Run4Love, a mHealth (WeChat-based) intervention to improve mental health of people living with HIV: a randomized controlled trial protocol

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Abstract

Background

People living with HIV (PLWH) suffer from high rates of mental illness; but targeted effective interventions are limited, especially in developing countries. High penetration of smartphone usage and widespread acceptance of social media applications provide an unprecedented opportunity for mobile-based health interventions (mHealth interventions) in resource-limited settings like China. The current report describes the design and sample characteristics of the Run4Love randomized controlled trial (RCT) aimed at improving mental health in PLWH in China.

Methods

A total of 300 PLWH with elevated depressive symptoms were recruited and randomized into either the intervention or control group. Participants in the intervention group received an adapted cognitive-behavioral stress management (CBSM) course delivered by the enhanced WeChat platform (for 3 months) and were motivated to engage in physical activities. Progress of the participants was automatically tracked and monitored with timely feedback and rewards. The control group received a brochure on nutrition for PLWH in addition to standard care. The outcome assessments are conducted at baseline, 3, 6, and 9 months using tablets. The primary outcome is depressive symptoms measured by the scale of the Center for Epidemiology Studies Depression (CES-D). Secondary outcomes include quality of life, chronic stress measured with biomarker of hair cortisol, and other measures of stress and depression, self-efficacy, coping, HIV-related stigma, physical activity, and patient satisfaction. Mixed effects model with repeated measures (MMRM) will be used to analyze the intervention effects.

Discussion

The Run4Love study is among the first efforts to develop and evaluate a multicomponent and integrated mHealth intervention to improve the mental health and quality of life of PLWH. Once proven effective, Run4Love could be scaled up and potentially integrated into the routine case management of PLWH and adapted to other populations with chronic diseases.

Trial registration

Chinese Clinical Trial Registry - ChiCTR-IPR-17012606, registered on 07 September 2017.

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Most cited references 24

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Hair cortisol as a biological marker of chronic stress: current status, future directions and unanswered questions.

Evan Russell, Gideon Koren, Michael Rieder … (2012)

The detrimental effects of stress on human health are being increasingly recognized. There is a critical need for the establishment of a biomarker that accurately measures its intensity and course over time. Such a biomarker would allow monitoring of stress, increase understanding of its pathophysiology and may help identify appropriate and successful management strategies. Whereas saliva and urine cortisol capture real-time levels, hair cortisol analysis presents a complementary means of monitoring stress, capturing systemic cortisol exposure over longer periods of time. This novel approach for cortisol quantification is being increasingly used to identify the effects of stress in a variety of pathological situations, from chronic pain to acute myocardial infarctions. Because of its ability to provide a long-term, month-by-month measure of systemic cortisol exposure, hair cortisol analysis is becoming a useful tool, capable of answering clinical questions that could previously not be answered by other tests. In this paper we review the development, current status, limitations and outstanding questions regarding the use of hair cortisol as a biomarker of chronic stress. Copyright Â© 2011 Elsevier Ltd. All rights reserved.

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Hair cortisol, stress exposure, and mental health in humans: a systematic review.

Sabine M. Staufenbiel, Brenda Penninx, Anne Spijker … (2013)

The deleterious effects of chronic stress on health and its contribution to the development of mental illness attract broad attention worldwide. An important development in the last few years has been the employment of hair cortisol analysis with its unique possibility to assess the long-term systematic levels of cortisol retrospectively. This review makes a first attempt to systematically synthesize the body of published research on hair cortisol, chronic stress, and mental health. The results of hair cortisol studies are contrasted and integrated with literature on acutely circulating cortisol as measured in bodily fluids, thereby combining cortisol baseline concentration and cortisol reactivity in an attempt to understand the cortisol dynamics in the development and/or maintenance of mental illnesses. The studies on hair cortisol and chronic stress show increased hair cortisol levels in a wide range of contexts/situations (e.g. endurance athletes, shift work, unemployment, chronic pain, stress in neonates, major life events). With respect to mental illnesses, the results differed between diagnoses. In major depression, the hair cortisol concentrations appear to be increased, whereas for bipolar disorder, cortisol concentrations were only increased in patients with a late age-of-onset. In patients with anxiety (generalized anxiety disorder, panic disorder), hair cortisol levels were reported to be decreased. The same holds true for patients with posttraumatic stress disorder, in whom - after an initial increase in cortisol release - the cortisol output decreases below baseline. The effect sizes are calculated when descriptive statistics are provided, to enable preliminary comparisons across the different laboratories. For exposure to chronic stressors, the effect sizes on hair cortisol levels were medium to large, whereas for psychopathology, the effect sizes were small to medium. This is a first implication that the dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis in the development and/or maintenance of psychopathology may be more subtle than it is in healthy but chronically stressed populations. Future research possibilities regarding the application of hair cortisol research in mental health and the need for multidisciplinary approaches are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

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Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild

Fabian Wahle, Tobias Kowatsch, Elgar Fleisch … (2016)

Background Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. Objective The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. Methods A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject’s preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery. Results A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively. Conclusions Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states.

