Why Use Mobile Tech in Research
The benefits of using digital and wearable technology in health studies.
Researchers are using mobile technologies more than ever, allowing them to conduct larger studies and gain insights from individuals in their everyday environment. This trend is expected to accelerate progress in mental health and other health and well-being research.
Mobile devices allow researchers to assess various physiological, environmental and contextual information simultaneously, both objectively (e.g., wearable sensors) and self-reported (e.g., app-acquired ecological momentary assessment). This low-burden and often passive assessment method enables researchers to collect data over extended periods in free-living conditions (outside medical settings.)
Benefits of using mobile technologies in health research
Depression, anxiety and other mental health issues often occur alongside medical conditions like diabetes, cancer or pregnancy. Mobile technologies can streamline the integration of mental health assessments into research on other health conditions. Traditionally, mental health studies relied on expensive lab tests and outdated questionnaires, which are often burdensome and limited in scope. These methods can also introduce recall biases, making it difficult to conduct large-scale, innovative research.
Mobile technologies offer a solution by collecting a wide range of data, including objective health metrics, self-reported information and environmental details. This approach helps researchers better understand mental health in everyday settings, test interventions in real-world scenarios, and explore how mental health interacts with other health conditions. By overcoming traditional research barriers, mobile technologies pave the way for more effective studies and improved health outcomes.
How mobile technologies can influence study design
The same mobile technologies that enable longitudinal data capture in free-living conditions allow for advanced study designs (such as ‘ecological momentary’ or real-time assessments) that accelerate groundbreaking discoveries and the development and deployment of precision interventions. Faculty at U-M’s Data Science for Dynamic Intervention Decision-Making Center (d3C) are working on developing novel research methods that are well-suited for studies using mobile devices, including:
- Adaptive Interventions
- Just-in-Time Adaptive Interventions (JITAI)
- Multimodal Adaptive Intervention (MADI)
- Multilevel Adaptive Implementation Strategies (MAISY)
- Personalized Just-in-Time Adaptive Interventions (P-JITAI)
Common Mobile Technologies Measurements
The chart below includes examples of objective health-related parameters captured by mobile technologies. This isn’t an exhaustive list, and available measures vary by device. For assistance with device selection, request a mobile consult or browse our resource center.
| DOMAIN | DESCRIPTION |
|---|---|
| Active Tasks | Replication of in-lab testing utilizing software and sensors from mobile technologies, such as speech recognition, accelerometer, gyroscope, motion detection, or camera. Examples include motor, auditory, visual, cognitive, speech, and physical functions. |
| Cardiovascular | Measurements of the heart and related autonomic nervous system measures, including heart rate, blood pressure, heart rate variability, RMMSD, cardiovascular age, and resting heart rate. |
| Demographics | De-identified demographics and summarized social determinants of health, such as age, gender identification, sexual orientation, ethnicity, and zip code. |
| Device Properties | Hardware and software information about the device, such as brand, model, firmware version, SIM IMSI, part number, serial number, etc. Note that some device properties may be considered PII, such as SIM and serial numbers, and may need to be avoided in the data set but can still be used in APIs and data pipelines. |
| Discrete Results | Single-value summarized results from surveys, tests, questionnaires, mood analysis models, etc. Also includes summarized motor, auditory and vision function tests and active tasks as well as medical diagnoses, lab results, and other miscellaneous discrete values not covered by another domain. |
| Environmental and Spatial | Measurements of a person's physical environment and surroundings, such as ambient temperature, ambient light, UV index, air quality index, weather, noise level, background sound and tone, room occupancy, smart home sensor measures, latitude and longitude, geolocation, geofencing, compass direction, beacon proximity, cell/Wi-Fi triangulation, and altitude. |
| Exercise Sessions | Detailed and summary physical activity data recorded during exercise sessions or active tasks, such as running speed, bike cadence, power meters, swim strokes, and calories burned. |
