Advancing dermatology patients’ quality of life through digital health innovations: a comprehensive systematic review

Abstract

Digital health interventions (DHIs) are increasingly used in dermatology to improve patients’ quality of life (QoL). This systematic review (PROSPERO: CRD420250628076) synthesized 14 studies categorized into four WHO intervention types, including telemedicine, targeted client communication, personal health tracking, and client-to-client communication, and evaluated for their effects on QoL and related psychosocial outcomes. Among telemedicine studies (n = 8), six reported non inferior or superior QoL versus in person care. Targeted client communication (n = 4) showed mixed QoL effects but improved mental health and self efficacy. Personal health tracking (n = 1) did not significantly improve QoL, while client to client communication (n = 1) provided psychosocial support. Overall, DHIs may improve QoL in dermatology, but evidence is limited by methodological heterogeneity, risk of bias, and short follow up. Future evaluations should prioritize standardized core outcome sets and integrate AI with in time adaptive interventions to deliver personalized psychological support, ensuring algorithmic equity from the outset.

Introduction

Dermatologic conditions affect over one-third of the global population, presenting as a diverse spectrum of disorders that result in lasting physical, psychological, and social impairments^1–5. While many of these conditions are chronic and inflammatory, such as psoriasis, acne, and atopic dermatitis, others include acute inflammatory states and skin malignancies such as melanoma. These diseases compromise not only skin integrity but also emotional stability, sleep quality, and self-image^6–8. The psychosomatic burden of visible skin disease is substantial-frequently reinforcing a cycle of stress-induced flare-ups, reduced social participation, and deteriorating mental health^9–14.

The concept of quality of life (QoL) in dermatology encompasses functional capacity, emotional well-being, social interaction, and perception of appearance¹⁵. In this context, traditional face-to-face care, while effective for clinical diagnosis and acute treatment, often fails to address the persistent, day-to-day psychosocial challenges patients face. Constraints such as limited consultation time, geographic barriers to specialists, and the episodic nature of clinic visits often leave patients without adequate longitudinal support for self-management and emotional resilience^16–18.

With technological advancements, digital health interventions (DHIs) have emerged to bridge these care gaps. The World Health Organization (WHO) defines DHIs as discrete functions of a digital technology to achieve health sector objectives¹⁹. In previous literature, DHIs are not merely passive conduits for traditional care; rather, their inherent digital functionalities such as asynchronous image sharing, real-time symptom tracking, and automated feedback, serve as active components and behavioral change techniques that may independently influence clinical and psychosocial outcomes^20,21. These approaches hold special relevance for dermatology due to its visual nature, need for ongoing self-management, and high prevalence of remote consultation use^22–27.

While prior research has explored specific DHIs in dermatology, few synthesize how such interventions holistically impact QoL across a broad range of dermatologic conditions^26,28,29. Moreover, mechanisms linking these interventions to clinical and psychosocial improvements remain under-characterized. Research has established that improvements in self-efficacy, emotional resilience, and social functioning often drive overall QoL enhancement in chronic dermatology³⁰. Consequently, evaluating the true impact of DHI requires a multidimensional approach that captures not only global QoL but also these closely related patient-centered psychosocial outcomes.

Therefore, this systematic review aims to comprehensively evaluate the efficacy of DHIs on QoL and associated psychosocial outcomes in dermatologic patients. To address the conceptual ambiguity between a delivery modality and the intervention itself, this review conceptualizes DHIs as complex interventions rather than passive communication platforms. To provide a standardized synthesis, we systematically mapped the evaluated DHIs based on the WHO Classification of DHIs³¹. By mapping these interventions to established WHO domains, this review seeks to clarify the differential effects of various DHIs on patient QoL, ultimately providing actionable evidence for integrating digital health into dermatologic care.

Results

Study selection and characteristics

A total of 1955 studies were initially retrieved from four major databases. After excluding non-original research and duplicates, 901 articles remained. These were screened based on predefined inclusion and exclusion criteria, ultimately resulting in 12 eligible studies^32–43. Additionally, two highly relevant articles published after the database search were identified through manual citation tracking and journal alerts^44,45, bringing the total number of included studies to 14 (Fig. 1).

Fig. 1. Preferred reporting items for systematic reviews flow diagram for selection of studies.

This figure was created using Microsoft PowerPoint based on the PRISMA template.

The methodological designs of the included literature exhibited significant diversity, including 6 randomized controlled trials (RCTs)^{33–35,39,40,45}, 2 cross-sectional studies^32,43, 5 single-arm interventional studies^{36–38,41,42} and 1 observational study⁴⁴. Geographically, the research was conducted across diverse regions, predominantly in Europe (n = 5; e.g., Germany, UK, Denmark, Iceland)^{35,40,42–44} and North America (n = 3; USA)^32–34, with additional studies from Australia (n = 2)^38,41, China (n = 1)³⁶, and a multinational setting (n = 3)^37,39,45. Regarding sampling approaches, RCTs frequently utilized random sampling^{33–35,39,40,45}, while single-arm, cross-sectional and observational studies largely employed convenience sampling^{32,36–38,41–44}.

