ArticleSensors (Basel, Switzerland)2026
Predicting Momentary Mood in Daily Life from Accelerometer Data: Evaluating Single vs. Multiple Sensor Locations Using Machine Learning.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Effects of Different Marker Set Configurations on Tennis Stroke Recognition Performance: A Three-Dimensional Kinematic Study Based on MiniRocket.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Physical activity is a key lifestyle factor for mental health prevention, yet the influence of accelerometer placement on mood prediction remains unclear. We merged high-resolution acceleration data and Ecological Momentary Assessment (EMA) mood reports from 259 healthy participants across three ambulatory studies (SedMood, 24 hrCog, HO). Additionally, 15 min pre-assessment movement windows consisting of raw triaxial acceleration (64 Hz) from hip, thigh, chest, and wrist sensors were paired with six-item mood EMA queries. Features (e.g., mean, entropy, spectral power) were extracted and fed into gradient-boosted decision tree models (XGBoost), trained separately for energetic arousal, valence, and calmness. Performance was measured using the metrics MAE, RMSE and R
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.