Project description:BackgroundA total of 75% of people with mental health disorders have an onset of illness between the ages of 12 and 24 years. Many in this age group report substantial obstacles to receiving quality youth-centered mental health care services. With the rapid development of technology and the recent COVID-19 pandemic, mobile health (mHealth) has presented new opportunities for youth mental health research, practice, and policy.ObjectiveThe research objectives were to (1) synthesize the current evidence supporting mHealth interventions for youths who experience mental health challenges and (2) identify current gaps in the mHealth field related to youth's access to mental health services and health outcomes.MethodsGuided by the methods of Arksey and O'Malley, we conducted a scoping review of peer-reviewed studies that used mHealth tools to improve youth mental health (January 2016-February 2022). We searched MEDLINE, PubMed, PsycINFO, and Embase databases using the following key terms: (1) mHealth; (2) youth and young adults; and (3) mental health. The current gaps were analyzed using content analysis.ResultsThe search produced 4270 records, of which 151 met inclusion criteria. Included articles highlight the comprehensive aspects of youth mHealth intervention resource allocation for targeted conditions, mHealth delivery methods, measurement tools, evaluation of mHealth intervention, and youth engagement. The median age for participants in all studies is 17 (IQR 14-21) years. Only 3 (2%) studies involved participants who reported their sex or gender outside of the binary option. Many studies (68/151, 45%) were published after the onset of the COVID-19 outbreak. Study types and designs varied, with 60 (40%) identified as randomized controlled trials. Notably, 143 out of 151 (95%) studies came from developed countries, suggesting an evidence shortfall on the feasibility of implementing mHealth services in lower-resourced settings. Additionally, the results highlight concerns related to inadequate resources devoted to self-harm and substance uses, weak study design, expert engagement, and the variety of outcome measures selected to capture impact or changes over time. There is also a lack of standardized regulations and guidelines for researching mHealth technologies for youths and the use of non-youth-centered approaches to implementing results.ConclusionsThis study may be used to inform future work as well as the development of youth-centered mHealth tools that can be implemented and sustained over time for diverse types of youths. Implementation science research that prioritizes youths' engagement is needed to advance the current understanding of mHealth implementation. Moreover, core outcome sets may support a youth-centered measurement strategy to capture outcomes in a systematic way that prioritizes equity, diversity, inclusion, and robust measurement science. Finally, this study suggests that future practice and policy research are needed to ensure the risk of mHealth is minimized and that this innovative health care service is meeting the emerging needs of youths over time.

Project description:Patient Selection: Three hospitals were involved in the study: Dong Thap Provincial and An Giang Provincial hospitals, both located in the Mekong Delta Region of Vietnam where most of the Typhoid patients were admitted, and the Hospital for Tropical Diseases, Ho Chi Minh City, where the malarial patients were admitted. Infection status of each patient was determined by blood culture for the Typhoid patients and by microscopic examination of stained thick and thin peripheral-blood slides for the patients suspected of having Malaria. Patients with blood cultures positive for S. Typhi or S. enterica serovar Paratyphi A (S. Paratyphi A) (samples designated ST) or peripheral blood smears positive for Plasmodium falciparum or Plasmodium vivax (samples designated PF) were consented and enrolled in the study. A total of 28 S. Typhi positive patients and one S. Paratyphi A positive patient were enrolled in the first phase of the study and a further 9 S. Typhi and one S. Paratyphi A positive patient were enrolled in the second phase. Nine patients with Malaria caused by P. falciparum and one caused by P. vivax were enrolled. Sixteen uninfected healthy individuals (samples designated HC) were also enrolled in the study (one sample each). The Typhoid patients were randomly chosen to receive either Azithromycin or Gatifloxicin (20mg/kg/day for 7 days) antibiotics. Uncomplicated malaria patients were treated for 3 days with Artekin (dihydroartemisinin with piperaquine phosphate). Venous blood (2.5ml) was collected for total RNA extraction into PaxGene RNA collection tubes (Qiagen) at various times. The first sample (T1) was taken immediately on admission or entry into the study, then samples were collected, 3 (T3), 6-9 (T9 for Typhoid, T7 for Malaria), and 28-45 (T28) days following entry into the study and an average of 9 months (T9M) later. The PaxGene tubes were stored at 4degC until RNA was extracted as per the manufacturers instructions. Purified RNA was shipped on dry ice to Stanford University, CA for use in Microarray analyses. Microarrays: Experimental and reference samples (Human Universal RNA reference {Stratagene}) were amplified using the MessageAmp II aRNA Kit (Ambion) as per the manufacturer's instructions. Four micrograms amplified RNA was labeled through indirect incorporation of Cy5 (experimental) and Cy3 (reference) dyes (protocols can be found at http://cmgm.stanford.edu/pbrown/protocols/index.html). The labeled probes were then mixed and hybridized to Stanford Human cDNA arrays with 42 K features. Arrays were scanned using a Genepix 4000A laser scanner and data extracted with Genepix Pro Software version 5.1 (Axon Instruments). The data were then uploaded into the Stanford Microarray Database (SMD). A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Compound Based Treatment: Drugs used for treatment Disease State: Malaria, Typhoid or Healthy Controls Keywords: disease_state_design

