Suppr超能文献

华盛顿州和华盛顿特区安全网医疗系统患者的移动健康技术知识与实践

Mobile Health Technology Knowledge and Practices Among Patients of Safety-Net Health Systems in Washington State and Washington, DC.

作者信息

Laing Sharon S, Alsayid Muhammad, Ocampo Carlota, Baugh Stacey

机构信息

Nursing and Healthcare Leadership Program, University of Washington Tacoma, Tacoma, WA.

Health Services Department, University of Washington School of Public Health, Seattle, WA.

出版信息

J Patient Cent Res Rev. 2018 Jul 30;5(3):204-217. doi: 10.17294/2330-0698.1622. eCollection 2018 Summer.

Abstract

PURPOSE

Mobile health technology (mHealth) can reduce health disparities, but research on the health behaviors of low-income patients is needed. This study evaluates mHealth knowledge and practices of low-resource safety-net patients.

METHODS

We administered a 47-item questionnaire to 164 low-income patients accessing services at community health centers in the state of Washington and Washington, DC. Predictor variables included demographic factors: age, race, ethnicity, income. Outcome variables were smartphone knowledge (smartphones as a wellness tool), medical app knowledge (availability of medical-based apps), smartphone practices (ever used smartphones for wellness), health apps practices (ever used health-based apps), and medical apps practices (ever used medical-based apps). Multivariate logistic regression assessed relationships between predictor and outcome variables.

RESULTS

Mean age was 35.2 years (median: 34), and study cohort (N=159) consisted of mostly women (68%), white race (36%), and income of <$20,000/year (63%). Outcomes: 71% and 58% reported knowledge of using smartphones for wellness and knowledge of medical apps, respectively; 76% used smartphones for wellness, with adults 50+ years of age significantly less likely than younger adults (odds ratio [OR]: 0.94, 95% confidence interval [CI]: 0.88-0.99); 48% used health apps, with adults 50+ years of age less likely than younger adults (OR: 0.95, 95% CI: 0.91-0.99) and respondents earning <$20,000/year less likely than higher earners (OR: 3.13, 95% CI: 1.02-9.57); and 58% used medical apps, with Hispanics/Latinos significantly more likely than non-Hispanics/Latinos (OR: 6.38, 95% CI: 1.04-39.02).

CONCLUSIONS

Safety-net patients use mobile devices for health promotion. Age and income are important predictive factors, suggesting a more tailored design of the technology is required for broad engagement and health equity.

摘要

目的

移动健康技术(mHealth)可减少健康差距,但仍需对低收入患者的健康行为进行研究。本研究评估了资源匮乏的安全网患者的移动健康知识与实践情况。

方法

我们对华盛顿州及华盛顿特区社区卫生中心的164名低收入患者进行了一项包含47个项目的问卷调查。预测变量包括人口统计学因素:年龄、种族、族裔、收入。结果变量包括智能手机知识(将智能手机作为健康工具)、医疗应用程序知识(基于医疗的应用程序的可用性)、智能手机使用情况(是否曾使用智能手机促进健康)、健康应用程序使用情况(是否曾使用基于健康的应用程序)以及医疗应用程序使用情况(是否曾使用基于医疗的应用程序)。多变量逻辑回归分析评估了预测变量与结果变量之间的关系。

结果

平均年龄为35.2岁(中位数:34岁),研究队列(N = 159)主要由女性(68%)、白人(36%)以及年收入低于20,000美元(63%)的人群组成。结果如下:分别有71%和58%的患者报告了解使用智能手机促进健康以及了解医疗应用程序;76%的患者使用智能手机促进健康,50岁及以上成年人使用智能手机促进健康的可能性显著低于年轻成年人(优势比[OR]:0.94,95%置信区间[CI]:0.88 - 0.99);48%的患者使用健康应用程序,50岁及以上成年人使用健康应用程序的可能性低于年轻成年人(OR:0.95,95% CI:0.91 - 0.99),年收入低于20,000美元的受访者使用健康应用程序的可能性低于高收入者(OR:3.13,95% CI:1.02 - 9.57);58%的患者使用医疗应用程序,西班牙裔/拉丁裔使用医疗应用程序的可能性显著高于非西班牙裔/拉丁裔(OR:6.38,95% CI:1.04 - 39.02)。

