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Background
Within the past decade, digital coaching programs (DCPs) have emerged as an evidence-based modality to improve mental well-being and emotional intelligence (EI), although there is limited evidence in real-world contexts.
Objective
This pragmatic retrospective cohort study aims to determine the preliminary effectiveness of a DCP in improving mental well-being and EI within a real-world context. We hypothesized that there would be a significant increase in mental well-being and EI.
Methods
This study included 588 people who voluntarily enrolled in an 8-week, blended care DCP offered through their employers from October 2021 to August 2024. The DCP included routine check-ins and consultations with certified coaches. Participants completed the World Health Organization-Five Well-Being Index (WHO-5) at baseline and then weekly until the end of the program, as well as the Brief Emotional Intelligence Scale-10 (BEIS-10) at baseline and the end of the program. Multivariable linear mixed models examined changes in WHO-5 (biweekly) and BEIS-10 (pre-post) scores, adjusting for age, gender, program engagement, and program completion. Multivariable logistic regression models evaluated correlates of clinically meaningful improvements on the WHO-5 (ie, at least a 10-point improvement). We calculated a reliable change index (RCI) for the BEIS-10 and the proportion of participants meeting the RCI criterion from baseline to end of treatment.
Results
In multivariate linear mixed models adjusting for demographics and program characteristics, we observed a significant increase in WHO-5 scores (baseline x¯=45.6; week 8 x¯=66.3; Cohen’s d=1.98; P<.001). Over half of the sample (55.4%) experienced a clinically meaningful improvement on the WHO-5. Multivariable logistic regression found that higher engagement was associated with an increased odds of a clinically meaningful improvement on the WHO-5 (adjusted odds ratio [aOR] 1.002, 95% CI 1.001‐1.003), while program noncompletion (aOR 0.27, 95% CI 0.15‐0.50) and higher baseline well-being (aOR 0.91, 95% CI 0.89‐0.92) were associated with reduced odds. BEIS-10 scores also significantly increased from baseline to the end of the program after adjusting for relevant correlates (baseline x¯=37.6; week 8 x¯=41.2; Cohen’s d=1.32; P<.001). The estimated RCI on the BEIS-10 was approximately 5, with 19.7% experiencing a meaningful improvement.
Conclusions
These results demonstrate that DCPs can be a viable option for individuals looking to improve their mental well-being. Additional efforts should focus on establishing reliable change metrics for EI measures. Studies using hybrid effectiveness-implementation trial designs are now needed to further evaluate the real-world effectiveness of this program.
mental health servicesimplementation scienceepidemiologydigital healthquality of lifeemotional intelligenceIntroduction
The United States is currently experiencing an unprecedented mental health crisis that was exacerbated by the acute phase of the COVID-19 pandemic [1,2]. US federal surveillance systems estimate that over 40% of adults are experiencing clinically significant levels of anxiety and depression symptoms in the past week [2]. The pervasiveness of mental health problems at the population level contributes to significant functional impairments, reduced workplace productivity, and disability [3-5], even among those experiencing serious psychological distress (SPD) who do not necessarily meet a clinical threshold for a formal diagnosis [6]. Moreover, research demonstrates that anxiety, depression, and SPD are highly comorbid with unhealthy behaviors and a variety of medical conditions [7,8]. As such, comprehensive prevention and treatment efforts are urgently needed to address population-level mental health problems.
Unfortunately, the US mental health care system has been experiencing significant structural barriers for over a decade [9]. The combination of stigma associated with mental health problems, inadequate reimbursement by health insurers, and a shortage of mental health providers has resulted in mental health being inadequately addressed [9,10]. It is estimated that only two-thirds of adults with depression receive any care each year, with only a small fraction of those receiving care getting high-quality, evidence-based treatment [11,12]. Even before the onset of the COVID-19 pandemic, long wait times for mental health care were common. This has been exacerbated by widespread shortages in the mental and behavioral health workforce [13]. Given the magnification of these structural barriers due to the pandemic, there remains an urgent need for new mental health care models.
Digital mental health interventions (DMHI) have recently emerged as a promising model of mental health care. DMHIs have been shown to address a number of structural barriers to care, including access, cost, and stigma [14,15]. Recent meta-analyses have found that DMHIs incorporating evidence-based modalities like cognitive behavioral therapy (CBT), mindfulness, and behavioral activation are as effective as traditional CBT in reducing depression and anxiety symptoms [16]. Because some DMHIs focus more on the treatment of clinically significant mental health symptoms rather than promoting mental well-being, they may not be the best fit for people experiencing SPD and other subclinical problems [17,18].
