Depression manifests with remarkable variability across individuals, presenting diverse symptom profiles, disease trajectories, and treatment responses that complicate standardized approaches (7). This heterogeneity extends to biological foundations, as research demonstrates that genetic polymorphisms, hormonal factors, inflammatory markers, and neural connectivity patterns all contribute to individual differences in depression presentation (8). Key symptoms of depressive disorders include reduced motivation and pleasure (anhedonia), difficulties in managing anxiety and worry, inflexible thought patterns leading to self-reproach and guilt, impaired processing of sensory and social information, cognitive deficits in attention and memory, and various physical disturbances such as changes in weight, appetite, and sleep patterns (9). Figure 1 maps the different neural dysfunctions associated with depressive episodes and their manifested symptoms (9, 10).

Genetic evidence strongly supports the notion that depression comprises multiple biologically distinct subtypes. For instance, early-onset, recurrent, and postpartum depression exhibit higher Single Nucleotide Polymorphisms (SNP)-based heritability, indicating stronger genetic loading. Conversely, late-onset and milder subtypes show lower genetic contributions (11). At the symptomatic level, clinical evidence suggests that the internal structure of depression is not uniform. For example, melancholic depression typically includes features such as anhedonia, early morning awakening, and psychomotor disturbance, whereas atypical depression includes mood reactivity, hypersomnia, and increased appetite. Such symptom-specific differences imply that some subtypes, particularly severe and suicidal depression, may be more treatment-resistant and require intensive, tailored interventions, while milder or situational forms might respond well to brief or low-intensity treatments (12). The biological correlates and symptom profiles of different depression subtypes are summarized in Table 1. Recent research using both Diagnostic Statistical Manual (DSM)-5 (25, 26) and data-driven methods consistently supports the existence of biologically distinct depression subtypes. Data-driven clustering reveals unique biological signatures, particularly in inflammatory markers and brain connectivity, for symptom groups such as neurovegetative, anhedonic, and anxious profiles (27, 28). However, progress is hindered by methodological variability, limited replication, and inconsistent reporting, underscoring the need for more standardized, multimodal approaches to fully delineate and validate these subtypes. This variability calls for more personalized interventions that address biological correlates in addition to symptom profiles (14, 24).

Clinical guidelines such as the DSM-5 and National Institute for Health and Care Excellence (NICE) acknowledge clinical heterogeneity in depression by incorporating specifiers and stratified care approaches rather than redefining novel biological subtypes. The DSM-5 introduces specifiers “with melancholic features”, “with atypical features”, “with anxious distress”, and others to capture distinct symptom patterns that may guide treatment selection—for example, considering electroconvulsive therapy in severe melancholic depression or MAO inhibitors in atypical presentations (25). Similarly, NICE stratifies patients by episode severity (“less severe” vs. “more severe”) and recognizes subgroups such as chronic depression, psychotic depression, and treatment-resistant depression, offering stepped-care algorithms and augmentation strategies tailored to each subgroup (29). By refining diagnostic descriptors and matching intervention intensity and modality to individual clinical profiles, these guidelines operationalize depression's heterogeneity within routine practice.

The diverse manifestations of depression directly impact clinical practice, particularly regarding diagnosis, treatment selection, and long-term management. Despite the well-documented heterogeneity of depression, primary care settings, where most depression cases are initially encountered, often rely on broad diagnostic criteria and generalized treatment guidelines, resulting in inconsistent treatment implementation (30). Only 30% of patients achieve remission with their first prescribed medication, raising the need for multiple treatment trials before finding effective interventions, extending suffering, and increasing healthcare costs. Furthermore, approximately 55% experience side effects, highlighting the urgent need for personalized treatment strategies (8, 16, 20).

Precision psychiatry aims to tailor mental health treatments to groups and ultimately to individual patients (personalized psychiatry) by integrating biological, clinical, and digital data to predict treatment response and optimize therapeutic outcomes. Despite its potential, precision psychiatry remains difficult to implement. A key challenge is the lack of biomarker-based diagnostics. While psychiatric diagnoses are often reproducible, their biological validity remains weak, making precise treatment selection difficult (20). Measurement-Based Care (MBC) can improve treatment outcomes by using validated scales to systematically track symptoms, guide clinical decisions, and enhance patient adherence. Research demonstrates that MBC significantly increases remission rates (74% vs. 29% in standard care) and doubles treatment response odds in primary care settings (31).

