Sage Journals: Discover world-class research

Abstract

Background

Postoperative wound care is critical for recovery after discharge, but the COVID-19 pandemic limited access to follow-up. Mobile health applications may bridge this gap. This study evaluated the usability and acceptability of an app for postoperative wound monitoring.

Methods

Patients undergoing hernia repair or abdominal surgery between 2020 and 2022 were enrolled. Through the app, patients uploaded wound images and received professional feedback. The study period was divided into non-COVID-19 and COVID-19 phases based on local outbreak severity. Usability was assessed by the System Usability Scale (SUS; score >68 considered usable). Acceptability was evaluated using the Net Promoter Score (NPS; range −100 to +100). Multivariable linear regression identified factors associated with SUS and NPS.

Results

Sixty patients were included (mean age: 65.0 years; 53.3% male). Of these, 20 underwent hernia repair and 40 abdominal surgery; 31 were enrolled in the non-COVID-19 period and 29 in the COVID-19 period. Mean SUS and NPS were 79.6 ± 4.2 and 80.9 ± 3.8, respectively. Scores were higher during the COVID-19 period (SUS: 82.7 vs. 76.8; NPS: 83.4 vs. 78.5; both P < 0.001). Regression analysis showed that COVID-19-period enrollment and rural residence were independently associated with higher SUS and NPS (P < 0.001).

Conclusions

The app was highly usable and acceptable for postoperative wound care. Greater reliance on remote care during the pandemic and among rural patients was linked to higher scores. These findings support integrating digital health into surgical practice to improve access.

Keywords

Application COVID-19 wound cares digital health postoperative cares

Introduction

Wound care is an essential component in the treatment of surgical patients, which is performed by the patients themselves, family, or caregivers after discharge. With the introduction of the enhanced recovery after surgery (ERAS) protocols,¹ multiple interdisciplinary bundles are implemented to provide fast recovery and early discharge. Although the burden of wound care diminishes with the extended implementation of minimally invasive surgery,² poor care and concerns of surgical site infection (SSI) are still observed, especially in patients with stoma or complex wounds.^3,4 Notably, SSI is one of the most common surgical complications, and delayed diagnosis deteriorates the outcomes with increased medical cost and poor quality of life.⁵ Consequently, research is needed to fill the care gap between medical institutions and home care in order to alleviate the anxiety from the reduced contact with healthcare providers.⁶

Care gaps are discontinuities between healthcare systems and the recommended best practices in healthcare,⁷ and they are caused by several factors. Coronavirus disease 2019 (COVID-19),⁸ a new variant of severe acute respiratory syndrome coronavirus 2, had rapidly spread worldwide since it was discovered in December 2019, which decreased healthcare utilization and medical supply even in developed countries. During the COVID-19 pandemic, many patients were unwilling to visit medical institutions due to fear of infection, which resulted in reduced follow-up compliance and delayed identification of wound complications.⁹

Digital health offers innovative functions by integrating patient-reported outcomes, electronic health records, connectivity, and computing power.¹⁰ The growing use of mobile health technologies has enabled applications to support a wide spectrum of patient needs, ranging from chronic disease management to postoperative care, thereby enhancing accessibility, patient engagement, and continuity of care.^10–12 Although the COVID-19 pandemic caused a devastating impact worldwide, it provided an opportunity to accelerate the adoption of digital health, which might bridge the gap for surgical patients and medical professionals. In particular, during the main study period, Taiwan remarkably controlled the sporadic outbreak of COVID-19 by implementing several public health responses.¹³ However, it still suffered from the pandemic in other study periods. Therefore, this provides a unique opportunity to determine whether the pandemic affected people's attitude on the use of digital health. The present study aimed to elucidate the usability and acceptability of an app focusing on postoperative wound care between the non-COVID-19 pandemic period (non-COVID-19 period) and COVID-19 pandemic period (COVID-19 period).⁹

Methods

This observational cohort study was conducted at an academic teaching hospital in Taiwan from January 2020 to December 2022. This study was approved by the Human Research Ethics Committee of National Taiwan University Hospital (201912086RINC), which followed good clinical practice standards. The COVID-19 period was defined as January 2020 to June 2021, during which Taiwan Centers for Disease Control and Prevention mandated mask wearing, visitor restriction, and telehealth promotion. The non-COVID period was defined as July 2021 to December 2022, following normalization of hospital policies.

