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Multiple Sclerosis and Related Disorders May 2023Current cognitive monitoring of people with multiple sclerosis (pwMS) is sporadic, resource intensive and insensitive for detection of real-world cognitive performance... (Review)
Review
BACKGROUND
Current cognitive monitoring of people with multiple sclerosis (pwMS) is sporadic, resource intensive and insensitive for detection of real-world cognitive performance and decline. Smartphone applications may provide us with a more sensitive biomarker for cognitive decline that reflects real-world performance. The goal of this study was to perform a systematic review and qualitative synthesis of all current smartphone apps monitoring cognition in pwMS.
METHODS
A systematic search of five major online databases (PubMed/Medline, Scopus, Web of Science, Cumulative Index of Nursing and Allied Health Literature and IEEE Xplore) was performed in accordance with the Cochrane Handbook and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. We included all studies with at least one measure of phone-based digital biomarkers for monitoring cognition in pwMS above the age of 18. Two authors independently screened the articles retrieved. Data on test-retest reliability, validity coefficients, feasibility and practice effects were extracted from the studies identified. Critical appraisal of the studies was performed using the National Institute of Health quality assessment tool for observational cohort and cross-sectional studies.
RESULTS
12 articles covering six smartphone apps were included in this review. All articles had a low risk of bias, though sample size calculation was rarely performed. Of the six apps, five used smartphone versions of the symbol digit modalities test. The final app examined keystroke features passively. Test-retest reliability ranged from good to excellent. Concurrent validity was demonstrated through moderate to strong correlation with neuropsychological tests and weak to moderate correlations with EDSS, radiological biomarkers and patient-reported outcomes. Mobile apps performed comparably, and in some cases outperformed established cognitive tests. Whilst reported acceptability was high, significant attrition rates were present in longitudinal cohorts. There were significant short and long-term practice effects. Overall, smartphone versions of the SDMT showed strong psychometric properties across multiple apps.
CONCLUSION
Smartphone applications are reliable and valid biomarkers of real-world cognition in pwMS. Further longitudinal data would allow for a better understanding of their predictive and ecological validity.
Topics: Humans; Smartphone; Reproducibility of Results; Multiple Sclerosis; Cross-Sectional Studies; Cognition
PubMed: 37001409
DOI: 10.1016/j.msard.2023.104674 -
Digital Health 2023mHealth can help with healthcare service delivery for various health issues, but there's a significant gap in the availability and use of mHealth systems between... (Review)
Review
BACKGROUND
mHealth can help with healthcare service delivery for various health issues, but there's a significant gap in the availability and use of mHealth systems between sub-Saharan Africa and Europe, despite the ongoing digitalization of the global healthcare system.
OBJECTIVE
This work aims to compare and investigate the use and availability of mHealth systems in sub-Saharan Africa and Europe, and identify gaps in current mHealth development and implementation in both regions.
METHODS
The study adhered to the PRISMA 2020 guidelines for article search and selection to ensure an unbiased comparison between sub-Saharan Africa and Europe. Four databases (Scopus, Web of Science, IEEE Xplore, and PubMed) were used, and articles were evaluated based on predetermined criteria. Details on the mHealth system type, goal, patient type, health concern, and development stage were collected and recorded in a Microsoft Excel worksheet.
RESULTS
The search query produced 1020 articles for sub-Saharan Africa and 2477 articles for Europe. After screening for eligibility, 86 articles for sub-Saharan Africa and 297 articles for Europe were included. To minimize bias, two reviewers conducted the article screening and data retrieval. Sub-Saharan Africa used SMS and call-based mHealth methods for consultation and diagnosis, mainly for young patients such as children and mothers, and for issues such as HIV, pregnancy, childbirth, and child care. Europe relied more on apps, sensors, and wearables for monitoring, with the elderly as the most common patient group, and the most common health issues being cardiovascular disease and heart failure.
