pursue signal makes the next useful one weaker.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "trust-and-uncertainty", "alsoIn": [], "citedBy": [ "should-i-trust-an-ai-with-advice-about-my-day", "when-is-it-better-for-an-ai-assistant-to-say-nothing" ] }, { "slug": "dzindolet-2003-trust-reliance", "kind": "study", "url": "https://tre-up.com/research/sources/dzindolet-2003-trust-reliance", "link": "https://doi.org/10.1016/S1071-5819(03)00038-7", "authors": "Dzindolet MT, Peterson SA, Pomranky RA, Pierce LG, Beck HP", "short": "Dzindolet et al., 2003", "year": 2003, "title": "The role of trust in automation reliance", "venue": "International Journal of Human-Computer Studies 58(6):697–718", "doi": "10.1016/S1071-5819(03)00038-7", "institutions": [], "design": "Lab experiments", "sample": null, "finding": "Before seeing it work, people expected an automated aid to do well, better than themselves. After seeing it make mistakes, they distrusted even a reliable aid, unless they were told why it might err. Knowing why kept trust and use up.", "limits": "Lab tasks with a decision aid; details of the samples are in the full paper, which we did not read.", "use": "When Pursue is unsure, it says why, in plain words.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "trust-and-uncertainty", "alsoIn": [], "citedBy": [ "should-i-trust-an-ai-with-advice-about-my-day", "how-should-an-ai-say-it-isnt-sure" ] }, { "slug": "logg-2019-algorithm-appreciation", "kind": "study", "url": "https://tre-up.com/research/sources/logg-2019-algorithm-appreciation", "link": "https://doi.org/10.1016/j.obhdp.2018.12.005", "authors": "Logg JM, Minson JA, Moore DA", "short": "Logg et al., 2019", "year": 2019, "title": "Algorithm appreciation: People prefer algorithmic to human judgment", "venue": "Organizational Behavior and Human Decision Processes 151:90–103", "doi": "10.1016/j.obhdp.2018.12.005", "institutions": [ { "name": "Harvard University", "country": "US", "countryName": "United States" }, { "name": "University of California, Berkeley", "country": "US", "countryName": "United States" } ], "design": "Six experiments", "sample": null, "finding": "Across six experiments, ordinary people followed advice more when they believed it came from an algorithm than from a person. The effect weakened when they had to choose between the algorithm and their own judgment, and among experienced forecasters.", "limits": "Estimation and forecasting tasks, not personal decisions about one's own day.", "use": "People may lean on an AI's suggestion more than it deserves. Pursue's job is to keep the person's own judgment in charge.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://escholarship.org/content/qt9v38k9m6/qt9v38k9m6.pdf", "topic": "trust-and-uncertainty", "alsoIn": [], "citedBy": [ "should-i-trust-an-ai-with-advice-about-my-day" ] }, { "slug": "bucinca-2021-cognitive-forcing", "kind": "study", "url": "https://tre-up.com/research/sources/bucinca-2021-cognitive-forcing", "link": "https://doi.org/10.1145/3449287", "authors": "Buçinca Z, Malaya MB, Gajos KZ", "short": "Buçinca et al., 2021", "year": 2021, "title": "To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making", "venue": "Proceedings of the ACM on Human-Computer Interaction 5(CSCW1)", "doi": "10.1145/3449287", "institutions": [ { "name": "Harvard University", "country": "US", "countryName": "United States" } ], "design": "Online experiment", "sample": "199 participants", "finding": "Designs that made people think before seeing the AI's answer reduced over-reliance on the AI more than simple explanations did. People liked those designs least.", "limits": "One decision task online. The most effective designs were the least popular, a real trade-off.", "use": "Pursue sometimes asks one question before offering a step, so the person's own thinking comes first.