Life sciences company fights smoking addiction with a patient engagement app

Life sciences company fights smoking addiction with a patient engagement app

A global life sciences company worked with Modus Create to modernize its smoking cessation app and expand into digital biomarkers. The project began with a gamified hybrid app that improved patient engagement and evolved into an AWS-powered digital therapeutics (DTx) platform, capable of analyzing millions of data points. The application has improved quit success rates by 34% and enabled new regulatory therapy submissions.

OUR WORK INVOLVED

• Hybrid app development with Ionic
• UX redesign with gamification
• EHR data integration and real-time data synchronization
• Predictive ML model training
• Serverless architecture with AWS Lambda

Every attempt to quit cigarettes tells a story. When cravings strike, how users respond, what support networks they lean on, and what physiological signals emerge from wearables. Yet most of these insights remain invisible, making an already difficult path to cessation even harder to navigate.

A global life sciences leader partnered with Modus Create to bridge this gap. Starting with developing a gamified mobile app to boost patient engagement, the company went on to build a cloud-native digital therapeutics application to increase quit success rates.

Challenge

Declining mobile app engagement

The life sciences company had built a mobile app for smoking cessation using behavioral analysis and targeted prompts. Although this sounded good in theory, the app wasn’t seeing enough engagement. The application’s triggers and prompts offered little incentive to sustain behavior change. It struggled to predict quit success and lacked proactive guidance to keep users on track. As a result, many who downloaded the app relapsed quickly, dropping out of their cessation journey without connecting to critical support groups or healthcare providers in time.

The company reached out to Modus Create to address fundamental gaps in both patient engagement and clinical research. Working alongside the pharma company’s IT team, we identified three major challenges:

• Inconsistent experience: The existing smoking cessation app required separate Android and iOS codebases. This doubled maintenance effort, increased costs, and left users with an inconsistent experience across devices.
• Missing behavioral data: The company had no reliable way to capture patterns such as craving frequency, social support interactions, or early engagement signals, all of which could predict cessation outcomes.
• Stringent compliance requirements: Any new platform had to securely process sensitive health data while meeting HIPAA standards and aligning with FDA requirements for digital biomarker validation.

Addressing these challenges required a completely new direction. The company needed to develop a unified hybrid app that could both engage users and serve as the backbone for a cloud-native biomarker discovery platform.

Solution

Smarter therapies by developing a hybrid digital therapeutics (DTx) app

A full-stack product development team joined the pharma company to work on developing a new hybrid application. We worked in two-week sprints, with clean deliverables for each phase.

Fighting addiction with gamification

The product team began by consolidating separate Android and iOS builds into a single hybrid codebase using theIonic Framework. This cut maintenance costs and delivered a consistent experience across devices.

Next came a full UX redesign with gamification at the center. We introduced features like progress tracking, achievement rewards, and timely notifications to turn the cessation journey into a motivating experience.

The modernized application leveraged Ionic, Angular2, Redux, Cordova, PouchDB, TravisCI, and custom API integrations. Users could register via email or Facebook, log cravings, follow guided steps, and access practical tips and articles. Most importantly, the gamified approach encouraged many to move beyond self-guided quitting and seek counseling or medication support, extending the app’s impact into clinical care.

Building a biomarker discovery platform

With the mobile app now driving stronger engagement, the next step was to transform user behavior into clinical insights.

The product team built a cloud-native biomarker discovery platform that supported thousands of concurrent users, securely ingesting behavioral and physiological data from wearables, electronic health records, and in-app activity. This gave the life sciences company a single, compliant environment to capture and act on patient signals in real time.

We used several AWS solutions to make this possible. With AWS Amplify, the team accelerated cross-platform delivery for iOS, Android, and web, while Amazon Cognito ensured HIPAA-compliant authentication and secure access.

The platform unified medical and behavioral data in a single, compliant format, making it easier to blend electronic health records with real-time patient signals. Community features and engagement metrics provided both patients and clinicians with an up-to-date view of progress. Behind the scenes, predictive models continuously analyzed this data to flag relapse risk, stratify patients, and recommend timely interventions

The biomarker engine analyzed more than 2.5 million data points, including craving frequency, early app engagement, and social support interactions. From this, it identified 12 unique behavioral indicators that predicted cessation outcomes with 85% accuracy, far outperforming traditional clinical predictors.

Impact

Improving quit rates by 34%

The newly developed application delivered impact on multiple fronts. Patients experienced a more engaging and consistent quit-smoking journey, while researchers gained new tools to generate regulatory-grade evidence. Operationally, the company lowered costs and scaled its capabilities to thousands of users in real time.

• 20,000+ downloads shortly after launch and a 64% user recommendation rate.
• Quit success rates improved by 34%, supported by gamification and guided workflows.
• Analysis of 2.5 million+ data points uncovered 12 behavioral indicators that predicted cessation outcomes with 85% accuracy.
• Machine learning models achieved 94% accuracy for 30-day outcomes and 89% accuracy for six-month abstinence. The AWS platform processed 500,000+ biomarker data points daily with sub-second latency.
• Findings supported three investigational therapy submissions to regulatory authorities.

This project showed what’s possible when patient engagement and advanced analytics come together: healthier outcomes for individuals, faster evidence for researchers, and a new digital foundation for tackling one of healthcare’s most persistent challenges.