Using Genetic Algorithms in a Large Nationally Representative American Sample to Abbreviate the Multidimensional Experiential Avoidance Questionnaire

Psychometric properties of the chinese version of multidimensional experiential avoidance questionnaire-30

BACKGROUND

Experiential avoidance represents the tendency to avoid negative internal experiences, which is a key concept in Acceptance and Commitment Therapy. However, existing measures of experiential avoidance (i.e., Acceptance and Action Questionnaire-II, AAQ-II) have some limitations. This study aims to assess the psychometric properties of the Chinese version of Multidimensional Experiential Avoidance Questionnaire-30 (MEAQ-30) and provide evidence for the reliability and validity of this new instrument.

METHODS

Two questionnaire surveys were conducted. The first sample (N = 546) was analyzed using classical test theory (CTT), and the second sample (N = 511) was analyzed using multidimensional item response theory (MIRT).

RESULTS

CTT supported the six-factor structure of MEAQ-30, indicating good internal consistency and measurement invariance across genders. Furthermore, the Chinese version of MEAQ-30 showed satisfactory convergent and discriminant validity. The incremental validity test showed that after controlling for the effects of neuroticism and AAQ-II, the Chinese version of MEAQ-30 could still significantly predict depression, anxiety, and stress. MIRT indicated that 30 items had good discrimination and difficulty, and the six subscales were sufficiently reliable across the continuum of experiential avoidance.

CONCLUSION

The Chinese version of MEAQ-30 has good reliability and validity and is suitable for assessing experiential avoidance among Chinese populations.

Targeting emotional regulation using an Internet-delivered psychological intervention for cancer survivors: A randomized controlled trial

OBJECTIVES

This trial assessed the efficacy of an emotion-focused, modular, Internet-delivered adaptation of the Unified Protocol (UP) in improving cancer survivors' emotion regulation strategies.

DESIGN

A two-arm randomized controlled trial (1:1) was used to compare the efficacy of two Internet-based interventions: UP-adapted CanCope Mind (CM) and lifestyle-focused active control CanCope Lifestyle (CL).

METHODS

N = 224 cancer survivors randomized to CM or CL were assessed at baseline, between-modules, at post-intervention and 3-month follow-up on emotion regulation outcomes targeted by each CM module (Module 1: beliefs about emotions; Module 2: mindfulness; Module 3: cognitive reappraisal skills, catastrophizing, refocus on planning; Module 4: experiential avoidance). Primary analyses were intention-to-treat linear regressions using Fisher randomization tests for p-values and intervals were used to compare groups with standardized mean difference (SMD) effect sizes.

RESULTS

CanCope Mind participants (n = 61 completers) experienced moderate-to-large improvements (SMDs from .44-.88) across all outcomes at post-intervention. CM's effects were larger than CL's (n = 75 completers) immediately post-intervention and at 3-month follow-up for beliefs about emotions, mindfulness, cognitive reappraisals and experiential avoidance (all p's < .05). CM experienced greater improvements in catastrophizing immediately post-intervention, with a trending effect at follow-up. However, we could not reject the null hypothesis of identical between-group effects for refocusing on planning both immediately post-intervention and at follow-up. Exploratory analyses revealed inconsistent between-module effects.

CONCLUSIONS

In its entirety, CM is a promising intervention for improving and maintaining cancer survivors' adaptive emotion regulation, especially for mindfulness and experiential avoidance. This may have important clinical implications for promoting cancer survivors' emotional functioning and general well-being.

Adaptation in Young Military Recruits: Protocol for the Advancing Research on Mechanisms of Resilience (ARMOR) Prospective Longitudinal Study

BACKGROUND

Military services provide a unique opportunity for studying resilience, a dynamic process of successful adaptation (ie, doing well in terms of functioning and symptoms) in response to significant adversity. Despite the tremendous interest in positive adaptation among military service members, little is known about the processes underlying their resilience. Understanding the neurobiological, cognitive, and social mechanisms underlying adaptive functioning following military stressor exposure is essential for enhancing the resilience of military service members.

OBJECTIVE

The primary objective of the Advancing Research on Mechanisms of Resilience (ARMOR) longitudinal study is to characterize the trajectories of positive adaptation among young military recruits in response to basic combat training (BCT), a well-defined, uniform, and 10-week period of intense stress (aim 1), and identify promotive and protective processes contributing to individual variations in resilience (aim 2). The secondary objective is to investigate the pathways by which neurobehavioral markers of self-regulation assessed using electroencephalography and magnetic resonance imaging contribute to adaptive trajectories (aim 3).

METHODS

ARMOR is an ongoing, prospective longitudinal cohort study of young military recruits who recently joined the National Guard but have not yet shipped out for BCT. Participants (N=1201) are assessed at 5 time points over the initial >2 years of military service beginning before BCT (baseline) and followed up at 2 weeks and 6, 12, and 18 months after BCT. Participants complete web-based questionnaires assessing vulnerability and protective factors, mental health, and socioemotional functioning at each time point and a battery of neurocognitive tests at time 0. A subset of participants also complete structured diagnostic interviews and additional self-report measures and perform neurobehavioral tasks before and after BCT during electroencephalography sessions and before BCT only during magnetic resonance imaging sessions.

