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Record W2883493213 · doi:10.2196/mhealth.8146

Using Mobile Health to Enhance Outcomes of Noncommunicable Diseases Care in Rural Settings and Refugee Camps: Randomized Controlled Trial

2018· article· en· W2883493213 on OpenAlexaffvenue
Shadi Saleh, Angie Farah, Hani Dimassi, Nour El Arnaout, Mona H. Osman, Christo El Morr, Mohamad Alameddine

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsYork University
FundersUnited Nations Relief and Works Agency for Palestine RefugeesMinistry of Public Health
KeywordsmHealthMedicinePsychological interventionRandomized controlled trialeHealthEnvironmental healthHealth careIntervention (counseling)Rural healthRefugeeDisadvantagedRural areaGerontologyFamily medicineNursingGeography

Abstract

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Background: Rural areas and refugee camps are characterized by poor access of patients to needed noncommunicable disease (NCD)–related health services, including diabetes and hypertension. Employing low-cost innovative eHealth interventions, such as mobile health (mHealth), may help improve NCDs prevention and control among disadvantaged populations. Objective: The aim of this study was to assess the effect of employing low-cost mHealth tools on the accessibility to health services and improvement of health indicators of individuals with NCDs in rural areas and refugee camps in Lebanon. Methods: This is a randomized controlled trial study in which centers were allocated randomly into control and intervention sites. The effect of an employed mHealth intervention is assessed through selected quality indicators examined in both control and intervention groups. Sixteen primary health care centers (eight controls, eight interventions) located in rural areas and Palestinian refugee camps across Lebanon were included in this study. Data on diabetic and hypertensive patients—1433 in the intervention group and 926 in the control group—was extracted from patient files in the pre and postintervention periods. The intervention entailed weekly short message service messages, including medical information, importance of compliance, and reminders of appointments or regular physician follow-up. Internationally established care indicators were utilized in this study. Descriptive analysis of baseline characteristics of participants, bivariate analysis, logistic and linear regression were conducted using SPSS (IBM Corp). Results: Bivariate analysis of quality indicators indicated that the intervention group had a significant increase in blood pressure control (P=.03), as well as a significant decrease in the mean systolic blood pressure (P=.02), mean glycated hemoglobin (HbA1c; P<.01), and in the proportion of HbA1c poor control (P=.02). Separate regression models controlling for age, gender, and setting showed a 28% increase in the odds of blood pressure control (P=.05) and a 38% decrease in the odds of HbA1c poor control (P=.04) among the intervention group in the posttest period. Females were at lower odds of HbA1c poor control (P=.01), and age was statistically associated with annual HbA1c testing (P<.01). Regression models for mean systolic blood pressure, mean diastolic blood pressure, and mean HbA1c showed that a mean decrease in HbA1c of 0.87% (P<.01) pretest to posttest period was observed among the intervention group. Patients in rural areas belonging to the intervention group had a lower HbA1c score as compared with those in refugee camps (P<.01). Conclusions: This study underlines the importance of employing integrative approaches of diseases prevention and control in which existing NCD programs in underserved communities (ie, rural and refugee camps settings) are coupled with innovative, low-cost approaches such as mHealth to provide an effective and amplified effect of traditional NCD-targeted care that can be reflected by improved NCD-related health indicators among the population. Trial Registration: ClinicalTrials.gov NCT03580330; https://clinicaltrials.gov/ct2/show/NCT03580330 (Archived by WebCite at http://www.webcitation.org/70mhVEUwQ)

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.495
Teacher spread0.454 · 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 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

Citations85
Published2018
Admission routes2
Has abstractyes

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