Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Actigraphic monitoring of sleep and circadian rest-activity rhythm in individuals with major depressive disorder or depressive symptoms: A meta-analysis

BACKGROUND

Disrupted sleep and rest-activity pattern are common clinical features in depressed individuals. This meta-analysis compared sleep and circadian rest-activity rhythms in people with major depressive disorder (MDD) or depressive symptoms and healthy controls.

METHODS

Eligible studies were identified in five databases up to December 2023. The search yielded 53 studies with a total of 11,115 participants, including 4000 depressed participants and 7115 healthy controls.

RESULTS

Pooled meta-analyses demonstrated that depressed individuals have significantly longer sleep latency (SMD = 0.23, 95 % CI: 0.12 to 0.33) and wake time after sleep onset (SMD = 0.37, 95 % CI: 0.22 to 0.52), lower sleep efficiency (SMD = -0.41, 95 % CI: -0.56 to -0.25), more nocturnal awakenings (SMD = 0.58, 95 % CI: 0.29 to 0.88), lower MESOR (SMD = -0.54, 95 % CI: -0.81 to -0.28), amplitude (SMD = -0.33, 95 % CI: -0.57 to -0.09), and interdaily stability (SMD = -0.17, 95 % CI: -0.28 to -0.05), less daytime (SMD = -0.79, 95 % CI: -1.08 to -0.49) and total activities (SMD = -0.89, 95 % CI: -1.28 to -0.50) when compared with healthy controls.

LIMITATIONS

Most of the included studies reported separate sleep and activity parameters instead of 24-hour rest-activity rhythms. The variabilities among actigraphy devices and the types of participants recruited also impede precise comparisons.

CONCLUSIONS

The findings emerging from this study offered a better understanding of sleep and rest-activity rhythm in individuals with MDD or depressive symptoms. Future studies could advocate for deriving objective, distinctive 24-hour rest-activity profiles contributing to the risk of depression.

PROSPERO REGISTRATION NUMBER

CRD42021259780.

Directional associations among real-time activity, sleep, mood, and daytime symptoms in major depressive disorder using actigraphy and ecological momentary assessment

Previous research has suggested that individuals with major depressive disorder (MDD) experienced alterations in sleep and activity levels. However, the temporal associations among sleep, activity levels, mood, and daytime symptoms in MDD have not been fully investigated. The present study aimed to fill this gap by utilizing real-time data collected across time points and days. 75 individuals with MDD and 75 age- and gender-matched healthy controls were recruited. Ecological momentary assessments (EMA) were adopted to assess real-time mood status for 7 days, and actigraphy was employed to measure day-to-day sleep-activity patterns. Multilevel modeling analyses were performed. Results revealed a bidirectional association between mood/daytime symptoms and activity levels across EMA intervals. Increased activity levels were predictive of higher alert cognition and positive mood, while an increase in positive mood also predicted more increase in activity levels in depressed individuals. A bidirectional association between sleep and daytime symptoms was also found. Alert cognition was found to be predictive of better sleep in the subsequent night. Contrariwise, higher sleep efficiency predicted improved alert cognition and sleepiness/fatigue the next day. A unidirectional association between sleep and activity levels suggested that higher daytime activity levels predicted a larger increase in sleep efficiency among depressed individuals. This study indicated how mood, activity levels, and sleep were temporally and intricately linked to each other in depressed individuals using actigraphy and EMA. It could pave the way for novel and efficacious treatments for depression that target not just mood but sleep and activity levels.

Designing daily-life research combining experience sampling method with parallel data

BACKGROUND

Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them.

METHODS

Literature and expert opinions were integrated to formulate relevant guiding principles.

RESULTS

Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it.

CONCLUSIONS

The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field.

An exploration of actigraphy in the context of ketamine and treatment-resistant depression

OBJECTIVES

This study explored the potential of non-parametric and complexity analysis metrics to detect changes in activity post-ketamine and their association with depressive symptomatology.

METHODS

Individuals with treatment-resistant depression (TRD: n = 27, 16F, 35.9 ± 10.8 years) and healthy volunteers (HVs: n = 9, 4F, 36.4 ± 9.59 years) had their activity monitored during an inpatient, double-blind, crossover study where they received an infusion of ketamine or saline placebo. All participants were 18-65 years old, medication-free, and had a MADRS score ≥20. Non-parametric metrics averaged over each study day, metrics derived from complexity analysis, and traditionally calculated non-parametric metrics averaged over two weeks were calculated from the actigraphy time series. A separate analysis was conducted for a subsample (n = 17) to assess the utility of these metrics in a hospital setting.

RESULTS

In HVs, lower intradaily variability was observed within daily rest/activity patterns post-ketamine versus post-placebo (F = 5.16(1,15), p = 0.04). No other significant effects of drug or drug-by-time or correlations between depressive symptomatology and activity were detected.

CONCLUSIONS

Weak associations between non-parametric variables and ketamine were found but were not consistent across actigraphy measures.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT00088699.

Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression

Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

BACKGROUND

Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services.

