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Cited by 110Digital Mental Health InterventionsMental Health Research TopicsMobile Health and mHealth Applications

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Open access
Matcham, Faith, Leightley, Daniel, Siddi, Sara, Lamers, Femke, White, Katie M., Annas, Peter, de Girolamo, Giovanni, Difrancesco, Sonia +28 more·2022·BMC Psychiatry
DOI 10.1186/s12888-022-03753-1PMC PMC8860359

Published version of a preprint

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Abstract

Background: Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. Methods: Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. Results: Individuals with a history of MDD ( n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. Conclusions: RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

AI snapshot

Written from the paper’s full text.

TL;DR

RADAR-MDD enrolled 623 people with recurrent major depressive disorder across three sites and followed them for 11–24 months using smartphone and wearable remote measurement technologies. The study reports high retention and outcome completion (~80%) and comparable active/passive data availability, with no convincing evidence that baseline depression symptom severity predicts data availability.

Key findings

  • Of 623 enrolled participants, 497 (79.8%) participated for the maximum possible duration and 126 (20.2%) withdrew prematurely; completion of primary and secondary outcomes was approximately 80% across follow-up, with 445 (71.4%) providing 1-year outcome data and 181 (29.1%) participating for a full 2 years.
  • Median study participation was 541 days (IQR 401–730); 2.9 terabytes of compressed data were collected, and 110 participants (17.7%) had >50% available data across all modes of data collection.
  • Active RMT: participants completed median 21 PHQ-8 questionnaires, 20 RSES questionnaires, and 12 speech tasks; any data were available for 95.3% (PHQ8), 94.5% (RSES), and 82.2% (speech). THINC-it tasks had median 5 completions each, with >84% of participants having any data for each test.
  • Wearable RMT: average Fitbit wear-time was 62.5% (SD 9.1 percentage points) and mean 15.1 h/day; step count was most available (~50% provided >75% expected data), while activity data were least available (only 5% had >75%). Activity availability differed by baseline depression status (X² = 14.1, p = 0.002); passive smartphone data showed no evidence of difference by baseline depression in the narrative.

Methods

  • Multi-centre, prospective observational cohort study (RADAR-MDD) within RADAR-CNS; 623 adults with recurrent MDD (DSM-5 non-psychotic MDD within past 2 years and ≥2 lifetime episodes) were recruited at King’s College London (UK), Amsterdam University Medical Centre (Netherlands), and CIBER (Barcelona, Spain), with follow-up of 11–24 months.
  • Participants used a wrist-worn Fitbit Charge 2/3, RADAR-base active and passive smartphone apps (questionnaires, speech tasks, ESM, smartphone sensors), a THINC-it cognitive app, and REDCap outcome surveys every 3 months.
  • Analyses described baseline characteristics; compared baseline symptomatic vs no/some depression symptoms using chi-squared tests, linear regressions, and t-tests for ESM; summarised data availability in quartiles; estimated Fitbit wear time from heart-rate values >0 per 15-min window; used STATA v16.0.

Limitations

  • Stated: sensor and data types have different temporal validity and aggregation requirements, making coherent summary challenging; iterative RADAR-base/app changes meant participants had different user experiences over time; COVID-19 may have affected research experience and data completion; the sample had a higher proportion of White and female individuals than typical populations, limiting generalisability; the paper describes data availability, not quality or completeness, and does not examine predictors of availability.
  • Evident from the text: “available” data may include complete, partial, corrupted, or invalid data; baseline depression status is static and may not capture symptom fluctuations relevant to technology use; no formal threshold for acceptable dropout is established, though the authors describe ~80% completion as excellent.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/s12888-022-03753-1

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 39 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Participants completed a median of 12 speech tasks.

    “Overall, participants completed a median of 21 (IQR:9–31) PHQ-8 questionnaires, 20 (IQR:9–30) RSES questionnaires, 12 (IQR:2–23) speech tasks.”
    Quote in the paperNot a substantial claim
  • Findingown data

    The symptomatic group had a higher proportion of females.

