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. 2025 Feb 17;64(3):692–701. doi: 10.1111/bjc.12532

Relevance of ecological momentary assessment for medication adherence in clinical settings: A precision psychiatry approach

Valentine Chirokoff 1,2, Arnaud Tessier 1,3, Fuschia Serre 4, Maud Dupuy 1, Marc Auriacombe 4,5, Sandra Chanraud 1,2, Sylvie Berthoz 1,6, Melina Fatseas 1,5,7,✉, David Misdrahi 1,3
PMCID: PMC12334975  PMID: 39960195

Abstract

Background

Medication non‐adherence is a leading cause of treatment failure in psychiatric populations. However, current studies highlight the lack of methodological guidance on medication assessments. Ecological Momentary Assessment (EMA), using smartphone‐based evaluations, shows promise for real‐time monitoring in everyday settings.

Aims

This study evaluated EMA's effectiveness in assessing medication adherence in patients with schizophrenia, depression, and substance use disorders (SUD), covering various treatment regimens.

Materials & Methods

A total of 133 participants (27 with schizophrenia, 20 with depression, 44 with SUDs, and 42 healthy controls) completed EMA via study‐provided smartphones five times daily over 1 week. Treatment regimens, categorized by mono vs. polytherapy and single vs. multiple daily doses, were documented. EMA adherence was calculated from the completion rate of the assessments, while medication adherence was assessed daily for patients. Both mean medication adherence and adherence variation over time were analysed by diagnosis and treatment regimen.

Results

All groups demonstrated high mean EMA and medication adherence, with minor variations across treatment types. Importantly, patients showed improved adherence over time, independently of diagnosis or regimen.

Discussion

These findings indicate EMA's potential as an effective method for capturing medication adherence in psychiatric populations.

Conclusion

The approach's capacity for real‐time, context‐sensitive data collection could reveal adherence patterns and changes not detectable by conventional methods, offering valuable insights for clinical practice.

Keywords: adherence, depression, EMA, precision psychiatry, schizophrenia, SUD

Practitioner Points.

  • EMA may represent an effective method for capturing medication adherence in psychiatric populations.

  • While there is heterogeneity in medication adherence across pathologies and treatment regimens, adherence improvement over time during EMA measurement is similar across all groups.

INTRODUCTION

Over the past two decades, advancements in mobile technologies have significantly transformed research in psychiatry. The predominant ambulatory method in psychiatric research is Ecological Momentary Assessment (EMA) (Stone & Shiffman, 1994; Swendsen & Salamon, 2012). EMA allows a dynamic exploration of psychological phenomena by capturing real‐time data on emotional states, behaviours and other relevant experiences as they occur in daily life. As a robust measurement tool, EMA has generated novel insights into the aetiology and physiopathology of mental disorders across various populations (Bouvard et al., 2018; Granholm et al., 2008; Husky et al., 2014; Johnson et al., 2009; Swendsen & Salamon, 2012).

