Behavior analysts have long improved the healthcare needs of those they serve. Behavior analysis as a practice, however, has developed in parallel to, rather than in concert with, the disciplines that most often address society’s healthcare needs. Quality improvement is a methodology for refining healthcare processes and improving healthcare outcomes—a methodology with many similarities to behavior analysis. Leveraging behavior science to support quality improvement initiatives can validate behavior analysts as integral members of the healthcare team and realign behavior analysis as a science and practice to be in concert with, rather than in parallel to, medical professionals. We discuss how behavior analysts can strategically integrate their science at each step of the quality improvement process to advance patient- and systems-level outcomes. We also discuss the mutual benefits of integration.
Healthcare expenditures in the United States are among the highest in the world, and behavioral healthcare costs account for a substantial portion of overall healthcare expenses. Injuries to hospital staff contribute to the high costs associated with delivering behavioral healthcare. Interventions to decrease aggression leading to staff injuries are paramount, but there are limited data on the features of patient physical aggression resulting in these injuries. We reviewed 960 narrative reports of staff injuries caused by patient physical aggression across two pediatric healthcare systems from 2022–2024. We analyzed the putative antecedents to physical aggression, injured staff’s occupation (e.g., nurse), body part injured, and whether the injury was reportable to government agencies. Direct care staff (e.g., 1:1 staff) and nurses were at the greatest risk for injuries from patient physical aggression. Most injuries across both systems were non-reportable to government agencies. At least one putative antecedent was clear within most reports, with intrusive strategies (e.g., restraint, seclusion, and response blocking) and the presence of aversive stimuli (e.g., medical care and staff directions) being most common. Contextualizing patient physical aggression can elucidate features (e.g., putative antecedents) informing strategies to mitigate the recurrence of such behavior and, relatedly, avoid many staff injuries. These outcomes support one avenue for behavior analysts to help healthcare systems better equip staff with ways to manage their safety by better understanding patient behavior.
Importance:During the 2024-2025 respiratory syncytial virus (RSV) season in the US, nirsevimab and maternal RSV vaccination became widely available to prevent severe RSV disease in infants. Assessments of the real-world effectiveness and impact of both products are needed to inform RSV prevention policy. Objectives:To estimate nirsevimab and maternal RSV vaccine effectiveness against medically attended RSV-associated acute respiratory illness (ARI) and to estimate the impact of these products on RSV-associated hospitalizations during 2024-2025. Design, Setting, and Participants:Population-based surveillance for medically attended ARI was conducted among children younger than 2 years with systematic molecular testing for RSV. Children were enrolled at 7 US pediatric medical centers from October 1, 2024, through April 30, 2025. A test-negative case-control design was used to estimate maternal RSV vaccine and nirsevimab effectiveness. Exposures:To estimate maternal RSV vaccine effectiveness, the exposure was maternal RSV vaccination among newborns and infants younger than 6 months at medical encounters; to estimate nirsevimab effectiveness, the exposure was nirsevimab receipt among newborns and infants younger than 8 months as of October 1, 2024, or born after that date. Main Outcomes and Measures:The primary outcome was medically attended RSV-associated ARI and RSV-associated hospitalization. Immunization effectiveness was calculated as (1 - adjusted odds ratio) × 100%. To estimate population-level impact of RSV prevention products, relative rate reductions were estimated by comparing observed RSV-associated hospitalization rates during 2024-2025 to (1) observed rates in 2017-2020 or (2) counterfactual 2024-2025 rates estimated by a difference-in-differences approach; estimates from both approaches are presented as ranges. Results:Overall, 5029 children younger than 2 years with medically attended ARI were enrolled during October 2024 to April 2025. Median (IQR) age was 10 months (5-16 months), and 2176 children (43.3%) were female. Among newborns and infants younger than 6 months, maternal RSV vaccine effectiveness was 64% (95% CI, 37%-79%) against any medically attended RSV-associated ARI and 70% (95% CI, 37%-86%) against RSV-associated hospitalization. Nirsevimab effectiveness was 81% (95% CI, 71%-87%) against RSV-associated hospitalization, and nirsevimab remained 77% (95% CI, 42%-92%) effective against RSV-associated hospitalization at 130 to 210 days after receipt. RSV-associated hospitalizations were reduced by 41% to 51% among newborns and infants aged 0 to 11 months, with the highest reduction of 56% to 63% in those aged 0 to 2 months. Conclusions and Relevance:According to the results of this population-based surveillance study, during 2024-2025, both maternal RSV vaccine and nirsevimab were estimated to be effective at protecting infants from RSV-associated hospitalizations in their first RSV season, and RSV-associated hospitalization rates in newborns and infants aged 0 to 11 months were reduced by up to half compared to seasons before these products were introduced.
Diabetes Technology & TherapeuticsVol. 26, No. S1 Original ArticlesFree AccessUsing Digital Health Technology to Prevent and Treat DiabetesMark A. Clements, Neal Kaufman, and Eran MelMark A. ClementsChildren's Mercy Hospital, Kansas City, MO.University of Missouri–Kansas City, Kansas City, MO.Search for more papers by this author, Neal KaufmanFielding School of Public Health, Geffen School of Medicine, University of California, Los Angeles, CA.Canary Health Inc., Los Angeles, CA.Search for more papers by this author, and Eran MelJesse Z. and Sara Lea Shaffer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.Search for more papers by this authorPublished Online:1 Mar 2024https://doi.org/10.1089/dia.2024.2506AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail IntroductionDiabetes digital health includes electronic health (eHealth, which can be web-based or computer software–based), mobile health (mHealth, which also includes digital therapeutics and digital companion apps), as well as telehealth, including remote monitoring of patients. This year's PubMed search for and review of articles addressing diabetes digital health yielded 22 publications that should be of significant interest to the field. The publications this year can best be summarized by the numbers: Five meta-analyses or systematic reviewsTwo implementation science approachesSix health economic (mostly cost-effectiveness) analysesEight randomized controlled trials (either initial reports or secondary analyses)Five pediatric studies of type 1 diabetes (T1D), type 2 diabetes (T2D), prediabetes, or obesityOne study on older adults with T2DOne study addressing rural compared with urban populationsFour studies of digital solutions to deliver the Diabetes Prevention ProgramSix platforms that can best be described as participatory health technologies or collaborative care platformsTwo studies specifically focused on digital therapeutics/digital companions, though many of the mobile phone–based digital health solutions described herein can be considered in those categoriesThree studies addressing aspects of remote patient monitoringOne study of a web-based diabetes education platform, with massively open online courses (MOOC)The range of topics and the increasing number of higher quality articles indicate an actively maturing field. A title- and abstract-based PubMed search for "diabetes" and "digital health" revealed 274 articles in 20 months from January 2022 in contrast with 193 in all of 2020 and 2021. A similar search focused on "mHealth" revealed 225 articles in the 20 months from January 2022 versus 235 in all of 2020 and 2021. When one searches for "telehealth"-related articles using the same strategy, one finds 335 articles published to date in 2022 and 2023 versus 282 in all of 2020 and 2021. Finally, a search for "remote patient monitoring" and "diabetes" reveals 40 published articles so far in 2022 and 2023 versus 24 in 2020 and 2021. This article focuses on the 23 articles of greatest interest published from July 1, 2022, to June 30, 2023, across this highly varied discipline.A few concepts of interest emerge in this year's selected articles, including the value of comparing the effectiveness of different forms (aka vehicles for delivery) of digital intervention and the concept of the digital phenotype. Increasing attention is being paid to the important role that bona fide "digital therapeutics" and "digital companions" can play in improving