Introduction

The worldwide use of surgical treatments is increasing, with approximately one in ten people undergoing a surgical procedure each year in high-income countries1,2. Following discharge, patients assume primary responsibility for monitoring their own recovery and differences in adhering with both this and related self-care recommendations, can produce variable outcomes. More than 10% of patients over 45 years old experience a major postoperative complication3,4,5, often following discharge6, which typically prompts readmission7 and is associated with increased postoperative mortality across a range of surgical populations7,8. However, even minor events following surgery, such as nausea and pain, are known to significantly affect patient satisfaction and wellbeing9,10,11,12,13.

Studies have already demonstrated that using digital health interventions (DHI) can help identify postoperative complications earlier, improve recovery, and provide safe follow-up which is acceptable to patients10,14,15,16,17,18. DHI, defined as ā€˜the use of mobile and wireless technologies for health to improve health system efficiency and health outcomesā€™19, provide the opportunity to connect patients and healthcare providers in real-time. For example, embedded sensors in mobile phones and wearable technology can capture data remotely, passively and continuously, providing opportunities to track physiological parameters and enable patients to self-report symptoms and signs, which can indicate their postoperative status. In surgery, DHI may include wearable activity trackers20, mobile phone applications21, real-time collection of patient-reported outcomes22 and/or multiple electronic devices forming a digital health kit23.

A growing body of literature evaluating DHI in surgery exists, including studies reporting its value in the assessment of postoperative recovery24,25,26 and its cost-effectiveness27. Meanwhile, the COVID-19 pandemic has accelerated the adoption of remote monitoring applications and use of digital health in all aspects of surgical workflow22. Medical professionals have increasingly utilised digital health interventions to monitor and review patients remotely28, encouraging resource expansion and potentially representing a paradigm shift in patient management29.

Previous systematic reviews reporting on digital health and surgery have focused on web-based interventions, where the use of mobile devices or real-time measurement of patient data was excluded27,30,31. In addition, the use of narrow inclusion criteria limit comparisons across the research field and hinder the identification of critical evidence gaps19. Despite the emergence of numerous DHI initiatives in surgery, there has been little discussion of the importance of rigorous reporting in this literature30,31.

We aimed to determine the current use, evidence base and reporting quality for mobile DHI in the postoperative period following surgery.

Results

Study characteristics

Our review resulted in 324 full-text articles assessed for eligibility after initially screening 6969, with 44 articles (Fig. 1) ultimately included in this review9,23,25,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72. Tables 1 and 2 provide descriptions of each study, recruiting 3890 patients in total across ten randomised controlled trials9,32,33,34,35,36,37,38,39,40, 17 prospective studies25,42,43,44,45,46,47,48,49,50,51,52,53,54,71 and 17 pilot or feasibility studies23,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,72.

Fig. 1: PRISMA diagram.
figure 1

Articles were published between January 2000 and May 2021, based on a search of Embase, Cochrane library, Web of Science, WHO Global Index Medicus, clinical trial registries and Google scholar databases (for details, see ā€œMethodsā€).

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Table 1 Summary of included randomised control trials.
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Table 2 Summary of included prospective studies.
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More than half of the studies were conducted in the United States (nā€‰=ā€‰24; 1556 patients)23,32,33,35,36,39,40,41,42,44,45,46,47,48,49,50,52,56,59,60,61,65,66,67, with only one originating from a low- or middle-income setting34. Orthopaedic procedures were represented in a quarter of studies (nā€‰=ā€‰10; 611 patients)25,33,34,38,46,52,57,58,63,64, with interventions taking place predominantly within the first 30 postoperative days9,23,34,36,39,42,43,44,45,46,47,48,49,50,51,52,54,55,56,57,58,59,60,61,62,65,66,67,68,69,72. Real-time data collection and autonomous delivery to clinicians for immediate review occurred in 31 studies9,23,32,34,36,37,39,40,44,45,46,47,48,50,51,52,53,54,55,56,58,60,61,62,63,65,66,67,68,69,71.