Recently, digital coaching programs (DCP) have emerged as viable interventions grounded in behavioral science principles to help people with subclinical mental health problems improve their mental well-being, reduce stress, and prevent the onset of mental disorders [19]. DCPs function to help people improve their well-being by building skills that increase emotional intelligence (EI), resilience, and self-actualization. Yet, studies evaluating DCPs and other coaching programs frequently lack an operational definition of what techniques were included in the interventions [20,21]. Moreover, information about the effectiveness of DCPs in real-world settings remains limited [22]. Given these gaps in knowledge and the importance of early intervention to prevent the onset of mental health problems, there is an urgent need for studies that investigate how DCPs may improve mental health and well-being.
This study evaluated the preliminary effectiveness of an 8-week DCP implemented in a real-world setting to improve mental well-being and increase EI. The primary hypothesis was that participation in the DCP would be associated with increased mental well-being and EI after adjusting for demographic and program-related factors. Exploratory analyses calculated reliable change scores for the EI measure and examined factors associated with meaningful improvements in EI.
MethodsStudy Sample and Procedures
Program participants were enrolled in a DCP through a provider of employer-sponsored mental health care (Meru Health) between October 1, 2021, and August 19, 2024. Participants were offered the DCP through their employer. During this period, there were 45 employers offering the DCP to approximately 100,000 people. Interested participants could schedule an initial screening assessment through a digital platform. As part of this process, potential participants completed the Patient Health Questionnaire-2 and Generalized Anxiety Disorder-2 to report on depression and anxiety symptoms, respectively. To be eligible, all participants were required to have a mobile phone, be at least 18 years old, and have scores of less than 3 on the Patient Health Questionnaire-2 and Generalized Anxiety Disorder-2 [23,24]. Those reporting current or recent suicidality, substance use disorders, eating disorders, or psychotic disorders were ineligible for the program. After the screening assessment, eligible participants participated in an intake video call with a certified coach to develop a personalized care plan. Those not meeting program eligibility requirements were provided information about the 12-week treatment program that is designed for clinically significant levels of depression and anxiety symptoms.
Ethical Considerations
This study was deemed exempt by the Pearl Institutional Review Board (21-MERU-114) for secondary analyses of previously collected and deidentified data from a program evaluation. As part of the DCP, participants consented to have their anonymized data used for evaluation and quality improvement purposes. We conducted a retrospective analysis with program data after the DCP took place. All data and protected health information were stored in a cloud storage environment compliant with the Health Insurance Portability and Accountability Act. All data were encrypted in transit, end-to-end, and at rest. Deidentified data from this study are not available in a public archive or for distribution to ensure privacy of the program participants.
Program Description
The Meru Health DCP is a blended care intervention designed to improve well-being among people experiencing subclinical problems through a mobile phone app over an 8-week period, including face-to-face sessions, skills-building content, regular chat engagement, and digital workshops. The intervention is grounded in Daniel Goleman’s EI Framework, which posits that mental well-being and functioning may be increased by building competencies in 4 core domains of emotional intelligence: self-awareness, self-regulation, social awareness and empathy, and relationship management skills [25,26]. Each week focuses on building competencies in the 4 core EI domains, with the first 4 weeks focusing on individual-level skills and the latter 4 weeks focusing on relational skills (Multimedia Appendix 1). Participants receive a total of 32 learning videos, 40 CBT exercises, and 24 meditations spread evenly across the 8-week program. In rare situations, a participant will be referred to the therapist-supported program if they determine with their coach that therapy will be more appropriate.
The certified coaches provided personalized, daily chat support to encourage and motivate participants. Coaches are certified (Emotional Intelligence Coaching Certification, International Coaching Federation, National Board Certified Health & Wellness Coach, and Certified International Health Coach) and have training in EI, CBT, motivational interviewing, and mindfulness-based stress reduction. Participants were able to communicate with coaches via a chat support function and regular face-to-face sessions. After their intake session, on average, participants spent about 45 minutes per week on the app and engaging with their coach on various activities. Coaches used a secure, web-based dashboard developed by Meru Health to communicate with participants and monitor participant engagement metrics.