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://arxiv.org/abs/2102.09692", "topic": "trust-and-uncertainty", "alsoIn": [], "citedBy": [ "should-i-trust-an-ai-with-advice-about-my-day" ] }, { "slug": "kizilcec-2016-transparency-trust", "kind": "study", "url": "https://tre-up.com/research/sources/kizilcec-2016-transparency-trust", "link": "https://doi.org/10.1145/2858036.2858402", "authors": "Kizilcec RF", "short": "Kizilcec, 2016", "year": 2016, "title": "How Much Information? Effects of Transparency on Trust in an Algorithmic Interface", "venue": "Proceedings of CHI 2016, 2390–2395", "doi": "10.1145/2858036.2858402", "institutions": [ { "name": "Stanford University", "country": "US", "countryName": "United States" } ], "design": "Online field experiment", "sample": null, "finding": "When an algorithm's result was worse than people expected, explaining how it worked kept their trust, but explaining too much eroded it again. People whose expectations were met were not affected.", "limits": "Peer grading in online courses; one setting.", "use": "Pursue explains itself in a sentence, not a page.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "trust-and-uncertainty", "alsoIn": [], "citedBy": [ "should-i-trust-an-ai-with-advice-about-my-day", "how-should-an-ai-say-it-isnt-sure" ] }, { "slug": "van-der-bles-2019-communicating-uncertainty", "kind": "study", "url": "https://tre-up.com/research/sources/van-der-bles-2019-communicating-uncertainty", "link": "https://doi.org/10.1098/rsos.181870", "authors": "van der Bles AM, van der Linden S, Freeman ALJ, Mitchell J, Galvao AB, Zaval L, Spiegelhalter DJ", "short": "van der Bles et al., 2019", "year": 2019, "title": "Communicating uncertainty about facts, numbers and science", "venue": "Royal Society Open Science 6(5):181870", "doi": "10.1098/rsos.181870", "institutions": [ { "name": "University of Cambridge", "country": "GB", "countryName": "United Kingdom" }, { "name": "University of Warwick", "country": "GB", "countryName": "United Kingdom" }, { "name": "Columbia University", "country": "US", "countryName": "United States" } ], "design": "Interdisciplinary review", "sample": null, "finding": "Offers a framework for saying how sure we are about facts, numbers and science. The limited evidence suggests that admitting uncertainty does not necessarily backfire on trust, though the effect varies between people and formats.", "limits": "A review of a thin literature; it calls for more experiments.", "use": "Pursue says 'I'm not sure' when it isn't. Honesty about uncertainty is not a trust risk to hide.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6549952/", "topic": "trust-and-uncertainty", "alsoIn": [], "citedBy": [ "how-should-an-ai-say-it-isnt-sure" ] }, { "slug": "guo-2017-calibration", "kind": "study", "url": "https://tre-up.com/research/sources/guo-2017-calibration", "link": "https://arxiv.org/abs/1706.04599", "authors": "Guo C, Pleiss G, Sun Y, Weinberger KQ", "short": "Guo et al., 2017", "year": 2017, "title": "On Calibration of Modern Neural Networks", "venue": "Proceedings of ICML 2017, PMLR 70:1321–1330", "doi": "arXiv:1706.04599", "institutions": [ { "name": "Cornell University", "country": "US", "countryName": "United States" } ], "design": "Machine-learning experiments", "sample": null, "finding": "Modern neural networks are often over-confident: the confidence they report does not match how often they are right. A simple adjustment after training, temperature scaling, fixed much of this on most datasets tested.", "limits": "Image and text classification benchmarks, not advice to people.", "use": "A model's own confidence score cannot be shown as certainty. Pursue never invents a confidence number.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "arXiv record", "openAccess": true, "freeFullText": "https://proceedings.mlr.press/v70/guo17a.html", "topic": "trust-and-uncertainty", "alsoIn": [], "citedBy": [ "how-should-an-ai-say-it-isnt-sure", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "geifman-el-yaniv-2017-selective-classification", "kind": "study", "url": "https://tre-up.com/research/sources/geifman-el-yaniv-2017-selective-classification", "link": "https://arxiv.org/abs/1705.08500", "authors": "Geifman Y, El-Yaniv R", "short": "Geifman & El-Yaniv, 2017", "year": 2017, "title": "Selective Classification for Deep Neural Networks", "venue": "Advances in Neural Information Processing Systems 30 (NeurIPS 2017)", "doi": "arXiv:1705.08500", "institutions": [ { "name": "Technion – Israel Institute of Technology", "country": "IL", "countryName": "Israel" } ], "design": "Machine-learning method and experiments", "sample": null, "finding": "A trained network can be given the option to decline when it is unsure, set so that its error rate stays under a chosen level. Answering fewer cases made the answers it did give far more reliable.", "limits": "Image classification benchmarks; the mapping to personal advice is an analogy.", "use": "Declining to answer is a legitimate action. Pursue can say nothing, or ask, instead of guessing.