This study explored the potential to improve clinical outcomes in patients at risk of moving to the top segment of the cost acuity pyramid. This randomized controlled trial evaluated the impact of a Stepped-Care approach (predictive analytics + tailored nurse-driven interventions) on healthcare utilization among 370 older adult patients enrolled in a homecare management program and using a Personal Emergency Response System. The Control group (CG) received care as usual, while the Intervention group (IG) received Stepped-Care during a 180-day intervention period. The primary outcome, decrease in emergency encounters, was not statistically significant (15%, p = 0.291). However, compared to the CG, the IG had significant reductions in total 90-day readmissions (68%, p = 0.007), patients with 90-day readmissions (76%, p = 0.011), total 180-day readmissions (53%, p = 0.020), and EMS encounters (49%, p = 0.006). Predictive analytics combined with tailored interventions could potentially improve clinical outcomes in older adults, supporting population health management in home or community settings.

This two-arm randomized controlled trial evaluated the impact of a Stepped-Care intervention (predictive analytics combined with tailored interventions) on the healthcare costs of older adults using a Personal Emergency Response System (PERS). A total of 370 patients aged 65 and over with healthcare costs in the middle segment of the cost pyramid for the fiscal year prior to their enrollment were enrolled for the study. During a 180-day intervention period, control group (CG) received standard care, while intervention group (IG) received the Stepped-Care intervention. The IG had 31% lower annualized inpatient cost per patient compared with the CG (3.7 K, $8.1 K vs. $11.8 K, p = 0.02). Both groups had similar annualized outpatient costs per patient ($6.1 K vs. $5.8 K, p = 0.10). The annualized total cost reduction per patient in the IG vs. CG was 20% (3.5 K, $17.7 K vs. $14.2 K, p = 0.04). Predictive analytics coupled with tailored interventions has great potential to reduce healthcare costs in older adults, thereby supporting population health management in home or community settings.

Background: Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models. Objective: This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task. Methods: We performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded. Results: The proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability. Conclusions: Machine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.

BACKGROUND The uptake of digital health technology (DHT) has been surprisingly low in clinical practice. Despite showing great promise to improve patient outcomes and disease management, there is limited information on the factors that contribute to the limited adoption of DHT, particularly for hypertension management. OBJECTIVE This scoping review provides a comprehensive summary of barriers to and facilitators of DHT adoption for hypertension management reported in the published literature with a focus on provider- and patient-related barriers and facilitators. METHODS This review followed the methodological framework developed by Arskey and O’Malley. Systematic literature searches were conducted on PubMed or Medical Literature Analysis and Retrieval System Online, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica database. Articles that reported on barriers to and/or facilitators of digital health adoption for hypertension management published in English between 2008 and 2017 were eligible. Studies not reporting on barriers or facilitators to DHT adoption for management of hypertension were excluded. A total of 2299 articles were identified based on the above criteria after removing duplicates, and they were assessed for eligibility. Of these, 2165 references did not meet the inclusion criteria. After assessing 134 studies in full text, 98 studies were excluded (full texts were either unavailable or studies did not fulfill the inclusion criteria), resulting in a final set of 32 articles. In addition, 4 handpicked articles were also included in the review, making it a total of 36 studies. RESULTS A total of 36 studies were selected for data extraction after abstract and full-text screening by 2 independent reviewers. All conflicts were resolved by a third reviewer. Thematic analysis was conducted to identify major themes pertaining to barriers and facilitators of DHT from both provider and patient perspectives. The key facilitators of DHT adoption by physicians that were identified include ease of integration with clinical workflow, improvement in patient outcomes, and technology usability and technical support. Technology usability and timely technical support improved self-management and patient experience, and positive impact on patient-provider communication were most frequently reported facilitators for patients. Barriers to use of DHTs reported by physicians include lack of integration with clinical workflow, lack of validation of technology, and lack of technology usability and technical support. Finally, lack of technology usability and technical support, interference with patient-provider relationship, and lack of validation of technology were the most commonly reported barriers by patients. CONCLUSIONS Findings suggest the settings and context in which DHTs are implemented and individuals involved in implementation influence adoption. Finally, to fully realize the potential of digitally enabled hypertension management, there is a greater need to validate these technologies to provide patients and providers with reliable and accurate information on both clinical outcomes and cost effectiveness.

