Effectiveness of a nurse-led digital care program for patients with external fixation: A randomized controlled trial

Abstract

Background

We aimed to determine whether a nurse-led digital care program improves postoperative outcomes compared to usual care in patients receiving external fixation. Specifically, we assessed its impact on complication rates, pain, function, psychological well-being, patient satisfaction, and adherence.

Methods

We conducted a single-center, parallel-group randomized controlled trial involving 100 adult patients (aged 18–75) undergoing external fixation for trauma or reconstructive procedures. Participants were randomized 1:1 to receive either a nurse-led digital care program or standard postoperative care. The digital intervention included daily symptom logging, structured educational modules, video consultations, and in-app nurse communication. The primary outcome was the occurrence of at least one postoperative complication by 24 weeks. Complication burden, defined as the number of postoperative complications per participant, was assessed as a supportive summary. Secondary outcomes included pain (VAS), function (LEFS), psychological status (HADS), and patient satisfaction. Data were collected at baseline, 4, 12, and 24 weeks. Analyses were performed using linear mixed-effects models.

Results

At 24 weeks, complication rates were similar between the digital and control groups (difference: -0.02; 95% CI, -0.205 to 0.165; p=0.829). Improvements in pain, LEFS scores and HADS were also similar between groups at 24 weeks. Patient satisfaction, particularly regarding ease of use and communication with nurses, was significantly higher in the digital group. Per-protocol analysis confirmed these findings.

Conclusions

The nurse-led digital care program did not reduce postoperative complications by 24 weeks. It was associated with modest improvements in pain and function at 4 and 12 weeks, with no sustained differences at 24 weeks. Patient satisfaction favored the digital care program. As a single centre trial among smartphone users, these findings should be interpreted as preliminary evidence of acceptability in this specific clinical context. Multicenter evaluation across diverse settings and patient populations is needed before broader implementation can be considered.

Keywords

level I,randomized controlled trial digital care nurse-led external fixation randomized controlled trial

Introduction

External fixation is a common orthopedic technique for stabilizing complex fractures, limb lengthening, and other reconstructive procedures. However, patients with external fixators face substantial postoperative risks. Pin tract infections are the most frequent complication of external fixation, reported in a significant proportion of patients.¹ In some series nearly all patients developed at least minor pin site infection, though definitions vary.² These infections impose a serious burden, frequently necessitating additional clinic visits, antibiotic therapy, and sometimes surgical intervention.² If early pin site infections are not properly managed, a small percentage can progress to deep tissue infection or osteomyelitis, occurring in roughly 3–5% of cases, which may compromise bone healing and fixator stability.² Besides infection, other complications such as delayed union or nonunion of fractures, malalignment, and hardware loosening or failure are well-documented in external fixator patients. These challenges underscore the critical need for vigilant postoperative care.

Nursing interventions play an indispensable role in the recovery of patients with external fixators. Orthopedic nurses are responsible for daily pin site care to prevent infection, monitoring the fixator apparatus and limb for signs of complications, managing wound care and pain, and educating patients on hygiene and weight-bearing precautions. Timely identification of pin site inflammation by nursing staff and prompt treatment can arrest the progression to severe infection. Indeed, studies have noted that inadequate patient instruction and follow-up in pin care is associated with higher infection rates, one report found 80% of external fixator patients received no wound care education post-discharge, highlighting a gap in standard care.² Proactive nursing follow-up and education are therefore essential to improve outcomes, as they empower patients to recognize early warning signs and practice proper pin site maintenance, thereby reducing preventable complications.² The complex needs of external fixation patients demand comprehensive postoperative management in which nursing care is pivotal.

Traditional postoperative follow-up for external fixation relies on scheduled clinic visits and telephone calls. This model can be insufficient to address complications that arise between visits, especially for patients in remote or underserved areas who may face barriers to frequent in-person. Digital health programs offer a promising strategy to augment postoperative monitoring and support. By leveraging telemedicine platforms, mobile applications, and remote monitoring tools, healthcare providers can maintain continuous contact with patients after discharge. Such connectivity enables earlier detection of issues like pin site infection and timely interventions before problems escalate. It also allows for ongoing reinforcement of patient education and adherence to rehabilitation plans via digital modules and reminders. Advances in telehealth have made it feasible to deliver high-quality orthopedic postoperative care across distance, with studies in tele-orthopedics showing that remote consultations can be safe, clinically effective, and associated with high patient satisfaction.³ A systematic review of 21 studies found that virtual orthopedic follow-ups yielded outcomes comparable to conventional in-person visits, while improving convenience and reducing travel burdens for patients.³ Notably, no significant differences in complication rates were observed, and both patients and clinicians reported positive experiences with telemedicine in orthopedic.³ Such evidence suggests that integrating digital follow-up does not compromise care and may even enhance certain aspects of quality and access.

In nursing practice, nurse-led digital interventions have demonstrated improvements in postoperative management. For example, a recent quasi-experimental study on postoperative bariatric surgery patients showed that a nurse-led telecare follow-up program significantly reduced emergency department visits and postoperative complications compared to standard care.⁴ Patients in the telecare group had only one-sixth the rate of unplanned hospital visits as those receiving usual care, reflecting more effective early management of issues via remote nursing suppor.⁴ The authors noted that the intervention improved the safety and quality of care, underscoring the value of personalized follow-up in the immediate post-surgery period.⁴ Similarly, in orthopedic populations, digital education and monitoring programs led by nurses have been well received and shown potential benefits. McDonall et al. reported that 99% of patients recovering from knee arthroplasty engaged with a nurse-led multimedia app for postoperative education, indicating high acceptability of digital tools in acute care settings.⁴ Such platforms can provide standardized, evidence-based guidance consistently to patients and encourage active participation in recovery.⁴ Furthermore, qualitative research suggests that patients favor perioperative digital interventions that offer interactive features, tailored information, and direct communication with healthcare providers, which can motivate adherence and improve confidence after surgery. By integrating these digital solutions into orthopedic nursing practice, it is anticipated that common post-external fixation issues can be addressed more promptly and effectively. In essence, a nurse-led digital care program harnesses technology to extend the reach of the care team beyond hospital walls, facilitating continuous monitoring, early complication management, and ongoing patient education. This approach builds upon the crucial role of nurses in postoperative care and has the potential to improve clinical outcomes and patient satisfaction in the aftermath of external fixation.

This study will determine if a nurse-led digital care program will significantly reduce postoperative complications and improve pain management, functional recovery, and patient satisfaction compared to usual care among patients undergoing external fixation.⁵

Methods

Study design and setting

This study was a single-center, parallel-group randomized controlled trial conducted at a tertiary teaching hospital in China. The trial design and reporting followed the Consolidated Standards of Reporting Trials (CONSORT) 2010 guidelines.⁶ Ethical approval was obtained from the hospital’s Institutional Review Board (IRB No. 2022-061-(1)), and the trial was prospectively registered in the Chinese Clinical Trial Registry (ChiCTR2500100132). The study compared a nurse-led digital care program (intervention) to usual postoperative care (control) for patients requiring external fixation after orthopedic surgery. The follow-up period for primary endpoint assessment was 24 weeks post-surgery.

