When is the right moment for a reminder or a nudge?

PursueUpdated 10 October 20267 sources

Short 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.

01Receptivity comes and goes

The research field of 'just-in-time adaptive interventions' starts from one observation: a person's ability to take in support changes quickly, so the right help at one moment can be too much at the next. Its framework asks designers to define decision points and explicit rules, and it counts giving nothing as one of the options[1].

A field study of 10,372 real notifications found that how fast people responded, and how disruptive they found it, depended on the kind of task they were doing, how complete it was and how complex. Even useful notifications were disruptive at the wrong moment[2].

02Same message, different moment

In a trial with 1,255 users of a workplace well-being app, identical tailored messages were randomly sent or withheld at six times a day. A message made people about 4% more likely to use the app in the next day, and around 12% more likely at 12:30 on weekends[3].

In a physical-activity trial, a suggestion raised steps in the next half hour, strongly at first and less as the weeks went on[4]. Holding notifications for a sensed good moment made people respond faster in a product used by very large numbers of people[5], and waiting for a natural break reduced frustration[6].

03Where the evidence stops

Most of these studies measure whether people open or respond, not whether they are better off. Most come from health apps. They support the idea that timing matters; they do not give a universal best time.

Something to try today

If you set reminders for yourself, put them at the natural seams of your day, such as the end of a meeting or the walk back from lunch, not in the middle of focused work.

Pursue

How Tre-Up approaches this

Pursue weighs the moment as heavily as the message. Its first question is whether a signal is worth the interruption it costs, a trade first described in Microsoft Research's work on attention-sensitive alerting[7].

Tre-Up is an iOS app in development. Pursue is the AI inside it. This note describes research and design intent, not results from the app. Nothing here is medical advice.

Sources

Summaries are written in our own words from each source's abstract, public record or official text. Follow the link to read the original. How we write these notes

  1. 1.Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA (2018). Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine 52(6):446–462.University of Michigan · Northwestern University · Pennsylvania State University · University of New MexicoOur summarydoi:10.1007/s12160-016-9830-8
  2. 2.Mehrotra A, Pejovic V, Vermeulen J, Hendley R, Musolesi M (2016). My Phone and Me: Understanding People's Receptivity to Mobile Notifications. Proceedings of CHI 2016, 1021–1032.University of Birmingham · University College London · University of Ljubljana · University of CalgaryOur summarydoi:10.1145/2858036.2858566
  3. 3.Bidargaddi N, Almirall D, Murphy S, Nahum-Shani I, Kovalcik M, Pituch T, Maaieh H, Strecher V (2018). To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App. JMIR mHealth and uHealth 6(11):e10123.Flinders University · University of Michigan · Harvard UniversityOur summarydoi:10.2196/10123
  4. 4.Klasnja P, Smith S, Seewald NJ, Lee A, Hall K, Luers B, Hekler EB, Murphy SA (2019). Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps. Annals of Behavioral Medicine 53(6):573–582.University of Michigan · University of California, San Diego · Harvard UniversityOur summarydoi:10.1093/abm/kay067
  5. 5.Okoshi T, Tsubouchi K, Tokuda H (2018). Real-world large-scale study on adaptive notification scheduling on smartphones. Pervasive and Mobile Computing 50:1–24.Keio University · Yahoo Japan CorporationOur summarydoi:10.1016/j.pmcj.2018.07.005
  6. 6.Iqbal ST, Bailey BP (2008). Effects of intelligent notification management on users and their tasks. Proceedings of CHI 2008.University of Illinois Urbana-ChampaignOur summarydoi:10.1145/1357054.1357070
  7. 7.Horvitz E, Jacobs A, Hovel D (1999). Attention-Sensitive Alerting. Proceedings of UAI 1999, 305–313.Microsoft ResearchOur summaryNo DOI

Keep reading

All notes
  1. 01When is it better for an AI assistant to say nothing?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).Timing and receptivity
  2. 02Do reminder apps change what people do, or just how often they open the app?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.Engagement is not benefit

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