Published on in Vol 7, No 11 (2020): November

Preprints (earlier versions) of this paper are available at, first published .
Testing the Efficacy of a Multicomponent, Self-Guided, Smartphone-Based Meditation App: Three-Armed Randomized Controlled Trial

Testing the Efficacy of a Multicomponent, Self-Guided, Smartphone-Based Meditation App: Three-Armed Randomized Controlled Trial

Testing the Efficacy of a Multicomponent, Self-Guided, Smartphone-Based Meditation App: Three-Armed Randomized Controlled Trial

Original Paper

1Center for Healthy Minds, University of Wisconsin, Madison, WI, United States

2Department of Counseling Psychology, University of Wisconsin-Madison, Madison, WI, United States

3Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI, United States

4Healthy Minds Innovations Inc, Madison, WI, United States

5Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States

6Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States

Corresponding Author:

Simon B Goldberg, PhD

Center for Healthy Minds

University of Wisconsin

625 W Washington Ave

University of Wisconsin-Madison

Madison, WI, 53703

United States

Phone: 1 6082658986

Fax:1 6082654174


Background: A growing number of randomized controlled trials (RCTs) suggest psychological benefits associated with meditation training delivered via mobile health. However, research in this area has primarily focused on mindfulness, only one of many meditative techniques.

Objective: This study aims to evaluate the efficacy of 2 versions of a self-guided, smartphone-based meditation app—the Healthy Minds Program (HMP)—which includes training in mindfulness (Awareness), along with practices designed to cultivate positive relationships (Connection) or insight into the nature of the self (Insight).

Methods: A three-arm, fully remote RCT compared 8 weeks of one of 2 HMP conditions (Awareness+Connection and Awareness+Insight) with a waitlist control. Adults (≥18 years) without extensive previous meditation experience were eligible. The primary outcome was psychological distress (depression, anxiety, and stress). Secondary outcomes were social connection, empathy, compassion, self-reflection, insight, rumination, defusion, and mindfulness. Measures were completed at pretest, midtreatment, and posttest between October 2019 and April 2020. Longitudinal data were analyzed using intention-to-treat principles with maximum likelihood.

Results: A total of 343 participants were randomized and 186 (54.2%) completed at least one posttest assessment. The majority (166/228, 72.8%) of those assigned to HMP conditions downloaded the app. The 2 HMP conditions did not differ from one another in terms of changes in any outcome. Relative to the waitlist control, the HMP conditions showed larger improvements in distress, social connectedness, mindfulness, and measures theoretically linked to insight training (d=–0.28 to 0.41; Ps≤.02), despite modest exposure to connection- and insight-related practice. The results were robust to some assumptions about nonrandom patterns of missing data. Improvements in distress were associated with days of use. Candidate mediators (social connection, insight, rumination, defusion, and mindfulness) and moderators (baseline rumination, defusion, and empathy) of changes in distress were identified.

Conclusions: This study provides initial evidence of efficacy for the HMP app in reducing distress and improving outcomes related to well-being, including social connectedness. Future studies should attempt to increase study retention and user engagement.

Trial Registration: NCT04139005;

JMIR Ment Health 2020;7(11):e23825




Mindfulness and meditation have become household words for many people in the United States and across the globe in the past 20 years. Derived from Buddhist and Hindu contemplative traditions [1], secularized meditative practices are being taught in schools, recommended by health care providers, and employed by businesses [2-4]. The use of meditation tripled in the United States between 2012 and 2017 (from 4.1% to 14.2%) [5]. Meta-analyses involving hundreds of randomized controlled trials (RCTs) suggest that meditation training can decrease psychological symptoms (eg, depression, anxiety, stress) and increase aspects of well-being and positive functioning (eg, meaning in life, compassion, prosocial behavior) [6-15].

To date, the vast majority of research on meditation has focused on interventions delivered in person. Standardized mindfulness-based interventions such as mindfulness-based stress reduction (MBSR) [16] and mindfulness-based cognitive therapy (MBCT) [17] were explicitly designed as group-based interventions delivered by trained instructors, and these interpersonal elements are viewed as central ingredients (eg, group format) [18]. Despite some meditation-based interventions (MBIs) being recommended as first-line treatments (eg, MBCT for depression relapse prevention) [19-21], their availability remains limited [22]. Barriers for the dissemination of MBIs are similar to those facing other evidence-based psychotherapies (eg, lack of available providers, cost, logistical challenges) [23-25].

Delivering interventions through mobile technology has been proposed as a solution for increasing access to psychological interventions, including MBIs [26,27]. Web- and smartphone-based interventions have obvious advantages over traditional in-person delivery in terms of cost and scalability. Furthermore, mobile health (mHealth) interventions can, in theory, do things that in-person interventions typically never do, such as providing access 24 hours a day or customizing content based on passively sensed data (eg, location) [28]. Among mHealth delivery platforms, smartphone-based interventions may be particularly promising, with these devices often kept within arm’s reach, charged, turned on, and being owned by the vast majority of the population [29].

There has been a dramatic increase in the past five years in RCTs testing smartphone-based interventions that include training in meditation [30]. These studies have begun to examine efficacy in various clinical and nonclinical populations [31-38]. Although preliminary, available evidence suggests that smartphone-based interventions that include training in meditation and mindfulness may provide psychological benefits that are similar to in-person MBIs (eg, decreased psychological symptoms, increased positive functioning), albeit smaller in magnitude [30,39-42].

Similar to the in-person MBI literature, RCTs testing the mobile delivery of MBIs have focused almost entirely on mindfulness. The term mindfulness is derived from the Pali word sati, which in Buddhism refers to the cultivation of receptive, present-moment awareness [43]. In the scientific literature, mindfulness can refer to a mental state, trait, or faculty amenable to training [44-47]. Mindfulness-based interventions commonly adopt the definition by Kabat-Zinn [48]: “paying attention in a particular way: on purpose, in the present moment, and nonjudgmentally.” Meta-analyses suggest that dispositional mindfulness along with both short-term (eg, mindfulness inductions) and long-term (eg, mindfulness-based interventions such as MBSR) training are associated with decreased psychiatric symptoms, negative affect, substance use, and neuroticism [9,49-53].

Importantly, mindfulness training represents the implementation of primarily one meditative approach drawn from rich contemplative traditions [54]. Although largely untested, it is possible that a variety of meditative techniques may serve as valuable complements or alternatives to mindfulness. Different meditation practices have shown distinct neural signatures [55,56] and can produce different psychological effects [57]. Dahl et al [54] provide a useful typology for situating mindfulness training within the broader contemplative practice landscape. Using a family resemblance approach, they describe attentional, constructive, and deconstructive families. Mindfulness meditation, as implemented in MBSR, falls primarily within the attentional family, with training focused on regulating attention. The constructive family includes practices designed to strengthen psychological habits conducive to psychosocial health. This includes connection-based practices that involve cultivating feelings of warmth and friendliness toward oneself and others (eg, gratitude, loving kindness, and compassion practices) [58,59]. Experimental evidence suggests that connection practices increase well-being and decrease psychological symptoms [8,60]. The deconstructive family includes practices designed to modify unhelpful cognitive patterns, particularly regarding one’s view of self and others. Practices in this family involve intentional self-inquiry into the dynamics of conscious experience and the nature of the self with the goal of generating an understanding of cognitive patterns (ie, insight). Deconstructive elements are present in MBCT and cognitive therapy more generally (eg, seeing thoughts as thoughts) [17,61]. However, research on deconstructive meditative practices has been limited.

Smartphone-based meditation interventions have almost exclusively focused on mindfulness training [30,62], although several studies have investigated internet-based interventions that include connection-related practices [63-65]. Although some interventions include connection-based practice as one of several guided practices within a general mindfulness framework [32], RCTs primarily examining constructive or deconstructive practices are rare (with some promising exceptions) [66,67].

This Study

This study sought to investigate the effects of a self-guided, smartphone-based meditation intervention that included explicit training in constructive and deconstructive families of practices [54]. In a three-arm RCT, we compared training in mindful awareness, paired with Connection or Insight practices, with a waitlist control. As both arms included the Awareness module first, we refer to them by their unique module (ie, Connection or Insight, rather than Awareness+Connection and Awareness+Insight). We included outcome measures designed to detect global effects (psychological distress) and practice-specific effects (eg, social connection, shift in relationship to one’s thoughts). Our primary hypothesis was that participants in both active conditions would show reduced psychological distress relative to the waitlist control. In addition, we expected those randomized to connection practices to show larger improvements in connection-related measures and those randomized to insight practice to show larger improvements in insight-related measures. We had several exploratory secondary hypotheses. We hypothesized that app usage would be positively associated with reduced distress. We hypothesized that improvements in connection- and insight-related measures would mediate effects on distress for those in the Connection and Insight arms, respectively. We hypothesized that those lower in mindfulness at baseline would show larger improvements in the active conditions and that those lower in connection- and insight-related measures would show larger improvements in the Connection and Insight arms, respectively. These hypotheses were preregistered at the Open Science Framework [68].


We conducted an 8-week, fully remote RCT comparing 2 active smartphone-based meditation interventions with a waitlist control. Participants were recruited through emails sent to faculty, staff, and students at the University of Wisconsin-Madison and through a database of individuals who had previously expressed interest in research at the Center for Healthy Minds. All screening procedures and data collection were web-based and carried out using REDCap [69]. Participants completed a screening protocol to determine eligibility and received their group assignment via an automated email following the completion of baseline questionnaires. Randomization was achieved by automatically allocating participants to groups based on sequentially assigned participant identification numbers (ie, 1:1:1 randomization ratio). Participants were contacted by email to complete questionnaires 4 and 8 weeks postbaseline.