Measures
The primary outcome was mental well-being, which was measured with the World Health Organization-5 Well-Being Index (WHO-5). The WHO-5 is a psychometrically validated screening instrument that assesses mental well-being in the past 2 weeks [27]. There are a total of 5 items that are scored on a 0‐5 Likert scale, with raw scores ranging from 0 to 25. A composite score ranging from 0 to 100 is calculated by multiplying the raw score by 4. Higher scores indicate better mental well-being. A score of 50 or less indicates poor mental well-being and suggests further clinical investigation. Participants completed the WHO-5 on a biweekly basis during the DCP. The secondary outcome was EI and measured with the Brief Emotional Intelligence Scale-10 (BEIS-10), which has established validity and reliability [28]. There are a total of 10 items scored on a 1‐5 Likert scale, with scores ranging from 10 to 50. The BEIS-10 captures dimensions of self-appraisal of emotions, appraisal of others’ emotions, self-regulation of emotions, regulation of others’ emotions, and use of emotions. Higher scores indicate better EI. Participants completed the BEIS-10 at baseline and week 8.
Program-related measures included engagement and completion. Engagement was defined as the total number of minutes participants used the app during the program. Completion was defined as the proportion of participants who actively engaged in the program for at least 4 weeks [29]. Demographics included age and gender identity (male and female). The version of the DCP (versions 1 and 2) was also included as a covariate to adjust for any potential effects of intervention modifications.
Statistical Analyses
We plotted the average level of engagement during the intervention and fit a mixed-effects model using the lme4 package to examine whether there was a significant change during the intervention [30]. A mixed-effects model was also used to explore significant changes in BEIS-10 scores pre- and post-intervention. Both models used age, gender, engagement, program completion, and program version as covariates. Cohen’s d was used to assess effect sizes in pre- and post-program WHO-5 and BEIS-10 scores. Effect sizes of 0.2, 0.5, and 0.8 were considered small, medium, and large effect sizes, respectively [31].
To determine clinically significant improvements in mental well-being and emotional intelligence [32], we calculated the proportion of participants experiencing at least a 10-point improvement on the WHO-5 from baseline to the end of the intervention [33]. Because there is no documented threshold for clinically significant improvement on the BEIS-10, we calculated a reliable change index (RCI) for the BEIS-10 and the proportion of participants meeting the RCI criterion from baseline to end of treatment [34]. We also fit a multivariable logistic regression model to determine the association between clinically significant improvement in mental well-being with program-related factors and demographics while controlling for baseline well-being, EI, and the program version.
All statistical analyses were conducted with RStudio (version 1.3.959). Statistical significance was defined as a 2-tailed P value≤ .05. Our analytical approach used intent-to-treat analyses whereby all participants who enrolled in the program were included regardless of engagement or completion [35]. Full information maximum likelihood was used to account for missing data within the mixed-effects models, and multiple imputation with chained equations was used to account for missingness in the modeling procedures for clinically significant improvements [36,37].
Results
Overall, there were 588 people who enrolled in the Meru Health DCP between January 1, 2022, and August 19, 2024. As illustrated in Table 1, the majority of participants were female (77%), and the average age was approximately 42 (SD 11) years. The average WHO-5 score at baseline was 43 (SD 18), and the average BEIS-10 score was 37.5 (SD 4.1). A large majority of the sample (77%) participated in the DCP for at least 4 out of 8 weeks, and the total number of minutes active during the DCP was 367 (SD 343). The estimated marginal mean (EMM) number of minutes participants were active in the DCP app was 49.15 (95% CI 44.1‐54.2) during the first week, then decreased in a linear fashion to 7.67 (95% CI 2.61‐12.7) minutes at week 8 (Figure 1). No significant differences between program versions were found in the demographic characteristics and engagement.
Characteristics of participants in an 8-week digital coaching program (N=588).
