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Abstract Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R²≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research. 

Abstract Teaching a new concept through gestures—hand movements that accompany speech—facilitates learning above‐and‐beyond instruction through speech alone (e.g., Singer & Goldin‐Meadow,). However, the mechanisms underlying this phenomenon are still under investigation. Here, we use eye tracking to explore one often proposed mechanism—gesture's ability to direct visual attention. Behaviorally, we replicate previous findings: Children perform significantly better on a posttest after learning through Speech+Gesture instruction than through Speech Alone instruction. Using eye tracking measures, we show that children who watch a math lesson with gesturedoallocate their visual attention differently from children who watch a math lesson without gesture—they look more to the problem being explained, less to the instructor, and are more likely to synchronize their visual attention with information presented in the instructor's speech (i.e.,follow along with speech) than children who watch the no‐gesture lesson. The striking finding is that, even though these looking patterns positively predict learning outcomes, the patterns do notmediatethe effects of training condition (Speech Alone vs. Speech+Gesture) on posttest success. We find instead a complex relation between gesture and visual attention in which gesturemoderatesthe impact of visual looking patterns on learning—following along with speechpredicts learning for children in the Speech+Gesture condition, but not for children in the Speech Alone condition. Gesture's beneficial effects on learning thus come not merely from its ability to guide visual attention, but also from its ability to synchronize with speech and affect what learners glean from that speech.