Participants

Eligible participants were adults aged 18–75 years who had undergone an external fixation procedure for fracture management or limb reconstruction and had access to a smartphone to use the digital care app. Participants needed to be able to read and communicate in Mandarin/English because the digital care program and study questionnaires were delivered in Mandarin/English. Patients needed to be able to provide informed consent and participate in follow-up assessments. Major exclusion criteria included pregnancy or breastfeeding, severe cognitive impairment or psychiatric illness precluding informed consent, and uncontrolled serious comorbid conditions, such as uncontrolled diabetes or heart disease, that could significantly interfere with recovery (Table 1). Consecutive patients meeting these criteria at the study site were invited to participate and provided written informed consent. Participants were enrolled post-surgery during their hospitalization or initial clinic visit and baseline data were collected prior to randomization. Baseline assessments included patient demographics, clinical characteristics, and patient-reported outcomes such as baseline pain and function.

Table 1.

Inclusion and exclusion criteria.

Inclusion criteria
Adults aged 18-75 years.
Diagnosed with a condition requiring external fixation (e.g., open fractures, bone transplantation, limb lengthening).
Being required to wear external fixation for a period over 24 weeks
Ability to provide informed consent.
Access to a smartphone or other digital device capable of receiving the digital care program. Participants needed to be able to read and communicate in Mandarin/English because the digital care program and study questionnaires were delivered in Mandarin/English.
Exclusion criteria
Pregnancy or breastfeeding.
Severe cognitive impairment or psychiatric illness preventing informed consent.
Uncontrolled comorbidities (e.g., diabetes, heart disease) that could interfere with recovery.
Non-English speakers (due to language limitations in digital program).

Interventions

Digital care program (intervention group)

The digital care program was delivered by orthopedic nurses from the study hospital who were registered nurses with routine clinical responsibility for postoperative external fixation care. Prior to trial enrolment, all nurses assigned to deliver the digital program completed a standardized training package developed for this trial. Training covered external fixation related complication recognition, pin site assessment, patient education content, remote communication skills, and use of the application workflow for symptom monitoring, education delivery, and teleconsultations. Training materials included a written intervention handbook specifying required nurse tasks, timing of follow up contacts, triage escalation thresholds, and documentation standards. Competency to deliver the intervention was confirmed through supervised practice and review by the study team before independent delivery. During the trial, intervention nurses received ongoing supervision from the senior orthopedic nursing lead and the orthopedic surgeon responsible for the external fixation service to maintain standardization in delivery and escalation decisions.

Patients randomized to the intervention received a nurse led digital health care program delivered via a smartphone application (Figure 1). This program was designed to supplement standard follow up and included several components. (1) Symptom Monitoring: Patients logged symptoms and recovery indicators through the app, including pain levels and pin site status. The app provided feedback and alerts based on input. A study nurse reviewed entries and could message patients with additional instructions as needed. (2) Educational Modules: The app delivered structured educational content covering pin site care, wound care, rehabilitation exercises, pain management, and mobility guidance. Content was updated and tailored to procedure and recovery stage. (3) Video Consultations: Scheduled telemedicine visits were conducted via secure video call between the patient and a trained orthopedic nurse who had completed the standardized training described above. These were planned at regular intervals over 24 weeks or as needed based on patient status. During video consultations, the nurse assessed external fixator sites visually, evaluated range of motion and functional status, answered questions, and reinforced self-care. (4) Communication and Alerts: Patients could contact the team through in app messaging. Automated reminders supported pin care, medications, and exercise adherence. Concerning trends triggered alerts for prompt follow up. Patients in the intervention group also received usual follow up, but the digital platform provided additional support between visits.

Figure 1.

The interface of the nurse-led digital post-external fixation care program delivered used in this study.

Usual care (control group)

Patients randomized to the control group received standard postoperative care as per hospital protocol for external fixation. All patients attended scheduled research assessment visits at 4 weeks, 12 weeks, and 24 weeks post-surgery for standardized outcome measurement. Clinical monitoring visits, including radiographic assessment, pin site evaluation, and physical examination, were scheduled by the treating surgeon according to the patient’s clinical indication and recovery trajectory. Patients undergoing distraction osteogenesis or bone transport typically required more frequent clinical visits during the active distraction phase, often at intervals of one to two weeks, for adjustment of the distraction rate and radiographic monitoring of regenerate formation. Patients with acute traumatic fractures managed by external fixation generally followed a less intensive clinical schedule after the early postoperative period. These clinical monitoring visits occurred in addition to the fixed research assessment visits and were not constrained by the trial protocol. Telephone consultations were available for all control group patients who had concerns between visits. Standard wound care instructions, rehabilitation advice, and pain management were provided at discharge, and patients managed their recovery with periodic check-ins at the hospital. The control group did not have access to the digital app or its associated monitoring and telehealth services. All other aspects of clinical management, including antibiotic prophylaxis, analgesia, and physical therapy referrals, were left to the treating surgeons’ discretion and were consistent between groups.

Both groups received appropriate postoperative care, including clinical monitoring visits at intervals determined by the treating surgeon on the basis of clinical indication and treatment phase. This approach reflects the pragmatic trial design, which intentionally permits clinical care to vary according to clinical need while standardizing research assessment timepoints for between group comparison. The primary difference between groups was the addition of the digital care program, which provided continuous remote symptom monitoring, structured education, and nurse communication between clinical visits. Importantly, patients in both groups had equal access to the hospital’s services for any urgent issues. The care team instructed all participants on pin-site hygiene and follow-up schedules at discharge. The intervention was implemented for the full 24-week follow-up period, after which the digital app access for participants was concluded. Adherence to the digital intervention (module completion and attendance of video calls) was tracked automatically by the app for process evaluation. The multifaceted design of the digital care program aligns with emerging evidence that telehealth and remote monitoring can safely augment postoperative follow-up in orthopedic patients.^7–9

Outcomes

Primary outcome

The primary outcome was the occurrence of a clinically significant postoperative complication related to external fixation within 24 weeks of surgery. The primary endpoint was binary and indicated whether a participant experienced at least one complication by week 24. Complications included any pin-site infection, defined as local signs of infection requiring antibiotic therapy or other intervention, as well as other major adverse events such as delayed union or nonunion of the fracture, or any unplanned reoperation related to the external fixator. Each participant’s complication status was assessed during follow-up visits and via review of medical records. We also quantified complication burden as the number of clinically significant complications per participant during follow up. All primary outcome assessments were conducted by orthopedic surgeons or study nurses who were blinded to group allocation (see Blinding below).

Secondary outcomes

Several secondary endpoints were measured to evaluate pain, function, psychological well-being, satisfaction, and intervention engagement:

1) Pain: Pain intensity was measured using the Visual Analog Scale (VAS), a 10-cm horizontal scale from 0 (“no pain”) to 10 (“worst pain imaginable”).¹⁰ Participants rated their average pain related to the injured limb at baseline (post-surgery, prior to intervention) and at each follow-up (4, 12, and 24 weeks). Pain VAS is a validated measure of acute and chronic pain intensity. A decrease in VAS score over time indicates pain improvement.

2) Functional Status: Lower extremity functional outcome was assessed with the Lower Extremity Functional Scale (LEFS).¹¹ The LEFS is a 20-item questionnaire evaluating difficulty in performing daily activities (each item scored 0–4); the scores are summed to a total score ranging from 0 to 80, with higher scores indicating better functional ability. LEFS was administered at baseline and at follow-ups for patients with lower limb external fixators.