Characteristic
Overall
V1a program (n=96)
V2 program (n=492)
P valueb
Completion, n/N (%)
.049
Completer
450/588 (77)
66/96 (69)
384/492 (78)
Dropout
138/588 (23)
30/96 (31)
108/492 (22)
Age (years), mean (SD)
42 (11)
42 (11)
42 (11)
.70
Gender, n/N (%)
.50
Female
451/588 (77)
76/96 (79)
375/492 (76)
Male
137/588 (23)
20/96 (21)
117/492 (24)
Total minutes active, mean (SD)
367 (343)
212 (178)
398 (359)
<.001
Baseline WHO-5c, mean (SD)
43 (18)
49 (16)
42 (18)
<.001
Baseline BEIS-10d, mean (SD)
37.5 (4.1)
38.1 (4.1)
37.4 (4.1)
.11
aV1 and V2 refer to the 2 versions of the digital coaching program.
bPearson chi-square test and Wilcoxon rank sum test.
cWHO-5: World Health Organization-5 Well-Being Index.
dBEIS-10: Brief Emotional Intelligence Scale-10.
Patterns of engagement during an 8-week digital coaching program (N=588).
Mental Well-Being Outcomes
We conducted mixed-effects modeling to determine WHO-5 outcomes across the coaching program. Figure 2 and Table 2 depict the results of the linear mixed-effects model to determine changes in WHO-5 scores during the DCP. At baseline, the EMM for the WHO-5 was 45.6 (95% CI 43.2‐48.0), which met the established clinical threshold of 50 or less for poor mental well-being requiring clinical attention. At the end of the program, a significant increase in the WHO-5 score was observed (EMM 66.3, 95% CI 63.6‐69.0). This represented a large improvement (Cohen’s d=1.98, 95% CI 1.81‐2.15) that brought participants’ average WHO-5 scores outside of the clinical threshold of 50 or less. There were 55.4% who experienced a clinically meaningful improvement on the WHO-5 (ie, at least a 10-point improvement). Figure 3 shows the results from the multivariable logistic regression model examining correlates of clinically meaningful improvements in mental well-being. Higher engagement was associated with an increased odds of a clinically meaningful improvement (adjusted odds ratio [aOR] 1.002, 95% CI 1.001‐1.003). That is, the odds of a clinically meaningful improvement increased by 0.2% for every unit increase in engagement. Program noncompletion (aOR 0.27, 95% CI 0.15‐0.50) and higher baseline well-being (aOR 0.91, 95% CI 0.89‐0.92) were associated with reduced odds of a clinically meaningful improvement.
Change in WHO-5 scores during an 8-week digital coaching program (N=588). Error bands indicate 95% CIs. WHO-5 scores range from 0 to 100, with higher scores indicating better mental well-being. Scores of 50 or less indicate poor mental health requiring clinical attention. WHO-5: World Health Organization-Five Well-Being Index.
Multivariable mixed-effects model results for WHO-5a scores across time among participants in an 8-week digital coaching program (N=588). Reference groups are included parenthetically. For intervention weeks, the baseline assessment served as the reference group.
Predictors
WHO-5a
Estimates
Standardized beta
95% CI
Standardized 95% CI
P value
Intercept
47.35
−0.45
37.94‐56.77
−0.66 to−0.24
<.001
Week 2
7.61
0.46
5.00‐10.22
0.30‐0.62
<.001
Week 4
9.03
0.55
5.98‐12.08
0.36‐0.73
<.001
Week 6
15.45
0.93
12.10‐18.80
0.73‐1.14
<.001
Week 8
17.37
1.05
13.80‐20.94
0.84‐1.27
<.001
Age
0.02
0.01
−0.20 to 0.24
−0.13 to 0.16
.87
Male gender (female)
−0.63
−0.04
−5.94 to 4.67
−0.36 to 0.28
.82
Total minutes active
0.00
0.06
−0.01 to 0.01
−0.09 to 0.22
.43
Version 2 (version 1)
0.07
0.00
−4.93‐5.07
−0.30‐0.31
.98
aWHO-5: World Health Organization-5 Well-Being Index.
Multivariate associations with clinically significant improvement in mental well-being (N=588) Program noncompletion was defined as engaging in the program for less than 4 weeks. Reference groups are included parenthetically. Total active minutes, age, baseline WHO-5, and baseline BEIS-10 do not show visible CIs due to their extremely narrow ranges relative to other predictors. * indicates continuous variables. WHO-5: World Health Organization-Five Well-Being Index; BEIS-10: Brief Emotional Intelligence Scale-10.