Progress through the material in the Healthy Minds Program (HMP) app was self-guided. There was minimal contact with the study staff. Participants were provided with a study email address to contact for technical support or study-related questions. All procedures were approved by the institutional review board. The study was registered at (NCT04139005).


Eligible participants were aged ≥18 years, had access to a smartphone or other device capable of running the intervention app (Android or iOS), and did not have extensive previous meditation experience defined as meditation retreat experience, meditation practice weekly for >1 year or daily practice within the previous 6 months; or previous training under the instruction of a meditation teacher, other than an introductory course. Participants received US $25 for completing the assessments.


Participants assigned to one of the 2 active intervention arms were instructed to download the HMP app through the Google Play or Apple App Store. The full HMP app includes 4 modules with practices designed to cultivate categories of mental and emotional skills linked to both hedonic and eudaimonic well-being [70,71]. These include the cultivation of mindful attention (Awareness), positive relationships with self and others (Connection), insight into the nature of self and internal experience (Insight), and purpose, values, and meaning in life (Purpose). In this study, the 2 active interventions included 4 weeks of Awareness training, followed by 4 weeks of either Connection or Insight training. This design was predicated on the view that training in the stabilization of attention is foundational to skills trained by Connection and Insight [72]. Each module included brief, podcast-style didactic material along with guided meditation practices. Didactic content included discussion of the scientific bases of the practices. Participants were encouraged to follow a prespecified sequence while going through the material. Participants could select the length of the guided practices (5-30 min) and a variety of practices were available in each module. For example, the Awareness module included practices focused on awareness of breathing and mindfulness of sound. The Connection module included gratitude and kindness practices. The Insight module included practices involving noticing the changing nature of the phenomenon (ie, impermanence) and examining how thoughts and emotions influence perception. Participants in the waitlist condition received access to the full HMP app (ie, all 4 modules) at the conclusion of the study.


A demographic questionnaire was completed at baseline. App usage was measured objectively using the HMP app. Additional information about the psychometric properties and theoretical relevance of the included measures is provided in Multimedia Appendix 1 [73-101].

Psychological Distress

A psychological distress composite score was created from measures of depression, anxiety, and stress. We computed the mean across scaled (z-transformed) scores for each measure. The 8-item Patient-Reported Outcome Measures Information System Depression and Anxiety Scales [73] assessed depression and anxiety. Items are rated on a 5-point scale (1=never; 5=always), with higher scores indicating greater severity in the past 7 days. T scores ≥55 suggest mild or greater severity [102,103]. Internal consistency was high (=.93 to .94).

The 14-item Perceived Stress Scale [74] assessed psychological stress. Items are rated on a 5-point scale (0=never; 4=very often), with higher scores indicating greater stress in the past month. Internal consistency was high (=.89).

Measures Related to the Connection Module

The 20-item Social Connectedness Scale-Revised [75] assessed interpersonal connections. Items are rated on a 6-point scale (1=strongly disagree; 6=strongly agree), with higher scores indicating higher social connectedness. Internal consistency was high (=.95).

The 28-item Interpersonal Reactivity Index (IRI) [76] assessed empathy. Items are rated based on how well they describe the respondent on a 5-point scale (0=not well; 4=very well), with higher scores indicating greater empathy. Internal consistency was high for the total score (=.83).

The 21-item Compassionate Love Scale [77] assesses feelings of compassion. Items are rated on a 7-point scale (1=not at all true of me; 7=very true of me). Higher scores indicate greater feelings of compassion. Internal consistency was high (=.95).

Measures Related to the Insight Module

The 20-item Self-Reflection and Insight Scale (SRIS) [78] assessed participants’ tendency toward self-reflection (eg, “I frequently examine my feelings”) and self-understanding or insight (eg, “I usually know why I feel the way I do”). Items are rated on a 6-point scale (1=strongly disagree; 6=strongly agree) and yield subscales for self-reflection and insight, with higher scores indicating greater self-reflection or insight. Internal consistency was high (=.88 to .92).

The 15-item Perseverative Thinking Questionnaire (PTQ) [79] assessed rumination. For simplicity, we used the term “rumination” to refer to repetitive negative thinking as captured by the PTQ, although it captures both rumination and worry. Items are rated on a 5-point scale (0=never; 4=almost always), with higher scores indicating greater rumination. Internal consistency was high (=.96).

The 10-item Drexel Defusion Scale [80] assessed the ability to achieve psychological distance from internal experiences (ie, defusion). Items are rated on a 6-point scale (0=not at all; 5=very much), with higher scores indicating greater defusion. Internal consistency was high (=.89).


The 39-item Five Facet Mindfulness Questionnaire [81] assessed mindfulness. Items are rated on a 5-point scale (1=never or very rarely true; 5=very often or always true), with higher scores indicating greater mindfulness. Internal consistency was high for the total score (=.94).

Data Analysis

Results from all preregistered primary and secondary measures are reported. For deviations made from the preregistered data analytic plan, see Multimedia Appendix 1.

Data were analyzed using intention-to-treat principles (ie, participants were not excluded based on engagement) [104]. Primary analyses used multilevel models (MLMs [82] with restricted information maximum likelihood estimation in the lme4 package [105] in R [106]. MLMs with a maximum likelihood estimator are generally robust to data that are missing at random (MAR) [83]. For each outcome, an MLM was specified in which a linear change (coded as 0, 1, 2, for pre-, mid-, and posttest, respectively) in outcome was assumed over time, with participant-level random intercepts. Intervention effects were evaluated by the interaction between linear growth and group status, with contrasts comparing the 2 active conditions (ie, Connection, Insight) as well as the combined active conditions relative to waitlist control (see Multimedia Appendix 1 for the model). A subsequent sensitivity analysis restricted the sample to participants above the clinical cut-off for depression or anxiety at baseline (T≥55) [103]. Sensitivity analyses were also conducted with outliers (ie, 3 SD from the mean) and each participant sequentially removed [84].

Additional analyses assessed the potential impact of attrition, which is common in fully remote RCTs [107]. In this study, it is plausible that missingness was related to the unobserved value itself (ie, missing not at random [MNAR]). For example, individuals who failed to benefit from the HMP app may have been less likely to complete the study and would have shown worse outcomes had they been observed. Therefore, we relaxed our MAR assumptions to evaluate the degree to which intervention effects would be maintained under MNAR assumptions. We examined intervention effects in the presence of different assumed outcomes for dropout-missing observations, focusing on residualized change scores (from baseline to posttest) to simplify the study of missingness implications. We coded outcomes for dropout missingness at different levels, ranging from no difference in outcomes (relative to those that remained in the study) to all dropout-missing values being the worst possible outcome of those in the study. As, operationally, it becomes easier to study this range of conditions using outcome ranks as opposed to retaining the metrics of the studied measures, we applied a nonparametric Wilcoxon rank sum test to compare the active conditions against the waitlist control under different missingness assumptions.

To test our exploratory mediation hypotheses, we used the mediation package in R [108]. In these models, active group status (Connection or Insight) served as the independent variable; pre-post changes in mindfulness or connection- or insight-related measures served as the mediators; and posttest distress (controlling for pretest) served as the dependent variable. Pre-post changes were examined as mediators as unique Connection and Insight content was provided after the midtreatment assessments. We used MLMs to examine the effect of app usage, testing the time×usage interaction with usage operationalized as the median split of days of use. As noted in Multimedia Appendix 1, a median split was used because of deviations from normality in usage metrics. To assess baseline characteristics as moderators of change in distress, we tested 3-way interactions between time, group, and baseline characteristics within MLMs. False discovery rate (FDR) adjustment [85] was applied to all analyses to control for inflation of a type I error.

Sample Size and Power

We planned to recruit 300 participants (100 per group), which would allow the detection of small-to-moderate differences between any 2 groups (d=0.40) and between the active and waitlist control conditions (d=0.34) at 80% power and P=.05. Power was estimated using the pwr.t.test and pwr.t2n.test functions in the pwr package in R [109].

Recruitment and Participant Characteristics

A total of 954 potential participants were assessed for eligibility, of which 343 met the inclusion criteria and were randomized to Connection (n=121), Insight (n=107), or waitlist (n=115; Figure 1). Demographics are reported in Table 1. The sample was predominantly White (280/343, 81.6%), female (290/343, 84.5%), and with graduate-level education (190/343, 55.4%). Income was more variable (89/343, 25.9% earned US $50,000 or less). The mean age was 41.74 (SD 12.52) years.

