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Record W3045108176 · doi:10.3389/fcomp.2020.00019

Mobile Phone-Based Persuasive Technology for Physical Activity and Sedentary Behavior: A Systematic Review

2020· review· en· W3045108176 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Computer Science · 2020
Typereview
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMobile phonePhoneSedentary behaviorPsychological interventionMobile technologyPersuasive technologyStrengths and weaknessesComputer sciencePhysical activityMultimediaInternet privacyMobile deviceHuman–computer interactionPsychologyWorld Wide WebTelecommunicationsMedicinePersuasionSocial psychology

Abstract

fetched live from OpenAlex

Mobile phone technology has been progressively employed in persuasive technology interventions design to promote physical activity (PA) and discourage sedentary behavior (SB). Because of the ubiquitous nature and seamless integration of mobile phones into the user’s daily lives, mobile phone-based persuasive technologies (PTs) have the potential to influence and change a user’s behavior or attitude continuously. This paper provides a systematic review of 15 years of research (80 papers) focusing on the effectiveness of mobile phone-based PT in promoting PA and reducing SB. Specifically, this review aims to: (1) assess the effectiveness of mobile phone-based PT in persuading users to be more physically active and less sedentary, (2) highlight research trends in this area including other technology platforms implemented along with mobile phone-based PT, (3) reveal some strengths and weaknesses of existing mobile phone interventions in PA and SB domains, and (4) provide recommendations to inform future research in this area.

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.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.454
Teacher spread0.402 · 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