    “In comparison to those with no/some depression symptoms at baseline, the symptomatic group were significantly younger, and had a higher proportion of individuals who were female, on long-term sick leave or unemployed, receiving benefits, and earning less than £/€15,000 per annum.”
    Quote in the paperNot a substantial claim
  • Findingown data

    79.8% of participants participated for the maximum possible duration.

    “A total of 497 people (79.8%) participated for the maximum possible duration (from their enrolment until the end of data collection in April 2021), and 126 people (20.2%) withdrew prematurely.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Participants completed a median of 5 Symbol Check tasks.

    “Overall, participants completed a median of 5 (IQR:2–10) THINC-it app® PDQ5 questionnaires, 5 (IQR:2–9) Code Breaker tasks, 5 (IQR:2–9) Spotter tasks, 5 (IQR-2-9) Symbol Check tasks, and 5 (IQR = 2–10) Trails tasks.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Fitbit wear-time did not significantly differ between those with no depression symptoms versus those with symptoms at baseline (X2(622, n = 623) = 525616, p=0.24).

    “wear-time did not significantly differ between those with no depression symptoms versus those with symptoms at baseline (X2(622, n = 623) = 525616, p=0.24).”
    Quote in the paperNot a substantial claim
  • May combine two claims. 32
  • Classified as probably not a substantial claim. 22

  • May combine two claims.

    Findingown data

    The symptomatic group had increased levels of current depression, anxiety, and functional disability.

    “Regarding clinical characteristics, the symptomatic group had a higher proportion of current smokers, medical comorbidities, as well as increased levels of current depression, anxiety, functional disability, and worsened illness perceptions, although lower levels of alcohol use.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Figure 5a shows lower wear-time between days 290 and 380, and Fig. 5b shows a dip in percentage wear-time in people with baseline depression symptoms after the first year.

    “Figure 5a shows a paler horizontal band of colour between days 290 and 380 of study participation, indicating lower levels of wear-time during these time-points, and Fig. 5b shows a dip in percentage wear-time in people with symptoms of depression at baseline after the first year of participation.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    Recruitment into RADAR-MDD was highly successful, exceeding recruitment targets despite the COVID-19 pandemic, due to the flexibility of face-to-face and remote enrolments.

    “Recruitment into RADAR-MDD was highly successful, with the flexibility of face-to-face and remote enrolments resulting in the study exceeding its recruitment targets despite the COVID-19 pandemic [42].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [42] Oetzmann C, White KM, Ivan A, Julie J, Leightley D, Lavelle G, et al. A framework for recruiting into a remote measurement technologies (RMTs) study: Experiences from a major depressive disorder cohort. OSF Preprint. https://doi.org/10.31219/osf.io/ns7dc. source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    Activity data were the least readily available wearable data, with only 5% of participants having >75% data availability.

    “Activity data (comprising a combination of data derived from Fitbit proprietary algorithms and via participants inputting their own activities manually) was the least readily available data, with only 5% of participants having > 75% data availability.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Step count data were the most frequently available wearable data, with almost 50% of participants providing >75% of expected data throughout follow-up.

    “Step count data were the most frequently available data, with almost 50% of participants providing > 75% of expected data throughout the course of follow-up.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    A total of 341 risk assessments were conducted, representing 9.0% of the 3777 depression measurements taken.

    “Throughout RADAR-MDD, a total of 341 risk assessments were conducted (9.0% of the 3777 depression measurements taken).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The most common reason for withdrawal was loss to follow-up (n = 47), followed by problems using the Android study phone (n = 14).

    “The most common reason for withdrawal across all timepoints was loss to follow-up (n = 47) and problems using the Android study phone (for those who had switched from an iPhone for the purposes of the study (n = 14), representing 37.3 and 11.1% of all withdrawals respectively.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The symptomatic group had lower levels of alcohol use.

    “Regarding clinical characteristics, the symptomatic group had a higher proportion of current smokers, medical comorbidities, as well as increased levels of current depression, anxiety, functional disability, and worsened illness perceptions, although lower levels of alcohol use.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    A total of 2.9 terabytes of compressed data were collected.

    “A total of 2.9 terabytes of compressed data were collected, with 110 (17.7%) participants having more than 50% available data across all modes of data collection.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The symptomatic group had a higher proportion of current smokers.