Among the factors affecting patients with severe and persistent mental illness, medication non‐adherence has been reported as a frequent phenomenon with rates of partial or total non‐adherence estimated as high as 40%–50% (Velligan et al., 2009). Medication adherence is a critical factor impacting the efficacy and safety of treatments, significantly influencing therapeutic outcomes and contributing to treatment failure (Velligan et al., 2009; World Health Organization ed, 2003). However recent publications have highlighted the lack of a ‘gold standard’ and guidance on adherence's assessments methods (Haag et al., 2020; Mantila et al., 2022; Velligan et al., 2020). Indeed, while medication adherence is commonly assessed via self‐report in clinical and research settings, such methods have limitations, including memory‐related issues (Shiffman et al., 1997) and social desirability bias, leading to an overestimation of medication adherence (Lam & Fresco, 2015). This underscores the critical need for improved assessment methods (World Health Organization ed, 2003). A review of clinical trials conducted in the European Union between 2010 and 2020 revealed that despite the potential for digital tools like EMA to facilitate more objective measurements of medication adherence and enable the investigation of time‐ specific effects, this methodology was employed in only 1.4% of studies (Mantila et al., 2022). In antidepressant clinical trials, EMA has been validated as a robust measurements tools (Wichers et al., 2009) that could even present a higher sensitivity and specificity to symptoms than regular clinical reports due to its high ecological validity (Barge‐Schaapveld & Nicolson, 2002). For instance, a study assessing quality of life in depression trials revealed prolonged deficits in daily life measurable via EMA that were not detected by conventional, retrospective measures (Barge‐Schaapveld & Nicolson, 2002). Another study conducted a randomized clinical trial comparing daily process assessments with standard weekly assessments in antidepressant medication trials (Lenderking et al., 2008). The authors highlighted the daily measures detected therapeutic effects more quickly than did standard weekly clinic assessments, with no additional perceived burden for participants, nor higher dropout rates. These preliminary findings in medication trials provide promising leads to improve patients care for which mobile technology has been pointed as a possible leverage to reduce provider burden, treatment costs and improve treatment engagement, and understanding patient's functioning in their natural environment (Moitra et al., 2017). Consequently, studies have been conducted to assess the validity of the integration of EMA with traditional retrospective measures in psychiatric patients undertaking treatments. In accordance with clinical trials results, findings in psychotic populations highlighted a correlation between EMA and traditional clinical assessments but with an improvement of accuracy and reports rates using EMA (Moitra et al., 2021). Hence, EMA stands as a promising tool for monitoring medication and accurately identifying non adherent patients.

Our aim was to expand the current literature by assessing the relevance of EMA assessments for medication adherence among three prevalent serious mental illnesses: Substance Use Disorders (SUD), Depression and Schizophrenia, spanning diverse treatment regimens. More specifically, we investigated both EMA adherence (missing assessments) and medication adherence, comparing mean adherence levels among each patients' group, healthy controls and various treatment regimens. Furthermore, we characterized the evolution of EMA and medication adherence over time and assessed how patients' group and treatment regimen types influenced these temporal effects.

MATERIALS AND METHODS

Participants

The present study form part of the MobiCog cohort for which additional studies are published (Chirokoff et al., 2023; Chirokoff, Berthoz, et al., 2024; Chirokoff, Pohl, et al., 2024; Dupuy et al., 2022). A total of 133 individuals including 27 patients with schizophrenia, 20 patients with depression, 44 patients with a SUD (cannabis, alcohol and tobacco), and 42 healthy controls participated in the study. Apart from the healthy control group, all patients were taking prescribed daily oral medications. All participants provided their written informed consent; the study was approved by the local ethical committee Comité de Protection des Personnes de Sud‐Ouest et Outre‐Mer III» (N° 2014‐A01668–39).

Procedures

DSM‐IV‐TR diagnoses were established based on criteria assessed by the Mini International Neuropsychiatric Interview French Version 5.0.0 (Sheehan et al., 1998). Patients were recruited from outpatient care services at Charles Perrens Hospital, where they were beginning regular outpatient treatment for SUD, depression, or schizophrenia. Patients were receiving usual care, including individual behavioural treatments and pharmacotherapy (psychotropic drugs) as appropriate. Healthy control participants were identified through community postings and were recruited in the absence of lifetime psychotic disorder, lifetime bipolar disorder, and lifetime substance dependence, as well as no other current DSM‐IV‐TR axis I disorder. Sociodemographic data and current prescribed medications were recorded during the inclusion visit. Treatment regimens were categorized based on whether they involved monotherapy or polytherapy, as well as by the frequency of intake per day, distinguishing between treatments requiring a single daily dose and those requiring multiple daily doses. For patients with prescribed injectable long‐acting antipsychotics, we only considered the co‐prescription of an oral psychotropic drug for the medication adherence analysis.