disease outcomes. A digital therapeutic is software as medical device that is supported by evidence to prevent, manage, or treat a medical disorder whereas a digital companion works alongside a medication or treatment to increase patient engagement in the treatment. An example of a digital therapeutic is a cognitive behavioral therapy–based mobile intervention for T2D. Note that digital therapeutics can include some digital companion-like functions (promoting engagement with taking medications). By contrast, a mobile insulin titration application, which is only designed to aid in dose optimization of either long- or short-acting insulins, serves as an example of a digital companion. Health economic analyses continue to reveal the noninferiority or even superiority of digital health solutions relative to nondigital care modalities. This article presents the 22 articles of greatest interest across these highly varied disciplines.Key Articles ReviewedEffectiveness, Reach, Uptake, and Feasibility of Digital Health Interventions for Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis of Randomised Controlled TrialsSiopis G, Moschonis G, Eweka E, Jung J, Kwasnicka D, Asare BY, Kodithuwakku V, Willems R, Verhaeghe N, Annemans L, Vedanthan R, Oldenburg B, Manios Y, for theDigiCare4You ConsortiumLancet Digit Health2023; 5:e144–e159Costs and Cost-effectiveness of Implementing a Digital Diabetes Prevention Program in a Large, Integrated Health SystemSmith DH, O'Keeffe-Rosetti M, Fitzpatrick SL, Mayhew M, Firemark AJ, Gruß I, Nyongesa DB, Smith N, Dickerson JF, Stevens VJ, Vollmer WM, Fortmann SPPerm J2022; 26:74–82The Aim2Be mHealth Intervention for Children with Overweight or Obesity and Their Parents: Person-centered Analyses to Uncover Digital PhenotypesDe-Jongh González O, Tugault-Lafleur CN, Buckler EJ, Hamilton J, Ho J, Buchholz A, Morrison KM, Ball GD, Mâsse LCJ Med Internet Res2022; 24:e35285Effects of E-health-based Interventions on Glycemic Control for Patients with Type 2 Diabetes: A Bayesian Network Meta-analysisZhang X, Zhang L, Lin Y, Liu Y, Yang X, Cao W, Ji Y, Chang CFront Endocrinol (Lausanne)2023; 14:1068254New Digital Health Technologies for Insulin Initiation and Optimization for People with Type 2 DiabetesKerr D, Edelman S, Vespasiani G, Khunti KEndocr Pract2022; 28:811–821Economic Evaluation Associated with Clinical-grade Mobile App-based Digital Therapeutic Interventions: Systematic ReviewSapanel Y, Tadeo X, Brenna CTA, Remus A, Koerber F, Cloutier LM, Tremblay G, Blasiak A, Hardesty CL, Yoong J, Ho DJ Med Internet Res2023; 25:e47094Efficacy of Personalized Diabetes Self-care Using an Electronic Medical Record–Integrated Mobile App in Patients with Type 2 Diabetes: 6-Month Randomized Controlled TrialLee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, Yoon KH, Hyun MK, Ko SHJ Med Internet Res2022; 24:e37430Randomized, Controlled Trial of a Digital Behavioral Therapeutic Application to Improve Glycemic Control in Adults with Type 2 DiabetesHsia J, Guthrie NL, Lupinacci P, Gubbi A, Denham D, Berman MA, Bonaca MPDiabetes Care2022; 45:2976–2981Economic Evaluations of mHealth Interventions for the Management of Type 2 Diabetes: A Scoping ReviewTornvall I, Kenny D, Wubishet BL, Russell A, Menon A, Comans TJ Diabetes Sci Technol. Published online July 3, 2023. doi: 10.1177/19322968231183956Effectiveness of a Smartphone App (MINISTOP 2.0) Integrated in Primary Child Health Care to Promote Healthy Diet and Physical Activity Behaviors and Prevent Obesity in Preschool-aged Children: Randomized Controlled TrialAlexandrou C, Henriksson H, Henström M, Henriksson P, Delisle Nyström C, Bendtsen M, Löf MInt J Behav Nutr Phys Act2023; 20:22A Quantitative Model to Ensure Capacity Sufficient for Timely Access to Care in a Remote Patient Monitoring ProgramChang A, Gao MZ, Ferstad JO, Dupenloup P, Zaharieva DP, Maahs DM, Prahalad P, Johari R, Scheinker DEndocrinol Diabetes Metab2023; 6:e435Remote Glucose Monitoring Is Feasible for Patients and Providers Using a Commercially Available Population Health PlatformCrossen SS, Romero CC, Lewis C, Glaser NSFront Endocrinol (Lausanne)2023; 14:1063290A Model to Design Financially Sustainable Algorithm-enabled Remote Patient Monitoring for Pediatric Type 1 Diabetes CareDupenloup P, Pei RL, Chang A, Gao MZ, Prahalad P, Johari R, Schulman K, Addala A, Zaharieva DP, Maahs DM, Scheinker D, on behalf of the 4T Research TeamFront Endocrinol (Lausanne)2022; 13:1021982Cost-utility of an Online Education Platform and Diabetes Personal Health Record: Analysis