Mood Monitoring, Mood Tracking, and Ambulatory Assessment Interventions in Depression and Bipolar Disorder: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Laurence Astill Wright; Georgina Shajan; Daljit Purewal; Jonathan Stone; Madiha Majid; Boliang Guo; Richard Morriss

doi:10.2196/84020

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Mood Monitoring, Mood Tracking og Ambulatory Assessment-interventioner ved depression og bipolar lidelse: Systematisk review og metaanalyse af randomiserede kontrollerede forsøg

Studiet undersøger, om hyppig monitorering af stemningsleje hos personer med depression eller bipolar lidelse har en effekt på symptomer og for hvem interventionerne kan være nyttige. Det fokuserer på både aktive og passive metoder til stemningsmåling og vurderer deres betydning for forebyggelse af tilbagefald.

Titel og resumé er skrevet på dansk med AI ud fra den originale publikation. Originalteksten står nedenfor.

Astill Wright L, Shajan G, Purewal D, Stone J, Majid M, Guo B, Morriss R. · JMIR mental health · 2026

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CC BY 4.0 · © Laurence Astill Wright, Georgina Shajan, Daljit Purewal, Jonathan Stone, Madiha Majid, Boliang Guo, Richard Morriss. Originally published in JMIR Mental Health

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Mood Monitoring, Mood Tracking, and Ambulatory Assessment Interventions in Depression and Bipolar Disorder: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Original dokumenttekst

Abstract

Background

Mood monitoring is widely used by people with depression and bipolar disorder (BD) to prevent relapse and improve insight into their condition, but it is unclear if these interventions have an impact on symptoms and for whom. As the capacity for passive mood monitoring increases, it is vital to improve our understanding of frequent mood assessment.

Objective

This systematic review and meta-analysis assessed the effect of mood monitoring interventions in people with depression and BD to decrease relapse risk and symptoms of depression and mania.

Methods

We conducted a systematic review and meta-analysis (PROSPERO, International Prospective Register of Systematic Reviews: CRD42023396473) and reported results according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. Randomized controlled trials with clinically important follow-up periods were identified via multiple database searches and rated for risk of bias using the Cochrane Risk of Bias tool. The primary outcomes were symptoms of depression and mania. Available data were pooled to calculate standardized mean differences (SMDs) for the primary outcomes: severity of depression, bipolar depression, and mania/hypomania.

Results

We included 8 trials of 1230 participants and 6 different mood monitoring protocols. In BD, meta-analysis found a small but not statistically significant effect of mood monitoring interventions on decreasing mania symptoms (6 comparisons, n=873; SMD 0.16, 95% CI−0.34 to 0.01; P=.06) and no effect on bipolar depression (6 comparisons, n=873; SMD −0.08, 95% CI −0.31 to 0.15; P=.02). In depression, we found a small effect in decreasing symptoms of depression of borderline statistical significance at 12 months (2 comparisons, n=262; SMD −0.25, 95% CI −0.49 to 0.00; P=.05) but not at 6 months (2 comparisons, n=268; SMD −0.21, 95% CI −0.54 to 0.12; P=.21). There was an absence of evidence on the effect of mood monitoring on decreased relapse rates or readmission rates. Studies had a low risk of bias. There was no evidence on mood monitoring through ecological momentary assessment.

Conclusions

Overall mood monitoring interventions do not increase or decrease mood symptoms in people with BD, nor is there robust evidence of such effects in people with unipolar depression. Further research is merited on different forms of mood monitoring and to determine under what circumstances mood monitoring might have beneficial or adverse effects. These results initially suggest that ambulatory assessment does not induce large placebo effects or significantly negatively or positively affect mood, and thus that mood monitoring may be an appropriate outcome measure for research or for clinical practice.

Introduction

Many people with bipolar disorder (BD) and depression track their mood symptoms over time, and there are multiple tools available freely on the web to do this [1-4]. A recent survey of people with BD found that 41.6% of participants reported using a self-management app related to mood or sleep [5 6]. Mood monitoring is widely used, specifically by people with mood disorders [7], for example, Bipolar UK’s mood tracking app has greater than 10,000 downloads on Android alone [8]. Mood tracking is one of the most common features of mental health smartphone apps—previous reviews have noted that mood/behavior tracking is present in over half of these apps [9-11]. Many smartphones include in-built self-tracking functions for health [12], and many studies are incorporating mood monitoring as a method of clinical outcome [13] and testing them as interventions [14].

Traditionally, mood monitoring is done place using paper-based charting [15]. However, many people with BD and depression prefer digital methods as they are convenient and store easily accessible records of mood, allowing people to more easily look back and identify patterns of improvement or worsening of symptoms [8]. Digital methods, where individuals record their mood in the moment, may also decrease recall bias, so there might be greater accuracy in charting and plotting mood rather than retrospective completion of data every few weeks [7]. The technology used in mood tracking and ambulatory assessment is wide-ranging, and some of the descriptive terms used throughout this paper are nuanced, often with overlapping definitions. Because of this, we have listed these important terms in Table 1 below.

Mood monitoring can be used as an intervention (both in randomized controlled trials [RCTs] and nonrandomized studies) and also as a method of ascertaining outcome (both in RCTs and nonrandomized studies). Passive data collection may reduce the burden of data completion and remind the participant less frequently about their mood [16 17]. Some mood monitoring may combine active and passive monitoring. For example, passive monitoring of certain activity or behavior may trigger active data collection from the user when there is a preset level of change in this activity/behavior. In depression, activity may be reduced, and in mania, it may increase [18]. Other forms of mood monitoring might randomly request the participant to actively complete data on mood without any passive monitoring [17]. These technological advances may provide new utility to a relatively old intervention methodology. However, there is a need to assess whether these newer approaches to mood monitoring have benefits or harms as well, and so we included all of these approaches in this review.

There is evidence that increasing awareness of mood fluctuations can improve insight, and the identification of early warning signs can prevent relapse in depression and BD [19]. This raises the question of whether mood tracking can have any direct clinical effects, either positive or negative [20]. Currently, it is unclear if mood monitoring or mood tracking as an intervention is effective in reducing symptom severity or in preventing relapse. It is also possible that mood monitoring interventions have negative effects on mood [21 22] or lead to a response bias whereby users complete the same score despite mood altering in response to being asked the same questions repeatedly. As the capacity for mood monitoring through digital assessment increases and these methods are used increasingly as assessment methods in research, it is vital to improve our understanding of frequent mood assessment [23].

