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Mindfulness training (MT) includes a variety of contemplative practices aimed at promoting intentional awareness of experience, coupled with attitudes of nonjudgment and curiosity. Following the success of 8-week, manualized group interventions, MT has been implemented in a variety of modalities, including smartphone apps that seek to replicate the success of group interventions. However, although smartphone apps are scalable and accessible to a wider swath of population, their benefits remain largely untested.
This study aimed to investigate a newly developed MT app called Wildflowers, which was codeveloped with the laboratory for use in mindfulness research. It was hypothesized that 3 weeks of MT through this app would improve subjective well-being, attentional control, and interoceptive integration, albeit with weaker effects than those published in the 8 week, manualized group intervention literature.
Undergraduate students completed 3 weeks of MT with Wildflowers (n=45) or 3 weeks of cognitive training with a game called 2048 (n=41). State training effects were assessed through pre- and postsession ratings of current mood, stress level, and heart rate. Trait training effects were assessed through pre- and postintervention questionnaires canvassing subjective well-being and behavioral task measures of attentional control and interoceptive integration. State and trait training data were analyzed in a multilevel model using emergent latent factors (acceptance, awareness, and openness) to summarize the trait questionnaire battery.
Analyses revealed both state and trait effects specific to MT; participants engaging in MT demonstrated improved mood (
MT, using a smartphone app, may provide immediate effects on mood and stress while also providing long-term benefits for attentional control. Although further investigation is warranted, there is evidence that with continued usage, MT via a smartphone app may provide long-term benefits in changing how one relates to their inner and outer experiences.
ClinicalTrials.gov NCT03783793; https://clinicaltrials.gov/ct2/show/NCT03783793 (Archived by WebCite at http://www.webcitation.org/75EF2ehst)
Mindfulness training (MT) is a collection of meditation, introspection, and yoga practices aimed at the cultivation of psychological resilience and the alleviation of mental health symptoms [
Mindfulness has been defined as “the awareness that emerges through paying attention on purpose, in the present moment, and nonjudgmentally to the unfolding of experience moment by moment” [
Some of the proposed mechanisms for the effectiveness of MT include increases in metacognitive awareness, acceptance, and attentional control [
Despite well-established benefits of mindfulness-based interventions, and some understanding of the mechanisms involved, MT dissemination can be difficult. For example, MBCT and MBSR require a commitment of weekly meetings and at-home practice of learned mindfulness skills for 8 weeks [
Growing awareness of MT-related benefits, coupled with uncertainty around the necessary components leading to these benefits, has allowed for a rapid expansion of MT delivery modalities, including implementation through technological platforms. Technology-delivered mindfulness-based interventions have proven to be successful in improving well-being [
Perhaps the fastest growing market for MT lies in smartphone apps for MT; the most popular current MT app, Headspace, boasted over 6 million users in 2016 [
Although these studies found some benefits from using these MT apps, they relied solely on subjective self-reports, which may be confounded with participant expectancy. For example, participants may believe that MT improves attention regulation [
With few investigations of the effectiveness of MT apps on well-being, further research is warranted. The goal of this study was to better evaluate the local and longitudinal effects of app-delivered MT, relative to a randomized active-control group. For this purpose, we employed a newly developed MT app that was designed to collect user’s ratings of current mood and stress level as well as heart rate before and after each guided meditation session. In the active control condition, a popular cognitive game was adapted to allow for the same collection of mood, stress, and heart rate data. To investigate subtle changes across domains related to optimal psychological experience and functioning, a broad definition of well-being was measured, including both hedonic (ie, pleasure vs pain) and eudemonic aspects (ie, realizing one’s true nature) [
As outcome variables, we attempted to provide several longitudinal and local MT targets. For longitudinal targets, we modeled 3 commonly cited MT benefits: improved subjective well-being, attentional control [
It was hypothesized that MT via a smartphone app would improve trait subjective well-being, attentional control, and interoceptive integration, albeit with weaker effects for a brief 3 weeks of MT with the app than those published in the 8-week manualized group intervention MT literature. In addition, it was expected that beneficial state MT effects would be observed in mood, heart rate, and perceived stress, suggesting the immediate benefits of brief mindfulness meditation.
