SoberVerse: A Personalized Addiction Recovery System for Relapse Prediction

SoberVerse: A Personalized Addiction Recovery System for Relapse Prediction

Mohammed Obaidullah

Department of Computer Science and Engineering Keshav Memorial Institute of Technology, Hyderabad, India

Dokka Sowmya

Department of Computer Science and Engineering Keshav Memorial Institute of Technology, Hyderabad, India

S Harshitha Kanthamani

Department of Computer Science and Engineering Keshav Memorial Institute of Technology Hyderabad, India

Voore Nithya

Department of Computer Science and Engineering Keshav Memorial Institute of Technology Hyderabad, India

Abstract – SoberVerse is a behaviour-aware addiction and recovery tracking system designed to provide data-driven insights into user habits by integrating emotional states, trigger factors and usage patterns. Existing solutions primarily focus on usage tracking and fail to capture contextual behavioural factors that influence relapse. To address this limitation, the system introduces a quantitative behavioural risk model that evaluates relapse probability using parameters such as mood, craving intensity and trigger frequency. The system is implemented using a reactive architecture with offline-first data management to ensure privacy and low-latency performance. User data is processed locally to generate real-time analytical insights and personalised interventions. Experimental evaluation demonstrates a reduction in high-risk usage patterns and improved behavioural awareness, with average craving frequency decreasing from 5.2 to 3.1 instances per day. The proposed approach provides a unified framework combining behavioural analytics, risk modelling and privacy-preserving system design for personalised recovery tracking.

KeywordsAddiction Recovery, Behavioural Analytics, Relapse Prediction, Risk Modelling, Habit Tracking, Digital Health Systems, Context-Aware Computing, Time-Series Analysis, Privacy-Preserving Systems

1. Introduction

   Addiction and habit-related behaviours pose significant challenges to individual health, often leading to relapse due to unrecognized behavioural patterns. Existing digital solutions primarily focus on tracking usage frequency and streaks, offering limited insight into contextual factors such as mood, cravings, and triggers. These limitations hinder effective intervention and long-term recovery. To address this gap, SoberVerse proposes a behaviour-aware system that integrates multi-dimensional behavioural data with predictive modeling techniques. By analyzing temporal patterns and contextual dependencies, the system aims to estimate relapse risk and provide personalized interventions, enabling proactive and data-driven support for sustained behavioural change in digital health environments.

2. Literature Review

   1. Existing Systems

      Current addiction and habit tracking systems primarily rely on rule-based mechanisms to record usage frequency, streaks, and reminder-driven notifications. While these tools facilitate basic behavioural monitoring, they lack predictive and analytical capabilities required for understanding complex behavioural patterns. Most existing platforms do not incorporate machine learning techniques to model relationships between contextual variables such as mood, craving intensity, and trigger conditions. As a result, these systems fail to provide accurate relapse prediction or adaptive intervention strategies, limiting their effectiveness in supporting long-term behavioural change.

   2. Advanced Approaches

      Recent research has explored data-driven methodologies such as Ecological Momentary Assessment (EMA) and Just-In-Time Adaptive Interventions (JITAI), which enable real-time behavioural data collection and context-aware feedback. Machine learning-based approaches, including classification and time-series models, have been applied to predict behavioural outcomes using user-generated data. Systems like MindShift leverage intelligent models to analyse user inputs and generate personalised interventions. These approaches demonstrate the effectiveness of integrating behavioural analytics with predictive modelling. However, many rely on static datasets or limited feature representations, reducing their adaptability in dynamic real-world environments.

   3. Research Gap

   Despite these advancements, existing solutions lack a unified machine learning framework that integrates temporal behavioural data, contextual features, and personalized risk prediction. Most approaches focus on short-term intervention without modelling sequential dependencies in user behaviour. Additionally, limited emphasis is placed on privacy-preserving architectures, as many systems depend on cloud-based data processing. There remains a significant gap in developing scalable, offline-capable systems that combine time-series modelling, behavioural feature extraction, and predictive risk analysis. Therefore, there is a need for a comprehensive framework that leverages machine learning to enable accurate relapse prediction and personalized intervention while ensuring data privacy and system efficiency.

3. System Design and Methodology

   1. System Overview

      The proposed SoberVerse system is designed as a

      • Personalization Layer: Continuously adapts model parameters based on user-specific behavioural patterns. Algorithm 1 Behaviour-Aware Risk Prediction Workflow Algorithm 1 Behaviour-Aware Risk Prediction

        behaviour-aware predictive framework that integrates multi-

        dimensional user data for relapse risk estimation. Unlike

        traditional tracking systems, the architecture follows a data-driven pipeline consisting of behavioural acquisition, feature extraction, predictive modeling, and intervention generation. The system processes temporal behavioural data to capture both contextual and sequential dependencies, enabling accurate and personalized risk prediction.