Sample sizes ranged significantly from 11 participants in a pilot feasibility trial⁴¹ to 677 participants in large-scale RCTs⁴⁰. Across the included studies, mean participant age ranged from 19.3 ± 3.4 years⁴⁰ to 67 (46–82)³⁸ years, only three studies specifically reported outcomes for older adults (mean age >60 years)^33,38,41. The gender distribution reflected the specific targeted conditions. The overall proportion of females ranged from 0% in androgenetic alopecia⁴³ to 92% in carers of children with atopic dermatitis⁴⁰. The DHIs targeted a broad spectrum of dermatologic conditions. The most frequently evaluated specific diseases were psoriasis (n = 3)^32,34,37 and atopic dermatitis or eczema (n = 3)^35,40,42. Other targeted conditions included melanoma (n = 2)^38,41, chronic spontaneous urticaria (n = 1)⁴⁴, acne (n = 1)³⁶, and androgenetic alopecia (n = 1)⁴³, while three studies enrolled patient cohorts with mixed general dermatologic conditions^33,39,45.

The types of DHI were also diverse. In studies utilizing controlled designs (n = 6), the primary comparators were face-to-face consultations^33,34,39,45 or usual clinical practice^35,40. Mapped to the WHO classification, the majority of studies (n = 8) investigated telemedicine, encompassing real-time video consultations or asynchronous store-and-forward image transfers^{33,34,38,39,41,43–45}. The remaining studies evaluated targeted client communication (n = 4)^36,37,40,42, personal health tracking (n = 1)³⁵, and client-to-client communication (n = 1)³². Follow-up durations ranged from single cross-sectional assessments to longitudinal follow-ups of up to 12 months. The key study characteristics are shown in Table 1. The sample recruitment and baseline information of included studies are listed in Supplementary Table 1. A detailed description of each digital health intervention following the Template for Intervention Description and Replication (TIDieR) checklist is provided in Supplementary Table 2. The yearly distribution and types of included studies are illustrated in Supplementary Fig. 1.

Table 1.

Key characteristics of included studies

Category	Study (Year, Country)	Study Design	Sample Size	Dermatologic Condition	DHIs	Comparator	Follow-up Duration	QoL measure	QoL Findings
Telemedicine	Whited et al., 2013 (USA)	RCT	N = 391	Mixed dermatologic conditions	Store-and-forward teledermatology for managing consultations	In-person dermatology consultation	9 months	Skindex-16	Significant improvement in Skindex-16 scores over 9 months
	Armstrong et al., 2019 (USA)	RCT	N = 296	Psoriasis	Online, collaborative connected-health model	In-person dermatology consultation	12 months	Skindex-16, DLQI	Online care model was found to be equivalent to in-person care in improving quality of life
	Lopez-Liria et al., 2022 (USA&Spain)	RCT	N = 450	Mixed dermatologic conditions	Asynchronous teledermatology(TD)	Monitored conventionally in the hospitalf (F-F/D)	6 months	EQ-5D-5L and Skindex-29	Improving HRQoL outcomes over conventional hospital visits
	Lai-Kwon et al., 2022 (Australia)	Single-arm interventional study	N = 31 (Consented) / 30 (Completed intervention)	Metastatic melanoma	Telehealth survivorship program consisted of nurse-led consultations (MELCARE)	None (Single-arm)	3 months	No (Psychosocial needs were assessed using the Distress Thermometer and a bespoke problem list)	The intervention led to a significant improvement in psychological well-being
	Crosby et al., 2023 (Australia)	Single-arm interventional study	N = 11	Advanced melanoma	Supervised telehealth exercise (accredited exercise physiologist (AEP) via the online video conferencing platform Zoom (Zoom Inc))	None (Single-arm)	8 weeks	EORTC QLQ-C30	QoL was preserved despite patients undergoing checkpoint inhibitor therapy
	von Büren et al., 2023 (Germany)	Cross-sectional study	N = 191	Androgenetic alopecia (AGA)	Direct-to-consumer teledermatology platform	None (Cross-sectional)	6 weeks	No (QoL-related outcomes were assessed using a custom questionnaire)	Teledermatology has potential to have a positive impact on QoL is demonstrated by the finding that self-esteem improved
	Hindelang et al., 2025 (Germany)	Prospective observational study	N = 24	Chronic spontaneous urticaria (CSU)	Digital platform (medflex)	None (Single-arm)	12 months	DLQI	DLQI improved significantly over the 12-month period
	Lopez-Villegas et al., 2025 (USA&Spain)	RCT	N = 450	Mixed dermatologic conditions	Digital platform (Telederma)	In-person dermatology consultation	6 months	EQ-5D-5L	Improving HRQoL outcomes over conventional hospital visits, and more Quality-Adjusted Life Years
Targeted client communication	Liu et al., 2020 (China)	Single-arm interventional study	N = 30	Acne	Dermatological Live Chat Bot	None (Pre-post comparison)	4 weeks	DLQI and CADI	Both CADI and DLQI scores decreased significantly after 4 weeks
	Fortune et al., 2022 (Ireland & USA)	Single-arm interventional study	N = 66 (Recruited) 34 (Completed)	Psoriasis	Digital therapeutic app (Allay)	None (Single-arm)	12 weeks	DLQI	Positive improvement trends in DLQI
	Santer et al., 2022 (UK)	Two independent RCTs	N = 677 (340 carers of children + 337 young people)	Eczema (Atopic Dermatitis)	Online behavioural interventions in addition to usual care(Eczema Care Online, ECO)	Usual care (Standard informational website)	24 weeks (primary) and up to 52 weeks	CHU-9D for children, EQ-5D-5L for young people	No differences between the DHIs and usual care cohorts
	Gudmundsdóttir et al., 2023 (Iceland)	Single-arm interventional study	N = 21	Atopic Dermatitis	Mobile app delivering education, medication reminders, and lifestyle coaching	None (Single-arm)	6 weeks	QoL PROS	QoL PROs improved over time
Personal health tracking	Joergensen et al., 2020 (Denmark)	RCT	N = 83	Atopic dermatitis	Memory button and supportive app for tracking topical treatments	1): control group 2): participants only received the memory buttons	1 month	DLQI	No significant improvement was observed in QoL
Client-to-client communication	Idriss et al., 2009 (USA)	Cross-sectional study	N = 260	Psoriasis	Online support communities providing resources and peer advice	None (Cross-sectional)	Cross-sectional	DLQI, Non-validated Questions	Around half the respondents perceived improvements in their QoL

