Remote monitoring in chronic immune-mediated inflammatory diseases: A systematic review of active and passive approaches

To the Editor: Chronic immune-mediated inflammatory diseases (IMIDs) such as atopic dermatitis (AD), psoriasis (PSO), and chronic spontaneous urticaria (CSU) are common, long-lasting conditions that strongly affect quality of life. Management traditionally relies on intermittent in-clinic assessments, which are subjective and fail to capture daily symptom fluctuations. Remote patient monitoring (RPM) has emerged to address this gap by collecting real-time data outside clinical settings. RPM approaches can be active, requiring patient input such as smartphone image capture, or passive, relying on wearables that continuously collect data.¹

A systematic review was conducted following PRISMA guidelines, searching 5 databases (PubMed, Embase, Web of Science, Cochrane, and Emcare) for original studies published up to January 2, 2025 (Supplementary Fig 1, available via Mendeley at https://doi.org/10.17632/gz5vcj4nyk.1). Inclusion was limited to non-invasive RPM tools for dermatology-specific symptoms or disease activity in IMIDs. Studies relying solely on electronic patient-reported outcomes or digital diaries were excluded. Risk of bias was assessed using validated tools (Supplementary Table II, available via Mendeley at https://doi.org/10.17632/gz5vcj4nyk.1).

In total, 42 studies across 5 conditions were identified with 6656 patients: AD (26 articles, 1507 patients), PSO (7 articles, 1978 patients), hidradenitis suppurativa (HS, 3 articles, 326 patients), CSU (2 articles, 2598 patients), and acne vulgaris (AV, 4 articles, 247 patients), together with 852 healthy controls (for full details, see Supplementary Table I, available via Mendeley at https://doi.org/10.17632/gz5vcj4nyk.1). Overall, 21 studies (50%) used only active tools, 20 studies (48%) used only passive tools, and 1 study (2%) included both. Most of the evidence came from AD and PSO, which contributed 79% of all included studies. RPM tool usage varied by indication (Table I). 73% of AD studies involved passive monitoring using wearables that quantified sleep disruption and nocturnal scratching. These objective measures correlated strongly with clinical severity scores and were able to track treatment response.² Conversely, 71% of PSO studies used active tools. Active tools in both conditions included smartphone applications for symptom tracking and patient-captured imaging, aligning with clinician scoring and improved follow-up outside the clinic.³ All HS, CSU, and AV studies used active monitoring. HS studies used patient-captured images and machine learning,⁴ while CSU and AV studies focused on application-based symptom tracking and automated image grading. Key findings on tools and designs are summarized in Table II.

Table I.

Overview of active and passive RPM approaches and patient centricity by indication

Indication	Total studies	Total number of patients	Active RPM studies (n, %)	Passive RPM studies (n, %)
AD	26	1507	8 (30)∗	19 (73)∗
PSO	7	1978	5 (71)	2 (29)
HS	3	326	3 (100)	0 (0)
CSU	2	2598	2 (100)	0 (0)
AV	4	247	4 (100)	0 (0)

^∗One of the studies used both active and passive RPM, and it is included in both columns.

Table II.

Overview of RPM tools, study designs, indications, measurement domains with key objectives, strengths, and limitations

RPM tool (n)	Study design (n)	Indication (n)	Measurement	Objectives	Strengths	Limitations
Passive
Wearables (21)	RCT (2), Validation (1), Observational (19∗), Experimental (1)	AD (19†) PSO (2)	Sleep disturbances Scratching behavior	Comparing objective metrics between patient and HC	Objective, continuous data collection	Limited evidence in other indications Discrepancies with subjective scores
Active
Digital imaging (12)	RCT (1) Validation (4), Observational (6) Single-center CT (1)	AD (3) PSO (3) HS (1) CSU (1) AV (4)	Lesion severity Visual clinical signs	Accuracy of ML based image analysis and remote severity scoring	High reliability for remote assessment. Agreement with physicians Image quality variability	Image quality variability Limited in darker skin types or severe disease
Smart-phone app (10)	RCT (1∗) Observational (7) Feasibility (2)	AD (5†) PSO (2) HS (2) CSU (1)	ePRO QoL	Facilitate self-monitoring and symptom reporting Real-time tracking	Patient engagement	Long-term adherence Technical limitations

n refers to the number of studies. Measurement domains and objectives represent general categories synthesized from the detailed study data provided in Supplementary Table I, available via Mendeley at https://doi.org/10.17632/gz5vcj4nyk.1.

AD, Atopic dermatitis; AV, acne vulgaris; CSU, chronic spontaneous urticaria; CT, clinical trial; ePRO, electronic patient reported outcomes; HC, healthy controls; HS, hidradenitis suppurativa; ML, machine learning; PSO, psoriasis; QoL, quality of life; RCT, randomized clinical trial; RPM, remote patient monitoring.

^∗Some studies included multiple methodological components such as observational, randomized controlled, and validation phases. These studies were therefore counted across more than 1 design category.

^†One of the studies used both active and passive RPM and it is included in both columns.

RPM can transform the management of cutaneous IMIDs by enhancing symptom monitoring and patient engagement, potentially lowering the burden for both patients and healthcare providers.^1,3 Passive tools might reduce patient burden by collecting data unobtrusively, while active tools can capture flare-ups and visible changes through real-time symptom or image uploads.^2,5 Successful integration of these tools requires aligning patient and physician needs while preserving patient physician contact.

The absence of randomized trials, small cohort sizes, and significant data gaps in pediatric populations limits the current evidence base. Furthermore, imaging validation across skin tones and disease severities, as well as standardized app features, are needed to confirm clinical utility. In conclusion, RPM shows particular promise, especially in AD and PSO, while AV, HS, and CSU remain underexplored and require standardized validation research.

Conflicts of interest

Dr van Doorn reported support from Novartis, AbbVie, Pfizer, LEO Pharma, Sanofi, Lilly, Janssen, UCB, BMS, Celgene, and Third Harmonic outside the submitted work. Oliver, Rousel, Schoones, Niehues, van Ee, Versteeg, van der Zon, Pruijn, Balak, Rissmann, and Exadaktylos have nothing to declare in the scope of this work.