Patients Managing Their Medical Data in Personal Electronic Health Records: Scoping Review.

BACKGROUND: Personal electronic health records (PEHRs) allow patients to view, generate, and manage their personal and medical data that are relevant across illness episodes, such as their medications, allergies, immunizations, and their medical, social, and family health history. Thus, patients can actively participate in the management of their health care by ensuring that their health care providers have an updated and accurate overview of the patients' medical records. However, the uptake of PEHRs remains low, especially in terms of patients entering and managing their personal and medical data in their PEHR. OBJECTIVE: This scoping review aimed to explore the barriers and facilitators that patients face when deciding to review, enter, update, or modify their personal and medical data in their PEHR. This review also explores the extent to which patient-generated and -managed data affect the quality and safety of care, patient engagement, patient satisfaction, and patients' health and health care services. METHODS: We searched the MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, and Google Scholar web-based databases, as well as reference lists of all primary and review articles using a predefined search query. RESULTS: Of the 182 eligible papers, 37 (20%) provided sufficient information about patients' data management activities. The results showed that patients tend to use their PEHRs passively rather than actively. Patients refrain from generating and managing their medical data in a PEHR, especially when these data are complex and sensitive. The reasons for patients' passive data management behavior were related to their concerns about the validity, applicability, and confidentiality of patient-generated data. Our synthesis also showed that patient-generated and -managed health data ensures that the medical record is complete and up to date and is positively associated with patient engagement and patient satisfaction. CONCLUSIONS: The findings of this study suggest recommendations for implementing design features within the PEHR and the construal of a dedicated policy to inform both clinical staff and patients about the added value of patient-generated data. Moreover, clinicians should be involved as important ambassadors in informing, reminding, and encouraging patients to manage the data in their PEHR.

Need for numbers: assessing cancer survivors' needs for personalized and generic statistical information.

BACKGROUND: Statistical information (e.g., on long-term survival or side effects) may be valuable for healthcare providers to share with their patients to facilitate shared decision making on treatment options. In this pre-registered study, we assessed cancer survivors' need for generic (population-based) versus personalized (tailored towards patient/tumor characteristics) statistical information after their diagnosis. We examined how information coping style, subjective numeracy, and anxiety levels of survivors relate to these needs and identified statistical need profiles. Additionally, we qualitatively explored survivors' considerations for (not) wanting statistical information. METHODS: Cancer survivors' need for statistics regarding incidence, survival, recurrence, side effects and quality of life were assessed with an online questionnaire. For each of these topics, survivors were asked to think back to their first cancer diagnosis and to indicate their need for generic and personalized statistics on a 4-point scale ('not at all'- 'very much'). Associations between information coping style, subjective numeracy, and anxiety with need for generic and personalized statistics were examined with Pearson's correlations. Statistical need profiles were identified using latent class analysis. Considerations for (not) wanting statistics were analyzed qualitatively. RESULTS: Overall, cancer survivors (n = 174) had a higher need for personalized than for generic statistics (p < .001, d = 0.74). Need for personalized statistics was associated with higher subjective numeracy (r = .29) and an information-seeking coping style (r = .41). Three statistical need profiles were identified (1) a strong need for both generic and personalized statistics (34%), (2) a stronger need for personalized than for generic statistics (55%), and (3) a little need for both generic and personalized statistics (11%). Considerations for wanting personalized cancer statistics ranged from feelings of being in control to making better informed decisions about treatment. Considerations for not wanting statistics related to negative experience with statistics and to the unpredictability of future events for individual patients. CONCLUSIONS: In light of the increased possibilities for using personalized statistics in clinical practice and decision aids, it appears that most cancer survivors want personalized statistical information during treatment decision-making. Subjective numeracy and information coping style seem important factors influencing this need. We encourage further development and implementation of data-driven personalized decision support technologies in oncological care to support patients in treatment decision making.

Heterogeneity in Quality of Life of Long-Term Colon Cancer Survivors: A Latent Class Analysis of the Population-Based PROFILES Registry.