Quality of life and related patient-centered outcomes

All 14 included studies reported at least one QoL-related outcome, either via validated instruments (n = 11)^{32–37,39–41,44,45} or custom psychosocial measures (n = 3)^38,42,43. The assessment of QoL relied on a variety of established instruments, most notably the Dermatology Life Quality Index (DLQI) (n = 6)^{32,34–37,44}, the EQ-5D (n = 3)^39,40,45, Skindex variants^33,34,39, Child Health Utility-9D (CHU-9D) for children⁴⁰, Cardiff Acne Disability Index (CADI) for acne patients³⁶, and oncology-specific questionnaires, like European Organization for the Research and Treatment of Cancer Core Quality of Life Questionnaire Version 3 (EORTC QLQ-C30)⁴¹.

Alongside global QoL instruments, 9 studies reported secondary patient-centered outcomes^{32–34,36–38,40,43,44}. All patient-related outcomes were categorized into three fundamental domains based on the PROMIS framework^46,47: 1) Mental Health; 2) Self-Efficacy; and 3) Social Health. Figure 2 shows the effectiveness of DHIs across outcome domains. The classification of these outcomes by study is presented in Supplementary Table 3.

Fig. 2. Effectiveness of digital health interventions across outcome domains.

This figure was created using Microsoft PowerPoint.

Telemedicine

Within the telemedicine domain (n = 8)^{33,34,38,39,41,43–45}, evaluations of QoL-related psychosocial constructs were conducted across 4 RCTs^33,34,39,45, 2 single-arm interventional studies^38,41, 1 observational study⁴⁴ and 1 cross-sectional study⁴³. These studies primarily utilized the DLQI, Skindex, and EQ-5D instruments.

Regarding overall QoL, 3 RCTs compared teledermatology to conventional face-to-face consultations^33,34,39. Armstrong et al. (N = 296) conducted an RCT evaluating an online collaborative care model for psoriasis, suggesting that Skindex-16 score reduction in the remote group was equivalent to that of the conventional in-person group³⁴. Similarly, Whited et al.(N = 389) evaluated an asynchronous store-and-forward system for mixed dermatologic conditions, showing non-inferior QoL improvements on the Skindex-16³³. Lopez-Liria et al. and Lopez-Villegas et al. (N = 450) found that an asynchronous teledermatology system improved QoL more than conventional face-to-face visits for patients with mixed dermatologic conditions^39,45. In an observational study, Hindelang et al. (N = 24) observed an improvement in DLQI scores over 12 months among chronic spontaneous urticaria patients using a digital platform for routine follow-ups, without a control group comparison⁴⁴. In oncology settings, Lai-Kwon et al. (N = 31) reported that MELCARE, a nurse-led telehealth survivorship program, maintained overall QoL in metastatic melanoma survivors³⁸.

In evaluating patient-related outcomes, the evidence within this domain was categorized into mental health (n = 5)^{33,34,38,43,44} and self-efficacy (n = 1)⁴⁴. Regarding mental health, both Whited et al. and Armstrong et al. suggested that telemedicine models are as effective as in-person care in reducing disease-related emotional distress^33,34. Similarly, Lai-Kwon et al. found that telehealth mitigated psychological burden³⁸, and Hindelang et al. noted that DHIs maintained patients’ well-being⁴⁴. von Büren et al. observed that teledermatology was associated with improvements in QoL, with 59% of men with androgenetic alopecia reporting improved self-esteem within six weeks of therapy via a digital platform⁴³. In the domain of self-efficacy, Hindelang et al. found that DHIs made it easier for patients to track their treatment plans and organize their lives more autonomously⁴⁴.

Targeted client communication

Among studies evaluating targeted client communication (n = 4)^36,37,40,42, QoL and patient-related outcomes were assessed through 1 RCT⁴⁰ and 3 single-arm interventional studies^36,37,42, relying on the DLQI, EQ-5D, CADI and custom PROs.

