Effect of a Mobile Monitoring System vs Usual Care on Depression Symptoms and Psychological Health

Place, Skyler; Blanch‐Hartigan, Danielle; Smith, Victoria; Erb, Jane L.; Marci, Carl D.; Ahern, David K.

doi:10.1001/jamanetworkopen.2019.19403

Cited by 12 publications

(12 citation statements)

References 6 publications

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“…Five studies were identified in the area of early detection (Multimedia Appendix 1). Two of the studies were conducted in Germany [9,10], 2 in the United States [11,12], and 1 in Romania [13]. Two studies were financed by the EU [9,10], and 3 were funded by countries [11][12][13].…”

Section: Data Description: Prevention/early Detectionmentioning

confidence: 99%

“…Optionally, the participants could also choose to receive motivational text messages regularly to ensure continuity. In Place et al [11], the intervention consisted of a monitoring system that collected different types of metadata and clinicians providing feedback. In Buntrock et al [9], the CG only received psychoeducation through the web-based platform.…”

Section: The Intervention Geton Mood Enhancermentioning

confidence: 99%

“…In Buntrock et al [9], the CG only received psychoeducation through the web-based platform. In 2 studies, participants of the CG had unlimited access to treatment as usual (TAU) during the study period and were given access to the web-based intervention after the study period [10,11].…”

Section: The Intervention Geton Mood Enhancermentioning

confidence: 99%

“…All studies described depression-specific symptoms as primary outcome [9][10][11][12][13]. Additionally, in 2 studies, adherence to the intervention [9,13] was indicated; in 1 study, quality of life [13] was.…”

Section: The Intervention Geton Mood Enhancermentioning

confidence: 99%

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Digital Health Interventions in Prevention, Relapse, and Therapy of Mild and Moderate Depression: Scoping Review

Tokgöz¹,

Hrynyschyn²,

Häfner³

et al. 2021

JMIR Ment Health

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Background Depression is a major cause for disability worldwide, and digital health interventions are expected to be an augmentative and effective treatment. According to the fast-growing field of information and communication technologies and its dissemination, there is a need for mapping the technological landscape and its benefits for users. Objective The purpose of this scoping review was to give an overview of the digital health interventions used for depression. The main goal of this review was to provide a comprehensive review of the system landscape and its technological state and functions, as well as its evidence and benefits for users. Methods A scoping review was conducted to provide a comprehensive overview of the field of digital health interventions for the treatment of depression. PubMed, PSYNDEX, and the Cochrane Library were searched by two independent researchers in October 2020 to identify relevant publications of the last 10 years, which were examined using the inclusion and exclusion criteria. To conduct the review, we used Rayyan, a freely available web tool. Results In total, 65 studies were included in the qualitative synthesis. After categorizing the studies into the areas of prevention, early detection, therapy, and relapse prevention, we found dominant numbers of studies in the area of therapy (n=52). There was only one study for prevention, 5 studies for early detection, and 7 studies for relapse prevention. The most dominant therapy approaches were cognitive behavioral therapy, acceptance and commitment therapy, and problem-solving therapy. Most of the studies revealed significant effects of digital health interventions when cognitive behavioral therapy was applied. Cognitive behavioral therapy as the most dominant form was often provided through web-based systems. Combined interventions consisting of web-based and smartphone-based approaches are increasingly found. Conclusions Digital health interventions for treating depression are quite comprehensive. There are different interventions focusing on different fields of care. While most interventions can be beneficial to achieve a better depression treatment, it can be difficult to determine which approaches are suitable. Cognitive behavioral therapy through digital health interventions has shown good effects in the treatment of depression, but treatment for depression still stays very individualistic.

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Section: Data Description: Prevention/early Detectionmentioning

confidence: 99%

Section: The Intervention Geton Mood Enhancermentioning

confidence: 99%

Section: The Intervention Geton Mood Enhancermentioning

confidence: 99%

Section: The Intervention Geton Mood Enhancermentioning

confidence: 99%

See 2 more Smart Citations

Digital Health Interventions in Prevention, Relapse, and Therapy of Mild and Moderate Depression: Scoping Review

Tokgöz¹,

Hrynyschyn²,

Häfner³

et al. 2021

JMIR Ment Health

View full text Add to dashboard Cite

show abstract

“…Many governments and philanthropic organizations have invested in the high‐risk research needed to explore the conditions through which fortifications in communication systems can improve health outcomes. Meta‐analyses of individually funded interventions have demonstrated that a strategic use of communication technology can: (a) reduce costs associated with in‐person visits (Chen et al, 2009); (b) can improve efficiencies and effectiveness of care (Buntin et al, 2011); (c) can improve outcomes through remote patient monitoring (Lee et al, 2018; Peterson et al, 2013); (d) can improve screening for early signs of mental distress (Place et al, 2020); and (e) can boost adherence to preventive measures equitably across populations (Wu et al, 2019). Typically, however, these benefits are demonstrated within a narrow sphere of application (Adler‐Milstein et al, 2014).…”

Section: A Global Priority To Strengthen Communication Systemsmentioning

confidence: 99%

“Don't drop the patient:” Health information in a postpandemic world

Hesse

Aronoff‐Spencer

Ahern

et al. 2022

World Med & Health Policy

Self Cite

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Building on technological advances and existing currents of a healthcare system in flux, the COVID‐19 pandemic has brought about perhaps the most rapid transformation in human health and healthcare seen in our times. Even as new opportunities such as telemedicine, remote care, and rapid precision health practices are poised to improve access and care for populations, the increasing sophistication of this transformation has brought with it new levels of complexity, fragmentation, and silos. The singular outcome of this system is the vast number of novel ways for miscommunication, loss of information in transition, and breakdowns in the cognitive continuity of care. We refer to these failure modes as “dropping the patient” and adopt a mantra of “don't drop the patient” which examines our emerging health system in the context of patient‐centered continuity. In this light, we investigate bright spots and pitfalls, and we offer insights from Human Factors Engineering, Human Centered Design, and Human System Integration, which provide tools and methods to codesign and codevelop continuous and resilient services that are inclusive, sustainable, and effective.

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A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data

Othmani

Zeghina

Muzammel

2022

Computer Methods and Programs in Biomedicine

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Effect of a Mobile Monitoring System vs Usual Care on Depression Symptoms and Psychological Health

Cited by 12 publications

References 6 publications

Digital Health Interventions in Prevention, Relapse, and Therapy of Mild and Moderate Depression: Scoping Review

Digital Health Interventions in Prevention, Relapse, and Therapy of Mild and Moderate Depression: Scoping Review

“Don't drop the patient:” Health information in a postpandemic world

A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data

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