3) Psychological Outcomes: Anxiety and depression symptoms were measured using the Hospital Anxiety and Depression Scale (HADS).¹² HADS is a 14-item questionnaire with two subscales: Anxiety (HADS-A) and Depression (HADS-D), each containing 7 items. Each subscale is scored 0–21 (higher scores indicate greater anxiety/depressive symptoms).¹² HADS was administered at baseline and at the 12-week and 24-week follow-ups to monitor psychological well-being during recovery. Changes in HADS scores were tracked, with a focus on whether the digital care intervention influenced anxiety or depression levels.

4) Patient Satisfaction: Patient satisfaction with postoperative care was evaluated at week 24 using a short questionnaire developed for this trial. The questionnaire included five items covering convenience of follow up, ease of access to information and resources, helpfulness of the information and support provided, communication with the nurse and healthcare team, and overall satisfaction. Each item used a 5 point Likert scale, where 1 indicated strongly satisfied and 5 indicated strongly dissatisfied. Both intervention and control participants completed this survey. This instrument was created for pragmatic assessment of patient experience aligned with the intervention targets. Formal psychometric validation and reliability testing were not performed prior to the trial. Satisfaction findings should therefore be interpreted as exploratory.

5) Adherence and Engagement: For the intervention group, we tracked objective usage metrics of the digital program as secondary outcomes. These included the percentage of prescribed educational modules completed (out of 30 total modules), the number of scheduled video consultations attended (out of a maximum of 4), the frequency of symptom log submissions, and responses to reminders. These metrics were automatically recorded by the app.

To support transparent interpretation and to define the per protocol population, we specified criteria for adequate engagement based on the intended program dose. Adequate engagement was defined as meeting at least two of three criteria: completion of at least 21 of 30 educational modules, attendance at at least three video consultations, and symptom log submissions on at least three days per week on average during the first 12 weeks. Insufficient engagement was defined as not meeting at least two criteria. Adherence outcomes were summarized descriptively. To support intervention fidelity, nurse delivered contacts were documented within the platform, including timing and type of contact, and were reviewed during supervision meetings to promote consistent delivery aligned with the intervention handbook. Frequency of patient nurse communications was also tracked as an engagement metric. These adherence and engagement outcomes helped interpret the intervention’s effectiveness and were summarized descriptively. We did not collect direct measures of nursing workload, staff time, healthcare utilization, or program costs.

All outcomes were collected by research staff at baseline and at follow-up visits at approximately 4 weeks, 12 weeks, and 24 weeks post-surgery. The 24-week visit constituted the final evaluation for primary and secondary outcomes. Wherever possible, outcome assessors were blinded to the patient’s group (see below). Standardized questionnaires (VAS, LEFS, HADS, satisfaction survey) were either self-completed by patients or administered by a blinded researcher. Clinical outcomes (complications, fracture healing) were determined by a physician unaware of treatment allocation.

Sample size calculation

The sample size was calculated for the primary outcome assessed at 24 weeks, with pin site infection expected to be the most frequent component of postoperative complications in external fixation care. A large systematic review reported a cumulative pin track infection rate of 27.4% across external fixation studies, supporting an assumed control group infection rate of 30% for planning.⁹ Infection rates vary widely across studies because definitions, follow up duration, and the unit of analysis differ, but this value is consistent with published estimates.^2,9 Comparative studies of intensified pin site management approaches have reported absolute reductions in patient level infection frequency on the order of about 16% to 32%.^2,9 On this basis, we selected a 20% absolute reduction as clinically meaningful and plausible for an intervention designed to improve adherence, monitoring, and early response to early symptoms. We therefore planned to detect a reduction from 30% to 10%. Using a two-sided chi square test with alpha 0.05 and target power 80%, the required sample size was approximately 100 participants, 50 per group. We note that continuous patient reported outcomes such as VAS pain and LEFS are generally more statistically efficient than binary endpoints at equivalent sample sizes, because continuous measures retain information that dichotomization discards.¹³ This sample was therefore expected to provide adequate power for detecting moderate between group differences in these secondary outcomes. Rather than relying on retrospective power calculations, which are recognized as uninformative once data have been collected,^14,15 we interpret the precision of the primary outcome through the observed confidence interval for the risk difference.

Randomization, allocation, and blinding

Participants were randomly assigned in a 1:1 ratio to either the digital care intervention or the usual care control. The randomization sequence was generated using a computer random number algorithm prior to study start. To ensure balance with respect to the injury type and clinical context, randomization was stratified by fracture type/indication for external fixation. Specifically, patients were stratified into blocks based on whether the external fixator was applied for an acute traumatic fracture (such as an open fracture), for a staged reconstruction (such as post-osteomyelitis or deformity correction), or for elective limb lengthening. This stratification was chosen because these categories may influence baseline complication risk and recovery trajectory. Within each stratum, randomization was carried out in random block sizes, and assignments were sealed in sequentially numbered opaque envelopes. An independent research coordinator (not involved in patient recruitment or assessment) prepared the envelopes containing group assignments according to the computer-generated sequence. After a patient was consented and completed all baseline assessments, the treating team opened the next envelope to reveal the allocation. This ensured allocation concealment up until the point of assignment.

Due to the nature of the intervention, blinding of patients and nurses providing care was not feasible. However, outcome assessors were blinded to group allocation. Research assistants conducting follow up assessments were not informed of study hypotheses or which group was expected to have better outcomes, to minimize expectancy effects. Importantly, all outcome measures were collected by research staff independent of the intervention team. Intervention nurses did not participate in outcome assessment and did not have access to outcome collection forms. Clinical outcomes such as radiographic evidence of fracture healing and pin site infection were evaluated by blinded clinicians. Specifically, the orthopedic surgeon determining fracture union at 24 weeks and the nurse assessing pin site infection did not know if the patient was in the intervention or control group. Patients were instructed not to disclose group assignment during assessments. Data collection forms were coded without indicating group. Additionally, the data analyst was provided with coded datasets with group labels masked as “A” and “B,” which was then reviewed by the study steering committee before revealing actual group identities. This assessor blinded design was employed to reduce bias in outcome measurement.

Statistical analysis

All analyses were conducted on an intention-to-treat basis, including all randomized participants in their assigned groups regardless of adherence or protocol deviations. Data from participants who withdrew or were lost to follow-up were included up to the point of dropout, and efforts were made to impute or carry forward their last observations for longitudinal outcomes when appropriate. We used IBM SPSS Statistics version 26 (IBM Corp., Armonk, NY, USA) for data management and initial analyses, and R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria) for advanced statistical modeling.

For baseline characteristics, descriptive statistics were used to summarize baseline demographic and clinical variables by group. Continuous variables were reported as mean ± standard deviation (SD), and categorical variables as frequencies and percentages. Group comparisons at baseline were made using independent-samples t-tests for continuous measures and Chi-square tests for categorical measures to assess the success of randomization.

The primary outcome, defined as experiencing at least one complication by 24 weeks, was compared between the two groups using a Chi square test, or Fisher’s exact test if the expected cell counts were small. We calculated the absolute risk difference and relative risk with 95% confidence intervals for having at least one complication in the digital care group versus the control group.

We also analyzed complication burden, defined as the number of complications per participant by 24 weeks, as a count outcome. In this supportive analysis, we used Poisson regression to compare the mean number of complications between groups, adjusting for fracture type strata.

