# Timing and receptivity

> When help lands, and when the same help is just noise.

- Page: https://tre-up.com/research/topics/timing-and-receptivity
- Topic 01 of 7 in [Research Notes](https://tre-up.com/research) · Last reviewed 2026-10-07

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.

## Questions in this topic

- [When is the right moment for a reminder or a nudge?](https://tre-up.com/research/when-is-the-right-moment-for-a-reminder): 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. Markdown: https://tre-up.com/research/when-is-the-right-moment-for-a-reminder.md
- [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): 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). Markdown: https://tre-up.com/research/when-is-it-better-for-an-ai-assistant-to-say-nothing.md
- [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): 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. Markdown: https://tre-up.com/research/is-there-an-app-that-notices-when-my-day-is-slipping.md

## What this topic rests on (15 sources)

- **Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support**. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA (2018). Annals of Behavioral Medicine 52(6):446–462. https://doi.org/10.1007/s12160-016-9830-8
  - Institutions: University of Michigan (United States); Northwestern University (United States); Pennsylvania State University (United States); University of New Mexico (United States)
  - Design: Design framework
  - What it found: 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.
  - Where the evidence stops: 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.
  - Our summary: https://tre-up.com/research/sources/nahum-shani-2018-jitai
- **Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?** Nahum-Shani I, Murphy SA (2026). Annual Review of Psychology 77:679–703. https://doi.org/10.1146/annurev-psych-121024-044244
  - Institutions: University of Michigan (United States); Harvard University (United States)
  - Design: Review
  - What it found: 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.
  - Where the evidence stops: A conceptual review. It sets out challenges and directions; it does not give thresholds or effect sizes.
  - Our summary: https://tre-up.com/research/sources/nahum-shani-murphy-2026-jitai-review
- **My Phone and Me: Understanding People's Receptivity to Mobile Notifications**. Mehrotra A, Pejovic V, Vermeulen J, Hendley R, Musolesi M (2016). Proceedings of CHI 2016, 1021–1032. https://doi.org/10.1145/2858036.2858566
  - Institutions: University of Birmingham (United Kingdom); University College London (United Kingdom); University of Ljubljana (Slovenia); University of Calgary (Canada)
  - Design: Field study with phone logging and experience sampling; sample: 20 people, 10,372 real notifications, 474 questionnaires
  - What it found: 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.
  - Where the evidence stops: Twenty people, with many notifications each, and correlational data. It measures response and felt disruption, not long-term benefit.
  - Our summary: https://tre-up.com/research/sources/mehrotra-2016-receptivity
- **Real-world large-scale study on adaptive notification scheduling on smartphones**. Okoshi T, Tsubouchi K, Tokuda H (2018). Pervasive and Mobile Computing 50:1–24. https://doi.org/10.1016/j.pmcj.2018.07.005
  - Institutions: Keio University (Japan); Yahoo Japan Corporation (Japan)
  - Design: Large-scale deployment in a commercial app
  - What it found: 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.
  - Where the evidence stops: The outcome is opening and clicking, which is engagement, not benefit to the person. Transfer from news notifications to personal guidance is an assumption.
  - Our summary: https://tre-up.com/research/sources/okoshi-2018-adaptive-scheduling
- **Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps**. Klasnja P, Smith S, Seewald NJ, Lee A, Hall K, Luers B, Hekler EB, Murphy SA (2019). Annals of Behavioral Medicine 53(6):573–582. https://doi.org/10.1093/abm/kay067
  - Institutions: University of Michigan (United States); University of California, San Diego (United States); Harvard University (United States)
  - Design: Micro-randomized trial; sample: 44 adults, 6 weeks
  - What it found: 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.
  - Where the evidence stops: A small trial about physical activity. Strong as a method for testing whether to step in; weak as a rule for other behaviours.
  - Our summary: https://tre-up.com/research/sources/klasnja-2019-heartsteps
- **To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App**. Bidargaddi N, Almirall D, Murphy S, Nahum-Shani I, Kovalcik M, Pituch T, Maaieh H, Strecher V (2018). JMIR mHealth and uHealth 6(11):e10123. https://doi.org/10.2196/10123
  - Institutions: Flinders University (Australia); University of Michigan (United States); Harvard University (United States)
  - Design: Micro-randomized trial; sample: 1,255 users of a workplace well-being app, 89 days
  - What it found: 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%.
  - Where the evidence stops: The outcome is opening the app, not a change in behaviour or well-being. One commercial app, one population.
  - Our summary: https://tre-up.com/research/sources/bidargaddi-2018-to-prompt-or-not
- **Micro-Randomized Trials: An Experimental Design for Developing Just-in-Time Adaptive Interventions**. Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, Murphy SA (2015). Health Psychology 34(Suppl):1220–1228. https://doi.org/10.1037/hea0000305
  - Institutions: University of Michigan (United States); Arizona State University (United States); University of Pittsburgh (United States)
  - Design: Methods paper
  - What it found: 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.
  - Where the evidence stops: A design for running experiments, not a finding about any behaviour.