Some people with BD report that mood monitoring helps them to reduce relapse risk, for example, through greater awareness of their current mental state, while others report it worsens their mood, for example, by reminding them of their mood problems, so they consider mood monitoring a burden [16 22]. Others report that it is relatively simple to carry out in their day-to-day lives [1]. Mood monitoring may represent an intervention that is usable, acceptable, and easy for individuals to implement in their lives, and it can be coupled quite easily with simple psychological interventions such as psychoeducation [24]. Questions remain, however, about definitive efficacy and potential for harm from mood monitoring alone.

The potential for efficacy or adverse events is particularly important as RCTs and observational studies increasingly move towards digital combinations of passive and active monitoring or ambulatory assessment outcomes [17 25]. It is thus integral to know whether the method of assessment itself may carry any therapeutic benefits or any adverse effects. Any such risk or benefit might bias any outcome assessment of other interventions in trials, potentially enhancing or obscuring any true benefit or suggesting a false benefit through measurement methods used in the trial rather than from the intervention itself [26]. Furthermore, if there were adverse effects, ambulatory assessment protocols would need to consider providing coping/mitigating strategies—currently, the risk is not known, and such mitigating strategies are not routinely provided [27 28]. The risk/benefit of mood monitoring might be best investigated through an analysis of RCTs of frequent mood assessment, although other approaches, such as reports of adverse events, qualitative monitoring, and surveys from practice, all have their place in responsible technological innovation [29].

Previous reviews have explored mood monitoring but have not assessed efficacy in high-quality RCTs [28 30 31]. This is the first systematic review and meta-analysis that we are aware of that examines mood monitoring as an intervention in RCTs in BD. The aim of this systematic review is to assess the effect of mood tracking in people with BD and depression on relapse risk and symptoms of bipolar depression, mania/hypomania, and depression.

Methods

Overview

We used the Cochrane Handbook for Systematic Reviews of Interventions methodology and used a Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist. The study was preregistered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023396473 [32]).

Inclusion Criteria

The inclusion criteria were as follows: self-monitoring/ecological momentary assessment (EMA)/repeated symptom assessment in people with BD or depression as an intervention over a minimum period of 3 months, with rating of symptoms weekly at a minimum. On discussion with patient and public involvement, mood monitoring over periods less than 3 months might be misleading in various contexts associated with temporary change, such as changes in medication, menstrual, seasonal, or life event effects on mood, or as the person becomes familiar with a new method of mood monitoring. The studies needed to use an appropriate nonmood monitoring/EMA control. The studies should either use a validated measure of mood or validate the chosen measure with a validated mood measure. The studies could be published in any language and could be digital or nondigital, although we acknowledged that the majority of studies would use digital technologies. We only included RCTs with 20 or more participants with BD or depression [33]. We did not exclude RCT studies where mood monitoring was included but not the primary focus of the intervention (but it so happened that in the included studies, the mood monitoring did comprise a significant part of the overall intervention). We searched the gray literature (eg, conference abstracts, dissertations, policy literature, reports via ProQuest and Google Scholar—full details below) for unpublished studies that were eligible for inclusion.

Search Strategy and Selection Criteria

The complete search strategy is listed in the Multimedia Appendix 1. We searched PsychINFO, EMBASE, SCOPUS, IEEE Xplore, Ovid MEDLINE, Proquest SciTech Collection, ProQuest Dissertations and Theses Global, and Google Scholar using the search terms. Search results were exported for appraisal and stored on Rayyan [34]. The initial search was conducted on March 03, 2023, and updated on October 28, 2024. All abstracts were appraised by 2 independent screeners (LAW, GM, GS, RP, MM, and DP), and any disagreements were discussed, and a consensus arrived upon, with adjudication by a third independent screener if required. The full text of any potentially relevant papers was acquired, and if we were unable to source the full text of the study, we then contacted the corresponding author to request the paper. To determine if potentially relevant studies met the inclusion criteria, the full text was reviewed separately by 2 authors, again with discussion and consensus with a third reviewer if necessary. All papers for inclusion were reference checked along with relevant systematic reviews [15 18 27 28 30 31 35-38 undefined undefined undefined]. Key authors were also emailed to see if the inclusion of any ongoing unpublished studies could be included.

Data Extraction

Two independent reviewers extracted data (as per symptom severity scores for different time points) from studies meeting the inclusion criteria using identical data extraction forms. Irregularities in the data extraction were discussed, and any discrepancies were resolved through discussion.

Assessment of Study Bias

The Cochrane Collaboration’s Risk of Bias 2 tool was used for each study [39]. Risk of bias was assessed by 2 independent reviewers (LAW and GS), and any disagreement was resolved via discussion.

The certainty of the evidence for the meta-analysis results for each outcome was assessed independently and in duplicate by 2 review authors (LAW and GS) using the Grading of Recommendations, Assessment, Development, and Evaluation framework. This involved an individual assessment of each of the 5 domains of risk of bias (inconsistency, indirectness, imprecision, and publication bias), resulting in an overall assessment of the certainty of the evidence as “high,” “moderate,” “low,” or “very low” [40].

Synthesis of Results

The primary outcome in the meta-analysis for treatment studies was a reduction in depression/mania/hypomania incidence/symptoms for people with BD, and a reduction in depressive symptoms at 6 months postintervention. This timeframe was chosen to demonstrate the stability of treatment effects. For mania/hypomania/depression severity, we calculated standardized mean differences (SMDs). Insufficient studies examined BD or depression incidence or relapse risk to meta-analyze.

For outcomes included in more than one study, we measured statistical heterogeneity by calculating the I² statistic [41]. An I² of less than 30% was taken to indicate mild heterogeneity, and a fixed-effects model was used. When the I² was greater than or equal to 30%, a random-effects model was used. All analyses were performed using Review Manager (version 5.3; Cochrane Collaboration).

Results

Overview

The search identified a total of 23,515 studies. No studies that were not in English were found to meet the inclusion criteria. Following title and abstract screening, 21,638 studies were excluded, resulting in a total of 758 studies being reviewed in full. A total of 5 trials in people with BD and 3 trials in people with depression met the eligibility criteria and were included in the review. The BD trials included 803 participants, and the depression trials included 427 participants. Tables 2-4 display detailed characteristics of the studies and the mood monitoring protocols used. Figure 1 and Checklist 1 detail the search strategy with the PRISMA flowchart and checklist.