Undergraduate students were recruited from the University of Toronto Mississauga and randomly assigned to train with 1 of 2 smartphone apps: Wildflowers, an MT app or 2048, a cognitive training app, which was used as an active control condition to control for expectancy and daily engagement. Both apps were described to participants as a cognitive training app that might promote well-being. This description was given to foster positive expectancy in the active control condition, without introducing any real stressor or emotion regulation training.
To be eligible to participate in this study, participants were expected to (1) have normal or corrected-to-normal vision and hearing, (2) be 18 years or older, (3) be fluent in English, and (4) own an iPhone, iPad, or iPod with access to the internet.
Upon recruitment, each participant was asked to come in to the laboratory to complete self-report questionnaires of well-being through a Web-based survey platform called Qualtrics and complete behavioral measures of attentional control and interoceptive integration on a computer in the laboratory. After completing the questionnaires and tasks, participants downloaded their assigned app and made sure it was working on their phone and they knew how to use it. Participants did not know their condition assignment until after completing the pretraining measures. Ratings of current mood, stress level, and heart rate were recorded within each app before and after each training session. Heart rate was sampled with the camera on the participants’ smartphone using a well-established algorithm. This technique included an internal reliability check where if reliability was low, heart rate data were not provided to the user or researchers [
Before participating in the study, undergraduate students gave written informed consent. Participants were aware that their usage data (date and usage time, mood, stress, and heart rate) from each of the apps was sent anonymously via email to the researchers. Students recruited through the university’s undergraduate recruitment site received course credit for their participation. Students recruited via flyers posted throughout the university received Can $10 for every hour spent in the laboratory and for using their assigned app, to a maximum of Can $90 in compensation for their participation. The research protocol was approved by the University of Toronto Social Sciences, Humanities, and Education Research Ethics Board (REB). This study was retrospectively registered on ClinicalTrials.gov; ID: NCT03783793.
Mindfulness training in the study was performed using a new app called Wildflowers (Mobio Interactive Inc, Toronto), which was developed in collaboration with our laboratory. This smartphone app incorporates features that have been deemed to be important to include in smartphone MT, as suggested by Mani and colleagues [
Using the Wildflowers app (
The Wildflowers MT app is freely available in the Apple App Store and on Google Play, with additional content and features available to subscribing customers. The training experience described in this study is available through the free features on the app.
The training app for the control condition was based on an open source code for a popular cognitive training app called 2048, which is marketed by Ketchapp in the Apple app store as a “fun and relaxing puzzle game” (
The Perceived Stress Scale (PSS) [
The Big Five Inventory (BFI) [
The Psychological Well-Being Scale (PWBS) [
The Acceptance and Action Questionnaire-II (AAQ-II) [
The Philadelphia Mindfulness Scale (PHLMS) [
The Multidimensional Assessment of Interoceptive Awareness (MAIA) [
The Spiritual Experience Index-Revised (SEI-R) [
The Meaning in Life Questionnaire (MLQ) [
The mood board is a visual representation of negative and positive emotions on a spectrum, ranging from intense emotions to mild emotions. This mood board provides a maximum of 32 emotions that a participant can select and yields 4 scores: degree of intense negative emotions, degree of intense positive emotions, degree of mild negative emotions, and degree of mild positive emotions. This questionnaire is currently under validation; however, the words chosen for the mood board are commonly used in other measures of mood [
For additional details and psychometric properties for each of the questionnaires used in this study, please see
The Centre for Research on Safe Driving-Attention Network Test (CRSD-ANT) is a 10-min version of the Attention Network Test (ANT) that measures 3 different functions of attention: alerting, orienting, and conflict monitoring [
The Respiration Integration Task (RIT) is a newly developed behavioral task created in our laboratory to assess interoceptive attention (see
The RIT has 3 phases, a vision only baseline, a respiration entraining practice period, and the respiration integration period. During the baseline, participants use vision alone to detect changes in circle frequency. Once this threshold is established, participants spend 60 seconds entraining their breath, that is, practicing matching respiration to the movement of the circle as it pulses at the reference frequency. Afterwards, in the integration period, participants repeat the task while matching their breathing to the expansion and contraction of the sphere. The visual and breath scores are calculated by taking the mean frequency across the final 6 trials from each of these conditions.
