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Longitudinal Studies

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

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Quantifying Maternal Health Using Digital Phenotyping: Protocol for a Longitudinal Observational Study.

Amanda Glime^1,2, Taysir Mahmoud³, Soni Rusagara²

¹Bouvé College of Health Sciences, Northeastern University, Boston, MA, United States.

JMIR Research Protocols

|October 8, 2025

Summary

This summary is machine-generated.

This study introduces a digital phenotyping protocol using smartwatches and smartphones to continuously monitor maternal health during pregnancy and postpartum. The goal is to improve understanding of maternal-infant well-being through objective, real-time data collection.

Keywords:

digital phenotyping ecological momentary assessment longitudinal data pregnancy smartphone smartwatch

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Area of Science:

Digital health
Maternal-fetal medicine
Behavioral science

Background:

A novel digital phenotyping protocol is presented for continuous, objective measurement of behavioral, physiological, and contextual data during pregnancy and postpartum.
This method utilizes passive sensing from smartwatches and smartphones, alongside ecological momentary assessments (EMAs), to capture critical temporal changes and maternal-infant outcomes.

Purpose of the Study:

To develop and optimize a longitudinal data collection protocol for digital phenotyping in pregnant and postpartum individuals.
To minimize participant burden, enhance retention, and evaluate the comparative value of wearables versus smartphones for data collection.

Main Methods:

Data collection from 30 nulliparous participants from the third trimester through 6 weeks postpartum.
Integration of surveys, daily/weekly EMAs, and passive sensing (activity, vitals, sleep, location) via smartphones and Garmin smartwatches.
Utilizing the Huckleberry app for newborn data logging post-delivery, in collaboration with specialized research labs.

Main Results:

Study funded in August 2024, with data collection projected from October 2025 to July 2026.
Planned assessments include retention rates, data completion rates, smartwatch wear time, and Huckleberry app data volume.
Digital phenotyping will be used to explore prediction of breastfeeding, delivery outcomes, and maternal-infant well-being.

Conclusions:

The protocol integrates digital phenotyping into pregnancy and postpartum research for real-time maternal well-being indicators.
Aims to establish data completion rates and determine sample size for future studies via power analysis.
Potential to transform maternal health clinical interventions through integrated smartphone and wearable sensor data.