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Record W3045330112 · doi:10.2196/17926

Psychometric Properties of the Korean Version of the PsyMate Scale Using a Smartphone App: Ecological Momentary Assessment Study

2020· article· en· W3045330112 on OpenAlexvenueno aff
Yong Yang, Gi Wook Ryu, Philippe Delespaul, Mona Choi

Bibliographic record

VenueJMIR mhealth and uhealth · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsIntraclass correlationPsychologyScale (ratio)Rating scaleDescriptive statisticsMann–Whitney U testTest (biology)Pearson product-moment correlation coefficientClinical psychologyMedicineStatisticsPsychometricsEcologyDevelopmental psychologyGeographyMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Ecological momentary assessment (EMA) is a method for capturing the changes in the variables in daily life with increased accuracy and decreased recall bias. The PsyMate scale assesses momentary moods in daily life and can be used in various settings. OBJECTIVE: The aim of this study was to develop a Korean version of the PsyMate (K-PsyMate) scale and evaluate its psychometric properties by using the EMA method in patients with moyamoya disease (MMD) in South Korea. METHODS: Patients with MMD aged over 18 years were recruited from July 2018 to January 2019 at the inpatient and outpatient departments of a university hospital in South Korea. The K-PsyMate scale comprising 13 items was developed following a translation/back translation approach of the English version and loaded onto a mobile app. Participants were instructed to enter their moods 4 times a day for 7 consecutive days. Content validity index, factor analysis, and Pearson's correlation were performed for validity analysis. For reliability, intraclass correlation coefficients between the first and last measurements were estimated by mean rating, absolute agreement, and a 2-way mixed-effects model. Usability was analyzed through a descriptive analysis, 2-tailed t test, and analysis of variance, and the results were confirmed by Mann-Whitney U test and the Kruskal-Wallis test, as the dependent variable was not normally distributed. RESULTS: In total, 1929 assessments from 93 patients were analyzed. The mean age of the participants was 40.59 (SD 10.06) years, and 66 (71%) of the 93 participants were women. Content validity was excellent as content validity index was 0.99, and 2 factors, negative affect and positive affect, were derived by an exploratory factor analysis. The correlations between the subdomains of the K-PsyMate scale and Hospital Anxiety and Depression Scale were significant (P<.001). The agreement between the first and last measurements was poor to moderate according to the obtained intraclass correlation coefficient values. Usability was evaluated by 67 (72%) out of the 93 participants. The participants rated the accuracy of assessing their momentary moods on the app at 4.13 (SD 0.97), easiness in understanding questions, operating, and inputting answers at 4.12 (SD 0.88), and interruption by the survey alarms at 2.48 (SD 1.02) out of 5. CONCLUSIONS: The K-PsyMate scale has good validity but poor to moderate agreement, which reflects the characteristics of the EMA data collected in real and natural living environments without control. The findings of our study show that the K-PsyMate scale uploaded in a mobile app can be a valid and reliable tool for evaluating the momentary mood of patients with MMD because using a mobile app is convenient and patients are familiar with their own smartphones, which they use in their daily lives.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.008
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.106
GPT teacher head0.387
Teacher spread0.281 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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