Sarah S Nouri1, Julia Adler-Milstein2, Crishyashi Thao2, Prasad Acharya3, Jill Barr-Walker4, Urmimala Sarkar1,5, Courtney Lyles1,5. 1. Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California, USA. 2. Center for Clinical Informatics and Improvement Research, School of Medicine, University of California, San Francisco, San Francisco, California, USA. 3. Chronic Disease Control Branch, Center for Healthy Communities, California Department of Public Health, Sacramento, California, USA. 4. Zuckerberg San Francisco General Hospital Library, University of California, San Francisco, San Francisco, California, USA. 5. UCSF Center for Vulnerable Populations, Zuckerberg San Francisco General Hospital, San Francisco, California, USA.
Abstract
OBJECTIVE: The study sought to determine which patient characteristics are associated with the use of patient-facing digital health tools in the United States. MATERIALS AND METHODS: We conducted a literature review of studies of patient-facing digital health tools that objectively evaluated use (eg, system/platform data representing frequency of use) by patient characteristics (eg, age, race or ethnicity, income, digital literacy). We included any type of patient-facing digital health tool except patient portals. We reran results using the subset of studies identified as having robust methodology to detect differences in patient characteristics. RESULTS: We included 29 studies; 13 had robust methodology. Most studies examined smartphone apps and text messaging programs for chronic disease management and evaluated only 1-3 patient characteristics, primarily age and gender. Overall, the majority of studies found no association between patient characteristics and use. Among the subset with robust methodology, white race and poor health status appeared to be associated with higher use. DISCUSSION: Given the substantial investment in digital health tools, it is surprising how little is known about the types of patients who use them. Strategies that engage diverse populations in digital health tool use appear to be needed. CONCLUSION: Few studies evaluate objective measures of digital health tool use by patient characteristics, and those that do include a narrow range of characteristics. Evidence suggests that resources and need drive use.
OBJECTIVE: The study sought to determine which patient characteristics are associated with the use of patient-facing digital health tools in the United States. MATERIALS AND METHODS: We conducted a literature review of studies of patient-facing digital health tools that objectively evaluated use (eg, system/platform data representing frequency of use) by patient characteristics (eg, age, race or ethnicity, income, digital literacy). We included any type of patient-facing digital health tool except patient portals. We reran results using the subset of studies identified as having robust methodology to detect differences in patient characteristics. RESULTS: We included 29 studies; 13 had robust methodology. Most studies examined smartphone apps and text messaging programs for chronic disease management and evaluated only 1-3 patient characteristics, primarily age and gender. Overall, the majority of studies found no association between patient characteristics and use. Among the subset with robust methodology, white race and poor health status appeared to be associated with higher use. DISCUSSION: Given the substantial investment in digital health tools, it is surprising how little is known about the types of patients who use them. Strategies that engage diverse populations in digital health tool use appear to be needed. CONCLUSION: Few studies evaluate objective measures of digital health tool use by patient characteristics, and those that do include a narrow range of characteristics. Evidence suggests that resources and need drive use.
Authors: Lyndsay A Nelson; Shelagh A Mulvaney; Tebeb Gebretsadik; Yun-Xian Ho; Kevin B Johnson; Chandra Y Osborn Journal: J Am Med Inform Assoc Date: 2015-07-17 Impact factor: 4.497
Authors: Rebecca G Mishuris; Max Stewart; Gemmae M Fix; Thomas Marcello; D Keith McInnes; Timothy P Hogan; Judith B Boardman; Steven R Simon Journal: Health Expect Date: 2014-05-12 Impact factor: 3.377
Authors: David E Gerber; Andrew L Laccetti; Beibei Chen; Jingsheng Yan; Jennifer Cai; Samantha Gates; Yang Xie; Simon J Craddock Lee Journal: J Oncol Pract Date: 2014-07-08 Impact factor: 3.840
Authors: Rosette J Chakkalakal; Sunil Kripalani; David G Schlundt; Tom A Elasy; Chandra Y Osborn Journal: Diabetes Care Date: 2014 Impact factor: 19.112
Authors: Victoria M Nielsen; Glory Song; Lea Susan Ojamaa; Ruth P Blodgett; Catherine M Rocchio; Jena N Pennock Journal: Public Health Rep Date: 2022-01-28 Impact factor: 2.792
Authors: Lyndsay A Nelson; Andrew J Spieker; Lindsay S Mayberry; Candace McNaughton; Robert A Greevy Journal: J Am Med Inform Assoc Date: 2021-12-28 Impact factor: 4.497
Authors: Ayomide Owoyemi; Joanne I Osuchukwu; Clark Azubuike; Ronald Kelechi Ikpe; Blessing C Nwachukwu; Cassandra B Akinde; Grace W Biokoro; Abisoye B Ajose; Ezechukwu Ikenna Nwokoma; Nehemiah E Mfon; Temitope O Benson; Anthony Ehimare; Daniel Irowa-Omoregie; Seun Olaniran Journal: Front Digit Health Date: 2022-06-03
Authors: Kim E Alexander; Theodora Ogle; Hana Hoberg; Libbie Linley; Natalie Bradford Journal: BMC Health Serv Res Date: 2021-02-15 Impact factor: 2.655
Authors: Joan C Medina; Aida Flix-Valle; Ana Rodríguez-Ortega; Rosa Hernández-Ribas; María Lleras de Frutos; Cristian Ochoa-Arnedo Journal: Cancers (Basel) Date: 2022-02-15 Impact factor: 6.639