The study participants were adults aged 19 to 59 years. The upper age limit was set to minimize potential confounding effects of age-related neurophysiological and structural brain changes [23-26]. The sample size was informed by previous EEG and ML studies in alcohol-related populations with comparable sample sizes [27-29]. A total of 70 participants who self-reported problematic drinking behaviors were recruited. Participants were recruited between July 2023 and July 2024 through an online advertisement posted on AllLiveC, a platform specializing in clinical trial and research participant recruitment, targeting individuals with self-reported problematic drinking behaviors. Participants were not receiving formal treatment for AUD during the study period. Exclusion criteria included inability to understand the consent form or provide informed consent, pregnancy or plans for pregnancy, participation in another clinical trial, use of more than 2 phones, and claustrophobia or contraindications for magnetic resonance imaging scanning. Two participants withdrew early from the study, and 7 participants were excluded after study initiation due to age, failure to enter alcohol information in the mobile app, or missing visit 1. Additionally, 1 participant was excluded due to excessive discontinuities that prevented the construction of valid sequential windows. As a result, the final sample consisted of 60 participants in the final analysis (Figure 1).

The study protocol was approved by the institutional review board of Severance Hospital, Yonsei University Health System (4-2023-1590) and Samsung Medical Center (2023-02-115), Seoul, South Korea. All participants provided informed written consent before enrollment. To protect participant privacy and confidentiality, participant personal information was managed in a pseudonymized form throughout the study. Participants received compensation according to their level of app-use participation, with compensation averaging approximately KRW 200,000 (US $149) per participant.

At the baseline visit (visit 1), participants’ demographic information was collected, and assessments for HRV, EEG, and psychological and cognitive function were conducted. During this visit at Samsung Medical Center, Seoul, participants also completed interviews with the Mini-International Neuropsychiatric Interview to rule out comorbid psychiatric diagnoses and were evaluated for AUD based on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [30] criteria. Cognitive function was assessed using the Korean Wechsler Adult Intelligence Scale-IV and several psychometric assessments. Additionally, EEG and HRV tests were performed.

On visit 2, the Harujjan application was installed on participants’ mobile devices, and they received detailed, step-by-step instructions on its usage. Participants were guided on how to log their daily entries, including mood, sleep quality, and alcohol intake, and set reminders to ensure consistent usage throughout the study. Ecological momentary assessment (EMA) assessments were administered once daily via a mobile app and were completed at the end of each day at a participant-selected time. Automated push notifications were provided at the selected time, and each survey took approximately 5 minutes to complete. The daily EMA survey comprised 17 items across 4 domains: mood (overall mood and interest/pleasure), diet (meal consumption and meal quantity), alcohol-related behaviors (alcohol craving, alcohol consumption, drinking time, beverage type, amount consumed, drinking companions, drinking duration, drinking location, drinking motives, and blackout experience), and sleep (sleep duration, sleep satisfaction, and sleep-related daytime interference). Some items were conditionally presented based on participants’ responses. If participants consumed additional food or alcohol after completing the survey, they could submit additional entries until the following morning. Surveys were completed daily regardless of whether craving was experienced; no event-contingent assessments were used.

At the final visit (visit 3), after 4-week mobile app usage, participants completed an HRV test and a psychometric assessment.

Feature scaling is an essential preprocessing step in ML that ensures variables are on a comparable scale, thereby improving model stability and performance [56]. We applied standardization using the standard scaler, a widely used normalization technique [57]. Standardization transforms each feature to have zero mean and unit variance, computed as:

where z is the transformed value, X is the original feature value, μ is the mean, and σ is the SD [58]. To prevent data leakage, the scaler was fitted exclusively on the training data. The same transformation parameters were then applied to the validation and test sets.

The demographic characteristics of the participants are presented in Table 1. The average age of the participants was 34.17 (SD 8.62) years, with 25 males and 35 females included.

Among the 60 participants included in the final analytic sample, participants provided EMA responses for a mean of 27.88 (SD 1.35; median 28, IQR 28‐29; range 21‐30) days. A total of 29 (48.3%) participants provided responses for exactly 28 days, while 47 (78.3%) participants provided responses for at least 28 days. Some participants continued to submit EMA entries beyond the planned 28-day period, with 18 (30%) participants providing more than 28 days of data.

Before construction of the next-day prediction samples, a total of 1673 alcohol craving reports were submitted. The mean EMA-reported alcohol craving score was 2.21 (SD 1.13), with a median of 2 (IQR 1‐3) and a range of 1‐5.

The unimodal and multimodal models were compared across the aforementioned evaluation metrics, including accuracy, weighted F₁-score, macro F₁-score, and MAE. The results of the 5-class classification are reported as the mean, SD, and 95% CI of the mean across 100 runs with different random seeds (Table 2).

