Evidence map›Paper›PMID 32831178›Full record

ArticleJMIR formative research2020

Using ADAPT-ITT to Modify a Telephone-Based HIV Prevention Intervention for SMS Delivery: Formative Study.

Teaniese Davis, Ralph Joseph DiClemente, Michael Prietula

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Teaniese Davis *Center for Research and Evaluation, Kaiser Permanente Georgia, Atlanta, GA, United States.ORCID https://orcid.org/0000-0001-7408-0291
Ralph Joseph DiClemente *Department of Social & Behavioral Sciences, School of Global Public Health, New York University, New York, NY, United States.ORCID https://orcid.org/0000-0002-4374-4611
Michael Prietula *Goizueta Business School & Hubert Department of Global Health, Emory University, Atlanta, GA, United States.ORCID https://orcid.org/0000-0002-9157-1719
Emory University · USKaiser Permanente · USNew York University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAfrican American adolescent females are disproportionately affected by sexually transmitted infections (STIs) and HIV. Given the elevated risk of STIs and HIV in African American women, there is an urgent need to identify innovative strategies to enhance the adoption and maintenance of STI and HIV preventive behaviors. Texting is a promising technology for creating preventive maintenance interventions (PMIs) that extend the efficacy of the original intervention. However, little guidance in public health literature is available for developing this type of application.

objectiveThis paper describes a formative pilot study that incorporates user experience methods to design and test PMI texts for Afiya, an original evidence-based intervention (EBI) specifically designed for African American adolescent females. This study aims to describe the adaptation process of health educator-led phone calling to text-based communication.

methodsThe formative process followed the assessment, decision, adaptation, production, topical experts-integration, training, testing (ADAPT-ITT) framework for adapting EBIs and using them in a new setting, for a new target population or a modified intervention strategy. This study presents the details of how the phases of the ADAPT-ITT framework were applied to the design of the adaptation. An advisory board was constituted from the target population, consisting of 6 African American women aged 18-24 years, participating in formative activities for 12 weeks, and involving components of the PMI design. As Afiya included a telephone-based PMI, developers of the original Afiya phone scripts crafted the initial design of the SMS-based texts and texting protocol. The advisory board participated in the 1-day Afiya workshop, followed by 4 weeks of texting PMI messages and a midcourse focus group, followed by 4 more weeks of texting PMI messages, ultimately ending with a final focus group. At the advisory board's request, this phase included an optional, additional week of text-based PMI messages.

resultsThe methods provided a rich source of data and insights into the fundamental issues involved when constructing SMS-based PMI for this target population and for this EBI. Prior contact and context are essential as the health educator was identified as a key persona in the process and the messages were situated in the original (workshop) context. Narrative adaptations for personas emerged from advisory board discussions. Suggestions on how to expand the PMI to current, specific social contexts indicated that the use of narrative analysis is warranted.

conclusionsThe use of existing EBIs incorporating telephone-based PMI scripts facilitated the initial design of the texts, with a subsequent narrative analysis of the advisory board data providing additional adjustments given the actual context. Additional examination of the advisory board feedback revealed that personas would offer insight into and opportunities for a persona-specific modification of texting narratives.

Indexed as

ADAPT-ITT frameworkadolescentAfrican Americansfemalehealth status disparitiesHIVintervention studymHealthrisk reduction behaviorshort message servicetextingyoung adult

Identifiers

PMID32831178
PMCPMC7576465
OpenAlexW3080577886

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.