The use of accelerometer-based wearable activity monitors in clinical settings: current practice, barriers, enablers, and future opportunities

Abstract

Abstract Background Wearable activity monitors (WAMs, e.g. Fitbits and research accelerometers) show promise for helping health care professionals (HCPs) measure and intervene on patients’ activity patterns. This study aimed to describe the clinical use of WAMs within South Australia, barriers and enablers, and future opportunities for large-scale clinical use. Methods A descriptive qualitative study was undertaken using semi-structured interviews. Participants were HCPs with experience using WAMs in South Australian clinical settings. Commencing with participants identified through the research team’s professional networks, snowball recruitment continued until all identified eligible HCPs had been invited. Semi-structured interviews were used to explore the research aims, with quantitative data analysed descriptively, and qualitative data analysed thematically. Results 18 participants (physiotherapists n = 8, exercise physiologists n = 6, medical consultants n = 2, and research personnel recommended by medical consultants n = 2), represented 12 discrete “hubs” of WAM use in clinical practice, spanning rehabilitation, orthopaedics, geriatrics, intensive care, and various inpatient-, outpatient-, community-based hospital and private-practice settings. Across the 12 hubs, five primarily used Fitbits® (various models), four used research-grade accelerometers (e.g. GENEActiv, ActivPAL and StepWatch accelerometers), one used Whoop Bands® and another used smartphone-based step counters. In three hubs, WAMs were used to observe natural activity levels without intervention, while in nine they were used to increase (i.e. intervene on) activity. Device selection was typically based on ease of availability (e.g. devices borrowed from another department) and cost-economy (e.g. Fitbits® are relatively affordable compared with research-grade devices). Enablers included device characteristics (e.g. accuracy, long battery life, simple metrics such as step count) and patient characteristics (e.g. motivation, rehabilitation population, tech-savvy), whilst barriers included the HCPs’ time to download and interpret the data, multidisciplinary team attitudes and lack of protocols for managing the devices. Conclusions At present, the use of WAMs in clinical practice appears to be fragmented and ad hoc, though holds promise for understanding patient outcomes and enhancing therapy. Future work may focus on developing protocols for optimal use, system-level approaches, and generating cost-benefit data to underpin continued health service funding for ongoing/wide-spread WAM use.