The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis

Failure to recognise and act on physiological indicators of worsening illness in acute hospital wards is a generic problem that was recognised over a decade ago [1, 2] and may contribute to increased emergency intensive care unit (ICU) admissions and hospital mortality [1, 3]. In the United Kingdom, the use of physiological early warning scoring (EWS) systems (which measure "standard" vital signs such as pulse rate, respiratory rate, blood pressure, oxygen saturation, and temperature) is still common practice in general wards, together with a graded response such as referral for a senior review or increasing monitoring frequency [4]. This frequency of observations is generally guided by the clinical condition of the patient, and due to the requirement of manual physiological measurements, it can be time-consuming for healthcare professionals [5]. As a result, the optimal monitoring frequency is often not achieved [6], limiting the efficacy of intermittent monitoring systems dependent on the frequency of manual observations [7]. Furthermore, even when the ideal frequency is achieved, patients can deteriorate between observation sets [8]. Higher risk patients are often continuously monitored (for example in critical care), improving early detection of deterioration [5]. However, in the UK, continuous monitoring is not commonly used in the ward environment [9], although one study suggests it may be feasible and cost-effective in surgical wards [10], with the potential to improve patient outcomes when compared to intermittent monitoring [8].

Despite the potential to promote earlier detection of deterioration, limitations in continuous vital sign monitoring technology can pose a barrier to implementation [5], such as restriction of patient mobility and independence due to wires and static devices [9, 11]. In response to this need, commercially available wearable monitoring devices are evolving rapidly [12]. Wearable devices may provide an alternative to static wired continuous monitors and offer a bridge between bedside wired monitoring and intermittent manual measurements. This development has the potential to promote patients’ mobility and comfort while reducing nursing time and improving the early detection of abnormal physiological parameters [13].

A recent meta-analysis assessed the impact of multi-parameter continuous non-invasive monitoring in hospital wards, including wired static devices, suggesting a 39% decreased mortality risk in monitored patients compared to those receiving standard care (intermittent manual observations), it also suggested a trend of reduced intensive care unit (ICU) transfer, rapid response team (RRT) activation and hospital length of stay (LoS) [14]. The validation, feasibility, costs and clinical outcomes of 13 different wearable devices were assessed in another systematic review, which demonstrated that the majority of studies were still at the validation and feasibility phases [15], emphasising the lack of evidence assessing the impact on economic and clinical outcomes as there is still uncertainty around the impact of wearable monitoring systems (WMS) in the hospital environment, hindering its implementation and clinical use [16]. Our review focused on these wearable monitoring devices and/or systems implemented inside the hospital (inclusive of all specialities, acuity and ages).

The objective of this systematic review and meta-analysis was to assess the impact of vital sign monitoring on the detection of physiological deterioration and related clinical outcomes of hospitalised patients using wearable monitoring systems in comparison with standard care.

Search terms were designed by a medical librarian with field expertise (NT). Relevant articles up to 27th August 2020 were identified through electronic searches on MEDLINE Ovid (including Epub Ahead of Print and In-Process & Other Non-Indexed Citations), Embase (Ovid), CINAHL (EBSCO) and Cochrane Database of Systematic Reviews (Cochrane Library, Wiley), Cochrane Central Register of Controlled Trials (CENTRAL) (Cochrane Library, Wiley) and Health Technology Assessment (HTA) database via https://​www.​crd.​york.​ac.​uk/​CRDWeb/​. Full search strategy and terms/combinations used in each database can be found in Additional file 2: Appendix 2.

Clinical trials and prospective studies registered up to 10th September 2020 in ClinicalTrials.gov via https://​clinicaltrials.​gov/​ and ISRCTN via https://​www.​isrctn.​com/​ were also identified. Search details in Additional file 3: Appendix 3.

Searches of unpublished grey literature and pre-print servers were also conducted manually (details in Additional file 4: Appendix 4) and additional studies published in these servers up to 16th December 2020 were identified.

Titles and abstracts of all potentially relevant articles were independently reviewed for possible inclusion by two authors (CA, CB). The full text of any citation considered potentially relevant by any reviewer was retrieved. The degree of interrater agreement for study selection was determined by using kappa, with standard definitions for poor (< 0.20), fair (0.21–0.40), moderate (0.41–0.60), good (0.61–0.80), and very good (0.81–1.00) agreement [21, 22]. The included abstracts full-texts were assessed for eligibility and disagreements resolved by discussion between the 2 review authors; if no agreement could be reached, a third author was consulted (SV). The full selection process is outlined in the published protocol [19].

Two reviewers (CA and CB) extracted the data independently from the included studies. Disagreements were resolved by discussion between the 2 review authors. When required, this was also discussed with a third author (MS) and a statistician (SG).