   2. Behavioural Data Representation

      User behaviour is modelled as a time-series sequence:

      = {, , , , }

      where represents normalized mood score, denotes craving intensity, is the trigger frequency vector,

      indicates usage pattern, and corresponds to sobriety duration. This formulation enables the system to capture

      evolving behavioural patterns over time, forming the foundation for predictive analysis.

   3. Feature Engineering Module

      Raw behavioural inputs are transformed into structured feature vectors:

      = {1, 2, . . . , }

      Key features include moving averages of craving intensity, mood variance, trigger frequency distributions, and temporal relapse indicators. These derived features enhance the models ability to capture hidden relationships and improve prediction accuracy.

   4. Risk Prediction Model

      The system employs a logistic regression-based predictive model to estimate relapse probability:

      1: Input: Behavioural data sequence X = {X1, X2, …, Xt} 2: Output: Predicted relapse risk score Rt

      3: for each time step t do

      4: Normalize behavioural inputs Xt Scale to [0,1] 5: Extract temporal features from Xt, Xt-1, …, Xt-n

      6: F computeFeatureVector(Xt) Statistical + temporal features

      7: Rt sigmoid(W · F + b) Risk probability

      8: if Rt > then

      9: TriggerIntervention(Rt) High-risk condition 10: else

      11: ContinueMonitoring() Normal state 12: end if

      13: UpdateModel(Xt) Incremental learning 14: end for

      15: Return Rt

4. Implementation Details

   1. System Implementation Overview

      The SoberVerse system is implemented as a modular, data-driven architecture designed to support real-time behavioural analysis and predictive modeling. The implementation integrates data acquisition, feature processing, machine learning-based risk prediction, and adaptive intervention within a unified pipeline. The system operates in an offline-first environment to ensure privacy, while maintaining efficient local computation for low-latency predictions.

   2. Data Collection and Preprocessing

      User-generated behavioural data is collected through structured input interfaces, capturing parameters such as mood, craving intensity, trigger occurrences, usage events,

      and sobriety duration. Each input is timestamped to enable

      (

      1

      = 1) = 1 + (1+2+3+4+)

      temporal analysis.

      The collected data undergoes preprocessing steps

      where represents relapse risk at time , and are learnable parameters. Additionally, temporal dependencies are incorporated using a sequential modeling approach:

      = (, 1, . . . , )

      This allows the system to account for historical behavioural patterns, improving predictive performance.

      1. System Modules

         The architecture consists of the following components:

         • Behaviour Acquisition Module: Captures user inputs including mood, cravings, triggers, and usage logs.

         • Feature Engineering Module: Processes and transforms raw data into meaningful features.

         • Risk Prediction Engine: Computes relapse probability using machine learning models.

         • Intervention Engine: Generates personalized recommendations when high-risk conditions are detected.

      including normalization, missing value handling, and noise reduction. Continuous variables such as mood and craving intensity are scaled to a uniform range [0,1], ensuring consistency across model inputs. Trigger data is encoded as a frequency-based vector, while usage patterns are represented as binary or count-based features. This preprocessing pipeline ensures high-quality input data for the prediction model.

   3. Feature Engineering and Temporal Processing

      To enhance predictive capability, raw behavioural inputs are transformed into higher-level features. Temporal feature extraction techniques are applied to capture trends and variations over time. Key engineered features include moving averages of craving intensity, mood variability, trigger recurrence rates, and relapse proximity indicators.

      Additionally, sliding window mechanisms are used to construct sequential data representations, enabling the system

      to incorporate historical behavioural patterns. This transformation allows the model to identify dependencies across multiple time steps, improving the accuracy of relapse prediction.

   4. Machine Learning Model Implementation

      The core prediction engine is implemented using a logistic regression model for probabilistic risk estimation. The model is trained to compute the likelihood of relapse based on the feature vector:

5. Results and Performance Evaluation

   1. Dataset Description

      The SoberVerse system was evaluated using a time-series behavioural dataset comprising mood, cravings, triggers, and usage patterns collected over a fixed period.

      Parameter	Value
      Average Cravings per Day	5.2
      Trigger Frequency	High
      Awareness Score	Low

      Table 1: Dataset Configuration

      1

      ( = 1) = 1 + (+)

      where represents the engineered feature vector,

      denotes the learned weights, and is the bias term.

      For temporal modeling, the system extends this approach by incorporating sequential inputs, allowing the model to approximate time-dependent behavioural patterns. Although lightweight models are used for efficient local execution, the architecture is designed to support advanced models such as Recurrent Neural Networks (RNN) or Long Short-Term Memory (LSTM) networks in future enhancements.

      1. Risk Evaluation and Decision Mechanism

         The computed probability score is compared against a predefined threshold to classify user states into low-risk and high-risk categories. Threshold selection is optimized based on validation performance to balance precision and recall. When the predicted risk exceeds the threshold, the system flags a potential relapse condition and triggers intervention mechanisms.