    “Regarding clinical characteristics, the symptomatic group had a higher proportion of current smokers, medical comorbidities, as well as increased levels of current depression, anxiety, functional disability, and worsened illness perceptions, although lower levels of alcohol use.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing), S4 (based).

    Findingown data

    The most passive RMT data were available for GPS location and battery level.

    “The most data were available for GPS location and battery level data.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The mean numbers of ESM items completed were 11.8 (SD 23.7) for those with baseline depression symptoms and 11.9 (SD 23.7) for those without.

    “The mean numbers of ESM items completed by those with and without depression symptoms at baseline throughout the study duration were 11.8 (SD = 23.7) and 11.9 (SD = 23.7) respectively, with t-tests demonstrating no significant difference in ESM data availability between these groups (p = 0.158).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    The first THINC-it data were received in March 2018, with the first 4 months of data collection excluding THINC-it data.

    “due to the technical requirements of integrating data from the separate THINC-it® app into the RADAR-base platform, the first THINC-it® data were received in March 2018, with the first 4-months of data collection excluding THINC-it® data.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    The RADAR-MDD sample has slightly lower levels of current depression severity and reduced WSAS functional disability than those recruited into STAR*D.

    “In terms of clinical presentation, our sample have slightly lower levels of current depression severity and reduced WSAS functional disability than those recruited into STAR*D [48].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [48] Trivedi MH, John Rush A, Wisniewski SR, Nierenberg AA, Warden D, Louise Ritz M, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163:28–40. source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    Over 90% of participants had data available for analysis from aRMT, with PHQ-8 and RSES having the largest amount of data available for the most people.

    “Over 90% of participants had data available for analysis from the aRMT, with the PHQ-8 and RSES having the largest amount of data available for the most people.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    RADAR-MDD had approximately 80% completion rates of outcomes across all follow-up timepoints, with 79.8% of enrolled individuals completing the study protocol for the maximum possible time.

    “Here we report ~ 80% completion rates of our outcomes across all follow-up timepoints, with 79.8% of all enrolled individuals completing the study protocol for the maximum amount of time possible”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse.

    “Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse.”
    • Also stated in: § Background · hedged
      “Whilst some may experience a single lifetime episode of MDD, it is becoming more widely recognised as a chronic condition, characterised by periods of relapse and recovery [6, 7].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [6] Verduijn J, Verhoeven JE, Milaneschi Y, Schoevers RA, van Hemert AM, Beekman ATF, et al. Reconsidering the prognosis of major depressive disorder across diagnostic boundaries: full recovery is the exception rather than the rule. BMC Med. 2017. https://doi.org/10.1186/s12916-017-0972-8. source

      Not checked in the free run.

    • cites [7] Blanco C, Okunda M, Markowitz JC, Liu S-M, Grant BF, Hasin DS. The epidemiology of chronic major depressive disorder and dysthymic disorder: results from the National Epidemiologic Survey on alcohol and related conditions. J Clin Psychiatr. 2010;17:1645–56. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The GAD7 threshold of ≥10 has previously been shown to have good levels of sensitivity and specificity.

    “This threshold has previously been shown to have good levels of sensitivity and specificity [34].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Jordan P, Shedden-Mora MC, Löwe B. Psychometric analysis of the generalized anxiety disorder scale (GAD-7) in primary care using modern item response theory. PLoS One. 2017. https://doi.org/10.1371/journal.pone.0182162. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    All participants provided written consent.

    “All participants provided written consent and provided detailed baseline assessments including sociodemographic, social environment, medical history, medical comorbidities and technology use questionnaires.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The study was co-developed with service users in a Patient Advisory Board, who were involved in choice of measures, timing, engagement, and analysis plan.

    “The study was co-developed with service users in our Patient Advisory Board. They were involved in the choice of measures, the timing and issues of engagement and have also been involved in developing the analysis plan and representative(s) are authors of this paper and critically reviewed it.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Follow-up levels of 50, 60 and 70% have previously been described as adequate, good and very good respectively.

