Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review.

Background: Unaddressed early-stage mental health issues, including stress, anxiety, and mild depression, can become a burden for individuals in the long term. Digital phenotyping involves capturing continuous behavioral data via digital smartphone devices to monitor human behavior and can potentially identify milder symptoms before they become serious.

Objective: This systematic literature review aimed to answer the following questions: (1) what is the evidence of the effectiveness of digital phenotyping using smartphones in identifying behavioral patterns related to stress, anxiety, and mild depression? and (2) in particular, which smartphone sensors are found to be effective, and what are the associated challenges?

Methods: We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process to identify 36 papers (reporting on 40 studies) to assess the key smartphone sensors related to stress, anxiety, and mild depression.

REAL-Time Smartphone Activity Classification Using Inertial Sensors-Recognition of Scrolling, Typing, and Watching Videos While Sitting or Walking.

By developing awareness of smartphone activities that the user is performing on their smartphone, such as scrolling feeds, typing and watching videos, we can develop application features that are beneficial to the users, such as personalization. It is currently not possible to access real-time smartphone activities directly, due to standard smartphone privileges and if internal movement sensors can detect them, there may be implications for access policies. Our research seeks to understand whether the sensor data from existing smartphone inertial measurement unit (IMU) sensors (triaxial accelerometers, gyroscopes and magnetometers) can be used to classify typical human smartphone activities.

User-centered design of a web-based self-management site for individuals with type 2 diabetes - providing a sense of control and community.

Background: To design and test a web-based self-management tool for patients with type 2 diabetes for its usability and feasibility.

Methods: An evidence-based, theory-driven website was created for patients with type 2 diabetes. Twenty-three patients with type 2 diabetes aged ≥ 25 years were recruited from 2 diabetes care centers in Toronto, Canada.

Usability evaluation of a clinical decision support tool for osteoporosis disease management.

Background: Osteoporosis affects over 200 million people worldwide at a high cost to healthcare systems. Although guidelines are available, patients are not receiving appropriate diagnostic testing or treatment. Findings from a systematic review of osteoporosis interventions and a series of focus groups were used to develop a functional multifaceted tool that can support clinical decision-making in osteoporosis disease management at the point of care.

Development of a prototype clinical decision support tool for osteoporosis disease management: a qualitative study of focus groups.

Background: Osteoporosis affects over 200 million people worldwide, and represents a significant cost burden. Although guidelines are available for best practice in osteoporosis, evidence indicates that patients are not receiving appropriate diagnostic testing or treatment according to guidelines. The use of clinical decision support systems (CDSSs) may be one solution because they can facilitate knowledge translation by providing high-quality evidence at the point of care.

Group differences in physician responses to handheld presentation of clinical evidence: a verbal protocol analysis.

Background: To identify individual differences in physicians' needs for the presentation of evidence resources and preferences for mobile devices.

Methods: Within-groups analysis of responses to semi-structured interviews. Interviews consisted of using prototypes in response to task-based scenarios.