over Ten YearsCunningham SG, Stoddart A, Wild SH, Conway NJ, Gray AM, Wake DJJ Diabetes Sci Technol2023; 17:715–726A Randomised Control Trial to Explore the Impact and Efficacy of the Healum Collaborative Care Planning Software and App on Condition Management in the Type 2 Diabetes Mellitus Population in NHS Primary CareHeald AH, Roberts S, Albeda Gimeno L, Gilingham E, James M, White A, Saboo A, Beresford L, Crofts A, Abraham JDiabetes Ther2023; 14:977–988Evaluating the Implementation of a Digital Diabetes Prevention Program in an Integrated Health Care Delivery System among Older Adults: Results of a Natural ExperimentFitzpatrick SL, Mayhew M, Rawlings AM, Smith N, Nyongesa DB, Vollmer WM, Stevens VJ, Grall SK, Fortmann SPClin Diabetes2022; 40:345–353The Effectiveness of Digital Health Technologies for Patients with Diabetes Mellitus: A Systematic ReviewStevens S, Gallagher S, Andrews T, Ashall-Payne L, Humphreys L, Leigh SFront Clin Diabetes Healthc2022; 3:936752Weight Loss in a Digital Diabetes Prevention Program for People in Health Professional Shortage and Rural AreasGraham SA, Auster-Gussman LA, Lockwood KG, Branch OHPopul Health Manag2023; 26:149–156Usage and Daily Attrition of a Smartphone-based Health Behavior Intervention: Randomized Controlled TrialEgilsson E, Bjarnason R, Njardvik UJMIR Mhealth Uhealth2023; 11:e45414Digital Health Technologies Enabling Partnerships in Chronic Care Management: Scoping ReviewWannheden C, Åberg-Wennerholm M, Dahlberg M, Revenäs Å, Tolf S, Eftimovska E, Brommels MJ Med Internet Res2022; 24:e38980Effects of Mobile Health Interventions on Health-related Outcomes in Older Adults with Type 2 Diabetes: A Systematic Review and Meta-analysisLee JJN, Abdul Aziz A, Chan ST, Raja Abdul Sahrizan RSFB, Ooi AYY, Teh YT, Iqbal U, Ismail NA, Yang A, Yang J, Teh DBL, Lim LLJ Diabetes2023; 15:47–57Cost and Cost-effectiveness Analysis of a Digital Diabetes Prevention Program: Results from the PREDICTS TrialMichaud TL, Wilson KE, Katula JA, You W, Estabrooks PATransl Behav Med2023; 13:501–510Effectiveness, Reach, Uptake, and Feasibility of Digital Health Interventions for Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis of Randomised Controlled TrialsSiopis G1,3, Moschonis G1, Eweka E4 , Jung J5, Kwasnicka D6, Asare BY7, Kodithuwakku V6, Willems R8, Verhaeghe N8,9, Annemans L8, Vedanthan R4, Oldenburg B2,6, Manios Y10,11, for theDigiCare4You Consortium1Department of Food, Nutrition and Dietetics, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC, Australia; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 2Academic and Research Collaborative in Health, La Trobe University, Melbourne, VIC, Australia; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 3Institute for Physical Activity and Nutrition, Deakin University, Geelong, VIC, Australia; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 4Department of Population Health, NYU Grossman School of Medicine, New York, NY; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 5Maternal, Child and Adolescent Health Program, Burnet Institute, Melbourne, VIC, Australia; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 6NHMRC CRE in Digital Technology to Transform Chronic Disease Outcomes, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 7Curtin School of Population Health, Curtin University, Perth, WA, Australia; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 8Department of Public Health and Primary Care, Faculty of Medicine, Ghent University, Ghent, Belgium; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 9Research Institute for Work and Society, HIVA KU Leuven, Leuven, Belgium; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 10Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece; Hellenic Mediterranean University Research Centre, Heraklion, Greece; 11Institute of Agri-food and Life Sciences, Hellenic Mediterranean University Research Centre, Heraklion, GreeceLancet Digit Health 2023;5:e144–e159Nine in 10 people worldwide own a phone with features and can receive SMS text messages, and four in five people have access to a smartphone. Smartphone applications are already available for diabetes management. Because comparisons have been sparse of the effectiveness and implementation of the different modes of digital health interventions in type 2 diabetes (T2D), this study compared the effectiveness of SMS texting, smartphone applications, and website-based interventions for improving glycemia in adults with T2D and reports on their reach, uptake, and feasibility.MethodsThis