Emotional Intelligence Outcomes
We also fit a separate mixed effects model with BEIS-10 scores as the outcome to determine how these scores changed from baseline to the end of treatment. Table 3 and Multimedia Appendix 2 depict the results of the model and demonstrate that from baseline to end of treatment, there was a significant increase in BEIS-10 scores. The EMM for baseline was 37.6 (95% CI 37.0‐38.1), while at the end of treatment, the EMM was 41.2 (95% CI 40.5‐41.9). This represented a large effect size (Cohen’s d=1.32, 95% CI 1.14‐1.50). Finally, we found an RCI of approximately 5 for the BEIS-10. Based on these criteria, 19.7% of individuals experienced a meaningful improvement in EI during the DCP.
Multivariable mixed-effects model results for BEIS-10a scores across time among participants in an 8-week digital coaching program (N=588). Reference groups are included parenthetically.
Predictors
BEIS-10a
Estimates
Standardized beta
95% CI
Standardized 95% CI
P value
Intercept
38.92
−0.15
36.55‐41.39
−0.36 to 0.06
<.001
Week 8 (baseline)
3.80
0.94
2.81‐4.80
0.69‐to 1.18
<.001
Age
−0.02
−0.04
−0.07 to 0.04
−0.19 to 0.11
.59
Male gender (female)
−0.85
−0.21
−2.26 to 0.56
−0.56 to 0.14
.24
Total minutes active
0.00
0.02
−0.00 to 0.00
−0.15 to 0.19
.79
Version 2 (version 1)
−0.38
−0.09
−1.71 to 0.94
−0.42 to 0.23
.59
aBEIS-10: Brief Emotional Intelligence Scale-10.
Discussion
This study investigated the preliminary effectiveness of an 8-week DCP among 588 people with subclinical mental health problems. Consistent with our primary hypothesis, we observed that participation in the DCP was associated with large increases in mental well-being as measured by the WHO-5 (Cohen’s d=1.98). Similarly, we found that participants who did not complete the program (less than 4 weeks) had 73% lower odds of experiencing a clinically meaningful improvement in mental well-being (ie, at least a 10-point improvement on the WHO-5) compared to program completers. Exploratory analyses also revealed large increases in EI during the DCP (Cohen’s d=1.32) and that nearly a fifth of participants (19.7%) experienced a meaningful improvement based upon an estimated RCI of 5 on the BEIS-10 [34].
Overall, this study demonstrates that participation in a blended care DCP was associated with improvements in mental well-being among a sample of 588 adults. The results are consistent with other research studies and support the further investigation of DCPs as viable interventions to prevent the onset of common mental health problems like depression and anxiety [38]. For example, a study evaluating a blended care cognitive behavioral program delivered via video observed large improvements in depression (Cohen’s d=1.08) and anxiety (Cohen’s d=1.33) [39], which strongly aligns with the improvements in mental well-being (Cohen’s d=1.98) and EI (Cohen’s d=1.32) found in this study. Similarly, a randomized controlled trial of 146 primary care patients found that those randomized to a self-guided DCP were more likely to experience clinically significant improvements in depression and anxiety compared to a waitlist control [40]. In this study, there were large, statistically significant increases in mental well-being during the program, although the inverse association between baseline WHO-5 scores and clinically significant improvement suggests that the program had the greatest effect on participants who started with the lowest mental well-being. Thus, our results provide additional evidence that DCPs targeting people with subclinical distress may help reduce the onset of mental disorders that cause significant disability and role impairments [4,5].
Another notable finding was the relatively higher levels of completion (77%) compared to other studies. In particular, a dose-response relationship between engagement and clinically meaningful improvements in mental well-being was observed. It is possible that the continuous involvement of trained coaches over the course of the program led to higher engagement and more positive outcomes compared to self-guided or text-based DCPs [41-43]. Direct communications and involvement of trained coaches may have facilitated a remote, continuous care approach that establishes trust with participants at program intake and fosters a therapeutic alliance over the duration of the program [44,45]. This approach may have influenced the higher rate of completion observed in this study compared to other DCPs [39,46], although additional studies are needed to better evaluate how interactions with coaches (eg, frequency of coach interactions, average amount of coach time per participant) may impact adherence and program outcomes [45].
