Literature DB >> 35331943

Mentalizing imagery therapy to augment skills training for dementia caregivers: Protocol for a randomized, controlled trial of a mobile application and digital phenotyping.

Felipe A Jain1, Olivia Okereke2, Laura Gitlin3, Paola Pedrelli4, Jukka-Pekka Onnela5, Maren Nyer4, Liliana A Ramirez Gomez6, Michael Pittman7, Abu Sikder8, D J Ursal7, David Mischoulon4.   

Abstract

More than 50 million people worldwide live with a dementia, and most are cared for by family members. Family caregivers often experience chronic stress and insomnia, resulting in decreased mental and physical health. Accessibility of in-person stress reduction therapy is limited due to caregiver time constraints and distance from therapy sites. Mentalizing imagery therapy (MIT) provides mindfulness and guided imagery tools to reduce stress, promote self and other understanding, and increase feelings of interconnectedness. Combining MIT with caregiver skills training might enable caregivers to both reduce stress and better utilize newly learned caregiving skills, but this has never been studied. Delivering MIT through a smartphone application (App) has the potential to overcome difficulties with scalability and dissemination and offers caregivers an easy-to-use format. Harnessing passive smartphone data provides an important opportunity to study behavioral changes continuously and with higher granularity than routine clinical assessments. This protocol describes a randomized, controlled, superiority trial in which 120 family dementia caregivers, aged 60 years or older, will be assigned to smartphone App delivery of caregiver skills with MIT (experimental condition) or without MIT (control condition). The primary objectives of the trial are to assess whether the experimental condition is superior to control on reducing family caregiver stress, insomnia and related outcomes and to demonstrate the feasibility of developing behavioral markers from passive smartphone data that predict health outcomes in older adults. Trial outcomes may inform the suitability of our intervention for caregivers and provide new methods for assessment of older adults.
Copyright © 2022 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Dementia; Digital phenotyping; Family caregivers; Mentalization; Mindfulness; Mobile application

Mesh:

Year:  2022        PMID: 35331943      PMCID: PMC9133149          DOI: 10.1016/j.cct.2022.106737

Source DB:  PubMed          Journal:  Contemp Clin Trials        ISSN: 1551-7144            Impact factor:   2.261


  49 in total

1.  The Zarit Burden Interview: a new short version and screening version.

Authors:  M Bédard; D W Molloy; L Squire; S Dubois; J A Lever; M O'Donnell
Journal:  Gerontologist       Date:  2001-10

2.  Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health.

Authors:  Jukka-Pekka Onnela; Scott L Rauch
Journal:  Neuropsychopharmacology       Date:  2016-01-28       Impact factor: 7.853

3.  Impact of three dementia-related behaviors on caregiver depression: The role of rejection of care, aggression, and agitation.

Authors:  Scott Seung W Choi; Chakra Budhathoki; Laura N Gitlin
Journal:  Int J Geriatr Psychiatry       Date:  2019-04-04       Impact factor: 3.485

4.  Measuring caregiving appraisal.

Authors:  M P Lawton; M H Kleban; M Moss; M Rovine; A Glicksman
Journal:  J Gerontol       Date:  1989-05

5.  Age Differences in the Use of Health Information Technology Among Adults in the United States: An Analysis of the Health Information National Trends Survey.

Authors:  Henry K Onyeaka; Perla Romero; Brian C Healy; Christopher M Celano
Journal:  J Aging Health       Date:  2020-10-08

6.  Relapse prediction in schizophrenia through digital phenotyping: a pilot study.

Authors:  Ian Barnett; John Torous; Patrick Staples; Luis Sandoval; Matcheri Keshavan; Jukka-Pekka Onnela
Journal:  Neuropsychopharmacology       Date:  2018-02-22       Impact factor: 7.853

7.  The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression.

Authors:  A John Rush; Madhukar H Trivedi; Hicham M Ibrahim; Thomas J Carmody; Bruce Arnow; Daniel N Klein; John C Markowitz; Philip T Ninan; Susan Kornstein; Rachel Manber; Michael E Thase; James H Kocsis; Martin B Keller
Journal:  Biol Psychiatry       Date:  2003-09-01       Impact factor: 13.382

Review 8.  Psychometric properties of the 16-item Quick Inventory of Depressive Symptomatology: a systematic review and meta-analysis.

Authors:  Thomas J Reilly; Steve A MacGillivray; Ian C Reid; Isobel M Cameron
Journal:  J Psychiatr Res       Date:  2014-09-20       Impact factor: 4.791

9.  Relationship of cortisol levels and genetic polymorphisms to antidepressant response to placebo and fluoxetine in patients with major depressive disorder: a prospective study.

Authors:  Raúl Ventura-Juncá; Adriana Symon; Pamela López; Jenny L Fiedler; Graciela Rojas; Cristóbal Heskia; Pamela Lara; Felipe Marín; Viviana Guajardo; A Verónica Araya; Jaime Sasso; Luisa Herrera
Journal:  BMC Psychiatry       Date:  2014-08-03       Impact factor: 3.630

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