    “follow-up levels of 50, 60 and 70% have previously been described as adequate, good and very good respectively [44].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [44] Kristman V, Manno M, Côté P. Loss to follow-up in cohort studies: how much is too much? Eur J Epidemiol. 2004;28:2272–81.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A recent systematic review identified 52 publications testing RMT in depression.

    “In a recent systematic review we identified 52 publications testing RMT in depression [17].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, et al. Digital health tools for the passive monitoring of depression: A systematic review of methods. npj Digital Medicine. In press. https://doi.org/10.1101/2021.07.19.21260786. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Many digital medicine technologies have poor uptake.

    “Whilst the wider field of digital medicine has seen vast growth and investment, many technologies have poor uptake [13, 14].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [13] Szinay D, Jones A, Chadborn T, Brown J, Naughton F. Influences on the uptake of and engagement with health and well-being smartphone apps: systematic review. JMIR. 2020. https://doi.org/10.2196/17572. source

      Not checked in the free run.

    • cites [14] Pham Q, Graham G, Carrion C, Morita PP, Seto E, Stinson JN, et al. A library of analytic indicators to evaluate effective engagement with consumer mHealth apps for chronic conditions: scoping review. JMIR mHealth uHealth. 2019;7:e11941. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The literature on RMT in depression is characterised by inconsistent reporting, and data on adherence to protocol are very rarely reported.

    “The literature was characterised by inconsistent reporting, and very rarely were data on adherence to protocol reported.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [17] De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, et al. Digital health tools for the passive monitoring of depression: A systematic review of methods. npj Digital Medicine. In press. https://doi.org/10.1101/2021.07.19.21260786. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    There is no recognised threshold for acceptable versus unacceptable dropout in longitudinal research.

    “whilst there is no recognised threshold for “acceptable” versus “unacceptable” dropout”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [44] Kristman V, Manno M, Côté P. Loss to follow-up in cohort studies: how much is too much? Eur J Epidemiol. 2004;28:2272–81.

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Both active and passive RMT data collection are feasible in this patient group.

    “We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.”
    • Also stated in: § Conclusion · boosted
      “demonstrating that both are feasible in this patient group.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Data availability is a valid indicator of the feasibility of implementing RMT protocols in people with MDD.
    • Rests on an assumption: The RADAR-MDD sample of people with a history of recurrent MDD is representative of the clinical population with MDD for assessing feasibility of RMT.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The speech task text is phonetically balanced across all three languages and has been shown to provide linguistic parameters indicative of low mood.

    “an Aesop’s fable which is phonetically balanced across all three languages and has been shown to provide linguistic parameters indicative of low mood [27].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [27] Williamson JR, Quatieri TF, Helfer BS, Ciccarellu G, Mehta DD. Vocal and facial biomarkers of dperession based on motor incoordination and timing. In: 4th International Audio/Visual Emotion Challenge and Workshop: Depression Challenge; 2014. https://doi.org/10.1145/2661806.2661809. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    RADAR-MDD is the largest multimodal RMT study in the field of mental health.

    “RADAR-MDD is the largest multimodal RMT study in the field of mental health.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductnone given

    RADAR-base and its associated apps were developed and piloted within the main data collection period, with iterative changes and updates made throughout follow-up.

    “RADAR-base and its associated apps were developed and piloted within the main data collection period, with iterative changes and updates being made throughout the course of follow-up.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Radin et al. used a non-clinical population, and barriers to long-term wearable use are likely different in MDD versus general population.

    “In contrast to our sample, Radin et al. [52] used a non-clinical population and the barriers to long-term use of a wearable device are likely to be different in an MDD versus general population sample [21].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [52] Radin JM, Wineinger NE, Topol EJ, Steinhubl SR. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit Heal. 2020;11:e85–93. source

      Not checked in the free run.

    • cites [21] Simblett S, Matcham F, Siddi S, Bulgari V, di San Pietro CB, López JH, et al. Barriers to and facilitators of engagement with mHealth technology for remote measurement and management of depression: qualitative analysis. JMIR mHealth uHealth. 2019. https://doi.org/10.2196/11325. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Ethnic groups across the two countries that collected ethnicity data are challenging to compare, meaning in-depth interrogation of racial differences in outcomes will be affected by small cell sizes unless groups are merged.

