Dataset Information

The importance of shared language in rural behavioral health interventions: An exploratory linguistic analysis.

ABSTRACT: A focus on the use of shared language to enhance congruence in interventionist-client dialogue is missing from traditional research on evidence-based practices and rural behavioral health. This study incorporates qualitative interactional sociolinguistics, which includes discourse analysis (typically written or audio recordings of face-to-face encounters with 11 clients and a study interventionist), to describe those speech patterns in a broad sense (dialect), as well as more specific use of communicative strategies to increase parity in the interaction between a rural interventionist delivering an evidence-based practice in the context of a research study with rural women opioid users in a non-therapeutic context. Study findings indicated that in the context of delivering the intervention, use of a shared language, language pattern congruence, and communication styles can greatly augment the intent of the approach with vulnerable populations. In addition, other communicative strategies connected with traditional Appalachian values - such as religion, home, and family - were also important. This study makes an important contribution to behavioral health research and practice by understanding critical factors that may influence evidence-based practice delivery, particularly in real-world settings with vulnerable populations. These findings have important implications for the utilization of creative approaches to understand critical components of the clinical interaction as indicators of fidelity.

SUBMITTER: Staton M

PROVIDER: S-EPMC7731584 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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The importance of shared language in rural behavioral health interventions: An exploratory linguistic analysis.

Staton Michele M Cramer Jennifer J Walker Robert R Snell-Rood Claire C Kheibari Athena A

Rural mental health 20190916 4

A focus on the use of shared language to enhance congruence in interventionist-client dialogue is missing from traditional research on evidence-based practices and rural behavioral health. This study incorporates qualitative interactional sociolinguistics, which includes discourse analysis (typically written or audio recordings of face-to-face encounters with 11 clients and a study interventionist), to describe those speech patterns in a broad sense (dialect), as well as more specific use of com ...[more]

PMID: 33312331

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Project description:BackgroundDepression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression.ObjectiveThe objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression.MethodsSmartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8-86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression.ResultsOf the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants' age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231 (16.81%) were depressed scores (PHQ-8 score ≥10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status-normalized entropy and depression (r=0.14, P<.001). LMM demonstrates an intraclass correlation of 0.7584 and a significant positive association between screen status-normalized entropy and depression (β=.48, P=.03). The best ML algorithms achieved the following metrics: precision, 85.55%-92.51%; recall, 92.19%-95.56%; F1, 88.73%-94.00%; area under the curve receiver operating characteristic, 94.69%-99.06%; Cohen κ, 86.61%-92.90%; and accuracy, 96.44%-98.14%. Including age group and gender as predictors improved the ML performances. Screen and internet connectivity features were the most influential in predicting depression.ConclusionsOur findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors' data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring.

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Dataset Information

The importance of shared language in rural behavioral health interventions: An exploratory linguistic analysis.

Publications

The importance of shared language in rural behavioral health interventions: An exploratory linguistic analysis.

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