Background: Long-term health conditions and multimorbidity are increasing globally placing an unsustainable pressure on healthcare systems. Mobile health technologies, or mHealth, enable the collection of patient-generated health data outside clinical settings, offering the potential to support personalised care and inform clinical decision-making. However, the ways in which mHealth patient data is being used in clinical practice remains unclear. Objective: To map and synthesise the existing literature on how patient-generated mHealth data is reportedly being used and influencing clinical decision-making for adults with long-term conditions in an outpatient care setting. Methods: A narrative scoping review was conducted on studies published between 2014 and 2025. Studies were eligible for inclusion if they were in English, had data on the use of patient generated mHealth data, went beyond feasibility testing, and had reference to clinician behaviour/patient interactions. Grey literature was not used to maintain a focus on peer reviewed and published evidence. Studies involving paediatric or adolescent populations were excluded. Searches were conducted across the following databases between 2014 and 2025: Embase, MEDLINE, Knowledge and Library Hub, British Nursing Indes, Proquest Health Research Premium Collection. Data were charted systematically and synthesised narratively. Key data included study characteristics, mHealth use, data types and visualisations, patient demographics, and the ways data informed clinical decision-making. Results: 16 studies met the inclusion requirements which were primarily high-income countries focusing on rheumatoid arthritis and diabetes. Studies reported on how mHealth data was integrated into workflows, influenced healthcare decisions, and shaped patient-provider interactions. mHealth patient data was found to support patient-centred care and facilitate proactive holistic care, though in some instances it was shown to reinforce medical agendas removing agency from patients. There is also a gap between the intended use of the data and its implementation in clinical practice. Reported barriers included professional scepticism, integration challenges, and concerns about data accuracy. Evidence was focused on feasibility rather than long-term outcomes, with limited evidence on the impacts of mHealth. Conclusions: PGHD has potential to enhance clinical decision-making and person-centred practices. However, integration into routine practice is hindered by technological challenges, professional hesitancy, and a lack of standardisation. Future research should prioritise supporting integration, improve data presentation, and evaluate the long-term effects on clinical workflows. Addressing these barriers and establishing clear policy frameworks will be crucial for realising the potential of mHealth in healthcare delivery.
Time Spent on Social Media Applications in Relation to Depressive Symptoms During Emerging Adulthood and the Mediating Role of Sleep Quality: Cross-Sectional Observational Study
Background: The link between social media use and depressive symptoms remains bidirectional. Findings in this area are often compromised by methodological limitations related to measurement