Telemedicine technologies are increasingly being used to deliver healthcare services because of their potential to eliminate distance barriers and improve access to care. Additionally, telemedicine has led to the improved clinical outcomes and cost-savings as demonstrated by multiple studies in different disease populations (1-4). Findings from these studies include reduction in hospitalizations and use of other acute healthcare services, as well as improvement in the quality of life, clinical outcomes and patient satisfaction particularly in patients with chronic diseases (1-4).

Background Studies show that good communication between doctors and patients and among all caregivers who interface with patients directly results in better clinical outcomes, reduced costs, greater patient satisfaction, and lower rates of physician burnout. The main purpose of this pilot study was to test the acceptability and usability of a mobile phone app (HARP), an app designed to improve communication and data collection among nonclinical care givers such as home health aides and case managers of patients who receive care at home. The home health aides collected information on patient’s mood, energy, medication adherence, potential falls and appetite level for the day. This information is summarized in postvisit, weekly and final discharge summary reports via an online dashboard and sent to the patient’s case manager at different time points. We assessed the usability and acceptability of the HARP mobile app. Objective This is a quality improvement pilot project geared towards assessing the usability and acceptability of a mobile app developed to facilitate patient data collection by trained home health aides who work together on a regular basis to provide home-based care to discrete subpopulations of patients. Methods Four home health aides were recruited from Partners Healthcare at Home to use the app to collect data on at least 12 patients. Eligible patients received care from 1 of the 4 home health aides for at least 23 days and scheduled to have at least 5 home visits during this time. Each of the patients were followed for a minimum of 23 days and a maximum 60 days in which home health aides collected patient data using the app. Postvisit reports, weekly reports and discharge summary reports were shared with the patient’s case managers. Data collection included acceptability and satisfaction data from all home health aides and case managers via surveys. A subgroup of 2 case managers and 2 home health aides participated in semistructured interviews. Results 9 have completed the project to date, 5 patients dropped out due to discharge from Partners Healthcare at Home care. The interim data included is from 8 case managers and 4 home health aides who provide care to one or more of the 9 patients who completed the project. Most case managers (75%) found postvisit and weekly reports useful and 87% found tracking mood and energy helpful. About 75% felt tracking appetite and falls via the HARP app helpful. Almost all case managers (87%), agreed that integrating a tool like the HARP app to the EMR would help them provide better care to their patients. Three out of four home health aides (75%) felt that the app easy to use or learn about once they received instructions and were willing to consider using the app in their workflow. Conclusions Acceptability and usability of HARP app was considerably high among case managers. The acceptability of the app varied among home health aides, some found information useful and believed the app has potential to help personalize patient care. Future research would require exploring other patient information that is useful to all staff involved in the clinical workflow and increase adoption of tool in clinical settings. Such tools could potentially reduce clinician burnout and improve patient outcomes.

Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within the coming three-month period. We trained and tested models using EHR data from 14,407 and 3,009 patients, respectively. The best model achieved an AUROC of 0.719, outperforming the simple, competitive baseline of relying prediction based on the last BP measure alone (0.634). Perhaps surprisingly, recurrent neural networks did not outperform a simple logistic regression for this task, suggesting that linear models should be included as strong baselines for predictive tasks using EHR

