Empirical Research
Inflexitext: A program assessing psychological inflexibility in unstructured verbal data

https://doi.org/10.1016/j.jcbs.2020.09.002Get rights and content

Highlights

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    Methods for classifying unstructured verbal data are needed.

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    Inflexitext is an automated program coding psychological inflexibility in text.

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    Inflexitext scores are related to questionnaire distress and inflexibility.

Abstract

This paper describes the development and initial support for Inflexitext, an automated program identifying psychological inflexibility in unstructured verbal data. Written in Python 3.7, Inflexitext produces a psychological inflexibility score based on patterns of word occurrence reflecting its contributing processes. Inflexitext performance was examined in a sample of 809 English speaking adults in the United States recruited using Amazon's Mechanical Turk platform. Participants wrote essays in response to a prompt to write about an emotional issue and completed self-report measures of distress and psychological flexibility relevant constructs. Participant essays were analyzed using Inflexitext and Linguistic Inquiry Word Count 2015 (LIWC), a popular text scoring program. Inflexitext scores demonstrated small positive correlations to self-report measures of experiential avoidance, cognitive fusion, challenges in progress towards one's values, and to symptoms of depression, anxiety, and stress and a medium positive correlation with LIWC coding of negative emotion. Inflexitext scores evidenced small negative correlations with progress towards one's values and LIWC scores on positive emotion. Overall, this initial examination provides preliminary support for the program, although further evaluation is needed and limitations are discussed. Potential applications for future development include unobtrusive ambient monitoring of verbal behavior and real time examination of psychological inflexibility as related to psychological functioning and therapeutic outcomes.

Section snippets

Inclusion and exclusion criteria

Participant inclusion criteria were being over the age of 18, speaking English, and living within the United States. There were no exclusion criteria beyond meeting these requirements.

Participants

Participants included in analyses were 809 adults residing in the United States and recruited through Amazon's Mechanical Turk platform. Demographic information is presented for these individuals. Among participants 64.6% identified as female and 35% identified as male. Participants ranged in age from 18 to 81,

Scoring rule development

Rules for the six psychological inflexibility processes (i.e., experiential avoidance, fusion, challenges in present moment attention, attachment to the conceptualized self, lack of values clarity, and inaction towards one's values; Levin et al., 2013) were developed by drawing on the definitions of constructs used in the literature (Hayes et al., 2012; Luoma et al., 2007). Feedback on these rules was obtained from clinicians and researchers working within the psychological flexibility

Questionnaires

Demographic information on participant gender, age, ethnicity, educational attainment, and religious orientation was collected.

Acceptance and Action Questionnaire-II (AAQ-II; Bond et al., 2011) is a seven-item questionnaire assessing experiential avoidance, or maladaptive efforts to avoid challenging thoughts, feelings, and other experiences (Hayes et al., 2012). Participants indicate the extent to which statements reflective of this process apply to them on a seven-point scale (1 = never true

Results

Descriptive information on text inflexibility and other measures is presented in Table 1. After data cleaning procedures, 0.6% of cases contained any missing values, suggesting that missing data was unlikely to influence analyses.

Discussion

The current study describes the development of Inflexitext, a program for identifying psychological inflexibility in unstructured verbal data. One of the challenges with developing approaches for scoring verbal data is the variability present within language. As such, ensuring that Inflexitext was able to successfully identify some occurrence of inflexibility was important. Our examination suggested that text inflexibility scores had sufficient variability: scores ranged from 0 to 159.09 and

Conclusions and future directions

The current study represents an initial effort to develop a rule-based system for classifying verbal data using open source Python 3.7 software. Identification of patterns consistent with psychological inflexibility and other psychological constructs in unstructured data available through our interactions with technology may extend our understanding of human behavior and processes contributing to dysfunction and therapeutic change. Subsequent examinations with more extensive validation efforts,

Author note

Python code used in the study is freely available for academic use and may be obtained by contacting the corresponding author. Study supported by funding from Texas A&M University Corpus Christi. We have no conflicts of interest to disclose.

Declaration of competing interest

This project was internally funded by the corresponding author's institution. We have no conflicts of interest to declare.

References (63)

  • A.T. Gloster et al.

    Psychological flexibility as a malleable public health target: Evidence from a representative sample

    Journal of Contextual Behavioral Science

    (2017)
  • S.C. Hayes et al.

    The role of the individual in the coming era of process-based therapy

    Behaviour Research and Therapy

    (2019)
  • S.C. Hayes et al.

    Acceptance and commitment therapy and contextual behavioral science: Examining the progress of a distinctive model of behavioral and cognitive therapy

    Behavior Therapy

    (2013)
  • H. Hesser et al.

    Clients' in-session acceptance and cognitive defusion behaviors in acceptance-based treatment of tinnitus distress

    Behaviour Research and Therapy

    (2009)
  • J. Krafft et al.

    Psychological inflexibility and stigma: A meta-analytic review

    Journal of Contextual Behavioral Science

    (2018)
  • P.F. Lovibond et al.

    The structure of negative emotional states: Comparison of the depression anxiety stress scales (DASS) with the beck depression and anxiety inventories

    Behaviour Research and Therapy

    (1995)
  • M. Smout et al.

    Development of the valuing questionnaire (VQ)

    Journal of Contextual Behavioral Science

    (2014)
  • A.N. Tibubos et al.

    Does self-acceptance captured by life narratives and self-report predict mental health? A longitudinal multi-method approach

    Journal of Research in Personality

    (2019)
  • M.P. Twohig

    Acceptance and commitment therapy

    Cognitive and Behavioral Practice

    (2012)
  • S. Wakefield et al.

    The evidence base of acceptance and commitment therapy (ACT) in psychosis: A systematic review

    Journal of Contextual Behavioral Science

    (2018)
  • M. Anderson

    June 13). Mobile technology and home broadband 2019

    (2019)
  • M.H. Aung et al.

    Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies

    Depression and Anxiety

    (2017)
  • R.A. Baer et al.

    Using self-report assessment methods to explore facets of mindfulness

    Assessment

    (2006)
  • D. Ben-Zeev et al.

    Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health

    Psychiatric Rehabilitation Journal

    (2015)
  • A.J. Bishara et al.

    Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches

    Psychological Methods

    (2012)
  • K.W. Brown et al.

    The benefits of being present: Mindfulness and its role in psychological well-being

    Journal of Personality and Social Psychology

    (2003)
  • L. Cardaciotto et al.

    The assessment of present-moment awareness and acceptance

    Assessment

    (2008)
  • C. Chan et al.

    An overview of the use of Mechanical Turk in behavioral sciences

    Research on Social Work Practice

    (2015)
  • J.E. Dudek et al.

    Quality of life in women with lipoedema: A contextual behavioral approach

    Quality of Life Research

    (2015)
  • J.C. Eichstaedt et al.

    Facebook language predicts depression in medical records

    Proceedings of the National Academy of Sciences

    (2018)
  • J.K. Flake et al.

    Measurement schmeasurement: Questionable measurement practices and how to avoid them

    (2019)

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