Regarding overall QoL, findings were mixed across studies. Santer et al. conducted two independent RCTs focusing on atopic dermatitis, including carers of 340 children and 337 young people⁴⁰. Their findings indicated that the use of an online behavioral intervention, Eczema Care Online, observed no differences between the DHI and usual care cohorts in improving QoL. Liu et al. investigated an intervention that combined a self‑regulation‑based patient education module with a mobile application featuring an artificial intelligence (AI)‑powered live chat bot for acne patients(N = 30)³⁶. After 4 weeks, the intervention reduced CADI scores (from 7.45 to 4.15, p < 0.001), and QoL also improved (with DLQI from 11.34 to 4.70, p < 0.001), indicating alleviation of disease‑related disability and daily life disruptions caused by acne. Furthermore, Fortune et al. (N = 66) reported that participants completing the digital therapeutic program experienced improvements in their psoriasis-specific quality of life, as measured by the DLQI (p = 0.002, d = 1.06), with covariate analysis revealing an interaction between physical symptom reduction and dermatology-specific QoL improvements (F[1,31] = 60.92, p = 0.001, d = 2.84)³⁷. Gudmundsdóttir et al. (N = 21) prospectively evaluated an app integrating health education and lifestyle coaching⁴², all users reported improvements in QoL PROs over time.

Beyond QoL improvements, targeted client communication interventions offer benefits in related patient-centered outcomes, specifically within the domains of mental health (n = 1)³⁷ and self-efficacy (n = 3)^36,37,40. Regarding mental health, Fortune et al. noted that DHI-based education reduced clinical depression rates (p = 0.004,d = 0.96)³⁷. In the domain of self-efficacy, Liu et al. reported improvements in disease-coping efficacy after a 4-week intervention (mean ASRI scores rising from 56.09 to 70.07, p < 0.001)³⁶. Similarly, Fortune et al. observed increased psychological resilience among users (p = 0.01,d = 0.66)³⁷. Santer et al. found that at 24 weeks, DHIs improved enablement scores for both parent/carer (adjusted mean difference: −0.7, 95% CI: −1.0 to −0.4) and young adult cohorts (adjusted mean difference: −0.9, 95% CI: −1.3 to −0.6), better equipping them to actively manage their condition⁴⁰.

Personal health tracking

Consistent with our inclusion criteria, one study evaluating a personal health tracking intervention was retained (n = 1)³⁵. This intervention involved medication reminders rather than monitoring alone. QoL was assessed using the DLQI, while objective disease severity metrics were reported separately as contextual clinical outcomes.

In this RCT, Joergensen et al. (N = 83) explored combining an electronic memory button with a tracking app for atopic dermatitis³⁵. After one month, the intervention group showed decreases in objective disease severity and daily symptom burden, but overall QoL did not significantly differ between groups.

Client-to-client communication

The single observational survey (n = 1) focusing on client-to-client communication utilized subjective self-reported assessments to evaluate the impact of online community engagement on overall QoL³².

Regarding overall QoL, Idriss et al. conducted a comprehensive survey evaluating 260 active participants across five online support communities for psoriasis³². The data suggested that although these active users generally faced significant QoL impairment at baseline, around half the respondents (49.5%) perceived improvements in their QoL since becoming an online support site user.

Beyond QoL improvements, client-to-client communication interventions offer benefits in related patient-centered outcomes, specifically within the domains of self-efficacy and social health (n = 1)³². Regarding self-efficacy, Idriss et al. found that 65.7% of surveyed patients felt online support communities helped them regain a sense of control over their condition. In the domain of social health, digital peer interactions were reported as valuable, with most users discovering peers who actively listened (59.2%) and helped with personal emotional issues (54.2%).

Implementation and engagement outcomes

Implementation-related outcomes, including engagement, acceptability, and satisfaction, were reported in 13 studies^{32–35,37–45}.

Engagement and feasibility

Eleven studies reported engagement and feasibility with DHIs, and together showed mixed findings^{32–35,37,38,40–44}. A majority of studies (n = 7)^{32,34,35,38,40,42,44} demonstrated high intervention completion and longitudinal retention rates. Specifically, several programs achieved high retention, such as Lai-Kwon et al. (97%)³⁸ and Hindelang et al. (96% over 12 months)⁴⁴. Gudmundsdóttir et al. reported a 95% short-term completion rate⁴². Long-term feasibility was further validated. Armstrong et al. found that an online care model achieved an 89% retention rate over 12 months, comparable to the 91% in traditional care³⁴. Santer et al. found the 24-week follow-up rates exceeding 90% for both carers and young patients⁴⁰. Additionally, Joergensen et al. reported an 86.5% completion rate for a multi-modal system³⁵, and Idriss et al. observed an 87.5% weekly engagement rate within online forums³².

Conversely, some studies (n = 4) reported challenges regarding initial uptake and long-term program completion^33,37,41,42. Whited et al. noted an initial uptake rate of 34%³³, and Crosby et al. reported a moderate initial recruitment rate of 48% among advanced melanoma patients⁴¹. Fortune et al. showed a 51.5% completion rate over 12 weeks³⁷. Furthermore, Gudmundsdóttir et al. found that while overall completion was high, participation in non-mandatory tasks was significantly lower⁴². Despite these variations, von Büren et al. reported that 87% of patients maintained engagement to the DHI protocols⁴³.

