# Imputation Matters: A Deeper Look into an Overlooked Step in Longitudinal Health and Behavior Sensing Research

arXiv: https://arxiv.org/abs/2412.06018 · HTML: https://mlsanity.com/paper/2412.06018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 89 references that are in the map.
- First posted on arXiv 8 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking](https://mlsanity.com/paper/2302.10902/) (2023)
- [GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization](https://mlsanity.com/paper/2211.02733/) (2022)
- [Generalized Kalman Smoothing: Modeling and Algorithms](https://mlsanity.com/paper/1609.06369/) (2016)
- [Detecting Receptivity for mHealth Interventions in the Natural Environment](https://mlsanity.com/paper/2011.08302/) (2020)
- [Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention](https://mlsanity.com/paper/2403.05584/) (2024)
- [Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK](https://mlsanity.com/paper/2302.08591/) (2023)
- [Leveraging WiFi Network Logs to Infer Student Collocation and its Relationship with Academic Performance](https://mlsanity.com/paper/2005.11228/) (2020)
- [SeSaMe: A Framework to Simulate Self-Reported Ground Truth for Mental Health Sensing Studies](https://mlsanity.com/paper/2403.17219/) (2024)

## Built on by (most cited first, 1 of 1)

- [Causal Stories from Sensor Traces: Auditing Epistemic Overreach in LLM-Generated Personal Sensing Explanations](https://mlsanity.com/paper/2605.08590/) (2026)

## Abstract (arXiv)

Longitudinal passive sensing studies for health and behavior outcomes often have missing and incomplete data. Handling missing data effectively is thus a critical data processing and modeling step. Our formative interviews with researchers working in longitudinal health and behavior passive sensing revealed a recurring theme: most researchers consider imputation a low-priority step in their analysis and inference pipeline, opting to use simple and off-the-shelf imputation strategies without comprehensively evaluating its impact on study outcomes. Through this paper, we call attention to the importance of imputation. Using publicly available passive sensing datasets for depression, we show that prioritizing imputation can significantly impact the study outcomes -- with our proposed imputation strategies resulting in up to 31% improvement in AUROC to predict depression over the original imputation strategy. We conclude by discussing the challenges and opportunities with effective imputation in longitudinal sensing studies.