Recognition of depression's heterogeneous nature has driven momentum toward personalized medicine approaches that tailor treatment strategies to individual patient characteristics. These personalization strategies incorporate biological, psychological, and digital tools to refine diagnosis, predict treatment response, and improve outcomes as detailed below.

This is perhaps the first review to discuss the heterogeneity of depression in relation to digital health. This systematic review aims to explore and analyze recent digital technology targeting personalized depression care, thereby making the case for the digital transformation of mental healthcare. The objectives are: a.

To summarize and critique the recent literature on personalized digital interventions.

This review focuses on a specific niche in depression care: the personalization of digital health solutions to target the biological correlates of depression subtypes. While much research has explored either depression heterogeneity or digital tools independently, few have bridged both areas. The search strategy targeting this specific niche is shown in Table 2. This review intentionally focused on personalized digital interventions for depression; studies without an explicit personalization component were excluded by design. Searches covered peer-reviewed literature in Scopus and IEEE Xplore, complemented by targeted grey literature from ClinicalTrials.gov and hand-searching of reference lists and forward citations. Searches were last run on 15 September 2025. Records published after this date were not considered. Following the PRISMA guidelines, 29 studies were identified that address heterogeneity of depression to varying levels: 20 papers and 9 clinical trial reports. The PRISMA scheme is shown in Figure 2.

Data was extracted using Microsoft Excel by a single author, G.A. The extracted information included research objectives, methods, personalization strategy, key findings, and other study details. Due to the heterogeneous nature of the studies, the data extraction and synthesis follow a narrative, descriptive approach organized along two orthogonal frameworks: (i) modality of intervention (passive sensing/forecasting; just-in-time adaptive interventions; conversational/coach-supported tools; decision-support/recommenders; physiology-coupled therapeutics) and (ii) personalization axis (timing & context, treatment format & dose, content & target). Data items include:

Bibliographic and Design Details: author(s), year, country, study design (e.g., randomized controlled trials, cohort, case series).

To systematically evaluate the empirical literature on personalized digital interventions for depression, SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was employed. This strategic framework is commonly used in healthcare research to critically assess innovation potential, implementation challenges, and contextual fit. In this review, SWOT was applied to 20 peer-reviewed studies, each offering a unique technological or methodological approach to personalized mental health care.

To evaluate the methodological rigor and reliability of 14 prospective studies, a qualitative Risk of Bias (RoB) (44) assessment was conducted. This process considers five core domains adapted from the Cochrane Risk of Bias framework and digital intervention research best practices. Each study was categorized as Low, Moderate, or High risk in each domain, leading to an overall risk of bias judgment. This qualitative appraisal supported a nuanced interpretation of findings across heterogeneous methodologies.

The Prediction Model Risk Of Bias Assessment Tool (PROBAST) (45) was used to assess the quality of the models used in the 3 modeling studies. The models were evaluated for signal problems in the four domains of PROBAST, and the results were categorized as “yes”, “probably yes”, “probably not”, “no”, or “no information”. This leaves 3 articles (13, 46, 47) that were not fit for these tools and were narratively appraised instead. Only one researcher, G.A., performed the risk of bias assessments.

Personalized digital interventions for depression show strong potential due to their scalability, multimodal integration (e.g., EMA, sensors), and promising engagement outcomes. Opportunities include early intervention, Explainable Artificial Intelligence (XAI), and integration with traditional care. However, weaknesses such as small samples, inconsistent evaluation, and reliance on self-report data persist. Major threats include data privacy issues and digital exclusion of underserved populations. The SWOT results are shown in Figure 3.

4 studies (19, 51, 53, 60) showed a low overall risk of bias, as shown in Figure 4. Most others raised concerns, especially regarding randomization, missing data, and selective reporting. The predictive models assessed in the 3 retrospective studies were found to exhibit low to intermediate risk of bias, as shown in Table 5. These limitations highlight the need for more rigorous designs and standardized outcome reporting in future digital mental health research.

