Chronic diseases account for 71% of all deaths globally. Furthermore, the recent rapid increase in the number of patients who are chronically ill will heavily burden the health care sector. This review focuses on the use of digital counseling environments among patients with cancer and cardiovascular, musculoskeletal, and colorectal diseases.

Patients who are chronically ill need a variety of counseling approaches at different stages of the disease. Patient counseling, which refers to the interaction between a patient and health care professionals, can strongly support the patient’s sense of responsibility in adhering to their treatment [1]. Most of the novel patient counseling methods, for example, mobile, digital, or ubiquitous counseling, can increase adherence to treatment, which has been worryingly low among patients who are chronically ill [2]. Digital counseling environments can provide peer support through interaction; motivate self-care; and offer understandable, reliable, and up-to-date information to help patients better understand their disease and make lifestyle changes [3-5]. In addition, novel counseling methods can help manage patients’ anxiety and fear, as well as enhance patient safety [2,6,7]. Nevertheless, the current knowledge base regarding digital counseling for anxiety and adherence to treatment among patients who are chronically ill seems to be fragmented, which highlights the need for a comprehensive summary of the available counseling approaches.

Digital, customer-oriented services may improve a patient's quality of life and functionality when the service is accessible regardless of place or time and tailored to the patient’s specific needs [8,9]. Various technologies now enable the provision of such services, which can provide individual counseling to patients at the correct time and in an appropriate manner [10-12]. The provision of materials in different formats promotes tailored counseling approaches [11-15], with previous research demonstrating that patients value inclusivity, comprehensibility, availability, and flexibility in these services [4,13,16-18].

In recent years, digitalization has offered numerous opportunities for providing health care through digital channels. The World Health Organization defines digital health as “a broad umbrella term encompassing e-health, as well as developing areas such as the use of advanced computer sciences.” Mobile health (mHealth) is a subarea of digital health, and is described as “the use of wireless mobile technologies for health,” whereas another subarea, ubiquitous health, is defined as services delivered through ubiquitous technologies such as tags, sensors, and biometric devices [19]. The main objective of digital health could be described as using various technologies to support the achievement of health goals through the internet. However, the realm of digital health is wide and, as such, various terms have been applied in digital health research. This review focuses on web-based solutions and mobile apps that integrate knowledge sharing to create participative elements for the patient. Studies focusing on SMS text messaging and gaming were excluded.

According to the World Health Organization, digital and mobile technologies support health care systems through targeted and untargeted patient communication, patient-to-patient communication, personal health tracking, and citizen-based reporting. An important objective of digital health interventions is the widespread promotion of positive changes in behavior to prevent the onset of chronic disease.

The impacts of various digital health interventions on the management of chronic diseases, especially diabetes mellitus [20-26], cardiovascular diseases, and cancer, have been studied extensively by systematic reviews during recent years [10,27-47]. However, many of the studies that have been reviewed suffer from methodological shortcomings, that is, insufficient power to detect changes in outcomes and relatively short study duration [31]. This has led to a fragmented knowledge base, and a comprehensive description of the available digital health solutions—along with their effectiveness—is needed to further develop counseling for patients who are chronically ill. Research implications for future eHealth studies have recently been identified, categorized, and prioritized [48]. For example, randomized controlled trial (RCT) studies with large sample sizes and long follow-up periods, along with investigations of the cost-effectiveness and user acceptance of eHealth interventions, should be conducted in the near future. Furthermore, decision-makers will need an improved understanding of which components of the studied interventions, for example, frequency, duration and delivery mode, or patient characteristics, contribute most to the overall effectiveness of an eHealth intervention. Ethical aspects, intervention safety, and translation of findings into a practical context were also identified as necessary research elements [48].

This systematic review aims to identify and synthesize the best available evidence on the effectiveness of digital counseling environments for managing anxiety, depression, and adherence to treatment among patients who are chronically ill.

This review answers the following question: What is the effectiveness of the digital counseling environments compared with control (eg, usual care) on anxiety and depression and clinical outcomes related to adherence to treatment?