| Food and Nutrition | Estimated or user-entered information about food and nutrition, such as food diaries, calories consumed, food temperature, macronutrients, carbohydrates, protein, and meal size. |
| Interactions | Measures about the utilization of mobile technology in the context of social life, such as phone screen time, text messages, calls, and app usage. |
| Medications | Measurements related to medication use and compliance, such as insulin delivery, medication compliance scores, medication timing and prescription history. |
| Mental Health | Summary mental health information, such as last depression and anxiety screening scores, mindfulness sessions, meditation sessions, breathing sessions, latest mood diary results, latest sleep diary results, and applicable interdisciplinary or cross-domain aggregate measures. |
| Metabolic and Endocrine | Measurements of the body and metabolism including glucose, CGM EGVs and calibration events, body temperature, weight, body composition, cholesterol and triglycerides, circumference measurements, perspiration, VO2 max, metabolic lab values, sexual health metrics, and women's health metrics. |
| Physicial Activity | Summary physical activity data, such as daily steps, total wheelchair distance traveled, stairs climbed, mean altitude changes, active time, time-in-range at specific HR zones, and total swimming distance. |
| Respiratory | Respiratory system and related measurements, such as blood oxygen saturation, SPO2 nadir and mean, respiration rate, and apnea events. |
| Sleep | Sleep-related measurements include total sleep time, sleep stages, wake during the sleep period, sleep onset and offset, sleep efficiency, and circadian phase. |
| Study Management | Data necessary for day-to-day internal study management tasks, including participants, study information, configuration, and mappings to external systems. The data in this domain may not be fully de-identified, so appropriate precautions are needed before exporting or archiving. |
| Surveys, tests and questionnaires | User-entered data include sleep diaries, physical and mental health questionnaires, mood logs, social determinants of health questionnaires, detailed test scores, custom surveys, eConsenting, and other study management tasks. Also includes detailed, section-level and question-level mood scores, mental health screening results, and cognitive function scores from tests and active tasks, such as Stroop test, spatial memory, peripheral vision test, reaction time, PHQ-9, Kessler-6, GAD-7, PC-PTSD-5, PROMIS, or mood diary. |
| Other Physiological | Physiological measurements not otherwise categorized such as autonomic measures, stress, electrodermal activity (EDA), and speech recognition. |
Types of mobile devices used in health research
Mobile technology is constantly changing, with new products launched yearly. There are numerous options for consumer-grade devices, many with data on performance compared to gold-standard or robust use in health research studies. Examples include:
- Wearables. Devices that are worn on the body or as a clothing accessory, such as smartwatches, fitness trackers, continuous glucose monitors, smart rings, smart glasses, body temperature patches, body positioning patches, heart rate/EKG patches, shoe pods, chest straps, armbands, smart earpieces, connected blood pressure cuffs, and many more. Wearables typically require a Bluetooth connection to a smartphone, though some can use Wi-Fi, cellular, near-field communication (NFC), Bluetooth low energy (BLE), or Ant+.
- Nearables. Devices kept near the participant, typically placed around the home or on sports equipment, or hand-held health monitors, such as weight and body composition scales, ambient light sensors, connected thermometers, smart home devices and sensors, hand-held EKG, hand-held health monitors, alcohol consumption breathalyzers, bed-side sleep sensors, sleep mats, smartphone built-in sensors, bike speed and cadence sensors. Nearables typically utilize one or more connectivity methods, such as Bluetooth, Bluetooth Low Energy (BLE), NFC, Wi-Fi, cellular, Matter, Zigbee, Z-Wave, ANT+ and others.
- Mobile Apps. Survey and questionnaire apps, women’s health apps, active tasks, sensor data capture apps, speech recognition apps, participant-facing study management apps, chat and texting, mindfulness apps, exercise tracking apps, electronic sleep diaries, food & nutrition apps, OEM device-specific apps, etc. The most common apps used in research are smartphone native apps, smartwatch apps and widgets, mobile-optimized web apps, virtual reality (VR), augmented reality (AR), and smart glasses apps.