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "arXiv record", "openAccess": true, "freeFullText": "https://arxiv.org/abs/1705.08500", "topic": "trust-and-uncertainty", "alsoIn": [ "timing-and-receptivity" ], "citedBy": [ "how-should-an-ai-say-it-isnt-sure", "when-is-it-better-for-an-ai-assistant-to-say-nothing" ] }, { "slug": "yuan-hernandez-2023-cold-start", "kind": "study", "url": "https://tre-up.com/research/sources/yuan-hernandez-2023-cold-start", "link": "https://doi.org/10.1109/ACCESS.2023.3338705", "authors": "Yuan H, Hernandez AA", "short": "Yuan & Hernandez, 2023", "year": 2023, "title": "User Cold Start Problem in Recommendation Systems: A Systematic Review", "venue": "IEEE Access 11:136958–136977", "doi": "10.1109/ACCESS.2023.3338705", "institutions": [], "design": "Systematic review", "sample": null, "finding": "Recommendation quality drops sharply for a user with no history, the 'cold start'. The review treats it as its own problem and maps the ways systems have tried to solve it, including simply asking the user.", "limits": "Recommender systems, not personal guidance. We could not reopen the abstract on 7 October 2026; this summary rests on the lab's earlier reading and its DOI check.", "use": "In its first days Pursue knows almost nothing about you, so it asks a few short questions instead of pretending to know.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://ieeexplore.ieee.org/ielx7/6287639/6514899/10339320.pdf", "topic": "learning-a-person", "alsoIn": [], "citedBy": [ "how-can-an-app-help-me-if-it-doesnt-know-me-yet", "is-there-an-app-that-notices-when-my-day-is-slipping" ] }, { "slug": "koren-2009-temporal-dynamics", "kind": "study", "url": "https://tre-up.com/research/sources/koren-2009-temporal-dynamics", "link": "https://doi.org/10.1145/1557019.1557072", "authors": "Koren Y", "short": "Koren, 2009", "year": 2009, "title": "Collaborative Filtering with Temporal Dynamics", "venue": "Proceedings of KDD 2009, 447–456", "doi": "10.1145/1557019.1557072", "institutions": [ { "name": "Yahoo! Research", "country": "IL", "countryName": "Israel" } ], "design": "Method with large-scale evaluation", "sample": null, "finding": "People's preferences drift over time. Models that track that drift, instead of treating old data like new, predicted ratings on a large Netflix dataset better than anything reported before.", "limits": "Movie ratings, not daily behaviour. No decay rate carries over.", "use": "What you tell Pursue today outranks a pattern from last month.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "learning-a-person", "alsoIn": [], "citedBy": [ "how-can-an-app-help-me-if-it-doesnt-know-me-yet" ] }, { "slug": "ma-2026-negative-sampling", "kind": "study", "url": "https://tre-up.com/research/sources/ma-2026-negative-sampling", "link": "https://doi.org/10.1145/3793855", "authors": "Ma H, Xie R, Meng L, Feng F, Du X, Sun X, Kang Z, Meng X", "short": "Ma et al., 2026", "year": 2026, "title": "Negative Sampling in Recommendation: A Survey and Future Directions", "venue": "ACM Transactions on Information Systems 44:1–44", "doi": "10.1145/3793855", "institutions": [ { "name": "Shandong University", "country": "CN", "countryName": "China" }, { "name": "Tencent", "country": "CN", "countryName": "China" }, { "name": "University of Science and Technology of China", "country": "CN", "countryName": "China" } ], "design": "Survey", "sample": null, "finding": "Surveys how recommender systems learn from what people do not choose. Positive signals get most of the attention; negative signals are noisier and easy to misread, and something a person did not see cannot be counted as a 'no'.", "limits": "A survey of recommender methods. Affiliations are taken from the preprint.", "use": "Ignoring a suggestion is not the same as rejecting it. Pursue weights what you say over what it infers.