EMA

After verification of eligibility criteria, participants were trained to operate a study‐dedicated smartphone for EMA (Samsung Galaxy S with a 10.6 cm screen, 12‐point font size). The surveys were conducted over a 7‐day period, five times per day at random intervals within five equal time epochs. Financial compensation was provided with 30€ in‐store purchase vouchers for the completion of the EMA, and to maximize compliance rate, an additional store voucher of 20€ was offered to participants who completed at least 75% of the assessments. The intake of prescribed medications since last assessments was assessed at each EMA check‐in (‘yes’ or ‘no’); EMA adherence was estimated from the number of completed questionnaire reports (as opposed to missing ones) during the study. Time effect EMA adherence was defined as the decrease or increase in missing EMA observations as a function of day throughout the study duration. Medication adherence was estimated each day in a dichotomous manner to be compared across treatment regimens. Therefore, each day, medication adherence was characterized as 1 if at least one medication intake was reported, and 0 if no medication intake was reported.

Data analysis

Descriptive analyses and group comparisons between patients and controls, and within patients' treatments regimen were performed using t‐tests. Within‐patient comparisons between psychiatric disorders were conducted using ANOVA (Bates et al., 2015). EMA‐adherence rates were determined by computing the average percentage of programmed EMA assessments completed by each individual during the study. The mean medication adherence rate throughout the study was measured. Time effect for EMA adherence and medication adherence were examined using Hierarchical Mixed Model available in the lme4 package for R (Bates et al., 2015). Using the time‐dependent models, we assessed whether the rate of missing data increased as a function of day of the EMA study (i.e. ‘EMA adherence’), as well as whether daily medication decreased or increased as a function of day of the EMA study (i.e. ‘medication adherence’). We assessed group‐specific time effects by conducting interaction analyses between changes in EMA adherence and medication adherence over time, considering the effects within different subgroups (patients versus controls, within psychiatric disorders, and treatment regimens). Day 1 of EMA was identified as a clear outlier using Mahalanobis distance and was discarded from the rest of the analyses. The mean EMA adherence for day 1 was 77%, while the mean for the rest of the week was 94%. The Mahalanobis distance was calculated to be 5.10, with a corresponding p‐value of 0.02. All analyses were performed on R statistical software.

RESULTS

The sociodemographic characteristics of the full sample are presented in Table 1. Patients were mostly treated for SUD (n = 44), followed by schizophrenia (n = 27), and depression (n = 20). Regarding treatment regimen, 43 patients were treated with monotherapy compared with 48 patients that received polytherapy. Forty‐two patients were prescribed multiple daily doses of medication, 42 received a single daily dose, 6 patients received injectable long‐acting antipsychotic treatment in combination with another oral psychotropic drug and 1 patient had missing data. Details on medications prescribed are presented in Table 2. Patients with schizophrenia were mostly (89%) treated with Second‐Generation Antipsychotics (SGA). They were more frequently prescribed polytherapy, which included antidepressants (22%), anxiolytics (41%), and mood stabilizers (26%). Patients diagnosed with depression received treatment with an antidepressant in 95% of cases (1 patient with a mood stabilizer). Patients with SUD received specific medications targeting their addiction in 84% of cases. Additionally, 45% of these SUD patients had a co‐prescription of anxiolytics, while 20% were prescribed antidepressants.

TABLE 1.

Descriptives statistics (n = 133).

tabular image

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Note: Differences Patients ≠ Controls * < .05; ** < .01; *** < .001; A, SUD ≠ Depression, B, SUD ≠ Schizophrenia, C, Depression ≠ Schizophrenia.

Abbreviations: M, Mean; SD, standard deviation; SUD, substance use disorder.

TABLE 2.

Details of treatments prescribed.

Antipsychotic Antidepressant Treatments for SUD* Anxiolytic Mood stabilizer
FGA SGA Oral Injectable
Schizophrenia (n = 27) n (%) 2 (7%) 24 (89%) 20 (74%) 6 (22%) 6 (22%) 2 (7%) 11 (41%) 7 (26%)
Depression (n = 20) n (%) 0 (0%) 1 (5%) 1 (5%) 0 (0%) 19 (95%) 4 (20%) 6 (30%) 1 (5%)
SUD (n = 44) n (%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 8 (18%) 37 (84%) 20 (45%) 0 (0%)
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Abbreviations: FGA, First‐Generation Antipsychotics; SUD, Substance use Disorder; SGA, Second‐Generation Antipsychotics.