RESULTS

This UG3/UH3 project was initially funded in August 2017, with the UG3 pilot work completed at the end of 2018. The UH3 phase of the project was funded in March 2019. Study enrollment for the UH3 phase began on April 14, 2019, and ended on October 16, 2021. A total of 1201 participants are enrolled in the study. Follow-up data collection for the UH3 phase is ongoing and projected to continue through February 2024. We will disseminate the findings through conferences, webinars, open access publications, and communications with participants and stakeholders.

CONCLUSIONS

The ARMOR study provides a rich data set to identify the predictors and mechanisms of resilient and nonresilient outcomes in the context of military stressors, which are intended to empirically inform the development of prevention and intervention strategies to enhance the resilience of military trainees and potentially other young people facing significant life challenges.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51235.

Advancing Research on Mechanisms of Resilience (ARMOR) Prospective Longitudinal Study of Adaptation in Young Military Recruits: Protocol and rationale for methods and measures

Background

Military service provides a unique opportunity for studying resilience, a dynamic process of successful adaptation (i.e., doing well in terms of functioning and symptoms) in response to significant adversity. Despite tremendous interest in positive adaptation among military service members, little is known about the processes underlying their resilience. Understanding neurobiological, cognitive, and social mechanisms underlying adaptive functioning following military stressor exposure is essential to enhance the resilience of military service members.

Objectives

The primary objective of the Advancing Research on Mechanisms of Resilience (ARMOR) longitudinal study is to characterize trajectories of positive adaptation among young military recruits in response to Basic Combat Training (BCT), a well-defined, uniform, 10-week period of intense stress (Aim 1) and identify promotive and protective processes contributing to individual variations in resilience (Aim 2). The secondary objective is to investigate pathways by which neurobehavioral markers of self-regulation assessed by electroencephalography (EEG) and magnetic resonance imaging (MRI) contribute to adaptive trajectories (Aim 3).

Methods

ARMOR is an ongoing, prospective longitudinal cohort study of young military recruits who recently joined the National Guard but have not yet shipped for BCT. Participants (N=1,201) are assessed at five timepoints over the initial 2+ years of military service beginning before BCT (baseline) and followed up at 2 weeks, 6, 12, and 18 months post-BCT. At each time point, participants complete online questionnaires assessing vulnerability and protective factors, mental health and social-emotional functioning, and, at Time 0 only, a battery of neurocognitive tests. A subset of participants also complete structured diagnostic interviews, additional self-report measures, and perform neurobehavioral tasks before and after BCT during EEG sessions, and, at pre-BCT only, during MRI sessions.

Results

Study enrollment began April 14, 2019 and ended in October 16, 2021. A total of 1,201 participants are enrolled in the study (68.9% male; mean age = 18.9, SD = 3.0). Follow-up data-collection is ongoing and projected to continue through March 2024. We will disseminate findings through conferences, webinars, open access publications, and communications with participants and stakeholders.

Conclusions

Results are expected to elucidate how young military recruits adapt to military stressors during the initial years of military service. Understanding positive adaptation of military recruits in the face of BCT has implications for developing prevention and intervention strategies to enhance resilience of military trainees and potentially other young people facing significant life challenges.

An Online Acceptance and Mindfulness Intervention for Chronic Pain in Veterans: Development and Protocol for a Pilot Feasibility Randomized Controlled Trial

BACKGROUND

In the veteran community, chronic pain is particularly prevalent and often debilitating. Until recently, veterans with chronic pain were offered primarily pharmacological intervention options, which rarely suffice and can also have negative health consequences. To better address chronic pain in veterans, the Veterans Health Administration has invested in novel, nonpharmacological behavior interventions that target both pain management and chronic pain-related functional issues. One approach, acceptance and commitment therapy (ACT) for chronic pain, is supported by decades of efficacy evidence for improving pain outcomes; however, ACT can be difficult to obtain owing to issues such as a lack of trained therapists or veterans having difficulty committing to the time and resources needed for the full clinician-led ACT protocol. Given the strong ACT evidence base combined with access limitations, we set out to develop and evaluate Veteran ACT for Chronic Pain (VACT-CP), an online program guided by an embodied conversational agent to improve pain management and functioning.

OBJECTIVE

The aims of this study are to develop, iteratively refine, and then conduct a pilot feasibility randomized controlled trial (RCT) of a VACT-CP group (n=20) versus a waitlist and treatment-as-usual control group (n=20).

METHODS

This research project includes 3 phases. In phase 1, our research team consulted with pain and virtual care experts, developed the preliminary VACT-CP online program, and conducted interviews with providers to obtain their feedback on the intervention. In phase 2, we incorporated feedback from phase 1 into the VACT-CP program and completed initial usability testing with veterans with chronic pain. In phase 3, we are conducting a small pilot feasibility RCT, with the primary outcome being assessment of usability of the VACT-CP system.

RESULTS

This study is currently in phase 3; recruitment for the RCT began in April 2022 and is expected to continue through April 2023. Data collection is expected to be completed by October 2023, with full data analysis completed by late 2023.

