The Promise of mHealth

Abstract

Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.

A Home-Based Telerehabilitation Program for Patients With Stroke

“…However, each of the 12 patients was assessed over 28 separate days, for a total of 336 telerehabilitation study days. In addition, a strength of the system that supports the validity of the current findings is the implementation of principles of neuroplasticity in game design, including the use of stimuli that are challenging, repeated many times, intense, fun, and adjustable 12–18 . As part of the research design, the current study limited therapy to 2 hours of therapy/day for 28 days, but greater gains may be possible with a longer duration of intensive therapy 37,38 , which may be pursued in future studies.…”

“…The approach was built on established principles of learning and plasticity: training was challenging, repeated many times, motivating, interesting, and intensive 12–18 . Game design started with therapists outlining movements to be performed by patients, then USB-based devices were selected to transduce these movements, and finally the device signals were used to drive game play.…”

Physical Activity and Type 1 Diabetes

“…For example, continuous data from ankle triaxial accelerometers can be transmitted from the home and community via Wi-Fi or a smartphone to a remote data analysis server and compile data and reports on walking speed, duration of exercise, and even the type, duration, and energy used during exercise. 46,47 Other devices can track food intake (entered by the user); calculate calories in and out; estimate carbohydrate intake (in grams); measure body weight, percentage body fat, and body mass index; estimate power (wattage) and transmit results wirelessly to online accounts; and display graphs, trends, daily, and weekly goals. 48 For people with T1D accurate estimation of carbohydrate (higher glycemic index carbohydrates in particular) and calorie intake, balanced with energy expenditure during exercise, is critical for maintaining euglycemia.…”

The Specific Requirements of Neural Repair Trials for Stroke

“…This data can augment the standard clinic-based 6-min walking distance and 10-m walking speed outcomes, and give insight into standard self-reports of activity. 7,48 .…”