These two summaries address different aspects of the same postoperative safety profile, risk versus burden. If these approaches were to suggest meaningfully different conclusions, we would prioritize the binary analysis for the main inference and use the count analysis to contextualize burden, after confirming event adjudication and counting rules.

Continuous secondary outcomes that were measured repeatedly over time (pain VAS, LEFS score, HADS anxiety and depression scores) were analyzed using linear mixed-effects models to account for the longitudinal nature of the data. In these models, we included fixed effects for group (intervention vs control), time (as categorical variable for 4, 12, 24 weeks), and the group×time interaction. Each model also included a random intercept for each patient to account for within-subject correlations over time. Baseline values of the outcome were included as a covariate (essentially analyzing changes from baseline, akin to an ANCOVA approach for repeated measures). This modeling approach yields estimates of the treatment effect at each follow-up time point as well as an overall time-averaged effect. For example, we estimated the mean difference in pain VAS between groups at 4 weeks, 12 weeks, and 24 weeks, with 95% CIs and p-values for each, as well as tested whether there was a significant group×time interaction indicating different recovery trajectories. Similarly, changes in LEFS and HADS subscale scores were assessed over time between groups. We checked model assumptions and if any outcome showed non-normal residuals, sensitivity analyses using non-parametric tests or data transformation were performed. In addition, we carried out cross-sectional comparisons at the 24-week endpoint for key continuous outcomes using independent t-tests (or Mann–Whitney U tests if not normally distributed) to complement the mixed model results.

The satisfaction Likert scores were compared between groups using nonparametric tests. For each satisfaction item, we used the Mann Whitney U test to determine whether rating distributions differed between the intervention and control group. We summarized the responses as median and interquartile range in each group. Because the questionnaire was study specific and items represent distinct domains, we did not compute a composite score or internal consistency statistics. The instrument has not undergone formal psychometric validation or reliability testing. These satisfaction analyses are descriptive and should be interpreted cautiously.

Adherence outcomes were only relevant to the intervention group; thus, we did not perform between group statistical tests for these measures. We report the mean (SD) and median (IQR) of module completion rate, the distribution of video consultations attended, and the proportion of intervention participants meeting the prespecified adequate engagement criteria.

All statistical tests were two-tailed, significance level was set at p < 0.05 for primary outcome. The Bonferroni correction was applied for multiple comparisons for secondary outcomes, According to the Bonferroni correction method, the significance level of 0.05 is divided by 8, resulting in a corrected threshold of P < 0.00625. For the mixed-effects model analyses, we used the R packages lme4 and lmerTest to obtain coefficient estimates and p-values. Model outputs are reported as the estimated mean difference (or effect) between the digital care and control groups at each time point, with 95% CI and associated p-value.

For patients who missed a follow-up visit, we attempted to collect outcome data via phone or mail. If outcome data were still missing, we used multiple imputation within the mixed models to handle missing longitudinal data under the assumption of data missing at random.

All analyses were performed according to the pre specified statistical analysis plan. The results are presented for the intention to treat population. A per protocol analysis was also conducted sensitivity analysis. The per protocol population was defined as participants with available week 24 primary outcome data and no major protocol deviations. Major protocol deviations were prespecified as withdrawal of consent, loss to follow up before week 24 primary outcome assessment, or discontinuation of the allocated care pathway before initial exposure. In the digital care group, per protocol also required meaningful exposure to the digital program, defined in the trial protocol, because the intervention effect cannot be interpreted without minimal receipt.

Data analysis and interpretation were conducted without unblinding the outcome assessors. The statistician prepared a report of the outcomes with group labels masked as “A” and “B,” which was then reviewed by the study steering committee before revealing the actual group identities. This process helped ensure objective interpretation of the findings. All data were stored securely, and analysis scripts were archived for reproducibility.

Results

Participants

A total of 100 patients were randomized equally into digital care (n=50) and usual care groups (n=50). The proportion of missing outcome data is 8% at 4 weeks follow-up, 6% at 12 weeks follow-up and 7% at 24 weeks follow-up. The flow diagram of the study was shown in Figure 2. The per protocol population included 45 participants in the digital care group and 44 participants in the usual care group. Most baseline characteristics were similar between groups. Differences occurred in occupation, manual workers 44% in the digital care group versus 70% in the usual care group, and in current alcohol use, 18% versus 36% respectively. These variables were included as covariates in the adjusted regression models for primary and secondary outcomes, together with fracture type strata and baseline values of the relevant outcomes (Table 2).

Figure 2.

The flow diagram of the study, the proportion of missing outcome data is 8% at 4 weeks follow-up, 6% at 12 weeks follow-up and 7% at 24 weeks follow-up.

Table 2.

Baseline characteristics of the digital care program group and usual care group.

	Characteristic*	Digital care group (N=50)	Usual care group (N=50)	P value
Characteristics	Male Patients (no. [%])	22 (44.00)	27 (54.00)	0.317
	Age (yrs)	35 (11.82)	35 (9.50)	0.823
	BMI (kg/m ² )	22 (2.09)	22 (1.83)	0.741
	Occupation (no. [%])			0.009
	Manual worker	22 (44.00)	35 (70.00)
	Non-manual worker	28 (56.00)	15 (30.00)
	Education level (no. [%])			0.130
	Lower than high school	31 (62.00)	38 (76.00)
	Equal/higher to high school	19 (38.00)	12 (24.00)
	Insurance type (no. [%])			0.131
	Government	31 (62.00)	40 (80.00)
	Commercial	10 (20.00)	6 (12.00)
	Self-financed	9 (18.00)	4 (8.00)
	Current Smoker (no. [%])	13 (26.00)	18 (36.00)	0.280
	Current Alcohol use (no. [%])	9 (18.00)	18 (36.00)	0.043
	Diabetes type 2 (no. [%])	8 (16.00)	7 (14.00)	0.779
	Hypertension (no. [%])	11 (22.00)	9 (18.00)	0.617
	Cardiovascular diseases (no. [%])	2 (4.00)	3 (6.00)	0.646
Medial History	History of previous etiology (no. [%])			0.405
	Open fracture	4 (8.00)	4 (8.00)
	Bone Lengthening (Distraction Osteogenesis)	7 (14.00)	12 (24.00)
	Bone Deformities	6 (12.00)	8 (16.00)
	Infected Fractures (Osteomyelitis)	7 (14.00)	5 (10.00)
	Arthrodesis (Joint Fusion)	8 (16.00)	3 (6.00)
	Burn or Severe Trauma	6 (12.00)	5 (10.00)
	Post-Surgical Bone Stabilization	6 (12.00)	10 (20.00)
	Nonunion or Malunion Fractures	3 (6.00)	3 (6.00)
	Fracture Stabilization in Pediatric Patients	3 (6.00)	0 (0.00)
	History of previous etiology (no. [%])			0.502
	Ilizarov Ring fixator	5 (10.00)	11 (22.00)
	Monolateral fixator	18 (36.00)	14 (28.00)
	Combined fixator	7 (14.00)	9 (18.00)
	Hybrid fixator (Ring + monolateral)	4 (8.00)	2 (4.00)
	Talyor fixator	7 (14.00)	8 (16.00)
	External fixator+internal fixator	9 (18.00)	6 (12.00)
PROMs	VAS score	6 (1.01)	6 (0.92)	0.679
	LEFS score if available (points)	31 (5.22)	31 (4.43)	1.000
	HADS (Hospital Anxiety and Depression Scale)	10 (1.71)	10 (1.36)	0.949
	HADS (Hospital Anxiety and Depression Scale)	9 (1.33)	9 (0.99)	0.932
Score of custom patient satisfaction questionnaire ^#	Convenience (1-5)	5 (0.24)	5 (0.57)	0.000
	Ease of use/access (1-5)	5 (0.30)	5 (0.39)	0.253
	Helpfulness (1-5)	5 (0.27)	5 (0.42)	0.051
	Communication with the nurse (1-5)	5 (0.24)	5 (0.46)	0.002

BMI: Body Mass Index; VAS: Visual Analogue Scale; LEFS: Lower Extremity Functional Scale; HADS: Hospital Anxiety and Depression Scale.

