Wearable sensors may enable objective monitoring of infant motor activity, posture, and movement in laboratory, clinic, and home contexts. However, evidence for the validity, generalizability, and implementation readiness of wearable-derived infant motor measures remains heterogeneous. This scoping review synthesized technical features, validation evidence, and clinical applications of wearable sensors monitoring infant motor activity, posture, or movement during awake periods in the first year of life. PRISMA-ScR guidelines were followed, and the protocol was prospectively registered on Open Science Framework. Searches for peer-reviewed articles on wearable-based monitoring of infant activity published between 2019 and 2024 were identified through searches of PubMed, CINAHL, IEEE Xplore, and the ACM Digital Library. A total of 41 studies were selected and classified using Keadle's five-phase validation framework, distinguishing technical validation (Phases I-III) from applied implementation (Phase IV). Fifteen studies (36.6%) included typically developing infants, 22 (53.7%) included infants with or at-risk for developmental disabilities, and 4 (9.8%) were unspecified. Fifteen studies (36.6%) focused on technical validation; 26 (63.4%) implemented sensors in applied contexts. Triaxial accelerometers (n = 15) and IMUs (n = 24) were most common, with sampling frequencies from 1-150 Hz. Posture classification accuracy was 95.8%-98.2% in studies directly comparing posture and movement, whereas fine-grained movement classification was near 81%-84%. Reference-standard correspondence was generally strong for accumulated posture duration (r = 0.80-0.99) but more variable for movement and clinical measures (r = 0.61-0.98). Application studies quantified physical activity, movement coordination and complexity, and intervention responsiveness, with over half collecting home-based data. Wearable sensors have evolved from proof-of-concept systems to family-centered digital health tools supporting developmental monitoring and physical activity promotion. When paired with caregiver feedback and infant-centered design, these technologies may support families, complement clinic-based assessment, and extend developmental support access. Future research should prioritize standardized reporting, affordability, and open-source analytic pipelines to advance equitable implementation.
Mycetoma is a neglected tropical disease characterised by chronic, destructive infections of subcutaneous tissues, predominantly affecting populations in endemic regions with limited healthcare resources. Delayed diagnosis, limited treatment options, and restricted access to specialised healthcare contribute to substantial morbidity, disability, and socioeconomic burden. Digital technologies offer promising opportunities to address these challenges and improve mycetoma care. Artificial intelligence (AI) can support early detection and diagnostic accuracy through the analysis of clinical images, radiological findings, and patient data. Telemedicine can facilitate remote specialist consultations and follow-up, particularly in rural and underserved communities, while electronic health records enable systematic data collection, disease surveillance, and monitoring of treatment outcomes. Mobile health applications can improve patient education, symptom monitoring, and treatment adherence, whereas computational drug discovery may accelerate the identification of potential therapeutic agents. Furthermore, digital platforms can strengthen international research collaborations, knowledge exchange, and capacity building. However, implementing these technologies in endemic regions faces significant barriers, including inadequate digital infrastructure, limited internet connectivity, insufficient digital literacy, data privacy concerns, and ethical challenges related to informed consent, data ownership, and algorithmic decision-making. Addressing these barriers requires investment in infrastructure, appropriate regulatory frameworks, stakeholder collaboration, and context-specific capacity-building initiatives. Integrating digital health solutions into national healthcare strategies could support sustainable and equitable improvements in mycetoma management. This review highlights the potential applications, opportunities, and challenges of digital technologies in mycetoma care and emphasises the need for inclusive, locally adapted approaches to enhance early diagnosis, treatment outcomes, research, and health equity in endemic communities.