BACKGROUND: Long-term colon cancer survivors present heterogeneous health-related quality of life (HRQOL) outcomes. We determined unobserved subgroups (classes) of survivors with similar HRQOL patterns and investigated their stability over time and the association of clinical covariates with these classes. MATERIALS AND METHODS: Data from the population-based PROFILES registry were used. Included were survivors with nonmetastatic (TNM stage I-III) colon cancer (n = 1,489). HRQOL was assessed with the Dutch translation of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 version 3.0. Based on survivors' HRQOL, latent class analysis (LCA) was used to identify unobserved classes of survivors. Moreover, latent transition analysis (LTA) was used to investigate changes in class membership over time. Furthermore, the effect of covariates on class membership was assessed using multinomial logistic regression. RESULTS: LCA identified five classes at baseline: class 1, excellent HRQOL (n = 555, 37.3%); class 2, good HRQOL with prevalence of insomnia (n = 464, 31.2%); class 3, moderate HRQOL with prevalence of fatigue (n = 213, 14.3%); class 4, good HRQOL with physical limitations (n = 134, 9.0%); and class 5, poor HRQOL (n = 123, 8.3%). All classes were stable with high self-transition probabilities. Longer time since the diagnosis, no comorbid conditions, and male sex were associated with class 1, whereas older age was associated with class 4. Clinical covariates were not associated with class membership. CONCLUSION: The identified classes are characterized by distinct patterns of HRQOL and can support patient-centered care. LCA and LTA are powerful tools for investigating HRQOL in cancer survivors. IMPLICATIONS FOR PRACTICE: Long-term colon cancer survivors show great heterogeneity in their health-related quality of life. This study identified five distinct clusters of survivors with similar patterns of health-related quality of life and showed that these clusters remain stable over time. It was also shown that these clusters do not significantly differ in tumor characteristics or received treatment. Cluster membership of long-term survivors can be identified by sociodemographic characteristics but is not predetermined by diagnosis and treatment.

A latent-class heteroskedastic hurdle trajectory model: patterns of adherence in obstructive sleep apnea patients on CPAP therapy.

BACKGROUND: Sleep apnea patients on CPAP therapy exhibit differences in how they adhere to the therapy. Previous studies have demonstrated the benefit of describing adherence in terms of discernible longitudinal patterns. However, these analyses have been done on a limited number of patients, and did not properly represent the temporal characteristics and heterogeneity of adherence. METHODS: We illustrate the potential of identifying patterns of adherence with a latent-class heteroskedastic hurdle trajectory approach using generalized additive modeling. The model represents the adherence trajectories on three aspects over time: the daily hurdle of using the therapy, the daily time spent on therapy, and the day-to-day variability. The combination of these three characteristics has not been studied before. RESULTS: Applying the proposed model to a dataset of 10,000 patients in their first three months of therapy resulted in nine adherence groups, among which 49% of patients exhibited a change in adherence over time. The identified group trajectories revealed a non-linear association between the change in the daily hurdle of using the therapy, and the average time on therapy. The largest difference between groups was observed in the patient motivation score. The adherence patterns were also associated with different levels of high residual AHI, and day-to-day variability in leakage. CONCLUSION: The inclusion of the hurdle model and the heteroskedastic model into the mixture model enabled the discovery of additional adherence patterns, and a more descriptive representation of patient behavior over time. Therapy adherence was mostly affected by a lack of attempts over time, suggesting that encouraging these patients to attempt therapy on a daily basis, irrespective of the number of hours used, could drive adherence. We believe the methodology is applicable to other domains of therapy or medication adherence.

Impact of hospitalisation on health-related quality of life in patients with chronic heart failure.