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://arxiv.org/abs/2409.07237", "topic": "learning-a-person", "alsoIn": [], "citedBy": [ "how-can-an-app-help-me-if-it-doesnt-know-me-yet" ] }, { "slug": "liao-2020-personalized-heartsteps", "kind": "study", "url": "https://tre-up.com/research/sources/liao-2020-personalized-heartsteps", "link": "https://doi.org/10.1145/3381007", "authors": "Liao P, Greenewald K, Klasnja P, Murphy S", "short": "Liao et al., 2020", "year": 2020, "title": "Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity", "venue": "Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4(1):18", "doi": "10.1145/3381007", "institutions": [ { "name": "University of Michigan", "country": "US", "countryName": "United States" }, { "name": "IBM Research", "country": "US", "countryName": "United States" }, { "name": "Harvard University", "country": "US", "countryName": "United States" } ], "design": "Learning algorithm with a pilot deployment", "sample": null, "finding": "An algorithm that learns, for each person, when a suggestion is worth sending, improving as their data comes in. In the HeartSteps app it decided up to five times a day whether to send an activity suggestion.", "limits": "An algorithm paper with a small pilot; not a trial of outcomes.", "use": "Personalisation should be learned for each person over time, not copied from everyone else.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://arxiv.org/abs/1909.03539", "topic": "learning-a-person", "alsoIn": [ "timing-and-receptivity" ], "citedBy": [ "how-can-an-app-help-me-if-it-doesnt-know-me-yet" ] }, { "slug": "ikegaya-2025-personalized-criteria", "kind": "study", "url": "https://tre-up.com/research/sources/ikegaya-2025-personalized-criteria", "link": "https://doi.org/10.2196/66750", "authors": "Ikegaya M, Foo JC, Murata T, Oshima K, Kim J", "short": "Ikegaya et al., 2025", "year": 2025, "title": "Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study", "venue": "JMIR Human Factors 12:e66750", "doi": "10.2196/66750", "institutions": [ { "name": "Shizuoka University", "country": "JP", "countryName": "Japan" }, { "name": "University of Alberta", "country": "CA", "countryName": "Canada" }, { "name": "Heidelberg University", "country": "DE", "countryName": "Germany" } ], "design": "Pilot study, two groups", "sample": "28 university students, 2 weeks", "finding": "Prompts to move were triggered either by each student's own first-week baseline or by a population average. Both raised activity in the hour after a prompt, and the personal threshold raised it more. Over the whole week, neither raised activity.", "limits": "Twenty-eight healthy students, a pilot; the two groups' thresholds came from different samples.", "use": "Personal baselines help, but only once there is data, and short-term lifts do not add up on their own.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": true, "freeFullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12129369/", "topic": "learning-a-person", "alsoIn": [ "timing-and-receptivity" ], "citedBy": [ "how-can-an-app-help-me-if-it-doesnt-know-me-yet" ] }, { "slug": "saltzer-schroeder-1975-protection", "kind": "study", "url": "https://tre-up.com/research/sources/saltzer-schroeder-1975-protection", "link": "https://doi.org/10.1109/PROC.1975.9939", "authors": "Saltzer JH, Schroeder MD", "short": "Saltzer & Schroeder, 1975", "year": 1975, "title": "The Protection of Information in Computer Systems", "venue": "Proceedings of the IEEE 63(9):1278–1308", "doi": "10.1109/PROC.1975.9939", "institutions": [ { "name": "Massachusetts Institute of Technology", "country": "US", "countryName": "United States" } ], "design": "Design principles", "sample": null, "finding": "The classic list of design principles for protecting information, including 'fail-safe defaults': when something goes wrong, a system should fall to the safe side, and access should be denied unless explicitly allowed.", "limits": "About computer security, not behaviour. We use it as an engineering principle, by analogy.", "use": "When Pursue cannot work reliably, it says so rather than producing something that looks like advice.", "useNote": "How this source informs Pursue's design — a design input, not evidence that Pursue works.", "summaryBasedOn": "abstract and public record", "citationCheckedAgainst": "DOI record", "openAccess": false, "freeFullText": null, "topic": "privacy-and-deletion", "alsoIn": [], "citedBy": [ "when-is-it-better-for-an-ai-assistant-to-say-nothing" ] }, { "slug": "gdpr-article-17", "kind": "document", "url": "https://tre-up.com/research/sources/gdpr-article-17", "link": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679", "issuer": "European Union", "title": "General Data Protection Regulation, Article 17: Right to erasure", "year": "2016", "country": "EU", "countryName": "European Union", "point": "A company must erase personal data 'without undue delay' when, for example, it is no longer needed or consent is withdrawn. The article sets no day count and says nothing specific about backups.