Figure 1. CONSORT (Consolidated Standards of Reporting Trials) diagram. Mid=week 4 assessment; Post=week 8 assessment.
Table 1. Sample demographics.
VariableOverall (n=343)Connection (n=121)Insight (n=107)Waitlist (n=115)P valuea
Race and ethnicity, n (%).91

White280 (81.6)99 (81.8)86 (80.4)95 (82.6)

Black6 (1.7)3 (2.5)1 (0.9)2 (1.7)

Latinx4 (1.2)0 (0.0)4 (3.7)0 (0.0)

Asian18 (5.2)7 (5.8)6 (5.6)5 (4.3)

Multiracial33 (9.6)12 (9.9)9 (8.4)12 (10.4)

Not reported2 (0.6)0 (0.0)1 (0.9)1 (0.9)
Gender, n (%).93

Female290 (84.5)101 (83.5)89 (83.2)98 (85.2)

Male51 (14.9)20 (16.5)15 (14.0)16 (13.9)

Nonbinary2 (0.6)0 (0.0)1 (0.9)1 (0.9)

Not reported2 (0.6)0 (0.0)2 (1.9)0 (0.0)
Income (US $), n (%).18

≤50,00089 (25.9)33 (27.3)26 (24.3)30 (26.1)

50,000-100,000120 (35.0)35 (28.9)37 (34.6)48 (41.7)

100,000-150,00076 (22.2)32 (26.4)22 (20.6)22 (19.1)

>150,00057 (16.6)21 (17.4)21 (19.6)15 (13.0)

Not reported1 (0.3)0 (0.0)1 (0.9)0 (0.0)
Education, n (%).45

Some high school1 (0.3)0 (0.0)1 (0.9)0 (0.0)

High school graduate6 (1.7)3 (2.5)0 (0.0)3 (2.6)

Some college32 (9.3)9 (7.4)11 (10.3)12 (10.4)

College graduate114 (33.2)37 (30.6)36 (33.6)41 (35.7)

Graduate school190 (55.4)72 (59.5)59 (55.1)59 (51.3)
Age (years), mean (SD)41.74 (12.52)42.31 (12.8)43.21 (12.39)39.78 (12.2).10
Elevated symptomsb, n (%)252 (73.5)86 (71.1)81 (75.7)85 (73.9).73

aP values based on a one-way analysis of variance with group (Connection, Insight, or waitlist) predicting demographics (White, female, high income [≥US $100,000], and graduate school).

bElevated symptoms: Patient-Reported Outcomes Measurement Information System (PROMIS) Depression or PROMIS Anxiety in the mild or higher range (T≥55).


Of those randomized to one of the 2 active conditions, 77.7% (94/121) of Connection participants and 67.3% (72/107) of Insight participants downloaded and used the HMP app at least once. By assigning values of zero to those who did not use the app, we found that average utilization was 10.52 days (SD 13.31; median 4), with 18.09 activities within the app (SD 23.30; median 7), 9.45 meditation practices (SD 13.34; median 3), and 102.16 total min of meditation practice (SD 187.74; median 26). All usage metrics were highly zero inflated (Multimedia Appendix 1). Days of use had the lowest skewness (1.34) and kurtosis (0.91), so a median split of days of use was used in the analyses. The median survival time (ie, time before last use) was 12 days. Group status (Connection vs Insight) was not associated with usage (P=.15), and survival time did not differ between groups (Multimedia Appendix 1; P=.24). As Connection or Insight content was provided at week 5 of the program, 32.2% (39/121) of Connection and 23.4% (25/107) of Insight participants engaged with the unique content. This proportion did not differ between groups (OR 0.64, 95% CI 0.35-1.15; P=.14).

Attrition Analysis

We examined baseline demographic and outcome variables as predictors of attrition. We constructed logistic regression models predicting the presence of any follow-up data (ie, midtreatment or posttest). Participants were invited to complete the posttest measures even if they had not completed the midtreatment measures. The average completion of at least one follow-up assessment (mid- or posttreatment) was 54.2% (186/343). Waitlist participants were more likely to complete follow-up assessments (77/115, 67.0% vs 109/228, 47.8%; waitlist n=77; Connection and Insight combined n=109; OR 2.21, 95% CI 1.39-3.56; P<.001). Completion of follow-up assessments did not differ between the Connection and Insight groups (OR 0.99, 95% CI 0.59-1.67; P=.97). However, participants who used the app at least once were more likely to complete the follow-up assessments (OR 3.66, 95% CI 1.95-7.16]; P<.001). Completion of follow-up was not associated with demographics (White, female, high income [≥US $100,000], and graduate education) or outcome measures at baseline (Ps≥.15), with one exception. Participants with higher empathy scores (IRI) at baseline were more likely to complete follow-up assessments (OR 1.02, 95% CI 1.00-1.04; P=.04).

Primary Analyses

Correlations between outcomes are reported in Multimedia Appendix 1. The 3 groups did not differ in any demographic or outcome measures at baseline (Ps≥.10; Tables 1 and 2). Within- and between-group effect sizes (Cohen d) and P values from MLMs are reported in Table 3. The 2 active conditions did not differ from one another in terms of change over time for distress or any secondary outcomes (time×group, Ps≥.29). Therefore, all subsequent analyses combined the 2 active groups. When compared with the waitlist control, the active conditions showed greater decreases in distress (d=0.28) and rumination (d=0.18) and greater increases in social connectedness, self-reflection, insight, defusion, and mindfulness (d=0.13 to 0.41; FDR-adjusted Ps≤.02; Figure 2). The active conditions did not differ from the waitlist on changes in empathy (d=0.02) or compassion (d=0.12). Significance tests for time×group interactions did not change when restricting to those with elevated symptoms at baseline (Table 2), when excluding outliers (with the exception of Self-Reflection, P=.05; Multimedia Appendix 1), nor when each case was excluded sequentially.

A larger proportion of participants in the active conditions showed a minimally important decrease in distress (d≤–0.30) [86] relative to the waitlist condition (70% vs 49%; Connection and Insight combined n=64/91; waitlist n=33/67; OR 2.44 [95% CI 1.27-4.75]; P=.008). A smaller proportion in the active condition showed a minimally important increase in distress (ie, deterioration, d≥0.30) relative to the waitlist condition (3% vs 16%; Connection and Insight combined n=3/91; waitlist n=11/67; OR 0.17 [0.04, 0.58]; P=.009).

Table 2. Descriptive statistics for repeated measures by group and timepoint.
Group and outcomePretestMidtreatmentPosttestP valuea

nMean (SD)nMean (SD)nMean (SD)

Psychological distressc1210.08 (0.90)490.74 (0.80)470.78 (0.78).49

Social connectiond12183.41 (21.43)4890.62 (17.29)4692.32 (18.30).73

Empathye12167.67 (12.33)4867.42 (12.55)4766.01 (12.90).41

Compassionf12199.18 (22.19)48102.52 (21.71)46101.68 (21.64).57

Self-reflection subscaleg12156.31 (11.13)4858.45 (10.29)4657.49 (11.10).97

Insight subscaleg12134.12 (7.46)4837.06 (6.17)4637.54 (6.20).46

Ruminationh12131.12 (12.65)4625.31 (9.63)4625.36 (10.71).90

Defusioni12122.95 (9.37)4828.54 (8.37)4730.75 (7.81).86

Mindfulnessj121122.16 (20.41)49136.28 (16.37)47139.63 (19.33).77

Psychological distress1070.04 (0.94)430.52 (0.88)440.62 (0.88)N/Al

Social connection10783.28 (19.04)4187.57 (19.28)4490.20 (20.64)N/A

Empathy10769.77 (12.45)4170.24 (11.76)4469.49 (11.05)N/A

Compassion107101.57 (21.44)41101.07 (24.52)44108.88 (23.29)N/A

Self-reflection subscale10755.99 (10.40)4155.90 (9.00)4457.92 (9.45)N/A

Insight subscale10732.84 (8.42)4133.67 (8.28)4436.14 (8.44)N/A

Rumination10730.49 (11.71)4127.21 (10.79)4423.97 (10.68)N/A

Defusion10723.62 (10.36)4127.36 (10.01)4430.49 (10.23)N/A

Mindfulness107121.55 (24.96)43128.56 (22.96)44139.11 (19.75)N/A

Psychological distress1150.05 (0.88)640.23 (1.01)670.36 (0.91)N/A

Social connection11581.53 (19.68)6082.78 (21.10)6384.63 (20.24)N/A

Empathy11569.31 (12.85)6071.09 (13.49)6367.96 (13.26)N/A

Compassion11598.62 (22.39)5899.53 (22.64)63100.85 (22.42)N/A

Self-reflection subscale11556.34 (10.78)5756.87 (11.14)6356.23 (11.26)N/A

Insight subscale11533.22 (8.02)5735.40 (8.29)6335.55 (7.83)N/A

Rumination11531.14 (11.56)5629.51 (13.04)6227.41 (11.92)N/A

Defusion11523.49 (9.58)6024.88 (9.95)6426.71 (9.86)N/A

Mindfulness115120.16 (18.93)62125.15 (20.21)65128.88 (20.23)N/A

aP value from a one-way analysis of variance predicting baseline values for outcome measures by group status.

bCO: Awareness+Connection.

cComposite of Patient-Reported Outcomes Measurement Information System (PROMIS) Depression, PROMIS Anxiety, and Perceived Stress Scale.

dSocial Connectedness Scale.

eInterpersonal Reactivity Index.

fCompassionate Love Scale.

gSubscales of the Self-Reflection and Insight Scale.

hPerseverative Thinking Questionnaire.

iDrexel Defusion Scale.

jTotal score of Five Facet Mindfulness Questionnaire.

kIN: Awareness+Insight.

lN/A: not applicable.

mWL: waitlist.

