The science

We spent eighteen years
making stress measurable.

Then we found out measurement was only a third of the problem. Here is what we learned about the other two, and the research behind all of it.

The problem

Step tracking works, but stress is different.

If you are a thousand steps short at 6 PM, you know what to do to meet your goal. You trust the number (accuracy), you already believe activity matters (motivation), and the fix is obvious (actionability). All three things have to be true before tracking makes a difference.

Stress had none of them. If people do not trust the number, nothing happens. If they trust it but do not see why it matters, nothing happens. And if they trust it and want to do something about it but it is not clear what to do to fix it, still nothing happens.

CuesHub solves all three, backed by science.

01Accuracy

We spent eighteen years getting the accuracy right.

Our team was the first one to be funded by NIH to track stress from wearables in real life rather than in a lab. When we started in 2007, there were no wearables available so our team built the first wearable to collect physiological data in the field.

People ask us why it took eighteen years to get it right. It took this long because we wanted convience and accuracy for people in their everyday lives, not just in a lab. Physiological data is messy and difficult to extract intelligence from about our mental states, especially when collected from conviently worn devices like smartwatches. We failed multiple times but refused to give up.

Our first success come with the Mobile Open Observation of Daily Stressors (MOODS) study. Our aim was to validate our AI model and gain a deeper understanding about the reasons for stress in people's lives.

The validation tested how ready our model was for world-wide deployment. Most stress tracking research trains a model and tests it on the same data afterwards, which tells you the model fits that data and not much else. We deployed ours pre trained, with no fine tuning on the study at all. If it worked, it would work for anyone, unchanged.

Then something happened we had not designed for. Across a hundred days, the hundred and twenty two participants simply seeing their own data started changing their behavior on their own, in fourteen distinct ways, and their stress came down: ten percent less intense, and ten fewer stressors a month. We did not expect that. Most stress interventions do something in the moment and nothing usually lasts past the study.

That is when we realized that our AI was ready to help people change their behaviors.

The model went into the study pre trained, with no fine tuning on study data. Whatever accuracy it showed is accuracy anyone can have.

CuesHub's model matched self reported stress about as closely as cortisol, the stress hormone, does.

The papers underneath

02Motivation

Stress kept our ancestors safe, but today it hurts our career, health, and longevity.

Almost everyone treats stress as the price of doing well. It is also a driver of chronic disease, but that damage is slow and invisible, so the connection never feels real. We needed a way to make it real, and we found one: your heartbeats.

Your stress response was built to save your life. It raises your heart rate and pushes energy to your limbs so you can fight or run. It still does that today. But you are in a chair, nothing uses that energy, and it turns into low grade inflammation instead, which is a known path to chronic disease.

That rise in heart rate is what we measure as Workload Heart Rate. Here is the part that changed how we think about it. You get a lifetime budget of heartbeats, and spending it slower means living longer: five beats per minute off your resting heart rate is worth about a year. Spend heartbeats on exercise and you get them back, because your heart runs slower the rest of the day. Spend them on stress and they are gone. Exercise is an asset. Stress is a liability.

It diminishes performance too. Under stress your brain is busy calming your nervous system, and it will take the decision that makes the stress stop over the decision that is right for you or your team.

It also impacts your relationships with people. Under stress you have less patience for others. If you lead a team, the impact is even bigger. This is why our executive users check they are calm before going to important meetings. They also track their calmness live in meetings to ensure they make better decisions.

Every five beat per minute reduction in resting heart rate is associated with about one more year of life.

Heartbeats spent on exercise are recovered later in the day. Heartbeats spent on stress are not.

The papers underneath

03Actionability

Learn from your past, change your future with predictive nudges.

This part is harder than it looks. Tell someone they had a stressful day and you have added disappointment to a bad day. Interrupt them while it is happening and you have broken their work and probably made the situation worse.

That is why just in time intervention has not worked for stress, and adding large language models to it did not fix anything.

So we built around the thing that did work. Reflection is what moved people in the MOODS study. We show stress and calmness overlays on peoples calendars to help them find their triggers.

On top of self-reflection goes a predictive nudge carrying your coach's own words, sent before a hard moment while you can still do something about it, rather than in the middle of one when you cannot.

Seeing their own data led the 122 MOODS participants to start fourteen kinds of behavior change on their own, and brought stress intensity down ten percent.

The papers underneath

Model lineage

Five results, fourteen years apart.

Every milestone links to its paper. Read them rather than take our word for it.

2011

AutoSense

The first wearable sensor suite able to infer the onset, causality and consequences of stress in the field rather than the lab.

Read the paper
2011

Continuous inference

The companion result: psychological stress inferred continuously from sensor measurements collected in the natural environment.

Read the paper
2015

cStress

A gold standard for continuous stress assessment in the mobile environment, deployed in longitudinal studies with thousands of participants.

Read the paper
2024

MOODS

The hundred day nationwide study that validated the model in the field, and found that seeing the data changed behavior on its own.

Read the paper
2025

Pulse-PPG

An open source, field trained PPG foundation model for wearables, benchmarked across both lab and field settings. It powers CuesHub's engine on the watch.

Read the paper

Bibliography

Everything cited, in one place.

