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Depression and Mood Disorders; Suicide Prevention (1290) Digital Biomarkers and Digital Phenotyping (178) Ecological Momentary Assessment (EMA) (297) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1317) Wearable Devices and Sensors (825)

Published on in Vol 6, No 11 (2019): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12814, first published .
Wearable Technology for High-Frequency Cognitive and Mood Assessment in Major Depressive Disorder: Longitudinal Observational Study

Wearable Technology for High-Frequency Cognitive and Mood Assessment in Major Depressive Disorder: Longitudinal Observational Study

Wearable Technology for High-Frequency Cognitive and Mood Assessment in Major Depressive Disorder: Longitudinal Observational Study

Authors of this article:

Francesca Cormack1, 2 Author Orcid Image ;   Maggie McCue3 Author Orcid Image ;   Nick Taptiklis1, 2 Author Orcid Image ;   Caroline Skirrow1, 4 Author Orcid Image ;   Emilie Glazer2, 5 Author Orcid Image ;   Elli Panagopoulos2, 5 Author Orcid Image ;   Tempest A van Schaik2, 5 Author Orcid Image ;   Ben Fehnert2, 5 Author Orcid Image ;   James King2, 5 Author Orcid Image ;   Jennifer H Barnett1, 2, 6 Author Orcid Image
Article Authors Cited by (40) Tweetations (42) Metrics

    Original Paper

    1Cambridge Cognition, Cambridge, United Kingdom

    2Cognition Kit, Cambridge, United Kingdom

    3Takeda Pharmaceuticals USA, Deerfield, IL, United States

    4School of Psychological Science, University of Bristol, Bristol, United Kingdom

    5Ctrl Group, London, United Kingdom

    6Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

    Corresponding Author:

    Francesca Cormack, PhD

    Cambridge Cognition

    Tunbridge Court

    Bottisham

    Cambridge, CB25 9TU

    United Kingdom

    Phone: 44 1223 810700 ext 686

    Fax:44 1223 810701

    Email: Francesca.Cormack@camcog.com


    Background: Cognitive symptoms are common in major depressive disorder and may help to identify patients who need treatment or who are not experiencing adequate treatment response. Digital tools providing real-time data assessing cognitive function could help support patient treatment and remediation of cognitive and mood symptoms.

    Objective: The aim of this study was to examine feasibility and validity of a wearable high-frequency cognitive and mood assessment app over 6 weeks, corresponding to when antidepressant pharmacotherapy begins to show efficacy.

    Methods: A total of 30 patients (aged 19-63 years; 19 women) with mild-to-moderate depression participated in the study. The new Cognition Kit app was delivered via the Apple Watch, providing a high-resolution touch screen display for task presentation and logging responses. Cognition was assessed by the n-back task up to 3 times daily and depressed mood by 3 short questions once daily. Adherence was defined as participants completing at least 1 assessment daily. Selected tests sensitive to depression from the Cambridge Neuropsychological Test Automated Battery and validated questionnaires of depression symptom severity were administered on 3 occasions (weeks 1, 3, and 6). Exploratory analyses examined the relationship between mood and cognitive measures acquired in low- and high-frequency assessment.

    Results: Adherence was excellent for mood and cognitive assessments (95% and 96%, respectively), did not deteriorate over time, and was not influenced by depression symptom severity or cognitive function at study onset. Analyses examining the relationship between high-frequency cognitive and mood assessment and validated measures showed good correspondence. Daily mood assessments correlated moderately with validated depression questionnaires (r=0.45-0.69 for total daily mood score), and daily cognitive assessments correlated moderately with validated cognitive tests sensitive to depression (r=0.37-0.50 for mean n-back).

    Conclusions: This study supports the feasibility and validity of high-frequency assessment of cognition and mood using wearable devices over an extended period in patients with major depressive disorder.

    JMIR Ment Health 2019;6(11):e12814

    doi:10.2196/12814

    Keywords

    depression; cognition; mood; mobile health; mHealth; mobile apps; ecological momentary assessment; digital phenotyping; digital biomarkers 


    Major depressive disorder (MDD) is characterized by symptoms of low mood, diminished interest and pleasure in daily activities, feelings of worthlessness or guilt, fatigue, sleeping and appetite disturbances, and thoughts of death or suicide. MDD is a leading cause of disease burden and disability worldwide [1,2]. Cognitive symptoms, including difficulty concentrating or making decisions, are features of MDD [3] that may offer a target for intervention [4].

    Cognitive symptoms of MDD include deficits in several domains, including processing speed, attention, executive function, learning, and memory [5-7]. Cognitive symptoms are seen in first-episode depression [6,8], persist beyond the symptoms of low mood [9-11], contribute to the risk of relapse [12], and worsen with repeated depressive episodes [13,14].

    Cognitive MDD symptoms contribute to disability burden [15]. Poorer memory [15,16], attention, and executive function [17] have been associated with impairment in activities of daily living. Cognitive symptoms have also been associated with poor occupational functioning [18] and unemployment [19], work-related disability, and adverse psychosocial outcomes [20-22]. Longitudinally, improved cognitive function has been associated with higher rates of employment at follow-up in a variety of psychiatric illnesses, including MDD [23]. Treating these symptoms has the potential to improve functional outcomes and quality of life.

    Research has highlighted discrepancies between objectively measured cognitive function and patients’ self-report from questionnaires, with the latter being affected by depressed mood [15,24,25]. This inconsistency highlights the need for subjective and objective data to be acquired to provide accurate clinical information. A key obstacle is the lack of readily available tools for cognitive assessments outside the clinic. Such tools could support the treatment and remediation of cognitive symptoms associated with MDD.

    Mobile digital technologies allow for sampling outside of the clinic and in the patient’s home or work environment, providing a shared platform for clinicians and patients to monitor symptoms [26]. In depression, mobile apps have tracked changes in patient-reported mood [27-29] and have been used as part of randomized controlled trials to evaluate treatment efficacy [30]. However, these studies have relied on quantitative self-report or simple sensing and monitoring technologies [26].

    This study examined feasibility, that is, viability of brief, high-frequency cognitive and mood assessment over an extended period of time (6 weeks) implemented on an Apple Watch app in individuals with MDD, and validity, defined as agreement between these high-frequency data and validated measures of mood and cognition. Coprimary endpoints were (1) adherence, examined separately for high-frequency cognitive and mood assessment and (2) correlations between daily measures of cognition with traditional full-length cognitive assessments, as specified in study details in the clinical trials registration [31].

    The following secondary outcomes were examined, as described in the study analysis plan [32]: (1) the relationship between daily mood measures with full-length validated questionnaires and (2) the reliability of heart rate and activity sensors acquired via the Apple iPhone and Apple Watch (Apple Inc) apps. In addition, exploratory analyses examined the interrelationship of mood and cognitive measures acquired in low- and high-frequency assessment.


