Aditya Ponnada

We are presenting two papers at UbiComp 2025 (Espoo, Finland), both of which focus on mobile and wearable experience sampling. Jixin Li will be leading the presentations (and is on the job market!). Say hello if you are around and ask a lot of questions 🙂

+ This work was funded by the NIH. This shows how agencies don't contribute to public health but also results in new tools and methods for data collection and AI development.

Congratulations to all the co-authors, @shirlen3.bsky.social , Jixin Li, @genevievedunton.bsky.social , Wei-Lin Wang, Don Hedeker, and Stephen Intille. The details of the TIME study can be found here: timestudydocumentation.github.io/docs/build/h... (Reach out for questions or more info 😀)

This is one of the most intense EMA data collection studies leveraging smartphones and smartwatches. The μEMA method can be leveraged reliably for large-scale personalized data collection, just-in-time adaptive interventions, and human-in-the-loop ML that requires human feedback.

Finally, if you are wondering whether μEMA and EMA collect similar data. We compared the user-level variability captured by μEMA and EMA across 11 affect-based constructs. We found a moderate to strong +ve correlation between μEMA and EMA variability across constructs.

Third, when we measure user burden at the end of 12 months of data collection (only those who completed the study), μEMA was still perceived as less burdensome among those with possible survivor bias in data collection.

Second, we observed that regardless of the users' engagement with the data collection study (e.g., those who completed vs. withdrew vs. unenrolled), μEMA was consistently perceived as less burdensome, despite much higher interruption longitudinally.

This means, users who withdrew or were unenrolled because of poor engagement with EMA were twice as likely to answer μEMA surveys in real-world settings. We also suspect some survivor biases among those users who successfully completed 12 months of data collection.

First, we found that μEMA response rates were highest among those users who were unenrolled by research staff or voluntarily withdrew from data collection because of EMA burden. This response rate difference was negligible among those who completed 12 months of data collection.

As a result, we modeled ~1.3 million μEMA surveys and 14.9K EMA surveys collected across N = 177 participants, resulting in ~50K data collection days.

But for μEMA, each interruption presented only one micro-question with a yes/no type answer that can be responded to with a quick micro-interaction (taking hardly 2 seconds). In EMA, users answered long surveys with multiple back-to-back questions.

We used data collected in the TIME study, where users responded to surveys using μEMA and EMA in a burst-based longitudinal experiment. The μEMA method collected data on a smartwatch 4 times/hr for ~270 days. The EMA method collected data once/hr for ~90 days.

🥳 Pleased to share that our paper "Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA)" has been accepted at #IMWUT. In this paper, we conducted the first large-scale longitudinal comparison of μEMA and #EMA over a period of one year.

Totally love the fact that Copilot in RStudio uses my code comments in markdown as prompts. Has improved my productivity 2X for mundane data programming

What’s common between Twitter (or X) vs. Bluesky? They both don’t care about Indians. X amplifies raciest attacks on Indians. Bluesky is busy discussing if Elon was on H1B or not. If you are an Indian on non-immigrant visa, you are on your own. Just do your thing, work hard, and keep progressing

I just found out (from the post below) that the DARPA project I've been working on for the past couple of years was unexpectedly axed. The goal of the project: investigate foreign influence campaigns. Feeling like this is a sign of things to come...

Contextual Biases in Microinteraction Ecological Momentary Assessment (μEMA) Non-response | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Ecological momentary assessment (EMA) is used to gather in-situ self-report on behaviors using mobile devices. Microinteraction EMA (μEMA), is a type of EMA where each survey is only one single questi...

Most of my research involved personalizing the experience sampling methods. We proposed a novel method of using smartwatch interactions to collect self report at scale. One paper showed how smartwatches may be less prone to non response bosses in longitudinal studies. dl.acm.org/doi/abs/10.1...

Also given that data was collected in early adoption of the platform, will it change the way people use it?

The Ultimate Directory of tools and applications for Bluesky A curated collection of all things relating to the Blue Sky social media platform.

I’m finding this starter pack directory useful to rebuild network. blueskydirectory.com

I’m treating the starter pack labels as a broad intent search query and so posts that are not relevant from the child accounts can be automatically hidden from my timeline. Just thinking aloud

Yeah that would be nice. Right now those tabs are manual. But also, let’s say I follow “HCI researchers” starter pack. I’m following folks with the expectation that it’ll be about relevant HCI research. In a perfect world, I’ll happy to not see non-HCI posts from those accounts

I hope @bsky.app uses starter pack names as some kind of content preference labels. If I follow someone from starter pack named “experience sampling researchers”, it’ll be cool to see only filtered posts from that user on experience sampling 🤔

Also wondering hope these EMA surveys are delivered. On a mobile phone, numeric rating scales tend to be below the field biasing responses. Usually fully labeled 5 point scale has worked better for us. Of course you compromise on sensitivity

Hey Chris! Fellow Quant UXR here! Can you add me to the pack? 🙏

Add me!

data-is-better-together (Data Is Better Together) Building better datasets together

I am very excited to launch a new community initiative next week.

Let's build the largest open community dataset to evaluate and improve image generation models.

Follow:
huggingface.co/data-is-bett...

And stay tuned here

Thanks Micheal! We also have an entire portfolio of work addressing non response and response quality biases with EMAs. Most up to date list here: adityaponnada.github.io//

Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Techn... Ecological momentary assessment (EMA) is an approach to collect self-reported data repeatedly on mobile devices in natural settings. EMAs allow for temporally dense, ecologically valid data collection...

In short, our approach asks questions in EMA only when new information is gained or learned about the user. Check out the paper here and contact us for questions or feedback. 👏 to Jixin Li, Wei-Lin Wang, Genevieve Dunton, Stephen Intille. Link: dl.acm.org/doi/10.1145/...

Compared to a random selection of survey questions, our proposed method reduces imputation errors by 15-50% and survey length by 34-56% across real-world datasets, making surveys personalized to each user with reduced burden.