Physical Sciences and Mathematics Commons^™

Detection And Monitoring Of Repetitions Using An Mhealth-Enabled Resistance Band, Curtis L. Peterson, Emily V. Wechsler, Ryan J. Halter, George G. Boateng, Patrick O. Proctor, David F. Kotz, Summer B. Cook, John A. Batsis

Dartmouth Scholarship

Sarcopenia is defined as an age-related loss of muscle mass and strength which impairs physical function leading to disability and frailty. Resistance exercises are effective treatments for sarcopenia and are critical in mitigating weight-loss induced sarcopenia in older adults attempting to lose weight. Yet, adherence to home-based regimens, which is a cornerstone to lifestyle therapies, is poor and cannot be ascertained by clinicians as no objective methods exist to determine patient compliance outside of a supervised setting. Our group developed a Bluetooth connected resistance band that tests the ability to detect exercise repetitions. We recruited 6 patients aged 65 years …

Application Memory Isolation On Ultra-Low-Power Mcus, Taylor Hardin, Ryan Scott, Patrick Proctor, Josiah Hester, Jacob Sorber, David Kotz

Dartmouth Scholarship

The proliferation of applications that handle sensitive user data on wearable platforms generates a critical need for embedded systems that offer strong security without sacrificing flexibility and long battery life. To secure sensitive information, such as health data, ultra-low-power wearables must isolate applications from each other and protect the underlying system from errant or malicious application code. These platforms typically use microcontrollers that lack sophisticated Memory Management Units (MMU). Some include a Memory Protection Unit (MPU), but current MPUs are inadequate to the task, leading platform developers to software-based memory-protection solutions. In this paper, we present our memory isolation technique, …

Workshop On Emerging Technology And Data Analytics For Behavioral Health, David Kotz, Sarah E. Lord, A. James O'Malley, Luke Stark, Lisa Marsch

Dartmouth Scholarship

Wearable and portable digital devices can support self-monitoring for patients with chronic medical conditions, individuals seeking to reduce stress, and people seeking to modify health-related behaviors such as substance use or overeating. The resulting data may be used directly by a consumer, or shared with a clinician for treatment, a caregiver for assistance, or a health coach for support. The data can also be used by researchers to develop and evaluate just-in-time interventions that leverage mobile technology to help individuals manage their symptoms and behavior in real time and as needed. Such wearable systems have huge potential for promoting delivery …

Nocloud: Experimenting With Network Disconnection By Design, Reza Rawassizadeh, Timothy Pierson, Ronald Peterson, David Kotz

Dartmouth Scholarship

Application developers often advocate uploading data to the cloud for analysis or storage, primarily due to concerns about the limited computational capability of ubiquitous devices. Today, however, many such devices can still effectively operate and execute complex algorithms without reliance on the cloud. The authors recommend prioritizing on-device analysis over uploading the data to another host, and if on-device analysis is not possible, favoring local network services over a cloud service.

Investigating Intervention Components And Exploring States Of Receptivity For A Smartphone App To Promote Physical Activity: Study Protocol Of The Ally Micro-Randomized Trial, Jan-Niklas Kramer, Florian Künzler, Varun Mishra, Bastien Presset, David Kotz, Shawna Smith, Urte Scholz, Tobias Kowatsch

Dartmouth Scholarship

Background: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- 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 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, …