1
|
Ho FYY, Poon CY, Wong VWH, Chan KW, Law KW, Yeung WF, Chung KF. Actigraphic monitoring of sleep and circadian rest-activity rhythm in individuals with major depressive disorder or depressive symptoms: A meta-analysis. J Affect Disord 2024:S0165-0327(24)00898-X. [PMID: 38851435 DOI: 10.1016/j.jad.2024.05.155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 05/10/2024] [Accepted: 05/28/2024] [Indexed: 06/10/2024]
Abstract
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.
Collapse
Affiliation(s)
- Fiona Yan-Yee Ho
- Department of Psychology, The Chinese University of Hong Kong, Hong Kong.
| | - Chun-Yin Poon
- Department of Psychology, The Chinese University of Hong Kong, Hong Kong
| | | | - Ka-Wai Chan
- Department of Psychology, The Chinese University of Hong Kong, Hong Kong
| | - Ka-Wai Law
- Department of Psychology, The Chinese University of Hong Kong, Hong Kong
| | - Wing-Fai Yeung
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong
| | - Ka-Fai Chung
- Department of Psychiatry, The University of Hong Kong, Hong Kong
| |
Collapse
|
2
|
Wescott DL, Franzen PL, Hasler BP, Miller MA, Soehner AM, Smagula SF, Wallace ML, Hall MH, Roecklein KA. Elusive hypersomnolence in seasonal affective disorder: actigraphic and self-reported sleep in and out of depressive episodes. Psychol Med 2023; 53:1313-1322. [PMID: 37010222 PMCID: PMC10071357 DOI: 10.1017/s003329172100283x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Hypersomnolence has been considered a prominent feature of seasonal affective disorder (SAD) despite mixed research findings. In the largest multi-season study conducted to date, we aimed to clarify the nature and extent of hypersomnolence in SAD using multiple measurements during winter depressive episodes and summer remission. METHODS Sleep measurements assessed in individuals with SAD and nonseasonal, never-depressed controls included actigraphy, daily sleep diaries, retrospective self-report questionnaires, and self-reported hypersomnia assessed via clinical interviews. To characterize hypersomnolence in SAD we (1) compared sleep between diagnostic groups and seasons, (2) examined correlates of self-reported hypersomnia in SAD, and (3) assessed agreement between commonly used measurement modalities. RESULTS In winter compared to summer, individuals with SAD (n = 64) reported sleeping 72 min longer based on clinical interviews (p < 0.001) and 23 min longer based on actigraphy (p = 0.011). Controls (n = 80) did not differ across seasons. There were no seasonal or group differences on total sleep time when assessed by sleep diaries or retrospective self-reports (p's > 0.05). Endorsement of winter hypersomnia in SAD participants was predicted by greater fatigue, total sleep time, time in bed, naps, and later sleep midpoints (p's < 0.05). CONCLUSION Despite a winter increase in total sleep time and year-round elevated daytime sleepiness, the average total sleep time (7 h) suggest hypersomnolence is a poor characterization of SAD. Importantly, self-reported hypersomnia captures multiple sleep disruptions, not solely lengthened sleep duration. We recommend using a multimodal assessment of hypersomnolence in mood disorders prior to sleep intervention.
Collapse
Affiliation(s)
| | - Peter L. Franzen
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Brant P. Hasler
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Megan A. Miller
- Rehabilitation Care Services, VA Puget Sound Healthcare System, Seattle, WA
| | - Adriane M. Soehner
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Stephen F. Smagula
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Meredith L. Wallace
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Statistics, University of Pittsburgh, Pittsburgh PA
| | - Martica H. Hall
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Kathryn A. Roecklein
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
- Center for the Neural Basis of Behavior, University of Pittsburgh, Pittsburgh, PA
| |
Collapse
|
3
|
De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, Lavelle G, Matcham F, Pace A, Mohr DC, Dobson R, Hotopf M. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med 2022; 5:3. [PMID: 35017634 PMCID: PMC8752685 DOI: 10.1038/s41746-021-00548-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/28/2021] [Indexed: 12/27/2022] Open
Abstract
The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.
