Predictors of dropout in a digital intervention for the prevention and treatment of depression in patients with chronic Back pain: secondary analysis of two randomized controlled trials
Open accessAbstract
BACKGROUND: Depression is a common comorbid condition in individuals with chronic back pain (CBP), leading to poorer treatment outcomes and increased medical complications. Digital interventions have demonstrated efficacy in the prevention and treatment of depression; however, high dropout rates are a major challenge, particularly in clinical settings. OBJECTIVE: This study aims to identify the predictors of dropout in a digital intervention for the treatment and prevention of depression in patients with comorbid CBP. We assessed which participant characteristics may be associated with dropout and whether intervention usage data could help improve the identification of individuals at risk of dropout early on in treatment. METHODS: Data were collected from 2 large-scale randomized controlled trials in which 253 patients with a diagnosis of CBP and major depressive disorder or subclinical depressive symptoms received a digital intervention for depression. In the first analysis, participants' baseline characteristics were examined as potential predictors of dropout. In the second analysis, we assessed the extent to which dropout could be predicted from a combination of participants' baseline characteristics and intervention usage variables following the completion of the first module. Dropout was defined as completing <6 modules. Analyses were conducted using logistic regression. RESULTS: : OR 1.59, 95% CI 1.13-2.23), medium versus high social support (OR 3.03, 95% CI 1.25-7.33), and a higher number of days to module completion (OR 1.05, 95% CI 1.02-1.08) predicted a higher risk of dropout, whereas a self-reported negative event in the previous week was associated with a lower risk of dropout (OR 0.24, 95% CI 0.08-0.69). A model that combined baseline characteristics and intervention usage data generated the most accurate predictions (area under the receiver operating curve [AUC]=0.72) and was significantly more accurate than models based on baseline characteristics only (AUC=0.70) or intervention usage data only (AUC=0.61). We found no significant influence of pain, disability, or depression severity on dropout. CONCLUSIONS: Dropout can be predicted by participant baseline variables, and the inclusion of intervention usage variables may improve the prediction of dropout early on in treatment. Being able to identify individuals at high risk of dropout from digital health interventions could provide intervention developers and supporting clinicians with the ability to intervene early and prevent dropout from occurring.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Dropout was associated with users' baseline characteristics and early engagement patterns, which also influence individuals' SE because the characteristics and patterns can shape users' confidence in their ability to engage with and successfully complete digital interventions.
“Moshe et al. [57] found that dropout was associated with users’ baseline characteristics and early engagement patterns, which also influence individuals’ SE because the characteristics and patterns can shape users’ confidence in their ability to engage with and successfully complete digital interventions.”
From Advancing Undergraduate Student Mental Healthcare of Social Anxiety Disorder: Evaluating the Acceptance of AR-Assisted Cognitive Behavioral Therapy Through TAM-Based Constructs. (2026) · cites this paper as [57]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredWe estimated a 45% dropout and aim to achieve a sample size of 232 participants for a pooled analysis of 7 countries.
“To be on the safe side, considering the dropout rates in previous studies of digital interventions (Moshe et al., 2022) and mental health interventions in low- and middle-income countries (Fernández et al., 2021), we estimated a 45% dropout. Thus, we aim to achieve a sample size of 232 participants for a pooled analysis of 7 countries.”
From Internet-based self-administered intervention to reduce anxiety and depression symptomatology and improve well-being in 7 countries: protocol of a randomized control trial (2024) · cites this paper as Moshe et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793 · see citing works
Hamilton, M. (1960) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.23.1.56 · Cited by 32,817 · see citing works
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302 · see citing works
<b>mice</b>: Multivariate Imputation by Chained Equations in<i>R</i>
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870 · see citing works
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · cited 1× · see citing works
“The pROC package was used to calculate the AUROC [63].”
Building Predictive Models in R Using the caret Package
Kühn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 9,986 · see citing works
Vos, T., Allen, C., Arora, M., Barber, R. M., Bhutta, Z. A., Brown, A., Carter, A., Casey, D. C., Charlson, F. J., Chen, A. Z., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31678-6 · Cited by 7,253 · see citing works
(2010) · Cited by 7,034 · see citing works
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072 · see citing works
Depression and Pain Comorbidity: A Literature Review
Bair, M. J., Robinson, R. L., Katon, W., & Kroenke, K. (2003) · Archives of Internal Medicine · doi:10.1001/archinte.163.20.2433 · Cited by 3,463 · see citing works
Eysenbach, G. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e11 · Cited by 2,524 · cited 5× · see citing works
“In most digital health interventions, there is a steady attrition over time, with most users dropping out after completing the first 1 or 2 modules—the “attrition-phase” [31,41,42].”