An
Following data analysis, a post hoc power analysis simulation, with 10,000 simulations, was conducted using the statistical platform R 3.4.3 [
Participants were excluded from analysis if they did not adhere to the study protocol. Minimal adherence was defined as 10 min of practice per day, missing no more than 4 of the 21 days, and completing both the pre- and posttraining assessment measures.
An exploratory factor analysis (EFA) was conducted on the scale measures listed above in the R statistical computing environment [
All statistical analyses were conducted using the statistical platform R 3.4.3 [
As shown in the participant flow diagram for the study (
The data were inspected to make sure that assumptions that could affect the interpretation of the results were satisfied. Inspection of the normality of residuals, influential cases, autocorrelation of residuals, and homogeneity of variances revealed no major violation of assumptions (see
Before conducting the EFA, the factorability of the 31 questionnaire subscales in this study was examined. It was determined that all of the subscales were suitable to include in the EFA (see
The 3-factor solution (
Participant flow diagram.
Factor loadings of well-being questionnaires entered into the exploratory factor analysis.
Scale/subscale | Acceptance factor loadings | Awareness factor loadings | Openness factor loadings |
PSSa-short version | −0.67b | 0.06 | −0.13 |
BFIc/Extraversion | 0.38b | 0.20 | 0.06 |
BFI/Agreeableness | 0.29 | 0.32b | 0.01 |
BFI/Conscientiousness | 0.41b | 0.04 | 0.26 |
BFI/Neuroticism | −0.65b | 0.08 | −0.06 |
BFI/Openness | −0.05 | 0.33 | 0.42b |
PWBSd/Autonomy | 0.50b | −0.01 | 0.38 |
PWBS/Environmental Mastery | 0.76b | 0.03 | 0.21 |
PWBS/Personal Growth | 0.21 | 0.20 | 0.46b |
PWBS/Positive Relations with Others | 0.47b | 0.25 | −0.02 |
PWBS/Purpose in Life | 0.65b | 0.15 | 0.15 |
PWBS/Self-Acceptance | 0.84b | −0.04 | 0.17 |
AAQ-IIe | 0.87b | −0.10 | −0.01 |
PHLMSf/Awareness Subscale | −0.15 | 0.66b | 0.20 |
PHLMS/Acceptance Subscale | 0.73b | −0.30 | −0.06 |
MAIAg/Noticing | −0.15 | 0.82b | −0.04 |
MAIA/Not Distracting | 0.48b | −0.07 | −0.23 |
MAIA/Not Worrying | 0.35b | −0.17 | 0.26 |
MAIA/Attention Regulation | 0.10 | 0.66b | 0.08 |
MAIA/Emotional Awareness | −0.18 | 0.90b | 0.02 |
MAIA/Self-Regulation | 0.01 | 0.68b | 0.11 |
MAIA/Body Listening | −0.04 | 0.66b | 0.00 |
MAIA/Trusting | 0.39 | 0.52b | 0.09 |
SEI-Rh/Support | 0.05 | 0.24b | −0.11 |
SEI-R/Openness | 0.19 | 0.12 | 0.36b |
MLQi/Presence of Meaning | 0.59b | 0.17 | −0.13 |
MLQ/Search for Meaning | −0.40b | 0.40 | 0.04 |
Mood Board/Intense Negative Emotions | −0.45 | − 0.13 | 0.54b |
Mood Board/Mild Negative Emotions | −0.56b | −0.12 | 0.47 |
Mood Board/Intense Positive Emotions | 0.08 | 0.00 | 0.66b |
Mood Board/Mild Positive Emotions | 0.00 | 0.00 | 0.61b |
aPSS: Perceived Stress Scale.
bRepresents the strongest loadings for each latent factor.
cBFI: Big Five Inventory.
dPWBS: Psychological Well-Being Scale.
eAAQ-II: Acceptance and Action Questionnaire-II.
fPHLMS: Philadelphia Mindfulness Scale.
gMAIA: Multidimensional Assessment of Interoceptive Awareness.
hSEI-R: Spiritual Experience Index-Revised.
iMLQ: Meaning in Life Questionnaire.
