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Exploring Risk Factors Related to Low Calf Circumference in Older Adults With Multimorbidity: Cross-Sectional Latent Class Analysis

Studies have confirmed that multimorbidity is not a simple random combination but has some underlying physiological and pathological connections [13]. Identifying the combination of different categories in multimorbidity will be beneficial in achieving an individual-centered medical care pattern and improving the clinical outcomes of patients with multimorbidity. The evidence from UK Biobank participants suggests a link between probable sarcopenia and long-term conditions as well as multimorbidity [14].

Xilin Peng, Xudong Chen, Ruihao Zhou, Fanfan Shi, Tao Zhu, Guo Chen

JMIR Aging 2025;8:e68760

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The Provision of Social Support in an Online Support Forum for Caregivers of People With Comorbid Dementia and Cancer: Content Analysis Study

Prevalence of both cancer and dementia increases with age [1,2], and owing to the aging population and rise in multimorbidity, a growing number of people are now living with comorbid dementia and cancer (CDC) [3-5]. A recent analysis of UK primary care data found that 1 in 13 people aged ≥75 years with cancer also have dementia [3], though this is likely to be an underestimation due to dementia underdiagnosis [6].

Mollie Louise Price, Claire Surr, Brendan Gough, David Howe, Laura Ashley

JMIR Cancer 2025;11:e72217

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Addressing Safety, Quality, and Cost of Care Through a Telehealth Outpatient Transitional Care Model: Protocol for a Pragmatic Randomized Controlled Trial

Multimorbidity, the presence of 2 or more chronic conditions, is common and becomes more common with increasing age [1]. It is estimated that 8% of Australians (9.7 million people) had 2 or more chronic disease conditions in 2022. This ranged from 11% of people aged between 0 and 14 years to 79% of people aged ≥85 years [2]. Due to increasing life expectancy and improvements in health care, the prevalence of multimorbidity is rising [3,4].

Kate Davis, Sepehr Shakib, Greg Sharplin, Lachlan Darch, Nicholas Marlow, Marion Eckert

JMIR Res Protoc 2025;14:e71847

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Guideline-Based Clinical Decision Support Framework for Multimorbidity: Protocol for a Formulation and Testing Study

An increasing number of people, especially older adults, are experiencing multimorbidity, a situation where the patient has multiple (chronic) conditions at the same time [1]. As a result, multimorbidity is becoming a defining challenge for health systems [2].

Zijun Wang, Ling Wang, Bingyi Wang, Hongfeng He, Zhewei Li, Di Zhu, Jie Zhang, Huayu Zhang, Yaolong Chen, Janne Estill

JMIR Res Protoc 2025;14:e63339

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Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong

The prevalence of multimorbidity, which refers to the presence of two or more medical conditions simultaneously, is steadily rising with improvements in life expectancy [2,3]. In the United States, 40% of patients with cancer have at least one other chronic condition, and 15% have two or more comorbidities [4]. Comorbidities are believed to influence cancer detection, treatment uptake, and treatment toxicity [5,6].

Chun Sing Lam, Rong Hua, Herbert Ho-Fung Loong, Chun-Kit Ngan, Yin Ting Cheung

JMIR Cancer 2025;11:e71937

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Impact of 12-Month mHealth Home Telemonitoring on Clinical Outcomes in Older Individuals With Hypertension and Type 2 Diabetes: Multicenter Randomized Controlled Trial

Older people face challenges such as multimorbidity, cognitive impairment, visual impairment, limited mobility, environmental dependency, and reluctance to adopt modern technologies, which may reduce its effectiveness [2,5,23,24]. Considering demographic shifts, it is imperative to evaluate the benefits of telemonitoring in this age group and to adapt interventions to address the specific needs of older people.

Matic Mihevc, Majda Mori Lukančič, Črt Zavrnik, Tina Virtič Potočnik, Nina Ružić Gorenjec, Marija Petek Šter, Zalika Klemenc-Ketiš, Antonija Poplas Susič

JMIR Mhealth Uhealth 2025;13:e59733

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Extracting Multifaceted Characteristics of Patients With Chronic Disease Comorbidity: Framework Development Using Large Language Models

Research on chronic multimorbidity has increasingly become a focal point with the aging of the population [1]. Major research directions include exploring multimorbidity patterns [2,3], investigating multimorbidity development and prediction [4,5], and examining the mutual interplay between multimorbidity and patient characteristics [6]. Amid ongoing research on frequently occurring diseases, extracting patient characteristics holds paramount importance.

Junyan Zhang, Junchen Zhou, Liqin Zhou, Zhichao Ba

JMIR Med Inform 2025;13:e70096

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Co-Occurring Diseases and Mortality in Patients With Chronic Heart Disease, Modeling Their Dynamically Expanding Disease Portfolios: Nationwide Register Study

The studies that consider multimorbidity either restrict their analyses to a subset of diagnosis combinations [7] or group diagnoses into multimorbidity clusters at baseline before analyzing the effects of the extracted clusters [5]. Despite modeling disease interactions, these kinds of analyses fail to capture the crucial dynamics in the HD disease trajectories, where additional diseases are cumulatively diagnosed before death [10], causing an augmented risk profile for the patient.

Nikolaj Normann Holm, Anne Frølich, Helena Dominguez, Kim Peder Dalhoff, Helle Gybel Juul-Larsen, Ove Andersen, Anders Stockmarr