The Cognitive Strategies Behind Multimodal Health Sensemaking: A Menstrual Health Tracking Case Study
Bibliographic record
Abstract
The proliferation of commodity devices for gathering health data has led to multimodal health trackers that provide increasingly holistic views of people's well-being. As these trackers become more complex, it becomes harder for users to interpret how different signals interrelate in order to derive actionable insights and make informed health decisions. Addressing this challenge first requires understanding the cognitive and behavioral processes through which users interpret and make sense of multimodal data. In this paper, we use menstrual health tracking as a case study for investigating how individuals interpret multimodal health data. We conducted a 100-day longitudinal study with 20 participants who used a variety of health trackers to monitor signals relevant to menstrual health (e.g., hormones, sleep, mood). Through surveys and interviews, we identified that participants aligned their health goals with each device's perceived scope and approached multimodal data with hypotheses that involved pairs of signals. Our findings shed light on how a person's confidence in the sensemaking processes shapes their engagement with multimodality, leading to design recommendations that scaffold trust between users and their devices while encouraging exploration and staying true to users' evolving health goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".