         The decision-making process is adaptive, allowing threshold values and model parameters to be refined based on user-specific behavioural patterns, thereby improving personalization over time.

      2. Intervention and Personalization Layer

         The intervention module generates context-aware recommendations based on predicted risk levels and behavioural context. These include motivational prompts, alternative activities, and awareness feedback tailored to the users current state.

         A personalization layer continuously updates model parameters using incremental learning principles, ensuring that the system adapts to individual behavioural variations. This dynamic adjustment enhances prediction accuracy and improves the relevance of interventions.

      3. System Integration and Performance Considerations

      The entire pipeline is integrated within a lightweight architecture that supports efficient local computation. Data storage and processing are handled on-device, minimizing latency and preserving user privacy. The system is optimized for real-time responsiveness, with prediction and intervention generation occurring within milliseconds of user input.

      This implementation ensures scalability, adaptability, and robustness, making SoberVerse a practical and effective solution for data-driven addiction recovery and behavioural risk prediction.

      This dataset enables the modelling of temporal behavioural patterns and supports effective training and evaluation of the predictive model.

   2. Model Performance Evaluation

      The proposed system was evaluated using standard machine learning metrics including accuracy, precision, recall, and F1-score. The performance was compared with a baseline linear scoring model to demonstrate the effectiveness of the proposed predictive approach.

      Table 2: Model Performance Comparison

      Model	Accuracy	Precision	Recall	F1-score
      Baseline (Linear)	0.68	0.65	0.70	0.67
      Logistic Regression	0.82	0.85	0.79	0.82
      Proposed Model	0.86	0.88	0.83	0.85

      The results demonstrate a noticeable reduction in high-risk behavioural indicators, indicating the effectiveness of predictive monitoring and intervention strategies.

   3. Behavioural Outcome Analysis

      To assess the impact of the system on user behaviour, key behavioural metrics were analysed bfore and after system usage.

      Table 3: Behavioural Outcome Analysis

      Metric	Before	After
      Average Craving Score	5.2	3.1
      High-Risk Days (%)	42%	21%
      Trigger Exposure Rate	3.8/day	2.1/day
      Predicted Risk Score	0.72	0.41

      The results demonstrate a noticeable reduction in high-risk behavioural indicators, indicating the effectiveness of predictive monitoring and intervention strategies.

   4. Feature Contribution Analysis

      To understand the influence of different behavioural factors on relapse prediction, feature importance analysis was performed. The results indicate that craving intensity and trigger frequency contribute most significantly to risk prediction, followed by mood variations and sobriety duration.

   5. Discussion

   The experimental results demonstrate that the integration of behavioural analytics with machine learning significantly enhances the systems ability to predict relapse risk. The proposed model effectively captures both contextual and temporal dependencies, leading to improved predictive accuracy. Additionally, the reduction in behavioural risk indicators suggests that the systems intervention mechanisms contribute positively to user outcomes. These findings validate the effectiveness of the proposed approach for data-driven addiction recovery and highlight its potential for real-world deployment.

6. Conclusion and Future Enhancements

   1. Limitations

      The proposed SoberVerse system has certain limitations. The dataset used for evaluation is limited in size and duration, which may affect the generalizability of the predictive model. Additionally, the current implementation relies on a logistic regression model, which may not fully capture complex behavioural patterns. The system also depends on self-reported user inputs, which can introduce bias. Furthermore, the absence of large-scale real-world deployment restricts comprehensive validation under diverse conditions.

   2. Future Work

      Future work will focus on enhancing the predictive capabilities of the system by integrating advanced machine learning models such as Long Short-Term Memory (LSTM) networks and deep learning-based sequence models to better capture temporal dependencies in behavioural data. The collection of large-scale real-world datasets will further improve model robustness and generalization. Additionally, the system can be extended to incorporate reinforcement learning for adaptive intervention strategies. Future enhancements will also include cross-platform deployment, integration with wearable devices for real-time data acquisition, and improved privacy-preserving mechanisms using federated learning techniques.

   3. Conclusion

   This paper presented SoberVerse, a behaviour-aware addiction recovery system that leverages machine learning techniques for relapse risk prediction. By integrating multi-dimensional behavioural data including mood, cravings, triggers, and usage patterns, the proposed system models temporal dependencies and generates personalized risk assessments. Experimental evaluation demonstrates improved predictive performance compared to baseline approaches, with higher accuracy, precision, and F1-score. The results also indicate a significant reduction in behavioural risk indicators, highlighting the effectiveness of the system in supporting recovery. The proposed framework establishes a scalable and data-driven approach for digital health interventions, contributing to the advancement of intelligent behavioural analytics systems.

7. References

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