Wearable Health Technology for Preoperative Risk Assessment in Elderly Patients: The WELCOME Study

Barriers and facilitators to smartwatch-based prehabilitation participation among frail surgery patients: a qualitative study

BACKGROUND

For older, frail adults, exercise before surgery through prehabilitation (prehab) may hasten return recovery and reduce postoperative complications. We developed a smartwatch-based prehab program (BeFitMe) for older adults that encourages and tracks at-home exercise. The objective of this study was to assess patient perceptions about facilitators and barriers to prehab generally and to using a smartwatch prehab program among older adult thoracic surgery patients to optimize future program implementation.

METHODS

We recruited patients, aged ≥50 years who had or were having surgery and were screened for frailty (Fried's Frailty Phenotype) at a thoracic surgery clinic at a single academic institution. Semi-structured interviews were conducted by telephone after obtaining informed consent. Participants were given a description of the BeFitMe program. The interview questions were informed by The Five "Rights" of Clinical Decision-Making framework (Information, Person, Time, Channel, and Format) and sought to identify the factors perceived to influence smartwatch prehab program participation. Interview transcripts were transcribed and independently coded to identify themes in for each of the Five "Rights" domains.

RESULTS

A total of 29 interviews were conducted. Participants were 52% men (n = 15), 48% Black (n = 14), and 59% pre-frail (n = 11) or frail (n = 6) with a mean age of 68 ± 9 years. Eleven total themes emerged. Facilitator themes included the importance of providers (right person) clearly explaining the significance of prehab (right information) during the preoperative visit (right time); providing written instructions and exercise prescriptions; and providing a preprogrammed and set-up (right format) Apple Watch (right channel). Barrier themes included pre-existing conditions and disinterest in exercise and/or technology. Participants provided suggestions to overcome the technology barrier, which included individualized training and support on usage and responsibilities.

CONCLUSIONS

This study reports the perceived facilitators and barriers to a smartwatch-based prehab program for pre-frail and frail thoracic surgery patients. The future BeFitMe implementation protocol must ensure surgical providers emphasize the beneficial impact of participating in prehab before surgery and provide a written prehab prescription; must include a thorough guide on smartwatch use along with the preprogrammed device to be successful. The findings are relevant to other smartwatch-based interventions for older adults.

Wearable Sensors as a Preoperative Assessment Tool: A Review

Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.

Digital technologies for step counting: between promises of reliability and risks of reductionism

Step counting is among the fundamental features of wearable technology, as it grounds several uses of wearables in biomedical research and clinical care, is at the center of emerging public health interventions and recommendations, and is gaining increasing scientific and political importance. This paper provides a perspective of step counting in wearable technology, identifying some limitations to the ways in which wearable technology measures steps and indicating caution in current uses of step counting as a proxy for physical activity. Based on an overview of the current state of the art of technologies and approaches to step counting in digital wearable technologies, we discuss limitations that are methodological as well as epistemic and ethical-limitations to the use of step counting as a basis to build scientific knowledge on physical activity (epistemic limitations) as well as limitations to the accessibility and representativity of these tools (ethical limitations). As such, using step counting as a proxy for physical activity should be considered a form of reductionism. This is not per se problematic, but there is a need for critical appreciation and awareness of the limitations of reductionistic approaches. Perspective research should focus on holistic approaches for better representation of physical activity levels and inclusivity of different user populations.

Development of Machine Learning Model for VO2max Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates

A cardiopulmonary exercise test (CPET) is essential for lung resection. However, performing a CPET can be challenging. This study aimed to develop a machine learning model to estimate maximal oxygen consumption (VO2max) using data collected through a patch-type single-lead electrocardiogram (ECG) monitoring device in candidates for lung resection. This prospective, single-center study included 42 patients who underwent a CPET at a tertiary teaching hospital from October 2021 to July 2022. During the CPET, a single-lead ECG monitoring device was applied to all patients, and the results obtained from the machine-learning algorithm using the information extracted from the ECG patch were compared with the CPET results. According to the Bland-Altman plot of measured and estimated VO2max, the VO2max values obtained from the machine learning model and the FRIEND equation showed lower differences from the reference value (bias: -0.33 mL·kg-1·min-1, bias: 0.30 mL·kg-1·min-1, respectively). In subgroup analysis, the developed model demonstrated greater consistency when applied to different maximal stage levels and sexes. In conclusion, our model provides a closer estimation of VO2max values measured using a CPET than existing equations. This model may be a promising tool for estimating VO2max and assessing cardiopulmonary reserve in lung resection candidates when a CPET is not feasible.

Management of hospital admission, patient information and education, and immediate preoperative care

An increasing proportion of surgical procedures involves elderly and frail patients in high-income countries, leading to an increased risk of postoperative complications. Complications significantly impact patient outcomes and costs, due to prolonged hospitalization and loss of autonomy. Consequently, it is crucial to evaluate preoperative functional status in older patients, to tailor the perioperative plan, and evaluate risks. The hospital environment often exacerbates cognitive impairments in elderly and frail patients, also increasing the risk of infection, falls, and malnutrition. Thus, it is essential to work on dedicated pathways to reduce hospital readmissions and favor discharges to a familiar environment. In this context, the use of wearable devices and telehealth has been promising. Telemedicine can be used for preoperative evaluations and to allow earlier discharges with continuous monitoring. Wearable devices can track patient vitals both preoperatively and postoperatively. Preoperative education of patient and caregivers can improve postoperative outcomes and is favored by technology-based approach that increases flexibility and reduce the need for in-person clinical visits and associated travel; moreover, such approaches empower patients with a greater understanding of possible risks, moving toward shared decision-making principles. Finally, caregivers play an integral role in patient improvement, for example, in the prevention of delirium. Hence, their inclusion in the care process is not only advantageous but essential to improve perioperative outcomes in this population.