App development and functions

In our previous work, we developed staged apps with postoperative care functions for patients undergoing gastrectomy.^11,14 In the present study, our team (surgeons and information technology experts) developed an iOS- and Android-compatible app mainly focusing on wound care (Figure 1). The mobile app used in this study was designed as a postoperative monitoring tool to support patient self-care and facilitate remote clinical oversight. Its interface included six primary functions: (1) Wound picture for uploading daily photos of the surgical site, (2) Body weight tracking to monitor fluid status, (3) Questionnaire for symptom logging, (4) Wound care instructions based on the patient's recovery stage, (5) Comments from clinical staff, and (6) Education information with frequently asked questions and recovery guidance. All patient data were transmitted to a cloud server, where it could be reviewed by the care team. If abnormal findings (e.g. signs of infection or sudden weight gain) were detected, the clinician in charge would proactively contact the patient or caregiver. The app emphasized structured, patient-initiated data sharing with clinician feedback based on review. Figure 2 illustrates that the system architecture consists of three integrated components: the patient-facing mobile application, a secure cloud server, and a hospital-based clinical dashboard. Data such as wound images, symptom reports, and body weight entries are encrypted and transmitted to the cloud platform. The backend system includes an artificial intelligence (AI)-based image analysis module for preliminary wound infection detection, which alerts the clinical specialist when signs of infection are suspected—based on algorithms developed in our prior work. The clinical team accesses a web-based dashboard to review submissions and determine follow-up actions. Although natural language processing has not yet been implemented, the architecture is designed to accommodate future modules capable of delivering interactive wound care education and predictive modeling by linking app inputs with the patient's longitudinal health records. Data collected by the app were encrypted in the patient's smartphone and study server.

Figure 1.

Home page: upper part of the page displays a summary of daily schedules, and lower part shows the six major functions of the app.

Figure 2.

System architecture: we developed a client/server architecture to integrate the app service, including hospital unit, cloud server, and the patient. The client side is the app itself for wound care, uploading wound picture, record of body weight, questionnaire for wound recovery, comments from the feedback of surgical staffs, and education information of wound cares.

Participant recruitment

Patients (age: ≥20 years) undergoing abdominal or hernia repair procedures under the care of two surgeons were eligible. The inclusion criteria were as follows: patients who have access to and are familiar with using a smartphone with/without assistance from their caregivers, are willing to use the app on their smartphone with Internet connectivity and camera capabilities, and are willing to provide scheduled input of questionnaires. The surgeons contacted potential participants, provided further information, and obtained informed consent. Participants with severe vision impairments or phobia of using smartphones were excluded.

Demographic measures and data collection

Clinicodemographic variables including age, gender, body mass index, care responsibilities, surgery type, and education level were collected. The number of outpatient clinic visits, emergency department visit, readmission within 30 days after discharge, and surgery during the COVID-19 period were also recorded.

For data collection, users began entering data in the app daily for 1 week and then every 3 days for 2 weeks. Data input included wound photographs and questionnaire regarding wound healing scales. Daily schedules were listed on the upper portion of the app's homepage to remind users.

The wound images were then transmitted to the server, which was implemented with AI-supported algorithm for evaluation.¹⁵ Furthermore, a trained telecare staff and surgeon (J-MW) made the final diagnosis of the report, which was sent to the patients’ app synchronously (with alarm signal on the feedback icon). With this comment, the patients or their caregivers would know the current condition of their surgical wounds.