Mobile phone-based interventions

Thirty one of the eligible studies used a mixture of mobile phone-based interventions9,32,33,34,36,37,38,39,40,41,44,45,46,47,48,49,50,51,52,54,55,56,58,60,61,62,63,68,69,70,71, with 20 using smartphone applications9,32,36,37,39,40,41,44,45,47,48,49,50,54,55,58,62,63,70,71. Remote assessment of wound images taken by the patient and evaluation of symptoms reported using validated tools were the most frequent aims of the mobile phone-based interventions39,45,47,49,50,55,58,60,62,63,68,69,70. In total, 19 individual mobile applications were described (Table 3). Only three of these were publicly available to download from either Android or Apple platforms32,41,48, while it was unclear what platform the others used. One application was available as a demonstration version, however, patient data entry was restricted62. Five studies used predetermined thresholds or algorithms to generate clinician alerts from patient responses36,39,48,49,54.

Table 3 Studies using mobile applications.
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Twenty-one studies required patients to own a mobile device9,34,36,37,38,39,40,41,44,45,46,48,49,50,51,54,58,60,61,62,66,71 excluding up to a third of patients approached as a result47,48. Where participants were provided with a mobile device, participant age was higher (56.1 vs. 53.1 years), with only two studies explicitly recruiting older patients (ā‰„60 years old)52,71.

Mobile phone-based interventions included multimodal patient feedback programmes32,34,37, postoperative recovery tracking39,57 and patient education9. These frequently reduced the requirement for postoperative in-person reviews and reduced inappropriate patient emergency department use39,45,54,70. Some interventions were demonstrated to encourage quicker postoperative recovery and reduce analgesic requirements33,37,41,46 while postoperative complications could be identified earlier through both mobile messaging and wound photographs60,63. However, complication rates were similar to control groups in all studies where reported (range 2.0ā€“7.1%)35,37. In those studies utilising predefined algorithms and thresholds, none had been previously validated within another patient cohort36,39,48,49,54.

Wearable devices

Accelerometer-based devices were the most commonly represented wearable device, measuring postoperative patient physical activity or intensity (nā€‰=ā€‰14) via FitBit25,43,49,52,61,72 smartwatch42,65,66 or other devices37,56,59,64,71. Eight studies required the synchronisation of wearable devices to a mobile phone, together with manual download by a clinician on study completion, to allow data analysis25,42,43,49,57,59,64,72. Studies using wearables for continuous patient monitoring were less common, with only three studies reporting the use of automated data feeds for real-time clinical analytics and feedback49,54,66.

Studies demonstrated that increases in step count postoperatively correlated with age52,61, body build61 and operative approach (open versus keyhole procedures)43,52. Accelerometer activity data also demonstrated postoperative complications could be identified at an earlier stage42, were associated with other physiological parameters56 and correlated with complication scores such as the Comprehensive Complication Index65. Activity recovery curves were also demonstrated for common abdominal and thoracic procedures42. Only one study utilised in-built smartphone accelerometers, which demonstrated postoperative complications reduced daily exertional activity compared to baseline up to 6 weeks after surgery41.

A single randomised trial37 used a wearable device as part of a multimodal intervention, however, only a proportion of patients received this device, as patients were required to own a compatible smartphone. The studyā€™s authors did not report results based on device data, with a return to normal activity measured through the validated Patient-Reported Outcomes Measurement Information SystemĀ® (PROMIS) score.

Measured outcomes

The majority of studies reported postoperative recovery as their main outcome (Table 4)9,25,33,34,37,38,41,42,43,52,54,56,59,61,64,65,66,67,72. Additional primary outcomes included the impact of DHI on pain management33,34,44,46, postoperative complications50,51,58,60,68, symptom monitoring36,40, surgical site infection35,47,55,62,69,70 and hospital resource use23,35,39,45,63. Two studies determined the ability of DHI to aid postoperative weight loss following bariatric surgery32,53, while four studies solely focused on determining the feasibility of a DHI in postoperative follow-up48,49,57,71.

Table 4 Outcomes measured across included studies.
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Differences in study methodology and outcome definitions limit conclusions on the effectiveness of DHI across each outcome. However, DHI demonstrated a strong ability to track postoperative analgesic requirements33,34,44,46 and patient recovery9,25,33,34,37,38,41,42,43,52,54,56,59,61,64,65,66,67,72 while consistently reducing hospital resource use in the postoperative period39,45,63,70. The capture of longer-term outcomes were also possible beyond 30 days, particularly for orthopaedic procedures25,34,38,63,64 and to monitor weight loss32,53. DHI were also able to identify complications at an early stage51,60 and correctly classify wound infection in the majority of patients47,55,62, demonstrating good agreement with physicians55,58.