A systematic review of RCTs was conducted according to the Centre for Reviews and Dissemination and Joanna Briggs Institute guidelines [49,50]. The research adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement [51] regarding the reporting of evidence.

The selection of studies was based on predefined inclusion and exclusion criteria, which are reported in the patient, intervention, comparison, and outcome format [50]. The review considered studies that included participants who were adult patients aged ≥18 years with chronic diseases; described interventions that were digital (mobile, web-based, or ubiquitous) counseling approaches; and reported outcomes that were patient outcomes (primary outcomes), that is, anxiety, depression, or adherence to treatment, and clinical indicators (secondary outcomes) related to adherence to treatment. The comparator was no treatment, standard care, or another type of intervention. All RCTs and quasi-experimental studies published in English, Finnish, or Swedish from 2008 to 2020 were considered; this specific time period was selected to reflect the growth and adoption of digital technologies.

Studies focusing on patients aged <18 years or describing patients with psychiatric disorders or substance abuse problems were excluded. Furthermore, studies focusing on traditional counseling, SMS text message counseling, or eHealth game development were excluded. Studies were also excluded if they measured any outcomes other than those defined in the inclusion criteria or were protocols, reviews, editorial papers, discussions, recommendations, or parts of books.

Systematic literature searches were conducted across 4 electronic databases (CINAHL, PubMed, Scopus, and Web of Science) in May 2019, after which the search was complemented for the years 2019-2020 in October 2020. The reference lists of the included studies were screened for studies that may be relevant to the study objective, yet were not identified during the systematic literature search. An information specialist assisted the researchers in forming a search strategy and conducting the literature search. The search strategy for different databases is presented in Multimedia Appendix 1.

A total of 2056 publications were retrieved during the database searches. These publications were then imported into Zotero reference manager software (Corporation for Digital Scholarship). From the 2056 publications, 549 (26.7%) duplicates were removed. Of the 1507 studies remaining, 1434 (95.16%) were excluded after title and abstract screening by 2 independent researchers (KPP and MK) using predefined inclusion criteria, leaving 73 (4.84%) full-text articles relevant to the study objectives. Minor disagreements between the reviewers were resolved, and the researchers reached agreement. At the completion of the screening process, of the 73 studies, 50 (68%) met the inclusion criteria and were included in the critical appraisal. A PRISMA flow diagram was used to present the study selection process (Figure 1).

The methodological quality of the 50 selected studies was independently assessed by 2 researchers (KPP and MK) using a standardized Joanna Briggs Institute critical appraisal tool for RCTs and quasi-experimental studies [52]. The methodological quality was evaluated by assigning points to each criterion of the appraisal tool. Studies that scored at least 60% (8/13 points for RCTs and 5/9 points for quasi-experimental studies) across the appraisal criteria were included in the review. Of the 50 selected studies, 26 (52%) were included in the final review, whereas 24 (48%) were excluded based on poor blinding, unreliable measurement of outcomes, or inappropriate statistical analysis. Critical appraisal of the selected randomized controlled trial studies is presented in Multimedia Appendix 2 [53-76].

In the critical appraisal of the selected quasi-experimental studies (2/26, 8%), both scored 8 points out of 9. Each study had a control group, and there were no differences among the participants in the compared groups. Other than the intervention of interest, there were no differences between the groups in terms of care received. Multiple measurements of the outcomes both before and after the intervention were collected in the same way in both studies, and appropriate statistical analysis was conducted. The only unclear criterion concerned whether the follow-up was complete, and if not, whether differences between the groups in terms of their follow-up were adequately described and analyzed.

Data from the original studies included in the review were extracted to meet the Centre for Reviews and Dissemination information requirements [49]. The first author (KPP) entered the extracted data into a standardized form (Multimedia Appendix 3 [53-78]) that also included the quality assessment scores. The second author (MV) confirmed the extracted data.

Because of the heterogeneity of outcomes reported in the identified RCTs, a meta-analysis was not possible [49]. Narrative synthesis was used to answer the research question.

A total of 26 studies were included in this review: 20 (77%) RCTs [53-72], 4 (15%) pilot RCTs [73-76], and 2 (8%) quasi-experimental studies [77,78] published from 2010 to 2020 in English in 13 countries. Details of the included studies are described in Multimedia Appendix 3.

