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Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain.

ABSTRACT: Nearly a quarter of visits to the emergency department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of "big data" sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease (SCD) community. SCD is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study, we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to well predict pain dynamics, given patients reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data-driven recommendations for treating chronic pain.

SUBMITTER: Clifton SM

PROVIDER: S-EPMC5510708 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain.

Clifton Sara M SM Kang Chaeryon C Li Jingyi Jessica JJ Long Qi Q Shah Nirmish N Abrams Daniel M DM

Journal of computational biology : a journal of computational molecular cell biology 20170605 7

Nearly a quarter of visits to the emergency department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of "big data" sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic sufferi ...[more]

PMID: 28581814

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Project description:ObjectiveThis study aims at evaluating the cost-effectiveness and cost-utility of a guided and unguided internet-based intervention for chronic pain patients (ACTonPainguided and ACTonPainunguided) compared with a waitlist control group (CG) as well as the comparative cost-effectiveness of the guided and the unguided version.DesignThis is a health economic evaluation alongside a three-arm randomised controlled trial from a societal perspective. Assessments were conducted at baseline, 9?weeks and 6?months after randomisation.SettingParticipants were recruited through online and offline strategies and in collaboration with a health insurance company.Participants302 adults (?18 years, pain for at least 6?months) were randomly allocated to one of the three groups (ACTonPainguided, ACTonPainunguided, CG).InterventionsACTonPain consists of seven modules and is based on Acceptance and Commitment Therapy. ACTonPainguided and ACTonPainunguided only differ in provision of human support.Primary and secondary outcome measuresMain outcomes of the cost-effectiveness and the cost-utility analyses were meaningful change in pain interference (treatment response) and quality-adjusted life years (QALYs), respectively. Economic evaluation estimates were the incremental cost-effectiveness and cost-utility ratio (ICER/ICUR).ResultsAt 6-month follow-up, treatment response and QALYs were highest in ACTonPainguided (44% and 0.280; mean costs = €6,945), followed by ACTonPainunguided (28% and 0.266; mean costs = €6,560) and the CG (16% and 0.244; mean costs = €6,908). ACTonPainguided vs CG revealed an ICER of €45 and an ICUR of €604.ACTonPainunguided dominated CG. At a willingness-to-pay of €0 the probability of being cost-effective was 50% for ACTonPainguided (vs CG, for both treatment response and QALY gained) and 67% for ACTonPainunguided (vs CG, for both treatment response and QALY gained). These probabilities rose to 95% when society's willingness-to-pay is €91,000 (ACTonPainguided) and €127,000 (ACTonPainunguided) per QALY gained. ACTonPainguided vs ACTonPainunguided revealed an ICER of €2,374 and an ICUR of €45,993.ConclusionsDepending on society's willingness-to-pay, ACTonPain is a potentially cost-effective adjunct to established pain treatment. ACTonPainunguided (vs CG) revealed lower costs at better health outcomes. However, uncertainty has to be considered. Direct comparison of the two interventions does not indicate a preference for ACTonPainguided.Trial registration numberDRKS00006183.

Project description:BackgroundMobile technologies are increasingly being used in health care and public health practice for patient communication, monitoring, and education. Mobile health (mHealth) tools have also been used to facilitate adherence to chronic musculoskeletal pain (CMP) management, which is critical to achieving improved pain outcomes, quality of life, and cost-effective health care.ObjectiveThe aim of this systematic review was to evaluate the 25-year trend of the literature on the adherence, usability, feasibility, and acceptability of mHealth interventions in CMP management among patients and health care providers.MethodsWe searched the PubMed, Cochrane CENTRAL, MEDLINE, EMBASE, and Web of Science databases for studies assessing the role of mHealth in CMP management from January 1999 to December 2023. Outcomes of interest included the effect of mHealth interventions on patient adherence; pain-specific clinical outcomes after the intervention; and the usability, feasibility, and acceptability of mHealth tools and platforms in chronic pain management among target end users.ResultsA total of 89 articles (26,429 participants) were included in the systematic review. Mobile apps were the most commonly used mHealth tools (78/89, 88%) among the included studies, followed by mobile app plus monitor (5/89, 6%), mobile app plus wearable sensor (4/89, 4%), and web-based mobile app plus monitor (1/89, 1%). Usability, feasibility, and acceptability or patient preferences for mHealth interventions were assessed in 26% (23/89) of the studies and observed to be generally high. Overall, 30% (27/89) of the studies used a randomized controlled trial (RCT), cohort, or pilot design to assess the impact of the mHealth intervention on patients' adherence, with significant improvements (all P<.05) observed in 93% (25/27) of these studies. Significant (judged at P<.05) between-group differences were reported in 27 of the 29 (93%) RCTs that measured the effect of mHealth on CMP-specific clinical outcomes.ConclusionsThere is great potential for mHealth tools to better facilitate adherence to CMP management, and the current evidence supporting their effectiveness is generally high. Further research should focus on the cost-effectiveness of mHealth interventions for better incorporating these tools into health care practices.Trial registrationInternational Prospective Register of Systematic Reviews (PROSPERO) CRD42024524634; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=524634.