    “It is also worth noting that ethnic groups across the two countries who collected ethnicity data are challenging to compare, meaning that in-depth interrogation of racial differences in outcomes will be affected by small cell sizes unless ethnic groups are merged into larger, less descriptive categories.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    It is likely that fluctuations in depression symptoms over time are more relevant for predicting technology use than a static baseline status.

    “it is likely that fluctuations in depression symptoms over time are more relevant for predicting technology use, rather than a static baseline status”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Radin et al. omitted missing wear time data and excluded measurements with wear-time lower than 1000 min/day, which inflates their wear-time statistics.

    “However, Radin et al. omitted missing wear time data, and excluded measurements with a wear-time lower than 1000 min per day which inflates their wear-time statistics.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [52] Radin JM, Wineinger NE, Topol EJ, Steinhubl SR. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit Heal. 2020;11:e85–93. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    RADAR-MDD remains the largest, most ambitious multimodal RMT study in depression.

    “Despite these limitations, RADAR-MDD remains the largest, most ambitious multimodal RMT study in depression.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, et al. Digital health tools for the passive monitoring of depression: A systematic review of methods. npj Digital Medicine. In press. https://doi.org/10.1101/2021.07.19.21260786. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The majority of recruitment occurred before the global pandemic, but the threat posed by COVID-19 may have affected existing participants' research experience and data completion.

    “Whilst the majority of our recruitment occurred before the global pandemic, the threat posed by COVID-19 may have affected existing participants’ research experience and data completion.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisereasoning

    For a multiparametric analysis, data across multiple sensor types will be needed.

    “For a multiparametric analysis, data across multiple sensor types will be needed.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    A recent systematic review found only 5 studies using passive and active smartphone-based measurements in people with MDD, with median sample size 5, median follow-up 4 weeks, and huge variability in reporting quality.

    “A recent systematic review summarising studies using passive and active smartphone-based measurements in affective disorders found only 5 studies in people with MDD, and these studies reported a median sample size of 5, and median follow-up time of 4 weeks, in addition to huge variability in the quality of reporting [17].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [17] De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, et al. Digital health tools for the passive monitoring of depression: A systematic review of methods. npj Digital Medicine. In press. https://doi.org/10.1101/2021.07.19.21260786. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    At a granular level, smartphone and wearable sensor data are so fine that no meaningful inferences can be gained, requiring some form of aggregation which may not be the same across different sensors.

    “At a granular level, data from smartphone and wearable sensors are so fine that no meaningful inferences can be gained, requiring some form of aggregation which may not be the same across different sensors.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Attrition rates in longitudinal research vary widely.

    “Attrition rates in longitudinal research vary widely [43]”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [43] Gustavson K, Von Soest T, Karevold E, Roysamb E. Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a Monte Carlo simulation study. BMC Public Health. 2012. https://doi.org/10.1186/1471-2458-12-918. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Given the high level of depressive symptoms in the sample, the pandemic and associated social interventions may have added burden resulting in increased dropout and reduced adherence.

    “Given the high level of depressive symptoms in our sample, the pandemic and its associated social interventions may have added a burden to participants resulting in an increased dropout rate and reduced adherence to the study protocol.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    The THINC-it app being separate with different branding may have made tasks appear separate and reduced adherence.

    “Secondly, the THINC-it® app is separate to the other RADAR-base apps, with different branding, design and feel to the RADAR-base apps. This may have made the tasks appear separate or “other” to the main protocol and reduced adherence to these tasks.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    The THINC-it app lacks an inbuilt notification system, requiring participants to switch between apps, which increases points at which interest or motivation may be lost.

    “The THINC-it® app does not have an inbuilt notification system - participants received notifications to complete the cognitive tasks via the RADAR-base aRMT app. Participants are required to switch between apps, which increases the number of points at which interest or motivation may be lost [50].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [50] Dahl AJ, D’Alessandro AM, Peltier JW, Swan EL. Differential effects of omni-channel touchpoints and digital behaviors on digital natives’ social cause engagement. J Res Interact Mark. 2018;12:258–73. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given

    Sleep data are only meaningful when aggregated from midday-midday, whereas activity data are more relevant when calculated from midnight-midnight.