BACKGROUND:Fever is an important vital sign and often the first one to be assessed in a sick child. In acutely ill children, caregivers are expected to monitor a child's body temperature at home after an initial medical consult. Fever literacy of many caregivers is known to be poor, leading to fever phobia. In children with a serious illness, the responsibility of periodically monitoring temperature can add substantially to the already stressful experience of caring for a sick child.OBJECTIVE:The objective of this pilot study was to assess the feasibility of using the iThermonitor, an automated temperature measurement device, for continuous temperature monitoring in postoperative and postchemotherapy pediatric patients.METHODS:We recruited 25 patient-caregiver dyads from the Pediatric Surgery Department at the Massachusetts General Hospital (MGH) and the Pediatric Cancer Centers at the MGH and the Dana Farber Cancer Institute. Enrolled dyads were asked to use the iThermonitor device for continuous temperature monitoring over a 2-week period. Surveys were administered to caregivers at enrollment and at study closeout. Caregivers were also asked to complete a daily event-monitoring log. The Generalized Anxiety Disorder-7 item questionnaire was also used to assess caregiver anxiety at enrollment and closeout.RESULTS:Overall, 19 participant dyads completed the study. All 19 caregivers reported to have viewed temperature data on the study-provided iPad tablet at least once per day, and more than a third caregivers did so six or more times per day. Of all participants, 74% (14/19) reported experiencing an out-of-range temperature alert at least once during the study. Majority of caregivers reported that it was easy to learn how to use the device and that they felt confident about monitoring their child's temperature with it. Only 21% (4/9) of caregivers reported concurrently using a device other than the iThermonitor to monitor their child's temperature during the study. Continuous temperature monitoring was not associated with an increase in caregiver anxiety.CONCLUSIONS:The study results reveal that the iThermonitor is a highly feasible and easy-to-use device for continuous temperature monitoring in pediatric oncology and surgery patients.TRIAL REGISTRATION:ClinicalTrials.gov NCT02410252; https://clinicaltrials.gov/ct2/show/NCT02410252 (Archived by WebCite at http://www.webcitation.org/73LnO7hel).

BACKGROUND:Soaring health care costs and a rapidly aging population, with multiple comorbidities, necessitates the development of innovative strategies to deliver high-quality, value-based care.OBJECTIVE:The goal of this study is to evaluate the impact of a risk assessment system (CareSage) and targeted interventions on health care utilization.METHODS:This is a two-arm randomized controlled trial recruiting 370 participants from a pool of high-risk patients receiving care at a home health agency. CareSage is a risk assessment system that utilizes both real-time data collected via a Personal Emergency Response Service and historical patient data collected from the electronic medical records. All patients will first be observed for 3 months (observation period) to allow the CareSage algorithm to calibrate based on patient data. During the next 6 months (intervention period), CareSage will use a predictive algorithm to classify patients in the intervention group as "high" or "low" risk for emergency transport every 30 days. All patients flagged as "high risk" by CareSage will receive nurse triage calls to assess their needs and personalized interventions including patient education, home visits, and tele-monitoring. The primary outcome is the number of 180-day emergency department visits. Secondary outcomes include the number of 90-day emergency department visits, total medical expenses, 180-day mortality rates, time to first readmission, total number of readmissions and avoidable readmissions, 30-, 90-, and 180-day readmission rates, as well as cost of intervention per patient. The two study groups will be compared using the Student t test (two-tailed) for normally distributed and Mann Whitney U test for skewed continuous variables, respectively. The chi-square test will be used for categorical variables. Time to event (readmission) and 180-day mortality between the two study groups will be compared by using the Kaplan-Meier survival plots and the log-rank test. Cox proportional hazard regression will be used to compute hazard ratio and compare outcomes between the two groups.RESULTS:We are actively enrolling participants and the study is expected to be completed by end of 2018; results are expected to be published in early 2019.CONCLUSIONS:Innovative solutions for identifying high-risk patients and personalizing interventions based on individual risk and needs may help facilitate the delivery of value-based care, improve long-term patient health outcomes and decrease health care costs.TRIAL REGISTRATION:ClinicalTrials.gov NCT03126565; https://clinicaltrials.gov/ct2/show/NCT03126565 (Archived by WebCite at http://www.webcitation.org/6ymDuAwQA).