Bipolar Disorder: Overview of Individual Study Findings

In BD, 5 trials [14 43-45 49-61 undefined undefined undefined undefined undefined undefined undefined undefined undefined undefined undefined undefined undefined undefined] used 3 different mood monitoring protocols. These were the MONARCA/Monsenso system, Livewell, and MoodSwings [45 52]. Only the MONARCA/Monsenso system incorporated passive data capture into the intervention. These were all digital; 3 used active and passive ambulatory assessment [43 44 51] while 2 just used active ambulatory assessment [14 45].

All studies assessed mania and depression severity using a mixture of self-report and clinical rating scales, with three of these as the primary outcome [43 45 62]. Two studies assessed relapse rate/psychiatric readmission or duration as the primary outcome [44]. Only one study [45] provided raw mania/depression severity data, and we contacted the authors of the other papers to obtain this for the analyses. Studies used similar inclusion criteria, but there were some key differences. All studies recruited individuals with BD from clinical services and confirmed BD via a structured clinical interview at baseline and then relied on self-report measures/clinical ratings for outcome assessment. All studies excluded individuals who were currently experiencing a major mood episode, either by using a cutoff on self-report scores/clinical rating scales (which varied between studies) [14 42 45] or by the participant completing treatment at a specialist mood disorder service [43], with one study recruiting individuals on discharge from inpatient care following hospitalization for an affective episode [44].

The results of the trials were mixed. The 2 trials [14 47] assessing relapse/readmission risk showed no effect of mood monitoring. Goulding et al [14] demonstrated an effect of decreased relapse risk for low-risk individuals, but no effect on percentage-time symptomatic for all participants. Three studies did not identify a decrease in depression or mania scores from mood monitoring on clinical ratings conducted blinded to allocation [43 51 63]. Gliddon et al [45] reported decreases in depressive symptoms when compared with the peer support control, Goulding et al [14] also reported improvements in depression severity alongside improved relational quality of life.

Faurholt-Jepsen et al [51] and Faurholt-Jepsen et al [43] found a nonstatistically significant trend for worsening depressive symptoms versus the control group.

Bipolar Disorder: Meta-Analysis

Concerning the primary outcome, there was no effect of mood monitoring interventions in reducing symptoms of mania/hypomania (Figure 2: 6 comparisons, n=873; SMD −0.16, 95% CI −0.34 to 0.01; P=.06; I²=36%) or bipolar depression (Figure 3: 6 comparisons, n=873; SMD −0.08, 95% CI −0.31 to 0.15; P=.02; I²=63%).

Depression: Overview of Individual Study Findings

In depression, 3 trials used 3 different mood tracking procedures. These were: interactive voice response telephone calls (IVR) [46], Monsenso [47], and eCare for Moods [48]. None of the trials used mood monitoring as the primary outcome; instead, using standardized infrequent assessments of mood (Tables2 4). One study was analog using the telephone [46], the other two were digital [47 48]; two used active ambulatory assessment [48], with one using active and passive ambulatory assessment [47].

The Monsenso [47] and eCare for Moods [48] protocols used daily monitoring, while IVR calls [46] were administered weekly. Only the Monsenso system incorporated passive data collection. All protocols incorporated clinical feedback of the assessment as an intervention. Monseno and eCare for Moods used digital technology, while IVR used telephone calls and voice messages. We did not identify any paper-based charting trials. The duration of ambulatory assessment protocols varied from 6 months to 24 months.

Two studies assessed relapse rate/psychiatric readmission or duration as the primary outcome [46 48]. Tonning et al [64] assessed the rate and accumulated duration of psychiatric readmissions as the primary outcome, assessing depression severity as a secondary outcome. Only one study [48] provided raw depression severity data, and we contacted the authors of the other papers to obtain this for the analyses. Studies used similar inclusion criteria, but there were some key differences. All studies recruited individuals with depression from clinical services. Two studies confirmed depression via a structured clinical interview at baseline, while Aikens et al [46] confirmed depression via the Patient Health Questionnaire-9. All studies then relied on self-report measures for outcome assessment. Aikens et al [46] excluded patients who were experiencing major psychiatric distress and recruited from community samples. Hunkeler et al [48] did not exclude individuals with suicidal ideation or a particular severity of depression, again recruiting from community clinics. Tonning et al [47] recruited people with depression receiving inpatient care, providing the intervention postdischarge.

The results of the 3 trials were mixed. Tonning et al [47] report no change in relapse risk or readmission duration, as well as no change in depressive symptoms. They did, however, report a range of benefits across tertiary outcomes when adjusted for age, sex, and Hamilton Depression Rating Scale scores. Patients in the intervention group reported statistically higher recovery, measured using the Recovery Assessment Scale, as well as a tendency (not statistically significant) towards higher quality of life, higher well-being, more satisfaction with treatment, and higher behavioral activation in the intervention group compared with the control group.

In eCareformoods, participants in the intervention group experienced a statistically significant reduction in depressive symptoms at 2 years. A higher proportion of those in the intervention group remained in recovery from their depression, and the number needed to treat was calculated at 8. Intervention participants also had improvements across a range of secondary outcomes, including improved general mental health, learning new coping skills, greater satisfaction with specialty care, and more confidence in managing depression. These were all statistically significant.

Aikens et al [46] report a statistically significant improvement in Patient Health Questionnaire-9 depression severity (2.5 points) at 6 months in the intervention group. This persisted for 12 months. Clinical response was more likely in the intervention group than the control at 6 months, but this difference decreased in size and lost statistical significance by 12 months.

Only Tonning et al [47] reported adverse effects, and these were as follows: 3 participants found the monitoring stressful, and 1 participant did not find it helpful.

Unipolar Depression: Meta-Analysis

There was a small effect of borderline statistical significance at 12 months (Figure 4: 2 comparisons, n=262; SMD −0.25, 95% CI −0.49 to 0.00; P=.05; I²=45%) but not at 6 months (Figure 5: 2 comparisons, n=268; SMD −0.21, 95% CI −0.54 to 0.12; P=.21; I²=12%). Only 2 trials were included in the meta-analysis as the appropriate data for Tonning et al [47] were not available.

