Intensive Longitudinal Methods Among Adults With Breast or Lung Cancer: Scoping Review

HumanInsight Intensive Longitudinal Methods Among Adults With Breast or Lung Cancer: Scoping Review

J Med Internet Res. 2024 Jun 12;26:e50224. doi: 10.2196/50224.

ABSTRACT

BACKGROUND: Intensive longitudinal methods offer a powerful tool for capturing daily experiences of individuals. However, its feasibility, effectiveness, and optimal methodological approaches for studying or monitoring experiences of oncology patients remain uncertain.

OBJECTIVE: This scoping review aims to describe to what extent intensive longitudinal methods with daily electronic assessments have been used among patients with breast or lung cancer and with which methodologies, associated outcomes, and influencing factors.

METHODS: We searched the electronic databases (PubMed, Embase, and PsycINFO) up to January 2024 and included studies reporting on the use of these methods among adults with breast or lung cancer. Data were extracted on population characteristics, intensive monitoring methodologies used, study findings, and factors influencing the implementation of these methods in research and clinical practice.

RESULTS: We identified 1311 articles and included 52 articles reporting on 41 studies. Study aims and intensive monitoring methodologies varied widely, but most studies focused on measuring physical and psychological symptom constructs, such as pain, anxiety, or depression. Compliance and attrition rates seemed acceptable for most studies, although complete methodological reporting was often lacking. Few studies specifically examined these methods among patients with advanced cancer. Factors influencing implementation were linked to both patient (eg, confidence with intensive monitoring system) and methodology (eg, option to use personal devices).

CONCLUSIONS: Intensive longitudinal methods with daily electronic assessments hold promise to provide unique insights into the daily lives of patients with cancer. Intensive longitudinal methods may be feasible among people with breast or lung cancer. Our findings encourage further research to determine optimal conditions for intensive monitoring, specifically in more advanced disease stages.

PMID:38865186 | DOI:10.2196/50224

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Mobile Health App and Web Platform (eDOL) for Medical Follow-Up of Patients With Chronic Pain: Cohort Study Involving the French eDOL National Cohort After 1 Year

HumanInsight Mobile Health App and Web Platform (eDOL) for Medical Follow-Up of Patients With Chronic Pain: Cohort Study Involving the French eDOL National Cohort After 1 Year

JMIR Mhealth Uhealth. 2024 Jun 12;12:e54579. doi: 10.2196/54579.

ABSTRACT

BACKGROUND: Chronic pain affects approximately 30% of the general population, severely degrades quality of life and professional life, and leads to additional health care costs. Moreover, the medical follow-up of patients with chronic pain remains complex and provides only fragmentary data on painful daily experiences. This situation makes the management of patients with chronic pain less than optimal and may partly explain the lack of effectiveness of current therapies. Real-life monitoring of subjective and objective markers of chronic pain using mobile health (mHealth) programs could better characterize patients, chronic pain, pain medications, and daily impact to help medical management.

OBJECTIVE: This cohort study aimed to assess the ability of our mHealth tool (eDOL) to collect extensive real-life medical data from chronic pain patients after 1 year of use. The data collected in this way would provide new epidemiological and pathophysiological data on chronic pain.

METHODS: A French national cohort of patients with chronic pain treated at 18 pain clinics has been established and followed up using mHealth tools. This cohort makes it possible to collect the determinants and repercussions of chronic pain and their evolutions in a real-life context, taking into account all environmental events likely to influence chronic pain. The patients were asked to complete several questionnaires, body schemes, and weekly meters, and were able to interact with a chatbot and use educational modules on chronic pain. Physicians could monitor their patients' progress in real time via an online platform.

RESULTS: The cohort study included 1427 patients and analyzed 1178 patients. The eDOL tool was able to collect various sociodemographic data; specific data for characterizing pain disorders, including body scheme; data on comorbidities related to chronic pain and its psychological and overall impact on patients' quality of life; data on drug and nondrug therapeutics and their benefit-to-risk ratio; and medical or treatment history. Among the patients completing weekly meters, 49.4% (497/1007) continued to complete them after 3 months of follow-up, and the proportion stabilized at 39.3% (108/275) after 12 months of follow-up. Overall, despite a fairly high attrition rate over the follow-up period, the eDOL tool collected extensive data. This amount of data will increase over time and provide a significant volume of health data of interest for future research involving the epidemiology, care pathways, trajectories, medical management, sociodemographic characteristics, and other aspects of patients with chronic pain.

