Continuous Measurement of Reconnaissance Marines in Training With Custom Smartphone App and Watch: Observational Cohort Study.


Journal

JMIR mHealth and uHealth
ISSN: 2291-5222
Titre abrégé: JMIR Mhealth Uhealth
Pays: Canada
ID NLM: 101624439

Informations de publication

Date de publication:
15 06 2020
Historique:
received: 22 03 2019
accepted: 24 01 2020
revised: 05 11 2019
pubmed: 30 4 2020
medline: 28 4 2021
entrez: 30 4 2020
Statut: epublish

Résumé

Specialized training for elite US military units is associated with high attrition due to intense psychological and physical demands. The need to graduate more service members without degrading performance standards necessitates the identification of factors to predict success or failure in targeted training interventions. The aim of this study was to continuously quantify the mental and physical status of trainees of an elite military unit to identify novel predictors of success in training. A total of 3 consecutive classes of a specialized training course were provided with an Apple iPhone, Watch, and specially designed mobile app. Baseline personality assessments and continuous daily measures of mental status, physical pain, heart rate, activity, sleep, hydration, and nutrition were collected from the app and Watch data. A total of 115 trainees enrolled and completed the study (100% male; age: mean 22 years, SD 4 years) and 64 (55.7%) successfully graduated. Most training withdrawals (27/115, 23.5%) occurred by day 7 (mean 5.5 days, SD 3.4 days; range 1-22 days). Extraversion, positive affect personality traits, and daily psychological profiles were associated with course completion; key psychological factors could predict withdrawals 1-2 days in advance (P=.009). Gathering accurate and continuous mental and physical status data during elite military training is possible with early predictors of withdrawal providing an opportunity for intervention.

Sections du résumé

BACKGROUND
Specialized training for elite US military units is associated with high attrition due to intense psychological and physical demands. The need to graduate more service members without degrading performance standards necessitates the identification of factors to predict success or failure in targeted training interventions.
OBJECTIVE
The aim of this study was to continuously quantify the mental and physical status of trainees of an elite military unit to identify novel predictors of success in training.
METHODS
A total of 3 consecutive classes of a specialized training course were provided with an Apple iPhone, Watch, and specially designed mobile app. Baseline personality assessments and continuous daily measures of mental status, physical pain, heart rate, activity, sleep, hydration, and nutrition were collected from the app and Watch data.
RESULTS
A total of 115 trainees enrolled and completed the study (100% male; age: mean 22 years, SD 4 years) and 64 (55.7%) successfully graduated. Most training withdrawals (27/115, 23.5%) occurred by day 7 (mean 5.5 days, SD 3.4 days; range 1-22 days). Extraversion, positive affect personality traits, and daily psychological profiles were associated with course completion; key psychological factors could predict withdrawals 1-2 days in advance (P=.009).
CONCLUSIONS
Gathering accurate and continuous mental and physical status data during elite military training is possible with early predictors of withdrawal providing an opportunity for intervention.

Identifiants

pubmed: 32348252
pii: v8i6e14116
doi: 10.2196/14116
pmc: PMC7324996
doi:

Types de publication

Journal Article Observational Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

e14116

Informations de copyright

©Leslie Saxon, Brooks DiPaula, Glenn R Fox, Rebecca Ebert, Josiah Duhaime, Luciano Nocera, Luan Tran, Mona Sobhani. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 15.06.2020.

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Auteurs

Leslie Saxon (L)

University of Southern California, Center for Body Computing, Keck School of Medicine, Playa Vista, CA, United States.

Brooks DiPaula (B)

University of Southern California, Center for Body Computing, Keck School of Medicine, Playa Vista, CA, United States.

Glenn R Fox (GR)

University of Southern California, Center for Body Computing, Keck School of Medicine, Playa Vista, CA, United States.
University of Southern California, Marshall School of Business, Los Angeles, CA, United States.

Rebecca Ebert (R)

University of Southern California, Center for Body Computing, Keck School of Medicine, Playa Vista, CA, United States.

Josiah Duhaime (J)

United States Marine Corps, Reconnaissance Training Company, Camp Pendleton, CA, United States.

Luciano Nocera (L)

University of Southern California, Department of Computer Science, Viterbi School of Engineering, Los Angeles, CA, United States.

Luan Tran (L)

University of Southern California, Department of Computer Science, Viterbi School of Engineering, Los Angeles, CA, United States.

Mona Sobhani (M)

University of Southern California, Center for Body Computing, Keck School of Medicine, Playa Vista, CA, United States.

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