Periodontal health is essential to overall oral health and well-being [1]. Periodontal health is generally defined as the absence of inflammatory periodontal disease [2], encompassing the health of the surrounding support system for the tooth, which includes the gingiva, periodontal ligament, and alveolar bone [3]. A standard definition of periodontal health is crucial for identifying a reference point to determine and assess treatment outcomes [2]. Building upon this understanding, periodontal health is classified into the following 4 levels, as proposed by Lang and Bartold [2], based on various factors such as the condition of the periodontium, the ability to control modifying factors, predisposing factors, and treatment outcomes:

Furthermore, the two primary periodontal conditions, gingivitis and periodontitis, play significant roles in oral health. Gingivitis, the mildest form of periodontal disease, is characterized by localized, reversible inflammation of the gingiva caused by dental plaque [4], whereas periodontitis is a microbe-associated, host-mediated inflammatory condition leading to progressive attachment loss [5]. Chronic conditions such as periodontal disease and dental caries can cause severe pain in advanced stages and may result in challenges with speaking and eating [6]. Additionally, there is a strong association between oral diseases and noncommunicable conditions such as diabetes [7] and cardiovascular disease [8].

In response to the evolving understanding of periodontal diseases, a global workshop was convened in 2017 to develop a new classification system to simplify diagnosing and treating these diseases in general dental practice [9]. This updated classification system addresses the form, severity, and extent of periodontitis and formally incorporates recognized risk factors and their associations with systemic diseases. Critical components of this classification system include staging, which categorizes the severity and extent of the disease, and grading, which predicts future risks and potential health impacts. This classification system allows for precise and focused definitions of periodontal health and gingivitis as follows: (1) patients with an intact periodontium, (2) patients with a reduced periodontium due to causes other than periodontitis, and (3) patients with a reduced periodontium due to periodontitis [9].

The previous classification was formally introduced in 1999 [10]; the notable distinction between the two classifications was the use of the term “periodontitis” instead of “aggressive and chronic periodontitis” [9]. Furthermore, smoking and diabetes were identified as major potential risk factors that could influence the staging of periodontal disease in the new classification [9].

As part of an accurate diagnosis and appropriate treatment strategy, having clear and focused definitions in a classification system is fundamental in promoting periodontal health. This understanding is vital as neglecting periodontal health can lead to significant conditions such as periodontal disease, bone loss, and ultimately tooth loss [10]. A combination of effective oral hygiene practices, routine dental examinations, and a healthy lifestyle is essential to maintain optimal periodontal health. Daily brushing and flossing help eliminate plaque, while professional scaling can remove calculus buildup [1]. The importance of effective self-performed oral hygiene in preventing oral diseases is well-established [11], including the integration of mobile health (mHealth) interventions to promote the self-management of oral health [12].

Indeed, the combination of mHealth technology in periodontal care has significantly impacted the field, offering a promising avenue for promoting oral health awareness and empowering patients to take control of their periodontal health [13,14]. mHealth is a medical and public health practice supported by mobile devices such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices [15]. These mobile apps gather users’ health data, offer customized guidance, and facilitate remote patient data monitoring, thereby fostering collaborative relationships between dental professionals and individuals [16]. The increasing interest in chronic disease management has created opportunities to enhance self-management capabilities [17].

In light of these developments, Toniazzo et al [12] performed a systematic review to summarize the role of mHealth in improving oral health, with the measured outcomes being the plaque index and gingival index (GI). The review findings demonstrated a significant improvement in dental plaque and gingivitis resulting from the use of mobile apps, suggesting that these tools may enhance oral health behaviors. However, the lack of regulation during app development increases the risk of patients accessing inaccurate information [18]. Therefore, this review is designed as a preliminary step prior to the development of an mHealth-promotion app. To our knowledge, no other systematic review has been performed to specifically evaluate mobile apps targeting patients with periodontal disease.

Overall, integrating mHealth technology in periodontal care presents exciting opportunities for advancing oral health promotion and disease management. By embracing these innovations, dental professionals can enhance patient care and improve oral health outcomes while maintaining a comprehensive understanding of periodontal diseases.

