Healthcare via Cell Phones: A Systematic Review
By S. Krishna, S. Boren, E. Balas. Telemedicine journal and e-health, 2009
956 citations recorded by the source corpus.
Subfields: Demography, General Health Professions
View Paper: https://www.semanticscholar.org/paper/73ba62bde2e4b60cdb0dbb98e0a6270dbc8ec5c7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Demography
In General Health Professions
- Heather J. Cole-Lewis
- Susan A. Murphy
- Nicola Diviani
- Maddalena Fiordelli
- Peter Johannes Schulz
- Hyeoun-Ae Park
- T. Kershaw
- Vikram Patel
Citations in the corpus
- The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review (2013, neutral): “, mobile phones [23–27], specific mobile technology functions (e.”
- The Smartphone Psychology Manifesto (2012, background): “They also offer new possibilities for diagnosis, treatment, interventions, applications, and training, for example, in clinical, educational, health, military, organizational, and sports psychology and also in psychiatry (see Krishna et al., 2009).”
- Increasing Physical Activity With Mobile Devices: A Meta-Analysis (2012, background): “Others have examined the use of mobile technology to aid in disease prevention and management [17], as well as the influence of SMS technology on various health behaviors (eg, smoking cessation, diabetes self-management, asthma self-management; [18]).”
- Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis (2019, background): “The conversation has shifted from patients and providers to individuals who can now engage in self-care around the clock outside of traditional health care settings (eg, [1-3]).”
- Mobile Health Technology Evaluation (2013, background): “This is a multi-step process that: (1) defines the classification problem; (2) divides the annotated data set into training and validation data sets; (3) decides what machine-learning classifiers will be tested (e.”
- The Impact of mHealth Interventions: Systematic Review of Systematic Reviews (2018, neutral): “Diabetes education and advice via mobile phone and SMS significantly reduced HbA1c [31].”
- Text Messaging as a Tool for Behavior Change in Disease Prevention and Management (2010, positive): “This evidence is consistent with existing literature suggesting that mobile phones are a useful tool for interventions seeking improvement in health outcomes (15, 18, 22).”
- A Re-conceptualization of Access for 21st Century Healthcare (2011, background): “46 Another important type of asynchronous digital patient-to-provider encounter includes remote monitoring involving the entering/forwarding of clinical information by the patient to their provider via smartphones, kiosks, web-based portals hosted by the healthcare organization (e.g., MyHealtheVet…”
- Experiences of Patients With a Diabetes Self-Care App Developed Based on the Information-Motivation-Behavioral Skills Model: Before-and-After Study (2019, background): “Diabetes self-care apps can provide various functions as listed below [6,7]:”
- Mapping mHealth Research: A Decade of Evolution (2013, methodology): “Although some literature reviews cover one part or the other of the field [6,11,12], an overall picture is still missing, possibly due to the field’s constant evolution.”
- The Effectiveness of Mobile-Health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta-Analysis (2013, positive): “The review expands and updates the findings of earlier systematic reviews that focussed on specific devices, and/or specific functions, and/or specific health topics [8,11,58].”