The theoretical definition of DHI refers to a discrete functionality of digital technology that is applied to achieve health objectives. Telemedicine, digital platforms, and mobile phone and SMS text messaging follow-up are all examples of DHIs [31].

Although the definition of usual care has not been standardized, it can include the routine care received by patients for prevention or treatment of diseases [32]. In this study, in addition to regular hospital care, usual care forms consisted of the following 3 main categories and their collocated use: outpatient clinics, primary care, and home care. The operational definitions or meanings of several types of DHIs and usual care covered in this paper are explained in Table 1.

The 2 researchers XB and HZ performed an independent electronic search in PubMed, Web of Science, EMBASE, Cochrane Library, CINAHL, China Knowledge Resource Integrated Database, Wanfang Database, Weipu Database, and Chinese Biomedical from their inception to August 1, 2023. The search strategy was developed using the PICO search framework (ie, patient, intervention, comparison, outcome, and study design) and the search terms were divided into 3 categories: patients with chronic wounds (population), digital health (intervention), and wound status (outcome). Each category was combined with MeSH (Medical Subject Headings) terms and natural language (Multimedia Appendix 2).

The inclusion criteria of this study, which followed the PICOS (Population, Intervention, Comparison, Outcomes, and Study) design framework [38], were as follows: (1) Population: adults aged 18 years or older with chronic wounds of any type and severity. Chronic wound was defined as a wound that “fail to proceed through an orderly and timely process to produce anatomical and functional integrity” and follow the 4 major classifications of the Wound Healing Society, ie, pressure ulcers, diabetic foot ulcers, venous ulcers, and arterial insufficiency ulcers [6]; (2) Interventions: study interventions must have used a DHI to capture, assess, create, or communicate wound status in patients with chronic wounds. DHIs were defined as “health promotion approaches aided by various digital technologies,” such as remote consultations, mobile applications, web-based platforms, mobile phones, wearables, SMS, and email; (3) Comparison: studies that assigned participants into either an experimental group or a control group including usual, routine, and conventional care, or waitlist as defined by the original research; (4) Outcomes: the primary outcome was the state of chronic wound healing (eg, wound healing, healing time, and change in wound size), and secondary outcomes include all-cause mortality, adverse events, patient satisfaction, and certain wound-specific scoring metrics; and (5) Study design: randomized controlled trials (RCTs), cohort studies, and quasi-experimental studies published in English and Chinese.

Articles were excluded if their main objective was to assess the acceptability of a newly developed DHI among patients or if the study did not contain a control group. Conference proceedings, magazines, news, electronic resources and reports, theses, dissertations, abstracts, editorials, and systematic reviews were also excluded.

Initially, search duplicates were removed using the reference manager tool EndNote (version X9.3.3; Clarivate). For final inclusion, each study was assessed independently by 2 researchers (XB and HZ), first by screening the title and abstract, and then through a full-text review. Disagreements on the selection of records between the 2 researchers were resolved by team discussion or by a third researcher (LH). In addition, we manually searched the reference lists of the included studies for additional studies. The PRISMA flow diagram was used to illustrate the study selection (refer to Figure 1).

XB and HZ independently extracted the data using a pre-designed form created with Excel. Disagreements were resolved through discussion or with assistance from a third reviewer (LH), if necessary. From each study, we extracted information about study details (eg, title, author, year, and country), study design (type of study, aims, sample methods, and inclusion or exclusion criteria), participants’ characteristics (number of persons surveyed, population characteristics, ie, age, gender, and demographics), specific wound data and complications (diagnostic methods, ulcer specifications including stage), and outcomes (eg, wound healing, healing time, wound size change, adverse event, all-cause mortality, cost, and patients’ satisfaction).

The risk of bias for each study was assessed by 2 independent reviewers (XB and HZ). For RCTs, the Cochrane risk-of-bias tool [39] was used to assign a “high risk,” “unclear risk,” or “low risk” according to 5 domains: (1) sequence generation, (2) allocation concealment, (3) blinding, (4) incomplete outcome data, and (5) selective outcome reporting. We used ROBINS-I [40] for cohort studies, which assesses 7 types of bias from confounding variables, selection of participants, measurement classification of interventions, deviations from intended interventions, incomplete outcome data, outcome assessment, and selective outcome reporting. For quasi-experimental studies, the checklists of the Joanna Briggs Institute Critical Appraisal tools were used, comprising 9 items that can be rated yes, no, unclear, or not applicable.

