Design Factors of Longitudinal Smartphone-based Health Surveys

Abstract

Phone-based surveys are increasingly being used in healthcare settings to collect data from potentially large numbers of subjects, e.g., to evaluate their levels of satisfaction with medical providers, to study behaviors and trends of specific populations, and to track their health and wellness. Often, subjects respond to such surveys once, but it has become increasingly important to capture their responses multiple times over an extended period to accurately and quickly detect and track changes. With the help of smartphones, it is now possible to automate such longitudinal data collections, e.g., push notifications can be used to alert a subject whenever a new survey is available. This paper investigates various design factors of a longitudinal smartphone-based health survey data collection that contribute to user compliance and quality of collected data. This work presents the design recommendations based on analysis of data collected from 17 subjects over a 1-month period.

Design, deployment, and usability of a mobile system for cardiovascular health monitoring within the electronic Framingham Heart Study

“…The ubiquity of personal smart devices has enabled new modalities for questionnaire-based data collection through app-based survey administration, and studies in recent years have aimed to optimize this mode of data collection and to understand its validity compared to traditional methods. 16 , 17 , 18 However, longitudinal adherence to mobile self-administered surveys is relatively underexplored, and the rate of decline in survey adherence differs dramatically depending on the study participants and context of survey deployment. 19 , 20 , 21 We observed a decrease in survey completion from baseline to the 3-month timepoint despite implementing reminder prompts through push notifications; however, this drop is less drastic than seen in other similar e-cohorts.…”

Fitness Monitoring System Based on Internet of Things and Big Data Analysis

“…The detection method of physiological parameters is single and cannot reflect the physical condition of the human body from many aspects [6]. Existing research mainly uses a single data source to evaluate the human body's physique, which cannot meet the need for personalized physique services [7][8][9]. Therefore, in the era of big data, data fusion algorithms are used to process human physiological data, so that the processed data can have important application value in human body fitness assessment [10,11].…”