BACKGROUND: The use of digital Patient- Reported Outcomes (PRO) tools has been shown to enhance the exchange of information and shared decision-making in medical encounters. However, their influence on patient-pharmacist interactions has not yet been explored.
OBJECTIVE: This study aimed to examine the impact of RxTalk™, a digital PRO tool, in supporting the communication between patients and pharmacists compared to usual care.
METHODS: Intervention: RxTalk™ was developed to collect information about medication adherence and beliefs, using a tablet computer.
METHODS: A pilot randomized controlled study was conducted at a community pharmacy in Wisconsin, USA. Sixty patients were randomized to either the intervention group who used RxTalk™ during medication pick-up or the control group who did not use the tool. Patients who used RxTalk™ received paper copies of their responses which were also shared with pharmacists. The consultation was audio-recorded for both groups and coded using the Active Patient Participation Coding scale. Follow-up phone interviews were conducted with both groups within one week of enrollment.
RESULTS: Patient tapes were analyzed. In the unadjusted model, patients in the intervention group had a higher active participation rate (p = 0.004) and raised significantly more concerns during consultations (p < 0.001) compared to the control group. Pharmacists asked twice as many questions while counseling patients in the intervention group compared to the control group (p < 0.001). After controlling for patients\' demographics and pharmacists\' questions, there was a statistical difference between the two patient groups in their odds of expressing at least one concern utterance.
CONCLUSIONS: This pilot study suggests that collecting PRO from patients with chronic illnesses and providing results to pharmacists and patients can help patients express their health and medication concerns. RxTalk™ would be useful for pharmacists who wish to improve the recognition and management of medication-related problems.

BACKGROUND: Digital diabetes prevention programs (dDPPs) are effective \"digital prescriptions\" but have high attrition rates and program noncompletion. To address this, we developed a personalized automatic messaging system (PAMS) that leverages SMS text messaging and data integration into clinical workflows to increase dDPP engagement via enhanced patient-provider communication. Preliminary data showed positive results. However, further investigation is needed to determine how to optimize the tailoring of support technology such as PAMS based on a user\'s preferences to boost their dDPP engagement.
OBJECTIVE: This study evaluates leveraging machine learning (ML) to develop digital engagement phenotypes of dDPP users and assess ML\'s accuracy in predicting engagement with dDPP activities. This research will be used in a PAMS optimization process to improve PAMS personalization by incorporating engagement prediction and digital phenotyping. This study aims (1) to prove the feasibility of using dDPP user-collected data to build an ML model that predicts engagement and contributes to identifying digital engagement phenotypes, (2) to describe methods for developing ML models with dDPP data sets and present preliminary results, and (3) to present preliminary data on user profiling based on ML model outputs.
METHODS: Using the gradient-boosted forest model, we predicted engagement in 4 dDPP individual activities (physical activity, lessons, social activity, and weigh-ins) and general activity (engagement in any activity) based on previous short- and long-term activity in the app. The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the Brier score metrics determined the performance of the model. Shapley values reflected the feature importance of the models and determined what variables informed user profiling through latent profile analysis.
RESULTS: We developed 2 models using weekly and daily DPP data sets (328,821 and 704,242 records, respectively), which yielded predictive accuracies above 90%. Although both models were highly accurate, the daily model better fitted our research plan because it predicted daily changes in individual activities, which was crucial for creating the \"digital phenotypes.\" To better understand the variables contributing to the model predictor, we calculated the Shapley values for both models to identify the features with the highest contribution to model fit; engagement with any activity in the dDPP in the last 7 days had the most predictive power. We profiled users with latent profile analysis after 2 weeks of engagement (Bayesian information criterion=-3222.46) with the dDPP and identified 6 profiles of users, including those with high engagement, minimal engagement, and attrition.
CONCLUSIONS: Preliminary results demonstrate that applying ML methods with predicting power is an acceptable mechanism to tailor and optimize messaging interventions to support patient engagement and adherence to digital prescriptions. The results enable future optimization of our existing messaging platform and expansion of this methodology to other clinical domains.
BACKGROUND: ClinicalTrials.gov NCT04773834; https://www.clinicaltrials.gov/ct2/show/NCT04773834.
UNASSIGNED: RR2-10.2196/26750.