Finally, factor 3 (eigenvalue=2.38) was best labeled as openness and included subscales measuring openness, personal growth, and the reporting of both negative and positive emotions. For the reliability analysis, a subscale was considered to be a part of a factor if its loading was greatest for that factor, relative to the other factors (values that show strongest loadings for each latent factor are shown in
To test the hypothesis that trait well-being would improve over time as a result of MT, each of the 3 factors (acceptance, awareness, and openness) were analyzed in a multilevel model using the nlme package [
Each of the 3 factors from the EFA were modeled as a function of time (pre- vs posttraining) and group (MT vs cognitive training). In addition, pairwise follow-up comparisons, Tukey Honest Significant Difference test corrected for multiple comparisons, using least-squares means were conducted using the lsmeans function from the lsmeans package [
Analysis of subjective well-being data revealed a significant main effect of time for the acceptance factor (
A significant main effect of time was observed for the awareness factor (
There was no main effect of time or interaction between time and group observed for the openness factor (
Uncorrected multilevel models were conducted for each of the individual questionnaire subscales (
To test the hypothesis that attentional control would improve as a result of MT, each of the 3 network scores from the CRSD-ANT (orienting effect, alerting effect, and conflict effect) were analyzed in a multilevel model. Each of the network scores were modeled as a function of time (pre- vs posttraining) and group (MT vs cognitive training). In addition, pairwise follow-up comparisons were conducted.
Analysis of the CRSD-ANT revealed no main effects or interactions for the alerting effect (
A significant interaction between time and group was observed for the conflict effect (
Multilevel models of trait well-being measures.
Dependent and independent variable | Estimate (SE) | Pearson |
|||
Time | 0.19 (0.09) | 2.12 (84) | .04a | 0.23 | |
Group | 0.28 (0.20) | 1.40 (84) | .17 | 0.15 | |
Time×group | 0.24 (0.12) | 1.93 (84) | .06b | 0.21 | |
Time | 0.30 (0.11) | 2.65 (84) | .01a | 0.28 | |
Group | 0.26 (0.20) | 1.28 (84) | .20 | 0.14 | |
Time×group | 0.13 (0.15) | 0.83 (84) | .41 | 0.10 | |
Time | 0.04 (0.10) | 0.43 (84) | .67 | 0.05 | |
Group | 0.47 (0.19) | 2.49 (84) | .01a | 0.26 | |
Time×group | −0.07 (0.14) | −0.50 (84) | .62 | −0.05 | |
Time | −0.03 (0.19) | −0.14 (84) | .89 | −0.02 | |
Group | −0.09 (0.22) | −0.43 (84) | .67 | −0.05 | |
Time×group | 0.37 (0.26) | 1.43 (84) | .16 | 0.15 | |
Time | −0.03 (0.18) | −0.18 (84) | .85 | −0.02 | |
Group | −0.10 (0.22) | −0.45 (84) | .65 | −0.05 | |
Time×group | 0.36 (0.25) | 1.43 (84) | .16 | 0.15 | |
Time | 0.10 (0.15) | 0.65 (84) | .52 | 0.07 | |
Group | 0.30 (0.22) | 1.40 (84) | .16 | 0.15 | |
Time×group | −0.47 (0.21) | −2.29 (84) | .02a | −0.24 |
aRepresents significant findings.
bRepresents marginal findings.
Changes in conflict effect before and after mindfulness training (MT) and cognitive training.
To test the hypothesis that behavioral interoceptive attention would improve as a result of MT, participants’ scores from the RIT were analyzed in a multilevel model, modeled as a function of group (MT vs cognitive training), time (pre- vs posttraining), and condition (visual baseline vs breath integration). In addition, pairwise follow-up comparisons were conducted.