    Participants and Recruitment

    A recruitment target of 30 was set for this study, commensurate with usual practice for feasibility studies [33]. A sample size of 30 allows estimation of a compliance rate of 80%, with 95% CIs of ±12.8%. This sample size also provides 80% power to detect correlations of r=0.5.

    A total of 556 adults underwent an initial screening for eligibility to participate in the study through a patient recruitment company with links to primary care providers and depression patient groups, to identify individuals with depression potentially suitable for the study. In total, 72 individuals were contacted for more detailed medical history information and to complete the Patient Health Questionnaire-9 (PHQ-9) [34] to obtain an index of depression severity. Participant eligibility was determined according to the following inclusion and exclusion criteria before study entry:

    Inclusion criteria were primary psychiatric diagnosis of MDD; treated with antidepressant monotherapy; mild-to-moderate depression, defined by PHQ-9 scores between 5 and 15; aged 18 to 65 years; able to read and understand English; and owning their own iPhone.

    Exclusion criteria were personal history of other psychiatric disorder (except nonprimary concurrent anxiety); manic or hypomanic episode; mental retardation, organic mental disorders, or mental disorders owing to a general medical condition as defined in the Diagnostic and Statistical Manual of Mental Disorders 5th Edition; neurological or neurodegenerative disorder; alcohol or other substance abuse or dependence (excluding nicotine or caffeine); responding only to combination or augmentation therapy in the current episode; hospitalization for MDD in 3 months or suicide attempt in 6 months before screening (or the participant was considered to be at significant risk of suicide or hospitalization); having received any investigational compound within 30 days before screening or 5 half-lives before screening, whichever is longer; concurrent participation in other clinical studies; or participation in 2 or more interventional studies in the year before screening.

    In total, 30 of the 72 screened individuals were recruited into the study. Of the remaining screened individuals, 7 were eligible but not recruited. Others were excluded because of lack of an iPhone (n=4), insufficient time on medication (n=18), lack of antidepressant medication treatment history (n=2), polypharmacy (n=3), other psychiatric diagnosis or neurological condition (n=5), PHQ-9 higher than 15 (n=1), or insufficient information obtained in screening (n=2).

    Procedure

    The study began with a visit to the study site and a short semistructured interview to explore each participant’s expectations and motivations for taking part. Researchers provided study hardware (an Apple Watch Series 2, paired with the participant’s own iPhone), presented the tasks, and gave participants the opportunity to practice using the tasks and device and ask questions. Participants were given contact details for the study center, where they could get in touch by email or phone if they experienced technical issues or had questions or concerns regarding their participation. Testing was completed in the subsequent 6 weeks (42 days), corresponding to the time when antidepressant pharmacotherapy shows efficacy in treating the mood symptoms of MDD. Participants were encouraged to respond to cognitive assessment wherever possible but not to worry when individual assessments were missed.

    Data collected on the Apple Watch and iPhone were transferred automatically through Wi-Fi or data roaming via the participant’s iPhone to a secure data center held on Amazon’s Web service. This service provided identity and access control mechanisms to ensure participants (and only participants) had write access, and study managers only had read access. Where data for individual participants were not uploaded for 4 days, the research team made contact to ensure that the study equipment was working and to gain a better understanding of why assessments were not completed.

    Full-length cognitive and validated self-report assessments were completed via a Web-based testing interface. Familiarization with the tests was completed during in-person assessments on the first day of participation. Full assessments were completed on 3 occasions: week 1 (between days 1 and 2), week 3 (days 18-24), and week 6 (days 40-46). Participants were sent a unique link to a secure Web page that delivered the test. On completion of assessment, and when the device established an internet connection, data were transferred to a secure Health Insurance Portability and Accountability Act of 1996 ̶ compliant data center in the United States.

    The study was completed with a 90-min, semistructured qualitative interview during week 6 at participants’ homes. Interviews explored participants’ experiences of assessment with the wearable technology, changes in motivation and adherence, and contextual factors that might have contributed to those changes. Study hardware was returned at this time.

    Measures

    Daily Mobile Digital Assessments

    The Apple Watch provides a small touch screen for the presentation of stimuli and collection of participant responses and contains a range of sensors, including accelerometers and heart rate sensor. Participants were asked to wear the watch from 8 am to 10 pm for 6 weeks and to respond to assessment prompts. Additional step count data were acquired via the iPhone. An illustration of mood and cognitive assessment is provided in Figure 1.

    ‎
    Figure 1. Symbol display for n-back (left) and mood assessment questions (right) presented on the Apple Watch. Participants were asked to tap the screen to respond to a match.
    High-Frequency Cognitive Assessments

    Participants were prompted to complete cognitive assessments 3 times daily (morning, afternoon, and evening). Multiple prompts for cognitive testing were delivered to improve flexibility for participants unable to complete cognitive testing at specific points in the day and to yield data with the potential to examine diurnal changes (not examined in the current report).

    Cognitive assessment was completed using a variant of the n-back task, a task which has shown sensitivity to impairments in MDD [35]. This variant was developed for brief high-frequency assessment after initial piloting indicated that a large pool of nonverbalizable stimuli were required to reduce ceiling effects over prolonged testing. A total of 9 symbols, randomly selected from a pool of 227, were presented for 600 ms 1 at a time over 30 trials. Participants were asked to respond when any symbol was the same as the symbol presented 2 trials previously. The primary outcome measure was dprime (the ratio of hits [correct detection of an n-back match] to false alarms [response during no match]). Each full assessment took 30 seconds to complete, after which participants were shown their n-back score.

    High-Frequency Mood Assessments

    Mood assessment was prompted up to twice daily (afternoon and evening). If participants completed the mood assessment in the afternoon, no prompt was delivered in the evening. Only 1 mood assessment was completed per day as participants were asked to reflect on and respond regarding their experiences over the past day.

    Mood was assessed with 2 questions adapted from the PHQ-2, a validated brief form of the PHQ-9, which assesses only low mood and loss of interest or pleasure and is sensitive to depression and suitable for brief assessments [36]. One additional item assessing self-perceived concentration was taken from the Perceived Deficits Questionnaire—Depression (PDQ-D) [37,38], a measure that assesses subjective cognitive dysfunction in depression. Questions were modified from asking about symptom presence over multiple weeks to asking about symptoms over the past day. Wording was also shortened to facilitate presentation on a small screen.

    Mood questions were presented in the following manner: How much have the following problems bothered you over the past day? Participants rated the following items: (1) lack of interest or pleasure in doing things; (2) feeling down, depressed, or hopeless; and (3) trouble concentrating on things (eg, newspaper, TV). Responses were coded on a 4-point scale of severity of symptoms (1=no problem, 2=slightly, 3=somewhat, 4=greatly). This scale was modified from the 4-point scale of the PHQ-9 to reflect within-day experiences and was kept consistent for the PDQ-D item.

    Web-Based Full-Length Assessments

    The Cambridge Neuropsychological Test Automated Battery (CANTAB) Connect Web-based testing interface was used to complete full-length cognitive testing and validated questionnaires on 3 occasions (weeks 1, 3, and 6). CANTAB cognitive assessments have shown sensitivity to a range of cognitive deficits in depression [10].