The following data were extracted for each study, where available: author list, country, date published, registration number, aim, design, setting and population, recruitment start and end dates, ethical approval and informed consent information, eligibility criteria, intervention description, included devices, period of device wear, vital signs measured by devices, frequency of wearable data availability, comparator type, EWS and frequency of manual measurements, sample size, demographics (e.g. age, gender, BMI, etc.), other clinical characteristics (e.g. type of admission, American Society of Anaesthesiologists, ASA, score, etc.), deterioration detection and related clinical outcomes summary data, total/median monitoring hours, alerting system information (e.g. thresholds and alarms description), study limitations, device FDA/CE mark information, funding and conflict of interest information.

Four tools, selected based on study design, were used to assess risk of bias. For randomised controlled trials the Cochrane risk of bias tool (RoB2) was used [23]; for non-randomised studies, the Newcastle Ottawa Scale (NOS) [24] and the “Risk Of Bias In Non-randomised Studies—of Interventions” (ROBINS-I) were applied [25]; and, in addition, the Mixed Methods Appraisal tool (MMAT) [26] was used for all studies. This was a change from the original protocol [19] as the Jadad scale was replaced by the ROBINS-I for assessment of non-randomised studies, as we found it more comparable with the ROB2 tool used for included RCTs. Two reviewers (CA and CB) independently appraised each study and disagreements were solved by discussion until consensus was reached with a third reviewer (SV).

All outcomes with results from at least three studies were considered for the meta-analysis. Outcomes with data from less than three studies were not included in the meta-analysis but reported in a narrative synthesis.

After removal of duplicates, 8706 studies were identified. Following title and abstract review 51 full texts remained, of which 10 met the inclusion criteria (Fig. 1). Four studies appeared to meet the inclusion criteria but were excluded at full-text review: two studies were excluded for not reporting a subset of their analysis for the patients using the WMS [35, 36]; one was excluded after confirming with the author that the device was not wearable at the time of the study [37], and another did not have a comparator group [38]. A total of 4433 patients were included in these studies.

The overall quality of included studies was moderate with some bias to take into account, as per Figs. 2 And 3. For the included RCTs, using the ROB2, two were identified as being at “low risk” of bias [33, 44] and a further three were assessed as raising “some concerns” [30, 40], including the cluster RCT [32]. The risk of bias, assessed by the ROBINS-I was “moderate” for all before-and-after studies [29, 39, 42, 43]. See Additional file 8: Appendix 8 for further details. The results of the bias assessment did not influence inclusion in the meta-analyses.

In total, data from seven studies were included in the meta-analysis of primary outcomes related to deterioration detection, analysed separately according to the three reported deterioration outcomes – ICU transfers, rapid response or cardiac arrest activation, and complications.

The two secondary outcomes of in-hospital mortality and hospital length of stay were also meta-analysed.

In this systematic review and meta-analysis, we identified 10 eligible studies of various designs comparing the impact of WMS on deterioration detection and clinical outcomes with standard care, including a total of 4433 patients. Our main findings suggest that there is currently no strong evidence to suggest WMS is superior to standard care; there is some indication of a trend towards WMS enabling a reduction in ICU admissions, RRT/cardiac arrest calls and complications in hospitalised patients, however without statistical significance. Our results are limited by the small number of studies, limited sample sizes, and overall moderate risk of bias, failing to provide a generalisable answer to our research question (GradePro summary in Additional file 5: Appendix 5). This review also suggests a trend towards WMS decreasing in-hospital mortality and length of stay, but again without strong statistical significance supporting these findings.

Although our review focused specifically on wearable monitoring devices, our results are in accordance with a previous systematic review which had a focus on the clinical impact of a broader range of multi-parameter continuous non-invasive monitoring of vital signs in non-intensive care unit patients. In this review, the authors included all non-invasive devices (including wired and static bed monitors such as EarlySense) and also found a trend towards decreased ICU transfers, RRT activations and hospital length of stay, with a suggestion of reduced hospital mortality for patients monitored with these devices [14]. Our study also updates this review, with the inclusion of an additional four more recent studies.

All the studies included in our review were conducted in a non-ICU environment (mostly in surgical patients) and most comparators were standard intermittent vital-sign monitoring with the use of local EWS. Previous evidence suggests that focus on non-critical care settings is due to WMS being unable to replace the continuous monitoring commonly used for high-dependency patients. Instead, WMS offers an intermediate level of monitoring between continuous high-dependency monitoring and intermittent manual measurements, with the potential to facilitate early deterioration detection in high-risk patients (e.g. post-ICU) [5]. In addition, a recent study in the paediatric population concluded that wireless monitoring is feasible and can identify more deteriorations. The authors suggest that by using this in combination with a paediatrics early warning (PEW) score, some life-threatening events may be prevented [38].

One study included in our review reported patients’, relatives’ and healthcare professionals’ perceptions of the use of WMS on the general ward, and found agreement between all interviewed groups that WMS could facilitate earlier deterioration detection and improve patient safety without posing a barrier to mobility, as well as reduce staff workload and hospital costs [40], agreeing with previous evidence [52‐54] and reinforcing the direction of our findings.