systematic review and meta-analysis examined the effectiveness of digital health interventions in reducing glycated hemoglobin A1c (HbA1c) in adults with T2D. Randomized controlled trials (RCTs) published in English were located via searches of CINAHL, Cochrane Central, Embase, MEDLINE, and PsycInfo for dates January 1, 2009, to May 25, 2022. Covidence was used for screening, and the data were extracted following Cochrane's guidelines. The primary end point assessed was the change in the mean plasma concentration of HbA1c at 3 months or more. Cochrane risk of bias 2 was used to assess risk of bias. Data on reach, uptake, and feasibility were summarized narratively, and data on HbA1c reduction were synthesized in a meta-analysis. Grading of Recommendations, Assessment, Development, and Evaluation criteria was used to evaluate the level of evidence.ResultsThe search identified 3236 records, from which 56 RCTs from 24 regions (n = 11,486 participants) were included in the narrative synthesis, and 26 studies (n = 4546 participants) in the meta-analysis. Text messages (SMS) were used in 20 studies as the primary mode of delivery of the digital health intervention, 25 used smartphone applications, and 11 used website interventions. The smartphone application interventions reported higher reach compared with the others, but the website-based interventions reported higher uptake compared with the others. Effective interventions, in general, included people with greater severity of their condition at baseline (i.e., higher HbA1c), and the intervention involved a higher intensity—such as more frequent use of a smartphone application. Overall, the digital health intervention group participants had a −0.30 (95% CI, −0.42 to −0.19) percentage point greater reduction in HbA1c, compared with the control group participants. The difference in HbA1c reduction between groups was statistically significant when intervention was delivered through a smartphone application (−0.42% [95% CI, −0.63 to −0.20]) or by text message (−0.37% [95% CI, −0.57 to −0.17]), but not when delivered via a website (−0.09% [95% CI, −0.64 to 0.46]). The level of evidence was moderate overall owing to the considerable heterogeneity of the included studies.ConclusionsSmartphone apps and text messaging (SMS)—but not website-based interventions—were associated with better glycemic control, but the studies' heterogeneity should be recognized. Considering that both smartphone app and SMS interventions are effective for diabetes management, clinicians should consider factors such as reach, uptake, patient preference, and context of the intervention when deciding on the mode of delivery of the intervention. Clinicians should familiarize themselves with these modalities of program delivery and encourage people with T2D to use evidence-based applications to improve their self-management of diabetes. Future research needs to describe in detail the factors involved in effective implementation of SMS and smartphone application interventions, such as the optimal dose, frequency, timing, user interface, and communication mode.CommentsThis systematic review and meta-analysis included a large number of randomized controlled trials and found that SMS and smartphone-based interventions (but not website-based interventions) improved hemoglobin A1c (HbA1c). The authors also considered implementation outcomes such as reach and uptake. This review introduces the concept that not all digital health interventions are created equally, and that mode of delivery may be as important a consideration as the content of the intervention. Buried in the discussion is a consideration of the impact that hardware and operating system (e.g., iPhone vs android) can have on the time it takes to complete health-related tasks, suggesting that digital health solutions should be considered in the context of the hardware through which they are delivered.Costs and Cost-effectiveness of Implementing a Digital Diabetes Prevention Program in a Large, Integrated Health SystemSmith DH, O'Keeffe-Rosetti M, Fitzpatrick SL, Mayhew M, Firemark AJ, Gruß I, Nyongesa DB, Smith N, Dickerson JF, Stevens VJ, Vollmer WM, Fortmann SP Kaiser Permanente Center for Health Research, Portland, ORPerm J 2022;26:74–82Now that the Diabetes Prevention Program (DPP) has been translated into digital formats, this report provides an economic evaluation of a digital DPP implemented in a large, integrated health-care system.MethodsElectronic medical record data were used to assess 4148 patients who were invited to