CONCLUSIONS: This work demonstrates that the mHealth tool eDOL is able to generate a considerable volume of data concerning the determinants and repercussions of chronic pain and their evolutions in a real-life context. The eDOL tool can incorporate numerous parameters to ensure the detailed characterization of patients with chronic pain for future research and pain management.

TRIAL REGISTRATION: ClinicalTrials.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096.

PMID:38865173 | DOI:10.2196/54579

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User experience of a family health history chatbot: A quantitative analysis

HumanInsight User experience of a family health history chatbot: A quantitative analysis

Health Informatics J. 2024 Apr-Jun;30(2):14604582241262251. doi: 10.1177/14604582241262251.

ABSTRACT

OBJECTIVE: Family health history (FHx) is an important tool in assessing one's risk towards specific health conditions. However, user experience of FHx collection tools is rarely studied. ItRunsInMyFamily.com (ItRuns) was developed to assess FHx and hereditary cancer risk. This study reports a quantitative user experience analysis of ItRuns.

METHODS: We conducted a public health campaign in November 2019 to promote FHx collection using ItRuns. We used software telemetry to quantify abandonment and time spent on ItRuns to identify user behaviors and potential areas of improvement.

RESULTS: Of 11,065 users who started the ItRuns assessment, 4305 (38.91%) reached the final step to receive recommendations about hereditary cancer risk. Highest abandonment rates were during Introduction (32.82%), Invite Friends (29.03%), and Family Cancer History (12.03%) subflows. Median time to complete the assessment was 636 s. Users spent the highest median time on Proband Cancer History (124.00 s) and Family Cancer History (119.00 s) subflows. Search list questions took the longest to complete (median 19.50 s), followed by free text email input (15.00 s).

CONCLUSION: Knowledge of objective user behaviors at a large scale and factors impacting optimal user experience will help enhance the ItRuns workflow and improve future FHx collection.

PMID:38865081 | DOI:10.1177/14604582241262251

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Effectiveness of mHealth apps on adherence and symptoms to oral anticancer medications: a systematic review and meta-analysis

HumanInsight Effectiveness of mHealth apps on adherence and symptoms to oral anticancer medications: a systematic review and meta-analysis

Support Care Cancer. 2024 Jun 12;32(7):426. doi: 10.1007/s00520-024-08635-8.

ABSTRACT

PURPOSE: Despite the rapid expansion of mHealth apps, their adoption has not always been based on evidence of effectiveness on patient outcomes. This systematic review aimed to determine the effect of mHealth apps on adherence and symptom to oral anticancer medications (OAMs) and identify the app design that led to such effects.

METHODS: Pubmed, Cochrane Central, PsycINFO, EMBASE, and WoS were searched from inception to April 2023. Randomised controlled trials (RCTs) that evaluated effects of mHealth apps on primary outcomes OAM adherence and symptom burden were included. Two reviewers independently assessed risk-of-bias using Cochrane Risk-of-Bias version 2 and extracted the data. Quality of evidence was assessed using GRADE. The protocol was registered in PROSPERO (CRD42023406024).

RESULTS: Four RCTs involving 806 patients with cancer met the eligibility criteria. mHealth apps features included a combinations of symptom reporting, medication reminder, automated alert to care team, OAM and side effect information, one study implemented structured follow-up by a nurse. The intervention group showed no significant difference in OAM adherence (relative ratio 1.20; 95% CI 1.00 to 1.43), but significantly improved symptoms to OAMs with a lower standardised mean symptom burden score of 0.49 (SMD - 0.49; 95% CI - 0.93 to - 0.06), and a 25% lower risk of grade 3 or 4 toxicity (risk ratio 0.75; 95% CI 0.58 to 0.95) compared to usual care.

CONCLUSION: These findings suggest a potential role for mHealth app in managing OAM side effect. Further research should explore the role of AI-guided algorithmic pathways on the interactive features of mHealth apps.

PMID:38864924 | DOI:10.1007/s00520-024-08635-8

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Surgical telementoring as teaching tool in the operating room: trans-Nordic IDEAL stage 2a telementored series of a robotic ventral mesh rectopexy learning curve

HumanInsight Surgical telementoring as teaching tool in the operating room: trans-Nordic IDEAL stage 2a telementored series of a robotic ventral mesh rectopexy learning curve

Br J Surg. 2024 Jun 12;111(6):znae123. doi: 10.1093/bjs/znae123.