BACKGROUND: Empirical identification of the direct impact of hospitalisation in the change in utility could provide an interpretation for some of the unexplained variance in quality of life responses in clinical practice and clinical trials and provide assistance to researchers in assessing the impact of a hospitalisation in the context of economic evaluations. This study had the goal of determining the impact of nonfatal hospitalisations on the quality of life of a cohort of patients previously diagnosed with heart failure by using their quality of life measurements before and after hospitalisation. METHODS: The impact of hospitalisation on health-related quality of life was estimated by calculating the difference in utility measured using the EQ-5D-3L in patients that were hospitalised and had records of utility before and after hospitalisation. The variation in differences between the utilities pre and post hospitalisation was explained through two multiple linear regression models using (1) the individual patient characteristics and (2) the hospitalisation characteristics as explanatory variables. RESULTS: The mean difference between health-related quality of life measurement pre and post hospitalisation was found to be 0.020 [95% CI: - 0.020, 0.059] when measured with the EQ-5D index, while there was a mean decrease of - 0.012 [95% CI: - 0.043, 0.020] in the utility measured with the visual analogue scale. Differences in utility variation according to the primary cause for hospitalisation were found. Regression models showed a statistically significant impact of body mass index and serum creatinine in the index utility differences and of serum creatinine for utilities measured with the visual analogue scale. CONCLUSIONS: Knowing the impact of hospitalisation on health-related quality of life is particularly relevant for informing cost-effectiveness studies designed to assess health technologies aimed at reducing hospital admissions. Through using patient-level data it was possible to estimate the variation in utilities before and after the average hospitalisation and for hospitalisations due to the most common causes for hospital admission. These estimates for (dis) utility could be used in the calculations of effectiveness on economic evaluations, especially when discrete event simulations are the employed modelling technique.

Communicating tailored risk information of cancer treatment side effects: Only words or also numbers?

BACKGROUND: The increased availability of patient reported outcome data makes it feasible to provide patients tailored risk information of cancer treatment side effects. However, it is unclear how such information influences patients' risk interpretations compared to generic population-based risks, and which message format should be used to communicate such individualized statistics. METHODS: A web-based experiment was conducted in which participants (n = 141) read a hypothetical treatment decision-making scenario about four side effect risks of adjuvant chemotherapy for advanced colon cancer. Participants were cancer patients or survivors who were recruited from an online Dutch cancer patient panel. All participants received two tailored risks (of which the reference class was based on their age, gender and tumor stage) and two generic risks conveying the likelihood of experiencing the side effects. The risks were presented either in words-only ('common' and 'very common'), or in a combination of words and corresponding numerical estimates ('common, 10 out of 100' and 'very common, 40 out of 100'). Participants' estimation of the probability, accuracy of their estimation, and perceived likelihood of occurrence were primary outcomes. Perceived personal relevance and perceived uncertainty were secondary outcomes. RESULTS: Tailored risks were estimated as higher and less accurate than generic risks, but only when they were presented in words; Such differences were not found in the verbal and numerical combined condition. Although tailoring risks did not impact participants' perceived likelihood of occurrence, tailored risks were perceived as more personally relevant than generic risks in both message formats. Finally, tailored risks were perceived as less uncertain than generic risks, but only in the verbal-only condition. CONCLUSIONS: Considering current interest in the use of personalized decision aids for improving shared decision-making in oncology, it is important that clinicians consider how tailored risks of treatment side effects should be communicated to patients. We recommend both clinicians who communicate probability information during consultations, and decision aid developers, that verbal descriptors of tailored risks should be supported by numerical estimates of risks levels, to avoid overestimation of risks.

Assessing the quality and communicative aspects of patient decision aids for early-stage breast cancer treatment: a systematic review.

PURPOSE: Decision aids (DAs) support patients in shared decision-making by providing balanced evidence-based treatment information and eliciting patients' preferences. The purpose of this systematic review was to assess the quality and communicative aspects of DAs for women diagnosed with early-stage breast cancer. METHODS: Twenty-one currently available patient DAs were identified through both published literature (MEDLINE, Embase, CINAHL, CENTRAL, and PsycINFO) and online sources. The DAs were reviewed for their quality by using the International Patient Decision Aid Standards (IPDAS) checklist, and subsequently assessed to what extent they paid attention to various communicative aspects, including (i) information presentation, (ii) personalization, (iii) interaction, (iv) information control, (v) accessibility, (vi) suitability, and (vii) source of information. RESULTS: The quality of the DAs varied substantially, with many failing to comply with all components of the IPDAS criteria (mean IPDAS score = 64%, range 31-92%). Five aids (24%) did not include any probability information, 10 (48%) presented multimodal descriptions of outcome probabilities (combining words, numbers, and visual aids), and only 2 (10%) provided personalized treatment outcomes based on patients and tumor characteristics. About half (12; 57%) used interaction methods for eliciting patients' preferences, 16 (76%) were too lengthy, and 5 (24%) were not fully accessible. CONCLUSIONS: In addition to the limited adherence to the IPDAS checklist, our findings suggest that communicative aspects receive even less attention. Future patient DA developments for breast cancer treatment should include communicative aspects that could influence the uptake of DAs in daily clinical practice.