This analysis revealed a main effect of condition for the RIT, with the breath condition associated with better detection thresholds than the visual baseline condition (
To test the hypothesis that participants in the MT group would demonstrate immediate effects on well-being, each of the in-app measures (mood, stress, and heart rate) were analyzed in a multilevel model. Each of these measures were modeled as a function of group (MT vs cognitive training), time (multiple training sessions per participant), and session (before vs after each training session), with subject, time, and session as random intercepts.
Multilevel model of respiration integration task performance.
Independent variable | Estimate (SE) | Pearson |
||
Time | −0.06 (0.13) | −0.50 (213) | .62 | −0.03 |
Group | 0.09 (0.15) | 0.57 (83) | .57 | 0.06 |
Condition | 0.26 (0.12) | 2.16 (213) | .03a | 0.15 |
Time×group | 0.02 (0.17) | 0.12 (213) | .91 | 0.01 |
Time×condition | −0.16 (0.18) | −0.94 (213) | .35 | −0.06 |
Group×condition | 0.10 (0.17) | 0.60 (213) | .55 | 0.04 |
Time×group×condition | −0.10 (0.24) | −0.41 (213) | .68 | −0.03 |
aRepresents significant findings.
Changes in respiratory integration task performance by task condition, group, and time. MT: mindfulness training.
Analysis revealed a significant interaction between group and session on mood (
A significant main effect of group; an interaction between group and session; and 3-way interaction between time, group, and session were demonstrated for ratings of stress level (
Multilevel models of state measures of well-being.
Dependent variable and independent variable | Estimate (SE) | Pearson |
|||
Time (days) | −0.01 (0.01) | −1.65 (1117) | .10 | −0.05 | |
Group | −0.01 (0.15) | −0.07 (76) | .95 | −0.01 | |
Session (pre vs post) | 0.02 (0.07) | 0.31 (1190) | .75 | 0.01 | |
Time×group | 0.01 (0.01) | 0.85 (1117) | .40 | 0.03 | |
Time×session | 0.004 (0.01) | 0.63 (1190) | .53 | 0.02 | |
Group×session | 0.47 (0.09) | 4.99 (1190) | <.001a | 0.14 | |
Time×group×session | −0.002 (0.01) | −0.21 (1190) | .84 | −0.01 | |
Time (days) | −0.004 (0.01) | −0.75 (1117) | .45 | −0.02 | |
Group | −0.55 (0.16) | −3.48 (76) | .001a | −0.37 | |
Session (pre vs post) | −0.01 (0.05) | −0.14 (1190) | .89 | −0.004 | |
Time×group | 0.01 (0.01) | 1.43 (1117) | .15 | 0.04 | |
Time×session | 0.01 (0.004) | 1.13 (1190) | .26 | 0.03 | |
Group×session | −0.31 (0.07) | −4.66 (1190) | <.001a | −0.13 | |
Time×group×session | −0.02 (0.01) | −2.78 (1190) | .005a | −0.08 | |
Time (days) | 0.001 (0.01) | 0.17 (1064) | .86 | 0.01 | |
Group | −0.08 (0.14) | −0.54 (75) | .59 | −0.06 | |
Session (pre vs post) | −0.01 (0.09) | −0.10 (1067) | .92 | −0.003 | |
Time×group | 0.01 (0.01) | 0.63 (1064) | .53 | 0.02 | |
Time×session | 0.01 (0.01) | 1.72 (1067) | .09 | 0.05 | |
Group×session | 0.13 (0.13) | 1.02 (1067) | .31 | 0.03 | |
Time×group×session | −0.03 (0.01) | −2.18 (1067) | .03a | −0.07 |
aRepresents significant findings.
Changes in mood before and after each training session over the course of training. MT: mindfulness training.
Changes in stress before and after each training session over the course of training. MT: mindfulness training.
Changes in heart rate before and after each training session over the course of training. MT: mindfulness training.