*Values were reported as mean (standard deviation) for age, BMI, Mechanical axis deviation (MAD), all variables of PROMs and Score of custom patient satisfaction questionnaire, others were reported as number (percentage).

#Baseline scores of the custom patient satisfaction questionnaire were evaluated after patients first tried the program before discharge. The patient satisfaction questionnaire was study specific. Each question used a 5-point Likert scale, 1 equals strongly satisfied and 5 equals strongly dissatisfied. Formal psychometric validation and reliability testing were not performed prior to the trial.

Primary and secondary outcomes

At 24 weeks, the number of patients who developed complications showed no significantly different between groups. The number of complications was also not significantly different between groups (risk difference: −0.02, 95% CI, −0.205 to 0.165; p=0.829). Pain scores improved significantly more at 4 weeks (difference: -0.42, 95% CI, -0.822 to -0.014; p=0.043) and 12 weeks (difference: -0.57, 95% CI, -1.037 to -0.111; p=0.016) in the digital care group but not at 24 weeks (difference: -0.50, 95% CI, -1.005 to 0.013; p=0.056). LEFS favored digital care at 4 weeks (difference: 2.17, 95% CI, 0.157 to 4.186; p=0.035), 12 weeks (difference: 2.65, 95% CI, 0.434 to 4.873; p=0.020) and 24 weeks (difference: 1.95, 95% CI, -1.502 to 5.408; p=0.268), but not differ significantly after correction. HADS did not differ significantly at any follow-up point. Patient-reported ease of use favored digital care significantly at all follow-up points (all p<0.001), as did convenience (difference at 24 weeks: 0.46, 95% CI, 0.181 to 0.739; p=0.001) and nurse communication at early follow-ups (p<0.05 at 4 and 12 weeks) (Tables 3 and 4).

Table 3.

Unadjusted changes in outcomes at weeks 4, 12 and 24 after the surgery (intention-to-treat population).

Outcome	4 weeks post-surgery			12 weeks post-surgery			24 weeks post-surgery
Outcome	Digital care group (N=50)	Usual care group (N=50)	P value	Digital care group (N=50)	Usual care group (N=50)	P value	Digital care group (N=50)	Usual care group (N=50)	P value
Number of patients who had complication	12 (24.00)	13 (26.00)	0.817	17 (34.00)	18 (36.00)	0.834	17 (34.00)	18 (40.00)	0.534
Number of Complications	0.24 (0.57)	0.26 (0.57)	0.855	0.40 (0.71)	0.40 (0.65)	1.000	0.43 (0.74)	0.48 (0.75)	0.735
VAS score	-2.43 (0.62)	-1.43 (0.58)	0.000	-3.34 (0.70)	-2.34 (0.76)	0.000	-3.38 (0.74)	-3.13 (1.26)	0.242
LEFS score (points)	17.72 (2.31)	13.93 (2.08)	0.000	24.70 (2.60)	20.83 (2.19)	0.000	27.81 (2.86)	27.93 (2.92)	0.834
HADS Anxiety subscale (HAD-A)	-2.61 (0.54)	-1.89 (0.48)	0.000	-5.19 (2.41)	-5.17 (1.66)	0.960	-4.66 (2.44)	-4.89 (1.57)	0.588
HADS Depression subscale (HAD-D)	-1.98 (0.45)	-2.00 (0.30)	0.784	-3.98 (0.44)	-4.02 (0.33)	0.598	-4.68 (0.84)	-4.59 (0.62)	0.540
Number of educational modules completed (times, Maximum to 30)	-10.22 (7.11)	-	-	-12.77 (7.02)	-	-	-14.13 (7.50)	-	-
Number of video consultations attended (Maximum to 4 times)	-1.83 (0.95)	-	-	-1.87 (1.10)	-	-	-1.77 (1.13)	-	-
Convenience (1-5)	-0.85 (0.92)	-1.13 (1.29)	0.230	-0.87 (0.88)	-1.02 (1.13)	0.477	-0.89 (0.84)	-1.13 (1.20)	0.273
Ease of use (1-5)	-1.37 (1.10)	-1.98 (1.50)	0.029	-0.79 (1.06)	-1.49 (1.23)	0.004	-0.72 (1.03)	-1.20 (1.02)	0.028
Helpfulness (1-5)	-0.80 (0.86)	-1.28 (1.13)	0.025	-0.77 (0.87)	-1.21 (1.16)	0.037	-0.81 (0.88)	-1.22 (1.15)	0.057
Communication with the nurse (1-5)	-0.35 (0.60)	-0.89 (0.99)	0.002	-0.47 (0.58)	-0.62 (0.95)	0.361	-0.43 (0.54)	-0.61 (0.98)	0.265

VAS: Visual Analogue Scale; LEFS: Lower Extremity Functional Scale; HADS: Hospital Anxiety and Depression Scale.

Values represent the mean change from baseline for each group, reported as mean (standard deviation), with P values comparing between-group differences at each follow-up point. All outcome measures were unadjusted.

Table 4.

Adjusted effectiveness estimates from linear mixed effects models (intention-to-treat population).

Outcome	4 weeks post-surgery			12 weeks post-surgery			24 weeks post-surgery
Outcome	Coefficient	95% CI	P value	Coefficient	95% CI	P value	Coefficient	95% CI	P value
Number of Complications	-0.010	(-0.614, 1.023)	0.854	-0.009	(-0.165, 0.148)	0.914	-0.020	(-0.205, 0.165)	0.829
VAS score	-0.418	(-0.822, -0.014)	0.043	-0.574	(-1.037, -0.111)	0.016	-0.496	(-1.005, 0.013)	0.056
LEFS score (points)	2.172	(0.157, 4.186)	0.035	2.654	(0.434, 4.873)	0.020	1.953	(-1.502, 5.408)	0.268
HADS Anxiety subscale (HAD-A)	-0.373	(-0.988, 0.242)	0.232	-0.290	(-0.832, 0.251)	0.286	-0.163	(-0.914, 0.589)	0.671
HADS Depression subscale (HAD-D)	0.048	(-0.434, 0.531)	0.844	0.078	(-0.530, 0.685)	0.797	0.035	(-0.648, 0.718)	0.915
Number of educational modules completed (times, maximum to 30)	0.482	(0.270, 0.695)	0.000	0.486	(0.259, 0.712)	0.000	0.497	(0.223, 0.770)	0.000
Number of video consultations attended (Maximum to 4 times)	0.382	(0.110, 0.654)	0.006	0.523	(0.280, 0.766)	0.000	0.520	(0.184, 0.856)	0.002
Convenience (1-5)	0.482	(0.270, 0.695)	0.000	0.486	(0.259, 0.712)	0.000	0.497	(0.223, 0.770)	0.000
Ease of use (1-5)	0.382	(0.110, 0.654)	0.006	0.523	(0.280, 0.766)	0.000	0.520	(0.184, 0.856)	0.002
Helpfulness (1-5)	0.396	(0.200, 0.592)	0.000	0.464	(0.279, 0.649)	0.000	0.460	(0.181, 0.739)	0.001
Communication with the nurse (1-5)	0.489	(0.323, 0.656)	0.000	0.455	(0.317, 0.593)	0.000	0.440	(0.242, 0.638)	0.000

VAS: Visual Analogue Scale; LEFS: Lower Extremity Functional Scale; HADS: Hospital Anxiety and Depression Scale.

