Sleep Clinical Trial
Official title:
IntelligentCare: the Impact of a Wearable-based Intervention in an Ambulatory Hospital Setting to Improve Health in Multimorbidity Through Physical Activity and Sleep
The benefits of physical activity and adequate sleep are well documented, and their importance strengthens with the increasing prevalence of chronic diseases and multimorbidity. Interventions to promote physical activity and sleep that use commercial activity trackers may be useful non-pharmacological approaches to managing individual health; however, limited evidence exists on their use to improve physical activity in elderly patients with multimorbidity. This study aims to measure the effects of behavioral change techniques (BCTs) delivered by a wearable device on physical activity (PA) and quality of sleep (QS) in elderly patients with multimorbidity. The investigators designed an open-label randomized controlled trial with participants recruited through primary care and a specialist outpatient clinic. Participants must be more than 65 years old, have multimorbidity, and have access to smartphones. All eligible participants will receive PA promotion content and will be randomly assigned to wear a smartwatch. The primary outcome will be the participants' PA measurement at six months using the IPAQ-SF. Secondary outcomes will include changes in the participants' frailty status, biometric measurements, quality of life, and biopsychosocial assessments. A sample size of 40 participants per arm was calculated to detect group differences, with 50 participants recruited and randomized into each arm. This study aims to contribute to a better understanding of PA patterns and the impact of smartwatch-based PA interventions in patients with multimorbidity. The trial's findings are expected to improve our understanding of how physical activity patterns, patient-reported outcomes (PROMs), and healthcare resource utilization are linked in patients with multiple medical conditions. The study will use a registry for locally developed PROMs and data from the participants' medical records to determine how wearable data and medical information data can be used to predict PROMs and unplanned hospital admissions.
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