Home cardiotocography (CTG) may support remote antenatal surveillance, reduce hospital attendances, and improve patient-centred maternity care. However, United Kingdom (UK) evidence remains limited, and international findings may not be generalisable due to differences in maternity pathways and digital implementation. We therefore evaluated the feasibility, acceptability, usability, and safety of home CTG monitoring in a UK cohort of high-risk pregnant women. In this single-centre prospective feasibility study, 50 English-speaking women with high-risk singleton pregnancies at ≥32 weeks' gestation were enrolled. Participants received a portable CTG device and were advised to complete one hour of home monitoring weekly, for up to six weeks, or until delivery. Recordings were reviewed synchronously. The primary outcome was completion of 20 minutes of continuous, interpretable monitoring per episode. Secondary outcomes included feasibility, anxiety, acceptability, usability, competency, technological issues, and safety. In total, 204 home monitoring sessions were completed. The primary outcome was achieved in 82.4% (95% Confidence interval (CI), 76.5%-87.0%) of sessions (168/204), with a mean continuous monitoring duration of 34.3 ± 16.8 minutes. Session number was not associated with achievement of the primary outcome (Odds Ratio (OR) 1.18, 95% CI 0.93-1.51; p = 0.19) or monitoring duration (p = 0.16). Maternal demographic characteristics showed no statistically significant association with monitoring competency in the exploratory analysis. Anxiety scores decreased significantly following participation (p = 0.0004), including before and after the first (p = 0.01) and fifth sessions (p = 0.007). Usability was rated highly (mean 77.6; grade B + , 80th percentile). Participants were advised to attend maternity triage following 13 sessions (6.4%). No prespecified adverse outcomes related to device use occurred. Overall, 78% would use home CTG instead of hospital attendance in a future pregnancy. Home CTG monitoring for high-risk pregnancies was feasible, acceptable, usable, with no safety concerns identified in this single-arm cohort. These findings support future implementation, pathway integration and development of effectiveness studies.
Digital point-of-care (POC) diagnostics, including connected and molecular platforms, have the potential to strengthen clinical decision-making and disease surveillance, particularly for febrile illness across African health systems. Yet context-relevant evidence on health system readiness and the institutional conditions shaping their integration and sustainability is limited. Following RAMESES standards, we conducted a realist review to examine how, why, and under what circumstances digital POC diagnostics for febrile illness are integrated into African health systems. We systematically searched PubMed and CINAHL (2005-2025) and supplemented these with purposive searches of the grey literature. Qualitative and mixed-methods studies addressing implementation, integration, or readiness were included. Data were synthesized using context-mechanism-outcome (CMO) configurations to develop and refine the program theory. Seventeen studies from multiple African countries were included. Readiness for integration was explained by five interacting system levers: (1) usability and workflow compatibility; (2) workforce competence, trust, and confidence; (3) reliable infrastructure and connectivity; (4) governance, policy alignment, and financing; and (5) data systems that generate actionable value. When these conditions aligned, mechanisms such as perceived usefulness, confidence, institutional legitimacy, and responsiveness were activated, leading to sustained use, improved diagnostic pathways, and enhanced surveillance. Conversely, weaknesses, particularly in infrastructure, procurement, or data-use capacity, triggered frustration, distrust, and discontinuation. The integration of digital POC diagnostics into African health systems depends more on the health system's readiness to promote trust, continuity, and data-driven action than on the performance of the tests themselves. A realist, system-focused approach is required to enable context-specific implementation and prevent the reinforcement of pre-existing inequities in digital health adoption.
Falls among older adults represent a major public health concern with substantial clinical, societal, and economic implications. People living with dementia (PlwD) are disproportionately affected, reflecting the combined impact of cognitive, motor, and pharmacological vulnerabilities. Traditional fall risk assessment tools have demonstrated limitations in capturing the complex, dynamic risk profiles of people living with dementia, highlighting the need for technological innovation in prediction, detection, and prevention. Despite advances in gerontechnology, evidence specific to dementia remains limited. To address this gap, a scoping review was conducted following PRISMA-ScR and Joanna Briggs Institute guidelines. Comprehensive searches across Scopus, Medline, Embase, APA PsycINFO, and CINAHL identified thirteen peer-reviewed studies (2015-2025) evaluating technology-based approaches for fall prediction, detection, or prevention among people living with dementia or populations used to model dementia-related functional impairments. Data were extracted and synthesized thematically by technological function. Thirteen studies met inclusion criteria, encompassing pressure-sensitive platforms, technology-enabled preventive interventions, wearable inertial measurement units, and ambient radar/vision-based systems, with the latter two most common. Sample sizes ranged from 3-309 participants with varying dementia severity. Wearables demonstrated strong fall detection performance in controlled laboratory settings but were limited by adherence challenges and reduced ecological validity. Ambient and radar-based systems demonstrated feasibility yet raised privacy and scalability concerns. Pressure-sensitive platforms showed promise for identifying pre-fall risk states but lacked clinical validation. Preventive interventions, including tablet-based exercise programs, demonstrated feasibility and preliminary evidence of reduced fall incidence. These technologies demonstrate promise for dementia care but require further validation, attention to usability, and consideration of ethical implementation before widespread adoption. Future work should emphasize dementia-specific trials, cost-effectiveness, and user-centered co-design to support sustainable implementation.
