The library
Every source behind the notes.
40 studies and 14 primary documents, grouped by topic. Each one has its own page: what it found, where the evidence stops, and a link to the original.
- studies
- 40
- laws, guidelines and standards
- 14
- institutions
- 50
- countries
- 10
- studies free to read online
- 21
01 · 8 sources
Timing and receptivity
When help lands, and when the same help is just noise. Read the topic
- 2018Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior SupportNahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA · Annals of Behavioral Medicine 52(6):446–462University of Michigan · Northwestern University · Pennsylvania State University · University of New MexicoDesign framework
- 2026Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?Nahum-Shani I, Murphy SA · Annual Review of Psychology 77:679–703University of Michigan · Harvard UniversityReview
- 2016My Phone and Me: Understanding People's Receptivity to Mobile NotificationsMehrotra A, Pejovic V, Vermeulen J, Hendley R, Musolesi M · Proceedings of CHI 2016, 1021–1032University of Birmingham · University College London · University of Ljubljana · University of CalgaryField study with phone logging and experience sampling
- 2018Real-world large-scale study on adaptive notification scheduling on smartphonesOkoshi T, Tsubouchi K, Tokuda H · Pervasive and Mobile Computing 50:1–24Keio University · Yahoo Japan CorporationLarge-scale deployment in a commercial app
- 2019Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartStepsKlasnja P, Smith S, Seewald NJ, Lee A, Hall K, Luers B, Hekler EB, Murphy SA · Annals of Behavioral Medicine 53(6):573–582University of Michigan · University of California, San Diego · Harvard UniversityMicro-randomized trial
- 2018To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health AppBidargaddi N, Almirall D, Murphy S, Nahum-Shani I, Kovalcik M, Pituch T, Maaieh H, Strecher V · JMIR mHealth and uHealth 6(11):e10123Flinders University · University of Michigan · Harvard UniversityMicro-randomized trial
- 2015Micro-Randomized Trials: An Experimental Design for Developing Just-in-Time Adaptive InterventionsKlasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, Murphy SA · Health Psychology 34(Suppl):1220–1228University of Michigan · Arizona State University · University of PittsburghMethods paper
- 1999Attention-Sensitive AlertingHorvitz E, Jacobs A, Hovel D · Proceedings of UAI 1999, 305–313Microsoft ResearchDecision-theory model and system
02 · 10 sources
Interruptions and attention
What a ping, a switch or an unfinished task costs the mind. Read the topic
- 2015The attentional cost of receiving a cell phone notificationStothart C, Mitchum A, Yehnert C · Journal of Experimental Psychology: Human Perception and Performance 41(4):893–897Florida State UniversityLab experiment
- 2016"Silence Your Phones": Smartphone Notifications Increase Inattention and Hyperactivity SymptomsKushlev K, Proulx JDE, Dunn EW · Proceedings of CHI 2016, 1011–1020University of Virginia · University of British ColumbiaField experiment, two one-week conditions
- 2014An in-situ study of mobile phone notificationsPielot M, Church K, de Oliveira R · Proceedings of MobileHCI 2014Telefónica ResearchField study with phone logging and diaries
- 2017Productive, Anxious, Lonely: 24 Hours Without Push NotificationsPielot M, Rello L · Proceedings of MobileHCI 2017Telefónica ResearchField experiment: a day without notifications against a normal day
- 2019Batching smartphone notifications can improve well-beingFitz N, Kushlev K, Jagannathan R, Lewis T, Paliwal D, Ariely D · Computers in Human Behavior 101:84–94Duke University · Georgetown UniversityRandomized field experiment
- 2015Checking email less frequently reduces stressKushlev K, Dunn EW · Computers in Human Behavior 43:220–228University of British ColumbiaWithin-person field experiment
- 2008Effects of intelligent notification management on users and their tasksIqbal ST, Bailey BP · Proceedings of CHI 2008University of Illinois Urbana-ChampaignSystem with two user studies
- 2008The cost of interrupted work: more speed and stressMark G, Gudith D, Klocke U · Proceedings of CHI 2008, 107–110University of California, Irvine · Humboldt-Universität zu BerlinLab experiment
- 2004If Not Now, When? The Effects of Interruption at Different Moments Within Task ExecutionAdamczyk PD, Bailey BP · Proceedings of CHI 2004, 271–278University of Illinois Urbana-ChampaignLab experiment
- 2009Why is it so hard to do my work? The challenge of attention residue when switching between work tasksLeroy S · Organizational Behavior and Human Decision Processes 109(2):168–181University of MinnesotaTwo lab experiments
