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Enregistrement W4410714440 · doi:10.31234/osf.io/wjuqm_v1

Boosting digital agency through a self-nudge app

2025· preprint· en· W4410714440 sur OpenAlexfundno aff
David Joachim Grüning

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineSocial Sciences
ThématiqueImpact of Technology on Adolescents
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of California, DavisUniversiteit van AmsterdamBrown UniversityYork UniversityYale University
Mots-clésBoosting (machine learning)Agency (philosophy)Nudge theoryPsychologyComputer scienceArtificial intelligenceSocial psychologyEpistemologyPhilosophy

Résumé

récupéré en direct d'OpenAlex

The development of smartphones has transformed the way people interact with the digital world, but it has also made problematic digital behavior – especially social media use – a growing concern. This dissertation addresses the core problem of how to effectively empower individuals to regain self-control in digital environments designed for engagement and distraction. Existing interventions, mostly nudges implemented on digital platforms, tend to be context-bound, opaque, and insufficient for lasting behavior change. The central claim of the dissertation is that effective user support must build users' skills to help them make deliberate, reflective choices in digital spaces. The dissertation's approach leads to the development and evaluation of a self-nudge app as a platform-independent, boosting-based intervention tool. The dissertation consists of two parts, including three papers each.The first paper presents a conceptual framework that categorizes digital interventions according to their timing: proactive, interactive or reactive. It reviews the landscape of scientifically tested interventions aimed at promoting positive digital behavior and highlights the dominance of platform-specific, behavioral approaches. The paper highlights the need for alternative strategies that are both practically feasible and theoretically robust. As a direct follow-up, the second paper in the thesis reports on an expert meeting we organized with 31 international scholars and practitioners to identify the most promising methodologies for future digital intervention research. The result is a consensus on the need for independent, flexible tools, particularly smartphone apps and browser extensions, as these allow researchers to deliver interventions across platforms and real-life settings – free from any platform gatekeeping. The third paper advances the theoretical argument that interventions for 'wicked', that is, overly complex environments such as social media, need to combine behavioral interventions with informational elements that promote user understanding. The paper proposes a shift towards competency-based designs, rooted in the boosting paradigm, that help users better understand both the structure of digital environments and their own behavioral patterns in order to intervene effectively with their behavior.Based on the conceptual insights, the next paper of the dissertation introduces and tests the smartphone app one sec, developed in collaboration with a commercial partner. The app integrates three features – reflection prompts, mental friction and a dismissal option – to control habitual app use. In a six-week field study (N = 280) and an online experiment (N = 500), the app significantly reduced app use and improved users' satisfaction and awareness of their smartphone behaviour. This paper provides the first empirical support for a large-scale self-nudge app intervention. The next paper extends these findings in a long-term field study, following 1,039 users for an average of 13 weeks (with some followed for up to a year). The results replicate and strengthen earlier findings on the effectiveness of the app intervention, showing that users not only opened apps less frequently, but also reduced the amount of time they spent in them. The study also documents different usage patterns, including strategic disabling and re-activating of the app intervention on different days in the week, providing a real-world view of how users adapt interventions to their lives. In the final paper, we explore how intervention effects vary across users by examining socio-cognitive characteristics of users as moderators. In a large-scale field study (N = 1,809), traits such as self-awareness, social sensitivity, and cognitive reflection were predictive of behavior change, improvements in subjective well-being, and adherence to the intervention. In contrast, traditional personality traits (Big Five) showed minimal explanatory power. A replication study with a different app (N = 1,932) confirmed the generalizability of these findings.This dissertation makes two main contributions. First, it introduces frameworks that help structure the field of digital interventions, highlighting gaps in the literature as well as researchers' consensus on future research steps and how they should be taken. Second, informed by these frameworks, the dissertation designs and tests a real-world, scalable intervention rooted in boosting theory, demonstrating the importance of user-specific characteristics in shaping intervention effectiveness. Practically, the dissertation provides a validated, fully available app (one sec) that is now used by over two million users worldwide. Theoretically, the dissertation's research advances a shift in digital design towards adaptive, user-centred intervention strategies. Future research should further explore personalized, modular intervention systems and use advanced study designs like micro-randomized trials to tailor support to individual users and contexts. The challenge ahead is to move beyond one-size-fits-all solutions and develop digital interventions that respond to the complexity of real people in real environments.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,046

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0020,003
Science ouverte0,0010,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0140,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,343
Écart entre enseignants0,314 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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