OBJECTIVE: This review aims to synthesize the current evidence on digital health interventions targeting adolescents and young people with mental health conditions, aged between 10-24 years, with a focus on effectiveness, cost-effectiveness, and generalizability to low-resource settings (eg, low- and middle-income countries).
METHODS: We searched MEDLINE, PubMed, PsycINFO, and Cochrane databases between January 2010 and June 2020 for systematic reviews and meta-analyses on digital mental health interventions targeting adolescents and young people aged between 10-24 years. Two authors independently screened the studies, extracted data, and assessed the quality of the reviews.
RESULTS: In this systematic overview, we included 18 systematic reviews and meta-analyses. We found evidence on the effectiveness of computerized cognitive behavioral therapy on anxiety and depression, whereas the effectiveness of other digital mental health interventions remains inconclusive. Interventions with an in-person element with a professional, peer, or parent were associated with greater effectiveness, adherence, and lower dropout than fully automatized or self-administered interventions. Despite the proposed utility of digital interventions for increasing accessibility of treatment across settings, no study has reported sample-specific metrics of social context (eg, socioeconomic background) or focused on low-resource settings.
CONCLUSIONS: Although digital interventions for mental health can be effective for both supplementing and supplanting traditional mental health treatment, only a small proportion of existing digital platforms are evidence based. Furthermore, their cost-effectiveness and effectiveness, including in low- and middle-income countries, have been understudied. Widespread adoption and scale-up of digital mental health interventions, especially in settings with limited resources for health, will require more rigorous and consistent demonstrations of effectiveness and cost-effectiveness vis-à-vis the type of service provided, target population, and the current standard of care.
DESIGN: Qualitative study by means of semistructured interviews with women and key informants, using social-ecological model as a conceptual framework.
SETTING: Interviews were conducted in Kota Bharu district, Kelantan, a northeast state in Peninsular Malaysia.
PARTICIPANTS: Eighteen women of reproductive age (18 to 44 years old) that experienced their first marriage below the age of 18, as well as five key informants, consisting of a government officer, a community leader, an officer from religious department and two mothers. The women were recruited from a reproductive health clinic. The key informants who had specialised knowledge related to child marriage were selectively chosen.
RESULTS: Three themes emerged that aligned with the social-ecological model: immaturity in decision-making, family poverty and religious and cultural norms.
CONCLUSIONS: The findings imply that sex education and awareness-building activities regarding the consequences of child marriage must be implemented to eradicate child marriage in Malaysia. Such implementation must be coordinated as a team-based approach involving experts in such fields as law, religion, psychology, social-welfare and public health. In order to increase the awareness of child marriage consequences, the target for awareness must extend not only to the adolescent girls and their families, but also to the community and society at large by clearly communicating the negative consequences of and addressing the drivers for child marriage.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.