Across three non-scored narrative appraisals, Woebot (WB001), MindBot, and HAYT are clearly described but differ in clinical maturity and evidentiary strength. WB001 provides the most complete clinical framing, an FDA Breakthrough, prescription 8-week Agent-delivered CBT and interpersonal therapy program with daily mood tracking and NLP-based crisis detection, yet the brief lacks detailed data-governance disclosures (13). MindBot offers a rigorous engineering overview (preprocessing, sentiment thresholds, LLM-augmented replies), but it provides limited bias-monitoring and escalation specifics (46). HAYT delineates a clinician-integrated workflow (NLP of diary entries, DSM-5 questionnaires, CBT prompts, secure messaging) with transparent data flow in principle, though results are based on synthetic data and do not validate real-world safety or clinical impact (47). Across all three, safety provisions are conceptually present but unspecified in performance terms. Credibility would be strengthened by explicit reporting on privacy, human-in-the-loop escalation, and algorithm update policies.

In terms of transparent research practices and open scientific publication, we observed heterogeneous adoption of open-science practices across the corpus. Trial registration was common in RCTs, but public protocols, pre-specified analysis plans, de-identified data, and analysis code were infrequently shared in most studies. We therefore flag transparent preregistration (e.g., ClinicalTrials.gov/OSF), protocol publication, and routine data- and code-sharing with privacy safeguards as concrete steps to reduce selective-reporting risk and improve reproducibility in this rapidly evolving field.

Gerczuk et al. (17) personalize mood estimation to speaker identity, capturing inter-speaker variance in acoustic features, yet remain uncustomized to symptom mechanisms (17). MindBot and HAYT demonstrate NLP and LLM-assisted pipelines and, in HAYT's case, a clinician-messaging architecture using synthetic data (46, 47). These works illustrate that personalization can be embedded at the interface layer; nonetheless, they largely bypass clinical variance, no discussion of clinical endpoints, no subtype stratification, and limited attention to safety, governance, or model drift. Proxy metrics (e.g., “emotional accuracy”) and synthetic diaries risk overstating clinical readiness while under-representing failure modes in non-standard language, high-distress states, or low-literacy populations.

These studies personalize primarily by schedule, adherence support, or single-channel signals. Rohani et al. (56) evaluated recommendation algorithms for pleasant event scheduling across clinical and non-clinical populations. While their approach accommodates intra-individual variability in activity-mood associations, it does not explicitly model symptom heterogeneity or depression subtypes, instead focusing on behavioral intervention optimization through reinforcement learning principles (56).

Malins et al. (54) investigated personalized relapse prevention messaging CBT, with messages tailored to individual recovery trajectories across distinct clinical states (wellness maintenance, early warning signs, full relapse). This strategy acknowledges heterogeneity in relapse vulnerability and symptom recognition patterns, though it relies on clinician-guided personalization without mechanistic or biomarker stratification (54). The m-Path social-support JITAI tailors timing and content via self-signaled need and momentary affective context (19). Adaptation is person-specific but not mechanism- or biotype-aware. This creates person-level adaptation on two axes: detection thresholds based to individual baselines and action menus refined by user preference. Yet the mechanism model remains indifferent to subtype. There is no personalization by symptoms, no biophysical state inputs, and no contextual signals (19).

Personalization in the living-lab for socially vulnerable older adults is primarily achieved through individualized baselining and longitudinal feedback rather than just-in-time control (50). Participants wear multimodal sensors (e.g., heart-rate variability, sleep efficiency/fragmentation, activity) and complete high-frequency chatbot PHQ check-ins. These streams are harmonized to compute each person's baseline and day-over-day deltas for mood and physiology. Personalization therefore resides in the personal reference model and tailored visualizations that support self-care and caregiver awareness. However, there is no policy that optimizes timing or content based on estimated treatment effects, and no stratification by symptom dimension or biotype (50).

Digital-navigator coaching personalizes engagement rather than content; coaches review mindLAMP activity and symptom self-reports and then tailor next steps like what module to try next and which homework to emphasize, via human-in-the-loop heuristics (55). Adaptation is driven by recent completion patterns, working alliance scores, and user-stated goals, with cadence modulated to sustain adherence. This raises dose and breadth of app use at the person level but personalization is pragmatic, not mechanistic (55).