    Cognitive Assessments
    1. Spatial working memory (SWM) [39] examined participants’ ability to retain and manipulate visuospatial information and to strategize. Between 4 and 8 boxes were presented on the screen. Participants were asked to find tokens in the boxes and move them to a collection area and were instructed that they would not find a token in the same box twice in the same trial. Outcome measures included the following: (1) between errors, the number of times the participant revisited a box in which a token had been found (range of possible scores 0-175); and (2) strategy, the number of unique boxes from which a participant started a new search (range of possible scores 4-28). For both outcomes, lower scores indicated better performance.
    2. The CANTAB rapid visual information processing (RVP) [40] test measured sustained attention and processing speed. Digits from 2 to 9 were presented successively at the rate of 100 digits per minute and in pseudorandom order. Participants were asked to respond to target sequences of digits (eg, 2-4-6, 3-5-7, 4-6-8). Two outcome measures were examined: (1) RVP A', a signal detection measure of sensitivity to the target regardless of response tendency (expected range is 0 to 1); and (2) RVP median latency of correct responses (maximum response time allowable 1800 ms).
    Validated Questionnaires
    1. The PHQ-9 [34] provided an index of depression severity, with higher scores reflecting greater symptom severity.
    2. The PDQ-D [37] subscales of attention/concentration and planning/organization were summated to provide an index of participant-perceived cognitive symptoms. Higher scores reflect greater perceived cognitive symptoms.
    3. The University of California Los Angeles Loneliness Scale (UCLA-LS) [41] measured subjective feelings of loneliness and social isolation. Higher scores reflect more severe loneliness and social isolation.
    Semistructured Interviews

    A copy of the discussion guides for semistructured interviews at study onset and end are provided in Multimedia Appendix 1.

    Statistical Analysis

    High-Frequency Data Preparation and Cleaning

    Adherence was assessed separately for cognitive function, mood reports, and activity. Adherence for mood and cognitive assessments was defined in line with methods described in the clinical trials registration [31]: each day was defined as adherent (with participants completing at least 1 full assessment each day) or nonadherent (days with no data). For Apple Watch activity and heart rate measures, nonwearing days (defined as days where <100 steps were recorded [42,43] [n=19 observations] or where heart rate was not recorded [n=6 additional observations]) were excluded from analyses. No minimum adherence was specified for participants to be included in analyses.

    Percentage of adherent days was examined separately for mood, cognitive function, and activity for the duration of the study (defined as percentage of 42 days completed) and calculated for individual study weeks (weeks 1-6). In addition, for cognitive assessments, where responses were prompted 3 times daily, percentage of responses to all possible assessments was examined.

    Daily dprime performance was calculated from the mean of all available n-back assessments within each day. Total daily mood was the summation of responses across the 3 questions presented during each assessment. Total step count from the iPhone and the Apple Watch was extracted for each day. Minimum, maximum, and mean daily heart rates for each day were obtained from the Apple Watch.

    Summary measures for daily assessments were obtained for total daily mood, daily dprime, average heart rate, and total step count; means of all available daily assessments were calculated across the entire assessment period (6 weeks) and for individual weeks (1-6) to document change over the assessment period. No corrections for missing data and no other adjustments to raw data were made. Normality of all summary measures was assessed with visual examination of the data and with the Shapiro-Wilk test before further analysis.

    Web-Based Full-Length Assessments Data Preparation and Cleaning

    Absolute scores from validated self-report questionnaires were computed by summating responses within scales and providing summed scores for PHQ-9, PDQ-D, and UCLA-LS at each time point. To reduce multiple comparisons, overall scores from self-report questionnaires and CANTAB cognitive testing were calculated by taking the mean of outcome measures obtained at weeks 1, 3, and 6. This yielded overall means for SWM between errors, SWM strategy, RVP A′, and RVP median latency, as well as for self-report questionnaires (PHQ-9, PDQ-D, and UCLA-LS). Normality of data was assessed with visual examination of the data and with the Shapiro-Wilk test before further analysis.

    Adherence Over Time

    To examine whether the binary variable of adherence (response vs nonresponse) improved or declined over time, a series of logistic regression mixed models were carried out with study day (days 1−42) as a fixed factor and the participant as a random effect. Logistic regressions were also repeated separately for morning, afternoon, and evening n-back assessments to identify changes in response by time of day over the duration of the study.

    Logistic regression models examined whether adherence to cognitive and mood assessments could be predicted by severity of depression symptoms at the onset of the study, as measured by the following covariates: PHQ-9, PDQ-D, and UCLA-LS scores from week 1. These included a covariate-by-day interaction term to examine variation by day. Assumptions of logistic regression models were investigated by examining the distribution and patterns of residuals versus fitted values.

    To test whether adherence was associated with cognitive symptoms at study onset, a series of bivariate correlations (Pearson correlations or Spearman rank correlation, as appropriate) were completed. These explored the relationship between overall adherence with CANTAB cognitive measures at week 1. As this was an exploratory study, no corrections for multiple comparisons were made.

    Daily Cognitive Assessment

    Cognitive performance on the n-back was modeled using a longitudinal mixed-effects model with daily dprime as response variable, a fixed effect of study day, and a random effect of participant with random intercept and random slope. No covariates were examined. For the fixed-effect part of the model, we compared linear, quadratic, and cubic trends via likelihood ratio test and compared model parameters via maximum likelihood. This allowed the examination of different learning curves on the n-back to identify the best fit for change in performance over time. Each participant’s intercept (representing initial level of performance) and slope (representing learning rate) were extracted.

    Summary n-back measures (mean, intercept, and slope) were correlated with overall means of CANTAB outcome measures and self-report questionnaires (PHQ-9, PDQ-D, and UCLA-LS). N-back slope was also correlated with the total number of n-back assessments completed over the study period, to examine the effects of practice on learning rate. Pearson or Spearman correlations were performed as appropriate.

    Daily Mood Assessment

    Multilevel reliability of the 3 mood items was examined using the multilevel.reliability command in the Psych package of R [44]. The package takes into consideration missing data by including components of variance derived from multilevel mixed modeling and examines multiple sources of variance for each score based on generalizability theory.

    Average daily mood was modeled using a longitudinal mixed-effects model with total daily mood as response variable, a fixed effect of study day, and a random effect of participant with random intercept and random slope. No covariates were examined. For the fixed-effect part of the model, linear, quadratic, and cubic trends were compared via likelihood ratio test, and model parameters were compared via maximum likelihood, identifying the best fit for change in mood over time.

    Overall means for daily mood assessment from the entire assessment period were correlated with overall means from full-length questionnaires and CANTAB assessments to investigate concurrent validity of daily mood assessments and the relationship between daily mood and full-length cognitive assessments. Parametric or nonparametric correlations were completed as appropriate.

