MétaCan
Menu
Back to cohort
Record W2787238207 · doi:10.1016/s2468-2667(17)30239-6

Effect of an interactive text-messaging service on patient retention during the first year of HIV care in Kenya (WelTel Retain): an open-label, randomised parallel-group study

2018· article· en· W2787238207 on OpenAlexafffundabout
Mia L. van der Kop, Samuel Muhula, Patrick I Nagide, Lehana Thabane, Lawrence Gelmon, Patricia Opondo Awiti, Bonface Abunah, Lennie Bazira Kyomuhangi, Matthew Budd, Carlo A. Marra, Anik R. Patel, Sarah Karanja, David Ojakaa, Edward J. Mills, Anna Mia Ekström, Richard Lester

Bibliographic record

VenueThe Lancet Public Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of ManitobaMcMaster UniversityImpactUniversity of British Columbia
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsMedicineAttendanceShort Message ServiceIntervention (counseling)PsychosocialFamily medicineRandomized controlled trialPhonePediatricsNursingPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Retention of patients in HIV care is crucial to ensure timely treatment initiation, viral suppression, and to avert AIDS-related deaths. We did a randomised trial to determine whether a text-messaging intervention improved retention during the first year of HIV care. METHODS: This unmasked, randomised parallel-group study was done at two clinics in informal settlements in Nairobi, Kenya. Eligible participants were aged 18 years or older, HIV-positive, had their own mobile phone or access to one, and were able to use simple text messaging (or have somebody who could text message on their behalf). Participants were randomly assigned (1:1), with random block sizes of 2, 4, and 6, to the intervention or control group. Participants in the intervention group received a weekly text message from the automated WelTel service for 1 year and were asked to respond within 48 h. Participants in the control group did not receive text messages. Participants in both groups received usual care, which comprised psychosocial support and counselling; patient education; CD4 cell count; treatment; screening for tuberculosis, opportunistic infections, and sexually transmitted infections; prevention of mother-to-child transmission and family planning services; and up to two telephone calls for missed appointments. The primary outcome was retention in care at 12 months (ie, clinic attendance 10-14 months after the first visit). Participants who did not attend this 12-month appointment were traced, and we considered as retained those who were confirmed to be active in care elsewhere. The data analyst and clinic staff were masked to the group assignment, whereas participants and research nurses were not. We analysed the intention-to-treat population. This trial is registered with ClinicalTrials.gov, number NCT01630304. FINDINGS: Between April 4, 2013, and June 4, 2015, we screened 1068 individuals, of whom 700 were recruited. 349 people were allocated to the intervention group and 351 to the control group. Participants were followed up for a median of 55 weeks (IQR 51-60). At 12 months, 277 (79%) of 349 participants in the intervention group were retained, compared with 285 (81%) of 351 participants in the control group (risk ratio 0·98, 95% CI 0·91-1·05; p=0·54). There was one mild adverse event related to the intervention, a domestic dispute that occurred when a participant's partner became suspicious of the weekly messages and follow-up calls. INTERPRETATION: This weekly text-messaging service did not improve retention of people in early HIV care. The intervention might have a modest role in improving self-perceived health-related quality of life in individuals in HIV care in similar settings. FUNDING: National Institutes of Health and Canadian Institutes of Health Research Canadian HIV Trials Network.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.066
GPT teacher head0.427
Teacher spread0.361 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations78
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueThe Lancet Public HealthSame topicMobile Health and mHealth ApplicationsFrench-language works237,207