BACKGROUND: Mindfulness-based treatments gained popularity for migraine treatment. In this manuscript we report the results of a single-arm open pilot study that evaluated the impact of a multimodal web-based intervention combining home-based medication withdrawal, patients\' education, and online mindfulness-based interventions. We aimed to address whether our program had the ability to show a change in the observed parameters and the study should therefore be intended as an early phase trial.
METHODS: Consecutive patients with chronic migraine associated with medication overuse headache were enrolled, followed-up for 12 months, in a program that included home-based medication withdrawal, education on the correct use of drugs and lifestyle issues, prescription of tailored pharmacological prophylaxis, and attendance to six online mindfulness-based sessions. We tested the effect of the program on improving headache frequency, medication intake, quality of life (QoL), headache impact, depression, self-efficacy, and pain catastrophizing.
RESULTS: A total of 37 patients completed the study (10 dropped out). We observed a large improvement in headache frequency, medication intake, headache impact, and QoL, a moderate improvement in pain catastrophizing and a mild improvement in depression symptoms; 70% to 76% of patients achieved 50% or more reduction in headache frequency from baseline to each follow-up (p < .01).
CONCLUSIONS: The results of our multimodal program showed significant improvements in headache frequency, medication intake, and patient-reported outcomes. Future studies are needed to better identify patients who might benefit most from Digital Health Interventions and to demonstrate at least an equivalence in outcome with in-person programs carried out in hospital settings.

BACKGROUND: Optimal rehabilitation programs for orthopedic joint replacement patients ensure faster return to function, earlier discharge from hospital, and improved patient satisfaction. Digital health interventions show promise as a supporting tool for re-enablement.
OBJECTIVE: The main goal of this mixed methods study was to examine the usability of the AIMS platform from the perspectives of both patients and clinicians. The aim of this study was to evaluate a re-enablement platform that we have developed that uses a holistic systems approach to address the de-enablement that occurs in hospitalized inpatients, with the older adult population most at risk. The Active and Independent Management System (AIMS) platform is anticipated to deliver improved patient participation in recovery and self-management through education and the ability to track rehabilitation progression in hospital and after patient discharge.
METHODS: Two well-known instruments were used to measure usability: the System Usability Scale (SUS) with 10 items and, for finer granularity, the User Experience Questionnaire (UEQ) with 26 items. In all, 26 physiotherapists and health care professionals evaluated the AIMS clinical portal; and 44 patients in hospital for total knee replacement, total hip replacement, or dynamic hip screw implant evaluated the AIMS app.
RESULTS: For the AIMS clinical portal, the mean SUS score obtained was 82.88 (SD 13.07, median 86.25), which would be considered good/excellent according to a validated adjective rating scale. For the UEQ, the means of the normalized scores (range -3 to +3) were as follows: attractiveness=2.683 (SD 0.100), perspicuity=2.775 (SD 0.150), efficiency=2.775 (SD 0.130), dependability=2.300 (SD 0.080), stimulation=1.950 (SD 0.120), and novelty=1.625 (SD 0.090). All dimensions were thus classed as excellent against the benchmarks, confirming the results from the SUS questionnaire. For the AIMS app, the mean SUS score obtained was 74.41 (SD 10.26), with a median of 77.50, which would be considered good according to the aforementioned adjective rating scale. For the UEQ, the means of the normalized scores were as follows: attractiveness=2.733 (SD 0.070), perspicuity=2.900 (SD 0.060), efficiency=2.800 (SD 0.090), dependability=2.425 (SD 0.060), stimulation=2.200 (SD 0.010), and novelty=1.450 (0.260). All dimensions were thus classed as excellent against the benchmarks (with the exception of novelty, which was classed as good), providing slightly better results than the SUS questionnaire.
CONCLUSIONS: The study has shown that both the AIMS clinical portal and the AIMS app have good to excellent usability scores, and the platform provides a solid foundation for the next phase of research, which will involve evaluating the effectiveness of the platform in improving patient outcomes after total knee replacement, total hip replacement, or dynamic hip screw.