Patient adherence

Twenty-five studies reported patient adherence with digital health interventions25,34,36,37,42,43,44,45,46,47,48,49,51,52,53,55,56,58,59,60,62,65,66,72 however this assessment varied widely (Tables 1 and 2). Patient adherence ranged between 42 to 96%, however, no included studies used a validated assessment method. Adherence was generally found to be highest within the first 2 weeks postoperatively55,58,72 with adherence falling for longer-term interventions34,55,62. Patients with complications were more likely to use DHI50, while limited use of mobile applications was associated with high rates of inappropriate emergency department presentation following major colorectal resection48. High patient satisfaction was reported in multiple studies23,33,39,45,47,53,54,57,69,71 however patients also found some DHI to be intrusive36,53,58,71 while none reported the carersā€™ use or experience of the intervention.

Reporting quality and bias

Overall, reporting quality was suboptimal, particularly within the items of data security, cost assessment and patient engagement during intervention development (Fig. 2a). Only one domain, the presentation of infrastructure availability to support technology within the study location (item 1), was consistently reported across all studies. Other domains, including data security, cost assessment and scalability; were frequently under-reported, demonstrating poor standardisation and limiting comparability across studies. The median score was 8 (range 2 to 15), while only nine (19%) studies scored above 1036,37,40,41,47,55,57,63,71 No obvious trends in reporting quality were detected over time, despite the publication of a mobile health evidence reporting and assessment (mERA) and World Health Organisation Monitoring and evaluating digital health interventions in 2016 (Fig. 2b). No association was found between study design, device and quality score.

Fig. 2: Reporting quality across included studies.
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Reporting quality for each mERA guideline domain (a) and temporal relationship (b). mERA guideline item number contained within parentheses.

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Critical appraisal revealed that all the eligible randomised studies had a high risk of bias in at least one defined outcome, primarily at the allocation and blinding stages (Fig. 3). Prospective studies also showed a high risk of bias, demonstrated during blinding and recruitment of consecutive patients (Supplementary Table 1). Furthermore, only two studies included a control group23,68 and only one performed a sample size calculation a priori56.

Fig. 3: Risk of bias assessment.
figure 3

Overall summary (a) and individual bias assessment (b) for included randomised controlled trials assessed using the Cochrane collaboration tool.

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Discussion

To our knowledge, this is the first systematic review to have investigated the use and effectiveness of mobile DHI in postsurgical care, including a rigorous assessment of current reporting quality. The increasing affordability and widespread use of mobile technologies presents new opportunities to remotely monitor patient-centred health metrics during the postoperative period. In this review, we evaluated the use of DHI to complement conventional postoperative care across 42 studies. The wide diversity in the types of patient population, intervention and outcome measures were reported, while methodological reporting was found to be suboptimal across multiple domains.

Overall, the results indicate that regular acquisition of objective wound data (from images), patient-reported outcome data (from validated self-report tools) or continuous activity data (from wearables) can improve the assessment of postoperative recovery26. Combining remote assessment with active clinical prompts or patient advice (whether via automated or manual checking) also has the potential to reduce complication rates. Randomised studies included in this review demonstrated that DHI may facilitate patient recovery following major operations9,37, reduce inappropriate service use39,40 and improve longer-term outcomes in bariatric surgery32,33. Despite these opportunities, our review revealed a number of issues with the existing evidence base which require to be addressed if this potential is to be fulfilled.

DHI can provide an opportunity for patient engagement, support and self-care73,74, providing a bridge between clinical services and patientsā€™ homes and helping to mitigate social isolation paving new ways to explore two-way interactions. Despite these opportunities, the research studies reviewed herein captured in this review made little reference to engaging patients in the development of the DHI and only one study was designed to engage patients in their care or in reviewing their own data37. Given the critical role of clinician-patient partnerships in the successful delivery of interventions and in supporting shared care, this seems like a missed opportunity and we would encourage future patient-centred research and interventions73. Many of the studies reported high levels of exclusion amongst patients who did not possess the relevant mobile technology, suggesting that more work on inclusive design is needed to avoid exacerbating the ā€˜digital health divideā€™75. The case for better patient engagement, or carers supporting an individualā€™s recovery, may also mitigate the well-known problem of patient attrition from digital health interventions76.