InfluentialFlexible Imputation of Missing Data, Second Edition
van Buuren, S. (2018) · doi:10.1201/9780429492259 · Cited by 2,273 · see citing works
Psychological therapies for the management of chronic pain (excluding headache) in adults
Williams, A. C. D. C., Eccleston, C., & Morley, S. (2012) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd007407.pub3 · Cited by 1,634 · cited 1× · see citing works
“Psychological interventions have been demonstrated to be effective in the treatment of depression for individuals with CBP [7].”
Wu, A., MARCH, L. M., Zheng, X.-Q., Huang, J., Wang, X., Zhao, J., Blyth, F., Smith, E. U. R., Buchbinder, R., & Hoy, D. G. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.02.175 · Cited by 1,560 · see citing works
Computer-based psychological treatments for depression: A systematic review and meta-analysis
Richards, D., & Richardson, T. (2012) · Clinical Psychology Review · doi:10.1016/j.cpr.2012.02.004 · Cited by 1,442 · see citing works
The treatment gap in mental health care.
(2004) · Europe PMC (PubMed Central) · Cited by 1,419 · see citing works
Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt‐Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J. (2021) · World Psychiatry · doi:10.1002/wps.20883 · Cited by 1,199 · cited 1× · see citing works
“…which deliver psychotherapeutic components via a web browser or smartphone app, have been proposed as a scalable, cost-effective way to meet the growing demand for psychological treatment and address many of the challenges associated with accessing traditional face-to-face therapy [11-15].”
Adherence in Internet Interventions for Anxiety and Depression
Christensen, H., Griffiths, K. M., & Farrer, L. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1194 · Cited by 1,133 · cited 3× · see citing works
“Variables were chosen on the basis of previous research pointing to demonstrated or hypothetical relationships between the predictor variables and intervention adherence or dropout [32-34, 45, 46, The process variables included internet affinity, as measured by the Internet Affinity Scale.”
Undertreatment of people with major depressive disorder in 21 countries
Thornicroft, G., Chatterji, S., Evans-Lacko, S., Gruber, M., Sampson, N., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., Andrade, L., Borges, G., et al. (2017) · British Journal of Psychiatry · doi:10.1192/bjp.bp.116.188078 · Cited by 1,121 · cited 1× · see citing works
“On average, less than 1 in 5 people in high-income countries and less than 1 in 27 people in low-and middle-income countries receive appropriate treatment [9], giving rise to a treatment gap in mental health care [10].”
A Systematic Review of the Impact of Adherence on the Effectiveness of e-Therapies
Donkin, L., Christensen, H., Naismith, S. L., Neal, B., Hickie, I. B., & Glozier, N. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1772 · Cited by 876 · cited 2× · see citing works
“Several studies have assessed predictors of dropout in digital mental health interventions [27,32-34].”
Karyotaki, E., Efthimiou, O., Miguel, C., Maas genannt Bermpohl, F., Furukawa, T. A., Cuijpers, P., Individual Patient Data Meta-Analyses for Depression (IPDMA-DE) Collaboration, Riper, H., Patel, V., Mira, A., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.4364 · Cited by 866 · see citing works
Managing patients with multimorbidity in primary care
Wallace, E., Salisbury, C., Guthrie, B., Lewis, C., Fahey, T. P., & Smith, S. M. (2015) · BMJ · doi:10.1136/bmj.h176 · Cited by 752 · cited 1× · see citing works
“As the treatment schedules of patients with multimorbidities can already be quite demanding [37,38], higher levels of pain disability—or lower confidence in performing activities while in pain (pain self-efficacy)—may therefore influence an individual’s ability to adhere to a digital intervention…”
Shimokawa, K., Lambert, M. J., & Smart, D. W. (2010) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0019247 · Cited by 657 · cited 2× · see citing works
“…feedback technology that identifies individuals at risk of deterioration during treatment has been shown to improve eventual treatment outcomes [43,44], dropout warning systems could be used to alert the supporting clinician, guide care pathways (eg, in stepped-care models), or personalize the…”
Inflammation Effects on Motivation and Motor Activity: Role of Dopamine
Felger, J. C., & Treadway, M. T. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.143 · Cited by 453 · cited 1× · see citing works
“This question may be particularly pertinent for these individuals as chronic pain and depression are often associated with reduced motivation to initiate or complete goal-directed tasks [35,36].”
