The Relationship between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multi-centre Longitudinal Observational Study

Research in mental health has implicated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography, is not suitable for long-term, continuous, monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. The main aim of this study was to devise and extract sleep features, from data collected using a wearable device, and analyse their correlation with depressive symptom severity and sleep quality, as measured by the self-assessed Patient Health Questionnaire 8-item. Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every two weeks by the PHQ-8. The data used in this paper included 2,812 PHQ-8 records from 368 participants recruited from three study sites in the Netherlands, Spain, and the UK.We extracted 21 sleep features from Fitbit data which describe sleep in the following five aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z-test was used to evaluate the significance of the coefficient of each feature. We tested our models on the entire dataset and individually on the data of three different study sites. We identified 16 sleep features that were significantly correlated with the PHQ-8 score on the entire dataset. Associations between sleep features and the PHQ-8 score varied across different sites, possibly due to the difference in the populations

Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device:Multicenter Longitudinal Observational Study

BACKGROUND: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. OBJECTIVE: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). METHODS: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. RESULTS: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. CONCLUSIONS: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant

Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity

Depression is the most common mental disorder and is negatively impactful to individuals and their social networks. Passive sensing of behavior via smartphones may help detect changes in depressive symptoms, which could be useful for tracking and understanding disorders. Here we look at a passive way to detect changes in depressive symptoms from data collected by users' smartphones. In particular, we take two modeling approaches to understand what features of physical activity, sleep, and user emotional wellbeing best predict changes in depressive symptoms. We find overlap in the features selected by our two modeling approaches, which implies the importance of certain features. Characteristics around sleep, such as change and irregularity of sleep duration, appear as meaningful predictors, as does personality. Our work corroborates prior results that sleep is strongly related to changes in depressive symptoms, but we show that even a very coarse measure has some predictive capability

Digital Interventions for Depression : Predictors and Moderators of Treatment Adherence and Outcomes