*

Acamprosate, Baclofen, Disulfiram, Naltrexone, Nalmefene, Nicotine patch.

EMA adherence

A high rate of adherence with the EMA method was observed, with more than 84% of the participants who completed assessments in all groups (Table 1), with the number of completed assessments being slightly but significantly higher in the control group (95%) compared to the overall patient group (90.30%; t = −5.475, p < .001, cohen's d = .168). Within patients, lower adherence with EMA was observed in those receiving monotherapy (88.9%) compared to those on polytherapy (91.7%), (t = −2.481, p = 0.013, cohen's d = .159). Regarding EMA adherence over the study period, no significant differences were found between patients undertaking one (91%) compared to multiple (90%) (t = 0.799, p = 0.42, cohen's d = .064) intake per day. Similarly, no differences were found between patients treated for SUD (92%), depression (84%), or Schizophrenia (92%) (F = 0.083, p = 0.774, ηp = .001).

Regarding time effect, no increase in missing EMA observations was observed as a function of day throughout the study duration (see Table 3). In the patients' groups no significant effect of time was found, while a decrease in missing EMA observations was observed in the control group. Additionally, no significant between‐group differences in the magnitude of these effects were found between diagnoses or treatment regimens (see Table 3).

TABLE 3.

Time varying effect in EMA and medication adherence.

Outcome
EMA adherence Medication adherence
Effect of time in group: g SE Z‐value p‐value g SE Z‐value p‐value
Control −.0364 .0155 −2.349 .018 ‐ ‐ ‐ ‐
Patients .0012 .0078 .157 .875 .1056 .0384 2.753 .005
Moderators:
Type of Pathology −.0186 .0097 −1.908 .056 −0.070 .0436 −1.604 .109
Mono vs. poly therapy .0225 .0158 1.422 .155 −.0495 .0774 −.640 .522
Single vs. multiple intakes per days. −.0143 .0162 −.881 .378 −.0035 .0821 −.042 .966
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Abbreviations: SE, standard error; γ, coefficient.

Medication adherence

Overall, medication adherence during the study period was high, with a mean adherence rate of 81% (see Table 1). Differences were observed between medication regimens: adherence rates were 73% for monotherapy and 88% for polytherapy (t = 9.9736, p < .001, cohen's d = .389), and 80% for single daily intake compared to 84% for multiple daily intake (t = −2.9061, p = .003 cohen's d = .117). No significant differences in adherence rates were found across the different groups of patients (F = 0.571, p = 0.452, ηp = .001).

Regarding time effect, daily medication adherence as a function of day throughout the study duration is presented in Table 3. All patients showed an increasing likelihood of taking their medication daily with each passing day of the study (coefficient = 0.1056, p = 0.005). The observed increase over time did not exhibit significant differences across various medication regimens (in terms of number of medications and frequency of intake per day) or diagnoses, as detailed in Table 3.

DISCUSSION

In the current study we aimed to assess the feasibility and relevance of EMA in evaluating medication adherence across various psychiatric disorders and treatment regimens. Overall, our results support the use of EMA to explore medication adherence in patients with severe mental disorders within a psychiatric precision approach. Our findings can be summarized as follows: (i) EMA adherence was high, exceeding 84% in all subgroups, with lower adherence in monotherapy patients, but no effect of the type of psychiatric disorder or treatment regimen (single vs. multiple intakes per day); (ii) No increase in missing EMA observations was observed over time; (iii) Medication adherence averaged 81%, with higher adherence in polytherapy and multiple dosages, but no differences between psychiatric diagnoses, (iv) medication adherence increased over time, irrespective of medication regimen or psychiatric diagnoses.