Ebert, D. D., Van Daele, T., Nordgreen, T., Karekla, M., Compare, A., Zarbo, C., Brugnera, A., Øverland, S., Trebbi, G., Jensen, K. L., et al. (2018) · European Psychologist · doi:10.1027/1016-9040/a000318 · Cited by 437 · see citing works
Self-citationPrognosis for patients with chronic low back pain: Inception cohort study
Costa, L. D. C. M., Maher, C. G., McAuley, J. H., Hancock, M. J., Herbert, R. D., Refshauge, K. M., & Henschke, N. (2009) · BMJ · doi:10.1136/bmj.b3829 · Cited by 430 · cited 1× · see citing works
“In patients with CBP, comorbid depression is often associated with lower treatment adherence, poorer treatment outcomes, increased medical complications, and higher health care use [4-6].”
Karyotaki, E., Kleiboer, A., Smit, F., Turner, D. T., Pastor, A. M., Andersson, G., Berger, T., Botella, C., Breton, J. M., Carlbring, P., et al. (2015) · Psychological Medicine · doi:10.1017/s0033291715000665 · Cited by 408 · cited 6× · see citing works
“In a meta-analysis of individual patient data, Karyotaki et al [33] found that being male, having a lower education level, younger age, and comorbid anxiety symptoms significantly increased the risk of dropout from self-guided digital interventions for depression.”
InfluentialWaller, R., & Gilbody, S. (2009) · Psychological Medicine · doi:10.1017/s0033291708004224 · Cited by 393 · cited 4× · see citing works
“…be because of the fact that experiencing a negative event (or being asked to report on one) provided greater intrinsic motivation to complete the module and is consistent with research demonstrating that some people drop out from an intervention because they no longer feel they need it [30,31,40].”
InfluentialDigital interventions for the treatment of depression: a meta-analytic review
Moshe, I., Terhorst, Y., Philippi, P., Domhardt, M., Cuijpers, P., Cristea, I., Pulkki-Råback, L., Baumeister, H., & Sander, L. B. (2021) · Psychological Bulletin · doi:10.1037/bul0000334 · Cited by 390 · cited 4× · see citing works
“There is now a large body of evidence supporting the efficacy of digital interventions for the prevention and treatment of depression [16-20], with recent studies providing promising evidence for the treatment of depression in patients with comorbid physical conditions [21-25].”
InfluentialSelf-citationICBT in routine care: A descriptive analysis of successful clinics in five countries
Titov, N., Dear, B., Nielssen, O., Staples, L., Hadjistavropoulos, H., Nugent, M., Adlam, K., Nordgreen, T., Bruvik, K. H., Hovland, A., et al. (2018) · Internet Interventions · doi:10.1016/j.invent.2018.07.006 · Cited by 353 · cited 2× · see citing works
“Given the high dropout rates found in real-world settings [16], this will become increasingly important as interventions are implemented within public and private health care systems to meet the growing demand for psychotherapy [47].”
Beatty, L., & Binnion, C. (2016) · International Journal of Behavioral Medicine · doi:10.1007/s12529-016-9556-9 · Cited by 347 · cited 1× · see citing works
“More pertinently, it points to the challenges of predicting which individuals are likely to drop out of a digital intervention based on baseline characteristics alone [70].”
Sieverink, F., Kelders, S. M., & van Gemert-Pijnen, J. E. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8578 · Cited by 331 · cited 1× · see citing works
“Finally, although dropout in this study was operationalized according to the usage intended by the clinicians who developed the intervention [76], it is important to highlight that it is not always necessary for patients to complete the full per-protocol treatment to benefit clinically [30].”
Addressing the public mental health challenge of COVID-19
Campion, J., Javed, A., Sartorius, N., & Marmot, M. G. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30240-6 · Cited by 290 · cited 1× · see citing works
“…which deliver psychotherapeutic components via a web browser or smartphone app, have been proposed as a scalable, cost-effective way to meet the growing demand for psychological treatment and address many of the challenges associated with accessing traditional face-to-face therapy [11-15].”
Formal education and back pain: a review
Dionne, C. E., Korff, M. V., Koepsell, T. D., Deyo, R. A., Barlow, W. E., & Checkoway, H. (2001) · Journal of Epidemiology & Community Health · doi:10.1136/jech.55.7.455 · Cited by 274 · cited 1× · see citing works
“Lower levels of education have also been associated with longer duration or higher occurrence of back pain [68], underscoring the need for additional research on digital interventions for this particular patient group.”
Farvolden, P., Denisoff, E., Selby, P., Bagby, R. M., & Rudy, L. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e7 · Cited by 223 · cited 1× · see citing works
“In most digital health interventions, there is a steady attrition over time, with most users dropping out after completing the first 1 or 2 modules—the “attrition-phase” [31,41,42].”