Background. Depression is the leading cause of disability worldwide. Although evidence-based treatments exist, less than one-in-five people in high-income countries and less than one-in-twenty-seven in low-income countries receive treatment, giving rise to a treatment gap in mental healthcare. Digital interventions have been proposed as a solution to address the treatment gap. As an increasing number of public and private healthcare providers adopt digital interventions to meet the growing demand for treatment, the current thesis set out to examine the latest evidence-base for digital depression interventions and the extent to which new technologies may be used to identify at-risk individuals. Methods. Study 1 assessed the efficacy of digital interventions for the treatment of depressive symptoms based on the largest meta-analysis of digital depression interventions conducted to-date. Databases were searched for RCTs of a computer-, internet-, or smartphone-based interventions for depression versus an active or passive control condition. Participants were individuals with elevated symptoms of depression at baseline. Using a random-effects multilevel metaregression model, we examined effect size of treatment versus control (Hedges’ g) and explored moderators of treatment outcome. Study II was a secondary analysis of data from two RCTs (N=253) of a digital intervention for the prevention and treatment of major depression. Using logistic regression, we first examined participant characteristics as potential predictors of intervention dropout. We then assessed to what extent dropout could be predicted following completion of the first module using a combination of participant characteristics and intervention usage data. Dropout was defined as completing less than six modules. Study III was an observational study of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring. A smartphone app (Delphi) continuously monitored participants’ location and smartphone usage behavior over a 4- week period. The Oura Ring provided measures of activity, sleep and heart rate variability (HRV). Participants were prompted to report their daily mood and self-reported measures of depression, anxiety and stress were collected at baseline, midpoint and the end of the study using the DASS-21. Multilevel regression models were used to predict the association between smartphone and wearable data and mental health scores. Study IV was a secondary analysis of data from Study III in which we compared the accuracy of five supervised machine learning algorithms in the classification of individuals with normal versus above normal symptoms of depression, as defined by the DASS-21. Results. A systematic search of the literature in Study I identified 83 trials (N = 15,530). The overall effect size of digital interventions versus all controls was g = .52. Significantly lower effect sizes were found in studies conducted in real-world settings (effectiveness trials; g = .30) versus laboratory settings (efficacy trials; g = .59). Significantly higher effect sizes were found in interventions that involved human therapeutic guidance (g = .63) compared with unguided, self- help interventions (g = .34). Additionally, we found significant differences in effect size depending on the type of control used (WLC: g = .70; attention: g = .36; TAU: g = .31). No significant difference in outcomes was found between human-guided digital interventions and face-to-face therapy, although the number of studies was low. In Study II we found that lower level of education (OR=3.33) and both lower and higher age (a quadratic effect; age: OR=0.62, age^2: OR=1.55) were significantly associated with higher risk of dropout. In the analysis that aimed to predict dropout following completion of the first module, lower and higher age (age: OR=0.61, age^2: OR=1.58), medium versus high social support (OR=3.40) and a higher number of days to module completion (OR=1.05) predicted higher risk of dropout, whilst a self-reported negative event in the previous week was associated with lower risk of dropout (OR=0.22). In Study III, we found a significant negative association between the variability of locations visited and symptoms of depression (b = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (b = 0.24, p = 0.023) and time in bed and depression (b = 0.26, p = 0.020). Additionally, we found that wake after sleep onset significantly predicted symptoms of anxiety (b = 0.23, p = 0.035). Study IV revealed that a Support Vector Machine using only sensor-based predictors had an accuracy of 75.90% and an Area Under the Curve of 74.89%, whilst an XGBoost model that combined mood and sensor data as predictors classified participants as belonging to the group with normal or above normal levels of depressive symptoms with an accuracy of 81.43% and an Area Under the Curve of 82.31%. Conclusion. The current thesis provided evidence of the efficacy of digital interventions for the treatment of depression in a variety of populations. Importantly, we provided the first meta-analytic evidence that digital interventions are effective in routine healthcare settings, but only when accompanied by human guidance. Notwithstanding, adherence to digital interventions remains a major challenge with little more than 25% of patients completing the full intervention on average in real-world settings. Finally, we demonstrated that data from smartphone and wearable devices may provide valuable sources of data in predicting symptoms of depression, thereby helping to identify at-risk individuals.Tausta. Masennus on maailmanlaajuisesti keskeisimpiä toimintakykyä alentavia tekijöitä. Vaikka masennuksen hoitoon on kehitetty näyttöön perustuvia hoitomuotoja, hoidon tarjonta ei