Despite growing popularity, associations between dosage and outcomes in meditation-app interventions have not been established. We examined this relationship using a range of operationalizations of dosage (e.g., minutes of use, days of use, number and type of activities completed) and strategies for modeling outcomes (e.g., ordinary least squares regression, multilevel modeling, latent class analysis). We used data from a recently completed randomized controlled trial that tested a meditation app (N = 662; 80.4% with elevated depression/anxiety) that included psychological distress as its preregistered primary outcome. Across 41 models, whether an association was detected and the shape and direction of this association varied. Although several models indicated that higher dosage was associated with larger decreases in psychological distress, many models failed to show this relationship, and some even showed the opposite. These results may have implications for optimizing and studying dosage in meditation apps and for open-science practices.
AdamsZ. W.SieverdesJ. C.Brunner-JacksonB.MuellerM.ChandlerJ.DiazV.PatelS.SoxL. R.WilderS.TreiberF. A. (2018). Meditation smartphone application effects on prehypertensive adults’ blood pressure: Dose-response feasibility trial. Health Psychology, 37(9), 850–860. https://doi.org/10.1037/hea0000584
2.
BakkZ.VermuntJ. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 20–31.
3.
BarkhamM.ConnellJ.StilesW. B.MilesJ. N. V.MargisonF.EvansC.Mellor-ClarkJ. (2006). Dose-effect relations and responsive regulation of treatment duration: The good enough level. Journal of Consulting and Clinical Psychology, 74(1), 160–167. https://doi.org/10.1037/0022-006X.74.1.160
4.
BatesD.MächlerM.BolkerB.WalkerS. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
5.
BaumelA.MuenchF.EdanS.KaneJ. M. (2019). Objective user engagement with mental health apps: Systematic search and panel-based usage analysis. Journal of Medical Internet Research, 21(9), Article e14567. https://doi.org/10.2196/14567
6.
BerryK.SalterA.MorrisR.JamesS.BucciS. (2018). Assessing therapeutic alliance in the context of mHealth interventions for mental health problems: Development of the mobile Agnew relationship measure (mARM) Questionnaire. Journal of Medical Internet Research, 20(4), Article e90. https://doi.org/10.2196/jmir.8252
7.
BolckA.CroonM.HagenaarsJ. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12, 3–27.
8.
CallahanC.KimberJ.HuE.TannerL.KunkleS. (2024). The real-world impact of app-based mindfulness on Headspace members with moderate and severe perceived stress: Observational study. JMIR mHealth and uHealth, 12(1), Article e52968. https://doi.org/10.2196/52968
ChoiS. W.SchaletB.CookK. F.CellaD. (2014). Establishing a common metric for depressive symptoms: Linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychological Assessment, 26(2), 513–527.
11.
CohenS.WilliamsonG. (1988). Perceived stress in a probability sample of the United States. In SpacapanS.OskampS. (Eds.), The social psychology of health: Claremont Symposium on Applied Social Psychology (pp. 31–67). Sage.
12.
CollinsL. M. (2018). Optimization of behavioral, biobehavioral, and biomedical interventions. Springer.
13.
CollinsL. M.LanzaS. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718). John Wiley & Sons.
14.
CreswellJ. D.LindsayE. K. (2014). How does mindfulness training affect health? A mindfulness stress buffering account. Current Directions in Psychological Science, 23(6), 401–407.
15.
DahlC. J.LutzA.DavidsonR. J. (2015). Reconstructing and deconstructing the self: Cognitive mechanisms in meditation practice. Trends in Cognitive Sciences, 19(9), 515–523.
16.
DahlC. J.Wilson-MendenhallC. D.DavidsonR. J. (2020). The plasticity of well-being: A training-based framework for the cultivation of human flourishing. Proceedings of the National Academy of Sciences, USA, 117(51), 32197–32206.
17.
DiggleP.DiggleP. J.HeagertyP.LiangK. Y.ZegerS. (2002). Analysis of longitudinal data. Oxford University Press.
18.
FlettJ. A.ConnerT. S.RiordanB. C.PattersonT.HayneH. (2020). App-based mindfulness meditation for psychological distress and adjustment to college in incoming university students: A pragmatic, randomised, waitlist-controlled trial. Psychology & Health, 35(9), 1049–1074.
19.
FredricksonB. L.ArizmendiC.Van CappellenP.FirestineA. M.BrantleyM. M.KimS. L.SalzbergS. (2019). Do contemplative moments matter? Effects of informal meditation on emotions and perceived social integration. Mindfulness, 10(9), 1915–1925.
20.
GálÉ.S,tefanS.CristeaI. A. (2021). The efficacy of mindfulness meditation apps in enhancing users’ well-being and mental health related outcomes: A meta-analysis of randomized controlled trials. Journal of Affective Disorders, 279, 131–142.
21.
GoldbergS. B. (2022). A common factors perspective on mindfulness-based interventions. Nature Reviews Psychology, 1, 605–619. https://doi.org/10.1038/s44159-022-00090-8
22.
GoldbergS. B.BaldwinS. A.RiordanK. M.TorousJ.DahlC. J.DavidsonR. J.HirshbergM. J. (2022). Alliance with an unguided smartphone app: Validation of the Digital Working Alliance Inventory. Assessment, 296(6), 1331–1345.
23.
GoldbergS. B.BoltD. M.DavidsonR. J. (2021). Data missing not at random in mobile health research: Assessment of the problem and a case for sensitivity analyses. Journal of Medical Internet Research, 23(6), Article e26749. https://doi.org/10.2196/26749
24.
GoldbergS. B.DahlC. J.BoltD. M.DavidsonR. J.HirshbergM. J. (in press). Does it matter how meditation feels? An experience sampling study. Journal of Consulting and Clinical Psychology.
