PeerJClinical TrialsPeer-reviewed journal article
A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors
Cinnamon S Bloss, Nathan E Wineinger, Melissa Peters, Debra L Boeldt, Lauren Ariniello, Ju Young Kim, Judy Sheard, Ravi Komatireddy, Paddy Barrett, Eric J Topol
Abstract
BackgroundMobile health and digital medicine technologies are becoming increasingly used by individuals with common, chronic diseases to monitor their health. Numerous devices, sensors, and apps are available to patients and consumers - some of which have been shown to lead to improved health management and health outcomes. However, no randomized controlled trials have been conducted which examine health care costs, and most have failed to provide study participants with a truly comprehensive monitoring system.\n\nMethodsWe conducted a prospective randomized controlled trial of adults who had submitted a 2012 health insurance claim associated with hypertension, diabetes, and/or cardiac arrhythmia. The intervention involved receipt of one or more mobile devices that corresponded to their condition(s) and an iPhone with linked tracking applications for a period of 6 months; the control group received a standard disease management program. Moreover, intervention study participants received access to an online health management system which provided participants detailed device tracking information over the course of the study. This was a monitoring system designed by leveraging collaborations with device manufacturers, a connected health leader, health care provider, and employee wellness program - making it both unique and inclusive. We hypothesized that health resource utilization with respect to health insurance claims may be influenced by the monitoring intervention. We also examined health-self management.\n\nResults & ConclusionsThere was little evidence of differences in health care costs or utilization as a result of the intervention. Furthermore, we found evidence that the control and intervention groups were equivalent with respect to most health care utilization outcomes. This result suggests there are not large short-term increases or decreases in health care costs or utilization associated with monitoring chronic health conditions using mobile health or digital medicine technologies. Among secondary outcomes there was some evidence of improvement in health self-management which was characterized by a decrease in the propensity to view health status as due to chance factors in the intervention group.\n\nDisclosure of FundingThis research is funded in part by a NIH/NCATS flagship Clinical and Translational Science Award Grant (1UL1 TR001114), Qualcomm Foundation Scripps Health Digital Medicine Research Grant, and Scripps Healths Division of Innovation and Human Capital and Division of Scripps Genomic Medicine. Support for the study is also provided by HealthComp Third Party Administrator, Sanofi, AliveCor, and Accenture.\n\nTrial Registration: clinicaltrials.gov Identifier NCT01975428
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
- approach: experimental assay
In-depth summary
Claude Opus 5.5 · xhigh effort · 5 Nov 2026 · from the full text of version 2 (14 Jan 2016) · CC BY 4.0
In a 6-month randomized trial of 160 adults with chronic disease, smartphone-connected biosensors yielded equivalent short-term healthcare claims to standard care while reducing chance-attributed health locus of control.
Smartphone-enabled biomedical sensors let patients self-monitor chronic diseases, raising questions about whether remote monitoring increases or decreases short-term healthcare utilization. No prior prospective randomized controlled trial had evaluated health insurance claims across an integrated multi-device mobile monitoring and care-coordination platform.
Key results
ShownSix-month healthcare utilization and insurance claims did not differ significantly between the mobile biosensor monitoring group and the standard disease management control group.
Among 130 completers (n = 65 monitoring, n = 65 control; 160 enrolled), mean 6-month enrollment claims were $6,026 in the monitoring arm vs. $5,596 in controls (p = 0.62), showing no statistically significant differences across office visits (p = 0.46), inpatient admissions (p = 0.82), emergency visits (p = 0.06), or prescription claims (p = 0.60). · Participant demographics and information; Health insurance claims
ShownEquivalence testing confirmed that short-term healthcare resource utilization was statistically equivalent between the mobile monitoring and control groups.
Using a two one-sided equivalence test with a similarity region of 0.5 standard deviations, the monitoring and control arms were statistically equivalent across total claim costs (p = 0.027), prescription claims (p = 0.037), office visits (p = 0.038), inpatient admissions (p = 0.042), and total hospital encounters (p = 0.014). · Statistical analyses & sample size justification; Health insurance claims
ShownParticipants randomized to mobile monitoring showed a reduced propensity to attribute their health status to chance compared with controls.
On the Multidimensional Health Locus of Control (MHLC) Form C Chance subscale, the monitoring group decreased by ~0.8 points while controls increased by ~1.3 points (Δ = 2.06, p = 0.020), whereas Stanford PERC self-efficacy (7.9 vs. 8.0, p = 0.85) and Patient Activation Measure (73 vs. 76, p = 0.68) did not differ. · Health self-management
ShownMonitoring participants logged 10,305 biosensor readings with 50% mean weekly compliance, though 28% experienced technical issues requiring help-desk tickets.
Completers recorded 10,305 readings (6,356 blood pressure, mean = 151, sd = 84; 3,440 blood glucose, mean = 248, sd = 268; 509 arrhythmia, mean = 57, sd = 54) and 3,670 portal sessions (mean compliance = 50%), while 21 of 75 enrolled monitoring participants (28%) needed help-desk tickets and >= 20 needed device replacement. · Device usage; Participant demographics and information; Device usage; DISCUSSION
Methods, data and reproducibility
In a 6-month prospective randomized controlled trial (NCT01975428), 160 adults insured by Scripps Health with hypertension, diabetes, or arrhythmia were assigned to standard nurse disease management or the same program plus an iPhone, wireless biosensors, and the HealthyCircles portal. Six-month insurance claims and survey scales were compared among 130 completers using paired and equivalence tests.
- Prospective randomized controlled trial cohort of adults with hypertension, diabetes, or arrhythmia · human · n = 160 enrolled; 130 completed (65 control, 65 monitoring) · NCT01975428
- Wireless biosensor readings, HealthyCircles web portal sessions, and health insurance claims · human · n = 10,305 device readings and 3,670 online sessions
- Methods & tools
- Withings Blood Pressure Monitor, Sanofi IGBStar, AliveCor Mobile ECG, HealthyCircles, HealthComp, SurveyMonkey, Google Analytics, MHLC Form C, Stanford PERC, Patient Activation Measure
- Reproducibility gaps
- Claims data are referenced as Supplemental Data without a public repository URL; Google Analytics could not provide individual-level portal usage, and Withings blood pressure usage logs were corrupted and treated as missing for 18 participants.
Limitations (3)
- The 6-month trial period is too short to capture potential long-term reductions in healthcare utilization and clinical decompensation resulting from chronic disease self-monitoring. · stated by the authors
- Technical malfunctions affected 28% (21 of 75) of monitoring participants, requiring at least 20 iPhone or biosensor replacements and causing loss of accurate Withings usage counts for 18 participants. · stated by the authors
- Device adherence varied across conditions, with 4 of 14 diabetes participants (29%) recording zero glucose readings, 4 of 42 hypertension participants (10%) recording fewer than three readings, and mean weekly compliance reaching 50%. · stated by the authors
Read this if you design digital health trials or evaluate how remote patient monitoring with connected biosensors affects short-term healthcare utilization and patient self-management.
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