03 · 3 sources
Engagement is not benefit
Why opening an app more often is not proof that it helps. Read the topic
- 2026Impact of Push Notifications on Physical Activity and Sodium Intake Among Patients with Hypertension: Microrandomized Trial of a Just-in-Time Adaptive InterventionGolbus JR, Dorsch MP, Chen Y, Basu T, Luff E, Klasnja P, Newman MW, Skolarus LE, Dempsey W, Nallamothu BK · Journal of Medical Internet Research 28:e78218University of MichiganMicro-randomized trial within a 6-month randomized trial
- 2023How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized TrialBell L, Garnett C, Bao Y, Cheng Z, Qian T, Perski O, Potts HWW, Williamson E · JMIR mHealth and uHealth 11:e38342London School of Hygiene & Tropical Medicine · University College London · Harvard UniversityMicro-randomized trial
- 2019Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage AnalysisBaumel A, Muench F, Edan S, Kane JM · Journal of Medical Internet Research 21(9):e14567University of Haifa · Partnership for Drug-Free Kids · Northwell HealthUsage analysis of real-world panel data
04 · 3 sources
Autonomy and wording
Why people push back on advice, and the words that make it easier to hear. Read the topic
- 2022Reducing Psychological Reactance to Health Promotion Messages: Comparing Preemptive and Postscript Mitigation StrategiesRichards AS, Bessarabova E, Banas JA, Bernard DR · Health Communication 37(3):366–374Affiliation not yet confirmedExperiment with a one-week follow-up
- 1966A Theory of Psychological ReactanceBrehm JW · Academic Press (book)Affiliation not yet confirmedTheory (book)
- 2000The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of BehaviorDeci EL, Ryan RM · Psychological Inquiry 11(4):227–268University of RochesterTheory paper
05 · 10 sources
Trust and uncertainty
How people come to rely on automated advice, and how a system should admit what it doesn't know. Read the topic
- 1999Principles of mixed-initiative user interfacesHorvitz E · Proceedings of CHI 1999, 159–166Microsoft ResearchDesign principles
- 2004Trust in Automation: Designing for Appropriate RelianceLee JD, See KA · Human Factors 46(1):50–80University of IowaReview
- 1997Humans and Automation: Use, Misuse, Disuse, AbuseParasuraman R, Riley V · Human Factors 39(2):230–253Catholic University of America · Honeywell Technology CenterReview
- 2003The role of trust in automation relianceDzindolet MT, Peterson SA, Pomranky RA, Pierce LG, Beck HP · International Journal of Human-Computer Studies 58(6):697–718Affiliation not yet confirmedLab experiments
- 2019Algorithm appreciation: People prefer algorithmic to human judgmentLogg JM, Minson JA, Moore DA · Organizational Behavior and Human Decision Processes 151:90–103Harvard University · University of California, BerkeleySix experiments
- 2021To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingBuçinca Z, Malaya MB, Gajos KZ · Proceedings of the ACM on Human-Computer Interaction 5(CSCW1)Harvard UniversityOnline experiment
- 2016How Much Information? Effects of Transparency on Trust in an Algorithmic InterfaceKizilcec RF · Proceedings of CHI 2016, 2390–2395Stanford UniversityOnline field experiment
- 2019Communicating uncertainty about facts, numbers and sciencevan der Bles AM, van der Linden S, Freeman ALJ, Mitchell J, Galvao AB, Zaval L, Spiegelhalter DJ · Royal Society Open Science 6(5):181870University of Cambridge · University of Warwick · Columbia UniversityInterdisciplinary review
- 2017On Calibration of Modern Neural NetworksGuo C, Pleiss G, Sun Y, Weinberger KQ · Proceedings of ICML 2017, PMLR 70:1321–1330Cornell UniversityMachine-learning experiments
- 2017Selective Classification for Deep Neural NetworksGeifman Y, El-Yaniv R · Advances in Neural Information Processing Systems 30 (NeurIPS 2017)Technion – Israel Institute of TechnologyMachine-learning method and experiments
06 · 5 sources
Learning a person
How a system can help someone it barely knows yet, and why fresh answers beat old patterns. Read the topic
- 2023User Cold Start Problem in Recommendation Systems: A Systematic ReviewYuan H, Hernandez AA · IEEE Access 11:136958–136977Affiliation not yet confirmedSystematic review
- 2009Collaborative Filtering with Temporal DynamicsKoren Y · Proceedings of KDD 2009, 447–456Yahoo! ResearchMethod with large-scale evaluation
- 2026Negative Sampling in Recommendation: A Survey and Future DirectionsMa H, Xie R, Meng L, Feng F, Du X, Sun X, Kang Z, Meng X · ACM Transactions on Information Systems 44:1–44Shandong University · Tencent · University of Science and Technology of ChinaSurvey