Several limitations are acknowledged. The lack of an experimental design precludes inferences about the DCP’s effectiveness in improving mental well-being and EI compared to other forms of care. Without a control group, our results are generalizable to other observational studies that cannot fully rule out natural remission of the documented changes in well-being and EI. Because participants voluntarily agreed to participate in an employer-sponsored program, we cannot rule out motivation to improve mental health and EI as an alternative explanation for the results. While self-selection may have impacted our results, the dose-response effect we observed in a real-world context is consistent with meaningful improvements in mental well-being and EI found in experimental and observational studies [18,20,39,46]. Similarly, the modest sample size represents another limitation to the study’s external validity, although our multivariate analyses were robust and highly comparable to other studies. In addition, this DCP only captured age and gender to reduce participant burden during intake, limiting inferences about race, ethnicity, and other key demographic categories. Finally, our approach to addressing missing data raises the potential for methodological artifacts. These methods assume that data for participants with missing responses on the WHO-5 and BEIS-10 may be predicted with data from those who provided responses. Because those with poorer outcomes are less likely to respond, outcomes imputed based upon the observed characteristics of participants may include inaccurate assumptions. Nonetheless, we used an intent-to-treat framework and rigorous missing data approaches rather than performing listwise deletion and conducting completer-only analyses that can lead to selection biases and inflated effect sizes [35-37,47].
Our findings indicated that participation in an 8-week DCP was associated with large improvements in mental well-being and EI. Follow-up studies with larger samples are now needed to replicate these findings and investigate the effectiveness of the DCP in preventing common mental health disorders. Hybrid implementation-effectiveness trials are also needed to generate stronger evidence of the real-world effectiveness of DCPs [48,49]. In addition, mixed methods approaches may help elucidate mechanisms related to acceptability, satisfaction, and participant-coach interactions [50]. In sum, this study provides preliminary evidence that DCPs may be viable, evidence-based interventions for improving mental well-being at the population level.
Data Availability
The datasets generated or analyzed during this study are not available in a public archive or through request to ensure privacy of the program participants.
AP, BS, OA, and ES report employment at Meru Health during the conduct of this study. NCP reports scientific consultancy fees from Meru Health and research support from the CDC Foundation, National Institutes of Health, Abbott Laboratories, and Gilead Sciences. JP has no financial disclosures or conflicts of interest. The content is solely the responsibility of the authors and does not necessarily represent the official views of Meru Health. All decisions about the research were made by the authors and were unrestricted.
AbbreviationsaOR
adjusted odds ratio
BEIS-10
Brief Emotional Intelligence Scale-10
CBT
cognitive behavioral therapy
DCP
digital coaching program
DMHI
digital mental health intervention
EI
emotional intelligence
EMM
estimated marginal mean
RCI
reliable change index
SPD
serious psychological distress
WHO-5
World Health Organization-Five Well-Being Index
ReferencesKolaLKumarMKohrtBAFatoduTOlayemiBAAdefolarinAOStrengthening public mental health during and after the acute phase of the COVID-19 pandemicLancet20220514399103381851185210.1016/S0140-6736(22)00523-235339230VahratianABlumbergSJTerlizziEPSchillerJSSymptoms of anxiety or depressive disorder and use of mental health care among adults during the COVID-19 pandemic—United States, August 2020–February 2021MMWR Morb Mortal Wkly Rep2020701349049410.15585/mmwr.mm7013e2BeckACrainALSolbergLISeverity of depression and magnitude of productivity lossAnn Fam Med20119430531110.1370/afm.126021747101HasinDSSarvetALMeyersJLEpidemiology of adult DSM-5 major depressive disorder and Its specifiers in the United StatesJAMA