  Chronic disease management and follow-up are vital for realizing sustained
patient well-being and optimal health outcomes. Recent advancements in wearable
sensing technologies, particularly wrist-worn devices, offer promising
solutions for longitudinal patient follow-up by shifting from subjective,
intermittent self-reporting to objective, continuous monitoring. However,
collecting and analyzing wearable data presents unique challenges, such as data
entry errors, non-wear periods, missing wearable data, and wearable artifacts.
We therefore present an in-depth exploration of data analysis challenges tied
to wrist-worn wearables and ambulatory label acquisition, using two real-world
datasets (i.e., mBrain21 and ETRI lifelog2020). We introduce novel practical
countermeasures, including participant compliance visualizations,
interaction-triggered questionnaires to assess personal bias, and an optimized
wearable non-wear detection pipeline. Further, we propose a visual analytics
approach to validate processing pipelines using scalable tools such as tsflex
and Plotly-Resampler. Lastly, we investigate the impact of missing wearable
data on "window-of-interest" analysis methodologies. Prioritizing transparency
and reproducibility, we offer open access to our detailed code examples,
facilitating adaptation in future wearable research. In conclusion, our
contributions provide actionable approaches for wearable data collection and
analysis in chronic disease management.