kohtaa kysyntää: korkean tulotason maissa vain viidennes hoitoa tarvitsevista saa hoitoa, ja matalan tulotason maissa hoitoa saavien osuus on vielä selkeästi alhaisempi. Hoidon saatavuusongelman ratkaisuksi on ehdotettu digitaalisia hoitomuotoja, ja digitaalisten masennushoitojen käyttö yleistyykin sekä julkisissa että yksityisissä hoitokonteksteissa. Tässä tutkimuksessa selvitettiin digitaalisten masennushoitojen tehokkuutta ja teknologiasovellusten käyttöä masennuksen riskiryhmien varhaisen tunnistamisen välineenä. Menetelmät. Ensimmäinen osatutkimus tarkasteli masennusoireiden hoidossa käytettävien digitaalisten hoitojen tehokkuutta. Tutkimuksessa toteutettiin tähän mennessä kattavin meta-analyysi satunnaistettuihin koeasetelmiin perustuvista masennusinterventiotutkimuksista, joissa hoitomuotona oli digitaalinen ohjelma ja aktiivinen tai passiivinen kontrollitilanne. Digitaaliset hoidot olivat internetissä tai muulla digitaalisella alustalla toteutettuja hoitoja (esimerkiksi tietokone- tai älypuhelinperustaisia hoitoja). Analyyseissä käytettiin monitasometaregressiomallinnusta, joka estimoi efektikoon koeryhmälle verrattuna kontrolliryhmään (Hedgesin g). Lisäksi tarkasteltiin digitaalisten hoitojen tehokkuuteen mahdollisesti vaikuttavia muokkaavia tekijöitä. Toisessa osatutkimuksessa selvitettiin digitaalisen hoidon keskeyttämistä ennakoivia tekijöitä kahden satunnaistetun vertailututkimuksen aineistossa (N=253) logistisilla regressiomalleilla. Tutkimuksessa tarkasteltiin yksilöllisten ominaisuuksien yhteyttä digitaalisen hoidon keskeyttämistodennäköisyyteen ja lisäksi sitä, miten yksilölliset ominaisuudet ja osallistujan käyttäytyminen digitaalisella alustalla ennustivat keskeyttämistodennäköisyyttä osallistujien suoritettua hoidon ensimmäisen moduulin. Kolmannessa osatutkimuksessa selvitettiin, voidaanko älylaitteilla kerätyillä käyttäytymiseen ja hyvinvointiin liittyvillä tiedoilla ennustaa mielenterveysoireilua. Tutkimuksen aineistona 60 24–68-vuotiasta aikuista, joita seurattiin Applen iPhone-sovelluksen (Delphi) ja Oura-sormuksen avulla neljän viikon ajan. Kerätty aineisto sisälsi osallistujien sijaintia ja puhelimen käyttöä koskevat tiedot sekä aktiivisuuden, unen ja syketaajuuden vaihtelun mittaukset ja päivittäin raportoidun mielialan. Masennus-, ahdistus- ja stressioireet mitattiin osallistujilta tutkimuksen alussa, puolivälissä ja lopussa itseraportointikyselyillä (DASS-21). Kerätyn aineiston ja päivittäin raportoidun mielialan yhteyttä masennus-, ahdistus- ja stressioireiluun tutkittiin monitasoregressiomalleilla. Neljäs osatutkimus toteutettiin samassa aineistossa kuin kolmas osatutkimus. Siinä vertailtiin viiden ohjatun koneoppimisalgoritmin tarkkuutta luokitella osallistujat masentuneiden ja terveiden luokkiin. Tulokset. Systemaattisen kirjallisuushaun perusteella ensimmäisen tutkimuksen meta-analyysiin sisällytettiin 83 tutkimusta (N=15,530). Digitaalisen intervention efektikoko kaikkiin kontrollitilanteisiin verrattuna oli g = 0.52. Kun digitaalinen hoito toteutettiin koeolosuhteiden ulkopuolella (ns. todellisessa elämässä), olivat efektikoot huomattavasti pienempiä (vaikuttavuus g = 0.30) kuin koeolosuhteissa havaitut (tehokkuus g = 0.59). Efektikoot olivat suurempia hoidoissa, joihin liittyi ohjaava ihmiskontakti (esimerkiksi terapeutti) (g = 0.63) verrattuna hoitoihin, joihin ei liittynyt ihmiskontaktia (g = 0.34). Efektikoot erosivat merkitsevästi myös kontrollitilanteesta riippuen (WLC: g = .70; attention: g = .36; TAU: g = .31). Ihmiskontaktin sisältävän digitaalisen hoidon havaittiin olevan yhtä tehokasta kuin kasvokkain tapahtuvan hoidon, joskin tutkimuksia tämän arvioimiseksi oli vain vähän. Toisessa osatutkimuksessa havaittiin, että matalampi koulutustaso (OR=3.33) sekä keskimääräistä(?) matalampi ja korkeampi ikä (kvadraattinen yhteys, ikä: OR=0.62, ikä^2: OR=1.55) ennustivat suurempaa todennäköisyyttä keskeyttää digitaalinen hoito. Niillä osallistujilla, jotka olivat suorittaneet hoidon ensimmäisen moduulin, keskeyttämistä ennustivat ikä (ikä: OR=0.61, ikä^2: OR=1.58), vähäisempi sosiaalinen tuki (OR=3.40) ja meneillään olevassa moduulissa jäljellä olevien päivien määrä (OR=1.05). Itseraportoitu ikävä tapahtuma edellisen viikon aikana oli puolestaan yhteydessä matalampaan keskeyttämistodennäköisyyteen (OR=0.22). Kolmannessa osatutkimuksessa havaittiin, että vähäisempi maantieteellinen liikkuvuus (b = −0.21, p = 0.037) ja suurempi unen (b = 0.24, p = 0.023) ja sängyssä vietetyn ajan määrä (b = 0.26, p = 0.020) olivat yhteydessä korkeampiin masennusoirepisteisiin. Lisäksi havaittiin yhteys nukahtamisen jälkeisen heräämisen ja ahdistuneisuusoireiden välillä (b = 0.23, p = 0.035). Neljäs tutkimus osoitti, että sensoripohjaisia ennustajia käyttävistä algoritmeista Support Vector Machine luokitteli ihmiset masennusoirepistemäärän perusteella oikein masentuneisiin ja terveisiin 75.90% tarkkuudella (käyrän alle jäävä pinta-ala (AUC) = 74.89%). Sensoripohjaisia ja päivittäisiin mielialamittauksiin perustuvia ennustajia yhdistävän XGBoost-algoritmin tarkkuus oli 81.43% (AUC = 82.31%). Johtopäätös. Tämä väitöskirjatutkimus tuotti uutta tietoa digitaalisten masennushoitojen tehokkuudesta. Tutkimuksessa esitettiin ensimmäinen kattava meta-analyysi, joka osoitti, että digitaaliset hoidot voivat olla tehokkaita psykiatrisen hoidon välineitä, mikäli digitaaliseen hoitoon sisältyy ohjaava ihmiskontakti. Digitaalisten hoitojen laajamittaisen käytön suurin haaste liittyy yhä hoitoon sitoutumiseen; keskimäärin vain joka neljäs potilas suorittaa hoidon loppuun. Tutkimustulosten mukaan kannettavien ja puettavien älylaitteiden avulla voidaan kerätä arvokasta tietoa, jonka avulla ennakoida masennusoireilua ja siten varhain tunnistaa erityisessä riskissä olevat