Using the Mindfulness-Based Interventions: Teaching Assessment Criteria (MBI:TAC) in Supervision

Mindfulness-based cognitive therapy vs. a health enhancement program for the treatment of late-life depression: Study protocol for a multi-site randomized controlled trial

Background

Late-life depression (LLD) affects up to 18% of older adults and has been linked to elevated dementia risk. Mindfulness-based cognitive therapy (MBCT) holds promise for treating symptoms of depression and ameliorating cognitive deficits in older adults. While preliminary findings are promising, a definitive RCT investigating its effects on late life depression and cognition have not yet been conducted. We present a protocol describing a multi-site blinded randomized controlled trial, comparing the effects of MBCT and of an active control, a Health Enhancement Program (HEP), on depressive symptoms, executive functioning, and brain biomarkers of LLD, among several other exploratory outcomes.

Methods

Two-hundred and thirteen (n = 213) patients with LLD will be recruited at various centers in Montreal, QC, Canada. Participants will undergo stratified randomization to either MBCT or HEP intervention groups. We will assess changes in (1) depression severity using the Hamilton Depression Rating Scale (HAM-D17), (2) processing speed and executive functioning, (3) brain biomarkers of LLD (hippocampal volume, default network resting-state functional connectivity and executive network resting-state functional connectivity), and (4) other exploratory physiological and mood-based measures, at baseline (0 weeks), post intervention (8 weeks), and 26 weeks after baseline.

Discussion

The proposed study will assess the clinical potential of MBCT to improve symptoms of depression, as well as examine its impact on cognitive impairments and neurobiological markers, and thus inform its use as a promising adjunct in the treatment of LLD.

Clinical trial registration

www.ClinicalTrials.gov, identifier: NCT05366088.

Introducing the Mindfulness-Based Interventions: Teaching and Learning Companion (The TLC)

Background:

Reflective practice is a key skill which can enable the development of teaching competence among Mindfulness-Based Program (MBP) teachers.

Purpose:

In this article, the Mindfulness-Based Interventions: Teaching and Learning Companion (TLC) is introduced. This new tool is based upon the established teaching competence assessment tool, the Mindfulness-Based Interventions: Teaching Assessment Criteria (MBI:TAC). The MBI:TAC has enabled benchmarking of international standards of MBP teaching which helps ensure high fidelity to MBP curricula and contributes to the overall integrity of the field. This in turn, underpins the potential of MBPs to be effective interventions for the enhancement of participants’ mental health and wellbeing.

Conclusions:

The TLC aims to facilitate MBP teachers’ development by enabling active reflection focused on the key features of MBP teaching skills.

The design and methods of the OPTIMUM study: A multisite pragmatic randomized clinical trial of a telehealth group mindfulness program for persons with chronic low back pain

Mindfulness-based stress reduction (MBSR) is an evidence-based non-pharmacological approach for chronic low back pain (cLBP), yet it is not readily available or reimbursable within primary care clinics. Primary care providers (PCPs) who wish to avoid prescribing opioids and other medications typically have few options for their cLBP patients. We present the protocol of a pragmatic clinical trial entitled OPTIMUM (Optimizing Pain Treatment In Medical settings Using Mindfulness). OPTIMUM is offered online via telehealth and includes medical group visits (MGV) with a PCP and a mindfulness meditation intervention modeled on MBSR for persons with cLBP. In diverse health-care settings in the US, such as a safety net hospital, federally qualified health centers, and a large academic health system, 450 patients will be assigned randomly to the MGV + MBSR or to usual PCP care alone. Participants will complete self-report surveys at baseline, following the 8-week program, and at 6- and 12-month follow-up. Health care utilization data will be obtained through electronic health records and via brief monthly surveys completed by participants. The primary outcome measure is the PEG (Pain, enjoyment, and general activity) at the 6-month follow-up. Additionally, we will assess psychological function, healthcare resource use, and opioid prescriptions. This trial, which is part of the NIH HEAL Initiative, has the potential to enhance primary care treatment of cLBP by combining PCP visits with a non-pharmacological treatment modeled on MBSR. Because it is offered online and integrated into primary care, it is expected to be scalable and accessible to underserved patients. Clinical Trials.gov: NCT04129450.

Implementing the Mindfulness-Based Interventions; Teaching Assessment Criteria (MBI:TAC) in Mindfulness-Based Teacher Training

The Mindfulness-Based Interventions: Teaching Assessment Criteria (MBI:TAC) was originally developed as a tool to assess the teaching competence of mindfulness-based program (MBP) teachers. The tool was made freely available and has since been used by mindfulness-based teacher training organisations internationally. During this time the MBI:TAC has evolved in its usage, from an assessment tool to one which informally supports how MBP teachers are trained. In this article, we first examine the rationale for implementing the MBI:TAC in MBP teacher training; second, we offer practical guidance on ways of integrating the tool into teacher training pathways with awareness of its potential and possible pitfalls; and third, we offer guidance on using the tool as a framework for giving effective feedback to trainees on their teaching practice. Implementing the MBI:TAC in teacher training may support the quality and integrity of MBP teacher training, and thus ensure high quality MBP teachers graduating. In turn this may help avoid the 'implementation cliff' - that is, the quality of an intervention delivery is delivered in optimal conditions when it is being researched, and drops in quality when delivered in sub-optimal, 'real world' conditions.