结论

安全网患者使用移动设备促进健康。年龄和收入是重要的预测因素,这表明需要对技术进行更具针对性的设计,以实现广泛参与和健康公平。

相似文献

1
Mobile Health Technology Knowledge and Practices Among Patients of Safety-Net Health Systems in Washington State and Washington, DC.
J Patient Cent Res Rev. 2018 Jul 30;5(3):204-217. doi: 10.17294/2330-0698.1622. eCollection 2018 Summer.
2
Physical Activity Support Predicts Safety-Net Patients' Digital Health-Care Engagement: Implications for Patient Care Delivery.
Am J Health Promot. 2020 Mar;34(3):311-315. doi: 10.1177/0890117119894508. Epub 2019 Dec 20.
3
Smartphone ownership and perspectives on health apps among a vulnerable population in East Harlem, New York.
Mhealth. 2018 Aug 8;4:31. doi: 10.21037/mhealth.2018.07.02. eCollection 2018.
4
Use of Mobile Health Applications for Health-Seeking Behavior Among US Adults.
J Med Syst. 2016 Jun;40(6):153. doi: 10.1007/s10916-016-0492-7. Epub 2016 May 4.
6
Using Smartphones and Health Apps to Change and Manage Health Behaviors: A Population-Based Survey.
J Med Internet Res. 2017 Apr 5;19(4):e101. doi: 10.2196/jmir.6838.
7
Use of eHealth and mHealth technology by persons with multiple sclerosis.
Mult Scler Relat Disord. 2019 Jan;27:13-19. doi: 10.1016/j.msard.2018.09.036. Epub 2018 Oct 2.

引用本文的文献

3
6
Social Media, Digital Health Literacy, and Digital Ethics in the Light of Health Equity.
Yearb Med Inform. 2022 Aug;31(1):82-87. doi: 10.1055/s-0042-1742503. Epub 2022 Jun 2.
8
An investigation of the use of app technology to support clinical management of patients with chronic myeloid leukaemia (CML).
J Oncol Pharm Pract. 2023 Jul;29(5):1083-1093. doi: 10.1177/10781552221090904. Epub 2022 Apr 11.
9
Mobile app development in health research: pitfalls and solutions.
Mhealth. 2021 Apr 20;7:32. doi: 10.21037/mhealth-19-263. eCollection 2021.
10
Influencing Factors of Acceptance and Use Behavior of Mobile Health Application Users: Systematic Review.
Healthcare (Basel). 2021 Mar 22;9(3):357. doi: 10.3390/healthcare9030357.

本文引用的文献

2
Usability of Commercially Available Mobile Applications for Diverse Patients.
J Gen Intern Med. 2016 Dec;31(12):1417-1426. doi: 10.1007/s11606-016-3771-6. Epub 2016 Jul 14.
6
Use of mobile health (mHealth) tools by primary care patients in the WWAMI region Practice and Research Network (WPRN).
J Am Board Fam Med. 2014 Nov-Dec;27(6):780-8. doi: 10.3122/jabfm.2014.06.140108.
7
Assessing the impact of mHealth interventions in low- and middle-income countries--what has been shown to work?
Glob Health Action. 2014 Oct 27;7:25606. doi: 10.3402/gha.v7.25606. eCollection 2014.
8
mHealth resources to strengthen health programs.
Glob Health Sci Pract. 2014 Feb 11;2(1):130-1. doi: 10.9745/GHSP-D-14-00013. eCollection 2014 Feb.
9
Using the lives saved tool (LiST) to model mHealth impact on neonatal survival in resource-limited settings.
PLoS One. 2014 Jul 11;9(7):e102224. doi: 10.1371/journal.pone.0102224. eCollection 2014.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验