Table 3. Results of multilevel models assessing differential change over time.
OutcomeCOa versus INbActive versus WLc

dCOddINddiffP valueePFDRfdActivegdWLddiffP valuePFDRElev PhElev PFDR
Psychological distressi–0.77–0.70––0.74–0.46–0.28<.001<.001<.001<.001
Social connectionj0.420.360.06.54.820.390.
Self-reflection subscalem0.110.18––
Insight subscalem0.460.390.07.98.980.420.

aCO: Awareness+Connection.

bIN: Awareness+Insight.

cWL: waitlist.

dCohen d calculated as pre-post for within-group effects and the difference between within-group effects (Connection−Insight, active−waitlist) for ddiff. For within-group, subscripted CO (ie, dCO), IN, Active, and WL refer to subgroups noted.

eP value from time×group interaction from multilevel models.

fFDR: false discovery rate adjusted P values.

gCombined Awareness+Connection and Awareness+Insight.

hActive versus waitlist time×group interaction restricted to sample with elevated depression or anxiety at baseline (T≥55).

iComposite of Patient-Reported Outcomes Measurement Information System (PROMIS) Depression, PROMIS Anxiety, and Perceived Stress Scale.

jSocial connection: Social Connectedness Scale.

kInterpersonal Reactivity Index.

lCompassionate Love Scale.

mSubscales of the Self-Reflection and Insight Scale.

nPerseverative Thinking Questionnaire.

oDrexel Defusion Scale.

pTotal score of Five Facet Mindfulness Questionnaire.

Figure 2. Longitudinal changes in psychological distress, social connectedness, defusion, and mindfulness by group. The figure displays observed means and SEs (error bars=1 SE) based on all available data (n=343). CO: Awareness+Connection; IN: Awareness+Insight; WL: waitlist.

Robustness Check: Sensitivity Analyses

Although maximum likelihood is robust to data MAR [83], subsequent analyses evaluated treatment effects based on varying assumptions under MNAR conditions. Using the completer sample, a Wilcoxon rank sum test on the residualized gain score mirrored the MLM results, with larger improvements in the active conditions relative to the waitlist on several outcomes (FDR-adjusted Ps≤.047; Multimedia Appendix 1). In the worst-case scenario model in which missing reflects the worst possible outcome across both active and waitlist groups, the groups did not differ, although the direction of the mean rank favored the waitlist group for all outcomes. Thus, we examined the results in between these extreme conditions to understand where significance goes away and where the direction of intervention effect reverses. When we assumed that missing values are on average 0.25 SD above the mean (implying worse than average outcomes for the missing observations), the results continued to favor the active conditions for changes in distress, social connectedness, defusion, and mindfulness (FDR-adjusted Ps≤.04; Multimedia Appendix 1). When we assumed that missing values were on average 0.50 SD above the mean, the differences between groups were not statistically significant for any outcome. The difference remained nonsignificant when we assumed that missing values are on average 0.75 SD above the mean. Thus, it appears that our results are robust to MNAR up to a point, specifically that missing outcomes are no more than 0.25 SD above the mean on average, under the assumption that missingness implies comparable outcomes for both the active and waitlist groups.

Secondary Analyses

The results of the usage analyses are reported in Table 4. HMP use above the median number of days was associated with larger improvements in distress, insight, defusion, and mindfulness (FDR-adjusted Ps≤.03; Figure 3).

The results of the mediation analyses are reported in Table 5. Changes in 5 candidate mechanisms showed a significant average causal mediation effect (FDR-adjusted Ps≤.04) in the expected direction (ie, improvements in social connection, insight, rumination, defusion, and mindfulness mediated improvements in distress). Changes in mindfulness were associated with the largest proportion mediated (0.45).

Table 4. Results of multilevel models predicting changes in outcomes from Healthy Minds Program app usage (n=228).
OutcomeTime×usage Bat test (df)bP valuecPFDRd
Psychological distresse–0.17–2.46 (207).02.03
Social connectionf2.421.61 (202).11.17
Empathyg–0.47–0.58 (200).56.56
Compassionh–1.16–0.76 (201).45.50
Self-reflection subscalei0.800.87 (218).38.49
Insight subscalei1.552.46 (211).02.03
Ruminationj–1.85–2.13 (197).03.06
Defusionk2.402.64 (229).009.03
Mindfulnessl5.172.93 (212).004.03

aMultilevel model regression coefficient. Usage: days of use split into high (median or above) and low (below median) groups.

bt statistic for time×usage interaction with associated degrees of freedom (df).

cP value for time×usage interaction.

dFDR: false discovery rate adjusted P values.

eComposite of Patient-Reported Outcomes Measurement Information System (PROMIS) Depression, PROMIS Anxiety, and Perceived Stress Scale.

fSocial Connectedness Scale.

gInterpersonal Reactivity Index.

hCompassionate Love Scale.

iSubscales of the Self-Reflection and Insight Scale.

jPerseverative Thinking Questionnaire.

kDrexel Defusion Scale.

lTotal score of Five Facet Mindfulness Questionnaire.

Figure 3. Healthy Minds Program app usage predicting longitudinal changes in psychological distress, defusion, insight, and mindfulness in active conditions (FDR-corrected Ps≤.04). Usage=median split of days of use (n=228). HMP: Healthy Minds Program; WL: waitlist.
Table 5. Results of mediation analyses predicting changes in psychological distress.
OutcomeACMEaADEbProp mediatedcP valuedPFDRe
Social connectionf–0.10–0.320.24.002.005
Self-reflection subscalei0.02–0.44–
Insight subscalei–0.06–0.370.13.03.04

aACME: average causal mediation effect (ie, indirect effect).

bADE: average direct effect (ie, from active to posttreatment distress controlling for pretreatment distress, when active=1 and waitlist=0).

cProportion mediated computed as indirect effect (ie, ACME) divided by total effect [108].

dP value based on quasi-Bayesian CIs.

eFDR: false discovery rate adjusted P values. Models examining pre-post change in constructs related to Awareness, Connection, and Insight modules as mediators of pre-post change in (composite of PROMIS Depression, PROMIS Anxiety, and Perceived Stress Scale). Proportion mediated can be negative in instances where direct effect and indirect effect have opposite signs.

fSocial connection: Social Connectedness Scale.

gInterpersonal Reactivity Index.

hCompassionate Love Scale.

iSubscales of the Self-Reflection and Insight Scale.

jPerseverative Thinking Questionnaire.

kDrexel Defusion Scale.

lTotal score of Five Facet Mindfulness Questionnaire.

The results of the baseline moderation analyses are reported in Table 6. A total of 3 baseline variables showed significant time×group×baseline interactions after FDR adjustment. Psychological vulnerability, as indicated by 2 outcomes (higher rumination and lower defusion) at baseline, was associated with significant improvements in distress in the HMP conditions relative to the waitlist condition. Baseline empathy showed the opposite pattern, with those higher at baseline showing significant improvements in distress in HMP relative to the waitlist condition (Multimedia Appendix 1).

Table 6. Baseline outcomes as moderators of longitudinal changes in psychological distress.
OutcomeTime×group BaTime×group×baseline Bt test (df)bP valuecPFDRd
Social connectione–0.640.012.16 (363).03.06
Empathyf0.67–0.01–3.23 (343).001.008
Compassiong0.070.00–1.20 (343).23.29
Self-reflection subscaleh–0.590.011.55 (339).12.19
Insight subscaleh–0.440.011.14 (359).25.29
Ruminationi0.21–0.01–3.15 (390).002.008
Defusionj–0.540.012.86 (361).004.01
Mindfulnessk–0.440.000.82 (372).41.41

aMultilevel model regression coefficient.

bt test: t statistic for time×group×baseline (with group coded as active=1, waitlist=0) with associated degrees of freedom (df).

cP value for time×group×baseline.

dFDR: false discovery rate adjusted P values.

eSocial Connectedness Scale.

fInterpersonal Reactivity Index.

gCompassionate Love Scale.

hSubscales of the Self-Reflection and Insight Scale.

iPerseverative Thinking Questionnaire.

jDrexel Defusion Scale.

kTotal score of Five Facet Mindfulness Questionnaire.

Principal Findings

This study sought to expand the scientific understanding of the impact of smartphone-delivered meditation training beyond mindfulness. To do so, we evaluated the effects of mindfulness training (Awareness) paired with practices designed to cultivate kindness toward oneself and others (Connection) or insight into the nature of self and internal experience (Insight). We assessed the effects on psychological distress and constructs theoretically linked to connection- and insight-based training [54].

Contrary to our expectations, there was no indication that training in connection produced differential effects relative to insight-related practices. There are several potential reasons for this. One likely explanation is that the actual content completed by each group was largely overlapping. Both groups began with foundational mindfulness training. Given the modest engagement (a perennial concern in mHealth interventions) [110,111], most participants did not engage with the unique Connection or Insight modules. It is also possible that meditation training produces similar effects for novices, regardless of the specific type of training. Novices may spend much of their initial meditation practice simply regathering a wandering attention, regardless of the actual practice instructions. Indeed, studies showing distinct neural signatures associated with various forms of meditation practice have primarily been conducted with long-term practitioners with thousands of hours of experience [55]. A third possibility is that various forms of meditation training contain common ingredients (eg, acceptance, curiosity) that may, especially early in training, be more potent than style-specific ingredients.

Despite the absence of differential effects, the results suggest that meditation delivered via smartphones produced small reductions in psychological distress (d=–0.28) and improvements in several candidate mechanisms relative to a waitlist control (d=–0.18 to 0.41). These results are generally consistent with meta-analyses of the broader mHealth and mHealth MBI literature, which has shown small benefits of self-guided smartphone apps on depression and anxiety symptoms (g=0.21 to 0.23) and measures of mindfulness and acceptance (g=0.27) [30]. These effects are considerably smaller than those produced by in-person MBIs (eg, d=0.55 vs waitlist) [9]. It is likely that mHealth MBIs may be less potent than in-person interventions, indicating trade-offs between scalability, cost, and potency. On the basis of those completing posttreatment measures, HMP appears safe in that the rates of clinically significant increases in distress were rare (3%) and were less common than the rates in the control condition (16%). This finding is consistent with a recent large-scale evaluation of the deterioration in MBSR [87].