Each coefficient represents the estimated between-group difference in the change from baseline (Intervention group minus Control group) for the specified outcome at that follow-up time point. Positive coefficients indicate higher scores in the Intervention group compared to the Control group, whereas negative values indicate lower scores in the Guide Plate group. Each estimate is presented with its 95% confidence interval (CI) and corresponding P value. All outcome measures were adjusted for baseline values in the model.

In the analysis of multiple secondary outcomes, the Bonferroni correction method was employed in this study to control for the accumulation of Type I errors. The underlying principle is that when multiple secondary endpoints are tested simultaneously, if the conventional significance level (α = 0.05) is still applied, the probability of at least one test showing a statistically significant difference due to random error increases with the number of tests performed, which may lead to an overestimation of the intervention effect. The Bonferroni correction mitigates this issue by equally distributing the overall significance level across each individual test, thereby maintaining the overall false positive risk at 0.05 and ensuring the stringency of the statistical inference. In practice, this study involved eight secondary outcomes: VAS score, LEFS score, HADS Anxiety subscale (HAD-A), HADS Depression subscale (HAD-D), Convenience, Ease of use, Helpfulness, and Communication with the nurse. Following the Bonferroni correction method, the significance level of 0.05 was divided by 8, yielding a corrected threshold of P < 0.00625.

Per-protocol analyses confirmed significant improvements at 24 weeks in VAS difference: -0.50, 95% CI, -1.036 to 0.035; p=0.066) and LEFS difference: 2.12, 95% CI, -1.463 to 5.703; p=0.246), consistent with intention-to-treat results; complication rates remained similar between groups (difference: -0.012, 95% CI, -0.214 to 0.190; p=0.906) (Supplemental Tables 1 and 2).

Discussion

This randomized controlled trial found no significant difference in the primary outcome of postoperative complications by 24 weeks between the nurse led digital care group and usual care. Complication burden was also similar. Several explanations merit consideration.

Both groups received standardized pin site care and scheduled clinical monitoring, which likely provided a strong baseline for complication prevention. Pin site infections are driven primarily by biological risk factors, surgical technique, and pin design rather than monitoring intensity.¹⁶ Camathias and colleagues showed in a randomized blinded trial that even the contrast between daily pin tract care and no care at all produced no significant difference in outcomes.¹⁷ A digital monitoring layer added on top of standardized care would therefore face diminishing returns for this endpoint. Additionally, digital monitoring may facilitate earlier detection of complications without reducing binary incidence. The TWIST trial of 492 emergency surgery patients found no difference in 30-day surgical site infection rates between smartphone monitoring and usual care, yet the monitoring group had 3.7-fold higher odds of diagnosis within the first seven postoperative days.¹⁸ In our trial, symptom tracking and wound photograph review between visits may have shortened the interval from onset to clinical response without altering the total event count at 24 weeks.

A floor effect likely also contributed. Our sample size assumed a 30% baseline complication rate from a systematic review spanning heterogeneous settings.⁹ The observed control group rate was lower, consistent with published data from academic centres with standardized protocols where rates of 3.9 to 11.2% have been reported.^19,20 Event rate overestimation is common in clinical trials, occurring in 61.1% of major cardiovascular studies reviewed by Olivier and colleagues.²¹ The null finding is also consistent with meta-analytic evidence across surgical digital health. Grygorian and colleagues pooled 19 studies with 10,536 patients and found a complication risk ratio of 1.05 with a 95% confidence interval of 0.77 to 1.43.²² Dawes and colleagues confirmed equivalent complication rates across 45 mobile health surgical studies.²³ These findings suggest complication equivalence is the typical pattern for digital surgical monitoring rather than evidence of intervention failure.²⁴

Pain and function improved significantly more in the digital care group at 4 and 12 weeks but not at 24 weeks. Because the intervention group received more contact than usual care, nonspecific attention effects may have contributed. However, each intervention component incorporated defined behaviour change techniques classified by the BCTTv1 taxonomy, including self monitoring, feedback, goal setting, and prompts.²⁵ Meta analytic evidence shows a dose response relationship between the number of behaviour change techniques and effect size, which is inconsistent with a purely nonspecific explanation.^25,26 The primary complication outcome was assessed by blinded clinicians, and meta epidemiological evidence confirms that blinded assessment eliminates observer bias regardless of participant blinding.^27,28 To assess clinical relevance, we compared early between group differences against published minimal clinically important difference thresholds. For VAS pain, a threshold of approximately 1 point on a 0 to 10 scale has been reported, and for LEFS a threshold of 9 points.¹¹ The observed differences were statistically significant but fell below these thresholds, suggesting that the average patient level clinical impact was modest.

Behavioural mechanisms may explain the pattern of early but not sustained benefit. Symptom logging, reminders, education, and two way communication correspond to established techniques supporting adherence and self efficacy.^29,30 A 2024 systematic review of internet-based telehealth for osteoarthritis found small improvements in pain, function, and self-efficacy,³¹ though durability is variable, and engagement does not always translate into sustained clinical gains.³² We measured engagement objectively but did not include a validated self-efficacy measure. Future trials should prespecify mechanistic outcomes and mediation analyses.

Patient satisfaction favoured the digital care group, while anxiety and depression did not differ significantly. Taken together, the overall impact should be interpreted as short term improvements in selected patient reported outcomes and experience, without sustained benefit in pain, function, or complications at 24 weeks.

