ArticleJMIR mental health2018
Measurement of Symptom Change Following Web-Based Psychotherapy: Statistical Characteristics and Analytical Methods for Measuring and Interpreting Change.
Article in JMIR mental health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 68 citations in OpenAlex.
- The wellbeing neuro course: a randomised controlled trial of an internet-delivered transdiagnostic psychological intervention for adults with neurological disorders.Psychological medicine · 2023Trial
- The impact of obesity and overweight on response to internet-delivered cognitive behavioural therapy for adults with chronic health conditions.International journal of obesity (2005) · 2023Trial
- Efficacy of an Online Workplace Mental Health Accommodations Psychoeducational Course: A Randomized Controlled Trial.International journal of environmental research and public health · 2023Trial
- Internet-Delivered Cognitive Behavioral Therapy for Postsecondary Students: Randomized Factorial Trial for Examining Motivational Interviewing and Booster Lessons.Journal of medical Internet research · 2022Trial
- Guided internet-delivered cognitive-behaviour therapy for persons with spinal cord injury: a feasibility trial.Spinal cord · 2020Trial
- Making Internet-delivered cognitive behaviour therapy scalable for cancer survivors: a randomized non-inferiority trial of self-guided and technician-guided therapy.Journal of cancer survivorship : research and practice · 2020Trial
- Article
- Implementation trial II: Clinical outcomes and acceptability of an internet-delivered intervention for anxiety and depression delivered as part of routine care for university students in Australia.Internet interventions · 2024Article
- Making the most out of timeseries symptom data: A machine learning study on symptom predictions of internet-based CBT.Internet interventions · 2024Article
- The Sleep Course: An inclusive trial examining the feasibility, acceptability, and preliminary efficacy of a digital sleep intervention for adults with self-reported sleep difficulties.Internet interventions · 2024Article
- Implementation trial I: Clinical outcomes and acceptability of an internet-delivered intervention for anxiety and depression delivered as part of routine care for university students in New Zealand.Internet interventions · 2024Article
- Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy.Communications medicine · 2024Article
- Psychometric evaluation of an adult post-COVID-19 symptom tool: a development and validation study.Scientific reports · 2024Article
- An open trial of the feasibility of brief internet-delivered acceptance and aommitment therapy (iACT) for chronic anxiety and depression.Internet interventions · 2023Article
- Predictive models for clinical decision making: Deep dives in practical machine learning.Journal of internal medicine · 2022Review
- Utilization, Patient Characteristics, and Longitudinal Improvements among Patients from a Provincially Funded Transdiagnostic Internet-delivered Cognitive Behavioural Therapy Program: Observational Study of Trends over 6 Years.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2022Observational
- A randomized factorial trial of internet-delivered cognitive behavioural therapy: An 8-week program with or without extended support and booster lesson.Internet interventions · 2022Article
- A Comparison of Self-Referral and Referral via Primary Care Providers, through Two Similar Digital Mental Health Services in Western Australia.International journal of environmental research and public health · 2022Observational
- Evaluation of a Commercial Mobile Health App for Depression and Anxiety (AbleTo Digital+): Retrospective Cohort Study.JMIR formative research · 2021Article
- Brief online motivational interviewing pre-treatment intervention for enhancing internet-delivered cognitive behaviour therapy: A randomized controlled trial.Internet interventions · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate measurement of treatment-related change is a key part of psychotherapy research and the investigation of treatment efficacy. For this reason, the ability to measure change with accurate and valid methods is critical for psychotherapy.
objectiveThe aims of this study were to (1) explore the underlying characteristics of depressive symptom change, measured with the nine-item Patient Health Questionnaire (PHQ-9), following psychotherapy, and (2) compare the suitability of different ways to measure and interpret symptom change. A treatment sample of Web-based psychotherapy participants (n=1098) and a waitlist sample (n=96) were used to (1) explore the statistical characteristics of depressive symptom change, and (2) compare the suitability of two common types of change functions: linear and proportional change.
methodsThese objectives were explored using hypotheses that tested (1) the relationship between baseline symptoms and the rate of change, (2) the shape of symptom score distribution following treatment, and (3) measurement error associated with linear and proportional measurement models.
resultsFindings demonstrated that (1) individuals with severe depressive baseline symptoms had greater reductions in symptom scores than individuals with mild baseline symptoms (11.4 vs 3.7); however, as a percentage measurement, change remained similar across individuals with mild, moderate, or severe baseline symptoms (50%-55%); (2) positive skewness was observed in PHQ-9 score distributions following treatment; and (3) models that measured symptom change as a proportional function resulted in greater model fit and reduced measurement error (<30%).
conclusionsThis study suggests that symptom scales, sharing an implicit feature of score bounding, are associated with a proportional function of change. Selecting statistics that overlook this proportional change (eg, Cohen d) is problematic and leads to (1) artificially increased estimates of change with higher baseline symptoms, (2) increased measurement error, and (3) confounded estimates of treatment efficacy and clinical change. Implications, limitations, and idiosyncrasies from these results are discussed.
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