Respiratory morbidity was an important consequence of prematurity in the first two years after discharge in three cohorts from 1996 to 2009.

AIM: This study aimed to evaluate the respiratory morbidity of preterm infants in the first two years after discharge in three cohorts from 1996 to 2009. METHODS: We included infants with a gestational age from 25 + 0 to 29 + 6 weeks, who were born in 1996-1997, 2003-2004 and 2008-2009 at the Leiden University Medical Center in the Netherlands. The following parameters were recorded: bronchopulmonary dysplasia (BPD), defined as oxygen demand or positive pressure at 36 weeks, mortality, duration of supplemental oxygen, discharge with supplemental oxygen and a nasogastric feeding tube, rehospitalisation and the use of inhaled medication. RESULTS: In line with our protocols, 106, 120 and 156 infants were analysed in the three study periods and 29%, 22% and 18% were diagnosed with BPD. Respiratory morbidity did not change over time in infants with and without BPD, except for an increase in rehospitalisation for respiratory issues in infants with BPD. This decreased in infants without BPD. Respiratory morbidity occurred more frequently in infants with BPD than without BPD, but this was not statistically significant. CONCLUSION: This study showed that when cohorts of preterm infants were compared over time, respiratory morbidity in the first two years of life remained an important consequence after discharge.

Improving manual oxygen titration in preterm infants by training and guideline implementation.

To study oxygen saturation (SpO2) targeting before and after training and guideline implementation of manual oxygen titration, two cohorts of preterm infants <30 weeks of gestation needing respiratory support and oxygen therapy were compared. The percentage of the time spent with SpO2 within the target range (85-95%) was calculated (%SpO2-wtr). SpO2 was collected every minute when oxygen is >21%. ABCs where oxygen therapy was given were identified and analyzed. After training and guideline implementation the %SpO2-wtr increased (median interquartile range (IQR)) 48.0 (19.6-63.9) % vs 61.9 (48.5-72.3) %; p < 0.005, with a decrease in the %SpO2 > 95% (44.0 (27.8-66.2) % vs 30.8 (22.6-44.5) %; p < 0.05). There was no effect on the %SpO2 < 85% (5.9 (2.8-7.9) % vs 6.2 (2.5-8) %; ns) and %SpO2 < 80% (1.9 (1.0-3.0) % vs 1.7 (0.8-2.6) %; ns). In total, 186 ABCs with oxygen therapy before and 168 ABCs after training and guideline implementation occurred. The duration of SpO2 < 80% reduced (2 (1-2) vs 1 (1-2) minutes; p < 0.05), the occurrence of SpO2 > 95% did not decrease (73% vs 64%; ns) but lasted shorter (2 (0-7) vs 1 (1-3) minute; p < 0.004). CONCLUSION: Training and guideline implementation in manual oxygen titration improved SpO2 targeting in preterm infants with more time spent within the target range and less frequent hyperoxaemia. The durations of hypoxaemia and hyperoxaemia during ABCs were shorter. What is Known: • Oxygen saturation targeting in preterm infants can be challenging and the compliance is low when oxygen is titrated manually. • Hyperoxaemia often occurs after oxygen therapy for oxygen desaturation during apnoeas. What is New: • Training and implementing guidelines improved oxygen saturation targeting and reduced hyperoxaemia. • Training and implementing guidelines improved manual oxygen titration during ABC.