Project description:BackgroundPrivacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments.ObjectiveThis scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed.MethodsWe followed the methodology defined by Arksey and O'Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy.ResultsThe database searches retrieved a total of 710 citations-24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps.ConclusionsThe privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.

Project description:BACKGROUND:The perinatal period is a vulnerable time during which depression and anxiety commonly occur. If left untreated or undertreated, there may be significant adverse effects; therefore, access to rapid, effective treatment is essential. Treatments for mild-to-moderate symptoms according to a stepped-care approach involve psychoeducation, peer support, and psychological therapy, all of which have been shown to be efficaciously delivered through digital means. Women experience significant barriers to care because of system- and individual-level factors, such as cost, accessibility, and availability of childcare. The use of mobile phones is widespread in this population, and the delivery of mental health services via mobile phones has been suggested as a means of reducing barriers. OBJECTIVE:This study aimed to understand the extent, range, and nature of mobile health (mHealth) tools for prevention, screening, and treatment of perinatal depression and anxiety in order to identify gaps and inform opportunities for future work. METHODS:Using a scoping review framework, 4 databases were searched for terms related to mobile phones, perinatal period, and either depression or anxiety. A total of 477 unique records were retrieved, 81 of which were reviewed by full text. Peer-reviewed publications were included if they described the population as women pregnant or up to 1 year postpartum and a tool explicitly delivered via a mobile phone for preventing, screening, or treating depression or anxiety. Studies published in 2007 or earlier, not in English, or as case reports were excluded. RESULTS:A total of 26 publications describing 22 unique studies were included (77% published after 2017). mHealth apps were slightly more common than texting-based interventions (12/22, 54% vs 10/22, 45%). Most tools were for either depression (12/22, 54%) or anxiety and depression (9/22, 41%); 1 tool was for anxiety only (1/22, 4%). Interventions starting in pregnancy and continuing into the postpartum period were rare (2/22, 9%). Tools were for prevention (10/22, 45%), screening (6/22, 27%), and treatment (6/22, 27%). Interventions delivered included psychoeducation (16/22, 73%), peer support (4/22, 18%), and psychological therapy (4/22, 18%). Cost was measured in 14% (3/22) studies. CONCLUSIONS:Future work in this growing area should incorporate active psychological treatment, address continuity of care across the perinatal period, and consider clinical sustainability to realize the potential of mHealth.

