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The running & exercise mental break optimisation (REMBO) app: design process and user evaluation.

2019· other· en· W7033859660 sur OpenAlexaboutno aff

Notice bibliographique

RevueTU/e Research Portal · 2019
Typeother
Langueen
DomaineEarth and Planetary Sciences
ThématiqueCold Fusion and Nuclear Reactions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOvertrainingMindsetProcess (computing)Set (abstract data type)PopularityMental healthGoal settingControl (management)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Introduction The popularity of running is steadily growing, which is great news considering its potential health benefits [1]. Less fortunate, however, is the associated injury rate in running. Recent research shows that no less than 52.2% of runners report a running-related injury (RRI) in the past 12 months [2]. These numbers suggests injury prevention in running is pivotal, but insufficiently practiced. The most common barriers for practicing injury prevention in running are “not knowing what to do” and “no history of RRI” [3] (p. 10). Therefore, we designed an application specifically for runners to prevent injuries and internalize a habitual preventative mindset concerning RRI in runners. We tested the application in a randomized controlled trial [4]. The application or app, which we aptly named the Running & Exercise Mental Break Optimisation (REMBO) app, contained various elements aimed at establishing the aforementioned goals, most importantly via a ‘running check’ questionnaire. The purpose of this short questionnaire was to determine ones’ personal training capacity via a set of questions and to give a personalized advice accordingly relating to their planned training load for that day. In doing so, its goal was to help runners stay within healthy boundaries of training, thereby preventing overtraining and the associated risk of injury. Thereby the app stimulated them to ensure adequate recovery had taken place before they engaged in running again. The main proposed workings of our app revolved around mental aspects of injury prevention, which are likely to be very important in injury prevention [4]. These aspects include physical, cognitive and emotional recovery [5], and obsessive and harmonious passion [6], which are described more in-depth in our design paper [4]. In order to evaluate the ease of use and effectiveness of this app we set out to gage user experiences. Using a semi-structured interview format, we set out to qualitatively explore how users experienced usage of the app and points for improvement they recommended. Note that predicted relations with injuries and/or results from the trial are not part of the current abstract and will be reported elsewhere. Methods As a summary of the app design and workings: we compared several online app designing platforms, eventually selecting one which facilitated such design via a (near-)drag & drop experience with basic HTML support. The earlier mentioned ‘running check’ consisted of 12 items mainly related to mental aspects, such as mental fatigue, feelings of obligation, and focus. Some items related to physical indicators were also included (e.g., joint pain). All items were rated on a 7-point Likert scale. The feedback mechanism on planned trainings based on this ‘running check’ was implemented using traffic lights, a common and effective approach in interventions [7]; green for: safe running, orange for: risky running, and red for: no running recommended at all. Categories were determined via an algorithm based on ‘running check’ scores of users. Orange and red traffic lights were accompanied by advice on reduction of or alternatives for participants’ planned training. The REMBO app was first tested in a randomized controlled trial [4]. After this trial we requested 37 people from the intervention group (n = 214) (i.e., those who had access to the app) to partake in an interview. Of those invited, 14 accepted and were interviewed in a semi-structured fashion by phone. During this interview 18 questions (i.e., closed, open, and follow-up questions based on certain answers) were used to explore the following facets of user experience: general perception of the app and the ‘run check’; outcomes resulting from app usage; anticipated future use; and possible improvements [8]. Results were analyzed according to Grounded Theory [9] using QDA Miner Lite (v2.0.6; Provalis Research, Montreal, Quebec, Canada). Results More positive than negative experiences were mentioned, with only a subset of these negative experiences pertaining to actual app content (c.f., app look). The ‘running check’ was nearly uniformly deemed a good indicator of their capacity, although some comments about its lack of physical questions and broadness of traffic light categories were mentioned. A wide variety of ideas were offered when asking for improvements, including the ability to save data and link the app with other apps. Most interviewees said the app influenced their opinion of running injuries by increasing awareness of mental aspects (e.g., detaching from ones’ sport), followed by a smaller share which said the app had not changed anything, following by a variety of yet smaller shares mentioning various positive outcomes other than awareness. Participants were nearly uniform (86%) in saying that the app would not require recurring usage but that its mechanism was internalized after a period of usage during the trial. Conclusion The goal of this study was to qualitatively evaluate the REMBO app among its users. Generally, the app was received well and achieved some of its intended effects, such as increased awareness of mental aspects (e.g., mentally detaching from ones’ sport) of injury prevention. Multiple points for improvement were offered by users, including collaboration with or implementation in other apps and the option to save ones’ data. As the goal of our app was rather small in scope (i.e., to test proposed mechanisms relating to RRI) our design process was not as elaborate as some similar studies [10]. Combining solely the functional mechanism of our application with other apps which already possesses adequate design and a benefitting user base may avoid issues pertaining to our basic design. The qualitative nature of this study can be considered both a strong and weak point, as the exploratory nature allows us to explore aspects otherwise missed, but the very nature (and sample size) of such studies generally complicate generalizability. Furthermore, some of our findings can also be considered ambiguous due to contrasting desires with comparable amounts of proponents on both sides of some issues. In conclusion, this study shows that the design and implementation of the REMBO app were received favorably among the interviewed runners and app usage resulted in increased awareness of the mental aspects (e.g., mental recovery, passion) of RRI prevention.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,083
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0290,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,094
Tête enseignante GPT0,366
Écart entre enseignants0,272 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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