Risk of Bias Assessments

The quality of these RCTs was good, with all trials having low risk of bias (Table 5). All trials used intention-to-treat analysis. All studies reported adherence and attrition.

Discussion

Principal Findings

This systematic review of mood monitoring interventions in people with BD found no effect either way on symptoms of mania/hypomania or bipolar depression in people with BD at 6‐12 months. There was no robust evidence of mood monitoring either increasing or decreasing symptoms of depression, with no effect at 6 months and borderline statistical improvement at 12 months in only 2 RCTs. There were some other benefits of mood monitoring across 3 RCTs in depression, but there was no consistency in what was measured or the outcomes that were improved.

Bipolar Disorder

Mood monitoring is theorized to work by improving understanding and insight to enable people to self-manage their BD [16]. People with BD seem to use the data provided to them by mood monitoring in varied ways [65]. These appear to be highly personalized and tailored to what works best for them. The mood monitoring protocol provides a platform for people to interpret their own mood data, devising highly personal ways of self-managing their BD subsequently [66]. This self-management may focus on sleep, medication, crisis planning, and communication in close relationships [16]. Thus, the variability in these outcomes might reflect the different ways in which participants used the mood monitoring information and their coping strategies in the face of depression and mania [8] as well as differences in populations and measurement of mood. Despite the equivocal results reported here, the practice remains hugely popular, and in the digital sphere, there are multiple different apps and protocols available aimed at people with BD, such as the Bipolar UK mood tracker [4], eMoods [42], Moodily, Moodnotes, etc [3].

Depression

There is some agreement with the findings from the meta-analysis we report from RCTs of mood monitoring in bipolar depression that, on the whole, mood monitoring has neutral effects on outcome, but there is quite a lot of heterogeneity, with some people reporting benefits, and others distress and burden. The findings are not robust. While all 3 RCTs report other benefits of mood monitoring, such as relapse, recovery, increased overall mental health, greater confidence in managing depression, and greater satisfaction with mental health services, there was no consistency in what or how these secondary or tertiary outcomes were measured. Only one of these RCTs reported adverse effects of mood monitoring, with 3 participants finding it a burden and stressful. From qualitative research, some variables that have bearing on the outcome of mood monitoring are context in which mood monitoring is taking place, the usual coping strategies, the nature of mood monitoring, and the population that is being examined. Only one RCT used passive mood monitoring, and none used EMA approaches, so there is no evidence available on modern developments in mood monitoring of depression symptoms in people with unipolar depression. Despite the modest effects reported here, mood monitoring remains popular for people with depression [67-70], and in the digital sphere, there are multiple different apps/protocols available [70]. This review highlights that the popularity of the process may be disproportionate to the direct effects of mood monitoring as an intervention. From the systematic reviews of qualitative data that we have performed, and like self-monitoring of BD, the popularity of mood monitoring in depression may be through the empowerment of the individual to use the data from mood monitoring in a variety of personal ways rather than any direct effect on any outcome [21 22]. However, mood monitoring is not for everyone; for some, it is a burden or a reminder of poor well-being.

Strengths and Limitations

Our search was thorough and in accordance with Cochrane methodology. We also consulted experts in the field, used a wide search, and used reference searching. Our conclusions, however, were limited due to a paucity of literature. This itself is an important finding, considering the possible implications of frequent mood assessment as ambulatory assessment and EMA approaches develop further. Sometimes there is definitional overlap between EMA protocols and mood monitoring protocols [17]. We did not identify a large enough group of studies using EMA approaches, passive monitoring, or a combination of the two to determine any outcomes or harms from these approaches in this review. We did not include mood monitoring protocols that used shorter follow-up periods, as some previous reviews have done [18-20], because we wanted to examine whether there was any high-quality evidence on benefits or harms. Therefore, we chose RCTs of mood monitoring interventions lasting at least 3 months and clinically relevant follow-up lengths of at least 6‐12 months.

Many other trials were excluded because control groups also had an intervention consisting of some element of mood monitoring. In all the included studies, the main therapeutic intervention applied to all participants in the intervention group was mood monitoring, but all of these trials included additional likely therapeutic elements, which makes isolating the effect of the mood monitoring challenging. With regard to the 5 BD trials, the MONARCA/Monsenseo [43 44 51] studies included data review and outreach by a nurse; the Livewell [14] and Moodswings [45] trials included additional coaching, psychoeducational materials, and planning for management of early warning signs and symptoms. In all 3 RCTs of mood monitoring in depression, there were other elements that might have improved or worsened depression symptoms. Two RCTs used cognitive behavior therapy modules [47 48], and one provided self-management guidance based on the severity of the participant’s symptoms [46]. These additional elements may prove therapeutic benefit but also obfuscate any effect directly from mood monitoring itself. Thus, it may be impossible to determine only the effects of mood monitoring on symptoms of depression or mania because interventions have elements of other interventions as well. There were no studies with just a basic mood monitoring element versus a nonmood monitoring control.

In addition to nonmood monitoring elements of the intervention, there may be factors other than the differences in the intervention that explain some of the mixed results we observe here. These include clinical characteristics of the sample such as the type/subtype of mood disorder (Bipolar 1: Bipolar 2 proportions reported in Tables2 4) and as the results of Goulding et al [14] suggest, there may be improved beneficial effects in lower-risk individuals with BD who are at a specific stage of their illness, and this is supported by qualitative work suggesting that there may be a right time for people with BD to be using mood tracking as an intervention [22 71]. Furthermore, while we did not assess adherence to mood tracking in this paper, we have addressed this in a separate meta-analysis [72], and this separate work demonstrates that all of the studies included here had >70% adherence. Thus, it is unclear if poor adherence levels may cause a failure of effect of mood tracking. Suboptimal adherence may obfuscate these effects, particularly if the effect size is small. However, poor adherence is pragmatic and reflective of real-life outcomes [73], and so these results provide a more pragmatic signal of effect.