    “For example, sleep data are only meaningful when aggregated from midday-midday, whereas activity data are more relevant when calculated from midnight-midnight.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Pedrelli et al. 2020 reported Empatica E4 wear-time estimates of 92-94% in 31 individuals with MDD, but follow-up was limited to 8 weeks.

    “Comparatively, Pedrelli et al., 2020 [53] report Empatica E4 wear-time estimates of 92–94% in their study involving 31 individuals with MDD, however their follow-up period was limited to only 8-weeks [53].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [53] Pedrelli P, Fedor S, Ghandeharioun A, Howe E, Ionescu DF, Bhathena D, et al. Monitoring changes in depression severity using wearable and mobile sensors. Front Psychiatry. 2020;11:584711. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    The approximately 80% completion and 79.8% protocol completion represent excellent availability of primary and secondary outcome measures.

    “representing excellent availability of our primary and secondary outcome measures.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    Cognitive tasks in THINC-it require more attention than conventional questionnaires, which may be more challenging for those experiencing depression symptoms.

    “Finally, the cognitive tasks offered as part of the THINC-it requires more attention than conventional questionnaires which may be more challenging for those who are experiencing depression symptoms [51].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [51] Peterson JZ, Porter RJ, Miskowiak KW. Clinical characteristics associated with the discrepancy between subjective and objective cognitive impairment indepression. J Affect Dis. 2019;246:763–74. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The higher proportion of White and female individuals reflects the tendency for White females to attend mental health services more often and their greater likelihood of participating in research studies.

    “reflecting the tendency for White females to attend mental health services more often than their male/non-White counterparts, and their greater likelihood of participating in research studies [46, 47].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [46] Manuel JI. Racial/ethnic and gender disparities in health care use and access. Health Serv Res. 2018. https://doi.org/10.1111/1475-6773.12705. source

      Not checked in the free run.

    • cites [47] Brown G, Marshall M, Bower P, Woodham A, Waheed W. Barriers to recruiting ethnic minorities to mental health research: a systematic review. Int J Methods Psychiatr Res. 2014. https://doi.org/10.1002/mpr.1434. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationown data

    The 110 individuals with >50% data is an indicator of the amount of resource and data collection required for multiparametric analyses.

    “It is important to acknowledge this as an indicator of the amount of resource and data collection required for multiparametric analyses.”
    Quote in the paperNot a substantial claim
48 of 48

  • The authors state this as an assumption.

    Data availability is a valid indicator of the feasibility of implementing RMT protocols in people with MDD.

    “We believe it is essential to include partial or incomplete data as part of this paper, as they are indicative not only of participant characteristics and study burden, but also of the underlying technical infrastructure.”
    “This concession gives greater insight into how data availability may fluctuate with changes in depressive state and provides a truer representation of the feasibility of implementing RMT protocols in people with MDD.”

  • The paper relies on this without support.

    The RADAR-MDD sample of people with a history of recurrent MDD is representative of the clinical population with MDD for assessing feasibility of RMT.

    “Here, we have shown that collecting RMT data from a clinical population is feasible.”

  • The paper relies on this without support.

    No remote measurement studies have reported the quantity of data collected via smartphone sensors.

    “To the best of our knowledge, no remote measurement studies have reported the quantity of data collected via smartphone sensors.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Passive-data summary contradicts its table

    Major

    The passive-data results say no difference in availability was identified, but Table 3 reports significant differences for app use (p = 0.02) and phone interactions (p = 0.03).

  • Calorie-intake availability percentage is incorrect

    Minor

    The 87 participants in this category represent 14.0% of the 623-person sample, not 19.6%.

    Reported 19.6 · recomputed 13.964687

  • Marital-status subgroup percentage is incorrect

    Minor

    The 119 participants in this category represent 48.6% of the 245-person no/some-symptoms group, not 47.8%.

    Reported 47.8 · recomputed 48.571429

  • Phone-interactions subgroup percentage is incorrect

    Minor

    Among the 245 participants with no/some baseline symptoms, 225 represent 91.8%, not 9.2%.

