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Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes

Technical Brief

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Key Messages

  • We conducted a thorough evidence review of automated-entry patient-generated health data (PGHD) devices and mobile apps for the prevention or treatment of 11 chronic conditions, specifically looking for evidence on their impact on health outcomes such as mortality, quality of life, and symptom improvement.
  • We included 114 controlled studies that used 118 unique devices and 26 mobile apps.
  • For three chronic conditions (coronary artery disease, heart failure, and asthma), we found a possible positive effect of PGHD technologies on health outcomes.
  • For obesity, we classified the health outcome data as unclear, and we found consistent evidence of a lack of effect of PGHD interventions on the surrogate outcome of body mass index/weight.
  • For hypertension, we classified the health outcome data as unclear, and we found evidence of a possible positive effect of PGHD interventions on the surrogate outcome of blood pressure.
  • For cardiac arrhythmias, we classified the health outcome data as unclear but found consistent evidence of a beneficial effect of PGHD interventions on the surrogate outcome of time to arrhythmia detection.
  • The evidence on both health outcomes and surrogate outcomes was unclear for the other five conditions (chronic obstructive pulmonary disease, diabetes prevention, sleep apnea, stroke, and Parkinson’s disease).
  • PGHD technologies are often provided as part of a multicomponent intervention, and future studies should attempt to determine the specific impact of PGHD, place a greater emphasis on the measurement of health outcomes, and study long-term effects.

Structured Abstract

Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation.

Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases.

Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition.

Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect.

The findings were "unclear" regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use.

Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.

Suggested Citation

Treadwell JR, Reston JT, Rouse B, Fontanarosa J, Patel N, Mull NK. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Technical Brief No. 38 (Prepared by the ECRI-Penn Evidence-based Practice Center under Contract No. 290-2015-00005-I.) AHRQ Publication No. 21-EHC012. Rockville, MD: Agency for Healthcare Research and Quality. March 2021. Posted final reports are located on the Effective Health Care Program search page. DOI: 10.23970/AHRQEPCTB38.