Overall, the multimodal model consistently outperformed the unimodal model across all evaluation metrics. In terms of accuracy, the multimodal model achieved 0.56 (SD 0.02; 95% CI 0.552-0.560) on the test set, compared to 0.45 (SD 0.01; 95% CI 0.443-0.447) for the unimodal dataset. Considering that a random classifier would achieve an accuracy of approximately 20% in a 5-class classification setting, and a majority-class classifier would yield approximately 37%, both models demonstrate meaningful predictive performance, with the multimodal model providing substantial improvement.

Given the imbalanced class distribution, F₁-scores were also evaluated. The unimodal model achieved a weighted F₁-score of 0.38 (SD 0.01) and a macro F₁-score of 0.24 (SD 0.04) on the test set, whereas the multimodal model improved these scores to 0.51 (SD 0.03) and 0.37 (SD 0.05), respectively. The improvement in macro F₁-scores suggests that the multimodal model better captures minority class patterns, leading to more balanced performance across classes. Furthermore, MAE was used to account for the magnitude of prediction errors given the ordinal nature of the prediction task. The unimodal model yielded an MAE of 0.90 (SD 0.03), whereas the multimodal model reduced this to 0.68 (SD 0.04). This indicates that the multimodal model not only improves the classification accuracy but also reduces the magnitude of prediction errors, producing predictions that are closer to the true ordinal labels. These results demonstrate that incorporating static baseline features into the temporal modeling framework provides complementary information that enhances both classification performance and error sensitivity.

Sample-level confusion matrices based on the modal prediction across the 100 runs for the unimodal and multimodal models, including raw-count and row-normalized representations, are provided in Figure S4 in Multimedia Appendix 1. The multimodal model showed improved class-specific prediction across several craving levels, including some less frequent classes. Notably, correct classification of craving level 5 increased from 0% in the unimodal model to 33% in the multimodal model, although performance for craving level 4 remained limited.

To further examine whether the multimodal model consistently improves performance at the individual level, we compared participant-wise accuracy between the unimodal and multimodal models. For each participant, the difference in accuracy between the multimodal and unimodal models was computed. The multimodal model consistently outperformed the unimodal model at the individual level, with 72% (43/60) of participants showing higher prediction accuracy with the multimodal model, indicating that the performance improvement was consistent across the majority of individuals. Participant-wise accuracy was significantly higher for the multimodal model than for the unimodal model. This improvement was confirmed by both the Wilcoxon signed-rank test (W=176; P<.001) and the paired t test (t₅₉=5.00; P<.001), treating the accuracy values as paired observations for each participant. The use of both nonparametric and parametric tests was motivated by uncertainty regarding the normality of the distribution. As illustrated in Figure 3, these findings demonstrate that the multimodal approach improves predictive performance not only at the aggregate level but also consistently across individuals.

To explore potential factors associated with participant-level prediction performance, we conducted post hoc Spearman correlation analyses. EMA response days were not significantly associated with accuracy in either the unimodal (ρ=0.035; P=.79) or multimodal (ρ=0.085; P=.52). In contrast, greater within-participant craving variability was associated with lower accuracy in both models (unimodal: ρ=−0.549; P<.001; multimodal: ρ=−0.671; P<.001). Craving entropy and the number of unique craving levels were likewise negatively associated with accuracy in both models, whereas the number of usable sequential samples was not significantly associated with accuracy. None of the examined participant characteristics was significantly associated with Δaccuracy. Detailed results are provided in Table S3 in Multimedia Appendix 1.

To examine the contribution of each baseline modality, we conducted a modality ablation analysis in which each baseline modality was used individually to initialize the RNN hidden state and compared with the unimodal zero-initialization model and the full multimodal model (Table 3). The modality ablation results provide interpretable insight into which signals drive performance gains. HRV features yield the strongest single-modality improvement, consistent with the established role of autonomic regulation in craving and relapse [19]. EEG features also contribute substantially, while psychological features alone produce smaller gains—possibly due to overlap with EMA-collected mood and behavioral data. These findings suggest that clinicians prioritizing resource-constrained deployment might first consider HRV monitoring as a complement to EMA, given its relative ease of collection compared to full EEG acquisition.

Figure 4 illustrates representative examples comparing predicted alcohol craving levels from the unimodal and multimodal models against the ground truth, respectively. For each prediction time point, the final predicted value was obtained by taking the mode of the 100 model outputs across different random seeds, with ties resolved by selecting the lower class label. Each row illustrates a distinct performance pattern. For participant 3, the multimodal model showed near-perfect tracking, whereas the unimodal model showed consistent deviation. For participant 6, the multimodal model achieved 5 of 5 correct predictions, compared with 2 of 5 for the unimodal model. For participant 27, both models showed comparable overall accuracy, but the multimodal model produced predictions closer to the true ordinal labels. For participant 44, high-craving minority class instances were better captured by the multimodal model. For participant 60, the multimodal model showed substantial accuracy improvement.