    Activity and Heart Rate Data

    Total step count from the iPhone and the Apple Watch was extracted for each day. Means were calculated for the duration of the study (overall means) and for each study week. Minimum, maximum, and average daily heart rates were obtained from the Apple Watch, and the mean daily heart rate was calculated over the study duration (overall mean) and for each study week. The correlation between overall means for steps measured from the iPhone and the Apple Watch was examined.

    Ethics Approval

    The study was reviewed and approved by the Proportionate Review Sub-Committee of the Wales Research Ethics Committee 6 at Swansea University (REC reference: 17/WA/0042) and performed in accordance with the current version of the Declaration of Helsinki. All participants provided written informed consent before enrollment.


    Participants

    Of the 37 eligible participants, 30 were enrolled (19 women and 11 men). Participants were aged between 19 and 63 years (mean age 37.2 years; SD 10.4) and had been on their current medication for an average of 9.9 months (range 0.4−94.3 months; SD 9.5). Current medications included serotonin antagonist and reuptake inhibitor (n=1), serotonin and norepinephrine reuptake inhibitors (n=5), selective serotonin reuptake inhibitors (n=20), and tricyclic antidepressants (n=4). Mean depression symptom severity, measured by the PHQ-9, was 9.1 (range 5-15; SD 3.1).

    Adherence

    Descriptive statistics for adherence across the duration of the study and by study week are shown in Table 1. Full adherence (100%, 42/42 days) was seen in 21 of 30 participants for cognitive assessment, 15 of 30 participants for mood testing, and 13 of 30 participants for activity assessment. Periods of low adherence tended to cluster temporally (Multimedia Appendix 2). Because of a technical issue on the final study day, the evening session was not administered, resulting in lower adherence on day 42. However, logistic mixed modeling showed no deterioration in adherence (ie, responding at least once daily) over time for assessments of mood, cognition, or activity. Logistic regression confirmed that self-reported depressive symptoms, assessed by the PHQ-9, PDQ-D, and UCLA-LS, were not associated with level of adherence in mood or cognitive assessments. Adherence was not significantly correlated with any CANTAB measures at week 1 (maximum rho=0.15; P=.44).

    Participants completed a mean of 86.8% of all possible n-back assessments (range 50%-99%, 63-125 of 126 assessments). Rate of responding in the morning (84%) was lower than the afternoon (87%) and evening (89%; χ22=12.9). Furthermore, although adherence (responding at least daily) remained high throughout the duration of the study, logistic regression confirmed modest reductions in individual assessments (morning, afternoon, and evening) over the study duration (morning: fixed-effects estimate=−0.03, P=.02; afternoon: fixed-effects estimate=−0.02, P<.001; evening: fixed-effects estimate=−0.08, P<.001).

    Table 1. Percentage adherence for cognitive (n-back) and mood assessments and percentage of watch-wearing days (step count) completed over the duration of the study (overall) and broken down by week (week 1 to week 6). Adherence for cognitive and mood assessments defined as participants completing at least 1 full assessment per day. Watch-wearing days for step count defined as days with a minimum of 100 steps and heart rate recorded.
    MeasureParticipant adherence (%)
    OverallWeek 1Week 2Week 3Week 4Week 5Week 6
    n-back







    		Mean (SD)95.63 (9.28)94.29 (19.35)93.33 (18.28)93.33 (20.8)99.05 (5.22)100 (0)93.81 (17.06)

    		Range66.7-1000-10028.6-10014.3-10071.4-10010028.6-100
    Mood







    		Mean (SD)94.60 (9.73)93.81 (19.38)92.38 (20.46)92.38 (22.11)98.57 (5.75)99.05 (3.63)91.43 (20.75)

    		Range66.7-1000-10028.6-10014.3-10071.4-10085.7-10014.3-100
    Step count







    		Mean (SD)90.08 (17.4)94.54 (14.99)92.12 (18.15)92.38 (20.11)92.38 (19.03)92.86 (17.09)85.22 (24.29)

    		Range21-10028-10014-10014-10028-10028-10014-100

    Daily Cognitive Assessment

    Descriptive data for n-back assessments are presented in Table 2. Multilevel analysis of dprime score by study day confirmed a better fit for a cubic term rather than quadratic or linear models (Bayesian information criterion=1298.05; likelihood ratio=10.36; P=.001), indicating an initial rapid improvement in performance followed by a plateau. Model fits for each study participant are shown in Figure 2. Dprime slope showed no significant relationship with the number of n-back assessments completed (rho=−0.02, 95% CIs −0.37 to 0.34; P=.91).

    Correlations between task performance metrics from the n-back and overall means from CANTAB cognitive assessments and self-report questionnaires were explored (Table 3). Participants with better performance on CANTAB showed higher intercept and better mean performance on the n-back. Depressive symptoms assessed with the PHQ-9 correlated with mean dprime, and correlations with dprime intercept approached but did not reach statistical significance (P=.06). No significant correlations were seen with PDQ-D or UCLA-LS, or dprime slope.

    Table 2. Descriptive data for main outcome variables.
    Outcome measure (daily)Weeks assessed
    OverallWeek 1Week 2Week 3Week 4Week 5Week 6
    Daily dprime (n-back)

    		Mean (SD)1.8 (0.53)1.38 (0.46)1.72 (0.56)1.79 (0.51)1.92 (0.60)1.97 (0.60)1.96 (0.58)

    		Range0.7-2.80.4-2.20.6-3.00.6-2.840.71-3.010.84-3.010.56-2.88
    Total mood score

    		Mean (SD)6.54 (2.41)6.96 (2.49)6.75 (2.45)6.54 (2.26)6.60 (2.45)6.15 (2.31)6.26 (2.41)

    		Range3-123-123-123-123-123-123-12
    iPhone step count

    		Mean (SD)3762.81 (3168.59)4124.48 (3610.95)3605.83 (3207.43)3961.29 (3282.68)3751.95 (3305.45)3404.67 (2438.33)3745.64 (3053.46)

    		Range100-20,183110-17,529109-20,183103-14,470122-18,393100-13,548104-15,485
    Apple Watch step count

    		Mean (SD)6429.64 (4242.01)6778.82 (4294.81)6607.06 (4093.61)6542.80 (4699.67)6106.60 (3980.54)6072.75 (4124.24)6487.93 (4223.08)

    		Range125-22,360133-19,560144-21,533448-22,360125-19,808155-22,130293-20,049
    Heart rate, bpma

    		Overall mean (SD)79.71 (10.47)78.63 (8.72)80.76 (11.98)80.33 (12.87)79.34 (9.65)79.50 (8.75)79.72 (10.17)

    		Range60-14763-11664-14760-14662-14061-11561-124

    abpm: beats per minute.