When possible, outcome data were analyzed quantitatively by calculating a pooled effect of different studies. A meta-analysis was performed when ≥2 studies with available data investigated the same outcome; otherwise, the outcomes were presented narratively. Considering the expected clinical heterogeneity among the included studies, the random-effects model was used to estimate the overall effect size and 95% CI. The pooled analysis was presented as a risk ratio (RR). The effect of the intervention on continuous outcomes was expressed as standardized mean difference (SMD) for outcomes reported with different measures and with 95% CI [41].

Heterogeneity among studies was estimated Cochran Q test and the I² statistic (I²>50% indicated substantial heterogeneity) [42]. We also performed sensitivity analyses to examine the robustness of the results. Sensitivity analysis was carried out by removing a single study at a time to see how it impacted the overall estimate. For chronic wounds’ outcomes, the forest plots were also constructed. When the number of included studies was more than 10 [43], the graphical symmetry of the funnel plot and the Egger test statistic were used to detect possible publication bias [44], because the power of the test is lower when the number of studies is small. In the absence of publication bias, the funnel plot is expected to be symmetrical, and the P values of Egger’s test are >.05 [45,46].

In addition, RCT and observational data were analyzed separately, and exploratory subgroup analyses were carried out for different modalities of DHI. All analyses were performed with RevMan (version 5.4.1; The Cochrane Collaboration) and Stata (version 15.0; StataCorp; XB and HZ).

In total, 25 studies were published between 2004 and 2023, with 9 (36%) conducted in China [24,25,50,54-56,64-66], 3 (12%) in Norway and France [52,53,57,58,60,61], and 2 (8%) in the United States [48,59], Australia [47,51], and Denmark [21,49], respectively. In addition, the following countries had 1 (4%) of the studies each, the United Kingdom [22], Canada [23], Sweden [62], and Israel [63]. Sample sizes varied across studies, ranging from the smallest sample of 26 subjects in Vowden and Vowden [22] to the largest of 1988 subjects in Wickstrom et al [62]. A total of 40% (10/25) of the studies were conducted on patients with chronic wounds of various etiologies [22,47,48,53,55, 56,60-62,66], 32% (8/25) focused on patients with pressure injuries [23-25,51,54,57,64,65], 16% (4/25) targeted patients with diabetic foot ulcers [49,52,58,59], 4% (1/25) recruited patients with lower extremity ulcers of various etiologies [63], 4% (1/25) included only patients with lower extremity venous ulcers [50], and a further 4% (1/25) excluded patients with pressure ulcers, surgical wounds, and cancer wounds [21].

Wound care in experimental groups varied, because of the clinical heterogeneity of DHIs between studies, with 15 (60%) studies collecting patients’ wound data via telemedicine, such as web-based programs and video conference [21,22,47-50, 53,54,57-59,61-63], 7 (28%) studies using the digital platform [24,25,55,56,64-66], and 3 (12%) studies using email and phone to facilitate the implementation. The follow-up periods were inconsistent among all studies and, where present, ranged from 3 to 35 months. In addition to hospitalization, the control group received wound usual care in a variety of settings, including outpatient clinics (14/25, 56%) [23-25,47-52,63-67], the home (3/25, 12%) [21,22,60], the community (2/25, 8%) [61,62], and home care combined with outpatient follow-up (1/25, 4%) [59]. With the exception of 2 (8%) studies, the remaining 23 (92%) studies were divided into 2 groups, where 3 groups were compared by Terry et al [48], group A (weekly visits with TM and wound care specialist consults), group B (weekly visits with weekly consults with WCS), and group C (routine care). Téot et al [53] divided the control group into group 1 (telemedicine), group 2a (home care), and group 2b (clinic care). Details of all 25 studies are summarized in Table S1 in Multimedia Appendix 3 [21-25,47-66].

Details of the assessment of the risk of bias are presented in Tables S2-S4 in Multimedia Appendix 4 [21-25,47-66]. Of the 14 RCTs, 4 (29%) RCTs were assessed as high risk in the “other bias” option, owing to unequal baseline characteristics [22,48,52,53], and 6 (43%) studies [49-51,55,57,58] demonstrated a low risk of bias, with bias in ≤1 domain.