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "gdpr-article-12", "kind": "document", "url": "https://tre-up.com/research/sources/gdpr-article-12", "link": "https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679", "issuer": "European Union", "title": "General Data Protection Regulation, Article 12(3): Time limits for responding to requests", "year": "2016", "country": "EU", "countryName": "European Union", "point": "A company must tell a person what it did about their request, including an erasure request, without undue delay and within one month, extendable by two more months for complex cases.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "ico-right-to-erasure", "kind": "document", "url": "https://tre-up.com/research/sources/ico-right-to-erasure", "link": "https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/individual-rights/individual-rights/right-to-erasure/", "issuer": "UK Information Commissioner's Office", "title": "Right to erasure (UK GDPR guidance)", "year": "current guidance", "country": "GB", "countryName": "United Kingdom", "point": "Erasure applies to backups too. If backup data cannot be overwritten at once, it must be put 'beyond use' until it is replaced on the normal schedule, and the person must be told clearly what will happen to their data.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "cnil-retention", "kind": "document", "url": "https://tre-up.com/research/sources/cnil-retention", "link": "https://www.cnil.fr/fr/passer-laction/les-durees-de-conservation-des-donnees", "issuer": "CNIL, French data protection authority", "title": "Les durées de conservation des données (data retention periods)", "year": "current guidance", "country": "FR", "countryName": "France", "point": "Describes a data life cycle from the active database to an isolated intermediate archive, kept only for legal obligations and open only to specifically authorised people. It sets no deadline for erasing backups.", "topic": "privacy-and-deletion", "citedBy": [] }, { "slug": "kvkk-deletion-regulation", "kind": "document", "url": "https://tre-up.com/research/sources/kvkk-deletion-regulation", "link": "https://www.resmigazete.gov.tr/eskiler/2017/10/20171028-10.htm", "issuer": "Turkish Personal Data Protection Authority (KVKK)", "title": "Regulation on the Deletion, Destruction or Anonymization of Personal Data", "year": "2017", "country": "TR", "countryName": "Türkiye", "point": "Deleted data must become inaccessible and unusable for the people who use it. Requests must be concluded within 30 days, and companies with a retention policy must run periodic destruction at least every six months.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "apple-guideline-5-1-1-v", "kind": "document", "url": "https://tre-up.com/research/sources/apple-guideline-5-1-1-v", "link": "https://developer.apple.com/app-store/review/guidelines/#data-collection-and-storage", "issuer": "Apple", "title": "App Store Review Guidelines 5.1.1(v): Account sign-in and deletion", "year": "current guidelines", "country": "US", "countryName": "United States", "point": "An app that lets people create an account must also let them delete it from inside the app. Deactivating it is not enough.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "google-cloud-data-deletion", "kind": "document", "url": "https://tre-up.com/research/sources/google-cloud-data-deletion", "link": "https://docs.cloud.google.com/docs/security/deletion", "issuer": "Google Cloud", "title": "Data deletion on Google Cloud", "year": "current documentation", "country": "US", "countryName": "United States", "point": "Deletion runs in stages: a recovery window of up to about 30 days, removal from active systems in about two months, then backups expiring on their normal cycles, with a commitment to delete customer data within about six months.