Our review focused on the effects of mood monitoring on mood symptoms. However, there might be other benefits from mood monitoring, such as the development of mental literacy about the condition early in its course by seeing how mood varies in severity across time or learning to recognize the symptoms of mania. It may improve confidence or coping strategies that exert some control over the symptoms through developing insights into the conditions. These elements may improve recovery and function [74]. For instance, a valuable role for mood monitoring might be to help with decision-making when seeking help for those who frequently relapse with depression or BD, and when to make important life decisions, such as new responsibilities or decisions with an element of risk, like taking a holiday abroad, away from usual sources of help. Thus, mood monitoring might also have different clinically important outcomes depending on the recency of diagnosis, course of illness, or where recovery and improvement of function are a clinical priority.

Future Research and Clinical Implications

Future research in mood disorders should evaluate the definitive efficacy of mood monitoring alone rather than additional components to assess for benefits and potential harms. Care should be taken over control groups to ensure that they do not unwittingly include mood monitoring or psychotherapeutic approaches that might obscure the effects of mood monitoring. It remains unclear for whom mood monitoring may be most effective, and future research should assess this. It could be, for example, that it is least effective and most harmful in people with mood disorders who cope with depression by suppressing these symptoms and have not developed other coping strategies. Other groups who may experience harm might be people with mood disorders that feature paranoia or where monitoring of the person was used as a form of coercion, for example, as a feature of morbid jealousy or other abusive relationships. The benefits and harms from more modern approaches to mood monitoring should be explored with a broader range of outcomes focusing on mood monitoring to improve recovery, function, capability, and quality of life.

The field would benefit from a definitive RCT assessing time to relapse in those in asymptomatic remission with an appropriate control group. This would use a mood monitoring intervention with minimal additional psychotherapeutic strategies. This work is important due to the increased use of ambulatory assessment measures as measures of treatment outcomes [75] in studies looking at other interventions and not primarily to explore the effects of measurement itself. These are, in many cases, indistinguishable from mood monitoring protocols that study mood monitoring as an intervention. Such a RCT of mood monitoring might benefit from qualitative work performed alongside to better understand how participants use information gleaned from mood monitoring, as they are not always passive consumers of such information [16]. Finally, we also need to examine mood monitoring in poorer and ethnically diverse populations. These populations may be excluded from digital interventions through poverty or other disadvantages [76], and the characteristics that predispose a population to digital disadvantage are the same as those that might put them at increased risk of mental illness [77 78]. If digital forms of monitoring and interventions are to be used more broadly in health care, they may have the effect of widening pre-existing health inequalities through lack of access to the technologies themselves, as well as to research, in disadvantaged populations. Technology could allow populations who might not otherwise easily access health care to access more relevant information or interventions, in languages other than the one used by the health care provider.

There is insufficient robust, high-quality evidence of benefits or harm to recommend active mood monitoring by participants as primary outcomes in an RCT. In fact, the heterogeneity of outcomes and variability in the way people with mood disorders appraise and use active mood monitoring suggests that active mood monitoring would be unsuitable for use as a primary outcome; a source of variance in outcome might be introduced that would be nonrandom and not necessarily predictable. Since many passive mood monitoring and ambulatory assessments are more implicit measures of mood, participants may not have as much agency in appraising and using such information. However, at this point, these measures are also not suitable as their effects on both outcomes and harms have not been sufficiently tested.

The meta-analysis showed small, nonsignificant, or borderline effects with the use of mood-monitoring, and understanding the practical implications of this is important, particularly considering the popularity of mood-tracking apps, some of which carry recommendations by organizations such as Bipolar UK. Mood monitoring, often coupled with other additional elements in the forms assessed here, did not have clinically important effects, and the qualitative research frequently reporting benefit does not align with the quantitative evidence presented here [21 79]. It is difficult to know under what circumstances mood monitoring may work for these individuals who do report a qualitative benefit. The qualitative research suggests that just mood monitoring is insufficient for any clinical effect and that individuals must incorporate this into a wide range of self-management strategies to keep them well [21 22 79]. The finding that there is no large clinical or placebo effect from mood monitoring suggests that these tools may make excellent outcome measures for research and clinical practice—fundamentally monitoring symptoms over time and using this information as an adjunct to make decisions around care (eg, improvements in shared decision making using more accurate data) and improving existing research outcomes.

Conclusions

As technological advances are applied to digital health and the capacity for more usable, passive mood monitoring increases, it is vital to understand whether these interventions work and for whom. It is also important to understand any positive or negative effects, as self-tracking is often used as a control or outcome assessment method in studies [17]. For BD, this review showed no effect of mood monitoring on symptoms of mania or bipolar depression, although the evidence was not robust with moderate to high heterogeneity in outcome. In people with depression, there was no robust evidence of the effects of mood monitoring on depression symptoms, with only 2 RCTs contributing to the meta-analysis. There was no evidence of the effect of mood monitoring using EMA. These results initially suggest that ambulatory assessment does not induce large placebo effects or significantly negatively/positively affect mood, and thus that mood monitoring may be an appropriate outcome measure for research or for clinical practice.

Funding: This work was supported by a Wellcome Trust Fellowship to LAW and MM.

Authors’ Contributions: LAW and RM were responsible for the original study design. LAW, GS, DP, and MM were responsible for the search, identification of relevant papers, data extraction, and risk of bias assessments. LAW was responsible for data analysis and initial drafting of the report. LAW and RM were responsible for the subsequent interpretation, editing, and rewriting of the report.

Conflicts of Interest: RM was chief investigator on a grant from the UK National Institute for Health and Care Research (NIHR) Applied Research Collaboration (ARC) East Midlands. RM has received other NIHR funding for research on interventions for depression and has received funding from Novartis to serve on a data management and ethics committee for 2 trials on the treatment of depression.