The personalized nutrition program for adolescent MDD individualizes targets at three layers: diet goals, eating-behavior, and a weekly menu plan tuned to family preferences and shopping logistics (61). Although the intervention is biologically plausible, leveraging Mediterranean-style patterns to influence inflammation, gut–brain signaling, and metabolic rhythms, the personalization engine does not measure or stratify by biological markers like CRP or microbiota, circadian rythm, or cognitive symptoms. As a result, “biology-aware” matching of diet to patient is not implemented and personalization remains preference-based rather than biology-directed (61).

Most of the reviewed clinical trials implement personalization strategies centered primarily on behavioral parameters (e.g., mood fluctuations, patient-reported outcomes, application engagement metrics) or symptom severity indices, without establishing explicit linkages to biological subtypes or neurobiological mechanisms. CRM forecasts personalized next-day mood from wearables and check-ins to trigger risk-contingent prompts (NCT05400785) (63). The response-based transdiagnostic internet trial adapts treatment format and dose by using early symptom trajectories to add brief therapist sessions for late responders (NCT07051148) (65). mHELP combines ML stress detection on phone and smartwatch streams with on-demand skills and two light telehealth touchpoints (NCT07017569) (64). MENTINA applies personalized rule-based feedback from questionnaire and sensor data, escalating to safety guidance when indicated (NCT06919133) (75). These approaches, while clinically valuable, conceptualize depression predominantly as a psychological or behavioral syndrome, neglecting to differentiate or specifically target distinct biological subtypes that may respond differentially to intervention strategies.

Chatterjee et al. (49) implemented explainable deep learning architectures for mood prediction using multimodal data streams (EMA, physiological parameters, cognitive assessments). Their methodological innovation lies in the application of interpretability techniques (SHAP, ALE, Anchors) to elucidate person-specific predictors of mood disturbance. This approach enables computational phenotyping aligned with natural symptom expression, though it does not explicitly classify clinical subtypes or stratify based on symptom dimensions (49). On the other hand, the Woebot WB001 device profile engages a specific clinical subtype (postpartum depression), illustrating how tailoring by life context can anchor personalization even at the caregiving stage (13).

Kathan et al. (48) conducted a comparative evaluation of multiple personalization strategies for symptom prediction, including transfer learning with shared and individualized components, subject-specific data preprocessing, and gender-based stratification. Their explicit assessment of model equity across demographic subgroups represents one of the few studies directly addressing fairness in computational personalization. This is a critical consideration given the documented disparities in depression presentation across demographic strata. Their multifaceted approach to heterogeneity encompasses both methodological innovation and equity considerations (48).

Together, these studies show that individualized models outperform pooled baselines and that equity assessment is feasible, marking a substantive step beyond undifferentiated prediction. However, they stop short of prospective decision rules: neither specifies thresholds that trigger action, nor do they test whether model outputs change behavior, adherence, or outcomes in real life. Small, intensively monitored cohorts also risk selection bias and limit generalizability. Moreover, depressive mechanisms are largely statistical rather than biological, in other words, predictive features are not validated against circuitry, inflammatory markers, or subtype taxonomies.

Several interventions tailor when and what to deliver using mechanisms that vary across people. Bell et al. (51) evaluated the Mello application, which delivers personalized cognitive-behavioral interventions based on real-time assessment of mood, rumination, location, and activity. By targeting a transdiagnostic mechanism, RNT, that presents with substantial inter-individual variability, their approach shows sophisticated engagement with heterogeneity beyond symptomatic expression. The intervention's significant efficacy (d = 0.50 for depression, d = 0.61 for anxiety, d = 0.87 for RNT) and mediation findings support the clinical utility of mechanism-focused personalization strategies (51). Similarly, the JITAI by Wang & Miller (52) measures receptivity and targets rumination, showing medium-to-large effects and demonstrating that targeting heterogeneous cognitive processes can outperform symptom-total heuristics (52). Still, mechanisms are inferred from self-reports, passive indicators that could address cognitive states (speech, mobility, physiology) would add more mechanistic information.

