Challenges for the evaluation of digital health solutions—A call for innovative evidence generation approaches

C Guo, H Ashrafian, S Ghafur, G Fontana… - NPJ digital …, 2020 - nature.com
The field of digital health, and its meaning, has evolved rapidly over the last 20 years. For
this article we followed the most recent definition provided by FDA in 2020. Emerging …

[HTML][HTML] Smartphone-based interventions for physical activity promotion: sco** review of the evidence over the last 10 years

A Domin, D Spruijt-Metz, D Theisen… - JMIR mHealth and …, 2021 - mhealth.jmir.org
Background: Several reviews of mobile health (mHealth) physical activity (PA) interventions
suggest their beneficial effects on behavior change in adolescents and adults. Owing to the …

Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity

P Liao, K Greenewald, P Klasnja… - Proceedings of the ACM on …, 2020 - dl.acm.org
With the recent proliferation of mobile health technologies, health scientists are increasingly
interested in develo** just-in-time adaptive interventions (JITAIs), typically delivered via …

Precision health: the role of the social and behavioral sciences in advancing the vision

E Hekler, JA Tiro, CM Hunter… - Annals of Behavioral …, 2020 - academic.oup.com
Abstract Background In 2015, Collins and Varmus articulated a vision for precision medicine
emphasizing molecular characterization of illness to identify actionable biomarkers to …

Personalized mobile technologies for lifestyle behavior change: a systematic review, meta-analysis, and meta-regression

HL Tong, JC Quiroz, AB Kocaballi, SCM Fat, KP Dao… - Preventive …, 2021 - Elsevier
Given that the one-size-fits-all approach to mobile health interventions have limited effects, a
personalized approach might be necessary to promote healthy behaviors and prevent …

Causal message-passing for experiments with unknown and general network interference

S Shirani, M Bayati - Proceedings of the National Academy of Sciences, 2024 - pnas.org
Randomized experiments are a powerful methodology for data-driven evaluation of
decisions or interventions. Yet, their validity may be undermined by network interference …

[HTML][HTML] Batch policy learning in average reward markov decision processes

P Liao, Z Qi, R Wan, P Klasnja, SA Murphy - Annals of statistics, 2022 - ncbi.nlm.nih.gov
We consider the batch (off-line) policy learning problem in the infinite horizon Markov
Decision Process. Motivated by mobile health applications, we focus on learning a policy …

Inference for batched bandits

K Zhang, L Janson, S Murphy - Advances in neural …, 2020 - proceedings.neurips.cc
As bandit algorithms are increasingly utilized in scientific studies and industrial applications,
there is an associated increasing need for reliable inference methods based on the resulting …

Designing m-Health interventions for precision mental health support

N Bidargaddi, G Schrader, P Klasnja, J Licinio… - Translational …, 2020 - nature.com
Mobile health (m-Health) resources are emerging as a significant tool to overcome mental
health support access barriers due to their ability to rapidly reach and provide support to …

Just-in-time adaptive interventions for suicide prevention: Promise, challenges, and future directions

DDL Coppersmith, W Dempsey, EM Kleiman… - Psychiatry, 2022 - Taylor & Francis
The suicide rate (currently 14 per 100,000) has barely changed in the United States over the
past 100 years. There is a need for new ways of preventing suicide. Further, research has …