Real world effectiveness of digital mental health services during the COVID-19 pandemic

Prescott, Maximo; Sagui-Henson, Sara J.; Chamberlain, Camille E. Welcome; Sweet, Cynthia Castro; Altman, Myra

doi:10.1371/journal.pone.0272162

Cited by 19 publications

(11 citation statements)

References 28 publications

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“…The results of this study support the existing body of literature showing that digital platforms are a valuable option for filling gaps in mental healthcare ( 10 – 12 ). Previous research has found a benefit in clinical outcomes and patient-reported well-being and satisfaction for people who leveraged telehealth services ( 21 ). Remotely delivered interventions, such as guided meditation or engaging in behavioral health care, have been shown to help reduce anxiety and improve overall mental health during pregnancy ( 13 , 22 , 23 ).…”

Section: Discussionmentioning

confidence: 99%

Utilization of digital prenatal services and management of depression and anxiety during pregnancy: A retrospective observational study

Rubin‐Miller¹,

Henrich²,

Peahl³

et al. 2023

Front. Digit. Health

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IntroductionWe examined how utilization of Maven, a digital healthcare platform that provides virtual prenatal services, is associated with improvements in perceived management of anxiety and depression during pregnancy, and how medical knowledge and support may influence this association.Materials and MethodsIn this retrospective study we used adjusted logistic regression to examine the relationship between digital platform use in pregnancy and perceived mental health management, and how perceived management of mental health is affected by user-reported improvements in medical knowledge and feeling supported by the platform. Effects were evaluated separately among users with and without a mental health condition. Demographics, medical history, and mental health management were self-reported.ResultsOf 5,659 users, 705 (12.5%) reported that Maven helped them manage anxiety and/or depression in the prenatal period. In adjusted models, users who read more articles, sent more messages to care advocates, or had more appointments with providers were more likely to report improved management of mental health in a dose-response manner (e.g., articles read: Q2 aOR 1.31 (95% CI 1.01–1.70), Q3 aOR 1.68 (95% CI 1.30–2.17), Q4 1.99 (95% CI 1.54–2.59)). Improvements in medical knowledge and high perceived support were both associated with better perceived mental health management. Results were similar in users with and without a mental health condition.DiscussionThese results suggest that access to a diverse set of digital resources provides multiple pathways to managing depression and anxiety during pregnancy for those with and without a diagnosed mental health condition.

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Section: Discussionmentioning

confidence: 99%

Utilization of digital prenatal services and management of depression and anxiety during pregnancy: A retrospective observational study

Rubin‐Miller¹,

Henrich²,

Peahl³

et al. 2023

Front. Digit. Health

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“…DMHIs may improve accessibility, can bridge social gaps, and allow users to log in anonymously whenever they want (Sorkin et al, 2021).Because DMHIs are believed to be an effective tool to address underutilization of professional mental health services (Garrido et al, 2019), several studies assessed their effectiveness. Overall, these studies supported the effectiveness of DMHIs in reducing anxiety and depression symptoms and showed that digital mental health programs can be as effective as in-person programs and sometimes more effective for adults (Cuijpers et al, 2010;Prescott et al, 2022) and children (Pǎsǎrelu et al, 2017). Several reviews of technology applications for mental health found positive outcomes and important benefits (Firth et al, 2017;Garrido et al, 2019).…”