Project description:BackgroundThe prevalence and consequences of obesity among children and adolescents remain a leading global public health concern, and evidence-based, multidisciplinary lifestyle interventions are the cornerstone of treatment. Mobile electronic devices are widely used across socioeconomic categories and may provide a means of extending the reach and efficiency of health care interventions.ObjectiveWe aimed to synthesize the evidence regarding mobile health (mHealth) for the treatment of childhood overweight and obesity to map the breadth and nature of the literature in this field and describe the characteristics of published studies.MethodsWe conducted a systematic scoping review in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews, by searching nine academic databases in addition to gray literature for studies describing acceptability, usability, feasibility, effectiveness, adherence, or cost-effectiveness of interventions assessing mHealth for childhood obesity treatment. We also hand searched the reference lists of relevant articles. Studies aimed at the prevention of overweight or obesity were excluded, as were studies in which mHealth was not the primary mode of treatment delivery for at least one study arm or was not independently assessed. A random portion of all abstracts and full texts was double screened by a second reviewer to ensure consistency. Data were charted according to study characteristics, including design, participants, intervention content, behavior change theory (BCT) underpinning the study, mode of delivery, and outcomes measured.ResultsWe identified 42 eligible studies assessing acceptability (n=7), usability (n=2), feasibility or pilot studies (n=15), treatment effect (n=17), and fidelity (n=1). Change in BMI z-scores or percentiles was most commonly measured, among a variety of dietary, physical activity, psychological, and usability or acceptability measures. SMS, mobile apps, and wearable devices made up the majority of mobile interventions, and 69% (29/42) of the studies specified a BCT used.ConclusionsPediatric weight management using mHealth is an emerging field, with most work to date aimed at developing and piloting such interventions. Few large trials are published, and these are heterogeneous in nature and rarely reported according to the Consolidated Standards of Reporting Trials for eHealth guidelines. There is an evidence gap in the cost-effectiveness analyses of such studies.

Project description:BackgroundThe proliferation of mobile health (mHealth) applications is partly driven by the advancements in sensing and communication technologies, as well as the integration of artificial intelligence techniques. Data collected from mHealth applications, for example, on sensor devices carried by patients, can be mined and analyzed using artificial intelligence-based solutions to facilitate remote and (near) real-time decision-making in health care settings. However, such data often sit in data silos, and patients are often concerned about the privacy implications of sharing their raw data. Federated learning (FL) is a potential solution, as it allows multiple data owners to collaboratively train a machine learning model without requiring access to each other's raw data.ObjectiveThe goal of this scoping review is to gain an understanding of FL and its potential in dealing with sensitive and heterogeneous data in mHealth applications. Through this review, various stakeholders, such as health care providers, practitioners, and policy makers, can gain insight into the limitations and challenges associated with using FL in mHealth and make informed decisions when considering implementing FL-based solutions.MethodsWe conducted a scoping review following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched 7 commonly used databases. The included studies were analyzed and summarized to identify the possible real-world applications and associated challenges of using FL in mHealth settings.ResultsA total of 1095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were included in the review. The analysis of these articles revealed 2 main application areas for FL in mHealth, that is, remote monitoring and diagnostic and treatment support. More specifically, FL was found to be commonly used for monitoring self-care ability, health status, and disease progression, as well as in diagnosis and treatment support of diseases. The review also identified several challenges (eg, expensive communication, statistical heterogeneity, and system heterogeneity) and potential solutions (eg, compression schemes, model personalization, and active sampling).ConclusionsThis scoping review has highlighted the potential of FL as a privacy-preserving approach in mHealth applications and identified the technical limitations associated with its use. The challenges and opportunities outlined in this review can inform the research agenda for future studies in this field, to overcome these limitations and further advance the use of FL in mHealth.

