ArticleJMIR mHealth and uHealth2019
Development and Implementation of a Person-Centered, Technology-Enhanced Care Model For Managing Chronic Conditions: Cohort Study.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Trial
- Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study.Headache · 2022Trial
- Opportunities and challenges in implementing digital patient-centred cancer care in Europe-a qualitative study of several ESMO Designated Centres.ESMO real world data and digital oncology · 2026Article
- Person-Centered Care in Digital Health Interventions for Chronic Diseases: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Balancing ideals and realities: health care professionals' perspectives of and attitudes toward digital patient-centered cancer care.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Beyond the hospital walls: community-based approaches to leukemia management in older adults.Annals of medicine and surgery (2012) · 2026Review
- Utilization of Artificial Intelligence in Reducing the Incidence of Medication Error: A Bibliometric Analysis.Health science reports · 2025Article
- Task effectiveness, usability, and acceptability of mHealth technologies among older adults at risk for cardiovascular disease: a feasibility study.Health and technology · 2025Article
- Current Perspective on the Potential Benefits of Smart Insulin Pens on Glycemic Control in Patients With Diabetes: Spanish Delphi Consensus.Journal of diabetes science and technology · 2025Article
- Definitions of digital biomarkers: a systematic mapping of the biomedical literature.BMJ health & care informatics · 2024Review
- Usability and feasibility analysis of an mHealth-tool for supporting physical activity in people with heart failure.BMC medical informatics and decision making · 2024Article
- Patterns and Predictors Associated With Long-Term Glycemic Control in Pediatric and Young Adult Patients with Type 1 Diabetes.Journal of diabetes science and technology · 2023Observational
- Connected Insulin Pens and Caps: An Expert's Recommendation from the Area of Diabetes of the Spanish Endocrinology and Nutrition Society (SEEN).Diabetes therapy : research, treatment and education of diabetes and related disorders · 2023Article
- Analysis of "Journal of diabetes science and technology · 2023Article
- Digital Health Technologies Enabling Partnerships in Chronic Care Management: Scoping Review.Journal of medical Internet research · 2022Article
- Challenges in Participant Engagement and Retention Using Mobile Health Apps: Literature Review.Journal of medical Internet research · 2022Review
- Key drivers involved in the telemonitoring of covid-19 for self-health management: an exploratory factor analysis.BMC health services research · 2022Article
- Computerized migraine diagnostic tools: a systematic review.Therapeutic advances in chronic disease · 2022Article
- Better self-care through co-care? A latent profile analysis of primary care patients' experiences of e-health-supported chronic care management.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCaring for individuals with chronic conditions is labor intensive, requiring ongoing appointments, treatments, and support. The growing number of individuals with chronic conditions makes this support model unsustainably burdensome on health care systems globally. Mobile health technologies are increasingly being used throughout health care to facilitate communication, track disease, and provide educational support to patients. Such technologies show promise, yet they are not being used to their full extent within US health care systems.
objectiveThe purpose of this study was to examine the use of staff and costs of a remote monitoring care model in persons with and without a chronic condition.
methodsAt Dartmouth-Hitchcock Health, 2894 employees volunteered to monitor their health, transmit data for analysis, and communicate digitally with a care team. Volunteers received Bluetooth-connected consumer-grade devices that were paired to a mobile phone app that facilitated digital communication with nursing and health behavior change staff. Health data were collected and automatically analyzed, and behavioral support communications were generated based on those analyses. Care support staff were automatically alerted according to purpose-developed algorithms. In a subgroup of participants and matched controls, we used difference-in-difference techniques to examine changes in per capita expenditures.
resultsParticipants averaged 41 years of age; 72.70% (2104/2894) were female and 12.99% (376/2894) had at least one chronic condition. On average each month, participants submitted 23 vital sign measurements, engaged in 1.96 conversations, and received 0.25 automated messages. Persons with chronic conditions accounted for 39.74% (8587/21,607) of all staff conversations, with higher per capita conversation rates for all shifts compared to those without chronic conditions (P<.001). Additionally, persons with chronic conditions engaged nursing staff more than those without chronic conditions (1.40 and 0.19 per capita conversations, respectively, P<.001). When compared to the same period in the prior year, per capita health care expenditures for persons with chronic conditions dropped by 15% (P=.06) more than did those for matched controls.
conclusionsThe technology-based chronic condition management care model was frequently used and demonstrated potential for cost savings among participants with chronic conditions. While further studies are necessary, this model appears to be a promising solution to efficiently provide patients with personalized care, when and where they need it.
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