    ‎
    Figure 2. Trajectories in n-back performance and mood over time for study participants; each study day is represented on the x-axis. Top: Each dprime (up to 3 daily) is shown on the y-axis (higher scores denote better performance). Bottom: total mood is shown on the y-axis (higher scores denote more depressive symptoms).
    Table 3. Correlation coefficients (95% CIs) for daily cognitive assessments with full-length aggregate Cambridge Neuropsychological Test Automated Battery cognitive assessment outcome measures and full-length aggregate self-report questionnaires.
    Outcome measures from full length assessmentn-back performance
    MeanInterceptSlope
    SWMa between errors−0.46b (−0.70 to −0.11)−0.51b (−0.51 to −0.17)−0.01 (−0.45 to 0.39)
    SWM strategy−0.37c (−0.65 to −0.01)−0.40c (−0.67 to −0.04)0.05 (−0.34 to 0.46)
    RVPd A\'0.50b (0.17 to 0.73)0.47b (0.12 to 0.71)0.29 (−0.10 to 0.58)
    RVP median latency−0.42c (−0.73 to 0.001)−0.46c (−0.75 to −0.07)−0.15 (−0.53 to 0.22)
    Depressive symptoms (PHQ-9)e−0.38c (−0.65 to −0.01)−0.36 (−0.65 to 0.01)−0.18 (−0.51 to 0.24)
    Cognitive problems (PDQ-D)f−0.25 (−0.57 to 0.12)−0.27 (−0.58 to 0.11)0.04 (−0.37 to 0.32)
    Loneliness (UCLA-LS)g−0.29 (−0.59 to 0.09)−0.26 (−0.57 to 0.12)−0.21 (−0.54 to 0.18)

    aSWM: spatial working memory.

    bP≤.01.

    cP≤.05.

    dRVP: rapid visual information processing.

    ePHQ-9: Patient Health Questionnaire-9.

    fPDQ-D: Perceived Difficulties Questionnaire—Depression.

    gUCLA-LS: University of California Los Angeles Loneliness Scale.

    Daily Mood Assessment

    The 3 mood items showed overall good reliability indices, supporting the combined use of the 3 question items. Between-person reliabilities were high (R=0.97 averaged over time and with time nested within individuals), and within-person generalizability was moderate to high (R=0.75 for within-person variation with time nested within individuals).

    Descriptive data for total mood are presented in Table 2. Multilevel analysis of total mood by study day confirmed the best fit for a linear model (Bayesian information criterion=73.38; likelihood ratio=6.14; P=.01). This model showed a modest overall linear improvement in mood over the course of the study (estimate of fixed effect of study day on mood=−0.0026, P=.01). However, there was a great deal of heterogeneity on mood trajectories over the study duration, as shown in model fits for each study participant in Figure 2.

    Mean overall scores from daily mood assessments were correlated with full-length self-report questionnaires, showing moderate correlations (Table 4). Self-reported depression (PHQ-9) and cognitive symptoms (PDQ-D) correlated more highly with daily mood assessments than self-reported loneliness as measured by the UCLA-LS.

    Significant correlations between dprime mean and intercept were seen for total mood scores, for question items assessing lack of interest, and for low mood (Table 4). Correlations between n-back performance and daily reported cognitive symptoms and all correlations with dprime slope were nonsignificant (P=.12-.79). Examining the relationship between daily mood assessment and CANTAB measures, SWM between errors and strategy showed moderate correlations with daily reported mood, whereas correlations with RVP outcome measures were nonsignificant.

    Table 4. Correlation coefficients (95% CIs) for daily mood assessments with full-length self-report measures of depression, daily cognitive assessments, and full-length cognitive assessments on Cambridge Neuropsychological Test Automated Battery.
    Outcome measures from full length assessments and daily cognitive assessmentsTotal mood scoreDaily question items
    Low mood (feeling down, depressed, hopeless)Cognitive symptoms (trouble concentrating on things)Lack of interest (lack of interest or pleasure)
    Depressive symptoms (PHQ-9)a0.69b (0.44 to 0.84)0.56b (0.24 to 0.77)0.69b (0.44 to 0.85)0.70b (0.45 to 0.85)
    Cognitive problems (PDQ-D)c0.65b (0.37 to 0.82)0.50b (0.16 to 0.73)0.68b (0.42 to 0.84)0.64b (0.36 to 0.82)
    Loneliness (UCLA-LS)d0.45e (0.10 to 0.70)0.47b (0.13 to 0.72)0.35 (−0.02 to −0.63)0.45e (0.10 to 0.70)
    dprime mean−0.41e (−0.67 to −0.06)−0.36e (−0.64 to 0.00)−0.28 (−0.58 to 0.08)−0.52b (−0.74 to −0.19)
    dprime intercept−0.42e (−0.68 to −0.07)−0.38e (−0.65 to −0.03)−0.29 (−0.59 to 0.08)−0.52b (−0.74 to −0.19)
    dprime slope−0.10 (−0.51 to 0.33)−0.02 (−0.52 to 0.38)−0.02 (−0.46 to 0.35)−0.13 (−0.51 to 0.29)
    SWMf between errors0.49b (0.15 to 0.73)0.52b (0.19 to 0.74)0.39e (0.03 to 0.66)0.49b (0.15 to 0.73)
    SWM strategy0.44e (0.09 to 0.70)0.43e (0.07 to 0.69)0.41e (0.05 to 0.68)0.41e (0.05 to 0.68)
    RVPg A\'−0.14 (−0.48 to 0.24)−0.06 (−0.41 to 0.32)−0.13 (−0.47 to 0.25)−0.20 (−0.53 to 0.18)
    RVP median latency0.23 (−0.18 to 0.58)0.27 (−0.09 to 0.57)0.18 (−0.19 to 0.51)0.28 (−0.11 to 0.61)

    aPHQ-9: Patient Health Questionnaire-9.

    bP≤.01.

    cPDQ-D: Perceived Difficulties Questionnaire—Depression.

    dUCLA-LS: University of California Los Angeles Loneliness Scale.

    eP≤.05.

    fSWM: spatial working memory.

    gRVP: rapid visual information processing.

    Activity and Heart Rate

    Descriptive statistics for step counts and heart rate are presented in Table 2. A moderate correlation was seen between step counts registered on the 2 devices (rho=0.61; 95% CI 0.57-0.65; P<.001), but there were also instances of marked discrepancy (Figure 3). Overall, the Apple Watch provided a higher step count estimate than the iPhone. Measurement issues were noted for heart rate using the Apple Watch, with individual heart rates registered including a minimum of 22 beats per minute, which was not biologically plausible.

    ‎
    Figure 3. Scatter plot of mean daily step count as measured by the Apple Watch vs. the iPhone, and reference line for perfect agreement between devices.

    Principal Findings

    This study demonstrated the feasibility of daily assessments of cognition and mood in mild-to-moderate MDD. The study spanned 6 weeks, corresponding to the time during which response to antidepressant pharmacotherapy efficacy would expect to be demonstrated, indicating that high levels of adherence can be achieved and retained over this time frame.

