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Record W2074983524 · doi:10.2196/mhealth.3347

Internet Use and Access Among Pregnant Women via Computer and Mobile Phone: Implications for Delivery of Perinatal Care

2015· article· en· W2074983524 on OpenAlexvenueno aff
Rachel Peragallo Urrutia, A Berger, Amber A Ivins, A. Jenna Beckham, John M. Thorp, Wanda K. Nicholson

Bibliographic record

VenueJMIR mhealth and uhealth · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsThe InternetMobile phonePsychological interventionInternet accessReferralmHealthMedicinePopulationLogistic regressionMobile technologyFamily medicineComputer scienceMobile deviceNursingWorld Wide WebTelecommunicationsEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The use of Internet-based behavioral programs may be an efficient, flexible method to enhance prenatal care and improve pregnancy outcomes. There are few data about access to, and use of, the Internet via computers and mobile phones among pregnant women. OBJECTIVE: We describe pregnant women's access to, and use of, computers, mobile phones, and computer technologies (eg, Internet, blogs, chat rooms) in a southern United States population. We describe the willingness of pregnant women to participate in Internet-supported weight-loss interventions delivered via computers or mobile phones. METHODS: We conducted a cross-sectional survey among 100 pregnant women at a tertiary referral center ultrasound clinic in the southeast United States. Data were analyzed using Stata version 10 (StataCorp) and R (R Core Team 2013). Means and frequency procedures were used to describe demographic characteristics, access to computers and mobile phones, and use of specific Internet modalities. Chi-square testing was used to determine whether there were differences in technology access and Internet modality use according to age, race/ethnicity, income, or children in the home. The Fisher's exact test was used to describe preferences to participate in Internet-based postpartum weight-loss interventions via computer versus mobile phone. Logistic regression was used to determine demographic characteristics associated with these preferences. RESULTS: The study sample was 61.0% white, 26.0% black, 6.0% Hispanic, and 7.0% Asian with a mean age of 31.0 (SD 5.1). Most participants had access to a computer (89/100, 89.0%) or mobile phone (88/100, 88.0%) for at least 8 hours per week. Access remained high (>74%) across age groups, racial/ethnic groups, income levels, and number of children in the home. Internet/Web (94/100, 94.0%), email (90/100, 90.0%), and Facebook (50/100, 50.0%) were the most commonly used Internet technologies. Women aged less than 30 years were more likely to report use of Twitter and chat rooms compared to women 30 years of age or older. Of the participants, 82.0% (82/100) were fairly willing or very willing to participate in postpartum lifestyle intervention. Of the participants, 83.0% (83/100) were fairly willing or very willing to participate in an Internet intervention delivered via computer, while only 49.0% (49/100) were fairly willing or very willing to do so via mobile phone technology. Older women and women with children tended to be less likely to desire a mobile phone-based program. CONCLUSIONS: There is broad access and use of computer and mobile phone technology among southern US pregnant women with varied demographic characteristics. Pregnant women are willing to participate in Internet-supported perinatal interventions. Our findings can inform the development of computer- and mobile phone-based approaches for the delivery of clinical and educational interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.114
GPT teacher head0.449
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations65
Published2015
Admission routes1
Has abstractyes

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