participate in digital DPP based on their clinical characteristics (glycated hemoglobin [HbA1c] level 5.7%–6.4% and body mass index ≥ 30 kg/m2). Using a propensity score we matched (1:1) the enrolled patients with nonenrolled patients for a total of 784. We identified high-risk patients (i.e., above the 50th percentile of risk; n = 202) by calculating each patient's 2-year chance of developing diabetes. The cost of the intervention, the costs of medical care over the 12- and 24-month follow-up period, and the incremental cost-effectiveness ratio as the cost per additional kilogram of weight loss at 24 months were determined.ResultsAt 12 months, the enrolled patients had lower total costs (8783 [95% CI, 5681–7538]). This pattern attenuated slightly at 24 months (enrolled 18,846 [95% CI, 14,097–16,688]). We found an incremental cost-effectiveness ratio of 150 per additional kilogram of weight loss; at the same willingness-to-pay, there was a 60% chance in the high-risk subgroup. The study's limitations include its nonrandomized design and potential volunteer bias.ConclusionsCompared with other lifestyle interventions, digital DPP had a favorable cost-effectiveness profile.CommentsThe biggest problem with the original Diabetes Prevention Program is that of scalability—and therefore reach. Digital DPPs can help solve those issues. Omada Health's digitally translated DPP program was previously found to be effective; the present analysis also demonstrates its cost-effectiveness. The authors also address in the discussion the concept that cost efficiency for this and presumably similar programs increases if one focuses on enrolling the highest risk individuals in a clinical cohort.The Aim2Be mHealth Intervention for Children with Overweight or Obesity and Their Parents: Person-centered Analyses to Uncover Digital PhenotypesDe-Jongh González O1, Tugault-Lafleur CN2, Buckler EJ3, Hamilton J4, Ho J5, Buchholz A6, Morrison KM7, Ball GD8, Mâsse LC11School of Population and Public Health, University of British Columbia, BC Children's Hospital Research Institute, Vancouver, BC, Canada; Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada; 2School of Nutrition Sciences, Faculty of Health Sciences, The University of Ottawa, Ottawa, ON, Canada; Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada; 5School of Exercise Science, Physical and Health Education, University of Victoria, Victoria, BC, Canada; Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada; 4Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada; 5Cumming School of Medicine, Department of Pediatrics, University of Calgary, Calgary, AB, Canada; Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada; 6Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada; Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada; 7Department of Pediatrics, Center for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada; Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada; 8Department of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, CanadaJ Med Internet Res 2022;24:e35285Few studies have characterized user typologies derived from individuals' patterns of interactions with specific app features (digital phenotypes) in mobile health (mHealth) interventions targeting childhood obesity. This study identified digital phenotypes among 214 parent-child dyads who used the Aim2Be mHealth app as part of a randomized controlled trial conducted between 2019 and 2020, and explored whether participants' characteristics and health outcomes differed across phenotypes.MethodsDistinct parent and child phenotypes were identified using latent class analysis based on their use of the app's features (behavioral, gamified, and social) over 3 months. Multinomial logistic regression models were used to assess whether the phenotypes differed by demographic characteristics. Covariate-adjusted mixed-effect models evaluated changes in BMI z scores (zBMI), diet, physical activity, and screen time across phenotypes.ResultsFive digital phenotypes were identified in the parent: socially engaged (35 of 214, 16.3%) and independently engaged (18 of 214, 8.4%), who used mainly the social or the behavioral features of the app, respectively; fully engaged (26 of 214, 12.1%); partially engaged (32 of 214, 15%); and unengaged (103 of 214, 48.1%) users. The married parents were more likely to be fully engaged than independently engaged (P = 0.02) or unengaged (P = 0.01). The socially engaged parents were older than the fully engaged (P = 0.02) and unengaged (P = 0.01). For