What makes depression apps safe, effective, and trustworthy?

By Hugo Francisco de SouzaReviewed by Lauren HardakerJan 15 2026

The research lays out a clearer path for identifying depression apps that are safe, effective, and worth recommending.

Study: Validation and selection of criteria for evaluating apps for managing depression: a Delphi study. Image credit: myboys.me/Shutterstock.com

In a recent study published in BMJ Open, researchers introduced a novel consensus-based framework that lays the groundwork for a future assessment tool designed to help experts and consumers evaluate the abundance of mobile health (mHealth) apps targeting depression and mental health support.

The framework employed a modified Delphi method, involving health professionals, technology experts, and patients, to propose and screen 51 potential evaluation metrics, which were subsequently distilled into 28 essential criteria intended for use in a structured app-assessment tool.

Study findings revealed a strong prioritisation among participants for data privacy and clinical effectiveness over other commonly emphasised app features, such as engagement and self-tracking. These findings will notably form the foundation of "EvalDepApps," a future tool designed to help users and clinicians identify safe, evidence-based digital interventions.

Why choosing a trustworthy depression app remains difficult

Depressive disorder, or commonly referred to as just depression, is a spectrum of mental conditions characterized by persistent feelings of sadness, often resulting in observable changes in behavior and daily (routine) functioning. The condition is alarmingly growing globally at unprecedented rates, reportedly affecting approximately 5 % of the adult population worldwide.

While the World Health Organization (WHO) emphasizes the potential of emergent digital technologies to bridge care gaps, the reality of the app store is often murky. A recently published analysis found that among 30 depression apps subjected to in-depth scrutiny, only 26.7 % were supported by scientific evidence.

Furthermore, mobile health (mHealth) apps targeting depression and mental health support rarely face any standardization or regulatory pressure. Currently, users are forced to rely on subjective or unverifiable reviews, which provide little insight into an app's medical validity, its handling of sensitive personal data, or its alignment with clinical best practice.

A depression-centric approach to mHealth evaluation would allow experts and patients alike to make informed choices when deciding upon their next digital anti-depression aid.

Using Delphi methods to define depression-specific app standards

The present study aimed to address this pressing need by proposing a specific, consensus-based set of criteria tailored exclusively for depression management apps. The study employed a modified Delphi study methodology, a structured communication technique used to reach a consensus among a panel of experts, to help consolidate the opinions, perceptions, and priorities of included stakeholders across two iterative rounds of scoring.

The stakeholders, referred to as study participants, comprised health professionals (psychiatrists, psychologists, nurses), health technology experts, and individuals diagnosed with depression. These participants (n = 43) were carefully selected to ensure a holistic review of the issue from both clinical and lived-experience perspectives.

The study methodology began with an author-conducted literature review to identify potential criteria for standardizing mHealth apps and assessing their performance. Of the 60 potential criteria identified, an internal review shortlisted 51 criteria, with nine criteria found to be redundant, for participant review.

The participant, or panel, review process was carried out over two rounds of voting:

1. Round 1: Participants rated the relevance of each criterion on a 6-point Likert scale (0 to 6).
2. Round 2: Criteria that did not reach a decisive consensus in the first round were sent back to the panel for re-evaluation alongside previous voting results and summary feedback.

Only the criteria meeting internal high-consensus thresholds were included in the study recommendations. Specifically, a criterion was considered high consensus only if 80 % or more of the respondents scored it a 5 or 6, corresponding to very important.

What stakeholders really want from depression apps

The two rounds of panel review resulted in the identification of 28 criteria, down from 51, meeting the preferences and requirements of both experts and patients. Panel participation was notably robust, with 59 % of invited experts responding in Round 1 and 53.4 % in Round 2.

Study outcomes revealed an overwhelming participant prioritization of:

1. Safety and privacy (25 % of included criteria): All proposed criteria regarding safety and privacy achieved maximum consensus in the very first round. For example, a criterion regarding the transfer of data to third parties achieved 100 % agreement.
2. Clinical effectiveness (25 %): The requirement that an app's recommendations be evidence-based achieved 95.7 % agreement, reflecting strong concern for demonstrable therapeutic valu

Unexpectedly, and in contrast to findings from broader mHealth evaluation studies, criteria related to health indicators, such as tracking sleep, diet, or sedentary habits, were largely deemed less essential, accounting for 7.1 % of the final criteria list. The authors note that this de-prioritisation does not imply these features lack value, but rather reflects limited evidence linking them to improved depression outcomes when used in isolation.

Finally, usability and functionality remained important, constituting 17.9 % of the final list. Participants emphasized that apps must be interpretable, quick to respond, and clearly communicate their aims to support sustained and meaningful use.

What these criteria mean for future mental health apps

The present study highlights that mHealth apps, particularly those targeting depression mitigation, must prioritize safety and scientific validity over less clinically substantiated features, such as sleep or diet tracking. It introduces 28 validated criteria for evaluating and screening mHealth apps, providing stakeholders with an informed path toward future assessments of digital tools for depression management, rather than an immediate endorsement of specific apps.

These criteria will be instrumental in optimizing the EvalDepApps assessment tool, which aims to empower healthcare professionals and users to identify high-quality apps, ensuring that digital mental health support is not only accessible but also safe and scientifically sound. The authors caution, however, that further validation, contextual adaptation, and real-world testing will be required before widespread implementation, particularly across different health systems and cultural settings.

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