With smartphones in nearly every pocket (around 91% of pockets in the US)1 and smartwatches on countless wrists, mobile health (mHealth) has become a powerful tool for managing and improving health. mHealth apps can be used to target behaviors such as to physical activity, nutrition, sleep, stress management, and mindfulness. But what exactly is mHealth, and how is it reshaping health behavior interventions? This article will look at what makes these technologies so impactful, their benefits and limitations, and how they compare to traditional methods of behavior change.
What is mHealth?
mHealth refers to the use of mobile phones and wireless technology to support medical care and health services. This includes everything from disease tracking to chronic disease management and even behavioral change interventions. Common applications of mHealth include consumer education for preventive healthcare, disease surveillance and epidemic tracking, treatment support for chronic conditions, and remote chronic disease management.2
mHealth solutions take many forms, including mobile apps, wearable sensors, and real-time tracking tools. Smartphone and smartwatch devices are packed with sensors that help track movement, heart rate, and environmental factors. For reference, the iPhone 15 has a proximity sensor, ambient light sensor, accelerometer (motion), gyroscope (rotation), barometer (pressure), magnetometer (compass), FaceID (recognition) and LiDAR (3D mapping).3 To reference smartwatch sensors, the newest Apple Watch has heart sensors (rate and rhythm), temperature sensor, compass, altimeter, accelerometer, gyroscope, and ambient light sensor.4
These sensors collect data and organize it into meaningful metrics that can be used for personalized health tracking, making mHealth a key player in modern healthcare. These tools alone have the potential to reach the masses.
How is mHealth different from telehealth?
While mHealth is a subset of telehealth, they are not the same. mHealth is strictly mobile-based tools, whereas telehealth can involve a range of digital communication methods such as video calls or other forms of remote patient monitoring (think remote heart monitors or continuous glucose monitors). The bottom line is telehealth can be delivered via mHealth platforms, but not all telehealth is mHealth. mHealth is often more accessible and portable, making it particularly useful for behavior change interventions, and may not require any new equipment.
mHealth is more than just a convenience for individuals; it has global health implications. It enables continuous health tracking without frequent visits to a provider’s office, facilitates direct communication between patients and providers, and integrates with electronic health records. These capabilities enhance care coordination and support more comprehensive healthcare.
How do mHealth approaches differ from traditional behavior change methods?
Outside of healthcare, mHealth has become widely adopted in the health behavior change field. There are over 350,000 mHealth applications available for download.5 Health behavior change has long been studied using psychological and behavioral theories, such as Social Cognitive Theory, Self-Determination Theory, and the Theory of Planned Behavior. These theories remain valuable and should still guide the design of mHealth applications.
However, traditional behavior change interventions differ in their delivery and follow-up. They often involve assessing baseline behavior, making predictions, and conducting follow-ups over time. This approach has several limitations, including reliance on self-reported data, which is often inaccurate due to recall issues and biases such as social desirability bias. Data collection at set intervals (e.g., baseline, six-month, and twelve-month follow-ups) provides only snapshots of behavior, potentially missing key contextual factors and behavioral triggers. Traditional interventions also have limited reach, often requiring participants to be in specific locations at specific times. Additionally, maintaining adherence to behavior change can be challenging due to a lack of consistent engagement strategies.
So, how does mHealth address these limitations and offer a more effective approach to sustained behavior change? The key advantage of mHealth applications lies in their ability to leverage real-time, real-world data and deliver intervention messages directly to the users’ mobile devices. Let’s look at a few examples of this in practice. mHealth solutions (can) provide objective measurement. Not all mHealth applications utilize objective measures, but the most effective ones do. Sensors embedded in smartphones, smartwatches, and smart rings provide continuous, accurate data without relying on self-reported information. mHealth solutions allow for real-time interventions. Apps can prompt behavior change at key moments, such as reminding users to stand up after prolonged sitting or reminding users that they are only 15 minutes away from reaching their daily activity goal. The ability to intervene in real-time is a game changer.
mHealth apps allow for personalization. Intervention messaging can be tailored based on individual data inputs. For example, a smartwatch can track a user’s typical activity levels, set goals based on baseline data, and encourage movement at strategic times throughout the day. Many mHealth apps use Just-in-Time Adaptive Interventions (JITAIs), which adjust interventions based on an individual’s needs, timing, and behavior patterns. Lastly, mHealth approaches are more scalable than traditional behavior change approaches. Digital solutions can reach large populations at minimal cost compared to in-person programs. They remove geographic barriers and allow for broader reach without being limited by time or location constraints. By addressing these challenges, mHealth provides a more dynamic, responsive, and scalable approach to behavior change than traditional methods.
What are the limitations and challenges of mHealth?
Like most tools, mHealth comes with challenges. Issues with data accuracy, long-term engagement, and privacy and security are key concerns. Some apps claim to measure activity or health metrics but may lack accuracy. Knowing which apps are validated is important. Additionally, many users abandon health apps once the initial enthusiasm fades. Applying user-centered design principles and incorporating engagement strategies are essential for sustaining behavior change.
Data privacy and security are also concerns, as with any technology. Users should understand data policies, how their data is protected, and who has access to it. Lastly, accessibility barriers and technology literacy can limit the reach of mHealth apps. Unfortunately, those who could benefit most from these interventions are often the ones who are missed.
What does the future look like for mHealth?
For mHealth to be truly effective, technology should be designed to fit into everyday life rather than requiring users to adapt their lives around it. Solutions that seamlessly integrate with daily routines—such as passive tracking, habit-forming nudges, and personalized interventions—are the most effective in driving long-term health behavior change.
While challenges remain, mHealth continues to be a powerful tool for health behavior change. As technology advances, mHealth apps will become more refined and effective in helping people lead healthier lives – one tailored intervention at a time.
References
- Pew Research Center (2024, Nov 13). Mobile Fact Sheet: Tech Adoption Trends. https://www.pewresearch.org/internet/fact-sheet/mobile/
- U.S. Food & Drug Administration (2020, Sept 22). What is Digital Health. Digital Health Center of Excellence. https://www.fda.gov/medical-devices/digital-health-center-excellence.
- Apple, Inc. (2025). iPhone 15 Tech Specs. https://www.apple.com/iphone-15/specs/
- Apple, Inc. (2025) Healthcare: Health on Apple Watch. https://www.apple.com/healthcare/apple-watch/
- Wells C, Spry C; Authors. An Overview of Smartphone Apps: CADTH Horizon Scan. Ottawa (ON): Canadian Agency for Drugs and Technologies in Health; 2022 Feb. Available from: https://www.ncbi.nlm.nih.gov/books/NBK595384/
Images were created using Microsoft Copilot AI.