Computer-Assisted Cognitive-Behavior Therapy for Depression: A Systematic Review and Meta-Analysis
Wright, J. H., Owen, J. J., Richards, D., Eells, T. D., Richardson, T., Brown, G. K., Barrett, M., Rasku, M. A., Polser, G., & Thase, M. E. (2019) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.18r12188 · Cited by 200 · see citing works
Musiat, P., Johnson, C., Atkinson, M., Wilksch, S., & Wade, T. (2022) · Psychological Medicine · doi:10.1017/s0033291721004621 · Cited by 166 · cited 1× · see citing works
“The same meta-analysis also identified that completing the full intervention was the strongest predictor of outcomes, underscoring the importance of treatment adherence [26].”
Richards, D., Enrique, A., Eilert, N., Franklin, M., Palacios, J., Duffy, D., Earley, C., Chapman, J., Jell, G., Sollesse, S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0293-8 · Cited by 154 · cited 1× · see citing works
“As digital mental health interventions are being increasingly adopted by health care systems worldwide [13,47], the ability to identify patients at risk of dropout may provide valuable information to improve the adherence, and thus effectiveness, of digital interventions.”
Delgadillo, J., de Jong, K., Lucock, M., Lutz, W., Rubel, J., Gilbody, S., Ali, S., Aguirre, E., Appleton, M., Nevin, J., et al. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(18)30162-7 · Cited by 152 · cited 2× · see citing works
“…feedback technology that identifies individuals at risk of deterioration during treatment has been shown to improve eventual treatment outcomes [43,44], dropout warning systems could be used to alert the supporting clinician, guide care pathways (eg, in stepped-care models), or personalize the…”
Paganini, S., Teigelkötter, W., Buntrock, C., & Baumeister, H. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.07.018 · Cited by 144 · see citing works
Self-citationPredictors of adherence among community users of a cognitive behavior therapy website.
(2008) · PubMed Central · Cited by 142 · see citing works
Mannion, A. F., Junge, A., Grob, D., Dvořák, J., & Fairbank, J. C. T. (2005) · European Spine Journal · doi:10.1007/s00586-004-0816-z · Cited by 124 · see citing works
Milesl, C. L., Pincusl, T., Carnesl, D., Homerl, K. E., Taylorl, S. J. C., Bremnerl, S. A., Rahmanl, A., & Underwoodl, M. (2011) · European Journal of Pain · doi:10.1016/j.ejpain.2011.01.016 · Cited by 119 · see citing works
O'Moore, K., Newby, J., Andrews, G., Hunter, D. J., Bennell, K., Smith, J., & Williams, A. D. (2018) · Arthritis care & research · doi:10.1002/acr.23257 · Cited by 113 · see citing works
Chien, I., Enrique, A., Palacios, J., Regan, T., Keegan, D., Carter, D., Tschiatschek, S., Nori, A., Thieme, A., Richards, D., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.10791 · Cited by 111 · cited 2× · see citing works
“…engagement with the intervention, for example, the number and timing of log-ins, interaction with specific components of the modules (eg, homework; [72]), data specifically related to the intervention target (eg, sleep data for an insomnia intervention; [46]), and additional self-report data such…”
Norlund, F., Wallin, E., Olsson, E. M. G., Wallert, J., Burell, G., von Essen, L., & Held, C. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9710 · Cited by 110 · cited 1× · see citing works
“In most digital health interventions, there is a steady attrition over time, with most users dropping out after completing the first 1 or 2 modules—the “attrition-phase” [31,41,42].”
Ebert, D. D., Nobis, S., Lehr, D., Baumeister, H., Riper, H., Auerbach, R. P., Snoek, F., Cuijpers, P., & Berking, M. (2017) · Diabetic Medicine · doi:10.1111/dme.13173 · Cited by 101 · see citing works
Self-citationPredictors of Dropout in Internet-Based Cognitive Behavioral Therapy for Depression
Ezawa, I. D., Forand, N. R., & Strunk, D. R. (2018) · Cognitive Therapy and Research · doi:10.1007/s10608-018-9979-5 · Cited by 92 · cited 3× · see citing works
“Participants with lower education levels were more likely to drop out of treatment, which is consistent with a large body of research on adherence to both digital interventions and face-to-face psychotherapy [33,51,65-67].”
Patient experience of computerised therapy for depression in primary care
Knowles, S. E., Lovell, K., Bower, P., Gilbody, S., Littlewood, E., & Lester, H. (2015) · BMJ Open · doi:10.1136/bmjopen-2015-008581 · Cited by 89 · cited 5× · see citing works
“…of log-ins, interaction with specific components of the modules (eg, homework; [72]), data specifically related to the intervention target (eg, sleep data for an insomnia intervention; [46]), and additional self-report data such as early measures of therapeutic alliance with the coach [40,73].”