Project description:BackgroundThe use of mobile health (mHealth) interventions has the potential to enhance chronic obstructive pulmonary disease (COPD) treatment outcomes. Further research is needed to determine which mHealth features are required to potentially enhance COPD self-management.AimThe aim of this study was to explore the potential features of an mHealth intervention for COPD management with healthcare providers (HCPs) and patients with COPD. It could inform the development and successful implementation of mHealth interventions for COPD management.MethodsThis was a qualitative study. We conducted semi-structured individual interviews with HCPs, including nurses, pharmacists and physicians who work directly with patients with COPD. Interviews were also conducted with a diverse sample of patients with COPD. Interview topics included demographics, mHealth usage, the potential use of medical devices and recommendations for features that would enhance an mHealth intervention for COPD management.ResultsA total of 40 people, including nurses, physicians and pharmacists, participated. The main recommendations for the proposed mHealth intervention were categorised into two categories: patient interface and HCP interface. The prevalent features suggested for the patient interface include educating patients, collecting baseline data, collecting subjective data, collecting objective data via compatible medical devices, providing a digital action plan, allowing patients to track their progress, enabling family members to access the mHealth intervention, tailoring the features based on the patient's unique needs, reminding patients about critical management tasks and rewarding patients for their positive behaviours. The most common features of the HCP interface include allowing HCPs to track their patients' progress, allowing HCPs to communicate with their patients, educating HCPs and rewarding HCPs.ConclusionThis study identifies important potential features so that the most effective, efficient and feasible mHealth intervention can be developed to improve the management of COPD.The reviews of this paper are available via the supplemental material section.

Project description:BackgroundUsing a mobile health (mHealth) intervention, consisting of a smartphone and compatible medical device, has the potential to enhance chronic obstructive pulmonary disease (COPD) treatment outcomes while mitigating health care costs.ObjectiveThe aim of this study was to explore the potential facilitators and barriers among health care providers (HCPs) regarding the use of mHealth interventions for COPD management.MethodsThis was a qualitative study. Semistructured individual interviews were conducted with HCPs, including nurses, pharmacists, and physicians who work directly with patients with COPD. A flexible prompts guide was used to facilitate discussions. Interview topics included the following: demographics, mHealth usage, perceptions toward challenges of mHealth adoption, factors facilitating mHealth adoption, and preferences regarding features of the mHealth intervention for COPD management. Interviews were conversational in nature, and items were not asked verbatim or in the order presented. The interviews were transcribed verbatim and compared against the digital recordings to ensure the accuracy of the content. After creating a codebook for analysis, 2 researchers independently coded the remaining interview data using pattern coding. They discussed commonalities and differences in coding until a consensus was reached.ResultsA total of 30 nurses, physicians, and pharmacists participated. The main facilitators to mHealth adoption are possible health benefits for patients, ease of use, educating patients and their HCPs, credibility, and reducing cost to the health care system. Alternatively, the barriers to adoption are technical issues, privacy and confidentiality issues, lack of awareness, potential limited uptake from the elderly, potential limited connection between patients and HCPs, and finances.ConclusionsIt is important to understand the perceptions of HCPs regarding the adoption of innovative mHealth interventions for COPD management. This study identifies some potential facilitators and barriers that may inform the successful development and implementation of mHealth interventions for COPD management.

Project description:BackgroundThere is a huge number of health-related apps available, and the numbers are growing fast. However, many of them have been developed without any kind of quality control. In an attempt to contribute to the development of high-quality apps and enable existing apps to be assessed, several guides have been developed.ObjectiveThe main aim of this study was to study the interrater reliability of a new guide - the Mobile App Development and Assessment Guide (MAG) - and compare it with one of the most used guides in the field, the Mobile App Rating Scale (MARS). Moreover, we also focused on whether the interrater reliability of the measures is consistent across multiple types of apps and stakeholders.MethodsIn order to study the interrater reliability of the MAG and MARS, we evaluated the 4 most downloaded health apps for chronic health conditions in the medical category of IOS and Android devices (ie, App Store and Google Play). A group of 8 reviewers, representative of individuals that would be most knowledgeable and interested in the use and development of health-related apps and including different types of stakeholders such as clinical researchers, engineers, health care professionals, and end users as potential patients, independently evaluated the quality of the apps using the MAG and MARS. We calculated the Krippendorff alpha for every category in the 2 guides, for each type of reviewer and every app, separately and combined, to study the interrater reliability.ResultsOnly a few categories of the MAG and MARS demonstrated a high interrater reliability. Although the MAG was found to be superior, there was considerable variation in the scores between the different types of reviewers. The categories with the highest interrater reliability in MAG were "Security" (α=0.78) and "Privacy" (α=0.73). In addition, 2 other categories, "Usability" and "Safety," were very close to compliance (health care professionals: α=0.62 and 0.61, respectively). The total interrater reliability of the MAG (ie, for all categories) was 0.45, whereas the total interrater reliability of the MARS was 0.29.ConclusionsThis study shows that some categories of MAG have significant interrater reliability. Importantly, the data show that the MAG scores are better than the ones provided by the MARS, which is the most commonly used guide in the area. However, there is great variability in the responses, which seems to be associated with subjective interpretation by the reviewers.