One important caveat for interpreting our findings is high attrition, particularly within the active conditions. Both high attrition and differential attrition are common in mHealth research [107,112]. Our overall attrition rate was almost identical to that typically found in RCTs testing smartphone interventions without telephone or in-person enrollment (45.8% in this study and 43.4% in the meta-analysis) [107]. In addition to employing maximum likelihood estimation in all MLMs (which is robust to MAR) [83], we conducted a series of sensitivity analyses to assess the effects of various MNAR assumptions. Most effects were robust to noncompleters having outcomes slightly worse than completers (SD 0.25). However, the effects did not persist with larger deviations (SD ≥0.50). It is impossible to directly test which of these scenarios is most likely (as is the case for other MNAR approaches) [113]. Future studies should include items specifically to predict missingness (eg, “how likely are you to drop out of this study”) [114]. Responses can then be included as auxiliary variables to improve the performance of MAR methods (effectively converting MNAR to MAR) [114].

In light of the degree of attrition, secondary analyses should be interpreted as exploratory. However, these models provide tentative possibilities to be examined further. We found evidence that higher usage (median or above days of use) was associated with larger improvements in distress and several other outcomes. This mirrors the dosage-outcome associations seen in the in-person MBI literature [115]. Mediation analyses suggest candidate mechanisms theoretically linked to each HMP module that may indirectly contribute to decreased distress (ie, mindfulness, social connectedness, defusion, rumination). This also mirrors reviews of the in-person MBI literature that have found changes in mindfulness and rumination mediate effects [116]. The possibility that social connectedness also plays a role should be explored further, particularly as it has been associated with numerous psychological and physical health outcomes [117,118]. Moderation analyses indicated larger improvements among those showing higher rumination or empathy and lower defusion at baseline. These are somewhat conflicting findings, with the rumination and defusion associations suggesting that HMP may be most effective for those with deficits at baseline, whereas the association with empathy suggesting a higher baseline level may be necessary to benefit most. Given that mHealth interventions could, in theory, be easily adapted to participant characteristics (eg, participants routed to receive a particular version based on baseline questionnaires), future experimental work can specifically examine who is likely to benefit from which kind of training (eg, randomizing to adapted vs nonadapted versions). The scalability of mHealth RCTs may allow recruitment of the sample sizes necessary for adequately powered tests of moderation [119].

Limitations and Future Directions

As noted, high and differential attrition are limitations of this study. Although attrition is commonplace in mHealth research [110], future studies should include study design features that decrease attrition (eg, telephone enrollment) [107]. Relatedly, engagement with the HMP app was relatively modest. Similar to attrition, low engagement is a rule rather than an exception for mHealth [120]. Presumably low engagement diminished any potential effects of the HMP app, making estimates of efficacy more ambiguous. Future studies could evaluate engagement strategies by randomizing participants to receive approaches found to improve adherence to medical regimens (eg, modifying dosage recommendations, providing reminders) [121]. Microrandomized trials could assess the impact of a variety of small manipulations on short-term engagement outcomes [122].

The lack of a follow-up assessment is another limitation, making it unclear whether any benefits were sustained. Furthermore, the lack of active control conditions makes it impossible to rule out effects due to a digital placebo [123]. Similarly, the included self-report measures are vulnerable to social desirability bias, although this may be less of an issue within a fully remote RCT [124]. Sampling procedures and sample demographics raise questions regarding generalizability, especially to racial/ethnic minority populations and those with lower levels of education. Participants in the Center for Healthy Minds database may have been particularly amenable to the HMP app (although those with prior meditation experience would have been excluded).

An obvious future study would assign participants to receive only Connection or Insight module content. This could clarify the unique effects of these types of practices. As we observed effects on distress that persisted when restricted to those with elevated symptoms at baseline, it would be worthwhile replicating this study within a clinical sample. For this, it could be important or even necessary, for safety reasons, to include some amount of professional guidance [41], perhaps telephone or text-based support [125]. Other extensions of this study could include the use of non–self-report measures, both to rule out social desirability as well as to clarify underlying mechanistic processes. Numerous biological and behavioral mechanisms have been assessed for in-person MBIs (eg, changes in attention, body awareness, stress physiology) [126-128] and may be operant within mHealth MBIs. A wide variety of extensions could also be made to the HMP app itself. For example, intervention components could be provided in response to passively assessed mood states (eg, using data streams routinely gathered through phone sensors). This would require not only the validation of passive measures [129,130] but also studies that clarify the optimal pairing of intervention components to mood. Microrandomized trials may be an ideal design for this purpose. The amount of engagement necessary for clinical benefits (ie, recommended dosage) should be clarified in future studies (eg, by random assignment to dosage conditions). RCTs using active control conditions can help clarify the role that nonspecific factors may play in mHealth MBIs. On the basis of the robust association between working alliances and outcomes within in-person interventions [131], future studies should assess its digital corollary [132]. Finally, a critical future direction is investigating the efficacy of mHealth MBIs specifically among (and ideally tailored for) [133,134] racial/ethnic minorities. Racial/ethnic minority populations are at increased risk for racism-related negative psychological and physical health consequences [135] and have been historically underrepresented in research on mindfulness [136,137].


Research into the mobile delivery of meditation training has expanded rapidly in the past five years. However, the vast majority of this work has focused exclusively on training mindfulness. We found preliminary evidence that 2 versions of a novel smartphone app that includes training in mindfulness, in addition to skills specifically targeted to improve social connection and intrapersonal insight, are associated with reduced psychological distress, increased social connectedness, and improvements in candidate mechanisms believed to underlie the beneficial effects of MBIs. Future studies, particularly those focusing on decreasing study dropout and increasing intervention engagement, are warranted.

Conflicts of Interest

RD is the founder, president, and serves on the board of directors for the nonprofit organization, Healthy Minds Innovations, Inc. CD is Chief Contemplative Officer for Healthy Minds Innovations, Inc. CW has served as a paid consultant and content contributor for Healthy Minds Innovations, Inc.

Multimedia Appendix 1

Healthy Minds Program randomized controlled trial supplemental materials.

DOCX File , 113 KB

Multimedia Appendix 2

CONSORT-eHealth (V 1.6.1).