CONCLUSIONS

The findings from this research project will provide information on the usability of the VACT-CP intervention, as well as secondary outcomes related to treatment satisfaction, pain outcomes (pain-related daily functioning and pain severity), ACT processes (pain acceptance, behavioral avoidance, and valued living), and mental and physical functioning.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03655132; https://clinicaltrials.gov/ct2/show/NCT03655132.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45887.

Development and Initial Validation of Two Brief Measures of Left-Wing Authoritarianism: A Machine Learning Approach

Although authoritarianism has predominantly been studied among political conservatives, authoritarian individuals exist on both "poles" of the political spectrum. A 39-item multidimensional measure of left-wing authoritarianism, the Left-wing Authoritarianism Index, was recently developed to extend the study of authoritarianism to members of the far-left. The present study coupled a fully automated machine learning approach (i.e., a genetic algorithm) with multidimensional item response theory in a large, demographically representative American sample (N = 834) to generate and evaluate two abbreviated versions of the Left-wing Authoritarianism Index. We subsequently used a second community sample (N = 477) to conduct extensive validational tests of the abbreviated measures, which comprise 25- and 13-items. The abbreviated forms demonstrated remarkable convergence with the full LWA Index in terms of their psychometric (e.g., internal consistency) and distributional (e.g., mean, standard deviation, skew, kurtosis) properties. This convergence extended to virtually identical cross-measure patterns of correlations with 14 external criteria, including need for chaos, political violence, anomia, low institutional trust. In light of these results, the LWA-25 and LWA-13 scales appeared to function effectively as measures of LWA.

Development of a Short-Form Stroke Impact Scale Using a Machine Learning Algorithm for Patients at the Subacute Stage

Importance: Several short forms of the Stroke Impact Scale Version 3.0 (SIS 3.0) have been proposed in order to decrease its administration time of about 20 min. However, none of the short-form scores are comparable to those of the original measure.

Objective: To develop a short-form SIS 3.0 using a machine learning algorithm (ML–SIS).

Design: We developed the ML–SIS in three stages. First, we calculated the frequencies of items having the highest contribution to predicting the original domain scores across 50 deep neural networks. Second, we iteratively selected the items showing the highest frequency until the coefficient of determination (R2) of each domain was ≥.90. Third, we examined the comparability and concurrent and convergent validity of the ML–SIS.

Setting: Hospitals.

Participants: We extracted complete data for 1,010 patients from an existing data set.

Results: Twenty-eight items were selected for the ML–SIS. High average R2s (.90–.96) and small average residuals (mean absolute errors and root-mean-square errors = 0.49–2.84) indicate good comparability. High correlations (rs = .95–.98) between the eight domain scores of the ML–SIS and the SIS 3.0 indicate sufficient concurrent validity. Similar interdomain correlations between the two measures indicate satisfactory convergent validity.

Conclusions and Relevance: The ML–SIS uses about half of the items in the SIS 3.0, has an estimated administration time of 10 min, and provides valid scores comparable to those of the original measure. Thus, the ML–SIS may be an efficient alternative to the SIS 3.0.

What This Article Adds: The ML–SIS, a short form of the SIS 3.0 developed using a machine learning algorithm, shows good potential to be an efficient and informative measure for clinical settings, providing scores that are valid and comparable to those of the original measure.

Assessing an Internet-Delivered, Emotion-Focused Intervention Compared With a Healthy Lifestyle Active Control Intervention in Improving Mental Health in Cancer Survivors: Protocol for a Randomized Controlled Trial

BACKGROUND

Cancer survivors are vulnerable to experiencing symptoms of anxiety and depression and may benefit from accessible interventions focused on improving emotion regulation. CanCope Mind (CM) was developed as an internet-delivered intervention adapted from the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders to improve emotion regulation and support the mental health of cancer survivors.

OBJECTIVE

This protocol aims to provide an outline of the CanCope Study, a trial comparing the efficacy of a Unified Protocol-adapted internet-delivered intervention (CM) designed for cancer survivors compared with an active control condition-an internet-delivered healthy lifestyle intervention, CanCope Lifestyle (CL). The primary aim is to assess and compare the efficacy of both interventions in improving emotion regulation, anxiety and depressive symptoms, and quality of life. The secondary aims involve assessing the mechanisms of the CM intervention.

METHODS

This trial is a 2-arm randomized controlled trial that allocates cancer survivors to either CM or CL. Both interventions comprise 4 web-based modules and are expected to take participants at least 8 weeks to complete. Participants' mental and physical health will be assessed via self-reported surveys at baseline (T0), between each module (T1, T2, and T3), immediately after the intervention (T4), and at 3-month follow-up (T5). The study aims to recruit 110 participants who have completed T4.

RESULTS

The CanCope study began recruitment in September 2020. A total of 224 participants have been randomized to the CM (n=110, 49.1%) and CL (n=114, 50.9%) groups.

CONCLUSIONS

This is one of the first trials to develop and investigate the efficacy of a web-based intervention for cancer survivors that specifically targets emotion regulation.

TRIAL REGISTRATION

Australian Clinical Trials ACTRN12620000943943; https://tinyurl.com/b3z9cjsp.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/36658.

