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Record W2996994829 · doi:10.4018/ijeach.2020010101

The Dual-Tasking Texting Effect of Cell Phone Technology on Walking

2019· article· en· W2996994829 on OpenAlexaff
Asher Mendelsohn, Carlos Zerpa

Bibliographic record

VenueInternational Journal of Extreme Automation and Connectivity in Healthcare · 2019
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsLakehead University
Fundersnot available
KeywordsPhoneAccelerometerGaitHuman multitaskingPhysical medicine and rehabilitationMobile phoneDual (grammatical number)Computer sciencePsychologyCognitive psychologyMedicineTelecommunications

Abstract

fetched live from OpenAlex

Dual-tasking is the simultaneous performance of two tasks causing a divided allocation of attentional resources. Dual-tasking is apparent in our society in the form of cell phone use while walking, which alters gait characteristics, resulting in an increased risk of injuries due to falls and collisions. This study explored the effect of cell phone texting on walking and validated the use of accelerometer technology to measure gait characteristics. Twelve young adult participants walked three times across electronic force platforms during regular walking (control), walking while reading a text (reading), walking while typing a text (texting). The results indicated that gait force patterns differed from control during texting. The results also indicated a significant correlation between measures of force and acceleration across walking conditions. The outcome of this study adds to existing literature regarding the effects of cell phone use on walking patterns and highlights the use of accelerometer technology to assess gait characteristics.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.353
Teacher spread0.332 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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