Publications by authors named "Richard J Siegert"

Introduction: Chronic cough is considered a disorder of neuronal hypersensitivity in which patients frequently report abnormal laryngeal and chest sensations, and excessive triggers. To facilitate clinical assessment, we developed the Cough Hypersensitivity Questionnaire (CHQ).

Methods: Candidate questionnaire items were developed following interviews with patients with refractory chronic cough (n=10, United Kingdom), and review by a multidisciplinary team.

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Background: Raising an autistic child is associated with increased parenting stress relative to raising typically developing children. Increased parenting stress is associated with lower parent wellbeing, which in turn can negatively impact child wellbeing.

Aims: The current study sought to quantify parenting stress and parent health-related quality of life (HRQOL) in the autism context, and further understand the relationship between them by employing a relatively novel statistical method, Network Analysis.

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Background: Various assessment tools that explore and assess mindfulness are available. Keeping in view both the origin of and the literature surrounding mindfulness assessment tools, this study aimed to evaluate the workability of one widely researched tool, the Five Facet Mindfulness Questionnaire (FFMQ), for establishing cross-cultural generalizability and utility in the Indian context.

Methods: We recruited 303 adults over 18 with proficiency in the Tamil language and no history of significant neurological trauma and/or psychiatric history.

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Purpose: Despite having the highest medical needs by population for weight loss treatment, Pacific patients in Aotearoa New Zealand face substantial levels of attrition in publicly funded weight loss surgery programs. In collaboration with the Auckland City Hospital bariatric surgery team, a Pacific-led preoperative weight loss surgery program was co-designed, delivered, and evaluated between 2020 and 2023.

Materials And Methods: This was a single-arm, prospective co-designed evaluation study that took place at Auckland City Hospital in Aotearoa New Zealand.

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The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature.

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The COVID-19 pandemic has elevated levels of distress and resulted in anti-Asian discrimination in many countries. We aimed to determine the 10-month prevalence of depression symptoms in Asian adults in New Zealand during the pandemic and to see if this was related to experience of racism. An online survey was conducted and a stratified sample of 402 respondents completed the brief Centre for Epidemiological Studies-Depression (CES-D) scale.

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Introduction: Increasing attention on workplace wellbeing and growth in workplace wellbeing interventions has highlighted the need to measure workers' wellbeing. This systematic review sought to identify the most valid and reliable published measure/s of wellbeing for workers developed between 2010 to 2020.

Methods: Electronic databases Health and Psychosocial Instruments, APA PsycInfo, and Scopus were searched.

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Objective: To examine (1) the concurrent validity of the Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC) with the criterion standard Coma Recovery Scale-Revised (CRS-R) for outcomes of awareness in patients with prolonged disorders of consciousness (PDoC), (2) the relationship between MATADOC items and CRS-R function subscales in similar domains, and (3) determine if items/function subscales measure different constructs.

Design: A prospective multicentric blinded study with repeated concurrent measures.

Setting: Three inpatient rehabilitation units.

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Objectives: Fear avoidance is associated with symptom persistence after mild traumatic brain injury (mTBI). In this study, we investigated whether fear avoidance was associated with other outcomes such as return to work-related activity (RTW).

Materials And Methods: We analyzed associations between fear avoidance and RTW 6-9 months after mTBI, in two merged prospective mTBI cohorts.

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Background: Prolonged Disorders of Consciousness (PDOC) resulting from severe acquired brain injury can lead to complex disabilities that make diagnosis challenging. The role of machine learning (ML) in diagnosing PDOC states and identifying intervention strategies is relatively under-explored, having focused on predicting mortality and poor outcome. This study aims to: (a) apply ML techniques to predict PDOC diagnostic states from variables obtained from two non-invasive neurobehavior assessment tools; and (b) apply network analysis for guiding possible intervention strategies.

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Dementia is a major health concern globally and cross-culturally with progressive decline in cognition, mobility and communication. There are few interventions for end-stage dementia (ESD) although music interventions have been observed to be accessible for people with mid to late-stage dementia. The lack of protocols and measures suited to ESD has limited research into the effects of music therapy.

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Background: Patients who have had prolonged stays in intensive care have ongoing rehabilitation needs. This is especially true of COVID-19 ICU patients, who can suffer diverse long-term ill effects. Currently there is no systematic data collection to guide the needs for therapy input for either of these groups nor to inform planning and development of rehabilitation services.