  - Our summary: https://tre-up.com/research/sources/klasnja-2015-micro-randomized-trials
- **Impact of Push Notifications on Physical Activity and Sodium Intake Among Patients with Hypertension: Microrandomized Trial of a Just-in-Time Adaptive Intervention**. Golbus JR, Dorsch MP, Chen Y, Basu T, Luff E, Klasnja P, Newman MW, Skolarus LE, Dempsey W, Nallamothu BK (2026). Journal of Medical Internet Research 28:e78218. https://doi.org/10.2196/78218
  - Institutions: University of Michigan (United States)
  - Design: Micro-randomized trial within a 6-month randomized trial; sample: 287 adults with self-reported hypertension; 187,517 randomization points
  - What it found: 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.
  - Where the evidence stops: A secondary analysis; the engagement results were exploratory. An older clinical group, so it transfers poorly to healthy adults with work goals.
  - Our summary: https://tre-up.com/research/sources/golbus-2026-mybpmylife
- **Effects of intelligent notification management on users and their tasks**. Iqbal ST, Bailey BP (2008). Proceedings of CHI 2008. https://doi.org/10.1145/1357054.1357070
  - Institutions: University of Illinois Urbana-Champaign (United States)
  - Design: System with two user studies
  - What it found: 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.
  - Where the evidence stops: Desktop tasks in a study setting. Detecting breakpoints worked, but telling the types apart was hard.
  - Our summary: https://tre-up.com/research/sources/iqbal-bailey-2008-breakpoints
- **If Not Now, When? The Effects of Interruption at Different Moments Within Task Execution**. Adamczyk PD, Bailey BP (2004). Proceedings of CHI 2004, 271–278. https://doi.org/10.1145/985692.985727
  - Institutions: University of Illinois Urbana-Champaign (United States)
  - Design: Lab experiment
  - What it found: 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.
  - Where the evidence stops: Lab tasks, short sessions.
  - Our summary: https://tre-up.com/research/sources/adamczyk-bailey-2004-if-not-now
- **Attention-Sensitive Alerting**. Horvitz E, Jacobs A, Hovel D (1999). Proceedings of UAI 1999, 305–313. https://www.microsoft.com/en-us/research/publication/attention-sensitive-alerting/
  - Institutions: Microsoft Research (United States)
  - Design: Decision-theory model and system
  - What it found: 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.
  - Where the evidence stops: A model and a system from 1999, not an outcome trial.
  - Our summary: https://tre-up.com/research/sources/horvitz-1999-attention-sensitive-alerting
- **Principles of mixed-initiative user interfaces**. Horvitz E (1999). Proceedings of CHI 1999, 159–166. https://doi.org/10.1145/302979.303030
  - Institutions: Microsoft Research (United States)
  - Design: Design principles
  - What it found: 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.
  - Where the evidence stops: Principles from experience with one system, not an experiment.
  - Our summary: https://tre-up.com/research/sources/horvitz-1999-mixed-initiative
- **Selective Classification for Deep Neural Networks**. Geifman Y, El-Yaniv R (2017). Advances in Neural Information Processing Systems 30 (NeurIPS 2017). https://arxiv.org/abs/1705.08500
  - Institutions: Technion – Israel Institute of Technology (Israel)
  - Design: Machine-learning method and experiments
  - What it found: 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.
  - Where the evidence stops: Image classification benchmarks; the mapping to personal advice is an analogy.
  - Our summary: https://tre-up.com/research/sources/geifman-el-yaniv-2017-selective-classification
- **Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity**. Liao P, Greenewald K, Klasnja P, Murphy S (2020). Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4(1):18. https://doi.org/10.1145/3381007
  - Institutions: University of Michigan (United States); IBM Research (United States); Harvard University (United States)
  - Design: Learning algorithm with a pilot deployment
  - What it found: 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.
  - Where the evidence stops: An algorithm paper with a small pilot; not a trial of outcomes.
  - Our summary: https://tre-up.com/research/sources/liao-2020-personalized-heartsteps
- **Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study**. Ikegaya M, Foo JC, Murata T, Oshima K, Kim J (2025). JMIR Human Factors 12:e66750. https://doi.org/10.2196/66750
  - Institutions: Shizuoka University (Japan); University of Alberta (Canada); Heidelberg University (Germany)
  - Design: Pilot study, two groups; sample: 28 university students, 2 weeks
  - What it found: 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.
  - Where the evidence stops: Twenty-eight healthy students, a pilot; the two groups' thresholds came from different samples.
  - Our summary: https://tre-up.com/research/sources/ikegaya-2025-personalized-criteria

## Other topics

- [Interruptions and attention](https://tre-up.com/research/topics/interruptions-and-attention): What a ping, a switch or an unfinished task costs the mind.
- [Engagement is not benefit](https://tre-up.com/research/topics/engagement-is-not-benefit): Why opening an app more often is not proof that it helps.
- [Autonomy and wording](https://tre-up.com/research/topics/autonomy-and-wording): Why people push back on advice, and the words that make it easier to hear.
- [Trust and uncertainty](https://tre-up.com/research/topics/trust-and-uncertainty): How people come to rely on automated advice, and how a system should admit what it doesn't know.
- [Learning a person](https://tre-up.com/research/topics/learning-a-person): How a system can help someone it barely knows yet, and why fresh answers beat old patterns.
- [Privacy and deletion](https://tre-up.com/research/topics/privacy-and-deletion): What deleting your data really means, from the law to the backup tape.

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