    Exploratory analyses examined the relationship between high-frequency mood and cognitive assessment and validated full-length cognitive assessments and questionnaires. These analyses aimed to establish the degree to which brief frequent assessments capture similar information to validated cognitive assessments and rating scales. Daily mood assessments showed moderate to strong correlations with validated self-report questionnaires of depression, cognitive problems, and loneliness. Correlations were highest for the PHQ-9, a scale designed as both a diagnostic instrument and a severity measure [34], which also showed the highest item overlap with high-frequency assessments. Daily n-back performance correlated moderately with performance on standardized tests of working memory and sustained attention. Findings support the concurrent validity of the measures examined during daily assessments.

    Adherence

    Adherence, defined as engaging with cognitive and mood assessments at least once daily, was very high (95%-96%), did not deteriorate over time, and was not predicted by depressive symptoms or cognitive function at study onset. These adherence rates, as well as the overall rate of responding to high-frequency assessments in the current study (≈87% for all possible cognitive assessments), are in keeping with previous compliance rates reported in high-frequency assessments in psychopharmacology, around 50% to 90% [45]. However, it is notable that although this study was significantly longer in duration than most previous high-frequency assessment studies, spanning 6 weeks rather than the typical 1- to 2-week duration, the daily frequency of assessment was lower, with most other studies typically sampling 5 to 10 times per day [45]. Previous studies in patients with mood disorders have shown good overall feasibility and acceptability of high-frequency assessments, although there is likely to be an interaction between protocol burden and burden of illness [46]. The brevity of the current protocol in conjunction with the proximity to wearable assessments may have helped to support the high levels of compliance seen here.

    Participants reported that completing assessments was easier when study sessions fit into their daily routines, and that periods of high and low mood affected their motivation to complete assessments. Adherence was also affected by technical problems for some participants, and by forgetting to wear the Apple Watch because of low mood or bereavement. Study center support and reminders during nonadherent periods provided a framework to enable participants to maintain a high level of engagement with the study.

    Change Over Time

    Participants’ performance on the n-back improved over time. Overall, mood symptoms showed a modest concurrent improvement, albeit with great heterogeneity in the trajectories observed over the assessment period. Participants were stabilized on monotherapy at the time of assessment, and many had started their current treatment many months before study participation (9.9 months on average). Improvements on the n-back, therefore, likely reflect the influence of practice effects and task specialization. Participants reported continued improvement in task performance as a motivator for engagement. This finding is supported by studies exploring gamification of tasks, where the use of game design elements (eg, points and scoreboards) can improve motivation [47,48].

    Importantly, very few participants reached and maintained ceiling levels of performance on the n-back. The symbols presented were designed to be hard to name, and each testing occasion drew 9 items from a stimulus pool of 227 items. Almost all participants felt that the task was challenging yet achievable. Attainability encouraged them to set personal goals to improve or maintain their scores, indicating that striking a balance between difficulty and attainability can promote engagement [49].

    Individual learning rates for each participant were reflected in their n-back slope, which did not correlate significantly with either CANTAB cognitive test measures or self-reported mood. This suggests that the capacity to improve performance is not directly affected by either depressive symptoms or cognitive impairment, consistent with research in a previous study showing that practice effects in cognitive tasks were not moderated by depressive symptomatology [50].

    Association Between Measures

    The n-back paradigm is commonly used alongside functional neuroimaging, where it activates a network of frontoparietal areas [51]. Research suggests that n-back is not simply a measure of working memory capacity but depends on functions such as updating, inhibition, and attention [52]. Consistent with this suggestion, n-back mean and intercept correlated with full-length CANTAB cognitive tests of attention and working memory, supporting the use of n-back performance as a sensitive but nonspecific marker of cognitive function.

    The trajectory of moods reported by patients during the course of the study was highly heterogeneous, showing no clear relationship with change in cognitive performance (Figure 2). However, we observed a significant association between aggregate daily mood measures with cognitive measures from CANTAB and n-back task performance (mean and intercept).

    Relationship With Full-Length Assessments

    The relationship between self-report questionnaires and high-frequency assessments of symptoms has been examined in a number of clinical conditions. Although in some cases the correlations are good [53], there can be a mismatch, with questionnaires relying on retrospective recall tending to overstate the severity and frequency of symptoms [54]. Retrospective recall shows distortion in favor of more salient or unique events at the expense of the more mundane [54], and depression is associated with negative biases in recollection during periods of low mood [55]. High-frequency assessment may be particularly useful in patients with MDD for ensuring accurate recording of the course of their illness and treatment response.

    In this study, correlations between daily measures and validated self-report questionnaires were moderate to high. Discrepancies between objectively and subjectively assessed cognitive function have been reported before, with the latter being affected by depressed mood [15,24,25]. Our results confirm this association. PDQ-D scores were correlated with daily mood assessments but not with cognitive performance, indicating that self-reported cognitive function cannot substitute for objective assessments.

    Limitations

    As our study focused on patients with mild-to-moderate MDD who volunteered for participation, it is unclear whether results would generalize to patients with different severity or to those who are less motivated. In addition, assessment using a small touch screen may not be feasible for patients with visual impairments or those requiring a larger typeface.

    Step counts collected via the Apple Watch and the iPhone were discrepant, which could be accounted for by differences in wearing patterns but undermines the reliability of activity data from either device. Measurement issues with heart rate data may reflect that the equipment was not of medical grade, or occasions when the Apple Watch was not fitted sufficiently tightly for reliable measures to be obtained. Although the wearable nature and ease of use of the technology allow for data to be collected over longer periods of time, our findings indicate that caution is required when this equipment is used to examine heart rate in scientific research. Variable accuracy for wrist-worn heart rate monitors, including the Apple Watch, compared with electrocardiogram measurement has also been noted previously in brief comparisons of bouts of exercise [56].

    Conclusions

    This study supports the feasibility and validity of high-frequency assessment on wearable devices to assess cognitive function and mood in patients with MDD. The study spanned 6 weeks, indicating that high levels of adherence can be achieved and retained over this time frame. Our study suggests that these methods can be used to monitor cognitive function and mood symptoms after the initiation of treatment for depression.

    Acknowledgments

    These data were presented in poster form at the CNS Summit in 2017. This work was sponsored and funded by Takeda Pharmaceuticals. The authors wish to thank Jennifer Schuster, BS, for her contributions to the project. All authors were involved in generating the study conception and design, analyzing and interpreting the data, and critically reviewing and revising the manuscript. All authors have approved this final manuscript for publication.

    Conflicts of Interest

    FC, NT, CS, and JHB are employees of Cambridge Cognition. TvS, EG, BF, and JK are employees of Ctrl Group. MM and JS are employees of Takeda Pharmaceuticals.

    Multimedia Appendix 1

    Discussion guides for semistructured interviews at study onset and end.

    PDF File (Adobe PDF File), 309 KB
    Multimedia Appendix 2

    Periods of adherence for cognition, mood, and activity assessment.