The assessment result for cohort studies revealed that 2 (33%) studies [21,60] had a moderate risk of bias, while 4 (67%) studies [59,61-63] had a high risk of bias.

Finally, for the quasi-experimental study, all study items related to the integrity of follow-up were rated as not applicable, and the items were rated as unclear mostly because it was unknown whether the other measures received by the groups were identical.

The funnel plot for all-cause mortality underlying the meta-analyses was symmetrical, which reflected a low risk of publication bias (Figure 5), the Egger test also showed consistent result (P=.37).

Sensitivity analysis was performed by omitting studies sequentially (Table 2). For wound healing around 1 year (Figure 6A), the pooled RR ranged from 1.05 (95% CI 0.93-1.18) to 1.19 (95% CI 0.95-1.47). For all-cause mortality (Figure 6B), the pooled RR ranged from 1.06 (95% CI 0.58-1.95) to 1.41 (95% CI 0.91-2.19). For adverse event (Figure 6C), the pooled RR ranged from 0.31 (95% CI 0.15-0.65) to 0.54 (95% CI 0.27-1.05). The results revealed that each outcome was relatively stable and would not change due to the elimination of a study; thus, the result of meta-analysis was robust.

This review provides a synthesis of high-quality evidence pertaining to the efficacy of DHIs for chronic wounds with different etiologies. Altogether, 12 RCTs, 5 cohort studies, and 3 quasi-experimental studies were identified and included in this meta-analysis. The magnitude of the intervention effect varied across studies, influenced by factors such as the nature of the intervention, assessment methods, and intervention duration.

The meta-analysis showed that DHIs did not significantly differ from usual care in terms of wound healing and all-cause mortality over around 1 year, which is consistent with findings of a previous meta-analysis targeting the effectiveness of telemedicine for chronic wound management [29]. Nevertheless, we believe it may be too early to conclude that DHIs are as equally effective as usual care in improving wound healing due to the lack of a sufficient number of high-quality randomized controlled studies and the fact that digital technology is always evolving.

Our study also suggests that DHIs can reduce adverse events in chronic wound management, which is inconsistent with existing evidence. A previous meta-analysis showed no significant difference in the number of adverse events between the telemedicine and usual care groups [67]. This may be due to the inclusion of other forms of DHI in addition to telemedicine in our study, and it also lends support to the idea of exploratory subgroup analyses of intervention types.

Subgroup analyses demonstrated a beneficial effect of digital platforms for hospitalized patients on wound healing, implying differences in chronic wound management outcomes among intervention types, which may be linked to the characteristics and limitations of the technologies. Specifically, teleconsultation scenarios in the form of videoconferencing are excellent [68], but require high levels of equipment and patient finances [51,69]. Although telephone and email follow-ups are more concise and convenient [70], visual diagnosis of wounds is lacking and the effectiveness of the interaction is difficult to ensure; for example, nurses are frequently unable to validate that patients have read and understood the content of the email [71]. Furthermore, digital platforms used for in-hospital patient pressure injury evaluation and management have shown promise in improving patients’ PUSH-score, but evidence from these studies is limited. Through further analysis of studies reporting this outcome metric, we discovered that the significant improvement in patients’ PUSH-score is closely linked to the real-time tracking function of the digital platform. This feature effectively aligns with the PUSH scale’s dynamic assessment of pressure injuries, enabling the quantification of dynamic changes in patients’ wounds [72]. Additionally, the platform's built-in automated analytics function provides critical feedback, offering a reliable predictive basis for nurses to identify the risk of pressure injury development in patients [64,73]. However, whether digital platforms provide better intervention outcomes than other types of DHIs still need to be validated and explored in higher-quality studies.

Of note, few studies have considered the impact of patient age on the effectiveness of technological interventions, and the groups included in the studies were predominantly middle-aged and older people, but the fact is that digital health care is more accepted in younger age groups [74]. Cost is another component that must be considered. Several studies have combined different forms of digital health technology to positively impacted wound outcomes; yet, it remains to be seen whether the increased cost of care as a result of the combination of technologies reduces patient adherence to treatment, which in turn affects wound outcomes [74]. Similarly, unequal access, use, and knowledge of information and communication technology among patients may also affect the effectiveness of DHIs.