UNASSIGNED: digital health has been demonstrated to improve the efficiency and scale of health service delivery in resource-limited settings. Understanding factors influencing its use could accelerate the process of its implementation in routine practice.
UNASSIGNED: we conducted a cross-sectional analytic study in Buea and Tiko health districts from January to May 2021. We included healthcare workers selected using multistage stratified sampling. Use of digital health was defined as using at least two digital tools and one digital health intervention (DHI) or at least two DHIs by a healthcare worker. Epi Info was used for statistical analysis. Binary logistic regression was used to evaluate factors associated with the use of digital health.
UNASSIGNED: in total, 221 participants were included in the study. The mean age was 33±9.1 years and 76.5% were female. Only 39.4% (n=87) of participants used digital health. The most frequently used digital tools for health-related purposes included: Microsoft (MS) Excel (29.9%), MS PowerPoint (26.8%) and MS Word (39.1%). The main DHIs used were research (30.2%) and diagnosing (24.1%) software. The main use of digital health was for research (75.6%). Owning a laptop (adjusted odds ratio (aOR)=1.98, 95% CI, 1.01 - 3.86), availability of internet connection in the health facility (1.99, 1.05 - 3.7) and receiving professional training in ICT/Computer Sciences (2.04, 1.06 - 3.93), were associated with higher odds of using digital health.
UNASSIGNED: this study shows a low level of use of digital health by healthcare workers. Providing newer devices, internet connection in health facilities and training in ICT for healthcare workers could improve its uptake.

OBJECTIVE: Treatment of periodontitis, a chronic inflammatory disease driven by biofilm dysbiosis, remains challenging due to patients\' poor performance and adherence to the necessary oral hygiene procedures. Novel, artificial intelligence-enabled multimodal-sensing toothbrushes (AI-MST) can guide patients\' oral hygiene practices in real-time and transmit valuable data to clinicians, thus enabling effective remote monitoring and guidance. The aim of this trial was to assess the effect of such a system as an adjunct to clinical practice guideline-conform treatment.
METHODS: This was a single-centre, double-blind, standard-of-care controlled, randomized, parallel-group, superiority trial. Male and female adults with generalized Stage II/III periodontitis were recruited at the Shanghai Ninth People\'s Hospital, China. Subjects received a standard-of-care oral hygiene regimen or a technology-enabled, theory-based digital intervention consisting of an AI-MST and targeted doctor\'s guidance by remote micromessaging. Additionally, both groups received guideline-conform periodontal treatment. The primary outcome was the resolution of inflamed periodontal pockets (≥4 mm with bleeding on probing) at 6 months. The intention-to-treat (ITT) analysis included all subjects who received the allocated treatment and at least one follow-up.
RESULTS: One hundred patients were randomized and treated (50 tests/controls) between 1 February and 30 November 2022. Forty-eight tests (19 females) and 47 controls (16 females) were analysed in the ITT population. At 6 months, the proportion of inflamed periodontal pockets decreased from 80.7% (95% confidence interval [CI] 76.5-84.8) to 52.3% (47.7-57.0) in the control group, and from 81.4% (77.1-85.6) to 44.4% (39.9-48.9) in the test group. The inter-group difference was 7.9% (1.6-14.6, p < .05). Test subjects achieved better levels of oral hygiene (p < .001). No significant adverse events were observed.
CONCLUSIONS: The tested digital health intervention significantly improved the outcome of periodontal therapy by enhancing the adherence and performance of self-performed oral hygiene. The model breaks the traditional model of oral health care and has the potential to improve efficiency and reduce costs (NCT05137392).