Primary outcome measures

The main outcome measures were usability and acceptability of the app. Users were asked to share their views on the app's usability and acceptability by filling out questionnaires in the app. The system usability scale (SUS), which included 10 five-point Likert scale items regarding the usability of an electronic device or system, was used to measure the app's usability.^16,17 SUS is commonly used to assess perceived usability by human–computer interaction researchers,¹⁸ and its score ranges from 0 to 100, with higher scores indicating better usability. The app was considered usable if the mean SUS score was >68.¹⁹ Meanwhile, the customer satisfaction score (five-point Likert scale to reflect short-term parameter of acceptability) and net promoter score (NPS; a 10-point scale to present long-term acceptability) were used to assess acceptability.¹³ The NPS ranges from −100 to +100, with higher scores indicating better acceptability.

Statistical analysis

All variables were calculated as percentages, and continuous variables were summarized as mean ± standard deviation. Descriptive statistics were performed using Stata software (version 13.1; StataCorp, College Station, TX, USA). The study subjects were divided into two groups (postoperative care during non-COVID-19 period vs. COVID-19 period). Student's t-test was used to compare the means of two continuous variables, and the chi-square test and Fisher's exact test were used to examine the correlation among categorical variables.

To identify factors associated with usability and acceptability, we performed multivariable linear regression analyses with the SUS and NPS as dependent variables. Candidate independent variables included age, sex, COVID-19 period, residence location (urban vs. rural), and surgical type (hernia vs. abdominal). These were selected based on clinical relevance and prior literature, and were screened using bivariate analysis with a threshold of P < 0.10. Variables that met this criterion were included in the final regression model. Results were reported as regression coefficients with corresponding 95% confidence intervals (CIs) and P-values. Statistical significance was considered at P < 0.05.

The sample size was calculated to detect a minimum clinically important difference of 5 points in both the SUS and NPS between the COVID-19 and non-COVID-19 groups. Based on prior literature, we assumed a standard deviation of 6.5 points for both scores. Using a two-sided significance level of 0.05 and power of 80%, a minimum of 26 participants per group was required to detect the anticipated difference. Accounting for an estimated 10% dropout rate, we targeted a total sample size of 60 participants. This sample size was considered sufficient to identify meaningful differences in usability and acceptability outcomes and to support multivariable linear regression modeling.

Results

Sixty patients (mean age: 65.0 ± 12.2 years) were enrolled in this study (Table 1). Among them, 53.3% were male. In terms of surgery type, 20 (33.3%) patients underwent hernia repair, and 40 (66.7%) patients underwent abdominal operations. Regarding the place of residence, 43 (71.7%) subjects lived in Taipei/New Taipei areas, and 17 patients (28.3%) lived in rural (non-Taipei) areas. In terms of study period, there were 31 (51.7%) cases during the non-COVID-19 period and 29 (48.3%) cases during the COVID-19 period. The mean SUS and NPS scores were 79.6 ± 4.2 and 80.9 ± 3.8, respectively. Figure 3 presents the flowchart of the study.

Figure 3.

Flowchart of the study.

Table 1.

Demographic characteristics of study subjects and associated scores.

Characteristic	Data (n = 60)
Age, mean ± SD	65.0 ± 12.2
Male, n (%)	32 (53.3%)
Body mass index, mean ± SD	23.0 ± 4.5
American Society of Anesthesiologists score >2	22 (36.7%)
Charlson comorbidity index >2	24 (40.0%)
Minimal invasive surgery	36 (60.0%)
Education level
Illiterate	1 (1.7%)
Up to high school	14 (23.3%)
>High school	45 (75.0%)
Place of residence
Taipei/New Taipei area	43 (71.7%)
Rural area	17 (28.3%)
Surgical procedure
Hernia repair	20 (33.3%)
Abdominal operation	40 (66.7%)
Method of participation, n (%)
Caregiver	15 (25.0%)
Independent	45 (75.0%)
System of application
Android	30 (50.0%)
iOS	30 (50.0%)
Study period
Non-COVID-19 pandemic	31 (51.7%)
COVID-19 pandemic	29 (48.3%)
System usability scale, mean ± SD	79.6 ± 4.2
Net promotor score, mean ± SD	80.9 ± 3.8

SD: standard deviation; COVID-19: coronavirus disease 2019.

