Drinking Declined During France’s January Abstinence Challenge, Even as App Dropout Rose
A national temporary abstinence campaign can change drinking behavior in measurable ways, but only if people stay engaged long enough to record it. In this real-world analysis from France’s 2024 “Défi de janvier,” daily self-reported alcohol consumption fell across baseline drinking-risk groups among users who remained active in a smartphone application, while retention proved uneven and sensitive to age and baseline risk level.
The pattern was not subtle. Among users with continued app activity, alcohol intake moved downward across January, and the largest absolute decreases appeared in those who started with the highest baseline consumption.
The study drew on anonymized app-based data collected from 1 to 31 January 2024 in 2,032 adults using the application during the campaign. Investigators examined day-to-day alcohol trajectories alongside retention, stratifying users by age, sex, and baseline WHO drinking-risk level assessed during the reference week before the challenge began. Retention was modeled with Cox proportional hazards methods, while consumption change over time was assessed with daily trajectories and linear mixed-effects models.
Baseline Risk and Age Shaped Whether Users Stayed in the App
Two features stood out immediately in the retention analysis: higher baseline WHO drinking-risk levels and younger age were independently associated with lower retention. In practical terms, the people who may have had the most room to benefit from a structured abstinence challenge were also the most likely to disappear from the dataset before the month was over.
Sex did not independently predict retention, and binge drinking during the reference week was not associated with dropout. Those findings suggest that the app’s engagement gap was not simply a function of sex-based differences or a single pattern of episodic heavy drinking. Instead, retention appears to have tracked more closely with the broader combination of age and baseline drinking severity.
For digital alcohol interventions, this matters because retention is not a neutral technical metric. If younger users and higher-risk drinkers disengage earlier, then the apparent effectiveness of the campaign can be inflated by the users most likely to persist: people already more capable of carrying through a month-long behavior change effort. The dataset still showed meaningful reductions in alcohol use, but the attrition pattern places a hard ceiling on how representative those reductions can be across all participants who entered the challenge.
Weekly Drinking Rhythms Persisted Despite the January Challenge
Users who stayed engaged showed a familiar weekly drinking cycle, with peaks on Fridays and Saturdays across all drinking-risk categories. Even during a temporary abstinence campaign, alcohol use did not flatten into a uniform line; it still rose and fell with the week’s social structure.
That rhythm is clinically familiar. Weekend peaks often reflect social drinking habits, household routines, and the clustering of discretionary consumption away from workdays. A digital abstinence challenge may dampen overall intake, but it does not erase the behavioral architecture of drinking. The fact that the weekly pattern persisted across risk strata suggests that the app captured more than a simple beginning-versus-end-of-month contrast; it also recorded the cadence of real-life drinking behavior as users navigated ordinary triggers.
This is where smartphone monitoring has a practical advantage. Unlike a single pre- and post-intervention questionnaire, daily app entries can detect whether reductions occur steadily, whether they hold across the most vulnerable days of the week, and whether participants drift back toward routine use after weekends. In this cohort, the January trajectory remained downward overall, but it moved against a backdrop of stable weekend peaks rather than a uniform suppression of drinking on every day.
Heavier Drinkers Reduced More in Absolute Terms
Among users with available longitudinal data, mean daily alcohol consumption declined across the month in every baseline risk group. The largest absolute reductions occurred in those with the highest starting consumption, while lower-risk drinkers also showed smaller but consistent decreases.
This distribution is biologically and behaviorally plausible. People starting from a higher baseline have more headroom to reduce, and a time-limited abstinence challenge may create a stronger behavioral reset when the original pattern is more entrenched. Lower-risk drinkers, by contrast, may already be closer to a low-intake ceiling, so their absolute reductions naturally appear smaller even when the direction of change is the same.
The result should not be mistaken for a one-size-fits-all effect. A campaign like “Défi de janvier” may encourage some participants to stop drinking altogether for a period, while others simply reduce frequency or volume. The linear mixed-effects approach used here is well suited to capturing those gradual shifts in self-reported intake rather than forcing the data into a binary success-or-failure frame.
What the Real-World App Data Can and Cannot Prove
This analysis offers useful real-world evidence, but its limits are built into the design. The data were observational, self-reported, and dependent on continued app engagement. Users who stopped entering information contributed less information over time, which leaves any estimate of later-month consumption vulnerable to attrition bias.
Still, the size of the cohort and the consistency of the trajectory across risk groups strengthen the signal that a national temporary abstinence challenge, supported by a smartphone application, can coincide with measurable reductions in drinking during the campaign month. The study also identifies the users most likely to need extra engagement support: younger participants and those with higher baseline drinking risk.
For public health planners, the practical implication is straightforward. Digital abstinence tools can capture and potentially support meaningful reductions in alcohol use, but retention will likely determine how much benefit is realized in practice. A future intervention that improves early engagement, reduces dropout among higher-risk users, and sustains participation through the weekends could produce a more durable effect than the current app-based model alone.
The unresolved issue is not whether drinking can fall during a temporary abstinence campaign; it did. The sharper question is which engagement strategies can keep the highest-risk and youngest users in the program long enough for that decline to matter beyond January.