the children, the latent class analysis revealed four phenotypes: fully engaged (32 of 214, 15%), partially engaged (61 of 214, 28.5%), dabblers (42 of 214, 19.6%), and unengaged (79 of 214, 36.9%). The fully engaged children were younger than the dabblers (P = 0.04) and unengaged (P = 0.003). The dabblers lived in higher-income households than fully and partially engaged (P = 0.03 and P = 0.047, respectively). Fully engaged children were more likely to have fully engaged (P < 0.001) and partially engaged (P < 0.001) parents than the unengaged children. Compared with unengaged children, the fully and partially engaged children had decreased total sugar (P = 0.006 and P = 0.004, respectively) and energy intake (P = 0.03 and P = 0.04, respectively) after 3 months of app use. The partially engaged children also had decreased sugary beverage intake compared with the unengaged children (P = 0.03). Over time, children with fully engaged parents had decreased zBMI; children with unengaged parents had increased zBMI (P = 0.005). Finally, children with independently engaged parents had decreased caloric intake compared with children with unengaged parents who had increased caloric intake over time (P = 0.02).ConclusionsThe success of mHealth interventions depends on full parent-child engagement. More research is needed into program design elements that can affect participants' engagement in supporting behavior change.CommentsThis article uses an interesting approach: latent class analyses to classify individuals based on their usage patterns of different features of the application. De-Jongh González and colleagues then identified the individual characteristics in both parent and child users that differed between the latent phenotypes. The study suggests that active parental engagement in mHealth interventions may be required to achieve a positive benefit for the child, that each mHealth intervention must have "active ingredients" that promote health behavior change, and that dose–response analysis by feature (rather than relying on overall app engagement metrics) may help to identify patterns in the use of various app features to elicit positive health behaviors. The digital phenotype concept may be critical to accelerating the design of successful mHealth applications.Effects of E-health-based Interventions on Glycemic Control for Patients with Type 2 Diabetes: A Bayesian Network Meta-analysisZhang X, Zhang L, Lin Y, Liu Y, Yang X, Cao W, Ji Y, Chang CDepartment of Social Medicine and Health Education, School of Public Health, Peking University Health Science Center, Beijing, ChinaFront Endocrinol (Lausanne) 2023;14:1068254The deep integration of the internet and health care has led to electronic tools and information technology devoted to disease management in type 2 diabetes (T2D). This study evaluated the effectiveness of different forms and durations of E-health interventions in achieving glycemic control in T2D patients.MethodsPubMed, Embase, Cochrane, and Clinical Trials databases were searched for randomized controlled trials reporting different forms of E-health interventions for glycemic control in T2D patients, including comprehensive measures, smartphone applications, phone calls, short message service (SMS texts), websites, wearable devices, and usual care. The inclusion criteria were adults aged ≥ 18 with T2D, intervention period of ≥ 1 month, glycated hemoglobin (HbA1c) (%) measured as outcome, and randomized control of E-health–based approaches. Cochrane tools were used to assess the risk of bias and R 4.1.2 to conduct the Bayesian network meta-analysis.ResultsThe analysis included a total of 88 studies comprising 13,972 T2D patients. Compared with the usual care group, the SMS-based intervention was superior in reducing HbA1c levels (mean difference, −0.56 [95% CI, −0.82 to −0.31), followed by smartphone apps (mean difference, −0.45 [95% CI, −0.61 to −0.30]), comprehensive measures (mean difference, −0.41 [95% CI, −0.57 to −0.25]), websites (mean difference, −0.39 [95% CI, −0.60 to −0.18]), and phone calls (mean difference, −0.32 [95% CI, −0.50 to −0.14]) (P < 0.05). The most effective interventions lasted ≤ 6 months.ConclusionsIn patients with T2D, all types of E-health–based approaches can improve glycemic control. The most effective intervention, SMS texting, is a high-frequency, low-barrier technology that achieves the best effect in lowering HbA1c, with ≤ 6 months as the optimal intervention duration.CommentsZhang and colleagues produced an impressive work that reviews 88 