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Author and article information

Contributors

Yan Guo: guoy8@mail.sysu.edu.cn

Y. Alicia Hong:

ORCID: http://orcid.org/0000-0002-1481-6495

yhong@sph.tamhsc.edu

Jiaying Qiao: qiaojy5@mail2.sysu.edu.cn

Zhimeng Xu: xuzhm7@mail2.sysu.edu.cn

Hanxi Zhang: zhanghx28@mail2.sysu.edu.cn

Chengbo Zeng: zengchb3@mail2.sysu.edu.cn

Weiping Cai: gz8hcwp@126.com

Linghua Li: llheliza@126.com

Cong Liu: gz8hlcm@126.com

Yiran Li: liyr9@mail2.sysu.edu.cn

Mengting Zhu: zhumt@mail2.sysu.edu.cn

Nathan Asher Harris: asher.harris@digipen.edu

Cui Yang: cyang29@jhu.edu

Journal

Journal ID (nlm-ta): BMC Public Health

Journal ID (iso-abbrev): BMC Public Health

Title: BMC Public Health

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2458

Publication date (Electronic): 26 June 2018

Publication date PMC-release: 26 June 2018

Publication date Collection: 2018

Volume: 18

Electronic Location Identifier: 793

Affiliations

[1 ]ISNI 0000 0001 2360 039X, GRID grid.12981.33, School of Public Health, , Sun Yat-sen University, ; #74 Zhongshan 2nd Road, Guangzhou, 510080 China

[2 ]ISNI 0000 0001 2360 039X, GRID grid.12981.33, Center for Migrant Health Policy, , Sun Yat-sen University, ; #74 Zhongshan 2nd Road, Guangzhou, 510080 China

[3 ]ISNI 0000 0001 2360 039X, GRID grid.12981.33, Sun Yat-sen Global Health Institute, Institute of State Governance, , Sun Yat-sen University, ; #74 Zhongshan 2nd Road, Guangzhou, 510080 China

[4 ]ISNI 0000 0004 4687 2082, GRID grid.264756.4, Department of Health Promotion and Community Health Sciences, School of Public Health, , Texas A&M University, ; 212 Adriance Lab Road, College Station, TX 77843 USA

[5 ]Department of Infectious Disease, Guangzhou Number Eight People’s Hospital, #627 Dongfeng Road, Guangzhou, 510080 China

[6 ]ISNI 0000 0001 2171 9311, GRID grid.21107.35, Johns Hopkins Bloomberg School of Public Health Baltimore, ; 615 N. Wolfe Street, Baltimore, MD 21205 USA

Author information

Y. Alicia Hong http://orcid.org/0000-0002-1481-6495

Article

Publisher ID: 5693

DOI: 10.1186/s12889-018-5693-1

PMC ID: 6019517

PubMed ID: 29940921

SO-VID: fa07c872-8dcf-4133-9441-368b2faffd7d

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 23 April 2018

Date accepted : 11 June 2018

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 71573290

Award Recipient : Yan Guo

Funded by: FundRef http://dx.doi.org/10.13039/100001547, China Medical Board;

Award ID: 17-271

Award Recipient : Yan Guo

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ScienceOpen disciplines: Public health

Keywords: mental health,mhealth intervention,people living with hiv (plwh),depression,randomized controlled trial

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ScienceOpen disciplines: Public health

Keywords: mental health, mhealth intervention, people living with hiv (plwh), depression, randomized controlled trial

Run4Love, a mHealth (WeChat-based) intervention to improve mental health of people living with HIV: a randomized controlled trial protocol

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Trial registration

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Special series on COVID-19 and Mental Health

Most cited references 24

Hair cortisol as a biological marker of chronic stress: current status, future directions and unanswered questions.

Hair cortisol, stress exposure, and mental health in humans: a systematic review.

Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild

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