CONCLUSION
Wearable technology and external sensors are heavily used in Europe, whereas they are seldom used in sub-Saharan Africa. More efforts should be made to use the mHealth system to improve health outcomes in both regions, incorporating more cutting-edge technologies like wearables internal and external sensors. Undertaking context-based studies, identifying determinants of mHealth systems use, and considering these determinants during mHealth system design could enhance mHealth availability and utilization.
PubMed: 37377558
DOI: 10.1177/20552076231180972 -
Digital Health 2024Stroke survivors often experience residual impairments and motor decline post-discharge. While digital home rehabilitation combined with supervision could be a promising... (Review)
Review
OBJECTIVE
Stroke survivors often experience residual impairments and motor decline post-discharge. While digital home rehabilitation combined with supervision could be a promising approach for reducing human resources, increasing motor ability, and supporting rehabilitation persistence there is a lack of reviews synthesizing the effects. Thus, this systematic review and meta-analysis aimed to synthesize the effect of digital home rehabilitation and supervision in improving motor ability of upper limb, static balance, stroke-related quality of life, and self-reported arm function among stroke survivors.
METHODS
Six electronic databases, grey literature, ongoing studies, and reference lists were searched for relevant studies. Two investigators independently reviewed titles, abstracts, screened full texts for eligibility and performed data extraction. Meta-analysis of 13 independent studies were grouped into four separate meta-analyses. The Grading of Recommendations, Assessments, Development and Evaluations (GRADE) tool was used for evaluating the overall quality of the evidence.
RESULTS
Meta-analyses showed no statistically significant difference between intervention (digital home rehabilitation) and control groups (home training/clinic-based) of all outcomes including motor ability of upper limb, static balance, stroke-related quality of life, and self-reported arm function. In the sub-group analysis digital home rehabilitation was associated with better quality of arm use (standardized mean difference = 0.68, 95% confidence interval: [0.27, 1.09], = 0.001).
CONCLUSIONS
This result indicated that digital home rehabilitation has similar effects and could potentially replace home training or clinic-based services. This review highlights better-targeted digital motor interventions to examine the effects of interventions further. The quality of evidence was moderate to high in motor and self-reported arm outcomes, and low for balance and quality of life.
PubMed: 38832099
DOI: 10.1177/20552076241256861 -
NPJ Digital Medicine Sep 2023Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images... (Review)
Review
Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86-98; n = 11), rosacea (94%, 90-97; n = 4), eczema (93%, 90-99; n = 9) and psoriasis (89%, 78-92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93-100%, n = 2), eczema (88%, n = 1), and acne (67-86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.
PubMed: 37758829
DOI: 10.1038/s41746-023-00914-8 -
NPJ Digital Medicine 2019While smartphone usage is ubiquitous, and the app market for smartphone apps targeted at mental health is growing rapidly, the evidence of standalone apps for treating... (Review)
Review
While smartphone usage is ubiquitous, and the app market for smartphone apps targeted at mental health is growing rapidly, the evidence of standalone apps for treating mental health symptoms is still unclear. This meta-analysis investigated the efficacy of standalone smartphone apps for mental health. A comprehensive literature search was conducted in February 2018 on randomized controlled trials investigating the effects of standalone apps for mental health in adults with heightened symptom severity, compared to a control group. A random-effects model was employed. When insufficient comparisons were available, data was presented in a narrative synthesis. Outcomes included assessments of mental health disorder symptom severity specifically targeted at by the app. In total, 5945 records were identified and 165 full-text articles were screened for inclusion by two independent researchers. Nineteen trials with 3681 participants were included in the analysis: depression ( = 6), anxiety ( = 4), substance use ( = 5), self-injurious thoughts and behaviors ( = 4), PTSD ( = 2), and sleep problems ( = 2). Effects on depression (Hedges' = 0.33, 95%CI 0.10-0.57, = 0.005, NNT = 5.43, = 59%) and on smoking behavior ( = 0.39, 95%CI 0.21-0.57, NNT = 4.59, ≤ 0.001, = 0%) were significant. No significant pooled effects were found for anxiety, suicidal ideation, self-injury, or alcohol use ( = -0.14 to 0.18). Effect sizes for single trials ranged from = -0.05 to 0.14 for PTSD and = 0.72 to 0.84 for insomnia. Although some trials showed potential of apps targeting mental health symptoms, using smartphone apps as standalone psychological interventions cannot be recommended based on the current level of evidence.