Psychiatry201804175433634610.1001/jamapsychiatry.2017.460229450462HendriksSMSpijkerJLichtCMMLong-term work disability and absenteeism in anxiety and depressive disordersJ Affect Disord201506117812113010.1016/j.jad.2015.03.00425805404OlfsonMWangSWallMMarcusSCBlancoCTrends in serious psychological distress and outpatient mental health care of US AdultsJAMA Psychiatry201902176215216110.1001/jamapsychiatry.2018.355030484838BiermanASTinettiMEPrecision medicine to precision care: managing multimorbidityLancet2016123388100612721272310.1016/S0140-6736(16)32232-227924764NelsonBWPeiperNCAschbacherKForman-HoffmanVLEvidence-based therapist-supported digital mental health intervention for patients experiencing medical multimorbidity: a retrospective cohort intent-to-treat studyPsychosom Med202486654755410.1097/PSY.000000000000131938718176AlegríaMAlvarezKChengMFalgas-BagueIRecent advances on social determinants of mental health: looking fast forwardAm J Psychiatry2023071180747348210.1176/appi.ajp.2023037137392038PetersonEBuschSAchieving mental health and substance use disorder treatment parity: a quarter century of policy making and researchAnnu Rev Public Health20180413942143510.1146/annurev-publhealth-040617-01360329328871NagataJMGansonKTBoninSLPrevalence and sociodemographic correlates of unmet need for mental health counseling among adults during the COVID-19 pandemicPsychiatr Serv202202173220620910.1176/appi.ps.20210011134189929WalkerERCummingsJRHockenberryJMDrussBGInsurance status, use of mental health services, and unmet need for mental health care in the United StatesPsychiatr Serv20150666657858410.1176/appi.ps.20140024825726980EriksonCSchenkEWestergaardSSalsbergESNew behavioral health workforce database paints a stark pictureHealth Affairs Forefront202210.1377/forefront.20220829.640971ConnollySLKuhnEPossematoKTorousJDigital clinics and mobile technology implementation for mental health careCurr Psychiatry Rep20210572373810.1007/s11920-021-01254-833961135LipschitzJMVan BoxtelRTorousJDigital mental health interventions for depression: scoping review of user engagementJ Med Internet Res202210142410e3920410.2196/3920436240001GoldbergSBLamSUSimonssonOTorousJSunSMobile phone-based interventions for mental health: a systematic meta-review of 14 meta-analyses of randomized controlled trialsPLOS Digit Health202211e000000210.1371/journal.pdig.000000235224559KaryotakiEEfthimiouOMiguelCInternet-based cognitive behavioral therapy for depression: a systematic review and individual patient data network meta-analysisJAMA Psychiatry202104178436137110.1001/jamapsychiatry.2020.436433471111ReinsJABuntrockCZimmermannJEfficacy and moderators of internet-based interventions in adults with subthreshold depression: an individual participant data meta-analysis of randomized controlled trialsPsychother Psychosom20219029410610.1159/00050781932544912HrynyschynRHafnerJSchönfeldSDockweilerCDigital health interventions in prevention, relapse, and therapy of mild and moderate depression: scoping reviewJMIR Ment Health2021041684e2626810.2196/2626833861201AmmentorpJUhrenfeldtLAngelFEhrensvärdMCarlsenEBKofoedPECan life coaching improve health outcomes?—a systematic review of intervention studiesBMC Health Serv Res2013102213142810.1186/1472-6963-13-42824148189Newnham-KanasCGorczynskiPMorrowDIrwinJAnnotated bibliography of life coaching and health researchInt J Evid Based Coach Mentor20092025-07-177139103https://psycnet.apa.org/record/2009-04458-004TheeboomTBeersmaBvan VianenAEMDoes coaching work? A meta-analysis on the effects of coaching on individual level outcomes in an organizational contextJ Posit Psychol20140129111810.1080/17439760.2013.837499LöweBKroenkeKGräfeKDetecting and monitoring depression with a two-item questionnaire (PHQ-2)J Psychosom Res20050258216317110.1016/j.jpsychores.2004.09.00615820844PlummerFManeaLTrepelDMcMillanDScreening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysisGen Hosp Psychiatry201639243110.1016/j.genhosppsych.2015.11.00526719105BoyatzisREGolemanDDharUOsiriJKThrive and survive: assessing personal sustainabilityConsulting Psychology Journal: Practice and Research2021731275010.1037/cpb0000193GolemanDWhat makes a leader?Harv Bus Rev19982025-07-2576693102https://pubmed.ncbi.nlm.nih.gov/10187249/10187249ToppCWØstergaardSDSøndergaardSBechPThe WHO-5 Well-Being Index: a systematic review of the