GoldbergS. B.KnoeppelC.DavidsonR. J.FlookL. (2020). Does practice quality mediate the relationship between practice time and outcome in mindfulness-based stress reduction?Journal of Counseling Psychology, 67(1), 115–122. https://doi.org/10.1037/cou0000369
27.
GoldbergS. B.LamS.SimonssonO.TorousJ.SunS. (2022). Mobile phone-based interventions for mental health: A systematic meta-review of 14 meta-analyses of randomized controlled trials. PLOS Digital Health, 1(1), Article e0000002. https://doi.org/10.1371/journal.pdig.0000002
28.
GoldbergS. B.TuckerR. P.GreeneP. A.DavidsonR. J.WampoldB. E.KearneyD. J.SimpsonT. L. (2018). Mindfulness-based interventions for psychiatric disorders: A systematic review and meta-analysis. Clinical Psychology Review, 59, 52–60.
29.
GrahamJ. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576.
30.
HirshbergM. J.DavidsonR. J.GoldbergS. B. (2023). Educators are not alright: Mental health during COVID-19. Educational Researcher, 52(1), 48–52. https://doi.org/10.3102/0013189X221142595
31.
HirshbergM. J.FryeC.DahlC. J.RiordanK. M.VackN. J.SachsJ.GoldmanR.DavidsonR. J.GoldbergS. B. (2022). A randomized controlled trial of a smartphone-based well-being training in public school system employees during the COVID-19 pandemic. Journal of Educational Psychology, 114(8), 1895–1911.
32.
JacobyW. G. (2000). Loess: A nonparametric, graphical tool for depicting relationships between variables. Electoral Studies, 19(4), 577–613.
33.
KikenL. G.GarlandE. L.BluthK.PalssonO. S.GaylordS. A. (2015). From a state to a trait: Trajectories of state mindfulness in meditation during intervention predict changes in trait mindfulness. Personality and Individual Differences, 81, 41–46.
34.
LeurentB.GomesM.FariaR.MorrisS.GrieveR.CarpenterJ. R. (2018). Sensitivity analysis for not-at-random missing data in trial-based cost-effectiveness analysis: A tutorial. PharmacoEconomics, 36(8), 889–901.
35.
LinardonJ.Fuller-TyszkiewiczM. (2020). Attrition and adherence in smartphone-delivered interventions for mental health problems: A systematic and meta-analytic review. Journal of Consulting and Clinical Psychology, 88(1), 1–13. https://doi.org/10.1037/ccp0000459
36.
LinardonJ.MesserM.GoldbergS. B.Fuller-TyszkiewiczM. (2023). The efficacy of mindfulness apps on symptoms of depression and anxiety: An updated meta-analysis of randomized controlled trials. Clinical Psychology Review, 107, Article 102370. https://doi.org/10.1016/j.cpr.2023.102370
37.
McVayM. A.BennettG. G.SteinbergD.VoilsC. I. (2019). Dose–response research in digital health interventions: Concepts, considerations, and challenges. Health Psychology, 38(12), 1168–1174. https://doi.org/10.1037/hea0000805
38.
MontoriV. M.GuyattG. H. (2001). Intention-to-treat principle. CMAJ, 165(10), 1339–1341.
39.
MuthénL. K.MuthénB. O. (2017). Mplus user’s guide (8th ed.).
40.
Nahum-ShaniI.ShawS. D.CarpenterS. M.MurphyS. A.YoonC. (2022). Engagement in digital interventions. American Psychologist, 77(7), 836–852.
41.
PilkonisP. A.ChoiS. W.ReiseS. P.StoverA. M.RileyW. T.CellaD., & PROMIS Cooperative Group. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS): Depression, anxiety, and anger. Assessment, 18(3), 263–283.
42.
QianT.WaltonA. E.CollinsL. M.KlasnjaP.LanzaS. T.Nahum-ShaniI.RabbiM.RussellM. A.WaltonM. A.YooH.MurphyS. A. (2022). The microrandomized trial for developing digital interventions: Experimental design and data analysis considerations. Psychological Methods, 27(5), 874–894.
43.
RobertiJ. W.HarringtonL. N.StorchE. A. (2006). Further psychometric support for the 10-item version of the perceived stress scale. Journal of College Counseling, 9(2), 135–147.
44.
SchaletB. D.CookK. F.ChoiS. W.CellaD. (2014). Establishing a common metric for self-reported anxiety: Linking the MASQ, PANAS, and GAD-7 to PROMIS Anxiety. Journal of Anxiety Disorders, 28(1), 88–96.
45.
SteegenS.TuerlinckxF.GelmanA.VanpaemelW. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702–712.
46.
TorousJ.AnderssonG.BertagnoliA.ChristensenH.CuijpersP.FirthJ.HaimA.HsinH.HollisC.LewisS.MohrD. C.PratapA.RouxS.SherrillJ.AreanP. A. (2019). Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry, 18(1), 97–98. https://doi.org/10.1002/wps.20592
47.
van BuurenS.Groothuis-OudshoornK. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.
48.
VermuntJ. K. (2010). Longitudinal research using mixture models. In van MontfortK.OudJ.SatorraA. (Eds.), Longitudinal research with latent variables (pp. 119–152). Springer.
49.
WasilA. R.GillespieS.PatelR.PetreA.Venturo-ConerlyK. E.ShingletonR. M.WeiszJ. R.DeRubeisR. J. (2020). Reassessing evidence-based content in popular smartphone apps for depression and anxiety: Developing and applying user-adjusted analyses. Journal of Consulting and Clinical Psychology, 88(11), 983–993. https://doi.org/10.1037/ccp0000604
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