Adherence to EMA with the multiple daily assessments was over 90% for patients, while adherence in the healthy group reached 95%. These values are far superior to those from a recent meta‐analysis including 477 EMA studies (N = 677,536), which obtained a mean compliance of 79% (Wrzus & Neubauer, 2023). The high compliance rate observed in the present study could be explained by methodological considerations already highlighted in the literature such as the short duration of EMA assessments (7 days) and financial incentives (Moitra et al., 2017). Overall, EMA compliance was slightly lower in the overall sample of patients compared to the healthy controls, as traditionally highlighted in EMA studies (Bouvard et al., 2018; Granholm et al., 2008; Husky et al., 2014; Johnson et al., 2009; Serre et al., 2015). No differences were found between psychiatric diagnoses (SUD, Depression or Schizophrenia) and treatment groups based on the number of medication intake per day. The only difference in EMA adherence was found between the polytherapy group (91.7%) compared to the monotherapy one (88.9%). When the rate of missing data was examined as a function of day of study to detect time‐related effect, a reverse association was found in the group of healthy controls indicating that these participants became more adherent over time. In the groups of patients, no time‐related evolution of EMA compliance was found, indicating that adherence neither decreased nor increased for this population during the study. Furthermore, we did not find any group interaction in term of diagnoses nor treatment regimen, highlighting a similar evolution in the three psychiatric groups. The high rates of completed assessments across disorders and treatment types hence provides further supports for EMA feasibility and acceptability in heterogenous populations with mental disorders.

Moreover, we highlighted a high rate of 81% mean medication adherence, slightly lower within patients with mono compared to poly therapy, and if undertaking one compared to multiple intakes per day. However, these treatment effects did not translate in any time related effect. During the study period, we observed a beneficial effect whereby patients demonstrated a significant increase in medication adherence over time, with no variations between type of pathology nor treatment regimen. Whereas concerns could exist regarding potential EMA related burden or reactivity phenomenon, our results do not corroborate such effects. Contrarily, besides its acceptability across treatment regimen and psychiatric diagnoses, the improvement of medication adherence over time further supports the relevance of medication‐related EMA assessments.

The validation of such tool is not only necessary to address the lack of gold standard in measurement methodology but also to provide more accurate assessments in clinical settings. Our study emphasizes that the contribution of EMA over a short period (7 days) can make it possible to monitor the initiation of a treatment in ambulatory conditions, to accurately assess patients' medication adherence and identify patients at risk of poor adherence in an individualized approach. The increase of medication over time in our study could indicate that EMA has the potential to act as a reminder and thus may improve adherence. Furthermore, whereas our study did not aim at identifying the relevance of a digital intervention for improving medication adherence, our results suggest its possible use as a step toward personalized medicine. Such clinical application of EMA, known as Ecological Momentary Intervention (EMI), is a growing topic of research, that extends the methodology of repeated within‐environment prompting into the domain of clinical intervention, providing treatment to people in real time and natural settings (Park et al., 2014). Regarding medication adherence, a systematic review has already demonstrated its interest in various psychiatrics populations (Park et al., 2014), emphasizing the clinical feasibility and interest of smartphone‐based monitoring in everyday life.