Acceptability and satisfaction

Patient acceptability and satisfaction were consistent across 9 studies^{32–34,37,38,41–44}. Quantitative evidence from Whited et al. showed no significant difference in satisfaction between teledermatology (86.8%) and in-person care (92.1%)³³. High acceptability was further supported by a 100% recommendation rate in Lai-Kwon et al.³⁸, a 92% preference for DHI-based care in Hindelang et al.⁴⁴, a high usability score of 6.2/7 in Gudmundsdóttir et al.⁴², and an 88% clinical tolerance rate in Crosby et al.⁴¹.

Qualitative data aligned with these findings. Fortune et al. noted that patients requested continued access post-study³⁷. Idriss et al. reported that the perceived utility of online support (86.5%) was higher than that of traditional consultations (51.9%)³². Armstrong et al. found that patients described the DHI as safe, accessible, equitable, and efficient³⁴. Regarding barriers to acceptability, Armstrong et al. found patient embarrassment regarding sensitive area imaging³⁴.

Healthcare efficiency and access

At the system level, several studies (n = 7) suggested that DHIs were associated with healthcare efficiency and access outcomes^{32–34,39,40,44,45}. Idriss et al. identified geographic and scheduling barriers associated with traditional care³². Similarly, Armstrong et al. reported a reduction in unnecessary specialist referrals³⁴, and Whited et al. observed a decrease in required clinic visits from 1.55 to 1.10 over 9 months³³. Physicians in the study by Hindelang et al. reported that remote applications were clinically reliable and time-saving⁴⁴.

Regarding economic outcomes, Armstrong et al. noted the near elimination of patient travel burdens³⁴. Lopez-Villegas et al. showed that teledermatology saved patients both time (59.73 minutes) and total personal costs (77.59%)³⁹, and further indicated that patients using this platform gained more Quality-Adjusted Life Years (+0.05 QALYs) compared to those receiving standard care⁴⁵. Finally, Santer et al. highlighted the public health value of these interventions, driven by their low cost, high scalability, and absence of identifiable harms⁴⁰.

Risk of bias and quality assessment

Overall, the methodological quality of the included studies (n = 14) varied substantially, ranging from low to high risk of bias across different study designs (Table 2). Among the RCTs (n = 6)^{33–35,39,40,45}, assessments using the Cochrane Risk of Bias 2 (RoB 2) tool⁴⁸ revealed mixed methodological quality (Figs. 3 & 4). Two studies^39,45 were judged to have a low risk of bias, two^34,40 raised some concerns, and two^33,35 were evaluated as high risk. The domains contributing to elevated risk ratings included outcome measurement and the randomization process. For non-randomized interventional studies (single-arm designs, n = 5)^{36–38,41,42}, evaluation using the ROBINS-I tool⁴⁹ indicated that two studies^36,41 were assessed at a moderate risk of bias, and serious risk in three^37,38,42 (Figs. 5 & 6). Observational and cross-sectional studies (n = 3)^32,43,44, evaluated using the Joanna Briggs Institute (JBI) critical appraisal tools⁵⁰, showed low risk of bias in two studies^32,44 and moderate risk in one study⁴³.

Table 2.

Summary of risk of bias and quality assessment

a Risk of Bias of the Randomized Controlled Trials (RCTs) Assessed Using the RoB 2.0 Tool
Study ID	Randomization process	Deviations from intended interventions	Mising outcome data	Measurement of the outcome	Selection of the reported result	Overall Bias
Lopez-Villegas et al., 2024	Low	Low	Low	Low	Low	Low
Lopez-Liria et al., 2022	Low	Low	Low	Low	Low	Low
Santer et al., 2022	Low	Low	Low	Some concerns	Low	Some concerns
Joergensen et al., 2020	Some concerns	Low	Low	High	Some concerns	High
Armstrong et al., 2019	Low	Low	Low	Some concerns	Low	Some concerns
Whited et al., 2013	Low	Low	Low	High	Low	High
b Risk of bias of the single-arm studies assessed using the ROBINS-I V2 tool
Study ID	Bias due to confounding	Bias in classification of interventions	Bias in selection of participants into the study	Bias due to missing data	Bias arising from measurement of the outcome	Bias in selection of the reported result	Overall assessment
Crosby et al., 2023	Moderate	Low	Low	Moderate	Moderate	Moderate	Moderate
Gudmundsdóttir et al., 2023	Moderate	Low	Low	Low	Serious	Low	Serious
Fortune et al., 2022	Serious	Low	Moderate	Moderate	Moderate	Low	Serious
Lai-Kwon et al., 2022	Moderate	Low	Low	Low	Serious	Low	Serious
Liu et al., 2020	Moderate	Low	Low	Low	Moderate	Low	Moderate
c Quality Assessment of Observational and Cross-sectional studies appraised using the Joanna Briggs Institute (JBI) critical appraisal tools
Study ID	Were the criteria for inclusion in the sample clearly defined?	Were the study subjects and the setting described in detail?	Was the exposure measured in a valid and reliable way?	Were objective, standard criteria used for measurement of the condition?	Were confounding factors identified?	Were strategies to deal with confounding factors stated?	Were the outcomes measured in a valid and reliable way?	Was appropriate statistical analysis used?	Overall assessment
Hindelang et al., 2024	Yes	Yes	Yes	Yes	Yes	Unclear	Yes	Yes	Low
von Büren et al., 2023	Yes	Yes	Yes	Unclear	Yes	Yes	Unclear	Yes	Moderate
Idriss et al., 2009	Yes	Yes	Unclear	Unclear	Yes	Yes	Yes	Yes	Low

Fig. 3. Risk of bias graph of RCTs (RoB 2 tool).