Published studies on the use of DHI in surgical populations came almost exclusively from high-income countries, particularly the USA. This is likely reflects both the research funding environment in different regions and the lack of financial accessibility of smartphones and wearables in resource-constrained countries. However, the often significant distance patients travel for surgical care in low- and middle-income countries, combined with difficulties in determining early outcomes in these settings77, offers huge potential for postoperative patient outcome reporting and is a legitimate candidate for global health research funding26.

Aggregated day level summaries of patient activity were commonly reported, with few exploring the potential of other accelerometer metrics to predict postoperative complications, such as sleep quality78,79 or activity intensity26,80. Wearable devices were found to generally associate well with operative characteristics and complication severity, however considerable variability within patient cohorts existed, highlighting the need to be developing more personalised models42,56,65,81 Large error values originating from manufacturersā€™ algorithms82,83, lack of standardised procedures for optimising accuracy82 and small patient cohorts may explain this variance. Data were also frequently only available to clinicians for ā€˜offlineā€™ analysis upon study completion, demonstrating the current limited ability of accelerometer technology to assist management of a larger population through preloaded signal analysis algorithms and timely clinical review84.

Companies often have a market strategy that relies on proprietary algorithms and closed data sets, making it difficult to evaluate these innovations. This problem is exacerbated when such algorithms are updated, complicating longitudinal comparisons of measures even within the same brand device. We recommend further research investment in Open Software and the sharing of appropriately anonymised datasets for meta-analysis, to encourage sustainable and trustworthy innovations of this type. This is particularly important as we move towards more automated methods involving artificial intelligence, where the ability to scrutinise algorithmic decision making will become increasingly crucial for patient safety and clinical accountability84.

Methodological reporting across the included studies was of variable quality. Current reporting inconsistency is problematic, limiting researchersā€™ and policy makersā€™ ability to understand programme details and determine the impact on health systems85. Moreover, continued suboptimal reporting will limit future comparison and study reproducibility. The lack of data security information is particularly concerning and in contrast to the high priority given to security and privacy in electronic health records in general55,86,87. Patients identify security as the single most important barrier to technology use postoperatively15 and future public confidence in DHI may be eroded if patient confidentiality is felt to be at-risk88,89.

Patient adherence reporting is a key component of the mERA guidelines to determine patient engagement, user interaction and DHI fidelity. However, there was wide variation in the definition and assessment of patient adherence within included studies, which restricted more detailed comparison. This suggests the development and validation of a standardised tool, detailing specific metrics on how patient adherence should be defined in DHI studies is needed.

Furthermore, cost assessment was also limited, with basic information on financial costs to design and develop DHI from the perspective of all end-users omitted. Digital health is often assumed to be cost-effective27, however a lack of evidence to substantiate this remains a barrier to implementation and policy investment90. Insufficient detail prevents meaningful comparison with existing care, while the cost of adoption in postoperative surgical settings cannot currently be justified without assessment relative to meaningful clinical outcomes91.

Despite widespread publication and being extensively accessed19,85,92 mERA guidelines were poorly represented within included studies. Designed to address the gaps in comprehensiveness and quality of reporting on the effectiveness of digital health programmes, by an expert committee convened by the World Health Organization (WHO), implementation of all items should be achievable across all income strata. We found no evidence of temporal change in reporting quality, with our findings demonstrating urgent action is required to achieve consistent and comprehensive reporting of digital health interventions. Therefore, we strongly recommend journal editors make mERA checklist completion a mandatory condition for acceptance, similar to other reporting guidelines93,94,95.