The participants in the included studies were adult patients aged ≥18 years with a range of diseases: various cancers [53,63,65,66,69,70,73,75-77], along with cardiovascular [54,56-58,64,68,71,72,74], musculoskeletal [59,62], colorectal [55,60,61], and kidney diseases [78]. The sample size ranged from 29 to 1000 patients, with, of the 26 studies, 9 (35%) having enrolled fewer than 99 participants [58-60,62,64,68,73,76,78], 9 (35%) having enrolled 100-200 participants [53,54,61,66,69,70,74,75,77], 7 (27%) having enrolled 201-500 participants [55-57,63,65,67,72], and 1 (4%) having enrolled 1000 participants [71]. The follow-up period ranged from days to 12 months, more specifically, 3 months or less in 46% (12/26) of the studies [59-61,64,66,68-70,73,74,76,78], 3-6 months in 23% (6/12) of the studies [58,62,63,65,67,71], and 6-12 months in 31% (8/26) of the studies [53-57,72,75,77].

This systematic review identified and synthesized the best available evidence regarding how effective digital counseling interventions are at managing anxiety, depression, and adherence to treatment among patients who are chronically ill.

The results indicate that an effective digital counseling environment includes both high-quality educational material, possibly enriched with multimedia elements, and activities that engage participants. Because of the heterogeneity of the studies included in this review, it was impossible to determine which type of digital intervention was the most effective at managing anxiety, depression, and adherence to treatment. Furthermore, determining the aspects responsible for changes in self-management was difficult. Overall, digital, web-based counseling environments designed for patients who are chronically ill seem to be more effective than, or at least comparable to, standard counseling methods. This indicates that well-accepted digital environments could complement standard counseling. Patients should be afforded a variety of web-based educational resources that correspond to their care objectives and needs. These services should also be provided at an appropriate time to ensure maximum benefits [39]. Previous reviews have identified the highly participative features of mHealth interventions, for example, reminders and continuous feedback, patient-centeredness, individually tailored content, and patient–provider communication, to be a large advantage of these services [28,31,34,41,80,81]. Furthermore, it has previously been suggested that digital environments have the potential to increase patient involvement, empowerment, and security through increased knowledge, symptom management, participation, engagement, and improved clinician–patient communication [82,83]. These types of services also do not depend on location, which will improve access to care for patients in remote locations where other services may not be available and, therefore, counteract care inequity. In light of the COVID-19 pandemic, digital environments can also support patients who are chronically ill and living in isolated circumstances [9].

Digital counseling environments can enhance clinical practice and care by empowering patients with chronic disease self-management, reducing dependency on health care professionals, and possibly changing the chronic disease course in the long term. Furthermore, digital counseling environments can be accommodated and used for other patient groups by enhancing diagnostic examination success and optimizing care procedures.

Nevertheless, digital environments can also contribute to care inequity if certain patients do not have the ability or resources to access digital environments. Moreover, digital environments can cause ambivalence and uncertainty if patients lack the digital skills and knowledge of how to use these environments [82].

Surprisingly, all the studies included in this review were based on basic technologies, that is, internet-based environments, websites, and mobile apps. There were no reports of interventions that applied emerging technologies such as augmented reality, virtual reality, mixed reality, or 360° virtual reality solutions. Furthermore, none of the presented digital counseling approaches used ubiquitous elements, for example, tags or sensors.

Digital interventions significantly improved anxiety and depression among patients with cancer [53,65] and cardiovascular disease [74]. Positive, albeit statistically nonsignificant, changes in anxiety and depression were also measured among patients with cancer [63,66,69,70,76,77], as well as individuals with colorectal [55], cardiovascular [56], and musculoskeletal [62] diseases. However, a pilot study [73] found that patients in the control group—who did not have access to the additional information provided by the mobile app—showed significantly lower anxiety and depression than the intervention population. As this particular study explored patients with cancer, it is possible that the increased amount of knowledge in the app reminded women of the cancer treatment they were going through. In contrast, a systematic review [38] reported that 17 studies found eHealth solutions to improve anxiety among patients with breast cancer. Nevertheless, other studies have reported eHealth interventions to exert contradictory effects on anxiety [40,83]. This includes the surprising finding that increased knowledge does not necessarily reduce anxiety. This area of research clearly needs to be investigated in more detail.