    PPTX File , 1552 KB
    1. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJL, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. Nov 2013;10(11):e1001547. [FREE Full text] [CrossRef] [Medline]
    2. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. Sep 8, 2007;370(9590):851-858. [CrossRef] [Medline]
    3. American Psychiatric Association. DSM-5 Diagnostic And Statistical Manual Of Mental Disorders. Fifth Edition. Washington, DC. American Psychiatric Publishing; 2013.
    4. Kaser M, Zaman R, Sahakian BJ. Cognition as a treatment target in depression. Psychol Med. Apr 2017;47(6):987-989. [CrossRef] [Medline]
    5. Hammar A, Ardal G. Cognitive functioning in major depression-a summary. Front Hum Neurosci. 2009;3:26. [FREE Full text] [CrossRef] [Medline]
    6. Ahern E, Semkovska M. Cognitive functioning in the first-episode of major depressive disorder: A systematic review and meta-analysis. Neuropsychology. Jan 2017;31(1):52-72. [CrossRef] [Medline]
    7. McClintock SM, Husain MM, Greer TL, Cullum CM. Association between depression severity and neurocognitive function in major depressive disorder: a review and synthesis. Neuropsychology. Jan 2010;24(1):9-34. [CrossRef] [Medline]
    8. Lee RS, Hermens DF, Porter MA, Redoblado-Hodge MA. A meta-analysis of cognitive deficits in first-episode Major Depressive Disorder. J Affect Disord. Oct 2012;140(2):113-124. [CrossRef] [Medline]
    9. Hasselbalch BJ, Knorr U, Kessing LV. Cognitive impairment in the remitted state of unipolar depressive disorder: a systematic review. J Affect Disord. Nov 2011;134(1-3):20-31. [CrossRef] [Medline]
    10. Rock PL, Roiser JP, Riedel WJ, Blackwell AD. Cognitive impairment in depression: a systematic review and meta-analysis. Psychol Med. Jul 2014;44(10):2029-2040. [CrossRef] [Medline]
    11. Bora E, Harrison BJ, Yücel M, Pantelis C. Cognitive impairment in euthymic major depressive disorder: a meta-analysis. Psychol Med. Oct 2013;43(10):2017-2026. [CrossRef] [Medline]
    12. Conradi HJ, Ormel J, de Jonge P. Presence of individual (residual) symptoms during depressive episodes and periods of remission: a 3-year prospective study. Psychol Med. Jun 2011;41(6):1165-1174. [CrossRef] [Medline]
    13. Basso MR, Bornstein RA. Relative memory deficits in recurrent versus first-episode major depression on a word-list learning task. Neuropsychology. Oct 1999;13(4):557-563. [CrossRef] [Medline]
    14. Hollon SD, Shelton RC, Wisniewski S, Warden D, Biggs MM, Friedman ES, et al. Presenting characteristics of depressed outpatients as a function of recurrence: preliminary findings from the STAR*D clinical trial. J Psychiatr Res. Feb 2006;40(1):59-69. [FREE Full text] [CrossRef] [Medline]
    15. Naismith SL, Longley WA, Scott EM, Hickie IB. Disability in major depression related to self-rated and objectively-measured cognitive deficits: a preliminary study. BMC Psychiatry. Jul 17, 2007;7:32. [FREE Full text] [CrossRef] [Medline]
    16. McCall WV, Dunn AG. Cognitive deficits are associated with functional impairment in severely depressed patients. Psychiatry Res. Dec 1, 2003;121(2):179-184. [CrossRef] [Medline]
    17. Gupta M, Holshausen K, Best MW, Jokic R, Milev R, Bernard T, et al. Relationships among neurocognition, symptoms, and functioning in treatment-resistant depression. Arch Clin Neuropsychol. May 2013;28(3):272-281. [CrossRef] [Medline]
    18. Woo YS, Rosenblat JD, Kakar R, Bahk W, McIntyre RS. Cognitive deficits as a mediator of poor occupational function in remitted major depressive disorder patients. Clin Psychopharmacol Neurosci. Feb 29, 2016;14(1):1-16. [FREE Full text] [CrossRef] [Medline]
    19. Baune BT, Miller R, McAfoose J, Johnson M, Quirk F, Mitchell D. The role of cognitive impairment in general functioning in major depression. Psychiatry Res. Apr 30, 2010;176(2-3):183-189. [CrossRef] [Medline]
    20. Evans VC, Chan SS, Iverson GL, Bond DJ, Yatham LN, Lam RW. Systematic review of neurocognition and occupational functioning in major depressive disorder. Neuropsychiatry. Feb 2013;3(1):97-105. [CrossRef]
    21. Evans VC, Iverson GL, Yatham LN, Lam RW. The relationship between neurocognitive and psychosocial functioning in major depressive disorder: a systematic review. J Clin Psychiatry. Dec 2014;75(12):1359-1370. [CrossRef] [Medline]
    22. Buist-Bouwman MA, Ormel J, de Graaf R, de Jonge P, van Sonderen E, Alonso J, et al. ESEMeD/MHEDEA 2000 investigators. Mediators of the association between depression and role functioning. Acta Psychiatr Scand. Dec 2008;118(6):451-458. [FREE Full text] [CrossRef] [Medline]
    23. Lee RS, Hermens DF, Naismith SL, Lagopoulos J, Jones A, Scott J, et al. Neuropsychological and functional outcomes in recent-onset major depression, bipolar disorder and schizophrenia-spectrum disorders: a longitudinal cohort study. Transl Psychiatry. Apr 28, 2015;5:e555. [FREE Full text] [CrossRef] [Medline]
    24. Popkin SJ, Gallagher D, Thompson LW, Moore M. Memory complaint and performance in normal and depressed older adults. Exp Aging Res. 1982;8(3-4):141-145. [CrossRef] [Medline]
    25. Antikainen R, Hänninen T, Honkalampi K, Hintikka J, Koivumaa-Honkanen H, Tanskanen A, et al. Mood improvement reduces memory complaints in depressed patients. Eur Arch Psychiatry Clin Neurosci. 2001;251(1):6-11. [CrossRef] [Medline]
    26. Marzano L, Bardill A, Fields B, Herd K, Veale D, Grey N, et al. The application of mHealth to mental health: opportunities and challenges. Lancet Psychiatry. Oct 2015;2(10):942-948. [CrossRef] [Medline]
    27. Kim J, Lim S, Min YH, Shin Y, Lee B, Sohn G, et al. Depression screening using daily mental-health ratings from a smartphone application for breast cancer patients. J Med Internet Res. Aug 4, 2016;18(8):e216. [FREE Full text] [CrossRef] [Medline]
    28. Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M, et al. Utilizing a personal smartphone custom app to assess the patient health questionnaire-9 (PHQ-9) depressive symptoms in patients with major depressive disorder. JMIR Ment Health. 2015;2(1):e8. [FREE Full text] [CrossRef] [Medline]
    29. Tsanas A, Saunders KE, Bilderbeck AC, Palmius N, Osipov M, Clifford GD, et al. Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder. J Affect Disord. Nov 15, 2016;205:225-233. [FREE Full text] [CrossRef] [Medline]