Twenty papers: the eighteen the app itself cites in the educational panel behind every number it shows you, plus the two that report the MOODS study directly.

  1. 1
    The neuroendocrinology of stress: the stress related continuum of chronic disease development

    Agorastos, A., & Chrousos, G. P. (2022). Molecular Psychiatry, 27(1), 502-513

  2. 2
    Everyday stress components and physical activity: examining reactivity, recovery and pileup

    Almeida, D. M., Marcusson-Clavertz, D., Conroy, D. E., Kim, J., Zawadzki, M. J., et al. (2020). Journal of Behavioral Medicine, 43(1), 108-120

  3. 3
    Sense2Stop: a micro randomized trial using wearable sensors to optimize a just in time adaptive stress management intervention

    Battalio, S. L., Conroy, D. E., Dempsey, W., Liao, P., Menictas, M., Murphy, S., et al. (2021). Contemporary Clinical Trials, 109, 106534

  4. 4
    Impact of changes in heart rate with age on all cause death and cardiovascular events in 50 year old men from the general population

    Chen, X. J., Barywani, S. B., Hansson, P. O., Ostgard Thunstrom, E., Rosengren, A., et al. (2019). Open Heart, 6(1), e000856

  5. 5
    Seeking positive strengths in buffering athletes' life stress burnout relationship: the moderating roles of athletic mental energy

    Chiou, S. S., Hsu, Y., Chiu, Y. H., Chou, C. C., Gill, D. L., & Lu, F. J. (2020). Frontiers in Psychology, 10, 3007

  6. 6
    AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field

    Ertin, E., Stohs, N., Kumar, S., Raij, A., Al'Absi, M., & Shah, S. (2011). ACM Conference on Embedded Networked Sensor SystemsCuesHub team

  7. 7
  8. 8
  9. 9
    Co variation of fatigue and psychobiological stress in couples' everyday life

    Doerr, J. M., Nater, U. M., Ehlert, U., & Ditzen, B. (2018). Psychoneuroendocrinology, 92, 135-141

  10. 10
    Association between change in heart rate over years and life span in the Paris Prospective 1, the Whitehall 1, and Framingham studies

    Gaye, B., Valentin, E., Xanthakis, V., Perier, M. C., Celermajer, D. S., Shipley, M., et al. (2024). Scientific Reports, 14(1), 20052

  11. 11
    cStress: towards a gold standard for continuous stress assessment in the mobile environment

    Hovsepian, K., Al'Absi, M., Ertin, E., Kamarck, T., Nakajima, M., & Kumar, S. (2015). ACM International Joint Conference on Pervasive and Ubiquitous ComputingCuesHub team

  12. 12
    Elevated resting heart rate, physical fitness and all cause mortality: a 16 year follow up in the Copenhagen Male Study

    Jensen, M. T., Suadicani, P., Hein, H. O., & Gyntelberg, F. (2013). Heart, 99(12), 882-887

  13. 13
    Brief review on physiological and biochemical evaluations of human mental workload

    Lean, Y., & Shan, F. (2012). Human Factors and Ergonomics in Manufacturing & Service Industries, 22(3), 177-187

  14. 14
    Inflammation: the common pathway of stress related diseases

    Liu, Y. Z., Wang, Y. X., & Jiang, C. L. (2017). Frontiers in Human Neuroscience, 11, 316

  15. 15
    Momentary stressor logging and reflective visualizations: implications for stress management with wearables

    Neupane, S., Saha, M., Ali, N., Hnat, T., Samiei, S. A., Nandugudi, A., et al. (2024). CHI Conference on Human Factors in Computing SystemsCuesHub team

  16. 16
    Continuous inference of psychological stress from sensory measurements collected in the natural environment

    Plarre, K., Raij, A., Hossain, S. M., Ali, A. A., Nakajima, M., Al'Absi, M., et al. (2011). ACM/IEEE International Conference on Information Processing in Sensor NetworksCuesHub team

  17. 17
    Pulse-PPG: An open source field trained PPG foundation model for wearable applications across lab and field settings

    Saha, M., Xu, M. A., Mao, W., Neupane, S., Rehg, J. M., & Kumar, S. (2025). Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(3)CuesHub team

  18. 18
    Wearable meets LLM for stress management: a duoethnographic study integrating wearable triggered stressors and LLM chatbots for personalized interventions

    Neupane, S., Dongre, P., Gracanin, D., & Kumar, S. (2025). Extended Abstracts of the CHI Conference on Human Factors in Computing SystemsCuesHub team

  19. 19
    Balancing exercise benefits against heartbeat consumption in elite cyclists

    Van Puyvelde, T., Janssens, K., Spencer, L., D'Ambrosio, P., Ray, M., Foulkes, S. J., et al. (2025). JACC: Advances, 4(10 Part 2), 102140

  20. 20
    Using minute ventilation for ambulatory estimation of additional heart rate

    Wilhelm, F. H., & Roth, W. T. (1998). Biological Psychology, 49(1-2), 137-150

Working on biosignals?

We publish, and we collaborate. If you are running a study that could use continuous, field grade strain measurement from consumer wearables, we would like to hear about it.

hello@cueshub.com