Short forms of the Body Appreciation Scale-2 (BAS-2SF): Item selection and psychometric evaluation

The 10-item Body Appreciation Scale-2 (BAS-2; Tylka & Wood-Barcalow, 2015) is a widely used contemporary measure of positive body image that assesses one's love for, acceptance and appreciation of, and respect for their body. Given the need for abbreviated measures to reduce participant burden and study cost, we aimed to generate a short form of the BAS-2 (BAS-2SF). Two versions of the BAS-2SF emerged: a 3-item version derived deductively from theory (items selected based on their unique contribution to the body appreciation construct), and a 2-item version generated empirically (items selected based on a genetic algorithm approach). Psychometric evidence was garnered across five studies, totaling 3114 participants. Both BAS-2SF versions correlated highly (rs = .93-.97) with the original 10-item BAS-2 and demonstrated internally consistent and stable scores. Factor analyses revealed high item-factor loadings, unidimensionality, and gender invariance of the 3-item BAS-2SF. Correlations with construct validity measures, as well as model pathways, were similar between both BAS-2SF versions and the 10-item BAS-2. BAS-2SF versions also evidenced incremental validity. Both BAS-2SF versions retain the psychometric integrity of the BAS-2 and are recommended for use; however, the 3-item BAS-2SF is an ideal option for researchers who wish to estimate a body appreciation latent factor.

Hiding the Rabbit: Using a genetic algorithm to investigate shape guidance in visual search

During visual search, attention is guided by specific features, including shape. Our understanding of shape guidance is limited to specific attributes (closures and line terminations) that do not fully explain the richness of preattentive shape processing. We used a novel genetic algorithm method to explore shape space and to stimulate hypotheses about shape guidance. Initially, observers searched for targets among 12 random distractors defined, in radial frequency space, by the amplitude and phase of 10 radial frequencies. Reaction time (RT) was the measure of “fitness.” To evolve toward an easier search task, distractors with faster RTs survived to the next generation, “mated,” and produced offspring (new distractors for the next generation of search). To evolve a harder search, surviving distractors were those yielding longer RTs. Within eight generations of evolution, the method succeeds in producing visual searches either harder or easier than the starting search. In radial frequency space, easy distractors evolve amplitude × frequency spectra that are dissimilar to the target, whereas hard distractors evolve spectra that are more similar to the target. This method also works with naturally shaped targets (e.g., rabbit silhouettes). Interestingly, the most inefficient distractors featured a combination of a body and ear distractors that did not resemble the rabbit (visually or in spectrum). Adding extra ears to these distractors did not impact the search spectrally and instead made it easier to confirm a rabbit, once it was found. In general, these experiments show that shapes that are clearly distinct when attended are similar to each other preattentively.

Using Machine Learning to develop a short-form measure assessing 5 functions in patients with stroke

OBJECTIVE

This study aimed to develop and validate a machine learning based short measure (the ML-5F) to assess 5 functions (activities of daily living (ADL), balance, upper extremity (UE) and lower extremity (LE) motor function, and mobility) in patients with stroke.

DESIGN

Secondary data from a previous study. A follow-up study assessed patients with stroke using the Barthel Index (BI), Postural Assessment Scale for Stroke (PASS), and Stroke Rehabilitation Assessment of Movement (STREAM) at hospital admission and discharge.

SETTING

A rehabilitation unit in a medical center.

PARTICIPANTS

A total of 307 patients.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The BI, PASS, and STREAM.

RESULTS

A machine learning algorithm, Extreme Gradient Boosting, was used to select 15 items from the BI, PASS, and STREAM, and transformed the raw scores of the selected items into the scores of the ML-5F. The ML-5F demonstrated good concurrent validity (Pearson's r = 0.88-0.98) and responsiveness (standardized response mean = 0.28-1.01).

CONCLUSIONS

The ML-5F comprises only 15 items but demonstrates sufficient concurrent validity and responsiveness to assess ADL, balance, UE and LE functions, and mobility in patients with stroke. The ML-5F shows great potential as an efficient outcome measure in clinical settings.

Psychometric and Machine Learning Approaches to Reduce the Length of Scales

Brief measures are important in psychology research because they reduce participant burden. Researchers can select items from longer measures either to build a short-form or to administer items conditional on a participant's previous responses. Researchers who carry out these item selection strategies either focus on estimating a precise score on the measure (typically carried out in a psychometric approach) or on predicting the score on the measure (possibly taking a machine learning approach). However, it is unclear how scores from the psychometric and machine learning approaches compare to each other. In this paper, the following four statistical approaches to select items are reviewed and illustrated: item response theory to build static short-forms, computerized adaptive testing, the genetic algorithm, and regression trees. Theoretical strengths and weaknesses between these four statistical approaches are discussed, and the overlap between the areas of psychometrics and machine learning is considered.

Piloting CanCope: An internet-delivered transdiagnostic intervention to improve mental health in cancer survivors

OBJECTIVE

CanCope is an internet-delivered, cognitive-behavioural intervention adapted from the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders to improve emotion regulation and support the mental health of cancer survivors. Four separate pilot studies evaluated each of CanCope's modules for (1) feasibility and participant satisfaction, and changes in (2) module-specific outcomes, and (3) global measures of emotion dysregulation and anxiety and depressive symptoms, from pre-to-post module delivery.

