skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective
Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology—and the unprecedented scope and quantity of data it generates—has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.  more » « less
Award ID(s):
2142794
PAR ID:
10465926
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Annual Review of Public Health
Volume:
44
Issue:
1
ISSN:
0163-7525
Page Range / eLocation ID:
131 to 150
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)
    Prolonged sedentary behavior poses significant health risks, calling for interventions that promote active lifestyles. For older adults, every physical activity, no matter how small or significant, plays a vital role in their quality of life. However, many interventions aimed at reducing sedentary behavior have overlooked the unique needs and preferences of older adults. In this study, we explore design opportunities for supporting behavior displacement---replacing sedentary time with active movements---as a potential strategy for intervening sedentary time among older adults. Through a 7-day diary study and interviews with 13 participants, we uncovered key factors, such as attention demand, productivity and quality of activities, physical fatigue, as well as social norms, that influence their decisions to engage in displacement. We also identified sequential and concurrent displacement strategies and the contexts in which each was employed. Our findings highlight the need for designing personalized, adaptive interventions that respect the diverse preferences and agency of older adults. 
    more » « less
  2. ; ; ; (, Youth & Society)
    The advent of Internet-enabled mobile digital devices has transformed U.S. adolescent technology use over the last decade, yet little is known about how these changes map onto other health-related behaviors. We provide a national profile of how contemporary technology use fits into adolescents’ daily health lifestyles compared with the previous generation, with particular attention to whether and for whom technology use displaces time spent in sleep or physical activity. Time diaries were collected from 11- to 17-year-olds in 2002-2003 ( N = 1,139) and 2014-2016 ( N = 527) through the U.S. Panel Study of Income Dynamics Child Development Supplement. Contemporary adolescents spent 40 minutes more per week in technology-focused activities, but their composition was more varied compared with the earlier cohort. Contemporary technology use was predictive of less time in physical activity, and adolescents who engaged in frequent video game play spent less time in physical activity compared with peers with other technology use profiles. 
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al (, ACM)
    Family informatics often uses shared data dashboards to promote awareness of each other’s health-related behaviors. However, these interfaces often stop short of providing families with needed guidance around how to improve family functioning and health behaviors. We consider the needs of family co-regulation with ADHD children to understand how in-home displays can support family well-being. We conducted three co-design sessions with each of eight families with ADHD children who had used a smartwatch for self-tracking. Results indicate that situated displays could nudge families to jointly use their data for learning and skill-building. Accommodating individual needs and preferences when family members are alone is also important, particularly to support parents exploring their co-regulation role, and assisting children with data interpretation and guidance on self and co-regulation. We discuss opportunities for displays to nurture multiple intents of use, such as joint or independent use, while potentially connecting with external expertise. 
    more » « less
  4. ; (, PLOS ONE)
    Patel, Sanjay Kumar (Ed.)
    Background Social capital has been associated with health outcomes in communities and can explain variations in different geographic localities. Social capital has also been associated with behaviors that promote better health and reduce the impacts of diseases. During the COVID-19 pandemic, social distancing, face masking, and vaccination have all been essential in controlling contagion. These behaviors have not been uniformly adopted by communities in the United States. Using different facets of social capital to explain the differences in public behaviors among communities during pandemics is lacking. Objective This study examines the relationship among public health behavior—vaccination, face masking, and physical distancing—during COVID-19 pandemic and social capital indices in counties in the United States. Methods We used publicly available vaccination data as of June 2021, face masking data in July 2020, and mobility data from mobile phones movements from the end of March 2020. Then, correlation analysis was conducted with county-level social capital index and its subindices (family unity, community health, institutional health, and collective efficacy) that were obtained from the Social Capital Project by the United States Senate. Results We found the social capital index and its subindices differentially correlate with different public health behaviors. Vaccination is associated with institutional health: positively with fully vaccinated population and negatively with vaccination hesitancy. Also, wearing masks negatively associates with community health, whereases reduced mobility associates with better community health. Further, residential mobility positively associates with family unity. By comparing correlation coefficients, we find that social capital and its subindices have largest effect sizes on vaccination and residential mobility. Conclusion Our results show that different facets of social capital are significantly associated with adoption of protective behaviors, e.g., social distancing, face masking, and vaccination. As such, our results suggest that differential facets of social capital imply a Swiss cheese model of pandemic control planning where, e.g., institutional health and community health, provide partially overlapping behavioral benefits. 
    more » « less
  5. ; ; ; ; ; ; (, JMIR Mental Health)
    null (Ed.)
    Background Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior. Objective The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application. Methods We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobile sensing system (mPulse) collected data from call logs, battery charging, and screen checking. To validate the model, we used mobile sensing features to predict common self-reported impulsivity traits, objective mobile behavioral and cognitive measures, and ecological momentary assessment (EMA) of state impulsivity and constructs related to impulsive behavior (ie, risk-taking, attention, and affect). Results Overall, the findings suggested that passive measures of mobile phone use such as call logs, battery charging, and screen checking can predict different facets of trait and state impulsivity and impulsive behavior. For impulsivity traits, the models significantly explained variance in sensation seeking, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting trait-based sensation seeking. On a daily level, the model successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions. Conclusions The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed. Trial Registration ClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email