While our study demonstrates the feasibility and potential utility of EMA for measuring medication adherence across different psychiatric populations, several important limitations should be acknowledged. Medication adherence was solely self‐reported through EMA. This should be considered as an important shortcoming to interpretating results regarding medication adherence. To date no gold standard is defined to assess medication adherence, however, objective measures such as blood tests, digital pills, tracer substances, and video verification are becoming possible options for a range of ‘gold‐standard’ approaches moving forward because they come closest to verifying ingestion. While our study did not utilize remote digital assessment methods such as photography to verify medication intake, future research should explore these approaches. However, these methods may raise privacy concerns and necessitate careful adherence to Protected Health Information (PHI) regulations. The high adherence rates observed suggest a possible selection bias toward more compliant patients. This may have created a ceiling effect, that limited the detection of meaningful differences or improvements. Additionally, participants' awareness of being monitored may have artificially inflated adherence rates, a phenomenon known as the Hawthorne Effect. Our study was conducted over a short period of time limited to a one‐week period of EMA assessments. Whereas this duration is enough to provide accurate measurement of a patient medication adherence, further studies are needed to evaluate the impact on medication using longer periods of assessment. Assessment schedule (the number of assessments per day and the total duration of assessment days) should also be balanced depending on the objectives and competing goals of studies, even if the current literature suggests little if any impact of such methodological choice on participants' compliance (Wrzus & Neubauer, 2023). Finally, the present sample may be biased as patients who agreed to participate were likely to be less hostile toward care and more compliant with treatment. As previously suggested, the patients who agree may experience greater motivation (Misdrahi et al., 2018).

Despite these considerations, the strong support found for the feasibility of EMA among persons prescribed daily medications provides essential information for pursuing this novel data‐collection strategy to enhance medication adherence monitoring.

CONCLUSION

Our study demonstrates the feasibility and utility of EMA for monitoring medication adherence across psychiatric populations, with adherence improving over time regardless of diagnosis or treatment regimen. Despite limitations such as population bias and ceiling effects, EMA shows promise as a clinical tool. Future research should integrate objective adherence measures alongside EMA to validate self‐reported data, extend study durations, and include diverse patient samples, particularly those with adherence challenges, to refine EMA's application and leading to more personalized and effective medication management strategies in psychiatric care.

AUTHOR CONTRIBUTIONS

Valentine Chirokoff: Writing – original draft; methodology; writing – review and editing; formal analysis; data curation; software. Arnaud Tessier: Investigation; writing – original draft; methodology; writing – review and editing; resources. Fuschia Serre: Investigation; validation; writing – review and editing; resources. Maud Dupuy: Investigation; validation; writing – review and editing; data curation; software. Marc AUriacombe: Conceptualization; investigation; funding acquisition; validation; supervision; project administration; writing – review and editing. Sandra Chanraud: Conceptualization; investigation; funding acquisition; validation; writing – review and editing; project administration; supervision. Sylvie Berthoz: Conceptualization; investigation; validation; writing – review and editing; project administration; supervision. Melina Fatseas: Conceptualization; funding acquisition; investigation; writing – original draft; validation; writing – review and editing; methodology; project administration; supervision. David Misdrahi: Conceptualization; investigation; funding acquisition; writing – original draft; methodology; validation; writing – review and editing; project administration; supervision.

FUNDING INFORMATION

This investigation was supported by the Mobicog Grant from the Agence Nationale de la Recherche to Joel Swendsen (ANR‐12BSH2‐0012) and the MobicogIM (NCT02334956) grant from the Fondation pour la Recherche Médicale to Joel Swendsen (DPA20140629807).

CONFLICT OF INTEREST STATEMENT

The authors report no conflicts of interest in this work.

ETHICS STATEMENT

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The study was approved by the local ethical committee ‘Comité de Protection des Personnes de Sud‐Ouest et Outre‐Mer III» (N° 2014‐A01668–39). All participants gave their written informed consent prior to their inclusion in the study.

ACKNOWLEDGEMENTS

This paper is dedicated to the memory of our dear colleague, Joel Swendsen, who passed away suddenly on July 14th, 2022. We thank him deeply for initiating, conducting, and supervising this study that resulted in this manuscript.

Chirokoff, V. , Tessier, A. , Serre, F. , Dupuy, M. , Auriacombe, M. , Chanraud, S. , Berthoz, S. , Fatseas, M. , & Misdrahi, D. (2025). Relevance of ecological momentary assessment for medication adherence in clinical settings: A precision psychiatry approach. British Journal of Clinical Psychology, 64, 692–701. 10.1111/bjc.12532

DATA AVAILABILITY STATEMENT

All data are available upon request to the corresponding author.

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Associated Data

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Data Availability Statement

All data are available upon request to the corresponding author.

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