METHODS

Eligible cancer survivors self-selected into one two-week online module designed to improve a specific aspect of emotion regulation ([1] understanding emotions, [2] mindfulness of emotions, [3] cognitive reappraisals, [4] challenging emotion-driven behaviours).

RESULTS

Across modules, post-intervention surveys were completed by 17-19 participants, (58.1%-90.5% completion rate for participants who received the intervention). Each module was feasible and participants reported high satisfaction. Moderate-to-large pre-to-post effect sizes in mean differences were observed in module-specific target outcomes (p's < 0.05). Emotion dysregulation significantly decreased across modules 1 to 3 (p's < 0.05) with a non-significant decrease for module 4 (p = 0.13). Anxiety symptoms significantly decreased across all modules (p's < 0.05). Depressive symptoms significantly decreased across modules 1 and 3 (p's < 0.05), with non-significant decreases across modules 2 (p = 0.08) and 4 (p = 0.06).

CONCLUSIONS

Each CanCope module demonstrated promise in targeting emotion regulation skills and supporting the mental health of cancer survivors. Randomised controlled trials are required to test the efficacy of CanCope as an intervention in its entirety.

A 10-item Fugl-Meyer Motor Scale Based on Machine Learning

OBJECTIVE

The Fugl-Meyer motor scale (FM) is a well-validated measure for assessing upper extremity and lower extremity motor functions in people with stroke. The FM contains numerous items (50), which reduces its clinical usability. The purpose of this study was to develop a short form of the FM for people with stroke using a machine-learning methodology (FM-ML) and compare the efficiency (ie, number of items) and psychometric properties of the FM-ML with those of other FM versions, including the original FM, the 37-item FM, and the 12-item FM.

METHODS

This observational study with follow-up used secondary data analysis. For developing the FM-ML, the random lasso method of ML was used to select the 10 most informative items (in terms of index of importance). Next, the scores of the FM-ML were calculated using an artificial neural network. Finally, the concurrent validity, predictive validity, responsiveness, and test-retest reliability of all FM versions were examined.

RESULTS

The FM-ML used fewer items (80% fewer than the FM, 73% fewer than the 37-item FM, and 17% fewer than the 12-item FM) to achieve psychometric properties comparable with those of the other FM versions (concurrent validity: Pearson r = 0.95-0.99 vs 0.91-0.97; responsiveness: Pearson r = 0.78-0.91 vs 0.33-0.72; and test-retest reliability: intraclass correlation coefficient = 0.88-0.92 vs 0.93-0.98).

CONCLUSION

The findings preliminarily support the efficiency and psychometric properties of the 10-item FM-ML.

IMPACT

The FM-ML has potential to substantially improve the efficiency of motor function assessments in patients with stroke.

An exploration of the relationships of experiential avoidance (as measured by the aaq-ii and meaq) with negative affect, perceived stress, and avoidant coping styles

Background

Current psychotherapies seek to reduce experiential avoidance (EA) which has also been put forth as a risk factor for anxiety disorders, depression, and post-traumatic stress disorder. EA is a potentially maladaptive self-regulatory tendency to avoid negative thoughts, feelings, memories, physical sensations, and other internal experiences. One unresolved issue with the most commonly used measures of EA, the Acceptance and Action Questionnaire-II (AAQ-II) which measures EA as a single factor and the Multidimensional Experiential Avoidance Questionnaire (MEAQ) which measures EA as six subdimensions, is what exactly is being measured. The AAQ-II appears to measure negative affect (NA), some aspects of avoidant coping, and psychological distress. In addition, the relationships of all the MEAQ subscales have not been thoroughly examined with these other constructs. In the current study, the relationships of AAQ-II and MEAQ scores with NA, avoidant coping styles, and perceived stress were examined.

Methods

Two-hundred undergraduates (154 females and 46 males) completed the AAQ-II and MEAQ, the Distressed Type D Personality Scale (DS-14) which includes a measure of NA, the Brief COPE which measures coping styles, and the Perceived Stress Scale.

Results

Scores on the AAQ-II had moderate positive relationships with the MEAQ total score and all MEAQ subscales with the exception of distress endurance which had a moderate negative relationship. The AAQ-II had a stronger relationship with NA, avoidant coping, and perceived stress than did the MEAQ. All MEAQ subscales had a positive relationship to NA, avoidant coping, and perceived stress with the exception of distress endurance which had a negative relationship with these constructs. While the AAQ-II is limited as a unitary measure of EA the multiple dimensions of the MEAQ may involve an extraneous factor of distress endurance. Future work should examine the relationships of the MEAQ with NA, avoidant coping and perceived stress with clinical populations.

Personalized Body Constitution Inquiry Based on Machine Learning

Background

Body constitution (BC) is the abstract concept indicating the state of a person's health in Traditional Chinese Medicine (TCM). The doctor identifies the body constitution of the patient through inspection and inquiry. Previous research simulates doctors to identify BC types according to a patient's objective physical indicators. However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor's diagnosis. The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency.

Methods

This paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients. The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients.

Results

The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It also improves the accuracy of recognizing BC. Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%.

Conclusions

The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient's BC types.