The risk of bias visualization was were performed by using Hiplot (ORG) (https://hiplot.org).

Fig. 4. Risk of bias summary of RCTs (RoB 2 tool).

The risk of bias visualization was were performed by using Hiplot (ORG) (https://hiplot.org).

Fig. 5. Risk of bias graph of non-randomized interventional studies (ROBINS-I V2 tool).

The risk of bias visualization was were performed by using Hiplot (ORG) (https://hiplot.org).

Fig. 6. Risk of bias summary of non-randomized interventional studies (ROBINS-I V2 tool).

The risk of bias visualization was were performed by using Hiplot (ORG) (https://hiplot.org).

Discussion

To our knowledge, this is the first systematic review to comprehensively evaluate both the specific types of DHIs and their multidimensional impacts on the QoL in dermatology patients. By synthesizing evidence from 14 eligible studies and mapping them to the WHO classification, our findings indicate that DHIs may provide potential benefits across multiple QoL domains, though efficacy varies significantly by intervention modality.

Among the evaluated types, telemedicine possesses the most substantial evidence base. RCTs comparing teledermatology with conventional face‑to‑face care found it to be non‑inferior in improving QoL^33,34,39,45, aligning with previous systematic reviews⁵¹. While traditional in-person consultations remain the gold standard for absolute diagnostic accuracy, previous studies demonstrate that teledermatology achieves high diagnostic concordance while reducing time to diagnosis and enhancing patient satisfaction^27,52. Furthermore, findings regarding cost-savings and QALY gains⁴⁵ echo the growing body of health economic evidence supporting telemedicine as a sustainable solution for overstretched healthcare systems⁵³. However, due to the highly variable risk of bias across these trials, the QoL benefits of telemedicine should be viewed as promising trends rather than definitive evidence.

Interventions using targeted client communication showed mixed effects on global QoL but consistently improved self‑efficacy and mental health^36,37,40,42. This pattern also finds support in systematic reviews of other chronic conditions, such as end‑stage kidney disease, where digital health interventions significantly improve depression but the evidence for overall QoL changes is of low quality⁵⁴. Personal health tracking reduced disease severity but did not translate into QoL improvement³⁵, a phenomenon likely attributable to “tracking fatigue” that can emerge when self-monitoring lacks integrated clinical feedback⁵⁵. Meanwhile, client‑to‑client communication showed that online communities were perceived as valuable by most users³². Given the profound psychosocial stigma and altered body image associated with visible skin conditions⁵⁶, digital peer-to-peer platforms may address an important unmet need by providing experiential knowledge and emotional validation that common clinical encounters often lack⁵⁷. However, the cross‑sectional design of current evidence precludes causal inference.

Most studies reported high retention rates (85‑97%) and satisfaction^{32,34,35,38,40,42,44}, suggesting that DHIs are acceptable in practice. Nonetheless, noted barriers such as low initial uptake^33,41, poor engagement with non‑mandatory tasks⁴², and patient embarrassment about imaging sensitive areas³⁴, highlight significant real-world challenges. Qualitative research further emphasizes concerns about data privacy, digital literacy among older patients, workflow integration, and reimbursement^58,59. Consequently, hybrid care models that integrate digital triage with traditional in-person consultations may represent a more realistic and acceptable pathway for implementation⁶⁰. Such hybrid approaches are exemplified by Liu et al., who combined in‑person educational module with an AI‑powered mobile chat bot as an adjunctive digital support for acne patients³⁶.

The evidence gaps identified in this review suggest that static, one‑size‑fits‑all DHIs may be insufficient for the complex psychosocial needs of dermatology patients. AI and personalisation are future directions. The evolution toward precision dermatology envisions AI-powered digital assistants capable of integrating multidimensional clinical and patient-generated data to predict disease flares⁶¹. To translate these predictive capabilities into meaningful psychosocial benefits, future digital health models may adopt the framework of Just-in-Time Adaptive Interventions (JITAIs). By leveraging real-time patient feedback, JITAIs can deliver context-sensitive behavioral nudges and tailored psychological support at moments of heightened vulnerability, potentially mitigating anxiety associated with unpredictable exacerbations and improving long-term engagement⁶². At the same time, the application of AI raises important challenges related to regulation, algorithmic equity, and the digital divide, which should be addressed early in the design and implementation of digital health interventions⁶³.

However, several limitations must be acknowledged. The methodological quality of the included studies was variable. Among the six RCTs, four presented some risk of bias, while three of the five single-arm studies were rated as serious risk due to the absence of comparator groups^37,38,42. Follow-up durations were generally ≤12 months, limiting the assessment of long-term effects. In addition, most studies did not report participants’ digital literacy, which may restrict generalisability to older or vulnerable populations.