Across other programs, personalization is implemented through distinct sensing and decision layers. Cue derives an individualized “social rhythm” baseline from passive smartphone traces (sleep–wake cycle, mobility, communication patterns) augmented by brief mood/energy check-ins. Deviations from that baseline trigger rule-mapped micro-interventions at a cadence designed to minimize alert fatigue by suppressing and rotating content and timing based on recent engagement. While this addresses symptom level and temporal heterogeneity, a fuller account should discuss biological correlates, particularly circadian regulation, as mechanistic levers for personalization (53).

Physiology-coupled systems (NEVERMIND, FeelDTx, smartwatch-CBT) learn per-user baselines of HR and HRV, electrodermal activity, sleep efficiency and fragmentation, temperature and activity; state detectors (rule-based or lightweight ML) fire context-appropriate prompts (paced breathing/HRV biofeedback for sympathetic arousal, mindfulness or reframing during negative-affect windows, sleep-hygiene guidance nocturnally) with refractory periods and weekly navigator/therapist touchpoints where applicable (58–60). While the addition of physiological markers introduces symptomatic and temporal heterogeneity to the intervetion, future iterations should incorporate biotype-aware targeting, subgroup calibration, and fairness audits to raise the overall level.

STAND and RESiLIENT operationalize personalization at the system level, addressing heterogeneity in treatment effects even if not biomarker-defined. Through repeated symptom and suicide-risk assessments, the STAND algorithm assigns the user to one of stepped tiers of care with real-time alerts and scripted escalation pathways (57). The RESiLIENT models use baseline PHQ-9 patterns and early symptoms to estimate individual treatment effects, then assign the “best next” skill block (15).

Multiple trials showed sophisticated engagement with neurobiological heterogeneity, representing a paradigmatic advancement in precision psychiatry. The Digital Therapeutics for Apathy in Late-life Depression trial (NCT05877885) (72) specifically targets network dysfunction underlying apathy, a clinically distinct dimension frequently observed in geriatric depression presentations. The intervention architecture is meticulously designed to modulate specific neural circuitry, particularly networks subserving attention allocation, salience detection, and cognitive control functions; all systems that are consistently implicated in motivational deficits across neuroimaging and neurobiological investigations.

Similarly, the FDA-regulated clinical trial Effectiveness of Digital Therapeutics in Major Depressive Disorder (74) evaluates Rejoyn, a digital therapeutic explicitly engineered to engage neural networks implicated in major depressive psychopathology, with particular emphasis on neuroplasticity mechanisms. Unlike conventional approaches that conceptualize depression as a unitary construct, this trial acknowledges fundamental neural circuitry heterogeneity in MDD, implementing structured neurobehavioral exercises specifically designed to recalibrate dysfunctional neural circuits. Notably, the trial documentation explicitly addresses limitations of traditional pharmacological interventions that primarily target neurochemical dysregulation, highlighting the critical importance of circuit-based therapeutic approaches.

The MASE project personalizes timing and content by learning each person's Activity–Subjective Energy Association from accelerometry and ecological diaries, then combining this with neurobiological profiling to prescribe micro-activities matched to an individual's brain phenotype (NCT07059234) (62). REJUVENATE adapts task parameters and progression rules across multiple cognitive domains using in-app telemetry, aiming to remediate person-specific cognitive control deficits via neuroplasticity-based training (NCT04961047) (66). MEL-T01 embeds continuous performance sensing into gameplay to dynamically adjust difficulty, stimuli, and reinforcement schedules to an individual's executive-function profile (NCT05426265) (70).

Collectively, these studies operationalize heterogeneity at multiple levels, behavioral dynamics, cognitive phenotype, and neural circuitry, thus moving beyond severity-only tailoring. Methodological challenges remain (e.g., ensuring reliability of neurobehavioral markers, external validity across biotypes, and fairness audits for model-guided decisions), but the personalization mechanisms are technically rich and mechanism-aligned.