The Effect of the Fear of COVID-19 on Healthcare Professionals' Psychological Adjustment Skills: Mediating Role of Experiential Avoidance and Psychological Resilience

As the COVID-19 outbreak is rapidly spreading all over the world, it's secondary consequences will negatively affect both societies and individuals. The target group, expected to be exposed to the secondary negative consequences most intensely during the pandemic process and afterward, is undoubtedly the healthcare professionals. In this research, the impact of the fear that healthcare professionals in Turkey developed against the outbreak of COVID-19 on their psychological adjustment skills is examined, and in this context, the mediating role of experiential avoidance and psychological resilience is examined. In this context, an answer was sought for the question "Does experiential avoidance and psychological resilience have a mediating role in the impact of COVID-19 fear on psychological adjustment skills of healthcare professionals?" The research was carried out with a total of 370 healthcare professionals reached via online data collection method. Structural equation modeling was used in the data analysis process, and as a result, it was determined that the fear of COVID-19 had a negative effect on the psychological adjustment in healthcare professionals; however, psychological resilience was found to have a protective function that limits this effect, and experiential avoidance has a risk factor that aggravates this effect. Findings obtained from the research are discussed in the context of the literature.

Developing brief versions of the Moral Foundations Vignettes using a genetic algorithm-based approach

The time-efficient assessment of moral values using systematically validated measures is a high priority in moral psychology research. However, few such options exist for researchers working with Moral Foundations Theory, one of the most popular theories in moral psychology. Across two samples totaling 1336 participants (756 Australian undergraduates and 580 American Mechanical Turk workers), we used a genetic algorithm-based (GA) approach to construct and validate abbreviated versions of the Moral Foundations Vignettes (MFV), a 90-item scale comprising vignettes of concrete violations of each of the six moral foundations. We constructed 36- and 18-item versions of the MFV, demonstrating close correspondence with the complete MFV, and adequate reliability, predictive validity, and factor-analytic goodness of fit for both abbreviated versions. Overall, the abbreviated scales achieve substantially reduced length with minimal loss of information, providing a useful resource for moral psychology researchers.

A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development

This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and relationship to external variables. Using a Monte Carlo simulation study, this study compared existing implementations of the ant colony optimization, Tabu search, and genetic algorithm to select short forms of scales, as well as a new implementation of the simulated annealing algorithm. Selection of short forms of scales with unidimensional, multidimensional, and bifactor structure were evaluated, with and without model misspecification and/or an external variable. The results showed that when the confirmatory factor analysis model of the full form of the scale was correctly specified or had only minor misspecification, the four algorithms produced short forms with good psychometric qualities that maintained the desired factor structure of the full scale. Major model misspecification resulted in worse performance for all algorithms, but including an external variable only had minor effects on results. The simulated annealing algorithm showed the best overall performance as well as robustness to model misspecification, while the genetic algorithm produced short forms with worse fit than the other algorithms under conditions with model misspecification.

An Investigation of the Effect of COVID-19 on OCD in Youth in the Context of Emotional Reactivity, Experiential Avoidance, Depression and Anxiety

In addition to the serious physical and medical effects on individuals, the COVID-19 pandemic is likely to have short- and long-term psycho-social consequences, especially for young people. Nowadays, with psychological problems becoming more widely recognized in adolescents, it is possible that the fear and anxiety caused by the pandemic will trigger various anxiety disorders, OCD and similar negative outcomes. Considering that psychological qualities such as emotional reactivity and experiential avoidance observed in adolescents may increase the risk of such psycho-social disorders, in this study the relationship between fear of COVID-19 and OCD was investigated in a Turkish sample of 598 adolescents, and the mediating role of emotional reactivity, experiential avoidance and depression-anxiety in this relationship was examined. The data collection was performed online rather than in person because of the COVID-19 threat. A structural equation model was used to determine the direct and indirect predictive effects between variables in data analysis. The results of the study show that the effect of COVID-19 fear on OCD is mediated by emotional reactivity, experiential avoidance and depression-anxiety.

Developing an Indonesia's health literacy short-form survey questionnaire (HLS-EU-SQ10-IDN) using the feature selection and genetic algorithm

BACKGROUND AND OBJECTIVE

Measuring health literacy becomes more important because its association with health status and healthcare outcomes. Studies have developed at least 133 measurement tools for health literacy. HLS-EU-Q47 is a questionnaire consisting of 12 sub-dimensions and 47 questions developed by the Europe Health Literacy Consortium. Many countries in Europe and Asia have used HLS-EU-Q47 as a tool for measuring health literacy in the general public. Indonesia has conducted general health literacy survey using HLS-EU-Q47 but finding the difficulties because of the time-consuming interview. A shorter version of HLS-EU-Q47 is needed to apply in health literacy researches in Indonesia. This paper reports the results of feature reduction to develop a short Indonesian version HLS-EU questionnaire and measures the accuracy of the model compared with other short form like HLS-EU-SQ16 or HLS-SF12.

METHOD

The analysis was performed on a population-based dataset from Indonesia-Semarang Health Literacy Survey for which there were specific target variables as the classification of health literacy level. All attributes were assessed as potential targets in the models derived from the full dataset and its subsets. The feature selection methods with genetic algorithm were used as the filter as well as validation (cross validation) and classification (k-NN:k-nearest neighbor). The predictive accuracy of health literacy level and the complexity of models based on the reduced datasets were compared among the methods and other short versions such as HLS-EU-SQ16, HLS-SF12.