Physical inactivity is one of the leading risk factors contributing to rising rates of chronic diseases and has been associated with deleterious health outcomes in patients with chronic disease conditions. FeatForward is a mobile phone app designed to encourage patients with cardiometabolic risk (CMR) factors to increase their levels of physical activity. To evaluate the effect of the FeatForward mobile phone app on physical activity levels (primary outcome) and global CMR factors (secondary outcomes) in patients with chronic conditions. In this 6-month, 2-arm randomized controlled trial, adult participants endorsing at least 1 study-eligible condition (obesity, [pre-]diabetes, [pre-]hypertension) were enrolled and assigned to either the intervention group (FeatForward app and standard care) or control group (standard care only). The primary and secondary outcomes were, respectively, change from baseline in physical activity (step count) and CMR factors (weight, body mass index [BMI], waist circumference, glycated hemoglobin [HbA1c], fasting blood glucose, systolic/diastolic blood pressures, serum lipids, C-reactive protein [CRP]). CMR data were collected at 3 time-points: baseline, 3 months, and 6 months. Step count data were recorded continuously by patients’ study-issued activity trackers and collected in batches at 3 and 6 months. At study end, patients’ weekly average step counts (WAS) were calculated as total steps taken divided by days of step data (0-7) for each of 26 study weeks. Mixed-effects linear regression models evaluated change over time between groups for the primary outcome and secondary outcomes. All models controlled for baseline values. The step count model additionally controlled for proportion of days without data, defined as (7 – days of data) / 7. Analyses were conducted for both groups overall, and by disease cohort (obesity, diabetes, hypertension). Step count and CMR data were analyzed for 128 intervention and 133 control patients. There were no demographic differences between groups. While there was an overall downward trend in WAS for both groups, the intervention group decreased significantly less than the control group, with a slope of -29.3 steps per week compared to controls’ -57.9 (P=.02). Intervention patients with obesity slightly increased their step count overtime, differing significantly from controls (slope of 0.9 vs -90.2; P<.001). Intervention patients significantly lowered their BMI per study month compared to controls (slopes -0.23 vs -0.02; P=.04). Additionally, intervention patients with hypertension significantly decreased weight (P=.003), BMI (P=.002), and CRP (P=.03) per month compared to the control group. Waist circumference, HbA1c, fasting blood glucose, blood pressure, and lipids did not differ significantly by group or disease cohort over time. While it is common for patient engagement with physical activity trackers to decrease over the course of a study, patients using the FeatFoward app had a slower decline in physical activity compared to controls. Intervention patients experienced a reduction in their BMI from a mean of 34.3 to 33.4, compared to controls’ 34.8 to 35.0. Patients with hypertension experienced significant decreases in BMI, weight, and CRP compared to controls. Future analyses will evaluate the impact of app engagement levels on step counts and CMR factors for the intervention group.

CORA is a personalized smartphone-based self-management app designed to help cancer patients on oral anti-cancer medications manage medication, medication side-effects, and symptoms with the overall goal of improving their quality of life. To evaluate the effect of CORA on quality of life in patients on oral anti-cancer medications. Eighty-four patients were randomized to either an intervention group that received CORA plus usual care or a control group that received usual care. Quality of life was measured using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale administered at enrollment, 6 and 12 weeks. Engagement with the app was assessed by determining the unique days using the app. We evaluated the effect of engagement on FACIT-F both as a continuous variable (days using the app) and as a categorical outcome (low, medium, and high). Group differences for all outcomes over the study period were assessed using repeated measures mixed model analysis. Relative to the control group, the intervention group improved FACIT-F by 0.36 (95% CI 0.10-0.61) P=.006 per week over the study period. As a continuous variable, each additional day using the app was significantly associated with an improved FACIT-F score per week in the study [0.0060 (95% CI -0.000034-0.012), P=.05]. Within the intervention group that used the app, those who were most engaged with the app were significantly more likely to improve their quality of life over the study relative to the least engaged group [0.37 per week (95% CI 0.19-1.94), P=.05]. CORA may have significantly improved quality of life (FACIT-F) in cancer patients over 12 weeks. Smartphone applications may positively impact health and behavioral outcomes in cancer patients on oral anti-cancer medications.

Antimicrobial resistance (AMR) is among the most challenging problems facing modern medical care and is associated with increased morbidity, mortality and economic costs. In addition to reducing unnecessary prescriptions, an important part of preventing AMR is optimizing the use of existing antibiotics. While national guidelines and antibiotic stewardship programs provide general guidance on the management of many infectious syndromes, they are not personalized to the history and exam of a given patient and do not explicitly account for the impact of their recommendations on the future development of AMR in the patient. We propose to apply machine learning algorithms to the problem of antibiotic treatment optimization on a cohort of patients who presented with bacterial infection over an 18-year period in Boston at the Massachusetts General Hospital and the Brigham & Women’s Hospital.