Some limitations should be highlighted. As our search was only limited to the English language, we may have excluded relevant publications if they were not published in English. In addition, the omission of studies originating from low and middle-income countries is possible, with underreporting of DHI known to occur in studies outside the United States or without an industry sponsor96. Due to the heterogeneity of included studies and the quality of methodological reporting, we were unable to answer how DHI can impact specific clinical outcomes. Therefore, reported findings should be cautiously interpreted towards the current assessment of how digital health can improve patient outcomes following surgery until additional, higher-quality studies are available.

DHI to monitor postoperative recovery has been used across a broad range of surgical specialities, particularly within the United States. Devices are generally acceptable to patients and have been shown to identify postoperative complications early. Current studies report findings on small cohorts, infrequently engage patients during the design or delivery of the intervention and utilise patient-generated data in a passive manner. The requirement to own a mobile device considerably limits patient inclusion, while urgent improvements in the reporting of data security and cost-effectiveness is needed.

In order to advocate for the widespread use of digital health in the monitoring of postoperative patient recovery, additional high-quality research is needed prior to integration into the healthcare environment. Particular attention to reporting quality is advised, to ensure these studies can be replicated and provide the opportunity for equitable comparison.

Methods

Design

An electronic systematic search of Embase, Cochrane Library, Web of Science, WHO Global Index Medicus, clinical trial registries and Google scholar databases in accordance with the PRISMA guidelines was performed93. The PROSPERO international systematic review registry97 was searched to ensure a similar review had not previously been performed and the protocol was registered accordingly (CRD42019138736).

A thorough search was undertaken using the following Medical Subject Heading (MeSH) terms: ā€˜cellular phoneā€™; ā€˜microcomputersā€™; ā€˜smartphoneā€™, ā€˜iphoneā€™; 'androidā€™; ā€˜mobileā€™; ā€˜ipadā€™; ā€˜tabletā€™; ā€˜text messageā€™; ā€˜smsā€™; ā€˜e-healthā€™; ā€˜telemedicineā€™; ā€˜digital healthā€™; ā€˜wearableā€™; ā€˜mobile healthā€™; ā€˜mHealthā€™; and ā€˜surgeryā€™; ā€˜postoperativeā€™. The search was structured to ensure variations such as capitalisation, plurals and alternative phrases were captured (Supplementary Information 1). Search limits applied were English language, full-text, humans and articles published from 2000 (last search 18 May 2021). Case reports and editorials were excluded, with conference abstracts and reviews screened to assist in identifying related full-text articles prior to exclusion.

The title and abstract of all identified articles were screened independently by two authors (S.R.K. and N.N.), with those meeting the inclusion criteria screened further by full-text review. Any disagreements were resolved by discussion to reach a consensus. Reference lists of relevant articles were reviewed, together with a search of grey literature and the National Clinical Trials Register (clinicaltrials.gov) to identify any further studies for inclusion.

Studies involving patients undergoing any surgery requiring skin incision where postoperative outcomes were measured using a DHI following hospital discharge were included. DHI were defined according to the mobile health evidence reporting and assessment (mERA) guidelines; 'the use of mobile and wireless technologies for health to improve health system efficiency and health outcomes'19, with web-based interventions excluded if stationary devices, such as a desktop computer, were only used27. The more generic term ā€˜digital healthā€™ was selected to ensure all potential approaches, including mhealth, were systematically captured within this review98. Interventions containing only teleconsultation or patient education components were excluded due to the number of previously published reviews in this area27,30,31.

Data extraction

Data were extracted using a standardised proforma (Supplementary Information 2), with partial duplication to ensure consistency. Included studies were evaluated for study design, participant number, participant characteristics, DHI and origin, study duration and main findings. The method used to assess patient adherence was also extracted and reported based on the original study authorsā€™ criteria. A wearable device was defined as a small computing device containing a sensor worn somewhere on the body99.

Quality assessment

Reporting quality was analysed using the validated mERA 16-item core checklist, which systematically assesses transparency and completeness in digital health studies19. All included publications and associated study protocols were reviewed independently for potential risk of bias by two authors (S.R.K. and N.N.), using the Cochrane Collaboration tools for randomised studies100 and the methodological index for non-randomised studies (MINORS)101, with the global ideal score varying between non-comparative (16) and comparative studies (24).

We aimed to determine the current use, evidence base and reporting quality for mobile DHI in the postoperative period following surgery.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.