Project description:BackgroundSalt substitutes are edible salts that contain less sodium chloride owing to its partial substitution with other minerals, which serve as an important and effective intervention and public health strategy targeting hypertension and its secondary diseases, despite a small degree of controversy.ObjectiveThis study aimed to identify the current salt substitute initiatives in various nations and intergovernmental organizations (IGOs) worldwide and summarize their types and characteristics.MethodsA scoping review was performed based on Arksey and O'Malley's framework and the latest guidance from the Joanna Briggs Institute. Searches were conducted on Google; government websites on food, health, and other related topics; PubMed; Web of Science; and Google Scholar from January 2022 to May 2022. Initiatives related to salt substitutes that were included in the study focused on the involvement of governments or IGOs through the publication of standards, actions, collaborations, funding, and so on. Data were extracted into Microsoft Excel (version 2019; Microsoft Corp) based on predefined items and analyzed using narrative synthesis and frequency count methods.ResultsA total of 35 initiatives from 11 countries (of which 9 are high-income countries) and 3 IGOs were identified. We classified all salt substitute initiatives into 5 types, namely benefit-risk assessments and cautions; plans and actions; regulations and standards; labels; and food reformulation, cooperation with the food industry, and media. More than half (18/35, 51%) of the salt substitute initiatives were launched within the past 5 years. Except for regulations and standards, salt substitute initiatives are, in general, part of the salt reduction framework. No nation or IGO has yet reported on the monitoring and implications of the use of salt substitutes.ConclusionsDespite the limited number of salt substitute initiatives worldwide at present, a review on the different types and characteristics of such initiatives could be helpful in providing a reference for policy makers and stakeholders. Given the great potential of salt substitutes in improving hypertension and stroke, we call on more nations to pay attention to these substitutes and propose salt substitute initiatives in line with their national conditions.

Project description:Patient Selection: Three hospitals were involved in the study: Dong Thap Provincial and An Giang Provincial hospitals, both located in the Mekong Delta Region of Vietnam where most of the Typhoid patients were admitted, and the Hospital for Tropical Diseases, Ho Chi Minh City, where the malarial patients were admitted. Infection status of each patient was determined by blood culture for the Typhoid patients and by microscopic examination of stained thick and thin peripheral-blood slides for the patients suspected of having Malaria. Patients with blood cultures positive for S. Typhi or S. enterica serovar Paratyphi A (S. Paratyphi A) (samples designated ST) or peripheral blood smears positive for Plasmodium falciparum or Plasmodium vivax (samples designated PF) were consented and enrolled in the study. A total of 28 S. Typhi positive patients and one S. Paratyphi A positive patient were enrolled in the first phase of the study and a further 9 S. Typhi and one S. Paratyphi A positive patient were enrolled in the second phase. Nine patients with Malaria caused by P. falciparum and one caused by P. vivax were enrolled. Sixteen uninfected healthy individuals (samples designated HC) were also enrolled in the study (one sample each). The Typhoid patients were randomly chosen to receive either Azithromycin or Gatifloxicin (20mg/kg/day for 7 days) antibiotics. Uncomplicated malaria patients were treated for 3 days with Artekin (dihydroartemisinin with piperaquine phosphate). Venous blood (2.5ml) was collected for total RNA extraction into PaxGene RNA collection tubes (Qiagen) at various times. The first sample (T1) was taken immediately on admission or entry into the study, then samples were collected, 3 (T3), 6-9 (T9 for Typhoid, T7 for Malaria), and 28-45 (T28) days following entry into the study and an average of 9 months (T9M) later. The PaxGene tubes were stored at 4degC until RNA was extracted as per the manufacturers instructions. Purified RNA was shipped on dry ice to Stanford University, CA for use in Microarray analyses. Microarrays: Experimental and reference samples (Human Universal RNA reference {Stratagene}) were amplified using the MessageAmp II aRNA Kit (Ambion) as per the manufacturer's instructions. Four micrograms amplified RNA was labeled through indirect incorporation of Cy5 (experimental) and Cy3 (reference) dyes (protocols can be found at http://cmgm.stanford.edu/pbrown/protocols/index.html). The labeled probes were then mixed and hybridized to Stanford Human cDNA arrays with 42 K features. Arrays were scanned using a Genepix 4000A laser scanner and data extracted with Genepix Pro Software version 5.1 (Axon Instruments). The data were then uploaded into the Stanford Microarray Database (SMD). A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Compound Based Treatment: Drugs used for treatment Disease State: Malaria, Typhoid or Healthy Controls Keywords: disease_state_design Complex

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Mobile Health Initiatives in Vietnam: Scoping Study.

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Mobile Health Initiatives in Vietnam: Scoping Study.

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