ABSTRACT
BACKGROUND: Mental health is an increasing concern among vulnerable populations, including college students and veterans. OBJECTIVE: The purpose of this study was to determine if mobile health technology combined with health coaching can better enable a user to self-manage their mental health. METHODS: This study evaluated the mobile app "Biofeedback" that provided health coaching on stress self-management for college student veterans' mental health concerns. Twenty-four college student veterans were recruited from a large public university in Texas during the spring 2020 semester, impacted by COVID-19. Ten participants were assigned to the intervention group where they used the mobile Biofeedback app on their smartphones and smartwatches, and 14 were assigned to the control group without the app; assignment was based on mobile phone compatibility. Both groups participated in one initial lab session where they learned a deep-breathing exercise technique. The intervention group was then asked to use the mobile Biofeedback app during their daily lives and a smartwatch, and the control group was asked to perform the breathing exercises on their own. Both groups filled out Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) self-assessments at 2-week intervals. At the end of the semester, both groups were given an exit interview to provide user experience and perceived benefits of health coaching via the mobile biofeedback app. RESULTS: The deep-breathing exercise in the initial lab session reduced stress in both groups. Over the course of the study, the app recorded 565 coached breathing exercises with a significant decrease (approximately 3 beats per minute) in participants' heart rate during the 6-minute time period immediately after conducting the breathing exercises (Spearman rank correlation coefficient -0.61, P<.001; S=9,816,176). There was no significant difference between the two groups for PHQ-9 and GAD-7 scores over the course of the semester. Exit interview responses indicated that participants perceived that the mobile Biofeedback app improved their health and helped them address stress challenges. All participants reported that the intervention helped them manage their stress better and expressed that health coaching via a mobile device would improve their overall health. CONCLUSIONS: Participants reported a positive perception of the app for their mental health self-management during a stressful semester. Future work should examine long-term effects of the app with a larger sample size balanced between male and female participants, randomized participant allocation, real-time detection of mental health symptoms, and additional features of the app.
ABSTRACT
OBJECTIVE: This article analyzes the changes in downloads and activity of users of select popular mental health mobile applications (mHealth apps) during coronavirus disease 2019 (COVID-19). BACKGROUND: The outbreak of the COVID-19 crisis has shown a negative impact on public mental health. Mobile health has the potential to help address the psychological needs of existing and new patients during the pandemic and beyond. METHOD: Downloads data of 16 widely used apps were analyzed. The quality of apps was reviewed using the Mobile Application Rating Scale (MARS) framework. Correlation analysis was conducted to investigate the relationship between app quality and app popularity. RESULTS: Among the 16 apps, 10 were meditational in nature, 13 showed increased downloads, with 11 apps showing above 10% increase in the downloads after the pandemic started. The popular apps were satisfactory in terms of functionality and esthetics but lacked clinical grounding and evidence base. There exists a gap between app quality and app popularity. CONCLUSION: This study provided evidence for increased downloads of mental mHealth apps (primarily meditation apps) during the COVID-19 pandemic but revealed several gaps and opportunities to address deficiencies in evidence-based design, usability and effective assessment, and integration into current workflows. APPLICATION: The COVID-19 pandemic is a potential turning point for mHealth applications for mental health care. Whereas the evidence suggests a need for alternative delivery of care, human factors and ergonomics methods should be utilized to ensure these tools are user-centered, easy to use, evidence-based, well-integrated with professional care, and used sustainably.
ABSTRACT
BACKGROUND: Health coaching is an intervention process for driving behavior change through goal-setting, education, encouragement, and feedback on health-related behaviors. Telehealth systems that include health coaching and remote monitoring are making inroads in managing chronic conditions and may be especially suited for older populations. OBJECTIVE: This literature review aimed to investigate the current status of health coaching interventions incorporating telehealth technology and the associated effectiveness of this intervention to deliver health care with an emphasis on older adults (aged 65 and older). METHODS: A literature review was conducted to identify the research conducted on health coaching combined with remote monitoring for delivering health care to older adults. The Ovid MEDLINE and CINAHL databases were queried using a combination of relevant search terms (including middle aged, aged, older adult, elderly, health coaching, and wellness coaching). The search retrieved 196 papers published from January 2010 to September 2019 in English. Following a systematic review process, the titles and abstracts of the papers retrieved were screened for applicability to health coaching for older adults to define a subset for further review. Papers were excluded if the studied population did not include older adults. The full text of the 42 papers in this subset was then reviewed, and 13 papers related to health coaching combined with remote monitoring for older adults were included in this review. RESULTS: Of the 13 studies reviewed, 10 found coaching supported by telehealth technology to provide effective outcomes. Effectiveness outcomes assessed in the studies included hospital admissions/re-admissions, mortality, hemoglobin A1c (HbA1c) level, body weight, blood pressure, physical activity level, fatigue, quality of life, and user acceptance of the coaching program and technology. CONCLUSIONS: Telehealth systems that include health coaching have been implemented in older populations as a viable intervention method for managing chronic conditions with mixed results. Health coaching combined with telehealth may be an effective solution for providing health care to older adults. However, health coaching is predominantly performed by human coaches with limited use of technology to augment or replace the human coach. The opportunity exists to expand health coaching to include automated coaching.