Despite promising advances, several critical limitations persist across the reviewed literature:

Limited Integration of Established Subtypes

Future work must begin by grounding personalization algorithms in empirically derived depressive subtypes. Rather than treating depression as a unidimensional construct, researchers could apply unsupervised learning techniques to multimodal datasets (77). These data-driven subgroups can then inform model architectures that tailor predictions to the unique symptom constellations of melancholic, atypical, anxious, or other clinically meaningful clusters. At the same time, interventions should move beyond composite mood scores and incorporate multidimensional symptom assessments, using network-analysis approaches to map the dynamic interrelationships among symptoms (78). By capturing how fatigue, anhedonia, sleep disturbance, and cognitive dysfunction co-activate and cascade over time, digital tools can offer more precise, subtype-specific feedback and treatment recommendations. Following the promising approaches discussed earlier, interventions should prioritize targeting transdiagnostic mechanisms with established heterogeneity in addition to general behavioral patterns and preferences.

Progress will hinge on moving beyond small, convenience samples toward adequately powered, diverse, and prospectively enrolled cohorts. Multi-site recruitment with stratified targets (age, sex/gender, race/ethnicity, socioeconomic status, language, device/OS) should be pre-specified and monitored. To curb overfitting and analytic bias, modeling work should adopt participant-level, temporally blocked, nested cross-validation and report both discrimination (e.g., MAE/RMSE, AUROC) and calibration (slope, intercept, expected calibration error).

Equity must become a core design principle rather than an afterthought. Future personalization frameworks should adopt fairness-aware optimization strategies—such as demographic parity or equalized odds constraints—to ensure consistent performance across gender, age, and cultural groups (79). Algorithmic bias can systematically skew who benefits and who is harmed. Disparate error rates (e.g., higher false positives for crisis alerts in one group, higher false negatives in another) distort triage, amplify clinician workload unevenly, and may delay care for those already underserved. Interventions would benefit from routine bias audits that quantify disparities in predictive accuracy and treatment suggestions, followed by algorithmic recalibration where needed (80). In parallel, there is a need for consensus on reporting standards, research protocols ought to include a “heterogeneity specification” checklist that details how subtypes were defined, which symptom dimensions were assessed, and what bias-mitigation techniques were implemented. Such standardized reporting will facilitate cross-study comparison, meta-analysis, and the cumulative advancement of the field. Moreover, to strengthen transparency and reproducibility, depression-focused digital trials should adopt open-science practices such as preregistration and open sharing of code and de-identified data. Practical steps like registering hypotheses and analysis plans before data collection, archiving code and data in trusted repositories, and publishing preprints, are feasible now and would materially improve credibility in this fast-moving field (81, 82).

Additionally, truly user-centered innovation requires embedding human-centered design (HCD) throughout the development lifecycle. Researchers should engage patients, clinicians, and caregivers in participatory co-design workshops, iteratively refining wireframes, feature sets, and interaction flows based on real-world feedback (83, 84). Usability testing, employing think-aloud protocols and standardized measures (e.g., the System Usability Scale), can uncover interaction bottlenecks before large-scale deployment. Accessibility and inclusivity audits, evaluating readability, language support, digital literacy, and disability accommodations, will further ensure that personalized digital mental health tools are equitable and resonate with diverse user populations. By integrating robust analytics, fairness safeguards, and rigorous HCD practices, the next generation of interventions can fulfill the promise of precision psychiatry in a way that is both scientifically sound and deeply humane.

Finally, clinical integration should move beyond app availability to workflow-embedded, measurement-based care. Health systems can operationalize digital tools through (i) EHR-integrated screening and triage that route patients to matched interventions; (ii) a defined digital navigation role for onboarding, troubleshooting, and engagement support; and (iii) scheduled reassessment checkpoints that trigger escalation, switching, or augmentation within stepped-care pathways (85, 86). Interoperability and clear reimbursement pathways are critical to sustain routine use. Implementation should be guided by RE-AIM/CFIR with pragmatic and adaptive trials embedded in care to learn which components drive benefit (87). Systems must add equity and safety guardrails, like stratified performance rates, multilingual/low-literacy designs, transparent data-governance, and ethical frameworks. Finally, routine workload and cost accounting should inform scalable resourcing decisions (85, 86). Together, these steps shift digital interventions from promising pilots to reliable, equitable infrastructure for depression care.