Collapse
Affiliation(s)
- Valeria De Angel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK.
| | - Serena Lewis
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology, University of Bath, Bath, UK
| | - Katie White
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Leightley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Emanuela Oprea
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Grace Lavelle
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alice Pace
- Chelsea And Westminster Hospital NHS Foundation Trust, London, UK
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Richard Dobson
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, 16 De Crespigny Park, London, SE5 8AF, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| |
Collapse
|
4
|
Aubourg T, Demongeot J, Provost H, Vuillerme N. Exploitation of Outgoing and Incoming Telephone Calls in the Context of Circadian Rhythms of Social Activity Among Elderly People: Observational Descriptive Study. JMIR Mhealth Uhealth 2020; 8:e13535. [PMID: 33242018 PMCID: PMC7728541 DOI: 10.2196/13535] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 06/26/2019] [Accepted: 01/28/2020] [Indexed: 01/07/2023] Open
Abstract
Background In the elderly population, analysis of the circadian rhythms of social activity may help in supervising homebound disabled and chronically ill populations. Circadian rhythms are monitored over time to determine, for example, the stability of the organization of daily social activity rhythms and the occurrence of particular desynchronizations in the way older adults act and react socially during the day. Recently, analysis of telephone call detail records has led to the possibility of determining circadian rhythms of social activity in an objective unobtrusive way for young patients from their outgoing telephone calls. At this stage, however, the analysis of incoming call rhythms and the comparison of their organization with respect to outgoing calls remains to be performed in underinvestigated populations (in particular, older populations). Objective This study investigated the persistence and synchronization of circadian rhythms in telephone communication by older adults. Methods The study used a longitudinal 12-month data set combining call detail records and questionnaire data from 26 volunteers aged 70 years or more to determine the existence of persistent and synchronized circadian rhythms in their telephone communications. The study worked with the following four specific telecommunication parameters: (1) recipient of the telephone call (alter), (2) time at which the call began, (3) duration of the call, and (4) direction of the call. We focused on the following two issues: (1) the existence of persistent circadian rhythms of outgoing and incoming telephone calls in the older population and (2) synchronization with circadian rhythms in the way the older population places and responds to telephone calls. Results The results showed that older adults have their own specific circadian rhythms for placing telephone calls and receiving telephone calls. These rhythms are partly structured by the way in which older adults allocate their communication time over the day. In addition, despite minor differences between circadian rhythms for outgoing and incoming calls, our analysis suggests the two rhythms could be synchronized. Conclusions These results suggest the existence of potential persistent and synchronized circadian rhythms in the outgoing and incoming telephone activities of older adults.
Collapse
Affiliation(s)
- Timothée Aubourg
- Orange Labs, Meylan, France.,Univ Grenoble Alpes, AGEIS, Grenoble, France.,LabCom Telecom4Health, Univ Grenoble Alpes & Orange Labs, Grenoble, France
| | - Jacques Demongeot
- Univ Grenoble Alpes, AGEIS, Grenoble, France.,LabCom Telecom4Health, Univ Grenoble Alpes & Orange Labs, Grenoble, France.,Institut Universitaire de France, Paris, France
| | - Hervé Provost
- Orange Labs, Meylan, France.,LabCom Telecom4Health, Univ Grenoble Alpes & Orange Labs, Grenoble, France
| | - Nicolas Vuillerme
- Univ Grenoble Alpes, AGEIS, Grenoble, France.,LabCom Telecom4Health, Univ Grenoble Alpes & Orange Labs, Grenoble, France.,Institut Universitaire de France, Paris, France
| |
Collapse
|
5
|
Abstract
OBJECTIVE Although cognitive behavior therapy (CBT) is efficacious for major depression in patients with heart failure (HF), approximately half of patients do not remit after CBT. To identify treatment moderators that may help guide treatment allocation strategies and serve as new treatment targets, we performed a secondary analysis of a randomized clinical trial. Based on evidence of their prognostic relevance, we evaluated whether clinical and activity characteristics moderate the effects of CBT. METHODS Participants were randomized to enhanced usual care (UC) alone or CBT plus enhanced UC. The single-blinded outcomes were 6-month changes in Beck Depression Inventory total scores and remission (defined as a Beck Depression Inventory ≤ 9). Actigraphy was used to assess daily physical activity patterns. We performed analyses to identify the specific activity and clinical moderators of the effects of CBT in 94 adults (mean age = 58, 49% female) with HF and major depressive disorder. RESULTS Patients benefited more from CBT (versus UC) if they had the following: more medically severe HF (i.e., a higher New York Heart Association class or a lower left ventricular ejection fraction), more stable activity patterns, wider active periods, and later evening settling times. These individual moderator effects were small (|r| = 0.10-0.21), but combining the moderators yielded a medium moderator effect size (r = 0.38; 95% CI = 0.20-0.52). CONCLUSIONS These findings suggest that increasing the cross-daily stability of activity patterns, and prolonging the daily active period, might help increase the efficacy of CBT. Given moderating effects of HF severity measures, research is also needed to clarify and address factors in patients with less severe HF that diminish the efficacy of CBT. CLINICAL TRIAL REGISTRATION clinicaltrials.gov identifier: NCT01028625.
Collapse
|