BACKGROUND: There is growing evidence that Technology Assisted Sexual Abuse (TASA) represents a serious problem for large numbers of children. To date, there are very few evidence-based interventions available to young people (YP) after they have been exposed to this form of abuse, and access to support services remains a challenge. Digital tools such as smartphones have the potential to increase access to mental health support and may provide an opportunity for YP to both manage their distress and reduce the possibility of further victimization. The current study explores the acceptability of a digital health intervention (DHI; the i-Minds app) which is a theory-driven, co-produced, mentalization-based DHI designed for YP aged 12-18 who have experienced TASA.
METHODS: Semi-structured interviews were conducted with 15 YP recruited through Child and Adolescent Mental Health Services, a Sexual Assault Referral Centre and an e-therapy provider who had access to the i-Minds app as part of a feasibility clinical trial. Interviews focused on the acceptability and usability of i-Minds and were coded to themes based on the Acceptability of Healthcare Interventions framework.
RESULTS: All participants found the i-Minds app acceptable. Many aspects of the app were seen as enjoyable and useful in helping YP understand their abuse, manage feelings, and change behavior. The app was seen as usable and easy to navigate, but for some participants the level of text was problematic and aspects of the content was, at times, emotionally distressing at times.
CONCLUSIONS: The i-Minds app is useful in the management of TASA and helping change some risk-related vulnerabilities. The app was designed, developed and evaluated with YP who had experienced TASA and this may account for the high levels of acceptability seen.
BACKGROUND: The trial was registered on the ISRCTN registry on the 12/04/2022 as i-Minds: a digital intervention for young people exposed to online sexual abuse (ISRCTN43130832).

BACKGROUND: Primary care plays a key role in the management of type 2 diabetes. Sodium-glucose cotransporter 2 (SGLT2) inhibitors have been demonstrated to reduce hospitalization and cardiac and renal complications. Tools that optimize management, including appropriate prescribing, are a priority for treating chronic diseases. Future Health Today (FHT) is software that facilitates clinical decision support and quality improvement. FHT applies algorithms to data stored in electronic medical records in general practice to identify patients who are at risk of a chronic disease or who have a chronic disease that may benefit from intensification of management. The platform continues to evolve because of rigorous evaluation, continuous improvement, and expansion of the conditions hosted on the platform. FHT currently displays recommendations for the identification and management of chronic kidney disease, cardiovascular disease, type 2 diabetes, and cancer risk. A new module will be introduced to FHT focusing on SGLT2 inhibitors in patients with type 2 diabetes who have chronic kidney diseases, cardiovascular diseases, or risk factors for cardiovascular disease.
OBJECTIVE: The study aims to explore the barriers and enablers to the implementation of an SGLT2 inhibitor module within the Future Health Today software.
METHODS: Clinic staff were recruited to participate in interviews on their experience in their use of a tool to improve prescribing behavior for SGLT2 inhibitors. Thematic analysis was guided by Clinical Performance Feedback Intervention Theory.
RESULTS: In total, 16 interviews were completed. Identified enablers of use included workflow alignment, clinical appropriateness, and active delivery of the module. Key barriers to use were competing priorities, staff engagement, and knowledge of the clinical topic.
CONCLUSIONS: There is a recognized benefit to the use of a clinical decision support tool to support type 2 diabetes management, but barriers were identified that impeded the usability and actionability of the module. Successful and effective implementation of this tool could support the optimization of patient management of type 2 diabetes in primary care.