An important consideration for real world implementation is digital equity. Our trial required smartphone access and digital literacy, which excluded patients who may have the greatest need for supported postoperative care. Several evidence-based strategies can address this barrier. First, hybrid stepped care models that combine digital monitoring with telephone based or in person alternatives for digitally excluded patients have demonstrated equivalent outcomes in orthopedic settings. Buvik and colleagues showed in a randomized trial that nurse assisted remote orthopedic consultations achieved equivalent quality of care and patient satisfaction compared with standard in person visits.³³ Second, caregiver proxy access should be designed into digital platforms, as caregivers frequently serve as intermediaries for patients with limited digital skills.³⁴ Third, brief digital health literacy training delivered before discharge can improve engagement among older adults. A meta-analysis of digital health literacy interventions in older adults found significant improvements in eHealth literacy, particularly when training was face to face and lasted four or more weeks.³⁵ Fourth, the Ophelia co design process, which uses health literacy profiling to identify distinct patient subgroups and develop tailored solutions including non-digital alternatives for each, offers a systematic framework for equitable implementation.³⁶ Future implementation of the digital care program should incorporate these strategies and should screen patients for digital readiness at enrolment using validated instruments such as the eHealth Literacy Scale.³⁷

Although this trial did not collect economic data, the published literature on digital surgical follow up provides relevant context. Steinbeck and colleagues reported the PROMoting Quality trial, a cost effectiveness analysis alongside a randomised controlled trial of 6,807 joint replacement patients across nine German hospitals, and found that remote digital monitoring with nurse alerts produced cost savings of approximately 375 euros per patient with improved outcomes, yielding a dominant cost effectiveness ratio.³⁸ Buvik and colleagues demonstrated in an orthopedic telehealth trial that remote consultations became cost effective from both societal and health sector perspectives above a threshold of approximately 150 consultations per year.³³ Armstrong and colleagues found that mobile application based follow up after ambulatory surgery reduced in person visits by 60% without compromising outcomes.³⁹ These findings suggest that digital postoperative programs can generate savings through reduced clinic attendances, decreased patient travel, and lower rates of unplanned healthcare contact. Whether similar economic benefits would apply to nurse led digital care for external fixation patients remains to be tested in a trial designed for that purpose.

This trial has several strengths, including CONSORT compliant design with blinded outcome assessors, stratified randomisation, and a multifaceted intervention reflecting real world clinical workflows. Adherence and satisfaction metrics were comprehensively captured.

Several limitations apply. The single centre design may limit generalisability. Clinical monitoring schedules varied by indication, which is inherent in external fixation care. Stratified randomisation mitigates systematic bias, but treatment effect heterogeneity across subgroups may exist. The observed complication rate was lower than the 30% assumed in the power calculation, reducing statistical power. The 95% confidence interval for the risk difference excludes the hypothesised 20-point reduction but does not exclude smaller effects a larger trial might detect. Continuous secondary outcomes had greater statistical efficiency than binary endpoints.¹³ The inability to blind participants may have introduced reporting bias in self-reported outcomes, and the additional contact in the digital care group may contribute to attention effects, particularly for subjective endpoints. Our pragmatic design intentionally compared the complete program against real world usual care, consistent with the PRECIS 2 framework and MRC guidance.^40,41 The satisfaction instrument lacked formal psychometric validation. Additionally, because the digital platform and questionnaires were delivered in Mandarin/English and required access to a smartphone, participants who could not read Mandarin/English or who lacked digital literacy or resources were excluded. This introduces selection bias toward younger, more educated, and more technologically engaged patients who may respond differently to digital interventions than the broader external fixation population. The requirement for smartphone access and digital literacy means the trial sample is not representative of all patients who undergo external fixation, particularly older adults, patients in rural or underserved regions, and those with lower socioeconomic status. These constraints limit the external validity of our findings and mean that effectiveness in a general, unselected external fixation population cannot be inferred from this trial. Future research using factorial designs could isolate specific component contributions and distinguish these from nonspecific contact effects. We did not collect cost, resource use, or nursing workload data, so scalability cannot be assessed from this trial. Long term outcomes beyond 24 weeks were not measured. Future trials should prospectively collect micro costing data on nursing time per patient, platform maintenance expenditure, and healthcare utilisation across both arms, and should conduct a formal cost effectiveness analysis alongside clinical outcome assessment.

Conclusion

This randomized controlled trial found no evidence that a nurse-led digital care program reduced postoperative complications by 24 weeks compared with usual care. The program was associated with modest improvements in pain and function at 4 and 12 weeks, with no sustained differences at 24 weeks. Patient satisfaction favored the digital care program. These findings are preliminary and specific to a single tertiary centre with a smartphone using population. They do not demonstrate durable improvement in recovery outcomes and cannot be generalized to other healthcare settings or patient populations without further evaluation. Costs, resource use, and nursing workload were not measured, so scalability cannot be inferred. Multicenter trials enrolling diverse patient populations with varying levels of digital access and literacy, and incorporating longer follow up and economic measures, are needed to determine whether the program improves clinically important outcomes in a sustainable and equitable way.

Supplemental material

Supplemental material - Effectiveness of a nurse-led digital care program for patients with external fixation: A randomized controlled trial

Supplemental material for Effectiveness of a nurse-led digital care program for patients with external fixation: A randomized controlled trial by Wenyan Xu, Huiling Yue, Lihua Huang, Wenqi Song, Qinglin Kang, Yun Shen, Shengdi Lu in DIGITAL HEALTH

Supplemental material

Supplemental material - Effectiveness of a nurse-led digital care program for patients with external fixation: A randomized controlled trial

Footnotes

Acknowledgments

The authors acknowledge the Shanghai Medmotion Clinic for providing the necessary equipment for the study.

ORCID iDs

Yun Shen

Shengdi Lu

Ethical considerations

The study received ethical approval from the Ethics Committee of Shanghai Sixth People’s Hospital (IRB approval no.: 2022-061-(1)) and was conducted in strict accordance with the principles outlined in the Declaration of Helsinki.

Consent to participate

All participants provided written informed consent prior to enrolment in the study.

Author contributions

Conceptualization: W. Xu and S. Lu; Methodology: W. Song and S. Lu; Formal Analysis: H. Yue; Investigation: Q. Kang, W. Xu and L. Huang; Writing – Original Draft Preparation: W. Xu; Writing – Review & Editing: S. Lu.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Academic Foundation of Shanghai Sixth People’s Hospital (Nos. YJHLKT2024-28).

Declaration of conflicting interests

The authors declare that there were no conflicts of interest with respect to the authorship or the publication of this article.

Trial registration number

ChiCTR2500100132 (Chinese Clinical Trial Registry, ).

Guarantor

Shengdi Lu is the guarantor who accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish.

Supplemental material

Supplemental material for this article is available online.

References

Kofman

Buckley

McGrouther

. Complications of transcutaneous metal devices. Eur J Plast Surg 2012; 35(9): 673–682. https://doi.org/10.1007/s00238-011-0642-6

Kazmers

Fragomen

Rozbruch

. Prevention of pin site infection in external fixation: a review of the literature. Strategies Trauma Limb Reconstr 2016; 11(2): 75–85. https://doi.org/10.1007/s11751-016-0256-4

Haider

Aweid

Subramanian

, et al. Telemedicine in orthopaedics during COVID-19 and beyond: a systematic review. J Telemed Telecare 2022; 28(6): 391–403. https://doi.org/10.1177/1357633X20938241

Maqueda Martinez

Ferrer

Garcia Redondo

, et al. Effectiveness of a nurse led telecare programme in the postoperative follow up of bariatric surgery patients: a quasi experimental study. Healthcare 2024; 12(23): 2448. https://doi.org/10.3390/healthcare12232448

McDonall

Redley

Livingston

, et al. A nurse led multimedia intervention to increase patient participation in recovery after knee arthroplasty: hybrid implementation study. JMIR Human Factors 2022; 9(2): e36959. https://doi.org/10.2196/36959

Schulz

Altman

Moher

. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ 2010; 340: c332. https://doi.org/10.1136/bmj.c332

Kane

Thakar

Jamgochian

, et al. The role of telehealth as a platform for postoperative visits following rotator cuff repair: a prospective, randomized controlled trial. J Shoulder Elbow Surg 2020; 29(4): 775–783. https://doi.org/10.1016/j.jse.2019.12.004

Muschol

Heinrich

Heiss

, et al. Assessing telemedicine efficiency in follow up care with video consultations for patients in orthopedic and trauma surgery in Germany: randomized controlled trial. J Med Internet Res 2022; 24(7): e36996. https://doi.org/10.2196/36996

Iobst

Liu

. A systematic review of incidence of pin track infections associated with external fixation. J Limb Lengthening Reconstr 2016; 2(1): 6–16. https://doi.org/10.4103/2455-3719.182570

10.