InfluentialThompson, E. L., Broadbent, J., Bertino, M. D., & Staiger, P. K. (2016) · The Clinical Journal of Pain · doi:10.1097/ajp.0000000000000235 · Cited by 84 · cited 1× · see citing works
“…with multimorbidities can already be quite demanding [37,38], higher levels of pain disability—or lower confidence in performing activities while in pain (pain self-efficacy)—may therefore influence an individual’s ability to adhere to a digital intervention and thus experience the benefits [39].”
Enrique, A., Palacios, J. E., Ryan, H., & Richards, D. (2019) · Journal of Medical Internet Research · doi:10.2196/12775 · Cited by 81 · cited 2× · see citing works
“…age predicted higher risk of dropout suggests that the relationship between age and dropout may be more complex than has been previously identified, either owing to the nonlinear relationship between the two or a possible interaction between age and other factors such as computer literacy [33,69].”
Depressive symptoms in patients with chronic pain
Nicholas, M. K., Coulston, C. M., Asghari, A., & Malhi, G. S. (2009) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.2009.tb02473.x · Cited by 73 · cited 1× · see citing works
“In patients with CBP, comorbid depression is often associated with lower treatment adherence, poorer treatment outcomes, increased medical complications, and higher health care use [4-6].”
Scaling up psychological treatments for common mental disorders: a call to action
Singla, D. R., Raviola, G., & Patel, V. (2018) · World Psychiatry · doi:10.1002/wps.20532 · Cited by 69 · see citing works
Pedersen, D. H., Mansourvar, M., Sortsø, C., & Schmidt, T. (2019) · Journal of Medical Internet Research · doi:10.2196/13617 · Cited by 68 · cited 4× · see citing works
“Variables were chosen on the basis of previous research pointing to demonstrated or hypothetical relationships between the predictor variables and intervention adherence or dropout [32-34, 45, 46, The process variables included internet affinity, as measured by the Internet Affinity Scale.”
InfluentialKnowles, S. E., Goulding, R., Senra, H., Bower, P., Locock, L., Protheroe, J., Sanders, C., & Daker‐White, G. (2017) · Health Expectations · doi:10.1111/hex.12648 · Cited by 63 · cited 1× · see citing works
“As the treatment schedules of patients with multimorbidities can already be quite demanding [37,38], higher levels of pain disability—or lower confidence in performing activities while in pain (pain self-efficacy)—may therefore influence an individual’s ability to adhere to a digital intervention…”
Sander, L. B., Paganini, S., Terhorst, Y., Schlicker, S., Lin, J., Spanhel, K., Buntrock, C., Ebert, D. D., & Baumeister, H. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.1021 · Cited by 63 · see citing works
Self-citationEisenberg, J. M., & Hershey, J. C. (1983) · Medical Decision Making · doi:10.1177/0272989x8300300203 · Cited by 58 · see citing works
Baumeister, H., Paganini, S., Sander, L. B., Lin, J., Schlicker, S., Terhorst, Y., Moshagen, M., Bengel, J., Lehr, D., & Ebert, D. D. (2021) · Psychotherapy and Psychosomatics · doi:10.1159/000511881 · Cited by 55 · cited 4× · see citing works
“This study was a secondary analysis of data from 2 trials that assessed the efficacy of a therapist-guided internet-based intervention for the treatment [48] and prevention [21] of depressive symptoms in patients with comorbid CBP.”
InfluentialSelf-citationAL-Asadi, A. M., Klein, B., & Meyer, D. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2989 · Cited by 53 · see citing works
Meyer, B., Weiss, M., Holtkamp, M., Arnold, S., Brückner, K., Schröder, J., Scheibe, F., & Nestoriuc, Y. (2019) · Epilepsia · doi:10.1111/epi.14673 · Cited by 53 · see citing works
Chekroud, A. M., Foster, D. F., Zheutlin, A. B., Gerhard, D. M., Roy, B., Koutsouleris, N., Chandra, A., Esposti, M. D., Subramanyan, G., Gueorguieva, R. V., et al. (2018) · Psychiatric Services · doi:10.1176/appi.ps.201800094 · Cited by 51 · cited 1× · see citing works
“The same applies to baseline characteristics, where studies have shown that data obtained from electronic medical records may be used to identify those at risk of dropout during face-to-face therapy [74,75].”
Jarrett, R. B., Minhajuddin, A., Kangas, J. L., Friedman, E. S., Callan, J. A., & Thase, M. E. (2013) · Behaviour Research and Therapy · doi:10.1016/j.brat.2013.01.006 · Cited by 48 · cited 1× · see citing works
“Participants with lower education levels were more likely to drop out of treatment, which is consistent with a large body of research on adherence to both digital interventions and face-to-face psychotherapy [33,51,65-67].”