BACKGROUND: Digitalization with minimal human resources could support self-management among women with gestational diabetes and improve maternal and neonatal outcomes.
OBJECTIVE: This study aimed to investigate if a periodic mobile application (eMOM) with wearable sensors improves maternal and neonatal outcomes among women with diet-controlled gestational diabetes without additional guidance from healthcare personnel.
METHODS: Women with gestational diabetes were randomly assigned in a 1:1 ratio at 24 to 28 weeks\' gestation to the intervention or the control arm. The intervention arm received standard care in combination with use of the periodic eMOM, whereas the control arm received only standard care. The intervention arm used eMOM with a continuous glucose monitor, an activity tracker, and a food diary 1 week/month until delivery. The primary outcome was the change in fasting plasma glucose from baseline to 35 to 37 weeks\' gestation. Secondary outcomes included capillary glucose, weight gain, nutrition, physical activity, pregnancy complications, and neonatal outcomes, such as macrosomia.
RESULTS: In total, 148 women (76 in the intervention arm, 72 in the control arm; average age, 34.1±4.0 years; body mass index, 27.1±5.0 kg/m2) were randomized. The intervention arm showed a lower mean change in fasting plasma glucose than the control arm (difference, -0.15 mmol/L vs -2.7 mg/mL; P=.022) and lower capillary fasting glucose levels (difference, -0.04 mmol/L vs -0.7 mg/mL; P=.002). The intervention arm also increased their intake of vegetables (difference, 11.8 g/MJ; P=.043), decreased their sedentary behavior (difference, -27.3 min/d; P=.043), and increased light physical activity (difference, 22.8 min/d; P=.009) when compared with the control arm. In addition, gestational weight gain was lower (difference, -1.3 kg; P=.015), and there were less newborns with macrosomia in the intervention arm (difference, -13.1 %; P=.036). Adherence to eMOM was high (daily use >90%), and the usage correlated with lower maternal fasting (P=.0006) and postprandial glucose levels (P=.017), weight gain (P=.028), intake of energy (P=.021) and carbohydrates (P=.003), and longer duration of the daily physical activity (P=.0006). There were no significant between-arm differences in terms of pregnancy complications.
CONCLUSIONS: Self-tracking of lifestyle factors and glucose levels without additional guidance improves self-management and the treatment of gestational diabetes, which also benefits newborns. The results of this study support the use of digital self-management and education tools in maternity care.

UNASSIGNED: Mental health recovery narratives are widely available to the public, and can benefit people affected by mental health problems. The NEON Intervention is a novel web-based digital health intervention providing access to the NEON Collection of recovery narratives. The NEON Intervention was found to be effective and cost-effective in the NEON-O Trial for people with nonpsychosis mental health problems (ISRCTN63197153), and has also been evaluated in the NEON Trial for people with psychosis experience (ISRCTN11152837). We aimed to document NEON Intervention experiences, through an integrated process evaluation.
UNASSIGNED: Analysis of interviews with a purposive sample of intervention arm participants who had completed trial participation.
UNASSIGNED: We interviewed 34 NEON Trial and 20 NEON-O Trial participants (mean age 40.4 years). Some users accessed narratives through the NEON Intervention almost daily, whilst others used it infrequently or not at all. Motivations for trial participation included: exploring the NEON Intervention as an alternative or addition to existing mental health provision; searching for answers about mental health experiences; developing their practice as a mental health professional (for a subset who were mental health professionals); claiming payment vouchers. High users (10 + narrative accesses) described three forms of appropriation: distracting from difficult mental health experiences; providing an emotional boost; sustaining a sense of having a social support network. Most participants valued the scale of the NEON Collection (n = 659 narratives), but some found it overwhelming. Many felt they could describe the characteristics of a desired narrative that would benefit their mental health. Finding a narrative meeting their desires enhanced engagement, but not finding one reduced engagement. Narratives in the NEON Collection were perceived as authentic if they acknowledged the difficult reality of mental health experiences, appeared to describe real world experiences, and described mental health experiences similar to those of the participant.
UNASSIGNED: We present recommendations for digital health interventions incorporating collections of digital narratives: (1) make the scale and diversity of the collection visible; (2) provide delivery mechanisms that afford appropriation; (3) enable contributors to produce authentic narratives; (4) enable learning by healthcare professionals; (5) consider use to address loneliness.