The aim of this systematic review and meta-analysis is to evaluate the effectiveness of existing mobile apps or text messages designed to enhance periodontal health among patients compared to the outcomes of patients without any mobile app or digital intervention to support their treatment.

This systematic review has been registered in PROSPERO under registration number CRD42022340827.

The Participants, Interventions, Comparators, Outcomes, and Study design (PICOS) framework is used to guide the eligibility criteria [19,20]. Participants include all patients, regardless of age or sex, with or without periodontal disease, except those who cannot participate independently. The intervention of interest is the use of mobile apps available on Android or iOS devices and mobile phones for sending SMS text messages or web-based interventions. As comparators, this review considers studies that compare the intervention to standard or usual care such as a placebo or control group without using mobile technology. The main outcome is improvement in the periodontal health of participants based on signs of progress in the following indicators: GI, bleeding index (BI), clinical attachment loss (CAL), and periodontal probing depth (PPD). All of these periodontal clinical parameters (described in further detail below) are considered crucial for assessing periodontal health because they provide valuable information about the condition of the gingiva and surrounding tissues [2].

All published randomized controlled trials (RCTs) published in English will be included in the review. Studies aimed at improving tooth mobility and the grey literature will be excluded from this review. There will be no limitations on the time frame for measuring the outcome of the intervention.

The search strategy for identifying potential studies will be designed by 3 team members (RM, DE, and FKD) to ensure a comprehensive exploration of eligible studies. This will involve performing a thorough search across various electronic databases, including PubMed, EBSCOhost, Dentistry & Oral Sciences, ScienceDirect, CINAHL Plus, and Scopus. The search will use combinations of Medical Subject Headings (MeSH) key terms in the title or abstract, including “mobile applications,” “periodontal health,” “periodontitis,” “mHealth,” “smartphone application,” and “app-based intervention.” These terms will be combined using Boolean operators such as “AND” and “OR” to capture relevant studies. Additionally, databases such as the Cochrane Central Register of Controlled Trials, International Clinical Trials Registry Platform [27], and ClinicalTrials.gov [28] will be searched. A final rerun of the search will be performed before the final meta-analysis to include any additional studies. An example of the search strategy for PubMed is provided in Multimedia Appendix 1.

Two independent reviewers (RM and DE) will review all included articles. The selection will be based on the eligibility criteria and discrepancies will be resolved through consultation with a third reviewer (FKD). The identified articles will be imported to Covidence software [29], which is a web software system that assesses the data management process by reviewers during the systematic review process. The screening process will be performed independently by the two reviewers in 2 stages: (1) screening the abstract and (2) screening the full texts. Duplicate records will be removed automatically by Covidence.

Ongoing trials with no results will be flagged and recorded in an “Ongoing studies” table. Articles not meeting the eligibility criteria will be excluded and listed in a “Characteristics of excluded studies” table along with the reasons for exclusion. The study selection process will be described in a PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) flowchart [30]. This protocol adheres to the PRISMA Protocols checklist (see Multimedia Appendix 2).

Two independent reviewers (RM and DE) will extract data into Covidence using a specific form designed for this purpose. Data categories to be extracted include the field of research, sample size, setting, authors, publication year, countries, number of participants, app name and platform, app purpose, intervention period, and outcomes. Any disagreements will be resolved through discussion between the two reviewers. In cases where disagreements are unresolved, the third reviewer (FKD) will be consulted to reach a consensus through discussion.

Two independent reviewers (RM and DE) will assess the methodological quality of the included RCTs using the Cochrane Risk of Bias tool [31]. Any disagreements with quality ratings will be settled by consensus-based discussion.

The following domains for the risk of bias will be assessed for all included studies: random sequence generation (selection bias); allocation concealment (selection bias); blinding (performance bias and detection bias), separated for blinding of participants and personnel and blinding of outcome assessment; incomplete outcome data (attrition bias); selective reporting (reporting bias); and other bias [31].

The risk of bias for each domain will be categorized as high, low, or unclear. The risk of bias assessment outcomes will be documented in a table with a justification of the decision. A description of the criteria for a decision of “low risk,” “high risk,” or “unclear risk” will be documented [31].

All data, regardless of publication quality, will be included in the review. If study details cannot be obtained, the librarian will be consulted. If the study remains unobtainable, it will not be included in the meta-analysis.