PDF File (Adobe PDF File), 3037 KB

  1. Harrington A, Dunne J. When mindfulness is therapy: ethical qualms, historical perspectives. Am Psychol. Oct 2015;70(7):621-631. [CrossRef] [Medline]
  2. Dunning DL, Griffiths K, Kuyken W, Crane C, Foulkes L, Parker J, et al. Research review: the effects of mindfulness-based interventions on cognition and mental health in children and adolescents - a meta-analysis of randomized controlled trials. J Child Psychol Psychiatry. Mar 2019;60(3):244-258. [FREE Full text] [CrossRef] [Medline]
  3. Jamieson SD, Tuckey MR. Mindfulness interventions in the workplace: a critique of the current state of the literature. J Occup Health Psychol. Apr 2017;22(2):180-193. [CrossRef] [Medline]
  4. Meditation: A Simple, Fast Way to Reduce Stress. Mayo Clinic Staff. URL: [accessed 2020-03-07]
  5. Clarke TC, Barnes PM, Black LI, Stussman BJ, Nahin RL. Use of yoga, meditation, and chiropractors among US adults aged 18 and over. NCHS Data Brief. Nov 2018;(325):1-8. [FREE Full text] [Medline]
  6. Chu ST, Mak WW. How mindfulness enhances meaning in life: a meta-analysis of correlational studies and randomized controlled trials. Mindfulness. Nov 28, 2019;11(1):177-193. [CrossRef]
  7. Donald JN, Sahdra BK, Van Zanden B, Duineveld JJ, Atkins PW, Marshall SL, et al. Does your mindfulness benefit others? A systematic review and meta-analysis of the link between mindfulness and prosocial behaviour. Br J Psychol. Feb 2019;110(1):101-125. [CrossRef] [Medline]
  8. Galante J, Galante I, Bekkers M, Gallacher J. Effect of kindness-based meditation on health and well-being: a systematic review and meta-analysis. J Consult Clin Psychol. Dec 2014;82(6):1101-1114. [CrossRef] [Medline]
  9. Goldberg SB, Tucker RP, Greene PA, Davidson RJ, Wampold BE, Kearney DJ, et al. Mindfulness-based interventions for psychiatric disorders: a systematic review and meta-analysis. Clin Psychol Rev. Feb 2018;59:52-60. [FREE Full text] [CrossRef] [Medline]
  10. Goyal M, Singh S, Sibinga EM, Gould NF, Rowland-Seymour A, Sharma R, et al. Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. JAMA Intern Med. Mar 2014;174(3):357-368. [FREE Full text] [CrossRef] [Medline]
  11. Kuyken W, Warren FC, Taylor RS, Whalley B, Crane C, Bondolfi G, et al. Efficacy of mindfulness-based cognitive therapy in prevention of depressive relapse: an individual patient data meta-analysis from randomized trials. JAMA Psychiatry. Jun 1, 2016;73(6):565-574. [FREE Full text] [CrossRef] [Medline]
  12. Luberto CM, Shinday N, Song R, Philpotts LL, Park ER, Fricchione GL, et al. A systematic review and meta-analysis of the effects of meditation on empathy, compassion, and prosocial behaviors. Mindfulness (N Y). Jun 2018;9(3):708-724. [FREE Full text] [CrossRef] [Medline]
  13. Sedlmeier P, Eberth J, Schwarz M, Zimmermann D, Haarig F, Jaeger S, et al. The psychological effects of meditation: a meta-analysis. Psychol Bull. Nov 2012;138(6):1139-1171. [CrossRef] [Medline]
  14. Strohmaier S. The relationship between doses of mindfulness-based programs and depression, anxiety, stress, and mindfulness: a dose-response meta-regression of randomized controlled trials. Mindfulness. Mar 2, 2020;11(6):1315-1335. [CrossRef]
  15. Goldberg S, Riordan K, Sun S, Davidson R. The empirical status of mindfulness-based interventions: a systematic review of 44 meta-analyses of randomized controlled trials. Perspect Psychol Sci. Jan 1, 2021:-. (forthcoming).
  16. Kabat-Zinn J. Full Catastrophe Living: How to Cope With Stress, Pain and Illness Using Mindfulness Meditation. Revised Edition. London, UK. Piatkus; 2013.
  17. Segal Z, Williams J, Teasdale J. Mindfulness-Based Cognitive Therapy for Depression. Second Edition. New York, USA. Guilford Press; 2013.
  18. Kabat-Zinn J. An outpatient program in behavioral medicine for chronic pain patients based on the practice of mindfulness meditation: theoretical considerations and preliminary results. Gen Hosp Psychiatry. Apr 1982;4(1):33-47. [CrossRef] [Medline]
  19. Goldberg S, Segal Z. Mindfulness-based Cognitive Therapy. American Psychological Association. URL: [accessed 2020-03-07]
  20. Goldberg SB, Tucker RP, Greene PA, Davidson RJ, Kearney DJ, Simpson TL. Mindfulness-based cognitive therapy for the treatment of current depressive symptoms: a meta-analysis. Cogn Behav Ther. Nov 2019;48(6):445-462. [CrossRef] [Medline]
  21. Depression in Adults: Recognition and Management. National Institute for Health and Care Excellence (NICE). 2009. URL: [accessed 2020-03-07]
  22. Rycroft-Malone J, Gradinger F, Griffiths H, Crane R, Gibson A, Mercer S, et al. Accessibility and implementation in the UK NHS services of an effective depression relapse prevention programme: Learning from mindfulness-based cognitive therapy through a mixed-methods study. Health Serv Deliv Res. 2017;5(14):-. [CrossRef] [Medline]
  23. Andrade LH, Alonso J, Mneimneh Z, Wells JE, Al-Hamzawi A, Borges G, et al. Barriers to mental health treatment: results from the WHO World Mental Health surveys. Psychol Med. Apr 2014;44(6):1303-1317. [FREE Full text] [CrossRef] [Medline]
  24. Beidas RS, Kendall PC. Training therapists in evidence-based practice: a critical review of studies from a systems-contextual perspective. Clin Psychol (New York). Mar 2010;17(1):1-30. [FREE Full text] [CrossRef] [Medline]
  25. Kazdin AE. Annual research review: expanding mental health services through novel models of intervention delivery. J Child Psychol Psychiatry. Apr 2019;60(4):455-472. [CrossRef] [Medline]
  26. Fairburn CG, Patel V. The impact of digital technology on psychological treatments and their dissemination. Behav Res Ther. Jan 2017;88:19-25. [FREE Full text] [CrossRef] [Medline]
  27. Segal Z, Dimidjian S, Vanderkruik R, Levy J. A maturing mindfulness-based cognitive therapy reflects on two critical issues. Curr Opin Psychol. Aug 2019;28:218-222. [FREE Full text] [CrossRef] [Medline]
  28. Gustafson DH, McTavish FM, Chih M, Atwood AK, Johnson RA, Boyle MG, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry. May 2014;71(5):566-572. [FREE Full text] [CrossRef] [Medline]
  29. Taylor K, Silver L. Smartphone Ownership is Growing Rapidly Around the World, but Not Always Equally. Pew Research Center. 2019. URL: https:/​/www.​​global/​2019/​02/​05/​smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/​ [accessed 2020-11-19]
  30. Linardon J. Can acceptance, mindfulness, and self-compassion be learned by smartphone apps? A systematic and meta-analytic review of randomized controlled trials. Behav Ther. Jul 2020;51(4):646-658. [CrossRef] [Medline]
  31. Cox CE, Hough CL, Jones DM, Ungar A, Reagan W, Key MD, et al. Effects of mindfulness training programmes delivered by a self-directed mobile app and by telephone compared with an education programme for survivors of critical illness: a pilot randomised clinical trial. Thorax. Jan 2019;74(1):33-42. [FREE Full text] [CrossRef] [Medline]
  32. Garrison KA, Pal P, O'Malley SS, Pittman BP, Gueorguieva R, Rojiani R, et al. Craving to quit: a randomized controlled trial of smartphone app-based mindfulness training for smoking cessation. Nicotine Tob Res. Mar 16, 2020;22(3):324-331. [FREE Full text] [CrossRef] [Medline]
  33. Noone C, Hogan MJ. A randomised active-controlled trial to examine the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample. BMC Psychol. Apr 5, 2018;6(1):13. [FREE Full text] [CrossRef] [Medline]
  34. Rosen KD, Paniagua SM, Kazanis W, Jones S, Potter JS. Quality of life among women diagnosed with breast cancer: a randomized waitlist controlled trial of commercially available mobile app-delivered mindfulness training. Psychooncology. Aug 2018;27(8):2023-2030. [CrossRef] [Medline]
  35. Segal ZV, Dimidjian S, Beck A, Boggs JM, Vanderkruik R, Metcalf CA, et al. Outcomes of online mindfulness-based cognitive therapy for patients with residual depressive symptoms: a randomized clinical trial. JAMA Psychiatry. Jun 1, 2020;77(6):563-573. [CrossRef] [Medline]
  36. Yang E, Schamber E, Meyer RM, Gold JI. Happier healers: randomized controlled trial of mobile mindfulness for stress management. J Altern Complement Med. May 2018;24(5):505-513. [CrossRef] [Medline]
  37. Spears CA, Abroms LC, Glass CR, Hedeker D, Eriksen MP, Cottrell-Daniels C, et al. Mindfulness-based smoking cessation enhanced with mobile technology (iQuit mindfully): pilot randomized controlled trial. JMIR Mhealth Uhealth. Jun 24, 2019;7(6):e13059. [FREE Full text] [CrossRef] [Medline]
  38. Huberty J, Green J, Glissmann C, Larkey L, Puzia M, Lee C. Efficacy of the mindfulness meditation mobile app 'calm' to reduce stress among college students: randomized controlled trial. JMIR Mhealth Uhealth. Jun 25, 2019;7(6):e14273. [FREE Full text] [CrossRef] [Medline]
  39. Firth J, Torous J, Nicholas J, Carney R, Rosenbaum S, Sarris J. Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. J Affect Disord. Aug 15, 2017;218:15-22. [FREE Full text] [CrossRef] [Medline]
  40. Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry. Oct 2017;16(3):287-298. [FREE Full text] [CrossRef] [Medline]
  41. Linardon J, Cuijpers P, Carlbring P, Messer M, Fuller-Tyszkiewicz M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry. Oct 2019;18(3):325-336. [FREE Full text] [CrossRef] [Medline]
  42. Spijkerman MP, Pots WT, Bohlmeijer ET. Effectiveness of online mindfulness-based interventions in improving mental health: a review and meta-analysis of randomised controlled trials. Clin Psychol Rev. Apr 2016;45:102-114. [FREE Full text] [CrossRef] [Medline]