Wells, M. J., Owen, J. J., McCray, L. W., Bishop, L. B., Eells, T. D., Brown, G. K., Richards, D., Thase, M. E., & Wright, J. H. (2018) · The Primary Care Companion For CNS Disorders · doi:10.4088/pcc.17r02196 · Cited by 46 · see citing works
Schlicker, S., Baumeister, H., Buntrock, C., Sander, L., Paganini, S., Lin, J., Berking, M., Lehr, D., & Ebert, D. D. (2020) · JMIR Mental Health · doi:10.2196/16398 · Cited by 46 · cited 1× · see citing works
“There is now a large body of evidence supporting the efficacy of digital interventions for the prevention and treatment of depression [16-20], with recent studies providing promising evidence for the treatment of depression in patients with comorbid physical conditions [21-25].”
Self-citationSander, L., Paganini, S., Lin, J., Schlicker, S., Ebert, D. D., Buntrock, C., & Baumeister, H. (2017) · BMC Psychiatry · doi:10.1186/s12888-017-1193-6 · Cited by 44 · cited 3× · see citing works
“Dropout was defined as completing 6 intervention modules, in accordance with the intervention developers [49,50].”
Lawler, K., Earley, C., Timulák, L., Enrique, Á., & Richards, D. (2021) · JMIR Formative Research · doi:10.2196/26221 · Cited by 38 · cited 1× · see citing works
“…of log-ins, interaction with specific components of the modules (eg, homework; [72]), data specifically related to the intervention target (eg, sleep data for an insomnia intervention; [46]), and additional self-report data such as early measures of therapeutic alliance with the coach [40,73].”
Symptoms and side effects in chronic non-cancer pain: patient report vs. systematic assessment
JONSSON, T., Christrup, L. L., Højsted, J., Villesen, H. H., ALBJERG, T. H., Ravn‐Nielsen, L. V., & Sjøgren, P. (2010) · Acta Anaesthesiologica Scandinavica · doi:10.1111/j.1399-6576.2010.02329.x · Cited by 29 · cited 1× · see citing works
“This question may be particularly pertinent for these individuals as chronic pain and depression are often associated with reduced motivation to initiate or complete goal-directed tasks [35,36].”
Bremer, V., Chow, P. I., Funk, B., Thorndike, F. P., & Ritterband, L. M. (2020) · doi:10.2196/preprints.17738 · Cited by 29 · cited 5× · see citing works
“In a study of a self-help digital intervention for the treatment of insomnia, Bremer et al [46] identified individuals at risk of dropout with an area under the receiver operating curve (AUC) of 0.719 using a combination of baseline characteristics (eg, self-reported stress levels) and intervention…”
InfluentialLin, J., Sander, L., Paganini, S., Schlicker, S., Ebert, D., Berking, M., Bengel, J., Nobis, S., Lehr, D., Mittag, O., et al. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-015226 · Cited by 27 · cited 3× · see citing works
“Dropout was defined as completing 6 intervention modules, in accordance with the intervention developers [49,50].”
Predicting patient engagement in IAPT services: a statistical analysis of electronic health records
Davis, A., Smith, T. R., Talbot, J., Eldridge, C., & Betts, D. (2020) · Evidence-Based Mental Health · doi:10.1136/ebmental-2019-300133 · Cited by 26 · cited 1× · see citing works
“The same applies to baseline characteristics, where studies have shown that data obtained from electronic medical records may be used to identify those at risk of dropout during face-to-face therapy [74,75].”
Adherence to a web-based pre-treatment for phobias in outpatient clinics
Kok, R. N., Beekman, A. T. F., Cuijpers, P. J., & van Straten, A. (2017) · Internet Interventions · doi:10.1016/j.invent.2017.05.004 · Cited by 18 · cited 2× · see citing works
“Variables were chosen on the basis of previous research pointing to demonstrated or hypothetical relationships between the predictor variables and intervention adherence or dropout [32-34, 45, 46, The process variables included internet affinity, as measured by the Internet Affinity Scale.”
Sander, L., Rausch, L., & Baumeister, H. (2016) · Systematic Reviews · doi:10.1186/s13643-016-0209-5 · Cited by 14 · see citing works
Flagged first, then by citations
1 of the 74 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Understanding Adherence to Digital Health Technologies: Systematic Review of Predictive Factors
Figueiredo, T., Alcântara, L., Carrilho, J., Paúl, C., & Costa, E. M. (2025) · Journal of Medical Internet Research · doi:10.2196/77362 · Cited by 27 · see citing works
“In these studies, increasing age was shown to positively influence adherence, negatively influence it [39,44,48,53,61,69,80,88], or have mixed effects [62].”