Overall, the multimodal model demonstrates more accurate and stable predictions compared to the unimodal model across participants. In several cases, the multimodal model achieves perfect or near-perfect accuracy, whereas the unimodal model exhibits substantial deviations from the true values. Importantly, the advantage of the multimodal model is not limited to overall accuracy. In cases where both models produce incorrect predictions, the multimodal model tends to generate predictions that are closer to the true ordinal label. This behavior is reflected in the lower MAE observed in the multimodal setting and suggests improved sensitivity to the ordinal structure of the target variable.

Furthermore, the multimodal model shows improved performance in capturing the minority class instances, allowing it to better capture less frequent but clinically important states. This is consistent with the observed improvement in the macro F₁-score. Although some participants exhibit limited improvement in overall accuracy, the multimodal model still produces predictions that are closer to the ground truth, indicating meaningful improvements beyond simple accuracy gains. Taken together, these results suggest that incorporating static baseline features enables the model to generate more precise, balanced, and clinically informative predictions at the individual level.

This study demonstrates that a multimodal AI approach improves the prediction of changes in next-day alcohol cravings over a 28-day period compared to a unimodal model based solely on digital phenotypes. By integrating digital behavioral data with neurophysiological and psychological features, the multimodal model achieved more accurate and consistent predictions across individuals. These findings suggest that static baseline characteristics provide subject-specific contextual information that shapes the interpretation of temporal behavioral patterns. By conditioning temporal predictions on individual-specific features, the model is better able to capture heterogeneity in craving dynamics across participants. Rather than assuming uniform temporal patterns across individuals, the multimodal approach enables more personalized and stable predictions of alcohol craving.

This study represents a novel attempt to integrate digital phenotypes collected through a smartphone app with various modalities using AI methods. A previous review study showed that smartphone-based digital phenotyping studies in AUD primarily use passive sensor data (eg, accelerometer, GPS, and app usage) to capture behavioral patterns such as movement and sociability, and rely mainly on self-reported alcohol use, craving, or mood [75]. Another study explored the association between smartphone-derived behavioral features and alcohol craving using EMAs [76]. That study demonstrated that sensor-derived features, such as location entropy and accelerometer-based activity, were significantly correlated with alcohol craving and mood states in individuals with AUD [76]. By contrast, this study advances this line of research by adopting a multimodal AI approach that enables individualized forecasting of daily alcohol craving. This study suggests that integrating all modalities provides better performance than using any single modality alone. This may be because the integration of these modalities captures diverse aspects of craving. This approach may not only improve predictive performance but also enhance the potential for clinical interventions.

While several approaches have been proposed to integrate static data with time-series data through feature concatenation [62,77], only a limited number of studies have explored incorporating static information by using feed-forward networks to summarize static features and initialize the hidden state of recurrent models [61,78]. While prior clinical studies adopting this framework predominantly rely on long short-term memory (LSTM) or gated recurrent unit (GRU)–based architectures, we demonstrate that a tiny lightweight vanilla RNN can achieve stable predictive performance in a small-sample multimodal setting. Given the short input sequence (window size=4) and limited sample size, we prioritized a lightweight vanilla RNN to minimize model complexity, consistent with the prior work noting that simpler RNN architectures require fewer computational resources and may be suitable for smaller datasets or short-term prediction settings [79]. LSTM architectures were originally developed to address difficulties in learning dependencies over extended temporal intervals [80], whereas gated recurrent architectures introduce additional parameters relative to vanilla RNNs [81]. Prior clinical time-series forecasting work has shown that vanilla RNN cells can remain competitive with LSTM and GRU cells while requiring fewer parameters and computational resources [81]. Accordingly, our primary aim was to evaluate multimodal integration within a deliberately compact recurrent framework rather than to identify the optimal sequence-learning architecture.

Combining all modalities yielded the greatest improvement in predictive performance compared with models based on each unimodality in predicting daily alcohol craving levels. In additional analyses, combining digital phenotype data with EEG or HRV improved model performance compared with using psychological features alone. A previous digital phenotype study showed that a combination of multimodal measurement and an AI approach has enhanced the performance of digital markers in phenotyping and diagnosis of major depressive disorder [82]. Prior work has similarly demonstrated that multimodal approaches outperform single-modality models in the diagnosis of internet gaming disorder and AUD, suggesting that integrating self-report–based neuropsychological measures with objective EEG-based neurophysiological data can enhance clinical assessment [83]. The improved performance of the multimodal AI model likely reflects the inherently multidimensional nature of alcohol craving, encompassing psychological, behavioral, and neurophysiological aspects [8,12,84]. Integrating frequency-specific EEG relative power features that reflect neurophysiological processes associated with craving, autonomic nervous system activity reflected by HRV, and subjective self-reports may provide complementary information that is critical for accurate prediction. Future research should continue to use multimodal frameworks to better account for the complex mechanisms underlying alcohol craving.