To better understand the WMS used in the included studies, we aggregated available information on the alerting methods and thresholds. As most studies used audio alarms, system iterations seemed to focus on reducing the rate of false alarms by adjusting/individualising each vital sign and/or overall score to avoid alarm fatigue for clinical staff. This has been previously discussed as an important factor for the successful deployment of monitoring technology [55‐58]. The exception was one study that used visual warnings alone, resulting in increased NEWS measurements in patients using WMS without clinical staff being aware of the potential deterioration. They identified three patients for whom WMS alarmed between two intermittent measurements and who were later diagnosed with pneumonia, atrial fibrillation and an anastomotic leakage [41]; in the same study, the authors also explored the delay between high MEWS measured by a device and next regular MEWS measurement by a nurse, ranging from 0 up to 10 h, and varying between day and night [41].

Finally, a review of known registries was conducted to address the issue of publication bias, finding that the majority of the included studies were registered either in ClinicalTrials.gov or ISRCTN databases and all registered primary outcomes were reported as well as most of the secondary (Additional file 7: Appendix 7). However, most studies were registered retrospectively, rather than prospectively. We did, however, identify four studies that might have contributed to this systematic review by either performing a subgroup analysis for patients using wearable devices [36, 48] and by publishing their results as registered in another two [50, 51]. Our registry search allowed us to highlight this under-reported and non-published evidence, which could have potentially impacted our results and contributed to the meta-analysis and overall body of evidence available in this field.

There were some limitations to this review. The number of studies included was limited and used a variety of designs, populations, outcomes, medical devices/systems, EWS and alerting thresholds. Our meta-analysis only included two RCTs (one being a pilot) and one pilot cluster RCT, all of which had small sample sizes which reduced the probability of significant difference in outcomes, despite large effect sizes. In contrast, the included before-and-after studies had larger sample sizes but increased bias and quality limitations, again posing a barrier to any significant conclusion. Additionally, most included studies had “some concerns” or “moderate” risk of bias, also affecting the quality of our results. This reflects the emerging nature of this area of research and highlights the need for a large, multicentre RCT that evaluates whether WMS may be beneficial for early deterioration detection and related clinical outcomes in hospitalised patients.

Despite the apparent variation in the interventions and methodologies between the different studies, the meta-analyses generally showed low heterogeneity of outcomes (assessed using the I² statistic). This could be because the results were generally quite close to the null for most outcomes, and therefore little variability would be expected. Additionally, the small number of studies will have hampered the ability to robustly assess heterogeneity.

One of the studies [32] had an extra non-randomised bay in their exploratory analysis. We did not include these data in our meta-analysis in accordance with Cochrane guidelines, so only the randomised groups were considered [59]. Similarly, another study compared the WMS group with a period before in the same unit and a period during and before in an alternative unit. In this case, we only used the data comparing the before period in the same unit, to minimise selection bias [59]. For RRT and cardiac team call analysis, it is important to note that the Weller and colleagues study accounted for 96.7% of the weight on these results [29]. In addition, studies exploring RRT and cardiac team calls were combined in the meta-analysis, and therefore results might differ if analysed separately.

As previously reported, bias may be present as clinical staff are aware of the WMS use in their patients. However, the practicalities of blinding in WMS studies may not be feasible and may potentially have a counterproductive impact on clinical outcomes [14]. Additionally, all studies failed to provide practical information of other potential confounders for the reported primary outcomes, for example, staff seniority throughout patient stay, local ICU capacity (that might impact transfers), the Do Not Attempt Resuscitation (DNAR) status of the patients who died, among other factors. This also reflects the need for a human-factors approach when designing and evaluating WMS implementation studies.

The results of this systematic review highlighted the need for more, bigger and better studies to support WMS implementation, and test its impact on early deterioration detection and clinical outcomes. Our findings are limited by the reduced sample size of the included RCTs and reduced methodological quality of the before-and-after studies. One of the included studies [33] conducted a subgroup analysis exploring clinical outcomes in high-risk participants. This further highlighted the potential impact of the WMS use in patients at higher risk of deterioration and further RCTs should consider including a similar analysis of their results.

In this review, we also explored the methods for alerting and the alarm parameters of the included studies; our results suggest that “less is more” and studies seemed to focus on reducing false alarms and, consequently, increase the number of actionable alarms. Future implementation research should consider this, as well as the time required for the alarm system optimisation before WMS deployment.

Remote monitoring systems are only beneficial to the clinical staff if they are easy to use and clinicians understand the potential benefit on clinical outcomes [60]. This review is part of a wider phased project in our research programme, the virtual High Dependency Unit (vHDU) study. So far, we have selected [61] and tested a number of wearable devices [62, 63], prior to selection and integration in a final WMS, which will be evaluated in a pilot study and then a full multicentre RCT.