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "nist-sp-800-88-r2", "kind": "document", "url": "https://tre-up.com/research/sources/nist-sp-800-88-r2", "link": "https://csrc.nist.gov/pubs/sp/800/88/r2/final", "issuer": "U.S. National Institute of Standards and Technology", "title": "SP 800-88 Rev. 2: Guidelines for Media Sanitization", "year": "2025", "country": "US", "countryName": "United States", "point": "Recognises 'cryptographic erase': destroying the keys that encrypt data, so every copy, including copies in backups, can no longer be read. It is far faster than wiping each copy.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "aws-kms-key-deletion", "kind": "document", "url": "https://tre-up.com/research/sources/aws-kms-key-deletion", "link": "https://docs.aws.amazon.com/kms/latest/developerguide/deleting-keys.html", "issuer": "Amazon Web Services", "title": "Deleting an AWS KMS key", "year": "current documentation", "country": "US", "countryName": "United States", "point": "Deleting an encryption key is irreversible: data encrypted under it can never be decrypted again. AWS enforces a waiting period of 7 to 30 days before the key is gone.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "aws-backup-vault-lock", "kind": "document", "url": "https://tre-up.com/research/sources/aws-backup-vault-lock", "link": "https://docs.aws.amazon.com/aws-backup/latest/devguide/vault-lock.html", "issuer": "Amazon Web Services", "title": "AWS Backup Vault Lock", "year": "current documentation", "country": "US", "countryName": "United States", "point": "Locked backup vaults are write-once: in compliance mode no one, including AWS, can delete a backup before its retention period ends. Immediate deletion of such a backup is technically impossible.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "amazon-s3-object-lock", "kind": "document", "url": "https://tre-up.com/research/sources/amazon-s3-object-lock", "link": "https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lock.html", "issuer": "Amazon Web Services", "title": "Locking objects with Object Lock", "year": "current documentation", "country": "US", "countryName": "United States", "point": "In compliance mode, a locked object cannot be overwritten or deleted by any user, including the account owner, until its retention period ends.", "topic": "privacy-and-deletion", "citedBy": [] }, { "slug": "azure-backup-soft-delete", "kind": "document", "url": "https://tre-up.com/research/sources/azure-backup-soft-delete", "link": "https://learn.microsoft.com/en-us/azure/backup/backup-azure-security-feature-cloud", "issuer": "Microsoft", "title": "Soft delete for Azure Backup", "year": "current documentation", "country": "US", "countryName": "United States", "point": "Deleted backup data stays recoverable for 14 days by default, extendable to 180. Microsoft is rolling out soft delete as always-on, so backups are not purged the moment they are deleted.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "azure-immutable-vault", "kind": "document", "url": "https://tre-up.com/research/sources/azure-immutable-vault", "link": "https://learn.microsoft.com/en-us/azure/backup/backup-azure-immutable-vault-concept", "issuer": "Microsoft", "title": "Immutable vault for Azure Backup", "year": "current documentation", "country": "US", "countryName": "United States", "point": "An immutable vault blocks any operation that would delete recovery points early. Once locked, immutability cannot be turned off.", "topic": "privacy-and-deletion", "citedBy": [ "does-deleting-my-data-delete-it-from-backups" ] }, { "slug": "edpb-guidelines-4-2019", "kind": "document", "url": "https://tre-up.com/research/sources/edpb-guidelines-4-2019", "link": "https://www.edpb.europa.eu/sites/default/files/files/file1/edpb_guidelines_201904_dataprotection_by_design_and_by_default_v2.0_en.pdf", "issuer": "European Data Protection Board", "title": "Guidelines 4/2019 on Article 25: Data Protection by Design and by Default", "year": "2020", "country": "EU", "countryName": "European Union", "point": "Privacy has to be built into a product from the start and set as the default. On erasure, the guidelines ask that inaccurate data be erased or corrected without delay; they say nothing specific about backups.", "topic": "privacy-and-deletion", "citedBy": [] } ] }