Post hoc analyses suggested that greater within-person craving variability and more diverse craving-level distributions were associated with lower participant-level prediction accuracy, whereas EMA completion and the number of usable sequential samples were not significantly associated with accuracy. Participants with greater craving variability and more diverse craving-level distributions may represent a more challenging forecasting problem. However, these associations were exploratory and should not be interpreted as causal.

Recently, just-in-time adaptive interventions have gained increasing attention as an approach to delivering interventions in real time by leveraging dynamically changing information about an individual’s state and context [85]. They adapt intervention strategies based on both dynamic data (eg, emotions, behaviors, and context) and stable characteristics (eg, demographics and baseline conditions) [85]. Our model may serve as a predictive component within the just-in-time adaptive interventions by identifying high-risk moments based on dynamic and individual-level data. For example, when predicted craving exceeds a predefined threshold, the system may trigger personalized interventions such as behavioral prompts or clinician alerts. Given that HRV alone provided substantial performance gains, a cost-effective deployment using wearable devices may be feasible without requiring full EEG acquisition. Although the sample size was relatively small, future studies with larger populations may help further validate our multimodal AI approaches, enhancing its potential for real-world clinical application. Moreover, the use of a tiny RNN to address temporal misalignment across modalities may represent a promising strategy for future studies.

The use of a tiny RNN architecture may have contributed to stable model performance by reducing the risk of overfitting in a relatively small sample size and short sequence settings. While more complex architectures such as LSTM or transformer models may offer additional flexibility, given the limited data and the use of short sliding windows, these models may introduce additional model complexity and a large number of parameters. Despite its simplicity, the present approach was sufficient to capture meaningful temporal dependencies in multimodal data.

Several limitations of this study should be noted. First, the sample size was relatively small to be considered representative of all problematic alcohol drinkers, which may limit the generalizability of the findings. Therefore, further investigations with larger sample sizes should be warranted. Second, this study was conducted in a single South Korean cohort without external validation using an independent dataset. This may limit the generalizability of the findings to other populations and settings. Future studies should therefore validate the model in independent and more diverse cohorts. Third, alternative sequence-learning and machine-learning approaches, including LSTM, GRU, temporal convolutional networks, and gradient-boosting methods, were not systematically evaluated in this study. We intentionally prioritized a lightweight vanilla RNN given the short temporal window and limited sample size; however, direct comparisons across alternative model classes are needed to determine whether the observed multimodal benefits generalize across architectures. Fourth, although only 29 (48.3%) of 60 participants provided EMA responses for exactly 28 days, all participants in the final analytic sample contributed at least 21 days of EMA data (range 21‐30 days). Within this observed range, EMA response days were not significantly associated with participant-level accuracy. Therefore, this study could not determine a lower minimum EMA completion threshold, such as 8 days, required for satisfactory prediction performance. Fifth, the overall predictive performance was modest, as reflected in the accuracy and weighted and macro F₁-scores, indicating the need for further model optimization and validation. Sixth, next-day forecasting may not fully capture individual and circadian variability in craving dynamics; therefore, future studies should consider more frequent assessments and alternative forecasting intervals [16].

Validation using larger and more diverse cohorts is necessary to enhance the generalizability of the model across broader populations of individuals with problematic alcohol use, particularly by incorporating heterogeneous clinical profiles and varying levels of craving variability. Second, future research should focus on improving model performance for individuals with high intra-individual variability in craving, potentially through advanced modeling approaches that better capture temporal dynamics, such as sophisticated sequence models or attention-based architectures.

Finally, integrating predicted high-risk moments of craving with timely and personalized interventions—such as alerts, behavioral strategies, or therapeutic support—may enable the model to serve as a core component of a digital therapeutic system for preventing alcohol use and relapse. Building on this study, future work will focus on developing an app-based intervention program for alcohol craving regulation that uses the predictive model to deliver real-time personalized support.

This study attempts a novel approach to predicting daily alcohol craving in individuals with problematic alcohol use using AI algorithms that integrate digital phenotypes and neurophysiological and psychological data. The multimodal model outperformed single-modality approaches in predicting daily craving levels. Notably, integrating all modalities showed better performance than models combining digital phenotypes with either EEG, HRV, or psychological features. This study emphasizes the importance of multimodal approaches for daily alcohol craving prediction and suggests that these approaches may enable timely interventions to prevent problematic alcohol use.