Implementation of Remote Activity Sensing to Support a Rehabilitation Aftercare Program: Observational Mixed Methods Study With Patients and Health Care Professionals

Abstract

Background Physical activity is central to maintaining the quality of life for patients with complex chronic conditions and is thus at the core of neurorehabilitation. However, maintaining activity improvements in daily life is challenging. The novel Stay With It program aims to promote physical activity after neurorehabilitation by cultivating self-monitoring skills and habits. Objective We examined the implementation of the Stay With It program at the Valens Rehabilitation Centre in Switzerland using the normalization process theory framework, focusing on 3 research aims. We aimed to examine the challenges and facilitators of program implementation from the perspectives of patients and health care professionals. We aimed to evaluate the potential of activity sensors to support program implementation and patient acceptance. Finally, we aimed to evaluate patients’ engagement in physical activity after rehabilitation, patients’ self-reported achievement of home activity goals, and factors influencing physical activity. Methods Patients were enrolled if they had a disease that was either chronic or at risk for chronicity and participated in the Stay With It program. Patients were assessed at baseline, the end of rehabilitation, and a 3-month follow-up. The health care professionals designated to deliver the program were surveyed before and after program implementation. We used a mixed methods approach combining standardized questionnaires, activity-sensing data (patients only), and free-text questions. Results This study included 23 patients and 13 health care professionals. The diverse needs of patients and organizational hurdles were major challenges to program implementation. Patients’ intrinsic motivation and health care professionals’ commitment to refining the program emerged as key facilitators. Both groups recognized the value of activity sensors in supporting program implementation and sustainability. Although patients appreciated the sensor’s ability to monitor, motivate, and quantify activity, health care professionals saw the sensor as a motivational tool but expressed concerns about technical difficulties and potential inaccuracies. Physical activity levels after patients returned home varied considerably, both within and between individuals. The self-reported achievement of activity goals at home also varied, in part because of vague definitions. Common barriers to maintaining activity at home were declining health and fatigue often resulting from heat and pain. At the 3-month follow-up, 35% (8/23) of the patients withdrew from the study, with most citing deteriorating physical health as the reason and that monitoring and discussing their low activity would negatively affect their mental health. Conclusions Integrating aftercare programs like Stay With It into routine care is vital for maintaining physical activity postrehabilitation. Although activity trackers show promise in promoting motivation through monitoring, they may lead to frustration during health declines. Their acceptability may also be influenced by an individual’s health status, habits, and technical skills. Our study highlights the importance of considering health care professionals’ perspectives when integrating new interventions into routine care.

Relationship Between Clinical, Psychological, Physiological, and Technical Parameters and the Use of Digital Tools During Cardiac Rehabilitation Including Home Training (Preprint)

“…These high QoL scores prior to OUT-III in combination with a small sample size of patients with MacNew data pre and post OUT-III, especially in the group with digital tools (n=13), might explain the difference between Jones et al and our results. The higher IMS scores in the group with digital tools are in line with Lu et al [7], who identified patients' intrinsic motivation as a key facilitator for successful rehabilitation of chronically ill/on the verge of being chronically ill patients with remote activity sensing. Our results indicate that digital tools can also help to motivate cardiac patients, without exposing patients to additional distress.…”

“…So far, there is little knowledge concerning the relationship of patients' intrinsic motivation and digital tools during home-based CR. In a small-scale study with 23 patients, Lu et al [7] identified patients' intrinsic motivation as a key facilitator for successful rehabilitation with remote activity sensing. The rehabilitation program, however, did not focus on cardiac patients and none of the patients were reported to have a cardiac disease.…”

From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal

“…These characteristics align with the typical demographics observed in MS populations with a more progressed disease state 29 – 31 . A follow-up study 23 involving participants with different characteristics and chronic illnesses, such as cardiovascular diseases, revealed conclusions consistent with the main BarKA-MS analyses, suggesting that the findings discussed in this lessons learned paper may be applicable to other chronic disease populations.…”

“…BarKA-MS is a semi-remote observational, longitudinal cohort pilot study program that explored the physical activity rehabilitation of PwMS, which informed several independent analyses as part of the program 18 , 23 – 26 . The methods and results of BarKA-MS are published elsewhere 22 , 24 – 26 .…”

“…In the following sections, we present our insights (lessons learned) from designing and implementing BarKA-MS, as well several independent analyses of sensor measurements and patient reported outcomes 18 , 24 – 26 , and a follow-up study that was modeled after BarKA-MS 23 that examined the implementation of a physical activity post-rehabilitation program from the perspectives of patients and healthcare professionals. We specifically selected insights that are relevant to the use of wearable sensor data for digital biomarker development.…”