Overactive bladder (OAB), defined by urinary urgency with or without urge urinary incontinence (UI), usually with frequency and nocturia, can significantly impact patient’s quality of life. Tracking symptoms is an important part of OAB management and has been shown to assist in enhancing patient interaction with health care providers (HCP) when discussing solutions for symptom management. The primary goal of this study was to assess the usability and acceptability of an Android smartphone mobile app designed to help participants learn about OAB symptom management through tracking and self-management. Secondarily, we also assessed engagement with the app over the three-month study period. Eligible participants were experiencing OAB symptoms without an existing enlarged prostate or urinary tract infection (BPH/UTI), and enrolled through referrals from within the Partners Healthcare network. The mobile app was installed at the enrollment visit, and participants were instructed to complete monthly, 3-day symptom journals, as well as surveys and optional free-text notes for 12 weeks. Additionally, medication reminders, Kegel and bladder training exercises were available for use in the app. A visit with their HCP was scheduled between weeks 6 and 12 of the study for the HCP and participant to review collected symptom data via an app-linked portal. Qualitative input from the HCP, closeout participant interviews and app usage data (percent viewed and number of hits) were used to assess participant engagement. Closeout interviews (n=10) also assessed usability of the various app features. Demographic and usability satisfaction data were collected via questionnaires developed by investigators. Descriptive analyses were conducted to present the demographic and usability data. NVivo for Mac (version 11) was used to conduct a thematic analysis on qualitative data. Of the total enrolled (n=33), 26 participants completed the study. Participant engagement with the app was 100% for months one and two of the study then dropped to 72% by month three. Most participants (80%) reported using the app as needed vs regularly. As a group, female participants >50 years demonstrated the highest engagement (75%) at closeout. The most used app feature was the free-text diary feature (100%; 5516 hits), followed by the “event log” (100%; 2105 hits). The majority of other app features were also rated as useful by participants (52-100%). Participant interviews found the app was a valuable OAB information source, simplifying symptom tracking and follow-through on clinician recommendations. Perceived usefulness of the portal varied between primary care providers and specialists. Participants indicated the app was “Easy to Learn” (96%), “Simple to Use” (92%), useful for understanding changes in symptoms (91%), enabled better symptom tracking (96%), and facilitated communication with their HCP (75%). A mobile app to increase awareness of OAB symptoms improved confidence in self-management for participants and increased access to data for decision making and participant communication for specialists. Participant-reported outcomes indicate that the tracking void frequency and urgency features were very useful, while other features such as medication reminders, pad usage, bladder and Kegel trainings were used less frequently among participants.

Oral chemotherapeutic medications are increasingly being used for the treatment of cancer. Their long-term use raises questions about patients’ adherence to prescribed regimens. Therefore, we developed CORA – a personalized smartphone-based self-management app to help cancer patients on oral anti-cancer medications manage symptoms, medication, and medication side-effects with the overall goal of improving adherence to medications. Our objective was to evaluate the effect of CORA on adherence to oral anti-cancer medications. 84 patients were randomized to either an intervention group that received CORA plus usual care or a control group that received usual care. Outcomes were evaluated after 12 weeks using data from electronic pill bottle collected continuously throughout the study and the Morisky Medication Adherence Scale (MMAS) assessed at enrollment, 6 and 12 weeks. Repeated measures mixed model analysis, Mann-Whitney U and Chi-Squared tests were used to evaluate group differences in the MMAS data. Median MMAS scores did not differ by group, in the intervention vs. the control group: 6 (25-75%: 5-7) vs. 6 (25-75%: 6-7) at enrollment (p=0.90), 7(25-75%: 6-7) vs. 7 (25-75%: 6-7) at midpoint (p=0.27), and 7(25-75%: 6,7) vs. 7 (25-75%:6,7) at closeout (p=0.79). At closeout, percentage of participants between the intervention and control groups varied from 13.33% vs. 17.65%, 66.67% vs. 58.82%, and 20.00% vs. 23.52%, in the high, medium, and low adherence categories, respectively (p-value=0.84). Limitation: Assessing adherence with pill bottles was not feasible as most participants did not store their medications in the bottles for reasons including, difficulty in opening bottles, inadequate space for medications, fear of contamination when transferring medications and multiple daily dosing. CORA did not increase adherence to oral anti-cancer medications. Identifying the “right” add-on technology specific for target population is critical to implementation success for digital health solutions.