    Reported 9.2 · recomputed 91.836735

  • Alcohol-dependence cutoff differs between methods and table

    Minor

    The methods specify scores greater than 20 as indicating likely alcohol dependence, whereas Table 1 labels scores greater than 19 that way. A score of 20 is classified differently.

  • Steps no-data percentage is incorrect

    Minor

    Table 2 lists 36 participants without steps data out of 623, which is 5.8%, not 6.8%.

    Reported 6.8 · recomputed 5.778491

  • Activity-data subgroup percentage is incorrect

    Minor

    In the 245-person no/some-symptoms group, 53 participants represent 21.6%, not 51.6%.

    Reported 51.6 · recomputed 21.632653

7 of 7

Cites

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Cited by

Sorted by citations

Related

Authors
Matcham, F., Leightley, D., Siddi, S., Lamers, F., White, K. M., Annas, P., de Girolamo, G., Difrancesco, S., Haro, J. M., Horsfall, M., Ivan, A., Lavelle, G., Li, Q., Lombardini, F., Mohr, D. C., Narayan, V. A., Oetzmann, C., Penninx, B. W. J. H., Bruce, S., Nica, R., Simblett, S. K., Wykes, T., Brasen, J. C., Myin-Germeys, I., Rintala, A., Conde, P., Dobson, R. J. B., Folarin, A. A., Stewart, C., Ranjan, Y., Rashid, Z., Cummins, N., Manyakov, N. V., Vairavan, S., Hotopf, M., & on behalf of the RADAR-CNS consortium
Affiliations
  • King's College London (GB)
  • Centro de Investigación Biomédica en Red de Salud Mental (ES)
  • Amsterdam Public Health (NL)
  • Lundbeck (Denmark) (DK)
  • Northwestern University (US)
  • LAB University of Applied Sciences (FI)
  • Janssen (Belgium) (BE)
Corresponding author
Matcham, Faith
ORCID
Venue
BMC Psychiatry
Publisher
Springer Science and Business Media LLC
Published
2022-02-21
Volume
22
Issue
1
Pages
136-136
ISSN
1471-244X (ISSN-L)
DOI
10.1186/s12888-022-03753-1
PMC
PMC8860359
OpenAlex
W4225751944
Cited by
110
References
55
Citations by year
2026: 18 · 2025: 26 · 2024: 35 · 2023: 16 · 2022: 14 · 2021: 1
FWCI
20.04
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Psychiatry (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, cc-by)
  • Pure Amsterdam UMC (publishedVersion, other-oa)
  • Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
  • Lirias (submittedVersion, cc-by)
  • OPUS (Augsburg University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • NHS Blood and Transplant
  • South London and Maudsley NHS Foundation Trust
  • Wellcome Trust (116074)
  • King's College Hospital NHS Foundation Trust
  • Scottish Government
  • European Federation of Pharmaceutical Industries and Associations (116074)
  • UK Research and Innovation
  • Alzheimer's Society
  • National Institute for Health and Care Research (NF-SI-0515-10102, 116074)
  • British Heart Foundation
  • Department of Health and Social Care
  • King's College London
  • University College London
  • European Commission (115902, 116074, HORIZON2020)
  • Public Health Agency
  • ZonMw (73305095003)
  • King's Health Partners
  • Innovative Medicines Initiative (116074)
  • Institut Català de la Salut
  • GIESKES-STRIJBIS FONDS
  • Chief Scientist Office, Scottish Government Health and Social Care Directorate
  • NIHR BioResource
  • NIHR Maudsley Biomedical Research Centre
  • Medical Research Council (MC_PC_17214)
  • Engineering and Physical Sciences Research Council
  • Economic and Social Research Council
  • Health and Social Care Research and Development Division
  • National Institute for Health Research Applied Research Collaboration South London
  • Health Data Research UK
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
major depressive disorder, recurrent depression, depression relapse, wearable devices, smartphone sensors, participant recruitment and retention, data completeness, multimodal data collection, passive data collection
MeSH terms
Smartphone, Chronic Disease, Major Depressive Disorder, Humans, Prospective Studies, Recurrence, Mobile Applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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