Clinical outcomes of preterm infants while using automated controllers during standard care: comparison of cohorts with different automated titration strategies.

OBJECTIVE: To compare short-term clinical outcome after using two different automated oxygen controllers (OxyGenie and CLiO2). DESIGN: Propensity score-matched retrospective observational study. SETTING: Tertiary-level neonatal unit in the Netherlands. PATIENTS: Preterm infants (OxyGenie n=121, CLiO2 n=121) born between 24+0-29+6 weeks of gestation. Median (IQR) gestational age in the OxyGenie cohort was 28+3 (26+3.5-29+0) vs 27+5 (26+5-28+3) in the CLiO2 cohort, respectively 42% and 46% of infants were male and mean (SD) birth weight was 1034 (266) g vs 1022 (242) g. INTERVENTIONS: Inspired oxygen was titrated by OxyGenie (SLE6000) or CLiO2 (AVEA) during respiratory support. MAIN OUTCOME MEASURES: Mortality, retinopathy of prematurity (ROP), bronchopulmonary dysplasia and necrotising enterocolitis. RESULTS: Fewer infants in the OxyGenie group received laser coagulation for ROP (1 infant vs 10; risk ratio 0.1 (95% CI 0.0 to 0.7); p=0.008), and infants stayed shorter in the neonatal intensive care unit (NICU) (28 (95% CI 15 to 42) vs 40 (95% CI 25 to 61) days; median difference 13.5 days (95% CI 8.5 to 19.5); p<0.001). Infants in the OxyGenie group had fewer days on continuous positive airway pressure (8.4 (95% CI 4.8 to 19.8) days vs 16.7 (95% CI 6.3 to 31.1); p<0.001) and a significantly shorter days on invasive ventilation (0 (95% CI 0 to 4.2) days vs 2.1 (95% CI 0 to 8.4); p=0.012). There were no statistically significant differences in all other morbidities. CONCLUSIONS: In this propensity score-matched retrospective study, the OxyGenie epoch was associated with less morbidity when compared with the CLiO2 epoch. There were significantly fewer infants that received treatment for ROP, received less intensive respiratory support and, although there were more supplemental oxygen days, the duration of stay in the NICU was shorter. A larger study will have to replicate these findings.

Comparison of two automated oxygen controllers in oxygen targeting in preterm infants during admission: an observational study.

OBJECTIVE: To compare the effect of two different automated oxygen control devices on time preterm infants spent in different oxygen saturation (SpO2) ranges during their entire stay in the neonatal intensive care unit (NICU). DESIGN: Retrospective cohort study of prospectively collected data. SETTING: Tertiary level neonatal unit in the Netherlands. PATIENTS: Preterm infants (OxyGenie 75 infants, CLiO2 111 infants) born at 24-29 weeks' gestation receiving at least 72 hours of respiratory support between October 2015 and November 2020. INTERVENTIONS: Inspired oxygen concentration was titrated by the OxyGenie controller (SLE6000 ventilator) between February 2019 and November 2020 and the CLiO2 controller (AVEA ventilator) between October 2015 and December 2018 as standard of care. MAIN OUTCOME MEASURES: Time spent within SpO2 target range (TR, 91-95% for either epoch) and other SpO2 ranges. RESULTS: Time spent within the SpO2 TR when receiving supplemental oxygen was higher during OxyGenie control (median 71.5 [IQR 64.6-77.0]% vs 51.3 [47.3-58.5]%, p<0.001). Infants under OxyGenie control spent less time in hypoxic and hyperoxic ranges (SpO2<80%: 0.7 [0.4-1.4]% vs 1.2 [0.7-2.3]%, p<0.001; SpO2>98%: 1.0 [0.5-2.4]% vs 4.0 [2.0-7.9]%, p<0.001). Both groups received a similar FiO2 (29.5 [28.0-33.2]% vs 29.6 [27.7-32.1]%, p=not significant). CONCLUSIONS: Oxygen saturation targeting was significantly different in the OxyGenie epoch in preterm infants, with less time in hypoxic and hyperoxic SpO2 ranges during their stay in the NICU.