A significant 3-way interaction between time, group, and session was observed for heart rate (
An exploratory analysis of the associations between change scores for the trait measures (pre- and posttraining) and change scores for the state measures (pre- and postpractice session) were conducted via correlation analysis. Results (
Correlations between state (pre- and postsession) and trait (pre- and postintervention) measures of well-being change.
State and trait well-being | State well-being | Trait well-being | |||||||
Heart Rate | Stress | Mood | Accepta | Awareb | Openc | Alerting | Orienting | Conflict | |
Stress | −.19 | ||||||||
Mood | −.17 | −.34d | |||||||
Acceptance | −.18 | −.34e | .42d | ||||||
Awareness | .01 | −.17 | .20 | .25e | |||||
Openness | .18 | −.08 | .19 | .14 | .40f | ||||
Alerting | −.47e | .11 | −.07 | −.06 | −.12 | −.09 | |||
Orienting | .06 | −.22 | .25 | .22e | .02 | −.01 | .22e | ||
Conflict | .03 | .11 | −.10 | .29d | −.04 | −.01 | .14 | .13 | |
Group | −.33 | −.61f | .57f | .21 | .09 | −.05 | .15 | .15 | −.24e |
aAccept: acceptance.
bAware: awareness.
cOpen: openness.
d
e
f
This was the first actively controlled study to investigate whether MT apps can promote the therapeutic effects associated with validated group MT interventions, namely, subjective well-being, attentional control, and interoceptive integration. A data-driven approach was used to allow for a broad canvassing of well-being, while also providing a parsimonious interpretation of observed changes in well-being. This approach yielded 3 latent factors: acceptance, awareness, and openness. The clear distinction between loadings onto an acceptance and awareness factor reflect the 2 subfactors of the PHLMS [
Subjective well-being was assessed both in terms of trait (pre- and posttraining) and state (pre- and postpractice session) self-reports. A trend toward MT-specific changes in acceptance from pre- to posttraining was observed, and closer inspection of the data suggested that the MT group might have driven a general increase in acceptance over time. This result was complemented by MT effects at the state level; relative to the cognitive training group, participants in the MT group demonstrated improved mood and reduced stress following each training session. Importantly, changes in acceptance across the intervention were correlated with session-specific changes in stress and mood. Although the overall effect of training on acceptance was weak, this is one of the first documented reports of state-effects of meditation contributing to interventional level effects on dispositional mindfulness.
These findings are consistent with a broader literature in which dispositional acceptance has been associated with reduced experiential avoidance [
Contrary to the study hypotheses, participants in the MT and cognitive training groups reported significant increases in both acceptance and awareness over the study period. One explanation for this finding may be the fact that participants in both groups recorded their mood and stress levels before and after each training session. Research has shown that recording mood and stress in and of itself may contribute to improvements in negative symptomatology by increasing emotional self-awareness [
There were no training effects for either group observed for the openness factor. This result is not entirely surprising in the context of research that has shown that those who choose to practice mindfulness demonstrate greater openness [
Attentional control was assessed on a trait level using the CRSD-ANT, which yielded alerting, orienting, and conflict effect scores. Analyses revealed training effects specific to MT; relative to the cognitive training group, 3 weeks of MT led to greater improvements in conflict monitoring. However, training effects were not observed for alerting effect or orienting effect. These results are in line with Tang and colleagues [
Conflict monitoring, also known as executive attention or switching [
Interoceptive attention was assessed with the respiration integration task. In terms of interoceptive attention, there were no training effects. However, participants in both groups demonstrated greater accuracy when using their breath to judge the circle rather than just using their visual abilities. These results suggest that interoceptive attention might facilitate accuracy on discrimination tasks but that such attention was not particularly impacted by the training paradigm.