- 2020Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical ActivityLiao P, Greenewald K, Klasnja P, Murphy S · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4(1):18University of Michigan · IBM Research · Harvard UniversityLearning algorithm with a pilot deployment
- 2025Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot StudyIkegaya M, Foo JC, Murata T, Oshima K, Kim J · JMIR Human Factors 12:e66750Shizuoka University · University of Alberta · Heidelberg UniversityPilot study, two groups
07 · 15 sources
Privacy and deletion
What deleting your data really means, from the law to the backup tape. Read the topic
- 1975The Protection of Information in Computer SystemsSaltzer JH, Schroeder MD · Proceedings of the IEEE 63(9):1278–1308Massachusetts Institute of TechnologyDesign principles
- 2016General Data Protection Regulation, Article 17: Right to erasureEuropean UnionEuropean UnionPrimary document
- 2016General Data Protection Regulation, Article 12(3): Time limits for responding to requestsEuropean UnionEuropean UnionPrimary document
- —Right to erasure (UK GDPR guidance)UK Information Commissioner's OfficeUnited KingdomPrimary document
- —Les durées de conservation des données (data retention periods)CNIL, French data protection authorityFrancePrimary document
- 2017Regulation on the Deletion, Destruction or Anonymization of Personal DataTurkish Personal Data Protection Authority (KVKK)TürkiyePrimary document
- —App Store Review Guidelines 5.1.1(v): Account sign-in and deletionAppleUnited StatesPrimary document
- —Data deletion on Google CloudGoogle CloudUnited StatesPrimary document
- 2025SP 800-88 Rev. 2: Guidelines for Media SanitizationU.S. National Institute of Standards and TechnologyUnited StatesPrimary document
- —Deleting an AWS KMS keyAmazon Web ServicesUnited StatesPrimary document
- —AWS Backup Vault LockAmazon Web ServicesUnited StatesPrimary document
- —Locking objects with Object LockAmazon Web ServicesUnited StatesPrimary document
- —Soft delete for Azure BackupMicrosoftUnited StatesPrimary document
- —Immutable vault for Azure BackupMicrosoftUnited StatesPrimary document
- 2020Guidelines 4/2019 on Article 25: Data Protection by Design and by DefaultEuropean Data Protection BoardEuropean UnionPrimary document
Where the research comes from
50 institutions in 10 countries.
Institutions are the authors' affiliations as printed in each publication. Naming them says where the research was done. They are not affiliated with Tre-Up and do not endorse Tre-Up or Pursue.
United States28 institutions
Arizona State University
Catholic University of America
Columbia University
Cornell University
Duke University
Florida State University
Georgetown University
Harvard University
Nahum-Shani & Murphy, 2026, Klasnja et al., 2019, Bidargaddi et al., 2018, Bell et al., 2023, Logg et al., 2019, Buçinca et al., 2021, Liao et al., 2020
Honeywell Technology Center
IBM Research
Massachusetts Institute of Technology
Microsoft Research
Northwell Health
Northwestern University
Partnership for Drug-Free Kids
Pennsylvania State University
Stanford University
University of California, Berkeley
University of California, Irvine
University of California, San Diego
University of Illinois Urbana-Champaign
University of Iowa
University of Michigan
Nahum-Shani et al., 2018, Nahum-Shani & Murphy, 2026, Klasnja et al., 2019, Bidargaddi et al., 2018, Klasnja et al., 2015, Golbus et al., 2026, Liao et al., 2020
University of Minnesota
University of New Mexico
University of Pittsburgh
University of Rochester
University of Virginia
United Kingdom5 institutions
London School of Hygiene & Tropical Medicine
University College London
University of Birmingham
University of Cambridge
University of Warwick
Canada3 institutions
University of Alberta
University of British Columbia
University of Calgary
China3 institutions
Shandong University
Tencent
University of Science and Technology of China
Israel3 institutions
Technion – Israel Institute of Technology
University of Haifa
Yahoo! Research
Japan3 institutions
Keio University
Shizuoka University
Yahoo Japan Corporation
Germany2 institutions
Heidelberg University
Humboldt-Universität zu Berlin
Australia1 institution
Flinders University
Slovenia1 institution
University of Ljubljana
Spain1 institution
Telefónica Research
The 14 primary documents come from the European Union, national data-protection authorities, Apple, Google, Amazon Web Services, Microsoft and the U.S. National Institute of Standards and Technology.
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