Outcome measures were heterogeneous (DLQI, Skindex, EQ-5D, CADI, CHU-9D, etc.). Previous teledermatology trials have reported more than 55 different instruments, with varying levels of validation, which complicates cross-study comparisons and limits the feasibility of meta-analysis⁶⁴. Adoption of standardized Core Outcome Sets (COS), such as those proposed by the Cochrane Skin Group (CSG-COUSIN), may improve consistency of PRO measurement in future studies⁶⁵. Furthermore, mediation analyses were rarely conducted, leaving the mechanisms linking DHIs to QoL improvements unclear.

This review also has limitations. Restricting inclusion to English-language publications and excluding grey literature may have introduced language and publication bias. In addition, most studies were conducted in high-income countries, potentially limiting generalisability to low- and middle-income settings. Finally, although the WHO classification provides a standardized framework, it may oversimplify complex multi-component interventions.

Overall, current evidence tentatively suggests that DHIs may improve quality of life and alleviate psychosocial burden among dermatology patients. However, the available evidence remains limited by methodological heterogeneity, variable risk of bias, and predominantly short-term follow-up. These findings should therefore be interpreted as exploratory rather than definitive. Future research should prioritize rigorously designed, long-term randomized trials, adopt standardized core outcome sets, and explore personalized, adaptive DHIs that better address individual psychosocial needs.

Methods

Study design and registration

This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines⁶⁶ and was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO; ID: CRD420250628076). The completed PRISMA 2020 checklist is provided in Supplementary Table 4 and PRISMA 2020 for abstracts checklist is in Supplementary Table 5.

Search strategy

The search strategy was developed iteratively by the research team based on preliminary scoping searches, controlled vocabulary (MeSH and Emtree terms), and previously published systematic reviews. A comprehensive search was conducted in PubMed, Embase, the Cochrane Library, and Web of Science from database inception to June 2024. Search terms were structured around three key concepts: dermatologic conditions; digital health interventions; and quality of life.

In addition to the database search, recently published studies (e.g., late 2024 to early 2025) were identified through manual screening of reference lists and were incorporated during manuscript preparation. Only studies published in English were included, and no restrictions were applied regarding publication year. The specific search queries used for each database are listed in Table 3.

Table 3.

Search queries used in four databases

Databases	Search queries
PubMed	(dermatolog*[Title/Abstract] OR skin disease*[Title/Abstract] OR skin disorder*[Title/Abstract] OR “Dermatology”[Mesh] OR “Skin Diseases”[Mesh]) AND (eHealth[Title/Abstract] OR mHealth[Title/Abstract] OR telehealth[Title/Abstract] OR telemedicine[Title/Abstract] OR digital health*[Title/Abstract] OR mobile health*[Title/Abstract] OR internet[Title/Abstract] OR “Telemedicine”[Mesh]) AND (“Quality of Life”[Mesh] OR quality of life[Title/Abstract] OR QoL[Title/Abstract] OR HRQoL[Title/Abstract] OR well-being[Title/Abstract] OR patient-reported outcome*[Title/Abstract]).
Web of science	ALL = ((((“Dermatology” OR “Skin Disease” OR “Skin Cancer” OR dermatolog* OR skin disorder* OR “Skin Diseases”) AND (“Digital Technology” OR “Digital Health” OR “EHealth” OR “Telemedicine” OR “telehealth” OR “Mobile Applications” OR “MHealth” OR “E-Health” OR “M-Health” OR “eHealth” OR “mHealth” OR “e-health” OR “m-health” OR “internet” OR “telehealth” OR “digital” OR “internet”)) OR “teledermatology”)AND (“Quality of Life” OR “Health-Related Quality of Life” OR “QoL” OR “HRQoL” OR “Patient-Reported Outcomes” OR “well-being”)).
Embase	(dermatos*:ab OR skin disease*:ab OR skin disorder*:ab OR ‘Dermatology’/em OR ‘Skin Diseases’/em) AND (eHealth:ab OR mHealth:ab OR telehealth:ab OR telemedicine:ab OR digital health*:ab OR mobile health*:ab OR internet:ab OR ‘Telemedicine’/em) AND (‘Quality of Life’/em OR quality of life:ab OR QoL:ab OR HRQoL:ab OR well-being:ab OR patient-reported outcome*:ab).
Scopus	TITLE-ABS-KEY ((((“Dermatology” OR “Skin Disease” OR “Skin Cancer” OR dermatolog* OR skin AND disorder* OR “Skin Diseases”) AND (“Digital Technology” OR “Digital Health” OR “EHealth” OR “Telemedicine” OR “telehealth” OR “Mobile Applications” OR “MHealth” OR “E-Health” OR “M-Health” OR “eHealth” OR “mHealth” OR “e-health” OR “m-health” OR “internet” OR “telehealth” OR “digital” OR “internet”)) OR “teledermatology”) AND (“Quality of Life” OR “Health-Related Quality of Life” OR “QoL” OR “HRQoL” OR “Patient-Reported Outcomes” OR “well-being”)).

