ReviewHealth psychology open2016
Relapse prediction: A meteorology-inspired mobile model.
Review in Health psychology open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition: Protocol for a Prospective Cohort Study.JMIR research protocols · 2025Article
- Self-guided Change: The most common form of long-term, maintained health behavior change.Health psychology openReview
- Toward the prevention of alcohol use disorders: Overdrinking (unintentional binge drinking) in a community sample.Health psychology openArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting relapses to binge drinking in non-dependent drinkers may now be possible with smartphones. Smartphones have been shown to help individuals reduce their drinking and may help binge drinkers accelerate that process. Predicting the weather has improved greatly over the past 50 years, but predicting a binge drinking episode may be less difficult. It is hypothesized that the number of factors with high predictive value for any particular individual may not be large. Collecting data over time, a smartphone should be able to learn which combination of factors has a high probability of leading to an episode of binge drinking.
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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 OpenQuestion graph.