    30. Anguera JA, Jordan JT, Castaneda D, Gazzaley A, Areán PA. Conducting a fully mobile and randomised clinical trial for depression: access, engagement and expense. BMJ Innov. Jan 2016;2(1):14-21. [FREE Full text] [CrossRef] [Medline]
    31. Clinical Trials. URL: https://www.clinicaltrials.gov/ct2/show/NCT03067506 [accessed 2019-03-20]
    32. Clinical Trials. 2017. URL: https://www.clinicaltrials.gov/ProvidedDocs/06/NCT03067506/SAP_001.pdf [accessed 2019-03-20]
    33. Billingham SA, Whitehead AL, Julious SA. An audit of sample sizes for pilot and feasibility trials being undertaken in the United Kingdom registered in the United Kingdom Clinical Research Network database. BMC Med Res Methodol. Aug 20, 2013;13:104. [FREE Full text] [CrossRef] [Medline]
    34. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. Sep 2001;16(9):606-613. [FREE Full text] [CrossRef] [Medline]
    35. Snyder HR. Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: a meta-analysis and review. Psychol Bull. Jan 2013;139(1):81-132. [FREE Full text] [CrossRef] [Medline]
    36. Löwe B, Kroenke K, Gräfe K. Detecting and monitoring depression with a two-item questionnaire (PHQ-2). J Psychosom Res. Feb 2005;58(2):163-171. [CrossRef] [Medline]
    37. Fehnel SE, Forsyth BH, DiBenedetti DB, Danchenko N, François C, Brevig T. Patient-centered assessment of cognitive symptoms of depression. CNS Spectr. Feb 2016;21(1):43-52. [CrossRef] [Medline]
    38. Lam RW, Lamy F, Danchenko N, Yarlas A, White MK, Rive B, et al. Psychometric validation of the Perceived Deficits Questionnaire-Depression (PDQ-D) instrument in US and UK respondents with major depressive disorder. Neuropsychiatr Dis Treat. 2018;14:2861-2877. [FREE Full text] [CrossRef] [Medline]
    39. Owen AM, Downes JJ, Sahakian BJ, Polkey CE, Robbins TW. Planning and spatial working memory following frontal lobe lesions in man. Neuropsychologia. 1990;28(10):1021-1034. [CrossRef] [Medline]
    40. Sahakian B, Jones G, Levy R, Gray J, Warburton D. The effects of nicotine on attention, information processing, and short-term memory in patients with dementia of the Alzheimer type. Br J Psychiatry. Jun 1989;154:797-800. [CrossRef] [Medline]
    41. Russell D, Peplau LA, Cutrona CE. The revised UCLA Loneliness Scale: concurrent and discriminant validity evidence. J Pers Soc Psychol. Sep 1980;39(3):472-480. [CrossRef] [Medline]
    42. Moy ML, Weston NA, Wilson EJ, Hess ML, Richardson CR. A pilot study of an internet walking program and pedometer in COPD. Respir Med. Sep 2012;106(9):1342-1350. [FREE Full text] [CrossRef] [Medline]
    43. Richardson CR, Buis LR, Janney AW, Goodrich DE, Sen A, Hess ML, et al. An online community improves adherence in an internet-mediated walking program. Part 1: results of a randomized controlled trial. J Med Internet Res. Dec 17, 2010;12(4):e71. [FREE Full text] [CrossRef] [Medline]
    44. Revelle WR, Wilt J. Analyzing dynamic data: A tutorial. Pers Individ Dif. 2019;136:38-51. [CrossRef]
    45. Bos FM, Schoevers RA, aan het Rot M. Experience sampling and ecological momentary assessment studies in psychopharmacology: a systematic review. Eur Neuropsychopharmacol. Nov 2015;25(11):1853-1864. [CrossRef] [Medline]
    46. Wenze SJ, Miller IW. Use of ecological momentary assessment in mood disorders research. Clin Psychol Rev. Aug 2010;30(6):794-804. [CrossRef] [Medline]
    47. Landers RN, Bauer KN, Callan RC. Gamification of task performance with leaderboards: a goal setting experiment. Comput Human Behav. 2017;71:508-515. [CrossRef]
    48. Hamari J, Koivisto J, Sarsa H. Does Gamification Work? — A Literature Review of Empirical Studies on Gamification. In: Proceedings of the 2014 47th Hawaii International Conference on System Sciences. 2014. Presented at: HICSS'14; January 6-9, 2014:3025-3034; Hawaii. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6758978 [CrossRef]
    49. Clarke SP, Crowe TP, Oades LG, Deane FP. Do goal-setting interventions improve the quality of goals in mental health services? Psychiatr Rehabil J. 2009;32(4):292-299. [CrossRef] [Medline]
    50. Duff K, Callister C, Dennett K, Tometich D. Practice effects: a unique cognitive variable. Clin Neuropsychol. 2012;26(7):1117-1127. [CrossRef] [Medline]
    51. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp. May 2005;25(1):46-59. [CrossRef] [Medline]
    52. Espeland MA, Katula JA, Rushing J, Kramer AF, Jennings JM, Sink KM, et al. LIFE Study Group. Performance of a computer-based assessment of cognitive function measures in two cohorts of seniors. Int J Geriatr Psychiatry. Dec 2013;28(12):1239-1250. [FREE Full text] [CrossRef] [Medline]
    53. Shrier LA, Shih M, Beardslee WR. Affect and sexual behavior in adolescents: a review of the literature and comparison of momentary sampling with diary and retrospective self-report methods of measurement. Pediatrics. May 2005;115(5):e573-e581. [FREE Full text] [CrossRef] [Medline]
    54. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1-32. [CrossRef] [Medline]
    55. Clark DM, Teasdale JD. Diurnal variation in clinical depression and accessibility of memories of positive and negative experiences. J Abnorm Psychol. Apr 1982;91(2):87-95. [CrossRef] [Medline]
    56. Wang R, Blackburn G, Desai M, Phelan D, Gillinov L, Houghtaling P, et al. Accuracy of wrist-worn heart rate monitors. JAMA Cardiol. Jan 1, 2017;2(1):104-106. [CrossRef] [Medline]

    ‎
    CANTAB: Cambridge Automated Neuropsychological Test Battery
    MDD: major depressive disorder
    PDQ-D: Perceived Difficulties Questionnaire in Depression
    PHQ-9: Patient Health Questionnaire-9
    RVP: rapid visual information processing
    SWM: spatial working memory
    UCLA-LS: University of California Los Angeles Loneliness Scale

    Edited by J Torous; submitted 20.11.18; peer-reviewed by H Riese, P Santangelo; comments to author 02.01.19; revised version received 03.07.19; accepted 07.08.19; published 18.11.19.

    Copyright

    ©Francesca Cormack, Maggie McCue, Nick Taptiklis, Caroline Skirrow, Emilie Glazer, Elli Panagopoulos, Tempest A van Schaik, Ben Fehnert, James King, Jennifer H Barnett. Originally published in JMIR Mental Health (http://mental.jmir.org), 18.11.2019.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.