Referencer citeret i publikationen

1. MajidS MorrissR FigueredoG ReevesS Exploring self-tracking practices for those with lived experience of bipolar disorder: learning from combined principles of patient and public involvement and HCIDIS ’22: Proceedings of the 2022 ACM Designing Interactive Systems ConferenceJun132022doi10.1145/3532106.3533531
2. MajidS ReevesS FigueredoG et al The extent of user involvement in the design of self-tracking technology for bipolar disorder: literature reviewJMIR Ment HealthDec202021812e27991doi10.2196/27991Medline34931992PMC8726024
3. HopeB Top 7 mood-tracking apps for bipolar disorderbphope2024URLhttps://www.bphope.com/bipolar-buzz/bipolar-lifestyle-top-6-apps-to-track-your-moods/Accessed17-9-2025
4.Track your moodBipolar UKURLhttps://www.bipolaruk.org/get-support/track-your-mood/Accessed17-08-2025
5. de Azevedo CardosoT KochharS TorousJ MortonE Digital tools to facilitate the detection and treatment of bipolar disorder: key developments and future directionsJMIR Ment Health202411e58631doi10.2196/5863138557724PMC11019420
6. MortonE NicholasJ YangL et al Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-managementInt J Bipolar DisordApr4202210110doi10.1186/s40345-022-00256-6Medline35368207PMC8977125
7. NicholasJ LarsenME ProudfootJ ChristensenH Mobile apps for bipolar disorder: a systematic review of features and content qualityJ Med Internet ResAug172015178e198doi10.2196/jmir.4581Medline26283290PMC4642376
8.Mood Tracker appBipolar UK2025URLhttps://www.bipolaruk.org/get-support/track-your-mood/mood-tracker-app/Accessed17-08-2025
9. LaganS D’MelloR VaidyamA BildenR TorousJ Assessing mental health apps marketplaces with objective metrics from 29,190 data points from 278 appsActa Psychiatr ScandAug20211442201210doi10.1111/acps.13306Medline33835483
10. QuC SasC Daudén RoquetC DohertyG Functionality of top-rated mobile apps for depression: systematic search and evaluationJMIR Ment Health0124202071e15321doi10.2196/15321Medline32012079PMC7007593
11. SchuellerSM NearyM LaiJ EpsteinDA Understanding people’s use of and perspectives on mood-tracking apps: interview studyJMIR Ment Health202188e29368doi10.2196/2936834383678PMC8387890
12. CaldeiraC ChenY ChanL PhamV ChenY ZhengK Mobile apps for mood tracking: an analysis of features and user reviewsAMIA Annu Symp Proc20172017495504Medline29854114PMC5977660
13. BaiR XiaoL GuoY et al Tracking and monitoring mood stability of patients with major depressive disorder by machine learning models using passive digital data: prospective naturalistic multicenter studyJMIR Mhealth Uhealth202193e24365doi10.2196/2436533683207PMC7985800
14. GouldingEH DopkeCA RossomR JonathanG MohrD KwasnyMJ Effects of a smartphone-based self-management intervention for individuals with bipolar disorder on relapse, symptom burden, and quality of life: a randomized clinical trialJAMA PsychiatryFeb12023802109118doi10.1001/jamapsychiatry.2022.4304Medline36542401PMC9857325
15. KoendersMA NolenWA GiltayEJ HoencampE SpijkerAT The use of the prospective NIMH Life Chart Method as a bipolar mood assessment method in research: a systematic review of different methods, outcome measures and interpretationsJ Affect DisordApr12015175260268doi10.1016/j.jad.2015.01.005Medline25658502
16. Astill WrightL MooreM ReevesS VallejosEP MorrissR Improving the utility, safety, and ethical use of a passive mood-tracking app for people with bipolar disorder using coproduction: qualitative focus group studyJMIR Form ResFeb720259e65140doi10.2196/65140Medline39918865PMC11845880
17. De AngelV LewisS WhiteK et al Digital health tools for the passive monitoring of depression: a systematic review of methodsNPJ Digit Med01112022513doi10.1038/s41746-021-00548-8Medline35017634PMC8752685
18. WenzeSJ MillerIW Use of ecological momentary assessment in mood disorders researchClin Psychol RevAug2010306794804doi10.1016/j.cpr.2010.06.007Medline20619520
19. MorrissR VinjamuriI FaizalMA BoltonCA McCarthyJP Training to recognise the early signs of recurrence in schizophreniaCochrane Database Syst RevFeb28201320132CD005147doi10.1002/14651858.CD005147.pub2Medline23450559PMC12066188
20. TorousJ ChoudhuryT BarnettI KeshavanM KaneJ Smartphone relapse prediction in serious mental illness: a pathway towards personalized preventive careWorld PsychiatryOct2020193308309doi10.1002/wps.2080532931109PMC7491614
21. Astill WrightL MajidM ShajanG et al The user experience of ambulatory assessment and mood monitoring in depression: a systematic review & meta-synthesisnpj Digit MedDec2202581737doi10.1038/s41746-025-02118-8Medline41331067PMC12672782
22. Astill WrightL MajidM MooreM et al The user experience of ecological momentary assessment and mood monitoring in bipolar disorder: a systematic review and meta-synthesis of qualitative studiesJ Med Internet Res202427e71525e71525doi10.2196/71525PMC1253393141105870
23. BrownS PloegerC GuoB et al When a test is more than just a test: findings from patient interviews and survey in the trial of a technology to measure antidepressant medication response (the PReDicT Trial)Compr Psychiatry072024132152467doi10.1016/j.comppsych.2024.152467Medline38608615
24. ColomF LamD Psychoeducation: improving outcomes in bipolar disorderEur PsychiatryAug2005205-6359364doi10.1016/j.eurpsy.2005.06.002Medline16112848
25. Van TilK McInnisMG CochranA A comparative study of engagement in mobile and wearable health monitoring for bipolar disorderBipolar DisordMar2020222182190doi10.1111/bdi.12849Medline31610074PMC7085979
26. BrownS PloegerC GuoB et al When a test is more than just a test: findings from patient interviews and survey in the trial of a technology to measure antidepressant medication response (the PReDicT Trial)Compr Psychiatry072024132152467doi10.1016/j.comppsych.2024.15246738608615
27. DubadM WinsperC MeyerC LivanouM MarwahaS A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young peoplePsychol Med012018482208228doi10.1017/S0033291717001659Medline28641609