RESULT

The accuracy of the existing short form models were 90.64% with the HLS-EU-SQ16 and 88.67% with the HLS-SF12. This study proposed a model with 10 features as the construct of a short Indonesian-version (proposed as the HLS-EU-SQ10-IDN) since the model was with higher accuracy than the HLS-SF12, but fewer features for measuring general health literacy index. Moreover, the short version only completed part of 12 dimensions of the full questionnare.

CONCLUSION

A data mining technique using feature selection with combination of genetic algorithm and k-NN algorithm was applied to develop a short version questionnaire and proved to have better accuracy, as compared with the short version developed by traditional statistical technique.

A preventive intervention to modify depression risk targets after breast cancer diagnosis: Design and single-arm pilot study

OBJECTIVE

Apply the National Institutes of Health (NIH) Stage Model to design and test an intervention to prevent depression in breast cancer patients at risk for depression.

METHODS

We identified mindful emotion awareness, along with approach and avoidance strategies for cancer-related coping and emotion regulation, as targets for a preventive intervention adapted from the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders. Patients' preferences for individual, in-person, and time-efficient sessions informed the design. Patients at risk for depression received a 6-week, 5-hour intervention with daily exercises. Intervention targets were assessed at baseline, before each session, and 4-weeks post intervention. Mixed effects analysis of variance (ANOVA) assessed change over the follow-up period, controlling for age, partnered status, and disease stage.

RESULTS

Fifty-five percent (40/72) of women screened within 6 months of diagnosis had elevated depression risk. Of these, 24 (60%) signed consent. Sixteen received intervention after five were excluded for current depressive disorder, cognitive impairment, or death. Three dropped out. Ninety-eight percent attendance and 77% practice days indicated feasibility. Effect sizes (Cohen's d) corrected for regression to the mean (RTM) were 0.82 for cancer-related acceptance coping, 0.65 for cancer-related emotional expression, and 0.32 and 0.42 for decreased cancer-related avoidance coping and depressive symptoms, respectively. Effect sizes for variables lacking data to correct for RTM were 1.0, 0.7, and 0.5 for decreased rumination, experiential avoidance, and fear of depression, respectively, and 1.3, 0.6, and 0.4 for increased cognitive flexibility, distress tolerance, and describing/not judging emotions, respectively.

CONCLUSIONS

The feasibility of this intervention and malleability of its targets support its further investigation.

The Upsides and Downsides of the Dark Side: A Longitudinal Study Into the Role of Prosocial and Antisocial Strategies in Close Friendship Formation

Resource control theory (RCT) posits that both antisocial and prosocial behaviors combine in unique ways to control resources such as friendships. We assessed students (N = 2,803; 49.7% male) yearly from junior (grades 8-10) to senior high school (11-12) on antisocial (A) and prosocial (P) behavior, peer nominated friendship, and well-being. Non-parametric cluster analyses of the joint trajectories of A and P identified four stable profiles: non-strategic (moderately low A and P), bi-strategic (moderately high on A and P), prosocial (moderately low A and moderately high on P), and antisocial (moderately low on P, and very high on A). There were clear benefits to youth using bi-strategic strategies in junior high: they attracted relatively high levels of opposite sex friendship nominations. However, this benefit disappeared in senior high. There were also clear costs: bi-strategic youth experienced relatively low well-being, and this effect was significantly more pronounced for females than males. Prosocial youth were the only ones who maintained both high friendship numbers and high well-being throughout high school. We discuss the cost/benefit trade-offs of different resource control strategies.

A genetic algorithm to find optimal reading test word subsets for estimating full-scale IQ

In clinical neuropsychology the cognitive abilities of neurological patients are commonly estimated using well-established paper-based tests. Typically, scores on some tests remain relatively well preserved, whilst others exhibit a significant and disproportionate decline. Scores on those tests that measure preserved cognitive functions (so-called 'hold' tests) may be used to estimate premorbid abilities, including scores in non-hold tests that would have been expected prior to the onset of cognitive impairment. Many hold tests entail word reading, with each word being graded as correctly or incorrectly pronounced. Inevitably, such tests are likely to contain words that provide little or no diagnostic power (i.e., can be eliminated without negatively affecting prediction accuracy). In this paper, a genetic algorithm is developed and demonstrated, using n = 92 neurologically healthy participants, to identify optimal word subsets from the National Adult Reading Test that minimize the mean error in predicting the most widely used clinical measure of IQ and cognitive ability, the Wechsler Adult Intelligence Scale Fourth Edition IQ. In addition to requiring only 17-20 of the original 50 words (suggesting that this test could be revised to be up to 66% shorter) and minimizing mean prediction error, the algorithm increases the proportion of the variance in the predicted variable explained in comparison to using all words (from r2 = 0.46 to r2 = 0.61). In a clinical setting this would improve estimates of premorbid cognitive function and, if an abbreviated revision to this test were to be adopted, reduce the arduousness of the test for patients. The proposed method is evaluated with jackknifing and leave one out cross validation. The general approach may be used to optimize the relationship between any two psychological tests by finding the question subset in one test that minimizes the prediction error in a second test by training the genetic algorithm using data collected from participants upon whom both tests have been administered. This approach may also be used to develop new predictive tests, since it provides a method to identify an optimal subset of a set of candidate questions (for which empirical data have been collected) that maximizes prediction accuracy and the proportion of variance in the predicted variable that can be explained.

