Background: We conducted a systematic literature review to identify key trends associated with remote patient monitoring (RPM) via noninvasive digital technologies over the last decade. Materials and Methods: A search was conducted in EMBASE and Ovid MEDLINE. Citations were screened for relevance against predefined selection criteria based on the PICOTS (Population, Intervention, Comparator, Outcomes, Timeframe, and Study Design) format. We included studies published between January 1, 2005 and September 15, 2015 that used RPM via noninvasive digital technology (smartphones/personal digital assistants [PDAs], wearables, biosensors, computerized systems, or multiple components of the formerly mentioned) in evaluating health outcomes compared to standard of care or another technology. Studies were quality appraised according to Critical Appraisal Skills Programme. Results: Of 347 articles identified, 62 met the selection criteria. Most studies were randomized control trials with older adult populations, small sample sizes, and limited follow-up. There was a trend toward multicomponent interventions (n = 26), followed by smartphones/PDAs (n = 12), wearables (n = 11), biosensor devices (n = 7), and computerized systems (n = 6). Another key trend was the monitoring of chronic conditions, including respiratory (23%), weight management (17%), metabolic (18%), and cardiovascular diseases (16%). Although substantial diversity in health-related outcomes was noted, studies predominantly reported positive findings. Conclusions: This review will help decision makers develop a better understanding of the current landscape of peer-reviewed literature, demonstrating the utility of noninvasive RPM in various patient populations. Future research is needed to determine the effectiveness of RPM via noninvasive digital technologies in delivering patient healthcare benefits and the feasibility of large-scale implementation.

Background Multiple sclerosis (MS) is a chronic neurodegenerative disease. Current monitoring practices predominantly rely on brief and infrequent assessments, which may not be representative of the real-world patient experience. Smartphone technology provides an opportunity to assess people’s daily-lived experience of MS on a frequent, regular basis outside of episodic clinical evaluations. Objective The objectives of this study were to evaluate the feasibility and utility of capturing real-world MS-related health data remotely using a smartphone app, “elevateMS,” to investigate the associations between self-reported MS severity and sensor-based active functional tests measurements, and the impact of local weather conditions on disease burden. Methods This was a 12-week, observational, digital health study involving 3 cohorts: self-referred participants who reported an MS diagnosis, clinic-referred participants with neurologist-confirmed MS, and participants without MS (controls). Participants downloaded the elevateMS app and completed baseline assessments, including self-reported physical ability (Patient-Determined Disease Steps [PDDS]), as well as longitudinal assessments of quality of life (Quality of Life in Neurological Disorders [Neuro-QoL] Cognitive, Upper Extremity, and Lower Extremity Function) and daily health (MS symptoms, triggers, health, mobility, pain). Participants also completed functional tests (finger-tapping, walk and balance, voice-based Digit Symbol Substitution Test [DSST], and finger-to-nose) as an independent assessment of MS-related cognition and motor activity. Local weather data were collected each time participants completed an active task. Associations between self-reported baseline/longitudinal assessments, functional tests, and weather were evaluated using linear (for cross-sectional data) and mixed-effects (for longitudinal data) regression models. Results A total of 660 individuals enrolled in the study; 31 withdrew, 495 had MS (n=359 self-referred, n=136 clinic-referred), and 134 were controls. Participation was highest in clinic-referred versus self-referred participants (median retention: 25.5 vs 7.0 days). The top 5 most common MS symptoms, reported at least once by participants with MS, were fatigue (310/495, 62.6%), weakness (222/495, 44.8%), memory/attention issues (209/495, 42.2%), and difficulty walking (205/495, 41.4%), and the most common triggers were high ambient temperature (259/495, 52.3%), stress (250/495, 50.5%), and late bedtime (221/495, 44.6%). Baseline PDDS was significantly associated with functional test performance in participants with MS (mixed model–based estimate of most significant feature across functional tests [β]: finger-tapping: β=–43.64, P<.001; DSST: β=–5.47, P=.005; walk and balance: β=–.39, P=.001; finger-to-nose: β=.01, P=.01). Longitudinal Neuro-QoL scores were also significantly associated with functional tests (finger-tapping with Upper Extremity Function: β=.40, P<.001; walk and balance with Lower Extremity Function: β=–99.18, P=.02; DSST with Cognitive Function: β=1.60, P=.03). Finally, local temperature was significantly associated with participants’ test performance (finger-tapping: β=–.14, P<.001; DSST: β=–.06, P=.009; finger-to-nose: β=–53.88, P<.001). Conclusions The elevateMS study app captured the real-world experience of MS, characterized some MS symptoms, and assessed the impact of environmental factors on symptom severity. Our study provides further evidence that supports smartphone app use to monitor MS with both active assessments and patient-reported measures of disease burden. App-based tracking may provide unique and timely real-world data for clinicians and patients, resulting in improved disease insights and management.

This study was funded by Novartis Pharmaceuticals. Novartis employees were involved in all aspects of this study. Vegesna and Sasane are employed by and own stock in Novartis. Nazareth and Ko were employees of Novartis at the time of this study. Frois, Demean, Carpenter, and Wu are or have been employed by Analysis Group, which received a grant from Novartis for this research. Navarro received consulting fees from Novartis for his involvement in this research. Study concept and design were contributed by Sasane, Frois, Nazareth, and Wu. Navarro, Demean, and Frois took the lead in data collection, assisted by Carpenter, Ko, and Nazareth. Data interpretation was provided by Frois, Carpenter, Nazareth, and Ko, along with Sasane, Demean, Wu, and Navarro. The manuscript was written by Frois, Demean, Nazareth, and Ko, along with Sasane, Carpenter, Wu, and Navarro, and revised by Frois, Ko, and Vegesna, along with Sasane, Nazareth, Wu, and Navarro.