literaturePsychother Psychosom201584316717610.1159/00037658525831962DaviesKALaneAMDevonportTJScottJAValidity and reliability of a Brief Emotional Intelligence Scale (BEIS-10)J Individ Differ20100131419820810.1027/1614-0001/a000028EreksonDMLambertMJEggettDLThe relationship between session frequency and psychotherapy outcome in a naturalistic settingJ Consult Clin Psychol2015128361097110710.1037/a003977426436645BatesDMächlerMBolkerBWalkerSFitting linear mixed-effects models using lme4J Stat Softw201567114810.18637/jss.v067.i01FritzCOMorrisPERichlerJJEffect size estimates: current use, calculations, and interpretationJ Exp Psychol Gen201202141121810.1037/a002433821823805KounaliDButtonKSLewisGHow much change is enough? Evidence from a longitudinal study on depression in UK primary carePsychol Med20220752101875188210.1017/S003329172000370033138872BechPLindbergLMoellerSBThe reliable change index (RCI) of the WHO-5 in primary prevention of mental disorders. A measurement-based pilot study in positive psychiatryNord J Psychiatry20180872640440810.1080/08039488.2018.146339329656671JacobsonNSTruaxPClinical significance: a statistical approach to defining meaningful change in psychotherapy researchJ Consult Clin Psychol1991591121910.1037/0022-006X.59.1.12Ten HaveTRNormandSLTMarcusSMBrownCHLavoriPDuanNIntent-to-treat vs. non-intent-to-treat analyses under treatment non-adherence in mental health randomized trialsPsychiatr Ann200812381277278310.3928/00485713-20081201-1020717484AzurMJStuartEAFrangakisCLeafPJMultiple imputation by chained equations: what is it and how does it work?Int J Methods Psychiatr Res201103201404910.1002/mpr.32921499542MattaTHFlournoyJCByrneMLMaking an unknown unknown a known unknown: missing data in longitudinal neuroimaging studiesDev Cogn Neurosci20181033839810.1016/j.dcn.2017.10.00129129673GrantFGuilleCSenSWell-being and the risk of depression under stressPLoS One201387e6739510.1371/journal.pone.006739523840872LunguAJunJJAzarmaneshOLeykinYChenCEJBlended care-cognitive behavioral therapy for depression and anxiety in real-world settings: pragmatic retrospective studyJ Med Internet Res2020076227e1872310.2196/1872332628120GrahamAKGreeneCJKwasnyMJCoached mobile app platform for the treatment of depression and anxiety among primary care patients: a randomized clinical trialJAMA Psychiatry202009177990691410.1001/jamapsychiatry.2020.101132432695FlemingTBavinLLucassenMStasiakKHopkinsSMerrySBeyond the trial: systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxietyJ Med Internet Res2018066206e19910.2196/jmir.927529875089KaryotakiEKemmerenLRiperHIs self-guided internet-based cognitive behavioural therapy (iCBT) harmful? An individual participant data meta-analysisPsychol Med20181148152456246610.1017/S003329171800064829540243BoucherEMRaikerJSEngagement and retention in digital mental health interventions: a narrative reviewBMC Digit Health20242112110.1186/s44247-024-00105-9D’AlfonsoSLedermanRBucciSBerryKThe digital therapeutic alliance and human-computer interactionJMIR Ment Health20201229712e2189510.2196/2189533372897SchiepekGAichhornWGruberMStrunkGBachlerEAasBReal-time monitoring of psychotherapeutic processes: concept and complianceFront Psychol2016760410.3389/fpsyg.2016.0060427199837LunguABooneMSChenSYChenCEWalserRDEffectiveness of a cognitive behavioral coaching program delivered via video in real world settingsTelemed J E Health202101271475410.1089/tmj.2019.031332311301SalimAMackinnonAChristensenHGriffithsKComparison of data analysis strategies for intent-to-treat analysis in pre-test-post-test designs with substantial dropout ratesPsychiatry Res20080930160333534510.1016/j.psychres.2007.08.00518718673KesslerRCLuedtkeAPragmatic precision psychiatry—a new direction for optimizing treatment selectionJAMA Psychiatry202112178121384139010.1001/jamapsychiatry.2021.250034550327CurranGMBauerMMittmanBPyneJMStetlerCEffectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impactMed Care20120350321722610.1097/MLR.0b013e318240881222310560GouldCECarlsonCMaFForman-HoffmanVRantaKKuhnEEffects of mobile app–based intervention for depression in middle-aged and older adults: mixed methods feasibility studyJMIR Form Res2021062956e2580810.2196/2580834185000
Weekly Themes and Competencies of an 8-Week Digital Coaching Program.
Change in BEIS-10 Scores During an 8-Week Digital Coaching Program (N=588).