OBJECTIVE

This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.

METHODS

We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis.

RESULTS

Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine.

CONCLUSIONS

Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies' results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.

Combining actigraphy and experience sampling to assess physical activity and sleep in patients with psychosis: A feasibility study

BACKGROUND

Sleep disorders and reduced physical activity are common in patients with psychosis and can be related to health-related outcomes such as symptomatology and functioning. Mobile health technologies and wearable sensor methods enable continuous and simultaneous monitoring of physical activity, sleep, and symptoms in one's day-to-day environment. Only a few studies have applied simultaneous assessment of these parameters. Therefore, we aimed to examine the feasibility of the simultaneous monitoring of physical activity, sleep, and symptoms and functioning in psychosis.

METHODS

Thirty three outpatients diagnosed with a schizophrenia or other psychotic disorder used an actigraphy watch and experience sampling method (ESM) smartphone app for 7 consecutive days to monitor physical activity, sleep, symptoms, and functioning. Participants wore the actigraphy watch during day and night and completed multiple short questionnaires (eight daily, one morning, and one evening) on their phone. Hereafter they completed evaluation questionnaires.

RESULTS

Of the 33 patients (25 male), 32 (97.0%) used the ESM and actigraphy during the instructed timeframe. ESM response was good: 64.0% for the daily, 90.6% for morning, and 82.6% for evening questionnaire(s). Participants were positive about the use of actigraphy and ESM.

CONCLUSION

The combination of wrist-worn actigraphy and smartphone-based ESM is feasible and acceptable in outpatients with psychosis. These novel methods can help both clinical practice and future research to gain more valid insight into physical activity and sleep as biobehavioral markers linked to psychopathological symptoms and functioning in psychosis. This can be used to investigate relationships between these outcomes and thereby improve individualized treatment and prediction.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

BACKGROUND

This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively.

AIMS

Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology.

RESULTS

Overall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales.

CONCLUSIONS

Findings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.

Actigraphically measured psychomotor slowing in depression: systematic review and meta-analysis

Psychomotor slowing is a key feature of depressive disorders. Despite its great clinical importance, the pathophysiology and prevalence across different diagnoses and mood states are still poorly understood. Actigraphy allows unbiased, objective, and naturalistic assessment of physical activity as a marker of psychomotor slowing. Yet, the true effect-sizes remain unclear as recent, large systematic reviews are missing. We conducted a novel meta-analysis on actigraphically measured slowing in depression with strict inclusion and exclusion criteria for diagnosis ascertainment and sample duplications. Medline/PubMed and Web-of-Science were searched with terms combining mood-keywords and actigraphy-keywords until September 2021. Original research measuring actigraphy for ⩾24 h in at least two groups of depressed, remitted, or healthy participants and applying operationalized diagnosis was included. Studies in somatically ill patients, N < 10 participants/group, and studies using consumer-devices were excluded. Activity-levels between groups were compared using random-effects models with standardized-mean-differences and several moderators were examined. In total, 34 studies (n = 1804 patients) were included. Patients had lower activity than controls [standardized mean difference (s.m.d.) = -0.78, 95% confidence interval (CI) -0.99 to -0.57]. Compared to controls, patients with unipolar and bipolar disorder had lower activity than controls whether in depressed (unipolar: s.m.d. = -0.82, 95% CI -1.07 to -0.56; bipolar: s.m.d. = -0.94, 95% CI -1.41 to -0.46), or remitted/euthymic mood (unipolar: s.m.d. = -0.28, 95% CI -0.56 to 0.0; bipolar: s.m.d. = -0.92, 95% CI -1.36 to -0.47). None of the examined moderators had any significant effect. To date, this is the largest meta-analysis on actigraphically measured slowing in mood disorders. They are associated with lower activity, even in the remitted/euthymic mood-state. Studying objective motor behavior via actigraphy holds promise for informing screening and staging of affective disorders.

Digital phenotype of mood disorders: A conceptual and critical review

BACKGROUND

Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care.

OBJECTIVE

The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders.

METHODS

We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.

RESULTS

Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).

CONCLUSION

The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.

Uncovering complexity details in actigraphy patterns to differentiate the depressed from the non-depressed

While the negative association between physical activity and depression has been well established, it is unclear what precise characteristics of physical activity patterns explain this association. Complexity measures may identify previously unexplored aspects of objectively measured activity patterns, such as the extent to which individuals show repetitive periods of physical activity and the diversity in durations of such repetitive activity patterns. We compared the complexity levels of actigraphy data gathered over 4 weeks (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim 40000$$\end{document}∼40000 data points each) for every individual, from non-depressed (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=25$$\end{document}n=25) and depressed (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=21$$\end{document}n=21) groups using recurrence plots. Significantly lower levels of complexity were detected in the actigraphy data from the depressed group as compared to non-depressed controls, both in terms of lower mean durations of periods of recurrent physical activity and less diversity in the duration of these periods. Further, diagnosis of depression was not significantly associated with mean activity levels or measures of circadian rhythm stability, and predicted depression status better than these.