[Communicating personalised health information]. / Gepersonaliseerde gezondheidsinformatie communiceren.

Due to the large increase in relevant data, we can increasingly better estimate medical chances and risks and tailor them to the individual patient. In this article, we discuss why communicating such personalized information is so complicated, and how doctors can best discuss it with their patients.

Comparing Descriptive Statistics for Retrospective Studies From One-per-Minute and One-per-Second Data.

Background: Large amounts of data are collected in neonatal intensive care units, which could be used for research. It is unclear whether these data, usually sampled at a lower frequency, are sufficient for retrospective studies. We investigated what to expect when using one-per-minute data for descriptive statistics. Methods: One-per-second inspiratory oxygen and saturation were processed to one-per-minute data and compared, on average, standard deviation, target range time, hypoxia, days of supplemental oxygen, and missing signal. Results: Outcomes calculated from data recordings (one-per-minute = 92, one-per-second = 92) showed very little to no difference. Sub analyses of recordings under 100 and 200 h showed no difference. Conclusion: In our study, descriptive statistics of one-per-minute data were comparable to one-per-second and could be used for retrospective analyses. Comparable routinely collected one-per-minute data could be used to develop algorithms or find associations, retrospectively.

Communication, perception, and use of personalized side-effect risks in prostate cancer treatment-decision making: An observational and interview study.

OBJECTIVE: We investigated how healthcare professionals (HPs) communicate personalized risks of treatment side-effects to patients with localized prostate cancer during consultations, and explored how these patients perceive and use such risks during treatment decision-making. METHODS: Patient consultations with nurse practitioners and urologists discussing personalized risks of urinary incontinence after prostatectomy were audiotaped, transcribed, and coded. Patients (n = 27) were then interviewed to explore their perceptions and use of personalized side-effect risks. RESULTS: HPs explained personalized risks by discussing risk factors, which was appreciated and recalled by patients. Personalized risks were typically communicated both numerically and verbally (70%). When using numbers, HPs always used percentages, but rarely used natural frequencies (14%). Uncertainty was disclosed in only 34% of consultations. One-third of patients used personalized risks in their treatment decision-making by either switching to another treatment or sticking to their initial preference. CONCLUSIONS: Patients value and use personalized side-effect risks during treatment decision-making. Clearly explaining the relationship between risk factors and personalized risk estimates may help patients understand and recall those. Practice implications HPs should not only give patients specific and precise numerical risk information, but should also put effort in explaining how the personalized side-effect risks are determined.

Comparison of two devices for automated oxygen control in preterm infants: a randomised crossover trial.

OBJECTIVE: To compare the effect of two different automated oxygen control devices on target range (TR) time and occurrence of hypoxaemic and hyperoxaemic episodes. DESIGN: Randomised cross-over study. SETTING: Tertiary level neonatal unit in the Netherlands. PATIENTS: Preterm infants (n=15) born between 24+0 and 29+6 days of gestation, receiving invasive or non-invasive respiratory support with oxygen saturation (SpO2) TR of 91%-95%. Median gestational age 26 weeks and 4 days (IQR 25 weeks 3 days-27 weeks 6 days) and postnatal age 19 (IQR 17-24) days. INTERVENTIONS: Inspired oxygen concentration was titrated by the OxyGenie controller (SLE6000 ventilator) and the CLiO2 controller (AVEA ventilator) for 24 hours each, in a random sequence, with the respiratory support mode kept constant. MAIN OUTCOME MEASURES: Time spent within set SpO2 TR (91%-95% with supplemental oxygen and 91%-100% without supplemental oxygen). RESULTS: Time spent within the SpO2 TR was higher during OxyGenie control (80.2 (72.6-82.4)% vs 68.5 (56.7-79.3)%, p<0.005). Less time was spent above TR while in supplemental oxygen (6.3 (5.1-9.9)% vs 15.9 (11.5-30.7)%, p<0.005) but more time spent below TR during OxyGenie control (14.7 (11.8%-17.2%) vs 9.3 (8.2-12.6)%, p<0.05). There was no significant difference in time with SpO2 <80% (0.5 (0.1-1.0)% vs 0.2 (0.1-0.4)%, p=0.061). Long-lasting SpO2 deviations occurred less frequently during OxyGenie control. CONCLUSIONS: The OxyGenie control algorithm was more effective in keeping the oxygen saturation within TR and preventing hyperoxaemia and equally effective in preventing hypoxaemia (SpO2 <80%), although at the cost of a small increase in mild hypoxaemia. TRIAL REGISTRY NUMBER: NCT03877198.

