Check health professions papers: citations, claims and retractions

Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with hypertension: a systematic review and meta-analysis of randomised controlled trials

Siopis, G., Moschonis, G., Eweka, E., Jung, J., Kwasnicka, D., Asare, B. Y.-A., Kodithuwakku, V., Willems, R., Verhaeghe, N., Annemans, L., et al. (2023) · The Lancet Digital Health

Mobile Health and mHealth ApplicationsBlood Pressure and Hypertension Studies

Cited by 87

Mobile apps for treatment of speech disorders in children: An evidence-based analysis of quality and efficacy

Furlong, L., Morris, M., Serry, T., & Erickson, S. (2018) · PLOS ONE

Mobile Health and mHealth ApplicationsDigital Mental Health Interventions

Cited by 87

Performance factors of mobile rich media job aids for community health workers

Florez-Arango, J. F., Iyengar, M. S., Dunn, K., & Zhang, J. (2011) · Journal of the American Medical Informatics Association

Mobile Health and mHealth ApplicationsICT in Developing Communities

Cited by 87

Developing steady clicks:

Trewin, S., Keates, S., & Moffatt, K. (2006) · Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility

Assistive Technology in Communication and MobilityGaze Tracking and Assistive Technology

Cited by 87

Stance Width Influences Postural Stability and Motion Sickness

Stoffregen, T. A., Yoshida, K., Villard, S., Scibora, L., & Bardy, B. G. (2010) · Ecological Psychology

Balance, Gait, and Falls PreventionVestibular and auditory disorders

Cited by 87

Disaster Mobile Health Technology: Lessons from Haiti

Callaway, D. W., Peabody, C. R., Hoffman, A., Cote, E., Moulton, S., Baez, A. A., & Nathanson, L. (2012) · Prehospital and Disaster Medicine

Disaster Response and ManagementMobile Health and mHealth Applications

Cited by 87

Goals of Chronic Pain Management: Do Patients and Primary Care Physicians Agree and Does it Matter?

Henry, S. G., Bell, R. A., Fenton, J. J., & Kravitz, R. L. (2017) · The Clinical Journal of Pain

Musculoskeletal pain and rehabilitationOpioid Use Disorder Treatment

Cited by 87

A novel ensemble of random forest for assisting diagnosis of Parkinson's disease on small handwritten dynamics dataset

Xu, S., & Pan, Z. (2020) · International Journal of Medical Informatics

Artificial Intelligence in HealthcareParkinson's Disease Mechanisms and Treatments

Cited by 87

Behavioral constituents of chronic pain acceptance: Results from factor analysis of the Chronic Pain Acceptance Questionnaire

McCracken, L. M. (1999) · Journal of Back and Musculoskeletal Rehabilitation

Musculoskeletal pain and rehabilitationMindfulness and Compassion Interventions

Cited by 87

Improving Diabetes Management With Mobile Health Technology

Sieverdes, J. C., Treiber, F., Jenkins, C., & Hermayer, K. (2013) · The American Journal of the Medical Sciences

Mobile Health and mHealth ApplicationsDiabetes Management and Education

Cited by 87

Identifying the barriers and enablers for a triage, treatment, and transfer clinical intervention to manage acute stroke patients in the emergency department: a systematic review using the theoretical domains framework (TDF)

Craig, L. E., McInnes, E., Taylor, N., Grimley, R., Cadilhac, D. A., Considine, J., & Middleton, S. (2016) · Implementation Science

Health Policy Implementation ScienceAcute Ischemic Stroke Management

Cited by 87

Identifying the Dominant Personality Profiles in Medical Students: Implications for Their Well-Being and Resilience

Eley, D. S., Leung, J., Hong, B. A., Cloninger, K. M., & Cloninger, C. R. (2016) · PLOS ONE

Healthcare professionals’ stress and burnoutPersonality Traits and Psychology

Cited by 87

Standards in healthcare data

Schulz, S., Stegwee, R., & Chronaki, C. (2019) · Fundamentals of Clinical Data Science

Electronic Health Records SystemsBiomedical Text Mining and Ontologies

Cited by 86

People trying to lose weight dislike calorie counting apps and want motivational support to help them achieve their goals

Solbrig, L., Jones, R., Kavanagh, D., May, J., Parkin, T., & Andrade, J. (2017) · Internet Interventions

Mobile Health and mHealth ApplicationsDigital Mental Health Interventions

Cited by 86

Wearable devices and mobile technologies for supporting behavioral weight loss among people with serious mental illness

Naslund, J. A., Aschbrenner, K. A., Scherer, E. A., McHugo, G. J., Marsch, L. A., & Bartels, S. J. (2016) · Psychiatry Research

Mobile Health and mHealth ApplicationsPhysical Activity and Health

Cited by 86

Toward a Wearable System for Predicting Freezing of Gait in People Affected by Parkinson's Disease

Demrozi, F., Bacchin, R., Tamburin, S., Cristani, M., & Pravadelli, G. (2020) · IEEE Journal of Biomedical and Health Informatics

Balance, Gait, and Falls PreventionContext-Aware Activity Recognition Systems

Cited by 86

Research designs for intervention research with small samples II: stepped wedge and interrupted time-series designs

Fok, C. C. T., Henry, D., & Allen, J. (2015) · Prevention Science

Health Policy Implementation ScienceAdvanced Causal Inference Techniques

Cited by 86

Effects of a Digital Diabetes Prevention Program: An RCT

Katula, J. A., Dressler, E. V., Kittel, C. A., Harvin, L. N., Almeida, F. A., Wilson, K. E., Michaud, T. L., Porter, G. C., Brito, F. A., Goessl, C. L., et al. (2022) · American Journal of Preventive Medicine

Mobile Health and mHealth ApplicationsDiabetes Management and Education

Cited by 86

The epidemiology of chronic pain in a general population: Results of a survey in southern Sweden

Jakobsson, U. (2010) · Scandinavian Journal of Rheumatology

Musculoskeletal pain and rehabilitationPain Mechanisms and Treatments

Cited by 86

Acceptability, usability, and views on deployment of Peek, a mobile phone mHealth intervention for eye care in Kenya: qualitative study

Lodhia, V., Karanja, S., Lees, S., & Bastawrous, A. (2016) · JMIR mHealth and uHealth

Mobile Health and mHealth ApplicationsOphthalmology and Visual Health Research

Cited by 86

Barriers and enablers to meaningful youth participation in mental health research: qualitative interviews with youth mental health researchers

Faithfull, S., Brophy, L., Pennell, K., & Simmons, M. B. (2019) · Journal of Mental Health

Mental Health and Patient InvolvementChildren's Rights and Participation

Cited by 86

Are We Heeding the Warning Signs? Examining Providers’ Overrides of Computerized Drug-Drug Interaction Alerts in Primary Care

Slight, S. P., Seger, D. L., Nanji, K. C., Cho, I., Maniam, N., Dykes, P. C., & Bates, D. W. (2013) · PLoS ONE

Electronic Health Records SystemsPharmaceutical Practices and Patient Outcomes

Cited by 86

Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease

San-Segundo, R., Navarro-Hellín, H., Torres-Sánchez, R., Hodgins, J., & De la Torre, F. (2019) · Electronics

Balance, Gait, and Falls PreventionContext-Aware Activity Recognition Systems

Cited by 86

Cardiovascular disease detection using a new ensemble classifier

Esfahani, H. A., & Ghazanfari, M. (2017) · 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)

Artificial Intelligence in HealthcareData Mining and Machine Learning Applications

Cited by 86