NO ABSTRACT

PMID:38864757 | DOI:10.1093/bjs/znae123

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Towards knowledge-infused automated disease diagnosis assistant

HumanInsight Towards knowledge-infused automated disease diagnosis assistant

Sci Rep. 2024 Jun 11;14(1):13442. doi: 10.1038/s41598-024-53042-y.

ABSTRACT

With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor-patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor's effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants.

PMID:38862529 | DOI:10.1038/s41598-024-53042-y

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The Effects of mHealth Interventions on Quality of Life, Anxiety, and Depression in Patients With Coronary Heart Disease: Meta-Analysis of Randomized Controlled Trials

HumanInsight The Effects of mHealth Interventions on Quality of Life, Anxiety, and Depression in Patients With Coronary Heart Disease: Meta-Analysis of Randomized Controlled Trials

J Med Internet Res. 2024 Jun 11;26:e52341. doi: 10.2196/52341.

ABSTRACT

BACKGROUND: Coronary heart disease (CHD) is the leading cause of death globally. In addition, 20% to 40% of the patients with CHD have comorbid mental health issues such as anxiety or depression, affecting the prognosis and quality of life (QoL). Mobile health (mHealth) interventions have been developed and are widely used; however, the evidence for the effects of mHealth interventions on QoL, anxiety, and depression in patients with CHD is currently ambiguous.

OBJECTIVE: In this study, we aimed to assess the effects of mHealth interventions on QoL, anxiety, and depression in patients with CHD.

METHODS: We searched the Cochrane Library, PubMed, Embase, CINAHL, Web of Science, China National Knowledge Infrastructure, and Wanfang databases from inception to August 12, 2023. Eligible studies were randomized controlled trials that involved patients with CHD who received mHealth interventions and that reported on QoL, anxiety, or depression outcomes. We used the Cochrane risk-of-bias tool for randomized trials to evaluate the risk of bias in the studies, ensuring a rigorous and methodologically sound analysis. Review Manager (desktop version 5.4; The Cochrane Collaboration) and Stata MP (version 17.0; StataCorp LLC) were used to conduct the meta-analysis. The effect size was calculated using the standardized mean difference (SMD) and its 95% CI.

RESULTS: The meta-analysis included 23 studies (5406 participants in total) and showed that mHealth interventions significantly improved QoL in patients with CHD (SMD 0.49, 95% CI 0.25-0.72; Z=4.07; P<.001) as well as relieved their anxiety (SMD -0.46, 95% CI -0.83 to -0.08; Z=2.38; P=.02) and depression (SMD -0.34, 95% CI -0.56 to -0.12; Z=3.00; P=.003) compared to usual care. The subgroup analyses indicated a significant effect favoring the mHealth intervention on reducing anxiety and depressive symptoms compared to usual care, especially when (1) the intervention duration was ≥6 months (P=.04 and P=.001), (2) the mHealth intervention was a simple one (only 1 mHealth intervention was used) (P=.01 and P<.001), (3) it was implemented during the COVID-19 pandemic (P=.04 and P=.01), (4) it was implemented in low- or middle-income countries (P=.01 and P=.02), (5) the intervention focused on mental health (P=.01 and P=.007), and (6) adherence rates were high (≥90%; P=.03 and P=.002). In addition, comparing mHealth interventions to usual care, there was an improvement in QoL when (1) the mHealth intervention was a simple one (P<.001), (2) it was implemented in low- or middle-income countries (P<.001), and (3) the intervention focused on mental health (P<.001).

CONCLUSIONS: On the basis of the existing evidence, mHealth interventions might be effective in improving QoL and reducing anxiety and depression in patients with CHD. However, large sample, high-quality, and rigorously designed randomized controlled trials are needed to provide further evidence.

TRIAL REGISTRATION: PROSPERO CRD42022383858; https://tinyurl.com/3ea2npxf.

PMID:38861710 | DOI:10.2196/52341

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Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial

HumanInsight Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial

J Med Internet Res. 2024 Jun 11;26:e49669. doi: 10.2196/49669.

ABSTRACT

BACKGROUND: Sleep disturbance is a major contributor to future health and occupational issues. Mobile health can provide interventions that address adverse health behaviors for individuals in a vulnerable health state in real-world settings (just-in-time adaptive intervention).

OBJECTIVE: This study aims to identify a subpopulation with vulnerable sleep state in daily life (study 1) and, immediately afterward, to test whether providing mobile health intervention improved habitual sleep behaviors and psychological wellness in real-world settings by conducting a microrandomized trial (study 2).