Project description:BACKGROUND:Studies have shown the effectiveness and user acceptance of mobile health (mHealth) technologies in managing patients with chronic kidney disease (CKD). However, incorporating mHealth technology into the standard care of patients with CKD still faces many challenges. To our knowledge, there are no reviews on mHealth interventions and their assessments concerning the management of patients undergoing dialysis. OBJECTIVE:This study provided a scoping review on existing apps and interventions of mHealth technologies in adult patients undergoing chronic dialysis and identified the gaps in patient outcome assessment of mHealth technologies in the literature. METHODS:We systematically searched PubMed (MEDLINE), Scopus, and the Cumulative Index to Nursing and Allied Health Literature databases, as well as gray literature sources. Two keywords, "mHealth" and "dialysis," were combined to address the main concepts of the objectives. Inclusion criteria were as follows: (1) mHealth interventions, which are on a smartphone, tablet, or web-based portals that are accessible through mobile devices; and (2) adult patients (age ?18 years) on chronic dialysis. Only English papers published from January 2008 to October 2018 were included. Studies with mHealth apps for other chronic conditions, based on e-consultation or videoconferencing, non-English publications, and review papers were excluded. RESULTS:Of the 1054 papers identified, 22 met the inclusion and exclusion criteria. Most studies (n=20) were randomized controlled trials and cohort studies. These studies were carried out in 7 countries. The main purposes of these mHealth interventions were as follows: nutrition or dietary self-monitoring (n=7), remote biometric monitoring (n=7), web-based portal (n=4), self-monitoring of in-session dialysis-specific information (n=3), and self-monitoring of lifestyle or behavioral change (n=1). The outcomes of the 22 included studies were organized into five categories: (1) patient satisfaction and acceptance, (2) clinical effectiveness, (3) economic assessment, (4) health-related quality of life, and (5) impact on lifestyle or behavioral change. The mHealth interventions showed neutral to positive results in chronic dialysis patient management, reporting no to significant improvement of dialysis-specific measurements and some components of the overall quality of life assessment. Evaluation of these mHealth interventions consistently demonstrated evidence in patients' satisfaction, high level of user acceptance, and reduced use of health resources and cost savings to health care services. However, there is a lack of studies evaluating safety, organizational, sociocultural, ethical, and legal aspects of mHealth technologies. Furthermore, a comprehensive cost-effectiveness and cost-benefit analysis of adopting mHealth technologies was not found in the literature. CONCLUSIONS:The gaps identified in this study will inform the creation of health policies and organizational support for mHealth implementation in patients undergoing dialysis. The findings of this review will inform the development of a comprehensive service model that utilizes mHealth technologies for home monitoring and self-management of patients undergoing chronic dialysis.

Project description:BackgroundIntegrated care can generate health and social care efficiencies through the defragmentation of care and adoption of patient-centered preventive models. eHealth can be a key enabling technology for integrated care.ObjectiveThe aim of this study was to assess the effectiveness and cost-effectiveness of the implementation of a mobile health (mHealth)-enabled integrated care model for complex chronic patients.MethodsAs part of the CONNECARE Horizon 2020 project, a prospective, pragmatic, two-arm, parallel implementation trial was held in a rural region of Catalonia, Spain. During 3 months, elderly patients with chronic obstructive pulmonary disease or heart failure and their carers experienced the combined benefits of the CONNECARE organizational integrated care model and the eHealth platform supporting it, consisting of a patient self-management app, a set of integrated sensors, and a web-based platform connecting professionals from different settings, or usual care. We assessed changes in health status with the 12-Item Short-Form Survey (SF-12), unplanned visits and admissions during a 6-month follow up, and the incremental cost-effectiveness ratio (ICER).ResultsA total of 48 patients were included in the integrated care arm and 28 patients receiving usual care were included in the control arm (mean age 82 years, SD 7 years; mean Charlson index 7, SD 2). Integrated care patients showed a significant increase in the SF-12 physical domain with a mean change of +3.7 (SD 8.4) (P=.004) and total SF-12 score with a mean change of +5.8 (SD 12.8) (P=.003); however, the differences in differences between groups were not statistically significant. Integrated care patients had 57% less unplanned visits (P=.004) and 50% less hospital admissions related to their main chronic diseases (P=.32). The integrated care program generated savings in different cost scenarios and the ICER demonstrated the cost-effectiveness of the program.ConclusionsThe implementation of a patient-centered mHealth-enabled integrated care model empowering the patient, and connecting primary, hospital, and social care professionals reduced unplanned contacts with the health system and health costs, and was cost-effective. These findings support the notion of system-wide cross-organizational care pathways supported by mHealth as a successful way to implement integrated care.

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Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain.

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Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain.

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