Background: Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context
of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and
increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’
moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and
self-reported assessment to further integrate the 2 methodologies.
Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that
are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor
data were only collected while the participants filled out the questionnaires on their smartphones.
Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week
ecological momentary assessment study.
Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be
used to infer individuals’ self-reported states at later measurement occasions. The mean R
2
was approximately 0.31 (SD 0.29),
and more than half of the participants (119/158, 75.3%) had an R
2
of ≥0.18. Accuracy was only slightly attenuated compared
with earlier studies and ranged from 38.41% to 51.38%.
Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a
sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect
the accuracy and reliability of the inference