Abstract
The widespread adoption of digital health technologies such as smartphone-based mobile applications, wearable activity trackers and Internet of Things systems has rapidly enabled new opportunities for predictive health monitoring. Leveraging digital health tools to track parameters relevant to human health is particularly important for the older segments of the population as old age is associated with multimorbidity and higher care needs. In order to assess the potential of these digital health technologies to improve health outcomes, it is paramount to investigate which digitally measurable parameters can effectively improve health outcomes among the elderly population. Currently, there is a lack of systematic evidence on this topic due to the inherent heterogeneity of the digital health domain and the lack of clinical validation of both novel prototypes and marketed devices. For this reason, the aim of the current study is to synthesize and systematically analyse which digitally measurable data may be effectively collected through digital health devices to improve health outcomes for older people. Using a modified PICO process and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we provide the results of a systematic review and subsequent meta-analysis of digitally measurable predictors of morbidity, hospitalization, and mortality among older adults aged 65 or older. These findings can inform both technology developers and clinicians involved in the design, development and clinical implementation of digital health technologies for elderly citizens.
Highlights
The growing field of digital health attests that digital technologies are increasingly converging with human health and the delivery of healthcare services
Being physically active, having increased body-mass index (BMI) and obesity were significantly associated with survival (HR 0.42; Confidence Intervals (CI) 0.20, 0.88; 2 studies, hazard ratios (HRs) 0.77; CI 0.61, 0.96; 5 studies and odds ratios (ORs) 0.71; CI 0.50, 0.99; 6 studies, respectively), whereas the results related to systolic blood pressure ≤90 (HR 1.91; CI 0.88, 4.13; 2 studies) were neither for mortality nor for survival statistically significant
Hypertension, diabetes and decreased BMI were identified as significant predictors of morbidity, while frailty, pulmonary comorbidity, obesity, pain, fatigue, and fever were identified as significant predictors of hospital admission or readmission
Summary
The growing field of digital health attests that digital technologies are increasingly converging with human health and the delivery of healthcare services. The first generation of wearable devices and mobile tools could collect data, and provide insights only related to a small portion of human health and physiology, mobility reports (e.g., daily steps, physical position). Novel applications have expanded their data sources and can record a broader variety of healthrelated parameters and underlying processes. This is due to a four-fold technological transformation. Self-quantification technologies have expanded in variety as to include data sources that previously could only be collected exclusively via medical devices such as heartbeat rate and electroencephalography [2]. Smartphone-sensing methods have improved in quality and reliability, permitting fine-grained, continuous and unobtrusive collection of novel health-related data such as sleep patterns and voice records [3]. Smartphone apps can be used to predict a person’s cognitive status from their responses to gamified cognitive tasks such as 3D virtual navigation [5]
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