METHODS: Japanese workers (n=182) were instructed to collect data on their habitual sleep behaviors and momentary symptoms (including depressive mood, anxiety, and subjective sleep quality) using digital devices in a real-world setting. In study 1, we calculated intraindividual mean and variability of sleep hours, midpoint of sleep, and sleep efficiency to characterize their habitual sleep behaviors. In study 2, we designed and conducted a sleep just-in-time adaptive intervention, which delivered objective push-type sleep feedback messages to improve their sleep hours for a subset of participants in study 1 (n=81). The feedback messages were generated based on their sleep data measured on previous nights and were randomly sent to participants with a 50% chance for each day (microrandomization).

RESULTS: In study 1, we applied hierarchical clustering to dichotomize the population into 2 clusters (group A and group B) and found that group B was characterized by unstable habitual sleep behaviors (large intraindividual variabilities). In addition, linear mixed-effect models showed that the interindividual variability of sleep hours was significantly associated with depressive mood (β=3.83; P=.004), anxiety (β=5.70; P=.03), and subjective sleep quality (β=-3.37; P=.03). In study 2, we found that providing sleep feedback prolonged subsequent sleep hours (increasing up to 40 min; P=.01), and this effect lasted for up to 7 days. Overall, the stability of sleep hours in study 2 was significantly improved among participants in group B compared with the participants in study 1 (P=.001).

CONCLUSIONS: This is the first study to demonstrate that providing sleep feedback can benefit the modification of habitual sleep behaviors in a microrandomized trial. The findings of this study encourage the use of digitalized health intervention that uses real-time health monitoring and personalized feedback.

PMID:38861313 | DOI:10.2196/49669

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Impact of BMI on serum 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D with calcifediol supplementation in young adults: a longitudinal study

HumanInsight Impact of BMI on serum 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D with calcifediol supplementation in young adults: a longitudinal study

Endocrine. 2024 Jun 11. doi: 10.1007/s12020-024-03895-0. Online ahead of print.

ABSTRACT

BACKGROUND: High body mass index (BMI) is a risk factor for vitamin D deficiency. The rise in serum 25-hydroxyvitamin D [25(OH)D] concentrations following cholecalciferol supplementation is suboptimal, owing to adipose tissue sequestration and/or volumetric dilution. Calcifediol is a proven potent oral alternative for vitamin D supplementation, but whether BMI adversely affects its efficacy in raising 25(OH)D concentrations, is not well known.

MATERIAL AND METHODS: Adults with serum concentrations of 25(OH)D < 30 ng/mL were recruited and stratified as normal, overweight, or obese using WHO criteria. Baseline evaluation included 25(OH)D, parathyroid hormone (PTH), and total 1,25-dihydroxyvitamin D [1,25(OH)2D] based on BMI category (n = 883). A subset of participants was supplemented with 50 µg calcifediol (n = 193) and assessed for the rise in serum concentrations of 25(OH)D at 3- and 6-months following supplementation.

RESULTS: Participants were stratified as obese (11.2%), overweight (32.1%), or normal weight (56.7%). There were no significant baseline differences in serum concentrations of 25(OH)D among the groups (13.1 ± 6.4 vs 12.8 ± 6.8 vs 11.6 ± 6.6 ng/mL, p = 0.62). Similarly, PTH or 1,25(OH)2D concentrations were not different among the groups. On follow-up, 25(OH)D concentrations increased in all three groups at 3 and 6 months from baseline. The increase in 25(OH)D was 74.4 ng/mL (IQR 35.3-115.3) in obese, followed by overweight 62.2 ng/mL (18.1-98.7) and normal weight groups 47.1 ng/mL (17.5-89.7) at 3 months. 1,25(OH)2D also increased in all groups, without any significant intergroup differences (p > 0.05).

CONCLUSION: BMI does not impede the rise in 25(OH)D concentrations following supplementation with calcifediol in young adults with vitamin D deficiency.

PMID:38861119 | DOI:10.1007/s12020-024-03895-0

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Commentary on: Home-based telerehabilitation for community-dwelling persons with stroke during the Covid-19 pandemic: a pilot study"

HumanInsight

Commentary on: Home-based telerehabilitation for community-dwelling persons with stroke during the Covid-19 pandemic: a pilot study"

J Rehabil Med. 2024 Jun 11;56:jrm40662. doi: 10.2340/jrm.v56.40662.

NO ABSTRACT

PMID:38860719 | DOI:10.2340/jrm.v56.40662

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