Idrees, A. R., Kraft, R., Mutter, A., Baumeister, H., Reichert, M., & Pryss, R. C. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000498 · Cited by 20 · see citing works
Zantvoort, K., Isacsson, N. H., Funk, B., & Kaldo, V. (2024) · Digital Health · doi:10.1177/20552076241248920 · Cited by 17 · see citing works
“30 21 Taking away an assumed 15% data for a holdout test set results in training data of 98 and 291 patients per single intervention.”
BackgroundInfluentialvan Doorn, M., Monsanto, A., Wang, C. L., Verfaillie, S. C. J., van Amelsvoort, T., Popma, A., Jaspers, M., Öry, F. G., Álvarez‐Jiménez, M., Gleeson, J., et al. (2023) · JMIR mhealth and uhealth · doi:10.2196/50636 · Cited by 14 · see citing works
“…have identified several predictors of dropout, such as psychosocial difficulties, symptom severity [99], being young, being male, the involvement of more than one therapist in treatment, having no history of psychiatric disorders [100], lower social support [101], and lower change motivation [102].”
BackgroundEffective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study
Kötting, L., Anand-Kumar, V., Keller, F. M., Henschel, N. T., & Lippke, S. (2024) · JMIR Human Factors · doi:10.2196/48218 · Cited by 9 · see citing works
“These findings match previous findings that identified age as a predictor of dropout from digital interventions [41,42].”
BackgroundResultSimon, L., Steinmetz, L., Bendig, E., Küchler, A., Riemann, D. W., Ebert, D. D., Spiegelhalder, K., & Baumeister, H. (2025) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2025.100598 · Cited by 8 · see citing works
“Some evidence exists that both in insomnia (Bremer et al. 2020) and in other mental health areas, as in depression (Moshe et al. 2022), the time needed to complete the first module is associated with dropout.”
Dominguez-Rodriguez, A., Herdoiza-Arroyo, P. E., González-Ramírez, L. P., Martínez-Arriaga, R. J., Villarreal-Zegarra, D., Santos da Silva, A. C., González-Cantero, J. O., Vargas Salinas, V. S., S. Mensorio, M., Cisneros Hernández, A. A., et al. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1279847 · Cited by 3 · see citing works
Days between sessions predict attrition in text-based internet intervention of Binge Eating Disorder
Linnet, J., Hertz, S. P. T., Jensen, E. S., Runge, E., Tarp, K., Holmberg, T. T., Mathiasen, K., & Lichtenstein, M. B. (2023) · Internet Interventions · doi:10.1016/j.invent.2023.100607 · Cited by 4 · see citing works
Hall, S. S., Hall, C., Partlett, C., Jeayes, A., Rattu, N. R., Henshaw, H., Martin, J., & Shoesmith, E. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001466 · Cited by 3 · see citing works
“The role of digital skills was only considered by the perspective piece [39] and 7/57 trials, typically in the form of assessing digital literacy/competence through self-report or purpose-built questionnaires [42,75,85,87,92].”
Iribarren, S., Rupp, J., Sprecher, J., Lutz, B. R., & Rubinstein, F. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001457 · Cited by 2 · see citing works
“App use declined from 78% at 60 days to 50% at 180 days, which mirrors trends observed in other mobile health interventions [28,33].”
Martins, D. S., Bermúdez, M. B., Costa, M. D. A., Fleck, M. P., Salum, G. A., Dreher, C. B., & Manfro, G. G. (2024) · Scientific Reports · doi:10.1038/s41598-024-81327-9 · Cited by 2 · see citing works
Goethem, V. V., Dierickx, S., Matthys, O., Deliëns, L., Lapeire, L., Surmont, V. F. M., Geboes, K., Renard, V., Grøenvold, M., D’Alton, P., et al. (2025) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2024.0101 · Cited by 1 · see citing works
“24,45,46 While ICT-related reasons and digital competence do not influence trial participation and retention, future research into which factors do is required.”
Yoshioka, T., Goto, A., Mizushima, T., Suzuki, Y., Ueda, Y., Yagi, A., Sekine, M., Kudo, R., Garland, S. M., Kumarasamy, S., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/67778 · Cited by 1 · see citing works
Kronthaler, S. M., Kosman, E., Tissen‐Diabaté, T., Mühle, E., Weber-Schallauer, L., Pross, T., Karsten, M. M., Beier, K. M., & Hatzler, D. L. (2026) · Journal of Medical Internet Research · doi:10.2196/86042 · Cited by 1 · see citing works
“Being partnered has been identified as a risk factor for sexual dysfunction with distress [12], whereas being single has already been associated with higher dropout [81].”