Eligibility criteria

Population

Studies were included if they involved patients with a primary diagnosis of a dermatologic condition, including but not limited to psoriasis, atopic dermatitis, acne, chronic urticaria, and skin cancers. Studies focusing primarily on non-dermatologic systemic diseases (e.g., systemic lupus erythematosus, multiple sclerosis, systemic sclerosis, or diabetic foot ulcers) were excluded, even if dermatologic manifestations were present.

Interventions

DHIs were defined as patient-facing interventions delivered through digital technologies, designed to improve health outcomes or patient experience^67,68. Interventions were categorized according to the WHO Classification of Digital Health Interventions into four domains³¹: telemedicine (2.4), targeted client communication (1.1), personal health tracking (1.4), and client-to-client communication (1.3).

Telemedicine includes remote consultations between clients and providers, remote monitoring of client health data, transmission of medical information, and case management between providers. In this review, telemedicine was treated as an active intervention rather than merely a delivery modality.

Targeted client communication refers to the transmission of health information, alerts, or reminders to specific client populations with the aim of influencing health behaviour or supporting self-management.

Personal health tracking refers to the use of mobile applications or wearable devices enabling patients to monitor their own health status. To qualify as an intervention, such tracking was required to include active components (e.g., reminders or educational content).

Client-to-client communication refers to communication between patients as peers within organized digital communities or support groups.

Because many real-world DHIs contain overlapping functions, each study was assigned to the domain that best reflected its primary intervention purpose.

Eligible interventions included teledermatology and digitally mediated care delivery; mobile applications and digital self-management tools; online educational or behavioral interventions; and digital psychosocial or peer-support programs.

Interventions were excluded if they consisted solely of diagnostic or triage-focused artificial intelligence tools, provider-only digital systems, or monitoring-only tools without a clear interventional intent.

Outcomes

The primary outcome was QoL, defined as: outcomes measured using validated QoL instruments (e.g., DLQI, EQ-5D, Skindex), or explicitly reported patient-centered outcomes related to QoL domains. All instruments listed above were used exclusively as validated outcome measures and did not serve as interventional components of any digital health intervention. Guided by the National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information System (PROMIS)^46,47, the secondary patient-centered outcomes were systematically categorized into three fundamental domains: mental health, self-efficacy, and social health.

Studies without outcome data related to QoL were excluded.

Study design

RCTs, prospective single-arm studies (encompassing feasibility, pretest-posttest, and interventional designs), observational and cross-sectional studies were included. Reviews, commentaries, case reports, conference abstracts, and non-peer-reviewed materials were excluded.

Study selection

All records were imported into Zotero for deduplication. Two reviewers (Y.C. and X.L.) independently screened titles and abstracts to exclude irrelevant studies, followed by full-text screening to assess eligibility based on the predefined criteria. Disagreements were resolved by a third reviewer (L.G.) through discussion and consensus.

Data extraction

A standardized data extraction form was used to collect key study characteristics, including the first author, year of publication, country, study design, sample size, recruitment method, follow-up duration, dermatologic condition, type of DHI, comparator, QoL assessment tools, and key outcomes.

While comprehensively validated instruments remain the standard for quantifying health-related QoL, dermatologic research frequently utilizes custom questionnaires or specific psychological metrics. To ensure a comprehensive synthesis of patient-reported outcomes and address this methodological heterogeneity, we adopted a multidimensional approach during data extraction using established frameworks.

Data extraction was conducted independently by two reviewers (Y.C. and X.L.). Any discrepancies were resolved through discussion, with involvement of a third reviewer (L. G.) where necessary.

Data synthesis

Given the substantial heterogeneity in study designs, intervention types, and outcome measures, a quantitative meta-analysis was not feasible. Therefore, we conducted a structured narrative synthesis. QoL and related patient-centered outcomes were grouped according to the four predefined WHO categories of DHIs: telemedicine, targeted client communication, personal health tracking, and client-to-client communication. For each category, we summarized the number of studies, study designs, participant characteristics, QoL measures, and direction of effect (positive, neutral, or negative) for global QoL and patient-centered outcomes (mental health, self-efficacy, social health). Implementation and engagement outcomes were grouped according to the NIH PROMIS: mental health; self-efficacy; and social health. Findings were reported descriptively without statistical pooling.

Risk of bias and quality assessment

Given the methodological heterogeneity of included studies, design-specific tools were used to ensure appropriate and rigorous assessment of risk of bias. Randomized controlled trials were assessed using the Cochrane RoB 2 tool⁴⁸. Non-randomized interventional studies were evaluated using the ROBINS-I tool⁴⁹. Observational and cross-sectional studies were appraised using the JBI critical appraisal checklists appropriate to each study design⁵⁰. All assessments were performed independently by two reviewers (Y.C. and X.L.), with disagreements resolved through discussion with a third reviewer (L.G.).

Author contributions

Conceptualization: Linghong Guo.Material preparation, data collection, and analysis: Xinjin Liu, Yifei Cheng, Xu Liu, Yichen Liu, Dingling Li, Weihong Guo, and Haoyu Jiang.Writing - original draft: Xinjin Liu and Yifei Cheng.Writing- review & editing: Xinjin Liu, Yifei Cheng, Xian Jiang, and Linghong Guo. All authors read and approved the final manuscript.

Data availability

Data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

No code was used for data processing or analysis.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1038/s41746-026-02891-0.