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Record W7070680471

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

2019· other· en· W7070680471 on OpenAlexaboutno aff

Bibliographic record

VenueTU/e Research Portal (Eindhoven University of Technology) · 2019
Typeother
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsOvertrainingMindsetProcess (computing)Set (abstract data type)Mental healthPopularityGoal settingControl (management)
DOInot available

Abstract

fetched live from OpenAlex

<b>Introduction</b><br/>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]. <br/>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].<br/>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. <br/><b>Methods</b><br/>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.<br/>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).<br/><b>Results</b><br/>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.<br/>\tA 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.<br/><br/><b>Conclusion</b><br/>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. <br/>\tAs 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. <br/>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.<br/>\tIn 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. <br/>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.067
GPT teacher head0.381
Teacher spread0.313 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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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