Furthermore, the study population was categorized into two groups: non-COVID-19 and COVID-19 groups (Table 2). No statistically significant differences were observed in terms of mean age (65.0 vs. 65.0 years; P = 0.990), male gender (19 vs. 13; P = 0.900), or body mass index (23.4 vs. 22.6; P = 0.510) between the two groups. Furthermore, the COVID-19 group had a higher proportion of patients residing in rural areas than the non-COVID-19 group (51.7% vs. 6.5%; P < 0.001). The COVID-19 group had significantly higher mean SUS (82.7 vs. 76.8; P < 0.001) and NPS scores (83.4 vs. 78.5; P < 0.001) than the non-COVID-19 group.

Table 2.

Comparison of the study subjects enrolled during non-COVID-19 pandemic period and during COVID-19 pandemic period in Taiwan.

Characteristic	Non-COVID-19 pandemic (n = 31)	COVID-19 pandemic (n = 29)	P value
Age^a, mean ± SD	65.0 ± 13.2	65.0 ± 11.4	0.990
Gender^b (male:female)	19:12	13:16	0.900
Body mass index^a, mean ± SD	23.4 ± 4.7	22.6 ± 4.3	0.510
American Society of Anesthesiologists score >2^b	13 (41.9%)	9 (31.0%)	0.380
Charlson comorbidity index >2^b	12 (38.7%)	6 (20.7%)	0.830
Minimal invasive surgery^b	18 (58.1%)	18 (62.1%)	0.750
Surgical procedure^b			0.950
Hernia repair	10 (32.3%)	10 (34.5%)
Abdominal operation	21 (67.7%)	19 (65.5%)
Place of residence^b			<0.001
Taipei/New Taipei area	29 (93.5%)	14 (48.3%)
Rural area	2 (6.5%)	15 (51.7%)
Method of participation, n (%)^b			0.870
Caregiver	8 (34.8%)	7 (24.1%)
Independent	23 (65.2%)	22 (75.9%)
System usability scale^a, mean ± SD	76.8 ± 2.5	82.7 ± 3.6	<0.001
Net promotor score^a, mean ± SD	78.5 ± 3.3	83.4 ± 2.1	<0.001

SD: standard deviation; COVID-19: coronavirus disease 2019.

^aStudent's t-test.

^bChi-square test and Fisher's exact test.

Next, two linear regression models with the adjustment of confounding factors were developed to predict the scores of SUS and NPS, respectively. Table 3 demonstrates the adjusted model to predict the scores of SUS. Both COVID-19 pandemic (coefficients: 3.63, 95% CI: 2.21–5.06; P < 0.001) and living in the rural areas (coefficients: 4.46, 95% CI: 2.89–6.02; P < 0.001) were significantly associated with higher scores of SUS.

Table 3.

Adjusted multivariate analysis to predict the system usability scale.

		95% confident interval
	Coefficients	Lower limit	Upper limit	P value
COVID-19 pandemic period (ref: non-COVID-19)	3.63	2.21	5.06	<0.001
Age	0.04	−0.01	0.10	0.111
Male gender (ref: female)	−1.55	−3.12	0.38	0.065
Body mass index	0.01	−0.13	0.14	0.916
Charlson comorbidity index >2 (ref: ≦2)	−0.85	−2.13	0.42	0.185
Rural area (ref: Taipei/New Taipei)	4.46	2.89	6.02	<0.001

COVID-19: coronavirus disease 2019.

Similarly, not only COVID-19 pandemic (coefficients: 4.09, 95% CI: 2.44–5.73; P < 0.001) but also living in the rural areas (coefficients: 2.13, 95% CI: 0.32–3.93; P = 0.022) were significantly associated with higher scores of NPS (Table 4).

Table 4.

Adjusted multivariate analysis to predict the net promotor score.

		95% confident interval
	Coefficients	Lower limit	Upper limit	P value
COVID-19 pandemic period (ref: non-COVID-19)	4.09	2.44	5.73	<0.001
Age	0.03	−0.25	0.10	0.227
Male gender (ref: female)	0.61	−0.95	2.19	0.436
Body mass index	−0.02	−0.19	0.13	0.714
Charlson comorbidity index >2 (ref: ≦2)	0.74	−0.73	2.21	0.318
Rural area (ref: Taipei/New Taipei)	2.13	0.32	3.93	0.022

COVID-19: coronavirus disease 2019.