randomized controlled trials that collectively studied e-health interventions in > 13,000 individuals with T2D. The authors identified that SMS-based and smartphone app–based interventions were the most successful in improving disease outcomes. This study essentially suggests that the success of an E-health intervention not only depends on psychological theory or the type of multimedia content, but also on the vehicle of delivery. My question is this: Given this information, why have we not seen more interventions that rely on both a smartphone application and text messaging channels? Would we see even greater efficacy with a combined strategy?New Digital Health Technologies for Insulin Initiation and Optimization for People with Type 2 DiabetesKerr D1, Edelman S2, Vespasiani G3, Khunti K41Sansum Diabetes Research Institute, Santa Barbara, CA; University of Leicester, Leicester General Hospital, Leicester, UK; 2University of California San Diego Veterans Affairs Medical Center, San Diego, CA; University of Leicester, Leicester General Hospital, Leicester, UK; 3METEDA S.r.l., Rome, Italy; University of Leicester, Leicester General Hospital, Leicester, UK; 4Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UKEndocr Pract2022; 28:811–821Many people with type 2 diabetes (T2D) eventually need insulin to help reduce their risk of serious associated complications, but the barriers to the initiation and/or optimization of insulin may expose them to sustained hyperglycemia. New and future technologies may provide opportunities to help overcome these barriers.MethodsSoftware tools and devices developed to support the initiation and/or optimization of insulin were identified via a focused literature search of PubMed and key scientific congresses then manually filtering > 300 publications and conference abstracts.ResultsMost new devices to support insulin therapy help track the dose and timing of insulin, and most of the available software tools have been developed for smartphones. Current published data suggest that the use of these technologies is associated with equivalent or improved glycemic outcomes compared with standard care; among the benefits are reduced time burden and improved knowledge of diabetes among health-care providers. Good-quality evidence remains in short supply, however.ConclusionsNew digital health tools may help to reduce barriers to optimal insulin therapy. An integrated solution that connects glucose monitoring, dose recording, and titration advice as well as records comorbidities and lifestyle factors has the potential to reduce the complexity and burden of treatment and may improve adherence to titration and treatment, resulting in better outcomes for people with diabetes.CommentsLet's f
BACKGROUND:Risk factors for severe respiratory syncytial virus (RSV) illness include early infancy, premature birth, and underlying medical conditions. However, the clinical significance of respiratory viral co-detection is unclear. We compared the clinical outcomes of young children with RSV-only detection and those with RSV viral co-detection. METHODS:We conducted active, population-based surveillance of children with medically attended fever or respiratory symptoms at 7 US medical centers (1 December 2016-31 March 2020). Demographic and clinical data were collected through parental interviews and chart abstractions. Nasal swabs, with or without throat swabs, were systematically tested for RSV and 6 other common respiratory virus groups. We compared clinical outcomes, including hospitalization, and among those hospitalized, length of stay, intensive care unit admission, supplemental oxygen use, and intubation, between children aged <2 years with RSV-only detection and those with RSV co-detection. RESULTS:We enrolled 18 008 children aged <2 years. Of 17 841 (99.1%) tested for RSV, 5099 (28.6%) were positive. RSV was singly detected in 3927 children (77.0%) and co-detected in 1172 (23.0%). RSV co-detection with parainfluenza virus or adenovirus was associated with significantly lower odds of hospitalization (adjusted odds ratio, 0.56; 95% confidence interval [CI]: .33-.95; P = .031) and supplemental oxygen use (adjusted odds ratio, 0.66; 95% CI: .46-.95; P = .026), respectively, than RSV-only detection. For all other comparisons, we did not identify a significant association between RSV co-detection and worse clinical outcomes. CONCLUSIONS:Co-detection of RSV with another respiratory virus was not significantly associated with worse clinical outcomes compared with RSV-only detection.