Various digital counseling approaches significantly improved adherence to treatment among patients requiring hemodialysis [78], as well as individuals with musculoskeletal [59], colorectal [55], and cardiovascular [74] diseases. In 23% (6/26) of the studies, although adherence to treatment increased among patients with cardiovascular [54,57,71] and colorectal diseases [60,61] and cancer [75], no statistically significant differences between the groups (intervention and control) were found. Digital interventions also significantly improved the clinical indicators related to adherence to treatment among patients with cardiovascular disease [58,68,72]. For example, eHealth interventions significantly improved adherence to treatment [84] and blood pressure control [29,37,41,42,45,85]. Recent systematic reviews of mHealth interventions for hypertension [28] and coronary artery disease [30] have provided evidence that mHealth interventions are effective for blood pressure control, self-management, and medication adherence. It should be noted that the overall risk of bias was relatively high in both these studies.

The lack of significant improvements in the outcomes of patients who are chronically ill after digital counseling interventions may be explained by various methodological issues such as a short follow-up period or insufficient power to detect changes in outcomes [31,33,34,85]. A recent umbrella review [31] concluded that telemedicine has the potential to improve clinical outcomes in patients with diabetes; however, it was not found to have a significant and clinically meaningful impact on blood pressure because the outcomes measuring blood pressure showed low overall certainty. Risk of bias; inconsistency; differences in patient populations, settings, and interventions; imprecision; publication bias; and the underreporting of relevant information have been listed as the main reasons why previous reports have only provided low-quality evidence concerning the effectiveness of digital counseling approaches. In addition, the heterogeneity in eHealth definitions also makes between-study comparisons difficult, which are necessary to provide health care professionals with evidence-based recommendations [30,48,86,87].

This review includes a few inherent limitations. The literature search conducted for this review excluded gray literature, which means that relevant studies may have been overlooked. Language limitations were not used during the search process, but only studies published in English, Finnish, and Swedish were considered during the screening process. This may have resulted in language bias.

A further limitation was the varying quality and heterogeneity of the selected studies, that is, sample sizes, type of interventions, and length of follow-up times, which differed among the studies. The sample sizes were small (fewer than 200 patients overall) in 65% (17/26) of the studies. The complex digital counseling interventions were diverse and heterogeneous in content and had various risks of bias in their methodology. Quality assessment was performed using a standardized Joanna Briggs Institute critical appraisal tool for RCTs and quasi-experimental studies [52] to avoid systematic bias. Of the 24 RCT studies, 9 (38%) scored at least 10 points out of 13, whereas 15 (62%) scored less than 10 points out of 13. Of the 24 RCT studies, 2 (8%) had the lowest score, 8 points out of 13. Both quasi-experimental studies were rated as good quality. The highest risk of bias in the selected studies related to blinding of participants and personnel, blinding of outcome assessment, and incomplete outcome data.

Of the 26 studies, 6 (23%) did not measure anxiety, depression, or adherence to treatment as a primary outcome of digital counseling interventions. In addition, several different scales were used to measure the selected primary outcomes. Because of the heterogeneity of the outcomes measured and scales used in the included studies, we could not perform a meta-analysis. This may have introduced additional bias in the results.

The review was strengthened by the use of a systematic and extensive search process that used several databases and was conducted with the assistance of an information specialist. To avoid subjective selection bias, studies were selected for inclusion by 2 researchers (KPP and MK) working independently.

Among the 26 included studies, 10 (38%) digital, web-based interventions demonstrated statistically significant positive effects on anxiety and depression, adherence to treatment, and clinical indicators related to adherence to treatment. Positive, albeit statistically nonsignificant, changes were reported in 69% (18/26) of the studies. These results indicate that digital environments may improve anxiety, depression, and adherence to treatment among patients who are chronically ill, and hence have significant repercussions for the health care sector.