Discussion

This study describes the usability and acceptability of app-based postoperative wound care. The results support the use of digital health for postoperative wound care among patients undergoing elective abdominal or hernia surgery. Furthermore, the postoperative period during the COVID-19 pandemic was significantly associated with higher scores based on the survey of usability and acceptability.

Traditional surgical care after discharge mainly depends on either telephone calls or in-person follow-up at the outpatient clinic, including the removal of stitches or drains. However, a part of surgical care during the in-person visit can be performed online, especially the evaluation of wound recovery. Although the COVID-19 pandemic has disrupted the delivery of routine care and resulted in a shortage of medical resources, it has accelerated the implementation of digital health into clinical practice.^6,20 These findings are in line with our results showing that the study subjects appreciated app-based virtual care based on its usability and acceptability. During the pandemic, Taiwan implemented several public health responses to control the dissemination of COVID-19 and maintained the regular supply of medical resources in the early period.¹³ However, sporadic outbreak of COVID-19 still occurred in some periods. This provided a unique opportunity to elucidate the impact of the pandemic on people's attitude regarding the use of telehealth.

Our findings showed that surgical digital health may be a potential tool to eliminate the care gap between medical institutions and home care. However, several barriers still exist. One cross-sectional study found that the rate of telehealth use in surgical specialties (11.4%) was significantly lower than that in other specialties (12.7–39.5%).²¹ With more promising studies on digital health during the pandemic, the surgical community may have more engagement in digital health in the future. Furthermore, patient-level barriers to digital health (e.g. digital literacy or lack of Internet connection/devices) remain to be overcome. Notably, there is a higher proportion of dropout among the elderly,²² and a ready-made product may be a solution to overcome this barrier.²³ Furthermore, policy-level barriers impede the widespread adoption of digital health. During the COVID-19 pandemic, a number of national health reimbursements worldwide supported and eliminated the restriction for the application of telehealth. Our preliminary data demonstrated that digital health can be a feasible intervention for postoperative surgical wound care. Thus, we should anticipate a significantly growing demand for telehealth services after the pandemic. In conjunction with academic research to support the benefits of telehealth, policymakers and healthcare planners play a critical role in telehealth in terms of creating a foundation and developing a vision.^24,25

In our study, living in rural areas was associated with higher SUS or NPS scores. To our knowledge, digital health may have the most value in rural or underserved areas where access to medical care is a major barrier.^20,26 The potential benefits of implementing digital health in rural areas include improved healthcare quality, reduced medical costs, and no need of long-distance travel. One study investigating the impact of digital health on underserved populations during the COVID-19 pandemic noted that digital health has significantly increased in utility and convenience and that subjects were satisfied with it. With the help of digital health, rural communities have the opportunity to access a wider range of healthcare services and specialists virtually. Although the direction appears to be on its way to support patient needs, further efforts should be made to overcome other barriers.

Another aim regarding the use of digital health for wound care is to expedite the diagnosis and treatment of SSI, which increasingly develops after discharge, with the move toward ERAS guidelines (multimodal perioperative care elements intended to achieve early recovery).¹ If early diagnosis and management of SSI are made, the burden of substantial morbidity can be alleviated, and medical costs can decrease.²⁷ One randomized clinical trial found that using a mobile digital intervention facilitated the early diagnosis of SSI (statistically significant in 7-day SSI rate, unsignificant in 30-day SSI rate) in patients undergoing emergency surgery compared with routine care.²⁰ This study may provide us with a future study direction. However, the SSI rate is lower in elective surgery than in emergency surgery. Thus, more cases should be enrolled.