Only 1 unique effect of cognitive training was observed: participants in the cognitive training group demonstrated an increase in heart rate over time postpractice session but not for the prepractice session or pre- and postpractice in the MT group. This result may suggest that with an increased focus on negative symptoms during mood monitoring, participants in the cognitive training group may have experienced increased negative reactivity [
It is interesting that changes in heart rate were not observed for the MT group, especially as previous research has found decreases in heart rate following the completion of an 8-week mindfulness-based intervention [
Although this study provides evidence for the beneficial effects of MT using a smartphone app, there are several limitations that should be noted. First, studying app training inherently reduces the generalizability of findings to the richest segments of the global population. More specifically, our sample was limited to participants with Apple devices. Second, this study used a female-dominated sample, a factor that may also reduce generalizability. However, these limitations highlight the importance of replicating the present results across different operating devices and with a more diverse participant group. Third, although practice was monitored, participants were only reminded to practice if they missed 3 consecutive days. Therefore, participants did not necessarily practice with their assigned app (Wildflowers or 2048) every day, which might affect the extent of the significant findings observed. On the other hand, this limitation adds more ecological validity to this study as people in the real world would not be monitored closely to ensure they are practicing every day. Fourth, state mindfulness was not measured during daily training sessions, so it is hard to know if the benefits to mood and stress observed were a result of transiently increased state mindfulness or a result of another factor that was not considered in this study. However, a study design that promotes daily reflection on state mindfulness may have introduced further unintended training effects to the control group. Fifth, although the results strongly support benefits of MT on state measures of subjective well-being, the marginal pre- to postintervention results on the acceptance factor make it inappropriate to draw strong conclusions about the relative efficacy of MT relative to active control. These marginal results may be because of the power of this study or to the short intervention time of only 3 weeks. Although the
This study provides preliminary evidence on the benefits of using an MT smartphone app. These findings suggest that future work should continue to investigate the benefits of MT apps in clinical populations. In addition, future studies should investigate the longitudinal effects of using MT apps. Finally, the results of this study on improvements in attention regulation warrant studies exploring neural changes as a result of MT using a smartphone app. For example, Tang and colleagues observed that 2 weeks of brief mindfulness training altered the resting state functional connectivity of large-scale brain networks [
The results of this study suggest that MT with a smartphone app may provide immediate effects on mood and stress while also providing long-term benefits for attentional control. Although further investigation is warranted, there is evidence that with continued usage, MT via a smartphone app may provide long-term benefits in changing how one relates to his or her inner and outer experiences.
Screenshots of Wildflowers mindfulness training condition.
Screenshots of 2048 cognitive training control conditions.
Additional details and psychometric properties of the questionnaires.
Theoretical rationale for the newly developed respiration integration task.
Additional details for statistical analyses.
Scree plot of Horn’s Parallel Analysis of Principal Components used to determine the appropriate factor solution for the exploratory factor analysis.
Multilevel model figures for acceptance, awareness, openness, alerting, and orienting.
Multilevel model results for the individual questionnaire subscales.
CONSORT‐EHEALTH checklist (V 1.6.1).
Acceptance and Action Questionnaire-II
Attention Network Test
Big Five Inventory
Centre for Research on Safe Driving Attention Network Test
exploratory factor analysis
integrated body-mind training
Multidimensional Assessment of Interoceptive Awareness
mindfulness-based cognitive therapy
mindfulness-based stress reduction
Meaning in Life Questionnaire
mindfulness training
Philadelphia Mindfulness Scale
Perceived Stress Scale
Psychological Well-Being Scale
Research Ethics Board
Respiration Integration Task
Spiritual Experience Index-Revised
BJS is the Chief Scientist and CEO of Mobio Interactive Inc, and he owned approximately 40% of the company at the time of manuscript acceptance. BJS exclusively served as a technical liaison for the study and did not contribute to study design, did not contribute to selecting the active control, or the primary surveys, nor did he directly contribute to, or have influence over, data collection or analysis. NASF is a scientific advisor and mindfulness guide for Mobio Interactive Inc, and he owned approximately 2% of the company at the time of manuscript acceptance. NASF was involved in all aspects of study design and data analysis, but did not directly contribute to, or have influence over, data collection; nor did he directly perform any of the analyses. No other authors have connections to Mobio Interactive Inc. None of the authors in this study received financial compensation or any other form of compensation for the research undertaken herein. Mobio Interactive Inc did, however, contribute Can $5000 to the Ontario Centre of Excellence research grant that, in part, funded this study, as mandated by the funding agency.