{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T20:12:21Z","timestamp":1786738341504,"version":"build-2736575974"},"reference-count":60,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Ment Health"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Digital therapeutics (DTx) have become increasingly prominent in mental health care, offering scalable, evidence-based interventions. A common assumption underlying their design and evaluation is that greater usage time leads to superior therapeutic outcomes, reflecting an implicit linear dose-response model. However, accumulating evidence challenges this simplified perspective and suggests that the relationship between usage time and clinical benefit is substantially more complex. In this viewpoint article, we critically examine the assumption that higher usage time is a necessary prerequisite for therapeutic success in digital mental health interventions. Drawing on psychotherapy dose-effect research, findings from digital intervention trials, and our own empirical work, we highlight several factors that complicate linear exposure models. These include rapid early response and plateau effects, substantial heterogeneity in user trajectories, motivational and contextual determinants of use, episodic patterns of engagement, and the distinction between on-platform activity and off-platform skill application. The linear dose metaphor, implicitly inherited from pharmacotherapy models, appears conceptually mismatched with learning-based digital interventions. Importantly, equating greater use with greater effectiveness risks conflating exposure with therapeutic mechanism, and may inadvertently promote evaluation frameworks that prioritize duration over meaningful change. Together, these insights suggest that usage time is an imperfect proxy for therapeutic engagement, and that high or continuous use should not be treated as a universal indicator of intervention quality. We argue for a shift toward mechanism-informed and individualized evaluation frameworks that prioritize the quality, timing, and functional impact of intervention use over cumulative exposure.<\/jats:p>","DOI":"10.2196\/98660","type":"journal-article","created":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T02:20:05Z","timestamp":1783822805000},"page":"e98660-e98660","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Usage Time: Rethinking Therapeutic Dose and Effect in Digital Therapeutics"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1100-0260","authenticated-orcid":false,"given":"Bj\u00f6rn","family":"Meyer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1741-4069","authenticated-orcid":false,"given":"Linda T","family":"Betz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2960-4540","authenticated-orcid":false,"given":"Mario","family":"Weiss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5141-3847","authenticated-orcid":false,"given":"Wolfgang","family":"Lutz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brat.2018.08.003","article-title":"A randomized controlled trial of three smartphone apps for enhancing public mental health","volume":"109","author":"Bakker","journal-title":"Behav Res Ther"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1192\/bjp.2025.10485","article-title":"Beyond counting clicks: rethinking engagement in digital mental health","author":"Cipriani","journal-title":"Br J Psychiatry"},{"issue":"3","key":"R3","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.1772","article-title":"A systematic review of the impact of adherence on the effectiveness of e-therapies","volume":"13","author":"Donkin","journal-title":"J Med Internet Res"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.1016\/j.invent.2021.100454","article-title":"Engagement with mobile health interventions for depression: a systematic review","volume":"26","author":"Molloy","journal-title":"Internet Interv"},{"issue":"2","key":"R5","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/s13142-016-0453-1","article-title":"Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis","volume":"7","author":"Perski","journal-title":"Transl Behav Med"},{"issue":"12","key":"R6","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.8578","article-title":"Clarifying the concept of adherence to eHealth technology: systematic review on when usage becomes adherence","volume":"19","author":"Sieverink","journal-title":"J Med Internet Res"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01778-w","article-title":"Engagement and attrition in digital mental health: current challenges and potential solutions","volume":"8","author":"Smith","journal-title":"NPJ Digit Med"},{"issue":"11","key":"R8","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.9397","article-title":"Measuring engagement in eHealth and mHealth behavior change interventions: viewpoint of methodologies","volume":"20","author":"Short","journal-title":"J Med Internet Res"},{"issue":"5","key":"R9","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.amepre.2016.06.015","article-title":"Understanding and promoting effective engagement with digital behavior change interventions","volume":"51","author":"Yardley","journal-title":"Am J Prev Med"},{"key":"R10","unstructured":"Lutz W de Jong K Rubel JA Delgadillo J . 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