Because of some limitations, the results of our study should be interpreted with caution. First, it was conducted at a single tertiary medical center with a modest sample size, which may restrict the generalizability of the findings to other institutions, healthcare systems, or surgical populations. Second, although patients were stratified into COVID-19 and non-COVID-19 groups, a transitional period in mid-2021 marked a gradual shift in digital health policies and public behavior. Residual influence from pandemic-driven digital health adoption may have persisted in the non-COVID group, potentially affecting engagement with the app. Third, although our regression models adjusted for key clinical variables such as age, sex, and type of surgery, we did not collect data on individual digital literacy, prior smartphone experience, or caregiver involvement. These factors likely impact usability perceptions and app adherence. The absence of these variables introduces potential residual confounding. Future research should incorporate validated assessments of digital competence and caregiving support to more comprehensively evaluate user-related influences. Fourth, the app was designed specifically for postoperative monitoring in abdominal and hernia surgeries. Its applicability to other surgical procedures, particularly those with differing recovery trajectories or wound characteristics, remains to be explored. Lastly, differences in follow-up intensity or adherence to app usage between the COVID-19 and non-COVID-19 groups may have influenced the outcomes. Although overall completion rates of usability surveys were similar across groups, we did not systematically measure daily app usage frequency or longitudinal adherence patterns. As such, variability in engagement levels may have introduced differential bias in perceived usability or satisfaction. Future studies should incorporate backend usage analytics to quantify engagement and evaluate its relationship with clinical outcomes and user perceptions.

Conclusion

This study demonstrated that a mobile app for postoperative wound care is both usable and acceptable to patients recovering from abdominal and hernia surgeries, with high completion and satisfaction rates. Patients treated during the COVID-19 period reported significantly higher usability and endorsement scores, suggesting that external context may influence engagement with digital health tools. The app's structured design and clinical oversight contributed to its effectiveness in supporting remote postoperative care. Further research in broader surgical populations and diverse clinical settings is warranted to confirm its generalizability and long-term impact.

Footnotes

ORCID iDs

Liu-Jyun Chen

Jin-Ming Wu

Ethical approval

This study was approved by the Human Research Ethics Committee of National Taiwan University Hospital (201912086RINC), which followed good clinical practice standards. Consent to participate and consent to publish are also proved and available.

Consent for publication

All authors agreed that our study was published.

Author contributions

Conceptualization: J-MW and L-JC; methodology: J-MW, H-HT, Y-CL, R-FC, and L-JC; software: J-MW, H-HT, Y-CL, R-FC, and L-JC; validation: J-MW, H-HT, Y-CL, R-FC, and L-JC; formal analysis: J-MW, H-HT, Y-CL, R-FC, and L-JC; investigation: Y-CL, R-FC and L-JC; resources: J-MW, H-HT, and Y-CL; data curation: J-MW, H-HT, Y-CL, R-FC, and L-JC; writing—original draft preparation: J-MW, H-HT, Y-CL, R-FC, and L-JC; writing—review and editing: J-MW, H-HT, Y-CL, R-FC, and L-JC; visualization: J-MW, H-HT, Y-CL, R-FC, and L-JC; supervision: J-MW, H-HT, Y-CL, R-FC, and L-JC; project administration: J-MW, H-HT, Y-CL, and R-FC; and funding acquisition: J-MW.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant (110-2221-E-002 -014 -MY3 & 113-2221-E-002 -047 -MY3) from the Ministry of Science and Technology and Good Liver Foundation, Taiwan, which had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of data and materials

All data would be provided on contacting the corresponding author.

References

Oodit

Biccard

Panieri

, et al. Guidelines for perioperative care in elective abdominal and pelvic surgery at primary and secondary hospitals in low-middle-income countries (LMIC's): enhanced recovery after surgery (ERAS) society recommendation. World J Surg 2022; 46: 1826–1843.

Warren

Love

. Incisional hernia repair: minimally invasive approaches. Surg Clin North Am 2018; 98: 537–559.

Berti-Hearn

Elliott

. A resource guide to improve nursing care and transition to self-care for patients with ostomies. Home Healthc Now 2018; 36: 43–49.

Kopp Lugli

Marti

Salm

, et al. The role of HbA1c as a positive perioperative predictor of surgical site and other postoperative infections: an explorative analysis in patients undergoing minor to major surgery. World J Surg 2022; 46: 391–399.