28. Faurholt-JepsenM MunkholmK FrostM BardramJE KessingLV Electronic self-monitoring of mood using IT platforms in adult patients with bipolar disorder: a systematic review of the validity and evidenceBMC Psychiatry01152016167doi10.1186/s12888-016-0713-0Medline26769120PMC4714425
29. JirotkaM GrimpeB StahlB EdenG HartswoodM Responsible research and innovation in the digital ageCommun ACMApr2420176056268doi10.1145/3064940
30. OrtizA MaslejMM HusainMI DaskalakisZJ MulsantBH Apps and gaps in bipolar disorder: a systematic review on electronic monitoring for episode predictionJ Affect DisordDec202129511901200doi10.1016/j.jad.2021.08.14034706433
31. van der WattASJ OdendaalW LouwK SeedatS Distant mood monitoring for depressive and bipolar disorders: a systematic reviewBMC Psychiatry07222020201383doi10.1186/s12888-020-02782-yMedline32698802PMC7374077
32. Astill WrightL MorrissR Mood-tracking and self-monitoring in bipolar disorder and depressionNIHR-PROSPERO2024URLhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42023396473Accessed23-12-2025
33.Bipolar disorder (update) – review protocolsNational Institute for Health and Care Excellence (NICE)2021URLhttps://www.nice.org.uk/guidance/cg185/documents/bipolar-disorder-update-review-questions2Accessed17-09-202531556980
34. OuzzaniM HammadyH FedorowiczZ ElmagarmidA Rayyan-a web and mobile app for systematic reviewsSyst RevDec5201651210doi10.1186/s13643-016-0384-4Medline27919275PMC5139140
35. Antosik-WójcińskaAZ DominiakM ChojnackaM et al Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modellingInt J Med InformJun2020138104131doi10.1016/j.ijmedinf.2020.104131Medline32305023
36. Palmier-ClausJ LobbanF MansellW et al Mood monitoring in bipolar disorder: Is it always helpful?Bipolar DisordJun2021234429431doi10.1111/bdi.13057Medline33570820
37. MalhiGS HamiltonA MorrisG MannieZ DasP OuthredT The promise of digital mood tracking technologies: are we heading on the right track?Evid Based Mental HealthNov2017204102107doi10.1136/eb-2017-102757PMC1051639728855245
38. Faurholt-JepsenM GeddesJR GoodwinGM et al Reporting guidelines on remotely collected electronic mood data in mood disorder (eMOOD)-recommendationsTransl PsychiatryJun7201991162doi10.1038/s41398-019-0484-8Medline31175283PMC6555812
39. HigginsJPT AltmanDG GotzschePC et al The cochrane collaboration’s tool for assessing risk of bias in randomised trialsBMJOct182011343oct18 2d5928d5928doi10.1136/bmj.d592822008217PMC3196245
40. AtkinsD BestD BrissPA et al Grading quality of evidence and strength of recommendationsBMJJun19200432874541490doi10.1136/bmj.328.7454.1490Medline15205295PMC428525
41. ChandlerJ CumpstonM LiT PageMJ WelchVA Cochrane Handbook for Systematic Reviews of Interventions Version 64Vol6Cochrane Collaboration20234
42. Faurholt-JepsenM FrostM RitzC et al Daily electronic self-monitoring in bipolar disorder using smartphones – the MONARCA I trial: a randomized, placebo-controlled, single-blind, parallel group trialPsychol MedOct2015451326912704doi10.1017/S003329171500041026220802
43. Faurholt-JepsenM FrostM ChristensenEM BardramJE VinbergM KessingLV The effect of smartphone-based monitoring on illness activity in bipolar disorder: the MONARCA II randomized controlled single-blinded trialPsychol MedApr2020505838848doi10.1017/S0033291719000710Medline30944054
44. Faurholt‐JepsenM Lindbjerg TønningM FrosM et al Reducing the rate of psychiatric re‐admissions in bipolar disorder using smartphones—the RADMIS trialActa Psychiatr Scand0520211435453465doi10.1111/acps.1327433354769
45. GliddonE CosgroveV BerkL et al A randomized controlled trial of MoodSwings 2.0: an internet-based self-management program for bipolar disorderBipolar DisordFeb20192112839doi10.1111/bdi.12669Medline29931798
46. AikensJE ValensteinM PlegueMA et al Technology-facilitated depression self-management linked with lay supporters and primary care clinics: randomized controlled trial in a low-income sampleTelemed J E HealthMar2022283399406doi10.1089/tmj.2021.0042Medline34086485PMC8968843
47. TønningML Faurholt-JepsenM FrostM et al The effect of smartphone-based monitoring and treatment on the rate and duration of psychiatric readmission in patients with unipolar depressive disorder: the RADMIS randomized controlled trialJ Affect DisordMar12021282354363doi10.1016/j.jad.2020.12.141Medline33421863
48. HunkelerEM HargreavesWA FiremanB et al A web-delivered care management and patient self-management program for recurrent depression: a randomized trialPsychiatr ServNov2012631110631071doi10.1176/appi.ps.005332011Medline22983558
49. BilderbeckAC AtkinsonLZ McMahonHC et al Psychoeducation and online mood tracking for patients with bipolar disorder: A randomised controlled trialJ Affect DisordNov2016205245251doi10.1016/j.jad.2016.06.06427454410
50. DenicoffKD AliSO SollingerAB Smith-JacksonEE LeverichGS PostRM Utility of the daily prospective National Institute of Mental Health Life-Chart Method (NIMH-LCM-p) ratings in clinical trials of bipolar disorderDepress Anxiety200215119doi10.1002/da.1078Medline11816046
51. Faurholt-JepsenM FrostM RitzC et al Daily electronic self-monitoring in bipolar disorder using smartphones - the MONARCA I trial: a randomized, placebo-controlled, single-blind, parallel group trialPsychol MedOct2015451326912704doi10.1017/S0033291715000410Medline26220802
52. LauderS ChesterA CastleD et al A randomized head to head trial of MoodSwings.net.au: an Internet based self-help program for bipolar disorderJ Affect Disord011520151711321doi10.1016/j.jad.2014.08.008Medline25282145
53. CastleD WhiteC ChamberlainJ et al Group-based psychosocial intervention for bipolar disorder: randomised controlled trialBr J Psychiatry0520101965383388doi10.1192/bjp.bp.108.058263Medline20435965
54. PetzoldJ Mayer-PelinskiR PilhatschM et al Short group psychoeducation followed by daily electronic self-monitoring in the long-term treatment of bipolar disorders: a multicenter, rater-blind, randomized controlled trialInt J Bipolar DisordNov420197123doi10.1186/s40345-019-0158-8Medline31680193PMC6826047