Many mobile apps have been designed to monitor physical activity. While they may have many downloads, most users eventually stop using the app and become disengaged. We created a hyper-personalized physical activity tracking app to promote engagement with physical activity (PA) among users. It is unknown if this increased engagement and how engagement level may affect measured outcomes. The purpose of this study was to determine how users engaged with a hyper personalized activity tracking app for 6 months and whether this engagement affected physical activity Participants with cardiometabolic risk (CMR) factors were given an activity watch to track their PA (step counts) and asked to use the study mobile app for 6 months. App features included step tracking, personalized educational and motivational messages, biometric tracking and connection to a portal where their clinician could monitor their activity. App usage data were collected at 3- and 6-month study visits to determine app usage metrics for the 6 months. App engagement was determined by app usage metrics such as overall page clicks (number of clicks per page), frequency of use of individual features (number of clicks) and session length (time spent on a page). Participants were grouped by level of engagement with app (high, medium, low, none) post hoc to determine engagement effects on steps. Information was collected on 128 participants. Over the 6-month study, 60 participants (47%) engaged with the app. Among users, app usage decreased by over 50% with the highest app usage during month 1 followed by month 4. There was no difference in the average app session lengths at 0 and 6 months (12 vs 11 seconds, respectively). The most commonly viewed feature was the personalized daily messages (92% of participants used feature, 20,902 clicks, 58% of total views). At least 85% of app users engaged with all the features. Each additional day of app use was associated with a nonsignificant increase of 13 steps in overall average daily step count. Median days of app use were used to define groups with high, medium, and low engagement (median 89, 35 and 3 days of use respectively). The low engagement group had an average 1220 less steps per day than the high engagement group (P<.001). High engagement group’s session length remained steady through the study period, compared to medium engagement group’s session length that fluctuated widely. While steps decreased over the 6 months, those in the medium engagement group decreased in weekly step counts at a steeper slope then other engagement groups. While participants engaged with most app features, we observed a 50% decrease in engagement over the 6-month study. Despite this result, those with high engagement were able to achieve more physical activity than those with low engagement. This increase in physical activity may lead to improvements in CMR factors and better quality of life.

Background: The prevalence of hypertension is around 30-45% among the general population. Hypertension contributes to 1 out of 7 deaths in the United States and approximately 70% of persons who have a first heart attack or stroke. Timely treatment and optimal management of hypertension is associated with substantial reductions in stroke incidence (35-40%), myocardial infarction (20-25%), and heart failure (>50%). Additionally, remote monitoring with active intervention by medical professionals (telemonitoring) improves drug compliance and increase the target blood pressure (BP) achievement rate.

Background: Physical inactivity is one of the leading risk factors contributing to the rising rates of chronic diseases and has been associated with deleterious health outcomes in patients with chronic disease conditions. We developed a mobile phone app, FeatForward, to increase the level of physical activity in patients with cardiometabolic risk (CMR) factors. This intervention is expected to result in an overall improvement in patient health outcomes.Objective: The objective of this study is to evaluate the effect of a mobile phone-based app, FeatForward, on physical activity levels and other CMR factors in patients with chronic conditions.Methods: The study will be implemented as a 2-arm randomized controlled trial with 300 adult patients with chronic conditions over a 6-month follow-up period. Participants will be assigned to either the intervention group receiving the FeatForward app and standard care versus a control group who will receive only usual care. The difference in physical activity levels between the control group and intervention group will be measured as the primary outcome. We will also evaluate the effect of this intervention on secondary measures including clinical outcome changes in global CMR factors (glycated hemoglobin, fasting blood glucose, blood pressure, waist circumference, Serum lipids, C-reactive protein), health-related quality of life, health care usage, including attendance of scheduled clinic visits and hospitalizations, usability, and satisfaction, participant engagement with the FeatForward app, physician engagement with physician portal, and willingness to engage in physical activity. Instruments that will be used in evaluating secondary outcomes include the Short-Form (SF)-12, app usability and satisfaction questionnaires, physician satisfaction questionnaire. The intention-to-treat approach will be used to evaluate outcomes. All outcomes will be measured longitudinally at baseline, midpoint (3 months), and 6 months. Our primary outcome, physical activity, will be assessed by mixed-model analysis of variance with intervention assignment as between-group factor and time as within-subject factor. A similar approach will be used to analyze continuous secondary outcomes while categorical outcomes will be analyzed by chi-square test.Results: The study is still in progress and we hope to have the results by the end of 2016.Conclusions: The mobile phone-based app, FeatForward, could lead to significant improvements in physical activity and other CMR factors in patients.