Meara

Leather

Hagander

, et al. Global surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet 2015; 386: 569–624.

Temple-Oberle

Yakaback

Webb

, et al. Effect of smartphone app postoperative home monitoring after oncologic surgery on quality of recovery: a randomized clinical trial. JAMA Surg 2023; 158: 693–699.

Cook

Render

Woods

. Gaps in the continuity of care and progress on patient safety. Br Med J 2000; 320: 791–794.

Zhu

Zhang

Wang

, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020; 382: 727–733.

Wang

Lin

, et al.

Coronavirus disease pandemic effect on medical-seeking behaviors even in one resource-competent community: a case controlled study

Int J Environ Res Public Health 2022; 19(17): 10822.

10.

Aalami

Ingraham

Arya

. Applications of mobile health technology in surgical innovation. JAMA Surg 2021; 156: 414–415.

11.

Chang

, et al. Wearable-based mobile health app in gastric cancer patients for postoperative physical activity monitoring: focus group study. JMIR mHealth uHealth 2019; 7: e11989.

12.

Sancı

Ergin

İE

Öztürk

, et al. Mobile app communication to prevent ER visits post-circumcision: a prospective observational study. Int Urol Nephrol 2025; 57: 1465–1471.

13.

Wang

Brook

. Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA 2020; 323: 1341–1342.

14.

, et al. Tablet PC-enabled application intervention for patients with gastric cancer undergoing gastrectomy. Comput Methods Programs Biomed 2015; 119: 101–109.

15.

Tsai

, et al. A unified framework for automatic detection of wound infection with artificial intelligence. Appl Sci-Basel 2020; 10: 5353.

16.

van der Linden

MJW

Nahar van Venrooij

LMW

Verdaasdonk

EGG

. Personal devices to monitor physical activity and nutritional intake after colorectal cancer surgery: feasibility study. JMIR Perioper Med 2022; 5: e40352.

17.

Brooke

. SUS: a ‘quick and dirty’ usability. Usability Eval Ind 1996; 189: 189–194.

18.

Lewis

. Measuring perceived usability: the CSUQ, SUS, and UMUX. Int J Hum–Comput Int 2018; 34: 1148–1156.

19.

Bangor

Kortum

Miller

. An empirical evaluation of the System Usability Scale. Int J Hum-Comput Int 2008; 24: 574–594.

20.

McLean

Mountain

Shaw

, et al. 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.

21.

Chao

Zhu

, et al. Use of telehealth by surgical specialties during the COVID-19 pandemic. JAMA Surg 2021; 156: 620–626.

22.

Schreiweis

Pobiruchin

Strotbaum

, et al. Barriers and facilitators to the implementation of eHealth services: systematic literature analysis. J Med Internet Res 2019; 21: e14197.

23.

Aljedaani

Babar

. Challenges with developing secure mobile health applications: systematic review. JMIR mHealth uHealth 2021; 9: e15654.

24.

Desveaux

Soobiah

Bhatia

, et al. Identifying and overcoming policy-level barriers to the implementation of digital health innovation: qualitative study. J Med Internet Res 2019; 21: e14994.

25.

Sulis

Mariani

Montagna

. A survey on agents applications in healthcare: opportunities, challenges and trends. Computer Methods Programs Biomed 2023; 236: 107525.

26.

Lam

Shi

, et al. Assessing telemedicine unreadiness among older adults in the United States during the COVID-19 pandemic. JAMA Intern Med 2020; 180: 1389–1391.

27.

Ban

Minei

Laronga

, et al. American College of Surgeons and Surgical Infection Society: surgical site infection guidelines, 2016 update. J Am College Surg 2017; 224: 59–74.

Using a mobile app for postoperative wound care at home during the COVID-19 pandemic: A usability and acceptability study

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

App development and functions

Participant recruitment

Demographic measures and data collection

Primary outcome measures

Statistical analysis

Results

Discussion

Conclusion

Footnotes

ORCID iDs

Ethical approval

Consent for publication

Author contributions

Funding

Declaration of conflicting interests

Availability of data and materials

References