Eicher, S., Fenski, F., Behr, S., Hammelrath, L., Boettcher, J., Schaeuffele, C., & Knaevelsrud, C. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02546-0 · Cited by 1 · see citing works
“Higher baseline symptom severity and lower quality of life are associated with increased dropout rates 23,45 .”
InfluentialSîrbu, V., Tomoiagă, C. T., & David, D. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2641702 · Cited by 1 · see citing works
Zhou, Z., Zhou, Y., Ouyang, B., Man, S. S., & Chan, A. H. S. (2026) · Healthcare · doi:10.3390/healthcare14131978
“Moshe et al. [57] found that dropout was associated with users’ baseline characteristics and early engagement patterns, which also influence individuals’ SE because the characteristics and patterns can shape users’ confidence in their ability to engage with and successfully complete digital…”
Yoshioka, T., Goto, A., Mizushima, T., Suzuki, Y., Ueda, Y., Yagi, A., Sekine, M., Kudo, R., Garland, S. M., Kumaresamy, S., et al. (2024) · doi:10.2196/preprints.67778
Meeting people where they are: Crowdsourcing goal-specific personalized wellness practices
Hidalgo, J. E., Kim, J., Llorin, J., Stanton, K., Cherian, J., Bloomfield, L. S. P., Fudolig, M. I., Price, M., Ha, J., Noble, N., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000650 · Cited by 1 · see citing works
Kim, M., Park, J. Y., & Chung, K. (2024) · doi:10.2196/preprints.70243
Hall, S. S., Hall, C. L., Partlett, C., Jeayes, A., Rattu, N. R., Henshaw, H., Martin, J. L., & Shoesmith, E. (2026) · Research Square · doi:10.21203/rs.3.rs-7565290/v1
Zhang, A., Chen, M., Weaver, A., Francis-Levin, N., Walters, C., Rappaport, A., & Himle, J. A. (2026) · Journal of Psychosocial Oncology · doi:10.1080/07347332.2026.2669385
Sičáková-Beblavá, E., Košutzká, Z., Sloboda, M., Borošová, S., Ukropec, J., Ukropcová, B., & Beblavý, M. (2026) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2026.103558
Tong, L., Panagiotopoulou, O.-M., Miguel, C., Harrer, M., Amarnath, A., Čihařová, M., Gemmill, A. W., Salamanca-Sanabria, A., Williams, A. D., Fonseca, A., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00707-4
“These findings replicated previous research that younger adults 13,18 , male participants 18 and individuals with lower education attainment exhibited reduced adherence to internet-based interventions 13,14,18 .”
Gerhardt, U., & Kreuzenbeck, C. C. J. (2026) · AI & Society · doi:10.1007/s00146-026-03264-7
“Regarding age, they found a concave relationship with adherence, as already described by Moshe et al. (2022).”
Alvarabie, R., Burjawi, E., & Xiao, J. (2026) · Health Science Reports · doi:10.1002/hsr2.73038
“Interventions lasted from 6 [20] to 8 weeks [18], with 2 [20] to 3 [19] sessions per week, each around 20 min [21], typically preceded by an induction phase and followed by structured activity.”
InfluentialElia, F. L., García‐Palacios, A., Botella, C., Penedo, J. M. G., Holtforth, M. G., Berger, T., & Fernández‐Álvarez, J. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1918892
Kempe, L., Iakimova, G., & Graziani, P. (2026) · Advances in Mental Health · doi:10.1080/18387357.2026.2733116
- Authors
- Moshe, I., Terhorst, Y., Paganini, S., Schlicker, S., Pulkki-Råback, L., Baumeister, H., Sander, L. B., & Ebert, D. D.
- Affiliations
- University of Helsinki (FI)
- Universität Ulm (DE)
- University of Freiburg (DE)
- Technical University of Munich (DE)
- Corresponding author
- Moshe, Isaac
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2022-08-30
- Volume
- 24
- Issue
- 8
- Pages
- e38261
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC9472049
- OpenAlex
- W4293575236
- Cited by
- 28
- References
- 74
- Citations by year
- 2026: 14 · 2025: 4 · 2024: 8 · 2023: 2
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Funders
- Suomen Kulttuurirahasto
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Pharmacology › Musculoskeletal pain and rehabilitation
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- student dropout prediction, digital interventions, depression, chronic back pain, major depressive disorder, subclinical depressive symptoms, social support, causes of dropout, digital mental health, comorbidities
- MeSH terms
- Back Pain, Child, Preschool, Depression, Depressive Disorder, Major, Major Depressive Disorder, Humans, Randomized Controlled Trials as Topic, Treatment Outcome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