Towards eHealth to support the health journey of headache patients: a scoping review.

OBJECTIVE: The aim of this study is to (1) review the digital health tools that have been used in headache studies, and (2) discuss the effectivity and reliability of these tools. BACKGROUND: Many headache patients travel a long and troublesome journey from first symptoms until a meaningful care plan. eHealth, mHealth, and digital therapeutic modalities have been advocated as the way forward to improve patient care. METHOD: Online databases PubMed, Cinahl, and PsycINFO were searched using a predefined search query. A data extraction form was used to gather relevant data elements from the selected papers. RESULTS: A total of 39 studies were selected. The studies included 94,127 participants. The majority of studies focused on diaries (N = 27 out of 39). Digital (cognitive) behavioral therapy were also quite common (N = 7 out of 39). Other digital health tool categories were tele-consultations, telemonitoring and patient portals. CONCLUSION: Many digital health tools for headache patients regarding diaries and behavioral/therapeutical treatment are described in scientific research with limited information on effectivity and reliability. Scientific knowledge with regard to other categories such as tele-consultations, patient portals, telemonitoring including medication adherence, online information resources, wearable, symptom checkers, digital peer support is still scarce or missing.

Exploring Cancer Survivor Needs and Preferences for Communicating Personalized Cancer Statistics From Registry Data: Qualitative Multimethod Study.

BACKGROUND: Disclosure of cancer statistics (eg, survival or incidence rates) based on a representative group of patients can help increase cancer survivors' understanding of their own diagnostic and prognostic situation, and care planning. More recently, there has been an increasing interest in the use of cancer registry data for disclosing and communicating personalized cancer statistics (tailored toward personal and clinical characteristics) to cancer survivors and relatives. OBJECTIVE: The aim of this study was to explore breast cancer (BCa) and prostate cancer (PCa) survivor needs and preferences for disclosing (what) and presenting (how) personalized statistics from a large Dutch population-based data set, the Netherlands Cancer Registry (NCR). METHODS: To elicit survivor needs and preferences for communicating personalized NCR statistics, we created different (non)interactive tools visualizing hypothetical scenarios and adopted a qualitative multimethod study design. We first conducted 2 focus groups (study 1; n=13) for collecting group data on BCa and PCa survivor needs and preferences, using noninteractive sketches of what a tool for communicating personalized statistics might look like. Based on these insights, we designed a revised interactive tool, which was used to further explore the needs and preferences of another group of cancer survivors during individual think-aloud observations and semistructured interviews (study 2; n=11). All sessions were audio-recorded, transcribed verbatim, analyzed using thematic (focus groups) and content analysis (think-aloud observations), and reported in compliance with qualitative research reporting criteria. RESULTS: In both studies, cancer survivors expressed the need to receive personalized statistics from a representative source, with especially a need for survival and conditional survival rates (ie, survival rate for those who have already survived for a certain period). Personalized statistics adjusted toward personal and clinical factors were deemed more relevant and useful to know than generic or average-based statistics. Participants also needed support for correctly interpreting the personalized statistics and putting them into perspective, for instance by adding contextual or comparative information. Furthermore, while thinking aloud, participants experienced a mix of positive (sense of hope) and negative emotions (feelings of distress) while viewing the personalized survival data. Overall, participants preferred simplicity and conciseness, and the ability to tailor the type of visualization and amount of (detailed) statistical information. CONCLUSIONS: The majority of our sample of cancer survivors wanted to receive personalized statistics from the NCR. Given the variation in patient needs and preferences for presenting personalized statistics, designers of similar information tools may consider potential tailoring strategies on multiple levels, as well as effective ways for providing supporting information to make sure that the personalized statistics are properly understood. This is encouraging for cancer registries to address this unmet need, but also for those who are developing or implementing personalized data-driven information tools for patients and relatives.