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Article Synopsis
  • - The study aimed to improve predictions of patients emerging from prolonged disorders of consciousness (PDOC) using machine learning techniques on clinical data from the UKROC database, focusing on variables like age and length of stay.
  • - A total of 1170 patients were analyzed, with results showing that certain characteristics, such as severe motor impairment and complex disability, were associated with either emergence or non-emergence from PDOC.
  • - The findings highlight the potential of using machine learning to refine clinical predictions and decision-making in rehabilitation, addressing the current lack of evidence-based criteria for these cases.
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Objective: To measure symptoms of anxiety, depression and hopelessness in a sample of young Pacific adults living in Auckland, New Zealand during the 2020/2021 COVID-19 pandemic and identify protective factors.

Methods: Participants were 267 Pacific adults (58% female) who completed a survey online. Analyses included descriptive statistics, correlations, linear regression and symptom network analysis.

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The increasing demand for palliative care in New Zealand presents a potential threat to the quality of service delivery. One strategy to overcome this is through the implementation of valid and reliable patient-reported outcome measures. This mixed-methods study aimed to (1) implement measurement-based palliative care (MBPC) in a community palliative care service in Auckland, New Zealand; (2) evaluate the clinical utility of MBPC perceived by clinicians; (3) describe patient characteristics as measured by the Integrated Palliative Care Outcome Scale (IPOS), the Australasian Modified Karnofsky Performance Scale (AKPS), and Phase of Illness (POI); and (4) evaluate the internal consistency of the IPOS.

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Aim: To develop and validate a questionnaire to measure health CE at governance level.

Method: This study used qualitative and quantitative methods (including focus groups, cognitive interviews and an international survey), and consisted of two phases. In Phase 1, an initial list of items was generated and refined with feedback from health consumer representatives.

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Purpose: 1: To determine whether Total UK FIM + FAM scores can identify patients in VS/MCS. 2: Using the identified cut-off points, to examine outcomes from specialist rehabilitation.

Methods: Part 1: Retrospective analysis of a consecutive clinical cohort ( = 388) presenting to a single specialist PDOC evaluation programme 2007-2021.

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Aims And Objectives: To identify symptom clusters and develop a symptom cluster model among people living with chronic obstructive pulmonary disease (COPD).

Background: The examination of symptom clusters in COPD patients is an emerging field of scientific inquiry directed towards symptom management. However, no studies have modelled the relationships among symptom clusters, associated factors and health-related quality of life.

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Context: The growth of patient reported outcome measures data in palliative care provides an opportunity for machine learning to identify patterns in patient responses signifying different phases of illness.

Objectives: The study will explore if machine learning and network analysis can identify phases in patient palliative status through symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS).

Methods: A partly cross-sectional and partially longitudinal observational study was undertaken using the Australasian Karnofsky Performance Scale (AKPS); Integrated Palliative Care Outcome Scale (IPOS); Phase of Illness (POI).

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Objectives: Despite successful treatment, people living with HIV experience persisting and burdensome multidimensional problems. We aimed to assess the validity, reliability and responsiveness of Positive Outcomes, a patient-reported outcome measure for use in clinical practice.

Methods: In all, 1392 outpatients in five European countries self-completed Positive Outcomes, PAM-13 (patient empowerment), PROQOL-HIV (quality of life) and FRAIL (frailty) at baseline and 12 months.

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Background: Prototype analyses of well-being have identified central characteristics and prototypicality for New Zealand teachers, lawyers, adolescents, and work well-being of nurses. What has not yet been explored is the broad construct of well-being in intensive care nurses.

Aims: To identify intensive care nurses' conceptions of general well-being and investigate whether their general well-being is prototypically organized.

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Accurate distinction between state and trait anxiety is necessary for assessment and monitoring of individual anxiety levels over time and developing effective interventions to reduce anxiety. Generalizability theory (G-theory) is a suitable method to distinguish between state and trait and to evaluate reliability of test scores and sources of measurement error in the assessment of affective conditions. We applied G-theory to the widely used 40-item State and Trait Anxiety Inventory (STAI) completed by 139 participants on three occasions separated by 2-week intervals.

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While evidence supports the feasibility of online mindfulness training (MT), the effect of this approach on cognition remains unclear. The present study investigated changes in cognition following a newly developed 6-week videoconference-delivered MT program on cognitive function in two groups. The first group ( = 17) had two baseline assessments prior to MT [3 weeks after group two ( = 15)] to allow for evaluation of practice and learning effects.

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Depression and anxiety are common sequelae of stroke, occurring in at least one-third of patients. This study evaluated the acceptability and feasibility of providing mindfulness training (MT) to stroke survivors with the aim of reducing depression and anxiety. Following a six-week one-on-one MT course, 17 participants were interviewed.

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