In addition to the issues mentioned above, a point of concern, however, was a lack of blinding in most studies. The nature of interventions made them difficult to blind participants or providers, as the conduct of interactions frequently necessitates that both parties understand what they are doing. Patients, for example, are required to prepare their own computers or visit certain specific websites to validate and register their personal identities when conducting real-time videoconferencing-supported wound diagnosis and follow-up, and it is difficult for doctors or nurses on the other end of the video to be unaware of the content and form of the intervention, which is closely related to their work experience and professionalism; when using mobile phones or SMS text messaging for interventions, patients and researchers frequently agree on the frequency of the intervention ahead of time and exchange contact information in case they miss or ignore the diagnostic content, care recommendations, or follow-up feedback. Future RCTs should address this aspect to strengthen the evidence on DHIs for the management of patients with chronic wounds.

To our knowledge, this is one of the most comprehensive and up‐to‐date reviews and meta‐analyses to evaluate the effects of DHIs on chronic wound outcomes across a broad spectrum of the population, combining data from RCTs, cohort studies, and quasi-experimental studies. While there have been studies looking at the use of digital health technology in chronic wound management, this study is unique in that it notes the diversity of interventions and attempts to quantify the effects that different types of DHIs show in terms of improving wound outcomes. For example, an explorable link was discovered between digital platforms and changes in PUSH-score in patients with pressure injuries. This provides new ideas for future research on whether different types of digital intervention techniques can be coupled to various etiologic wound evaluation tools to improve intervention outcomes. Furthermore, studies included in this review cover a wide range of DHIs, which differ in terms of the persons engaged, the intervention management, and the technology used, and are applied to populations with various wound etiologies, allowing the results to be generalizable.

Despite the clinical implications and strength of our review, certain limitations need acknowledgment. First, few researchers have specified the wound staging of included patients, which prevents us from drawing firm conclusions about this. Additionally, differences in participants, intervention contents, methods, frequency, time, and measurements in the control group also resulted in heterogeneity. Moreover, due to the significant variation in intervention design, it was difficult to extract and classify interventions in a very standardized way. Even though we divided it into 3 types of intervention subgroups, the interventions are still not standardized within each subgroup, and many parameters are implicitly variable, which may influence the results of our review. Finally, it must be explained that we included other study types in the meta-analysis in addition to RCTs, which is not recommended by the official guidelines for meta-analysis. Therefore, to overcome this limitation, we have clearly categorized and described in detail the results of the RCTs and non-RCTs included in this paper to improve the clarity and reference value of the results.

The results of the subgroup analyses point to the benefits of digital platforms for chronic wound management in hospitalized patients, but a limitation that cannot be ignored is that most of the studies were quasi-experimental and the platforms they used varied in terms of both structural design and technological quality. Therefore, future studies should be based on evidence-based practice, attempt to develop a digital platform that can be replicated on a large scale, and conduct more RCTs, considering the context and needs of the population, such as the acceptability of the technology, economic disparities, and the use of other services. At the same time, given the association between age and digital health literacy, it is necessary to provide interventions for each age group to clearly confirm effects in future studies.

In addition to these clinical implications, there are several possible directions for further research. Considering the heterogeneity of the interventions and the wound etiology, we recommend further research to investigate whether there are certain associations between the types of digital health interventions and patient characteristics to provide a valid reference for the clinical construction of a systematic digital wound management program, such as differences in the effectiveness of the same interventions for patients with wounds of different etiologies, and the relationship between patients’ digital health literacy and the effectiveness of the interventions. Moreover, regarding the review process we mentioned in the principal results section, we found that only a few studies blinded wound therapists, nurses, or patients. We recommend that future studies consider using existing high-quality patient digital information collection programs or web-based data platforms and rationally using the automated analytical capabilities of the technology to conduct single-blind experiments in which patients with comparable baseline information are randomly grouped with the consent of the patient, and therapists are implemented blinding of assessors by performing software-based outcome measures for all primary and secondary outcomes and automatically storing patient self-reported data. Data review should be done in a blinded manner until analyses were performed, and data analysis was also done using blinded subgroups to improve the quality of evidence generated by the study.

In summary, this study suggests that DHIs are effective for chronic wound management, but the jury is still out on who is better between them and usual care. We also found indications that digital platforms can help with chronic wound management in hospitalized patients, warranting further investigations. Moreover, future high-quality research is needed, to identify factors contributing to improved patient-centered interventions with better care outcomes, as well as more careful consideration of individual patient characteristics.