mentioning

confidence: 85%

Editorial: Digital mental health: Interventions and assessment

2022

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Editorial on the Research Topic Digital mental health: Interventions and assessmentRobotic, web-based, virtual reality (VR), or mobile-based digital mental health interventions (DMHI) offer tremendous potential to promote mental health and well-being across a range of age groups (Neguţ et al., 2016;Aboujaoude et al., 2020;Voinescu et al., 2021). More than 70% of young people use digital devices, but their mental health problems remain under-diagnosed and under-treated (UNICEF, 2018; World Health Organization, 2020). Therefore, DMHIs could provide excellent opportunities to expand access to diagnostic and intervention services, as well as to improve empowerment, participation, help-seeking, and essential resources to address the stigma associated with mental health (Kaushik et al., 2016;Freeman et al., 2022). In addition, interventions designed to enhance psychological well-being may lessen the negative consequences of the COVID-19 epidemic (De Kock et al., 2022). Several investigations during the COVID-19 epidemic in the community showed a strong negative correlation between psychological well-being and anxiety, despair, and distress (see meta-analysis Salari et al., 2020). DMHIs may improve accessibility, can bridge social gaps, and allow users to log in anonymously whenever they want (Sorkin et al., 2021).Because DMHIs are believed to be an effective tool to address underutilization of professional mental health services (Garrido et al., 2019), several studies assessed their effectiveness. Overall, these studies supported the effectiveness of DMHIs in reducing anxiety and depression symptoms and showed that digital mental health programs can be as effective as in-person programs and sometimes more effective for adults (Cuijpers et al., 2010;Prescott et al., 2022) and children (Pǎsǎrelu et al., 2017). Several reviews of technology applications for mental health found positive outcomes and important benefits (Firth et al., 2017;Garrido et al., 2019). However, more research is required for vulnerable populations such as older adults with mental health problems, as they are at high risk of being excluded from DMHI due to a number of factors such as lack of knowledge and support in using digital tools (Seifert et al., 2019). Another problem with using DMHIs among the general population is that the dropout rates are very high (Torous et al., 2018).

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“…The outcomes and impact of using computer models in real-world settings are still being evaluated, but early studies suggest that they have the potential to improve diagnosis accuracy, enhance cost-effectiveness, personalized treatment and real time monitoring. Real world effectiveness of digital mental health services are also monitored during COVID-19 also [18].…”

Section: Outcomes and Impact Of Using Computer Models In Real-worldmentioning

confidence: 99%

Artificial Intelligence Enabled Cognitive Computer-Centered Digital Analysis Model for Examination of the Children’s Mental Health

Agarwal

Sharma

2023

Preprint

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Mental health plays an important role for how a person act, think and feel. Specially in the case of students it’s become very crucial and they suffer from various kind of mental disorders due to educational , emotional and societal pressure. This paper proposes a Artificial Intelligence enabled cognitive computer-centered digital analysis model for the examination of children’s mental health. The objective of children mental health analysis using vocal spectrogram, a pseudo scientific technology, is to deduce deceit from mental health that is detected in the voice. With excessive stress being viewed as an indication of deceit, the technology seeks to distinguish between good mental health and bad mental health outputs in response to stimuli. In this study, we provide a Convolutional Neural network (CNN) based cognitive computer based model for detecting children mental health based on speech cues and deep learning. Automatic children mental health detection is developing into an intriguing study topic since communication between intelligent systems and humans is demanded to happen more frequently. Although the level of particular hormones, like as corti-sol, can be accurately measured to identify children’s mental health. Proposed CNN model validation is done using five different usability factors (learnability, efficiency, effectiveness, satisfaction and under-standability) to assess its utility and effectiveness and the results of 1 Springer Nature 2021 L A T E X template Artificial Intelligence Enabled Cognitive Computer-Centered Digital Analysis Model fo the initial tests indicate that the proposed model provides a promising tool for the examination of children’s mental health. This paper also discusses about how Reinforcement Learning(RL) and CNN algorithms can be used for continuous improvement of the proposed model.

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Real world effectiveness of digital mental health services during the COVID-19 pandemic

Cited by 19 publications

References 28 publications

Utilization of digital prenatal services and management of depression and anxiety during pregnancy: A retrospective observational study

Utilization of digital prenatal services and management of depression and anxiety during pregnancy: A retrospective observational study

Editorial: Digital mental health: Interventions and assessment

Artificial Intelligence Enabled Cognitive Computer-Centered Digital Analysis Model for Examination of the Children’s Mental Health

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