Identifying Measurement Invariant Item Sets in Cross-Cultural Settings Using an Automated Item Selection Procedure

Abstract. In the construction of scales intended for the use in cross-cultural studies, the selection of items needs to be guided not only by traditional criteria of item quality, but has to take information about the measurement invariance of the scale into account. We present an approach to automated item selection which depicts the process as a combinatorial optimization problem and aims at finding a scale which fulfils predefined target criteria – such as measurement invariance across cultures. The search for an optimal solution is performed using an adaptation of the [Formula: see text] Ant System algorithm. The approach is illustrated using an application to item selection for a personality scale assuming measurement invariance across multiple countries.

Designation and psychometric properties of the Short Form Postpartum Quality of Life Questionnaire (SF-PQOL): an application of multidimensional item response theory and genetic algorithm

Background: Utilizing multidimensional item response theory (MIRT) and genetic algorithm (GA) we aimed to design and test the psychometric properties of the short form Postpartum Quality of Life Questionnaire (PQOL). Methods: In this methodological study, 500 women aged 18 to 42 were enrolled through a multistage random sampling scheme in Tabriz, Iran. We used MIRT model and GA to identify a short form of the 40-item PQOL measure (SF-PQOL). Construct and criterion validity of the SF-PQOL was assessed by confirmatory factor analysis (CFA) and the correlation between SFPQOL scores with a 12-item short form of QOL (SF-12) and Edinburg Postnatal Depression Scale (EPDS) scores, respectively. The internal consistency, test-retest reliability and feasibility of the measure were evaluated. Results: sixteen- and 13-item SF-PQOL were identified based on MIRT and GA, respectively.The results indicate the better performance of the MIRT based 13-item SF-PQOL; Construct and criterion validity, the test-retest and internal consistency reliability, and the feasibility were confirmed in the MIRT based SF-PQOL, but not in the GA-based SF-PQOL. Conclusion: The MIRT suggests a 13-item SF-PQOL with adequate content which demonstrated satisfactory validity, reliability, and feasibility. SF-PQOL could be used across the population for both research and clinical objectives.

Body Image-Acceptance and Action Questionnaire-5: An Abbreviation Using Genetic Algorithms

Body image concerns are typically linked with negative outcomes such as disordered eating and diminished well-being, but some people can exhibit psychological flexibility and remain committed to their valued goals despite being dissatisfied about their bodies. Such flexibility is most frequently measured by the Body Image-Acceptance and Action Questionnaire (BI-AAQ). This study used a recently validated, fully automated method based on genetic algorithms (GAs) on data from an American community sample (N₁=538, 71.5% female, Age: M = 40.87, SD = 13.5) to abbreviate the 12-item BI-AAQ to a 5-item short form, BI-AAQ-5. Validation tests were conducted on data from an independent community sample (N₂₌ 762, 44.6% female, Age: M = 40.65, SD = 13.06). The short form performed comparably to the long form in terms of its factor structure and correlations with theoretically relevant constructs, including body image dissatisfaction, stigma, internalization of societal norms of appearance, self-compassion, and poor mental health. Further, preliminary analyses using structural equation modeling showed that body image flexibility, as measured by either the long or short form, was associated with almost all the criterion variables, even while controlling for a highly related construct of body image dissatisfaction. These results demonstrate the potential discriminant validity of both the long and short form of the BI-AAQ, and show that the BI-AAQ-5 is a suitable alternative to its long form. We discuss how psychological flexibility with respect to body image dissatisfaction can be conducive to positive functioning.

Are People Mindful in Different Ways? Disentangling the Quantity and Quality of Mindfulness in Latent Profiles and Exploring Their Links to Mental Health and Life Effectiveness

We sought to disambiguate the quantitative and qualitative components of mindfulness profiles, examine whether including ‘nonattachment’ as a subcomponent of mindfulness alters the profiles, and evaluate the extent to which the person–centred approach to understanding mindfulness adds predictive power beyond a more parsimonious variable–centred approach. Using data from a nationally representative sample of Americans ( N = 7884; 52% female; Age: M = 47.9, SD = 16), we utilized bifactor exploratory structural equation modelling and latent profile analysis to separate the level and shape of previously identified profiles of mindfulness (Pearson, Lawless, Brown, & Bravo, 2015). Consistent with past research, we identified a judgmentally observing profile and a non–judgmentally aware group, but inconsistent with past research, we did not find profiles that showed high or low levels on all specific aspects of mindfulness. Adding nonattachment did not alter the shape of the profiles. Profile membership was meaningfully related to demographic variables. In models testing the distinctive predictive utility of the profiles, the judgmentally observing profile, compared to the other profiles, showed the highest levels of mental ill–health, but also the highest levels of life satisfaction and effectiveness. We discuss the implications of our study for clinical interventions and understanding the varieties of mindfulness. Copyright © 2017 European Association of Personality Psychology

Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm

The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.