PubMed: 31815193
DOI: 10.1038/s41746-019-0188-8 -
Frontiers in Digital Health 2022Digital health interventions (DHIs) have increased exponentially all over the world. Furthermore, the interest in the sustainability of digital health interventions is... (Review)
Review
BACKGROUND
Digital health interventions (DHIs) have increased exponentially all over the world. Furthermore, the interest in the sustainability of digital health interventions is growing significantly. However, a systematic synthesis of digital health intervention sustainability challenges is lacking. This systematic review aimed to identify the barriers and facilitators for the sustainability of digital health intervention in low and middle-income countries.
METHODS
Three electronic databases (PubMed, Embase and Web of Science) were searched. Two independent reviewers selected eligible publications based on inclusion and exclusion criteria. Data were extracted and quality assessed by four team members. Qualitative, quantitative or mixed studies conducted in low and middle-income countries and published from January 2000 to May 2022 were included.
RESULTS
The sustainability of digital health interventions is very complex and multidimensional. Successful sustainability of digital health interventions depends on interdependent complex factors that influence the implementation and scale-up level in the short, middle and long term. Barriers identified among others are associated with infrastructure, equipment, internet, electricity and the DHIs. As for the facilitators, they are more focused on the strong commitment and involvement of relevant stakeholders: Government, institutional, sectoral, stakeholders' support, collaborative networks with implementing partners, improved satisfaction, convenience, privacy, confidentiality and trust in clients, experience and confidence in using the system, motivation and competence of staff. All stakeholders play an essential role in the process of sustainability. Digital technology can have long term impacts on health workers, patients, and the health system, by improving data management for decision-making, the standard of healthcare service delivery and boosting attendance at health facilities and using services. Therefore, management changes with effective monitoring and evaluation before, during, and after DHIs are essential.
CONCLUSION
The sustainability of digital health interventions is crucial to maintain good quality healthcare, especially in low and middle-income countries. Considering potential barriers and facilitators for the sustainability of digital health interventions should inform all stakeholders, from their planning until their scaling up. Besides, it would be appropriate at the health facilities level to consolidate facilitators and efficiently manage barriers with the participation of all stakeholders.
PubMed: 36518563
DOI: 10.3389/fdgth.2022.1014375 -
Digital Health 2023Although the pedometer- and accelerometer-based interventions (PABI) have demonstrated efficacy in improving physical activity (PA) and health-related outcomes, the... (Review)
Review
Effectiveness of pedometer- and accelerometer-based interventions in improving physical activity and health-related outcomes among college students: A systematic review and meta-analysis.
BACKGROUND
Although the pedometer- and accelerometer-based interventions (PABI) have demonstrated efficacy in improving physical activity (PA) and health-related outcomes, the dearth of empirical evidence in college students warrants further investigation.
OBJECTIVE
This systematic review and meta-analysis aim to examine the effects of PABI on improving PA and health-related outcomes among college students.
METHODS
PubMed, Web of Science, Embase, Cochrane Library, and PsycINFO were searched for relevant literature from inception to 20 February 2022. Randomized controlled trials (RCTs) conducted among college students with PABI to increase objectively measured PA as the primary outcome were included in this study.
RESULTS
A total of nine RCTs with 527 participants were included in this study. The combined results showed that PABI significantly improved PA (standardized mean difference = 0.41, 95% confidence interval (CI): 0.08, 0.74, = 0.016) and significantly contributed to weight loss (mean differences (MD) = -1.56 kg, 95% CI: -2.40 kg, -0.73 kg, < 0.01), and lower body mass index (MD = -0.33 kg/m, 95% CI: -0.66 kg/m, 0.00 kg/m, = 0.05) compared to the control group, but no significant effects were observed on improvements of body fat (%) and exercise self-efficacy. Interventions in the group of step, general students, pedometer-based intervention, theory, and developed region were significantly more effective in subgroup analyses.
