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Ally: A Digital Assistant to Lift Your Level of Activity

Family Physical Activity

Background: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user-varying and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring.

Objective: The primary objective of this study is to quantify the main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through the collection of smartphone sensor data.

Methods: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a micro-randomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up.

Results: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants.

Video Clip


Kramer, J., Künzler, F., Mishra, V., Smith, S.N., Kotz, D.F., Scholz, U., Fleisch, E., Kowatsch, T. (2020) Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results from an Optimization Trial, Annals of Behavioral Medicine. (Web)

Presset, B., Kramer, J., Kowatsch, T., Ohl, F. (2020) The social meaning of steps: User reception of a mobile health intervention on physical activity, Critical Public Health. (Web)

Künzler, F., Mishra, V., Kramer, J.-N., Fleisch, E., Kotz, D.F. & T. Kowatsch (2019) Exploring the State-of-Receptivity for mHealth Interventions, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Issue 4, December. (Web)

Kramer, J., Künzler, F., Mishra, V., Presset, B., Smith, S.N., Scholz, U., Kotz, D.F., Kowatsch, T. (2019) Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial, JMIR Research Protocols, 8(1), e11540. (PDF)

Kowatsch, T., Fischer-Taeschler, D., Putzing, F., Bürki, P., Stettler, C., Chiesa-Tanner, G., Fleisch, E., Die digitale Pille für chronische Krankheiten, in: Digitale Transformation von Dienstleistungen im Gesundheitswesen VI – Impulse für die Forschung, M. Pfannstiel, P. Da-Cruz and H. Mehlich (eds.), Springer Gabler, Heidelberg, Germany, 205-231. (PDF)

Kramer, J., Künzler, F., Tinschert, P., Kowatsch, T. (2019) Trajectories of Engagement with a Digital Physical Activity Coach: Secondary Analysis of a Micro-Randomized Trial, Abstract presented at the International Society for Research on Internet Interventions (ISRII) Meeting 2019, Auckland, New Zealand. (PDF)

Künzler, F. (2019) Context-aware notification management systems for just-in-time adaptive interventions, PhD Forum at the IEEE International Conference on Pervasive Computing and Communications (PerCom), Kyoto, Japan, 435-436. (PDF)

Künzler, F., Kramer, J., Kowatsch, T. (2017) Efficacy of Mobile Context-aware Notification Management Systems: A Systematic Literature Review and Meta-Analysis, IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Rome, Italy, 131-138. (PDF)

Related Work

Kowatsch, T., Volland, D., Shih, I., Rüegger, D., Künzler, F., Barata, F., Filler, A., Büchter, D., Brogle, B., Heldt, K., Gindrat, P., Farpour-Lambert, N., l’Allemand, D. (2017) Design and Evaluation of a Mobile Chat App for the Open Source Behavioral Health Intervention Platform MobileCoach, In: Maedche A., vom Brocke J., Hevner A. (eds) Designing the Digital Transformation. DESRIST 2017. Lecture Notes in Computer Science, vol 10243. Springer: Berlin; Germany, 485-489. (Paper-PDF | Poster-PDF | Slide-PDF | Screencast)

Filler, A., Kowatsch, T., Haug, S., Wahle, F., Staake, T. & Fleisch, E. (2015) MobileCoach: A Novel Open Source Platform for the Design of Evidence-based, Scalable and Low-Cost Behavioral Health Interventions – Overview and Preliminary Evaluation in the Public Health Context. Wireless Telecommunications Symposium 2015 (WTS 2015), New York, USA. ***Outstanding Paper Award & Best Graduate Student Paper Award*** PDF

CDHI Research Team

Jan-Niklas Kramer, Florian Künzler, Matthias Heuberger, Prof. Dr. Elgar Fleisch & Prof. Dr. Tobias Kowatsch


Jan 2017 – Sep 2019


Varun Mishra & Prof. David F. Kotz, PhD (Center for Technology and Behavioral Health, Dartmouth College), Prof. Shawna N. Smith, PhD (University of Michigan) & Prof. Dr. Urte Scholz (University of Zurich)

Dartmouth College
University of Michigan
University of Zurich
CSS Insurance
Dartmouth College
CSS Insurance