  43. Goldstein J. Mindfulness: a Practical Guide to Awakening. Boulder, CO. Sounds True; 2013.
  44. Davidson RJ, Kaszniak AW. Conceptual and methodological issues in research on mindfulness and meditation. Am Psychol. Oct 2015;70(7):581-592. [FREE Full text] [CrossRef] [Medline]
  45. Grossman P, Van Dam NT. Mindfulness, by any other name…: trials and tribulations of in western psychology and science. Contemporary Buddhism. Jun 14, 2011;12(1):219-239. [CrossRef]
  46. Lutz A, Jha AP, Dunne JD, Saron CD. Investigating the phenomenological matrix of mindfulness-related practices from a neurocognitive perspective. Am Psychol. Oct 2015;70(7):632-658. [FREE Full text] [CrossRef] [Medline]
  47. van Dam NT, van Vugt MK, Vago DR, Schmalzl L, Saron CD, Olendzki A, et al. Mind the hype: a critical evaluation and prescriptive agenda for research on mindfulness and meditation. Perspect Psychol Sci. Jan 2018;13(1):36-61. [FREE Full text] [CrossRef] [Medline]
  48. Kabat-Zinn J. Wherever You Go, There You Are. New York, USA. Hyperion; 1994.
  49. Carpenter JK, Conroy K, Gomez AF, Curren LC, Hofmann SG. The relationship between trait mindfulness and affective symptoms: a meta-analysis of the five facet mindfulness questionnaire (FFMQ). Clin Psychol Rev. Dec 2019;74:101785. [CrossRef] [Medline]
  50. Giluk TL. Mindfulness, big five personality, and affect: a meta-analysis. Personal Individ Differ. Dec 2009;47(8):805-811. [CrossRef]
  51. Karyadi KA, VanderVeen JD, Cyders MA. A meta-analysis of the relationship between trait mindfulness and substance use behaviors. Drug Alcohol Depend. Oct 1, 2014;143:1-10. [FREE Full text] [CrossRef] [Medline]
  52. Mattes J. Systematic review and meta-analysis of correlates of FFMQ mindfulness facets. Front Psychol. 2019;10:2684. [FREE Full text] [CrossRef] [Medline]
  53. Schumer MC, Lindsay EK, Creswell JD. Brief mindfulness training for negative affectivity: a systematic review and meta-analysis. J Consult Clin Psychol. Jul 2018;86(7):569-583. [FREE Full text] [CrossRef] [Medline]
  54. Dahl CJ, Lutz A, Davidson RJ. Reconstructing and deconstructing the self: cognitive mechanisms in meditation practice. Trends Cogn Sci. Sep 2015;19(9):515-523. [FREE Full text] [CrossRef] [Medline]
  55. Fox KC, Dixon ML, Nijeboer S, Girn M, Floman JL, Lifshitz M, et al. Functional neuroanatomy of meditation: a review and meta-analysis of 78 functional neuroimaging investigations. Neurosci Biobehav Rev. Jun 2016;65:208-228. [CrossRef] [Medline]
  56. Singer T, Engert V. It matters what you practice: differential training effects on subjective experience, behavior, brain and body in the ReSource Project. Curr Opin Psychol. Aug 2019;28:151-158. [CrossRef] [Medline]
  57. Kok BE, Singer T. Phenomenological fingerprints of four meditations: differential state changes in affect, mind-wandering, meta-cognition, and interoception before and after daily practice across 9 months of training. Mindfulness (N Y). 2017;8(1):218-231. [FREE Full text] [CrossRef] [Medline]
  58. Hutcherson CA, Seppala EM, Gross JJ. Loving-kindness meditation increases social connectedness. Emotion. Oct 2008;8(5):720-724. [CrossRef] [Medline]
  59. Salzberg S. Lovingkindness: the Revolutionary Art of Happiness. Boston, UK. Shambhala; 2004.
  60. Kirby JN, Tellegen CL, Steindl SR. A meta-analysis of compassion-based interventions: current state of knowledge and future directions. Behav Ther. Nov 2017;48(6):778-792. [CrossRef] [Medline]
  61. Beck A, Rush A, Shaw B, Emery G. Cognitive Therapy of Depression. New York, USA. Guilford Press; 1979.
  62. Mani M, Kavanagh DJ, Hides L, Stoyanov SR. Review and evaluation of mindfulness-based iPhone apps. JMIR Mhealth Uhealth. Aug 19, 2015;3(3):e82. [FREE Full text] [CrossRef] [Medline]
  63. Galante J, Bekkers M, Mitchell C, Gallacher J. Loving-kindness meditation effects on well-being and altruism: a mixed-methods online RCT. Appl Psychol Health Well Being. Nov 2016;8(3):322-350. [CrossRef] [Medline]
  64. Krieger T, Reber F, von Glutz B, Urech A, Moser CT, Schulz A, et al. An internet-based compassion-focused intervention for increased self-criticism: a randomized controlled trial. Behav Ther. Mar 2019;50(2):430-445. [CrossRef] [Medline]
  65. Sommers-Spijkerman MP, Trompetter HR, Schreurs KM, Bohlmeijer ET. Compassion-focused therapy as guided self-help for enhancing public mental health: a randomized controlled trial. J Consult Clin Psychol. Feb 2018;86(2):101-115. [CrossRef] [Medline]
  66. Mak WW, Tong AC, Yip SY, Lui WW, Chio FH, Chan AT, et al. Efficacy and moderation of mobile app-based programs for mindfulness-based training, self-compassion training, and cognitive behavioral psychoeducation on mental health: randomized controlled noninferiority trial. JMIR Ment Health. Oct 11, 2018;5(4):e60. [FREE Full text] [CrossRef] [Medline]
  67. Rodgers RF, Donovan E, Cousineau T, Yates K, McGowan K, Cook E, et al. BodiMojo: efficacy of a mobile-based intervention in improving body image and self-compassion among adolescents. J Youth Adolesc. Jul 2018;47(7):1363-1372. [CrossRef] [Medline]
  68. Imhoff-Smith T, Rosenkranz M, Goldberg S. Impact of Targeted Mental Exercises on Awareness, Connection, Insight: a Pilot RCT. Open Science Framework. 2019. URL: [accessed 2020-03-07]
  69. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. REDCap Consortium. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. Jul 2019;95:103208. [FREE Full text] [CrossRef] [Medline]
  70. Diener E, Suh EM, Lucas RE, Smith HL. Subjective well-being: three decades of progress. Psychol Bull. 1999;125(2):276-302. [CrossRef]
  71. Ryff CD. Psychological well-being revisited: advances in the science and practice of eudaimonia. Psychother Psychosom. 2014;83(1):10-28. [FREE Full text] [CrossRef] [Medline]
  72. Lutz A, Slagter HA, Dunne JD, Davidson RJ. Attention regulation and monitoring in meditation. Trends Cogn Sci. Apr 2008;12(4):163-169. [FREE Full text] [CrossRef] [Medline]
  73. Pilkonis PA, Choi SW, Reise SP, Stover AM, Riley WT, Cella D, et al. PROMIS Cooperative Group. Item banks for measuring emotional distress from the patient-reported outcomes measurement information system (PROMIS): depression, anxiety, and anger. Assessment. Sep 2011;18(3):263-283. [FREE Full text] [CrossRef] [Medline]
  74. Cohen S, Williamson GM. Perceived stress in a probability sample of the United States. In: The Social Psychology of Health: Claremont Symposium on Applied Social Psychology. Newbury Park, CA. Sage; 1988.
  75. Lee RM, Draper M, Lee S. Social connectedness, dysfunctional interpersonal behaviors, and psychological distress: testing a mediator model. J Couns Psychol. 2001;48(3):310-318. [CrossRef]
  76. Davis MH. Measuring individual differences in empathy: evidence for a multidimensional approach. J Pers Soc Psychol. Jan 1983;44(1):113-126. [CrossRef]
  77. Sprecher S, Fehr B. Compassionate love for close others and humanity. J Soc Pers Relatsh. Jun 30, 2016;22(5):629-651. [CrossRef]
  78. Grant AM, Franklin J, Langford P. The self-reflection and insight scale: a new measure of private self-consciousness. Soc Behav Pers. Jan 1, 2002;30(8):821-835. [CrossRef]
  79. Ehring T, Zetsche U, Weidacker K, Wahl K, Schönfeld S, Ehlers A. The perseverative thinking questionnaire (PTQ): validation of a content-independent measure of repetitive negative thinking. J Behav Ther Exp Psychiatry. Jun 2011;42(2):225-232. [FREE Full text] [CrossRef] [Medline]
  80. Forman EM, Herbert JD, Juarascio AS, Yeomans PD, Zebell JA, Goetter EM, et al. The Drexel defusion scale: a new measure of experiential distancing. J Context Behav Sci. Dec 2012;1(1-2):55-65. [CrossRef]
  81. Baer RA, Smith GT, Hopkins J, Krietemeyer J, Toney L. Using self-report assessment methods to explore facets of mindfulness. Assessment. Mar 2006;13(1):27-45. [CrossRef] [Medline]
  82. Snijders T, Bosker R. Multilevel Analysis: an Introduction to Basic and Advance Multilevel Modeling. Second Edition. London, UK. Sage; 2012.
  83. Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009;60:549-576. [CrossRef] [Medline]
  84. Nieuwenhuis R, Grotenhuis M, Pelzer B. tools for detecting influential data in mixed effects models. R J. 2012;4(2):38. [CrossRef]
  85. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. Dec 5, 2018;57(1):289-300. [CrossRef]
  86. Schalet BD, Pilkonis PA, Yu L, Dodds N, Johnston KL, Yount S, et al. Clinical validity of PROMIS depression, anxiety, and anger across diverse clinical samples. J Clin Epidemiol. May 2016;73:119-127. [FREE Full text] [CrossRef] [Medline]
  87. Hirshberg MJ, Goldberg SB, Rosenkranz M, Davidson RJ. Prevalence of harm in mindfulness-based stress reduction. Psychol Med. Aug 18, 2020:1-9. [CrossRef] [Medline]
  88. Choi SW, Schalet B, Cook KF, Cella D. Establishing a common metric for depressive symptoms: linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychol Assess. Jun 2014;26(2):513-527. [FREE Full text] [CrossRef] [Medline]
  89. Schalet BD, Cook KF, Choi SW, Cella D. Establishing a common metric for self-reported anxiety: linking the MASQ, PANAS, and GAD-7 to PROMIS Anxiety. J Anxiety Disord. Jan 2014;28(1):88-96. [FREE Full text] [CrossRef] [Medline]
  90. Roberti JW, Harrington LN, Storch EA. Further psychometric support for the 10‐item version of the Perceived Stress Scale. J Coll Couns. 2006;9(2):135-147. [CrossRef]
  91. Lee RM, Dean BL, Jung K. Social connectedness, extraversion, and subjective well-being: Testing a mediation model. Personal Individ Differ. Oct 2008;45(5):414-419. [CrossRef]