CONCLUSIONS
PABI was found to be effective in promoting PA and weight loss among college students. Future research is needed to further explore the long-term effects of PABI and the characteristics of multiple intervention models.
PubMed: 37492032
DOI: 10.1177/20552076231188213 -
Hand (New York, N.Y.) Sep 2022Metacarpal shaft fractures are common hand injuries that predominantly affect younger patients. There is wide variability in their treatment with no consensus on best...
Metacarpal shaft fractures are common hand injuries that predominantly affect younger patients. There is wide variability in their treatment with no consensus on best practice. We performed a systematic review to assess the breadth and quality of available evidence supporting different treatment modalities for metacarpal shaft fractures of the finger digits in adults. A comprehensive search was conducted across multiple databases, in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 1600 records were identified; 7 studies fulfilled eligibility criteria and were included. No randomized controlled trials directly comparing surgery with nonsurgical treatment were found. One retrospective study compared nonsurgical with surgical treatment, whereas 6 compared surgical or nonsurgical treatments. Considerable heterogeneity between studies along with a high or critical risk of bias restricts direct comparison and conclusions. There is a lack of high-quality evidence to guide treatment, supporting the need for well-designed, multicenter trials to identify the most effective and cost-efficient treatment for metacarpal shaft fractures in adults.
Topics: Adult; Fracture Fixation, Internal; Fractures, Bone; Hand Injuries; Humans; Metacarpal Bones; Retrospective Studies
PubMed: 33252278
DOI: 10.1177/1558944720974363 -
NPJ Digital Medicine Oct 2021Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited... (Review)
Review
Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.
PubMed: 34663863
DOI: 10.1038/s41746-021-00514-4 -
Frontiers in Digital Health 2023This review focuses on studies about digital health interventions in sub-Saharan Africa. Digital health interventions in sub-Saharan Africa are increasingly adopting... (Review)
Review
BACKGROUND
This review focuses on studies about digital health interventions in sub-Saharan Africa. Digital health interventions in sub-Saharan Africa are increasingly adopting gender-transformative approaches to address factors that derail women's access to maternal healthcare services. However, there remains a paucity of synthesized evidence on gender-transformative digital health programs for maternal healthcare and the corresponding research, program and policy implications. Therefore, this systematic review aims to synthesize evidence of approaches to transformative gender integration in digital health programs (specifically mHealth) for maternal health in sub-Saharan Africa.
METHOD
The following key terms "mobile health", "gender", "maternal health", "sub-Saharan Africa" were used to conduct electronic searches in the following databases: PsycInfo, EMBASE, Medline (OVID), CINAHL, and Global Health databases. The method and results are reported as consistent with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Data synthesis followed a convergent approach for mixed-method systematic review recommended by the JBI (Joanna Briggs Institute).
RESULTS
Of the 394 studies retrieved from the databases, 11 were included in the review. Out of these, six studies were qualitative in nature, three were randomized control trials, and two were mixed-method studies. Findings show that gender transformative programs addressed one or more of the following categories: (1) gender norms/roles/relations, (2) women's specific needs, (3) causes of gender-based health inequities, (4) ways to transform harmful gender norms, (5) promoting gender equality, (6) progressive changes in power relationships between women and men. The most common mHealth delivery system was text messages via short message service on mobile phones. The majority of mHealth programs for maternal healthcare were focused on reducing unintended pregnancies through the promotion of contraceptive use. The most employed gender transformative approach was a focus on women's specific needs.
CONCLUSION
Findings from gender transformative mHealth programs indicate positive results overall. Those reporting negative results indicated the need for a more explicit focus on gender in mHealth programs. Highlighting gender transformative approaches adds to discussions on how best to promote mHealth for maternal health through a gender transformative lens and provides evidence relevant to policy and research.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO CRD42023346631.
PubMed: 38026837
DOI: 10.3389/fdgth.2023.1263488