Huskisson

. Measurement of pain. Lancet 1974; 2(7889): 1127–1131. https://doi.org/10.1016/s0140-6736(74)90884-8

11.

Binkley

Stratford

Lott

, et al. The Lower Extremity Functional Scale: scale development, measurement properties, and clinical application. Phys Ther 1999; 79(4): 371–383.

12.

Zigmond

Snaith

. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983; 67(6): 361–370. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x

13.

Altman

Royston

. The cost of dichotomising continuous variables. BMJ 2006; 332(7549): 1080. https://doi.org/10.1136/bmj.332.7549.1080

14.

Hoenig

Heisey

. The abuse of power: the pervasive fallacy of power calculations for data analysis. Am Stat 2001; 55(1): 19–24. https://doi.org/10.1198/000313001300339897

15.

Brydges

. Post hoc power is not informative. Genet Epidemiol 2022; 46(7): 515–518.

16.

Jennison

McNally

Pandit

. Prevention of pin site infection in external fixation: a review of the literature. Strategies Trauma Limb Reconstr 2016; 11(2): 75–85.

17.

Camathias

Valderrabano

Oberli

. Routine pin tract care in external fixation is unnecessary: a randomised, prospective, blinded controlled study. Injury 2012; 43(11): 1969–1973. https://doi.org/10.1016/j.injury.2012.08.010

18.

McLean

Mountain

Shaw

, et al. COVIDSurg Collaborative, GlobalSurg Collaborative. Remote diagnosis of surgical site infection using a mobile digital intervention: a randomised controlled trial in emergency surgery patients. npj Digit Med 2021; 4: 160. https://doi.org/10.1038/s41746-021-00526-0

19.

Parameswaran

Roberts

Seligson

, et al.

Pin tract infection with contemporary external fixation: how much of a problem?

J Orthop Trauma 2003; 17(7): 503–507. https://doi.org/10.1097/00005131-200308000-00005

20.

Ferreira

Marais

. Pin tract sepsis: incidence with the use of circular fixators in a limb reconstruction unit. SA Orthop J 2012; 11(1): 10–18.

21.

Olivier

Brenet

Laffy Beaufils

, et al. Accuracy of event rate and effect size estimation in major cardiovascular trials: a systematic review. JAMA Netw Open 2024; 7(4): e248818. https://doi.org/10.1001/jamanetworkopen.2024.8818

22.

Grygorian

Montano

Shojaa

, et al. Digital health interventions and patient safety in abdominal surgery: a systematic review and meta analysis. JAMA Netw Open 2024; 7(4): e248555. https://doi.org/10.1001/jamanetworkopen.2024.8555

23.

Dawes

Lin

Varghese

, et al. Mobile health technology for remote home monitoring after surgery: a meta analysis. Br J Surg 2021; 108(11): 1304–1314. https://doi.org/10.1093/bjs/znab323

24.

Michie

Richardson

Johnston

, et al. The behavior change technique taxonomy v1 of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013; 46: 81–95. https://doi.org/10.1007/s12160-013-9486-6

25.

Michie

Abraham

Whittington

, et al. Effective techniques in healthy eating and physical activity interventions: a meta regression. Health Psychol 2009; 28(6): 690–701. https://doi.org/10.1037/a0016136

26.

Webb

Joseph

Yardley

, et al. Using the internet to promote health behavior change: a systematic review and meta analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res 2010; 12(1): e4. https://doi.org/10.2196/jmir.1376

27.

Wood

Egger

Gluud

, et al. Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta epidemiological study. BMJ 2008; 336(7644): 601–605. https://doi.org/10.1136/bmj.39465.451748.AD

28.

Hrobjartsson

Thomsen

Emanuelsson

, et al. Observer bias in randomised clinical trials with binary outcomes: systematic review of trials with both blinded and non blinded outcome assessors. BMJ 2012; 344: e1119. https://doi.org/10.1136/bmj.e1119

29.

Bandura

. Self efficacy: toward a unifying theory of behavioral change. Psychological Review 1977; 84: 191–215. https://doi.org/10.1037//0033-295x.84.2.191

30.

Wang

Luo

Liu

, et al. Effectiveness of internet based telehealth programs in patients with hip or knee osteoarthritis: systematic review and meta analysis. J Med Internet Res 2024; 26: e55576. https://doi.org/10.2196/55576

31.

Lee

ASY

Yung

PSH

Ong

MTY

, et al. Effectiveness of a theory driven mobile health intervention in promoting post surgery rehabilitation adherence in patients who had anterior cruciate ligament reconstruction: a randomized clinical trial. Soc Sci Med 2023; 335: 116219.

32.

Eustache

El Kefraoui

Ekmekjian

, et al. Do postoperative telemedicine interventions with a communication feature reduce emergency department visits and readmissions? A systematic review and meta analysis. Surg Endosc 2021; 35(11): 5889–5904. https://doi.org/10.1007/s00464-021-08607-7

33.

Buvik

Bergmo

Bugge

, et al. Cost effectiveness of telemedicine in remote orthopedic consultations: randomized controlled trial. J Med Internet Res 2019; 21(2): e11330. https://doi.org/10.2196/11330

34.

Cho

Kim

Lee

, et al. Digital health equity and tailored health care service for people with disability: user centered design and usability study. J Med Internet Res 2023; 25: e50029.

35.

Dong

Wang

Dai

, et al. Effectiveness of digital health literacy interventions in older adults: single arm meta analysis. J Med Internet Res 2023; 25: e48166. https://doi.org/10.2196/48166

36.

Cheng

Elsworth

Osborne

. Co designing eHealth and equity solutions: application of the Ophelia process. Front Public Health 2020; 8: 604401. https://doi.org/10.3389/fpubh.2020.604401

37.

Welch

Norheim

Jull

, et al. CONSORT Equity 2017 extension and elaboration for better reporting of health equity in randomised trials. BMJ 2017; 359: j5085. https://doi.org/10.1136/bmj.j5085

38.

Steinbeck

Ehlig

Gress

, et al. Cost effectiveness of a patient reported outcome based remote monitoring and alert intervention for early detection of critical recovery after joint replacement: a randomised controlled trial. PLoS Med 2024; 21(10): e1004459.

39.

Armstrong

Coyte

Brown

, et al. Effect of home monitoring via mobile app on the number of in person visits following ambulatory surgery: a randomized clinical trial. JAMA Surg 2017; 152(7): 622–627. https://doi.org/10.1001/jamasurg.2017.0111

40.

Loudon

Treweek

Sullivan

, et al. The PRECIS 2 tool: designing trials that are fit for purpose. BMJ 2015; 350: h2147. https://doi.org/10.1136/bmj.h2147

41.

Craig

Dieppe

Macintyre

, et al. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ (Clinical research ed). 2008; 337: a1655. https://doi.org/10.1136/bmj.a1655

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