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning.

BACKGROUND: Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant's goal achievement. METHODS: We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. RESULTS: From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69-0.73) and (0.71 95%CI 0.67-0.76), respectively, followed by cannabis (0.67 95%CI 0.59-0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. DISCUSSION: Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention.

Tidal volumes at birth as predictor for adverse outcome in congenital diaphragmatic hernia.

OBJECTIVE: To assess the predictive value of tidal volume (Vt) of spontaneous breaths at birth in infants with congenital diaphragmatic hernia (CDH). DESIGN: Prospective study. SETTING: Tertiary neonatal intensive care unit. PATIENTS: Thirty infants with antenatally diagnosed CDH born at Hospital Sant Joan de Déu in Barcelona from September 2013 to September 2015. INTERVENTIONS: Spontaneous breaths and inflations given in the first 10 min after intubation at birth were recorded using respiratory function monitor. Only expired Vt of uninterrupted spontaneous breaths was included for analysis. Receiver operating characteristics (ROC) analysis was performed and the area under the curve (AUC) was estimated to assess the predictive accuracy of Vt. MAIN OUTCOME MEASURES: Mortality before hospital discharge and chronic lung disease (CLD) at day 28 of life. RESULTS: There were 1.233 uninterrupted spontaneous breaths measured, and the overall mean Vt was 2.8±2.1 mL/kg. A lower Vt was found in infants who died (n=14) compared with survivors (n=16) (1.7±1.6 vs 3.7±2.1 mL/kg; p=0.008). Vt was lower in infants who died during admission or had CLD (n=20) compared with survivors without CLD (n=10) (2.0±1.7 vs 4.3±2.2 mL/kg; p=0.004). ROC analysis showed that Vt ≤2.2 mL/kg predicted mortality with 79% sensitivity and 81% specificity (AUC=0.77, p=0.013). Vt ≤3.4 mL/kg was a good predictor of death or CLD (AUC=0.80, p=0.008) with 85% sensitivity and 70% specificity. CONCLUSION: Vt of spontaneous breaths measured immediately after birth is associated with mortality and CLD. Vt seems to be a reliable predictor but is not an independent predictor after adjustment for observed/expected lung to head ratio and liver position.

Communicative aspects of decision aids for localized prostate cancer treatment - A systematic review.

CONTEXT: Despite increasing interest in the development and use of decision aids (DAs) for patients with localized prostate cancer (LPC), little attention has been paid to communicative aspects (CAs) of such tools. OBJECTIVE: To identify DAs for LPC treatment, and review these tools for various CAs. MATERIALS AND METHODS: DAs were identified through both published literature (MEDLINE, Embase, CINAHL, CENTRAL, and PsycINFO; 1990-2018) and online sources, in compliance with the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. Identified DAs were reviewed for the International Patient Decision Aid Standards criteria, and analyzed on CAs, including information presentation, personalization, interaction, information control, accessibility, suitability, and source of information. Nineteen DAs were identified. RESULTS: International Patient Decision Aid Standards scores varied greatly among DAs. Crucially, substantial variations in use of CAs by DAs were identified: (1) few DAs used visual aids to communicate statistical information, (2) none were personalized in terms of outcome probabilities or mode of communication, (3) a minority used interactive methods to elicit patients' values and preferences, (4) most included biased cross tables to compare treatment options, and (5) issues were observed in suitability and accessibility that could hinder implementation in clinical practice. CONCLUSIONS: Our review suggests that DAs for LPC treatment could be further improved by adding CAs such as personalized outcome predictions and interaction methods to the DAs. Clinicians who are using or developing such tools might therefore consider these CAs in order to enhance patient participation in treatment decision-making.