MétaCan
Menu
Back to cohort
Record W2147891722

The Effect of Mobile Phone Short Messages System on Physical Activity and Anthropometric Measures among Postmenopausal Women

2014· article· en· W2147891722 on OpenAlexvenueno aff
Parvin Abedi, Saeed Shirali, Amir Jamshidnezhad, Mahdis Vakili, Maryam Sharafi, Seyed Ahmad Hosseini

Bibliographic record

VenueJournal of academic and applied studies · 2014
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsWaistAnthropometryMedicinePhysical therapyBody mass indexCircumferencePhysical activityIntervention (counseling)Randomized controlled trialWaist–hip ratioMathematicsInternal medicineNursing
DOInot available

Abstract

fetched live from OpenAlex

The main objective of this study was to evaluate the impact of mobile phone short messages system on physical activity and anthropometric measures among postmenopausal women in Iran. This was a randomized controlled trial in which 100 postmenopausal women with body mass index (BMI) ≥25were recruited randomly in Ahvaz, Iran. Weight, height, waist circumference, hip circumference, waist-hip ratio and physical activity were measured at the beginning and four months after intervention. Intervention was including; 49 short messages with the content of motivating the subjects to enhance their daily physical activity that sent for participants every other day. The control group received the routine care. The descriptive, independent t-test, paired t-test and chi-square test were utilized for statistical purposes. At the end of the study, weight reduced significantly in the control group compared to the intervention group (p=0.004) and physical activity level was reduced in both groups after four months; however the reduction was more evident in the control group. There was not any significant difference between two groups regarding weight, BMI, waist circumference, hip circumference, waisthip ratio and physical activity. Further studies with aim of compare mobile short messages with other educational methods are recommended.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.030
GPT teacher head0.412
Teacher spread0.382 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of academic and applied studiesSame topicMobile Health and mHealth ApplicationsFrench-language works237,207