55. van den BergKC HendricksonAT HalesSA VonckenM KeijsersGPJ Comparing the effectiveness of imagery focussed cognitive therapy to group psychoeducation for patients with bipolar disorder: a randomised trialJ Affect Disord012023320691700doi10.1016/j.jad.2022.09.16036206888
56. GoldbergJF BowdenCL CalabreseJR et al Six-month prospective life charting of mood symptoms with lamotrigine monotherapy versus placebo in rapid cycling bipolar disorderBiol Psychiatry Cogn Neurosci Neuroimaging012008631125130doi10.1016/j.biopsych.2006.12.03117543894
57. LangoschJM DrielingT BiedermannNC et al Efficacy of quetiapine monotherapy in rapid-cycling bipolar disorder in comparison with sodium valproateJ Clin Psychopharmacol2008285555560doi10.1097/JCP.0b013e318185e75f18794653
58. LiebermanDZ KellyTF DouglasL GoodwinFK A randomized comparison of online and paper mood charts for people with bipolar disorderJ Affect Disord0720101241-28589doi10.1016/j.jad.2009.10.01919896202
59. DeppCA KimDH de DiosLV WangV CeglowskiJ A pilot study of mood ratings captured by mobile phone versus paper-and-pencil mood charts in bipolar disorderJ Dual Diagn011201284326332doi10.1080/15504263.2012.723318Medline23646035PMC3640573
60. LeverichGS AltshulerLL FryeMA et al Risk of switch in mood polarity to hypomania or mania in patients with bipolar depression during acute and continuation trials of venlafaxine, sertraline, and bupropion as adjuncts to mood stabilizersAm J PsychiatryFeb20061632232239doi10.1176/appi.ajp.163.2.232Medline16449476
61. PahwaM McElroySL PriesmeyerR et al KIOS: a smartphone app for self-monitoring for patients with bipolar disorderBipolar DisordFeb20242618492doi10.1111/bdi.13362Medline37340215PMC10730767
62. Faurholt-JepsenM BrageS VinbergM et al Electronic monitoring of psychomotor activity as a supplementary objective measure of depression severityNord J PsychiatryFeb2015692118125doi10.3109/08039488.2014.936501Medline25131795
63. Faurholt-JepsenM FrostM MartinyK et al Reducing the rate and duration of Re-ADMISsions among patients with unipolar disorder and bipolar disorder using smartphone-based monitoring and treatment – the RADMIS trials: study protocol for two randomized controlled trialsTrialsDec2017181doi10.1186/s13063-017-2015-3PMC547288628619114
64. TønningML Faurholt-JepsenM FrostM et al The effect of smartphone-based monitoring and treatment on the rate and duration of psychiatric readmission in patients with unipolar depressive disorder: the RADMIS randomized controlled trialJ Affect DisordMar12021282Supplement 1354363Accessed31-12-2025doi10.1016/j.jad.2020.12.141Medline33421863
65. JonathanG GouldingE DopkeC et al Understand the mechanisms of behavior change in livewell: a smartphone intervention for bipolar disorderPresented at22nd Annual ISBD Conference 20/20: Vision for Bipolar Disorder and DepressionJun 18-21, 2020Accessed31-12-2025doi10.1111/bdi.12939
66. SaundersKEA BilderbeckAC PanchalP AtkinsonLZ GeddesJR GoodwinGM Experiences of remote mood and activity monitoring in bipolar disorder: a qualitative studyEur psychiatr2017411115121doi10.1016/j.eurpsy.2016.11.005PMC594781728135594
67.Wellness trackerDepression and Bipolar Support AllianceURLhttps://www.dbsalliance.org/wellness/wellness-toolbox/wellness-tracker/Accessed17-08-2025
68.Daily mood chartBlack Dog InstituteURLhttps://www.blackdoginstitute.org.au/wp-content/uploads/2020/04/19-dailymoodchart.pdfAccessed17-08-2025
69.Successfully manage bipolar disorder, anxiety, and depressionMoodTrackerURLhttps://www.moodtracker.com/Accessed17-08-2025
70. LinardonJ TorousJ FirthJ CuijpersP MesserM Fuller‐TyszkiewiczM Current evidence on the efficacy of mental health smartphone apps for symptoms of depression and anxiety. a meta‐analysis of 176 randomized controlled trialsWorld PsychiatryFeb2024231139149doi10.1002/wps.2118338214614PMC10785982
71. Astill WrightL BaksteinE SaundersK GuoB MorrissR Performance of active and passive ambulatory assessment measures and mood monitoring in bipolar disorder: a systematic reviewInt J Bipolar Disord (forthcoming)Preprint posted online on2024doi10.1186/s40345-025-00407-5PMC1285256541575653
72. Astill WrightL RoeJ GuoB MorrissR Attrition, adherence, and compliance in mood monitoring and ambulatory assessment studies for depression and bipolar disorder: systematic review and meta-analysisJMIR Ment Health (forthcoming)2026doi10.2196/83765PMC1254882641129719
73. KohJ TngGYQ HartantoA Potential and pitfalls of mobile mental health apps in traditional treatment: an umbrella reviewJ Pers MedAug2520221291376doi10.3390/jpm12091376Medline36143161PMC9505389
74. WangM LiuQ YangX et al Relationship of insight to neurocognitive function and risk of recurrence in depression: a naturalistic follow-up studyFront Psychiatry2023141084993doi10.3389/fpsyt.2023.108499337009118PMC10060510
75. aan het RotM HogenelstK SchoeversRA Mood disorders in everyday life: a systematic review of experience sampling and ecological momentary assessment studiesClin Psychol RevAug2012326510523doi10.1016/j.cpr.2012.05.00722721999
76. WesternMJ SmitES GültzowT et al Bridging the digital health divide: a narrative review of the causes, implications, and solutions for digital health inequalitiesHealth Psychol Behav Med20251312493139doi10.1080/21642850.2025.2493139Medline40276490PMC12020140
77. GnanapragasamSN Astill WrightL PembertonM BhugraD Outside/inside: social determinants of mental healthIr J Psychol MedMar20234016373doi10.1017/ipm.2021.4934193324
78. SalemM RobensonJ The impact of socioeconomic factors on mental health: a conceptual frameworkCureus2025177e88244doi10.7759/cureus.8824440827172PMC12358166
79. Astill WrightL MajidM MooreM et al The user experience of ambulatory assessment and mood monitoring in bipolar disorder: systematic review and meta-synthesis of qualitative studiesJ Med Internet ResOct17202527e71525doi10.2196/71525Medline41105870PMC12533931

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