  92. Cordier R, Milbourn B, Martin R, Buchanan A, Chung D, Speyer R. A systematic review evaluating the psychometric properties of measures of social inclusion. PLoS One. 2017;12(6):e0179109. [FREE Full text] [CrossRef] [Medline]
  93. Lang AJ, Malaktaris AL, Casmar P, Baca SA, Golshan S, Harrison T, et al. Compassion meditation for posttraumatic stress disorder in veterans: a randomized proof of concept study. J Trauma Stress. Apr 2019;32(2):299-309. [CrossRef] [Medline]
  94. Cliffordson C. The hierarchical structure of empathy: dimensional organization and relations to social functioning. Scand J Psychol. Feb 2002;43(1):49-59. [CrossRef] [Medline]
  95. Hojat M, Mangione S, Kane GC, Gonnella JS. Relationships between scores of the Jefferson Scale of Physician Empathy (JSPE) and the Interpersonal Reactivity Index (IRI). Med Teach. Nov 2005;27(7):625-628. [CrossRef] [Medline]
  96. Harrington R, Loffredo DA, Perz CA. Dispositional mindfulness as a positive predictor of psychological well-being and the role of the private self-consciousness insight factor. Personal Individ Differ. Dec 2014;71:15-18. [CrossRef]
  97. Van Dam NT, Earleywine M, Danoff-Burg S. Differential item function across meditators and non-meditators on the Five Facet Mindfulness Questionnaire. Personal Individ Differ. Oct 2009;47(5):516-521. [CrossRef]
  98. Goldberg SB, Del Re AC, Hoyt WT, Davis JM. The secret ingredient in mindfulness interventions? A case for practice quality over quantity. J Couns Psychol. Jul 2014;61(3):491-497. [FREE Full text] [CrossRef] [Medline]
  99. Lykins EL, Baer RA. Psychological functioning in a sample of long-term practitioners of mindfulness meditation. J Cogn Psychother. Aug 01, 2009;23(3):226-241. [CrossRef]
  100. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. Dec 1986;51(6):1173-1182. [CrossRef] [Medline]
  101. Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods. Dec 2010;15(4):309-334. [CrossRef] [Medline]
  102. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, et al. PROMIS Cooperative Group. The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clin Epidemiol. Nov 2010;63(11):1179-1194. [CrossRef] [Medline]
  103. PROMIS Score Cut Points. Health Measures. URL: [accessed 2020-03-07]
  104. Polit DF, Gillespie BM. Intention-to-treat in randomized controlled trials: recommendations for a total trial strategy. Res Nurs Health. Aug 2010;33(4):355-368. [CrossRef] [Medline]
  105. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Soft. 2015;67(1):-. [CrossRef]
  106. R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2018. URL: [accessed 2020-03-07]
  107. Linardon J, Fuller-Tyszkiewicz M. Attrition and adherence in smartphone-delivered interventions for mental health problems: a systematic and meta-analytic review. J Consult Clin Psychol. Jan 2020;88(1):1-13. [CrossRef] [Medline]
  108. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. Package for causal mediation analysis. J Stat Soft. 2014;59(5):-. [CrossRef]
  109. Champley S. PWR: Basic Functions for Power Analysis. The Comprehensive R Archive Network. URL: [accessed 2020-03-07]
  110. Eysenbach G. The law of attrition. J Med Internet Res. Mar 31, 2005;7(1):e11. [FREE Full text] [CrossRef] [Medline]
  111. Blankers M, Koeter MW, Schippers GM. Missing data approaches in eHealth research: simulation study and a tutorial for nonmathematically inclined researchers. J Med Internet Res. Dec 19, 2010;12(5):e54. [CrossRef] [Medline]
  112. Christensen H, Griffiths KM, Farrer L. Adherence in internet interventions for anxiety and depression. J Med Internet Res. Apr 24, 2009;11(2):e13. [FREE Full text] [CrossRef] [Medline]
  113. Enders CK. Applied Missing Data Analysis. New York. Guilford Press; 2010.
  114. Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. Jun 2002;7(2):147-177. [Medline]
  115. Parsons CE, Crane C, Parsons LJ, Fjorback LO, Kuyken W. Home practice in mindfulness-based cognitive therapy and mindfulness-based stress reduction: a systematic review and meta-analysis of participants' mindfulness practice and its association with outcomes. Behav Res Ther. Aug 2017;95:29-41. [FREE Full text] [CrossRef] [Medline]
  116. Gu J, Strauss C, Bond R, Cavanagh K. How do mindfulness-based cognitive therapy and mindfulness-based stress reduction improve mental health and wellbeing? A systematic review and meta-analysis of mediation studies. Clin Psychol Rev. Apr 2015;37:1-12. [CrossRef] [Medline]
  117. Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. Mar 2015;10(2):227-237. [CrossRef] [Medline]
  118. Leigh-Hunt N, Bagguley D, Bash K, Turner V, Turnbull S, Valtorta N, et al. An overview of systematic reviews on the public health consequences of social isolation and loneliness. Public Health. Nov 2017;152:157-171. [CrossRef] [Medline]
  119. Cohen J, Cohen P, West S, Aiken L. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Third Edition. Mahwah, New Jersey. Lawrence Erlbaum Associates Publishers; 2003.
  120. Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop. J Med Internet Res. Jun 29, 2017;19(6):e232. [FREE Full text] [CrossRef] [Medline]
  121. van Dulmen S, Sluijs E, van Dijk L, de Ridder D, Heerdink R, Bensing J. Patient adherence to medical treatment: a review of reviews. BMC Health Serv Res. Apr 17, 2007;7:55. [FREE Full text] [CrossRef] [Medline]
  122. Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, et al. Microrandomized trials: an experimental design for developing just-in-time adaptive interventions. Health Psychol. Dec 2015;34S:1220-1228. [FREE Full text] [CrossRef] [Medline]
  123. Torous J, Firth J. The digital placebo effect: mobile mental health meets clinical psychiatry. Lancet Psychiatry. Feb 2016;3(2):100-102. [CrossRef] [Medline]
  124. Tracey TJ. A note on socially desirable responding. J Couns Psychol. Mar 2016;63(2):224-232. [CrossRef] [Medline]
  125. Ly KH, Trüschel A, Jarl L, Magnusson S, Windahl T, Johansson R, et al. Behavioural activation versus mindfulness-based guided self-help treatment administered through a smartphone application: a randomised controlled trial. BMJ Open. Jan 9, 2014;4(1):e003440. [FREE Full text] [CrossRef] [Medline]
  126. Chiesa A, Calati R, Serretti A. Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clin Psychol Rev. Apr 2011;31(3):449-464. [CrossRef] [Medline]
  127. Pascoe MC, Thompson DR, Jenkins ZM, Ski CF. Mindfulness mediates the physiological markers of stress: systematic review and meta-analysis. J Psychiatr Res. Dec 2017;95:156-178. [CrossRef] [Medline]
  128. Treves IN, Tello LY, Davidson RJ, Goldberg SB. The relationship between mindfulness and objective measures of body awareness: a meta-analysis. Sci Rep. Nov 22, 2019;9(1):17386. [FREE Full text] [CrossRef] [Medline]
  129. Stachl C, Au Q, Schoedel R, Gosling SD, Harari GM, Buschek D, et al. Predicting personality from patterns of behavior collected with smartphones. Proc Natl Acad Sci U S A. Jul 28, 2020;117(30):17680-17687. [FREE Full text] [CrossRef] [Medline]
  130. Pratap A, Atkins DC, Renn BN, Tanana MJ, Mooney SD, Anguera JA, et al. The accuracy of passive phone sensors in predicting daily mood. Depress Anxiety. Jan 2019;36(1):72-81. [CrossRef] [Medline]
  131. Flückiger C, Del Re AC, Wampold BE, Horvath AO. The alliance in adult psychotherapy: a meta-analytic synthesis. Psychotherapy (Chic). Dec 2018;55(4):316-340. [CrossRef] [Medline]
  132. Henson P, Wisniewski H, Hollis C, Keshavan M, Torous J. Digital mental health apps and the therapeutic alliance: initial review. BJPsych Open. Jan 2019;5(1):e15. [FREE Full text] [CrossRef] [Medline]
  133. Benish SG, Quintana S, Wampold BE. Culturally adapted psychotherapy and the legitimacy of myth: a direct-comparison meta-analysis. J Couns Psychol. Jul 2011;58(3):279-289. [CrossRef] [Medline]
  134. Griner D, Smith TB. Culturally adapted mental health intervention: a meta-analytic review. Psychotherapy (Chic). 2006;43(4):531-548. [CrossRef] [Medline]
  135. Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, et al. Racism as a determinant of health: a systematic review and meta-analysis. PLoS One. 2015;10(9):e0138511. [FREE Full text] [CrossRef] [Medline]
  136. DeLuca SM, Kelman AR, Waelde LC. A systematic review of ethnoracial representation and cultural adaptation of mindfulness- and meditation-based interventions. Psychol Stud. May 22, 2018;63(2):117-129. [CrossRef]
  137. Waldron EM, Hong S, Moskowitz JT, Burnett-Zeigler I. A systematic review of the demographic characteristics of participants in US-based randomized controlled trials of mindfulness-based interventions. Mindfulness. Mar 12, 2018;9(6):1671-1692. [CrossRef]

FDR: false discovery rate
HMP: Healthy Minds Program
IRI: Interpersonal Reactivity Index
MAR: missing at random
MBCT: mindfulness-based cognitive therapy
MBI: meditation-based intervention
MBSR: mindfulness-based stress reduction
mHealth: mobile health
MLM: multilevel model
MNAR: missing not at random
PTQ: Perseverative Thinking Questionnaire
RCT: randomized controlled trial

Edited by J Torous; submitted 28.08.20; peer-reviewed by A Ferguson, T Krieger, O Bur; comments to author 19.10.20; revised version received 23.10.20; accepted 26.10.20; published 27.11.20.


©Simon B Goldberg, Theodore Imhoff-Smith, Daniel M Bolt, Christine D Wilson-Mendenhall, Cortland J Dahl, Richard J Davidson, Melissa A Rosenkranz. Originally published in JMIR Mental Health (, 27.11.2020.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, 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, as well as this copyright and license information must be included.