| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432282 |
| 005 | | 20200224120212 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781392233603 |
| 035 | |
▼a (MiAaPQ)AAI13896452 |
| 035 | |
▼a (MiAaPQ)ucla:17786 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 614 |
| 100 | 1 |
▼a Mak, Selene Synn-Lum. |
| 245 | 10 |
▼a Mobile Health Technology Use in Vulnerable Populations. |
| 260 | |
▼a [S.l.]:
▼b University of California, Los Angeles.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 187 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 80-12, Section: B. |
| 500 | |
▼a Publisher info.: Dissertation/Thesis. |
| 500 | |
▼a Advisor: Needleman, Jack. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Los Angeles, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Given the potential for mobile health technologies (mHealth) to reduce access barriers, there has been increased interest in understanding mHealth use among vulnerable populations. Vulnerable populations are at risk of lower mHealth use, and gaps in access to and use of mHealth between vulnerable and more affluent, more educated, and younger populations could exacerbate health disparities.Using a mixed methods approach, we analyzed survey and focus group data from a study sponsored by a foundation of a large health insurer on mHealth use in vulnerable populations. A sample of low-income adults (n=345) was recruited by local social services organizations in Miami, FL, Louisville, KY, South Bronx, NY. In the first study, we assessed sociodemographic correlates of mHealth use with multivariable logistic regression analyses. In the second study, we estimated direct and indirect effects of sociodemographic characteristics on mHealth use with structural equation modeling and examined the role of digital health literacy in this relationship. In the third study, we conducted a qualitative analysis of focus group interviews with older adults to contextualize mHealth acceptance and adoption.Factor analysis identified two composite outcome variables to represent mHealth use: those activities related to searching for information and those involving greater engagement with technology for health, such as downloading and using a health app.Lower age was associated with higher search-related mHealth use. Education influenced search-related mHealth use indirectly through digital health literacy. Age and education had indirect effects on engagement-related mHealth use through digital health literacy and search-related mHealth use. Qualitative findings revealed many older adults had minimal experience with mHealth and mHealth acceptance and adoption were influenced by perception of the usefulness of mHealth, the complexity of using mHealth, and facilitating conditions such as cost and technical assistance.These findings can inform interventions used to encourage greater mHealth use in vulnerable and older populations. |
| 590 | |
▼a School code: 0031. |
| 650 | 4 |
▼a Public health. |
| 690 | |
▼a 0573 |
| 710 | 20 |
▼a University of California, Los Angeles.
▼b Health Policy and Management 007I. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 80-12B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0031 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491712
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |