JMIR mHealth and uHealth

BackgroundThe rising rates of cannabis use, cannabis-related problems, and hospitalization rates related to cannabis usage warrant further research into better treatment methods and interventions. PurposeThis study aims to identify the quality of free cannabis cessation applications available on both the Apple App Store and the Google Play Store and analyse their features, content, and adherence to evidence-based practices. MethodsA systematic search was conducted in April 2023 using a variety of keywords. The applications were deemed eligible if they were free, in English, available on both the Apple App Store and the Google Play Store and were related to cannabis cessation. Each application was used for at least one month and were rated on the Mobile App Rating Scale by two users. There was excellent agreement determined between the two reviewers ([≤]2 points on all categories). ResultsA total of 4 applications were included in the overall quality and content analysis. The mean total quality scores of applications were determined to be 3.36 out of 5 indicating a poor to acceptable quality of applications. It was determined that there was a very limited number of available applications for users and those that were available were not of high quality with few applications incorporating evidence-based practices. ConclusionsThere are a very limited number of cannabis cessation applications available, and the currently available products have poor to acceptable quality.

ObjectiveThis study aimed to investigate the key demographics and evaluate the real-world contraceptive failure and continuation rates of the Natural Cycles app in a cohort of women from Canada. MethodsThis was a real-world, prospective cohort study. Demographics were assessed via in-app questionnaires. Contraceptive failure rates in typical and perfect use were calculated using the 13-cycle cumulative pregnancy probability (Kaplan-Meier survival analysis) and the one-year Pearl Index (PI). One-year continuation rates were estimated through survival analysis. ResultsThe study included 8 798 women who contributed an average of 9.2 months of data, amounting to a total of 7 063 woman-years of exposure. The average user was 27.3 years old, had a body mass index of 24.6, and reported being in a stable relationship. With typical use, the app demonstrated a 13-cycle cumulative pregnancy probability of 4.8 [95% CI: 4.3, 5.4] and a Pearl Index of 4.3 [95% CI: 3.9, 4.8]. Under perfect use, the contraceptive failure rate was 2.3 [95% CI: 0.7, 3.9] for life table analysis and 1.7 [95% CI: 0.5, 2.8] for the 1-year PI. The contraceptive methods continuation rate after one year was 62.4%. ConclusionsThe data presented in this study offer valuable insights into the cohort of women using the Natural Cycles app in Canada and provide country-specific effectiveness estimates. The apps contraceptive effectiveness aligns with previously published data on Natural Cycles.

Consumer-grade heart rate (HR) sensors including chest straps, wrist-worn watches and rings have become very popular in recent years for tracking individual physiological state, training for sports and even measuring stress levels and emotional changes. While the majority of these consumer sensors are not medical devices, they can still offer insights for consumers and researchers if used correctly taking into account their limitations. Multiple previous studies have been done using a large variety of consumer sensors including Polar(R) devices, Apple(R) watches, and Fitbit(R) wrist bands. The vast majority of prior studies have been done in laboratory settings where collecting data is relatively straight-forward. However, using consumer sensors in naturalistic settings that present significant challenges, including noise artefacts and missing data, has not been as extensively investigated. Additionally, the majority of prior studies focused on wrist-worn optical HR sensors. Arm-worn sensors have not been extensively investigated either. In the present study, we validate HR measurements obtained with an arm-worn optical sensor (Polar OH1) against those obtained with a chest-strap electrical sensor (Polar H10) from 16 participants over a 2-week study period in naturalistic settings. We also investigated the impact of physical activity measured with 3-D accelerometers embedded in the H10 chest strap and OH1 armband sensors on the agreement between the two sensors. Overall, we find that the arm-worn optical Polar OH1 sensor provides a good estimate of HR (Pearson r = 0.90, p <0.01). Filtering the signal that corresponds to physical activity further improves the HR estimates but only slightly (Pearson r = 0.91, p <0.01). Based on these preliminary findings, we conclude that the arm-worn Polar OH1 sensor provides usable HR measurements in daily living conditions, with some caveats discussed in the paper.

Background & ObjectiveDigital Service Providers have come up with certain Digital Well-Being Features as a step towards tackling harmful effects of screen overuse on physical and mental health. However, the awareness and use of the same remains scant. Our objective was to assess the knowledge, attitudes and practices regarding Digital Well-being features in the adult population of Maharashtra, India and the associations and correlations of the practice of using these features with screen time and degree of screen addiction. MethodsA cross-sectional online questionnaire-based study was conducted among 335 participants who were selected using quota sampling and were administered a Smartphone Addiction Scale and a self-designed questionnaire. ResultsKnowledge attitudes were good and total of 65.4% participants were digital wellbeing feature users. Correlation of digital hygiene score and digital wellbeing score was found neither with addiction nor with the average screen time. Interpretation & ConclusionKnowledge, attitude and practices regarding Digital Well-Being features were adequate among the urban population of Maharashtra. However, their use was not found to be associated with reduced screen time or a low screen addiction score. With further development and standardization, these features can be a useful tool for prevention of screen overuse and addiction.

Wearables, apps and other remote smart devices can capture rich, objective physiologic, metabolic, and behavioral information that is particularly relevant to pregnancy. The objectives of this paper were to 1) characterize individual level pregnancy self-reported symptoms and objective features from wearables compared to the aggregate; 2) determine whether pregnancy self-reported symptoms and objective features can differentiate pregnancy-related conditions; and 3) describe associations between self-reported symptoms and objective features. Data are from the Better Understanding the Metamorphosis of Pregnancy study, which followed individuals from preconception to three-months postpartum. Participants (18-40 years) were provided with an Oura smart ring, a Garmin smartwatch, and a Bodyport Cardiac Scale. They also used a study smartphone app with surveys and tasks to measure symptoms. Analyses included descriptive spaghetti plots for both individual-level data and cohort averages for select weekly reported symptoms and objective measures from wearables. This data was further stratified by pregnancy-related clinical conditions such as preeclampsia and preterm birth. Mean Spearman correlations between pairs of self-reported symptoms and objective features were estimated. Self-reported symptoms and objective features during pregnancy were highly heterogeneous between individuals. While some aggregate trends were notable, including an inflection in heart rate variability approximately eight weeks prior to delivery, these average trends were highly variable at the n-of-1 level, even among healthy individuals. Pregnancy conditions were not well differentiated by objective features. With the exception of self-reported swelling and body fluid volume, self-reported symptoms and objective features were weakly correlated (mean Spearman correlations <0.1). High heterogeneity and complexities of associations between subjective experiences and objective features across individuals pose challenges for researchers and highlights the dangers in reliance on aggregate approaches in the use of wearable data in pregnant individuals. Innovation in machine learning and AI approaches at the n-of-1 level could help to accelerate the field. Author SummaryThe objective physiological and behavioral information from wearable and other smart devices is uniquely relevant to pregnancy. The objectives of this study were to: 1) describe the individual-level variability of pregnancy self-reported symptoms and objective wearable measures; 2) determine whether this variability can be explained by pregnancy clinical conditions; and 3) determine whether pregnancy self-reported symptoms are associated with objective wearable measures. Data are from the Better Understanding the Metamorphosis of Pregnancy study, which followed individuals from preconception to three-months postpartum. Participants (18-40 years) used an Oura smartring, a Garmin smartwatch, and a Bodyport Cardiac Scale alongside a study app to track self-reported symptoms. High heterogeneity was observed in self-reported pregnancy symptoms, and objective measures such as heart rate variability, activity and sleep over pregnancy that were dissimilar to the population average of these measures. Pregnancy clinical conditions did not explain well the observed high variability in objective wearable measures while self-reported symptoms were weakly correlated with objective wearable measures over pregnancy. In sum, high heterogeneity and complexities of associations between subjective experiences and objective measures from wearables across pregnant individuals pose challenges for researchers. Innovation in machine learning and AI individual level approaches will help to accelerate the field.

BackgroundPostpartum weight retention (PPWR) is a key contributor to long-term obesity risk and an important determinant of maternal health. Mobile health (mHealth) interventions may support postpartum change to a healthier lifestyle, but their effectiveness may depends on usability. This study examined whether perceived usability of the INTER-ACT app was associated with weight loss at 6 months postpartum and whether this association was mediated by app use frequency, motivational power, and implementation of lifestyle recommendations. MethodsThis secondary analysis used data from the intervention arm of the INTER-ACT randomized controlled trial (RCT), which provided postpartum lifestyle coaching and a supportive app to women with excessive gestational weight gain (EGWG). Participants (n=138) completed the process evaluation survey at 6 months postpartum, which included the System Usability Scale (SUS), perceived motivational power, and perceived implementation of lifestyle recommendations. Weight loss was calculated between 6 weeks and 6 months postpartum. Pearson correlations and parallel mediation analyses tested associations between usability, engagement-related variables, and weight loss, adjusting for maternal age, parity, and pre-pregnancy BMI. ResultsThe INTER-ACT app demonstrated moderate usability (mean SUS=60.6). Usability was positively associated with app use frequency (B=.05, p<.001), perceived motivational power (B=.003, p<.001), and perceived implementation of lifestyle recommendations (B=.01, p=.036). However, none of these were associated with weight loss at 6 months postpartum. Usability showed no total, direct, or indirect effect on weight loss. Gestational weight gain (GWG) was the only significant predictor (B=0.24, p=0.003). Higher usability was also associated with more positive and fewer negative emotional responses (p<.05). ConclusionApp usability was associated with engagement and positive emotional experience, but did not translate into reduced PPWR. GWG remained the main determinant of postpartum weight outcomes. Future interventions should prioritize preventing EGWG and complement this with emotionally supportive postpartum mHealth tools as part of a multi-faceted strategy.

The postpartum period presents a critical window for maternal health, yet it is often characterized by limited clinical follow-up. This study evaluated the feasibility of implementing a mobile health (mHealth) system for remote monitoring of Blood Pressure (BP) and mental health among postpartum individuals. A total of 98 postpartum participants were enrolled, of whom 60 with complete and verifiable data were monitored for up to 12 weeks via the MOYO-Mom platform, with all participants self-identified as African Americans. In addition to evaluating user engagement and adherence to the study protocol, associations were examined between depressive symptoms, measured by the Edinburgh Postnatal Depression Scale (EPDS), and key variables including BP, age, and Hypertensive Disorders of Pregnancy (HDP). Of the enrolled cohort, 45 participants contributed 727 valid home BP measurements and 44 completed the EPDS at least once, providing longitudinal physiological and mental-health data for analysis. Findings from this study indicate that lower systolic BP was significantly associated with higher EPDS scores (p < 0.001), suggesting a physiological link to postpartum depression. Additionally, individuals with higher depression scores and those diagnosed with HDP demonstrated lower engagement with the mHealth platform. In contrast, younger participants showed higher adherence to the study protocol. These results--grounded in participant-level data from 423 BP readings and EPDS assessments--support the feasibility of mHealth-enabled postpartum monitoring while highlighting differential engagement across risk profiles. These findings highlight the potential of mHealth tools for postpartum care and underscore the importance of designing targeted strategies to enhance engagement,

PurposeThe SmartSleep Study is established to comprehensively assess the impact of night-time smartphone use on sleep patterns and health. An innovative combination of large-scale repeated survey information, high-resolution sensor-driven smartphone data, in-depth clinical examination and registry linkage allow for detailed investigations into multisystem physiological dysregulation and long-term health consequences associated with night-time smartphone use and sleep impairment. ParticipantsThe SmartSleep Study consists of three interconnected data samples, which combined include 30,673 individuals with information on smartphone use, sleep and health. Subsamples of the study population also include high-resolution tracking data (n=5,927) collected via a customized app and deep clinical phenotypic data (n=245). A total of 7,208 participants will be followed in nationwide health registries with full data coverage and long-term follow-up. Findings to dateWe highlight previous findings on the relation between smartphone use and sleep in the SmartSleep Study, and we evaluate the interventional potential of the citizen science approach used in one of the data samples. We also present new results from an analysis in which we utilize 803,000 data-points from the high-resolution tracking data to identify clusters of temporal trajectories of night-time smartphone use that characterize distinct use patterns. Based on these objective tracking data, we characterize four clusters of night-time smartphone use. Future plansThe unprecedented size and coverage of the SmartSleep Study allow for a comprehensive documentation of smartphone activity during the entire sleep span. The study will be expanded by linkage to nationwide registers, which will allow for further investigations into the long-term health and social consequences of night-time smartphone use. We also plan new rounds of data collection in the coming years. STRENGTHS and LIMITATIONS of this studyO_LIThe unprecedented size and coverage of the SmartSleep Study allow for a comprehensive objective and subjective documentation of smartphone activity during the entire sleep span. C_LIO_LIThe data in the SmartSleep Study are sampled by three different strategies, which allow us to test robustness and validate findings across samples. This aligns with the principles of triangulation, which aims at obtaining more reliable answers to complex research questions through the integration of results from different approaches with different sources of bias. C_LIO_LIThe SmartSleep Study is readily available for research projects: the data sources have already been linked, the data have been cleaned and prepared for future analyses. C_LIO_LIThe SmartSleep Study is not fully representative of the general population due to the sampling procedures, and we are currently creating weights that can be used in the statistical analysis to compensate for this imbalance. C_LI

The postpartum period involves profound physical, emotional, and social changes, yet many women report fragmented, infant-centered care that leaves their own needs insufficiently addressed. Digital health tools, including mobile apps, wearables, telehealth, and online resources, are increasingly used by postpartum women to seek information, support, and reassurance; however, little is known about how women experience these tools in their everyday lives. This qualitative study employed thematic analysis to explore the perspectives of postpartum women on digital health. Postpartum women ([&le;]12 months after birth) living in Alberta, Canada, were recruited through maternity clinics and targeted social media advertisements. Four virtual focus groups (4-6 participants in each; 18 participants overall) were conducted via Zoom using a semi-structured guide on postpartum healthcare experiences, use of digital tools (apps, wearables, telehealth, AI), and perceived barriers and facilitators to adoption. Sessions were audio-recorded, transcribed verbatim, and coded by multiple researchers. Thematic analysis identified 32 codes, organized into 12 subthemes and four overarching themes: navigating postpartum support networks; empowerment through digital health tools; conditions for acceptable digital health design; and when technology feels like a burden. Women appreciated multiple sources of support from midwives, public health nurses, peers, and online communities, but described care that quickly became infant-focused, leaving their own recovery and mental health under-addressed, particularly in rural settings. Digital tools helped mothers structure infant and self-care, track symptoms, and prepare for appointments, yet also created new forms of burden, including information overload, usability challenges, privacy concerns, and feelings of surveillance or pressure to perform. Participants emphasized personalization (flexible notifications, mother-focused content), embedded mental health support, integration with trusted providers, and co-designed, credible platforms endorsed by Canadian health systems. Overall, to be acceptable and effective, tools must center mothers needs and be embedded within a broader ecosystem of responsive, continuous care. Author summaryBecoming a parent is a major life change, and many women feel that support from the health system drops off once the baby is born. At the same time, new mothers are increasingly turning to mobile phone apps, wearable devices, online groups, and video visits to answer questions, track health, and feel less alone. We wanted to understand women lived experience with these digital tools after giving birth: what feels helpful, what feels burdensome, and what they would want in an ideal tool. Our research team, consisting of three PhD students, held four online focus group discussions (4-6 participants per group; 18 participants overall) with women in Alberta, Canada, who had given birth within the past year. They described digital tools as both empowering and exhausting. Apps and wearables helped them track feeding, sleep, and symptoms, organize daily life, and come better prepared for medical appointments. At the same time, constant tracking, frequent notifications, and unclear data practices could feel overwhelming, guilt-inducing, or intrusive. This study is an important first step in a larger co-design work. By listening closely to mothers stories, we gathered practical ideas about what a supportive postpartum app should (and should not) do. In future phases, we plan to work directly with postpartum women and frontline clinicians to turn these ideas into a user-friendly, trustworthy digital tool that supports both mothers and babies health.

2Widespread access to over-the-counter at-home COVID-19 tests was a crucial component of the United States public health response to the COVID-19 pandemic. For at-home testing to be effective, it is essential for the tests to be not only available but also usable and accessible to people with disabilities. In the present study, a survey was conducted to collect feedback about peoples experiences using over-the-counter COVID-19 tests, with an aim to identify barriers to the accessibility of COVID-19 tests for people with disabilities. Survey respondents self-identified as having little to no vision, low vision, or limited dexterity. The survey covered ten at-home COVID-19 test brands. For each test that a respondent had used, the survey included questions about whether the user was able to complete the test independently or whether they needed assistance, the ease of use for performing each part of the testing procedure, and any difficulties that the user encountered while using the test. The survey also included questions for each test regarding the use of mobile applications, digital instructions, and customer service. This report presents the results of nearly 400 responses to the accessibility survey. The results offer an improved understanding of accessibility barriers for at-home COVID-19 tests; designers of at-home diagnostic tests can apply this knowledge in order to improve the accessibility of over-the-counter COVID-19 tests and other at-home diagnostic tests to people with disabilities.

Background/ObjectivesMobility is a meaningful aspect of an individuals health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring. Our objective was to characterize the accuracy and reliability performance of a suite of digital measures of walking behaviors, as critical aspects in the practical implementation of digital measures into clinical studies. MethodsWe collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without history of gait/walking impairment in a real world setting. Based on step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, peak 30-minute walking pace. To characterize accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: Intraclass Correlation Coefficient (ICC), Pearson R, Mean Error (ME), Mean Absolute Error (MAE). To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time-to-reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1-30 days, and analyzing test-retest reliability based on ICCs between adjacent (non-overlapping) time windows for each measure. ResultsIn the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (N=35 participants; median observation time, 5 days). Agreement with the reference device-based readouts in the testing subcohort (n=35) for the eight measurements under evaluation, as reflected by ICCs, ranged between 0.7-0.9; Pearson R values were all greater than 0.75. For the time-to-reliability characterization, we collected data for a total of 15,120 participant days (overall cohort N=234; median observation time, 119 days). Here, all digital measures achieved an ICC between adjacent readouts > 0.75 by 16 days of wear time. ConclusionsWe characterized accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.

BackgroundNowadays the use of smartphones and the development of health-related mobile applications has increased worldwide. Menstrual cycle tracking applications (MCTAs) have become especially popular among women because of their practicality in recording menstrual cycles, characteristics of bleeding and prediction of cycle stages. There are various studies regarding the use of MCTAs for different aspects of womens health such as estimating a fertility window for both conception and contraception, help register last menstrual period for calculation of gestational age, record pre-menstrual symptoms, among others. However, effects of MCTAs have not been analyzed in a systematic review. ObjectiveThe aim of this study is to evaluate the effect of mobile applications for menstrual cycle or fertility trackers on womens health. MethodsA systematic review will be conducted, starting with a search in PubMed, CENTRAL and Scopus using search terms related to mobile applications and menstrual or fertility tracking. Only randomized controlled trials will be screened with a sample of child-bearing aged women that use menstrual or fertility tracking mobile applications. Selected studies will be fully analyzed and the results will be recorded on a spread sheet. Study selection and data extraction will be conducted by two reviewers independently and the Cochrane Risk of Bias Tool for RCT will be used for assessment of risk of bias. Discrepancies will be reviewed with a third reviewer. ConclusionCurrently, there is a lack of information on the effects of using MCTAs on womens health. This systematic review aims to provide an analysis on the outcomes of the usage of these applications and evaluate any potential effects. Conflicts of interestAll authors declare to have no conflicts of interest.

BackgroundUnderstanding the drivers of protective behavior during infectious disease outbreaks is critical for public health policy. App-based experimental epidemic games (epigames) offer a novel method to study these behaviors empirically, but their external validity--how well in-game choices reflect real-life beliefs--still needs to be rigorously tested. MethodsWe conducted a two-week randomized controlled trial (N=567) using the Epigames smartphone app at the American University of Iraq - Baghdad (AUIB) campus in the Middle East. This app used Bluetooth communication to sample the contact network between participants in sub-minute resolution and simulated the spread of a hypothetical respiratory virus through this network. Participants were randomized into two groups with differing opportunity costs (in-game points) for adopting voluntary quarantine within the game that would protect them from the simulated infection: Group 1 (Low Barrier) faced a small point difference between quarantine and non-quarantine choices, while Group 2 (High Barrier) faced a much larger difference. The optimal point difference between groups was determined by a Willing to Accept (WTA) pilot survey prior to the epigame. We measured real-life health beliefs and in-game beliefs through surveys at the beginning of the epigame, including questions on susceptibility, severity, self-efficacy, and benefits, to calculate the correlation between the two and to construct a Health Belief Model (HBM) parameterized by game data. We also measured self-assessment of game realism and behavior via an exit survey. ResultsReal-life and in-game beliefs showed moderate positive (Spearmans coefficient {rho} from 0.13 to 0.36) and statistically significant (p-value < 0.05) correlation across all survey measures, suggesting indicator (behavior) parallelism between real-life and the epigame. The exit survey also yielded positive self-assessment of context and behavior realism during the epigame. While simple aggregate analysis showed no significant difference in quarantine rates between Groups 1 and 2, a Poisson regression model revealed a significant crossover interaction. High economic barriers significantly reduced quarantine adoption among participants with low in-game motivation (interaction coef. = -2.22, p < 0.01). Perceived benefits appeared to moderate this effect at a near significance level (coef. = +0.33, p < 0.08). Demographic factors such as gender appeared to be significantly correlated with quarantine choice (p < 0.01). Analysis of the contact network measured with the app through Bluetooth showed assortative properties of several belief variables. ConclusionThis is the first systematic pre-registered study on the external validity of app-based epigames and the impact of economic cost and individual beliefs on protective behaviors. We found that economic barriers act as a "gatekeeper" for quarantine during the game, suppressing action among the skeptical while allowing highly motivated individuals to act. The significant correlation between real-life, in-game beliefs, and network structure suggests that epigames are a valid experimental tool for network-aware behavioral epidemiology. This warrants further studies for replication, examining variance across settings, and addressing limitations (e.g., crosstalk between groups) and technical issues (e.g., Bluetooth connectivity) in the study design. Study pre-registration: https://osf.io/qev6n/

Abstract Objective Wrist-worn accelerometers are common in large-scale epidemiological studies, but their ability to measure sedentary behaviour in free-living environments is unknown. We therefore aimed to evaluate the accuracy of openly-available methods to infer sedentary time from wrist-worn accelerometers. Methods We analysed data from 662 working-age adults in the SMART Work & Life study (20-70 years; mean age 45 years; 72% female) who concurrently wore wrist- and thigh-worn accelerometers for up to eight free-living days. Reference measurements of sedentary time were derived from the thigh accelerometer data using proprietary algorithms. Wrist accelerometer data were processed using widely used, publicly available activity recognition models. Performance was evaluated at 30-second epochs to generate per-participant metrics, alongside comparisons of mean daily sedentary time, mean daily number of prolonged sedentary bouts ([&ge;] 30 minutes) and proportion of sedentary time in prolonged bouts. Model performance was examined across subgroups defined by age, sex, body mass index, season, recruitment centre, and in sensitivity analyses restricted to daytime hours (08:00-22:00). Results The best performing machine learning model (Actinet) accurately classified sedentary time from wrist-worn accelerometer data with a mean per-participant accuracy of 0.87 and F1 score of 0.85. Cut point-based approaches demonstrated lower accuracy of 0.80 (F1 score of 0.79). The ActiNet machine learning model showed strong agreement in daily sedentary time, daily number of prolonged sedentary bouts and proportion of sedentary time in prolonged bouts, all within 10% of the free-living thigh reference. Findings were consistent across subgroups and in analyses restricted to daytime hours. Conclusion Wrist-worn accelerometers can provide accurate measurements of sedentary behaviour in free-living settings, when assessed using current machine learning models, particularly ActiNet. This work provides confidence in future epidemiological research to examine sedentary behaviour patterns from wrist-worn accelerometers and their associations with health outcomes.

BackgroundVoice-based systems such as Amazon Alexa may be useful to collect self-reported information in realtime from participants of epidemiology studies, using verbal input. We demonstrate the technical feasibility of using Alexa, investigate participant acceptability, and provide an initial evaluation of the validity of the collected data. We use food and drink information as an exemplar. MethodsWe recruited 45 staff and students at the University of Bristol (UK). Participants were asked to tell Alexa what they ate or drank for 7 days, and also to submit this information using a web form. Questionnaires asked for basic demographic information and about their experience during the study and acceptability of using Alexa. ResultsOf the 37 participants with valid data, most were 20-39 years old (N=30; 81%) and 23 (62%) were female. Across 29 participants with Alexa and web entries corresponding to the same intake event, 357 Alexa entries (61%) contained the same food/drink information as the corresponding web entry. Participants often reported that Alexa interjected, and this was worse when entering the food and drink information compared with the event date and time. The majority said they would be happy to use a voice-controlled system for future research. ConclusionsWhile usability of our skill was poor, largely due to the conversational nature and because Alexa interjected if there was a pause in speech, participants were mostly open to participating in future research studies using Alexa. Many more studies are needed, in particular, to trial less conversational interfaces. KEY MESSAGESO_LIOver the last few years voice-controlled smart systems have emerged giving the possibility of collecting self-reported data using a voice-based approach. C_LIO_LIWe successfully collected epidemiology food and drink information in real-time, demonstrating that voice-based collection of self-reported data is technically feasible. C_LIO_LIThe conversational design of our skill meant that usability was poor, for example, most participants (86%) reported that Alexa either occasionally, often or always interjected during use, and the majority of participants who had previously used a paper diary or my fitness pal did not find Alexa as efficient to use compared with these approaches. C_LIO_LIAfter participating in this study, the majority of participants would be happy to use Alexa again, either at home or on a wearable device. C_LIO_LIOur results highlight that further work is needed to evaluate use of voice-based systems, including comparing Amazon Alexa with the Google Assistant, and trialling less conversational interfaces. C_LI

While wearable activity trackers (WATs) are widely used in the present day, with device ownership increasing, some individuals subsequently discontinue device use. Existing research primarily examines the initiation and maintenance of device use, with less focus on device discontinuation. Examining this phenomenon can provide valuable insight into human-computer interactions and habit reversal. Therefore, the current study examined the perceived emotional and behavioural impact of WAT discontinuation, alongside reasons for this action in former WAT users. Fifteen former WAT users (9 female, aged 23 to 56 years) who reported either full or partial device discontinuation were interviewed. Three themes and nine sub-themes were identified which detailed the impacts of device discontinuation. Participants reported a mindset shift around ones body image, exercise performance and exercise motivation. Device discontinuation removed numerical feedback provision which led to participants gaining bodily intuition and a sense of freedom. However, discontinuation also resulted in short-term negative emotions including frustration around the loss of external praise and envy in current WAT users. Current findings hold important implications around digital safety from user perspective, highlighting the need for guidance around healthy WAT use and vulnerable user profiles. More broadly, findings also raise the need for physical activity promotion whilst protecting individuals well-being.

BackgroundAs the COVID-19 global pandemic continues, digital exposure notification systems are increasingly used to support traditional contact tracing and other preventive strategies. Likewise, a plethora of COVID-19 mobile apps have emerged. ObjectiveTo characterize the global landscape of pandemic related mobile apps, including digital exposure notification and contact tracing tools. Data Sources and MethodsThe following queries were entered into the Google search engine: "(*country name* COVID app) OR (COVID app *country name*) OR (COVID app *country name*+) OR (*country name*+ COVID app)". The App Store, Google Play, and official government websites were then accessed to collect descriptive data for each app. Descriptive data were qualified and quantified using standard methods. COVID-19 Exposure Notification Systems (ENS) and non-Exposure Notification products were categorized and summarized to provide a global landscape review. ResultsOur search resulted in a global count of 224 COVID-19 mobile apps, in 127 countries. Of these 224 apps, 128 supported exposure notification, with 75 employing the Google Apple Exposure Notification (GAEN) app programming interface (API). Of the 75 apps using the GAEN API, 15 apps were developed using Exposure Notification Express, a GAEN turnkey solution. COVID-19 apps that did not include exposure notifications (n=96) focused on COVID-19 Self-Assessment (35{middle dot}4%), COVID-19 Statistics and Information (32{middle dot}3%), and COVID-19 Health Advice (29{middle dot}2%). ConclusionsThe digital response to COVID-19 generated diverse and novel solutions to support non-pharmacologic public health interventions. More research is needed to evaluate the extent to which these services and strategies were useful in reducing viral transmission. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSThe COVID-19 pandemic created a role for technology to complement traditional contact tracing and mitigate the spread of disease. How countries responded with technology - specifically, how they utilized mobile apps to support public health was a focus of this research. The search process consisted of searching the Google Search Engine using queries "(*country name* COVID app) OR (COVID app *country name*) OR (COVID app *country name*+) OR (*country name*+ COVID app)." Apps that were found on the App Store, Google Play, and official government format that fit the pre-defined eligibility criteria were included in the Apps list considered in this search. Apps that did not match those criteria were excluded from the process. All 195 countries and associated COVID-19 apps were considered for inclusion if they were official COVID-19 apps adopted by the governments of those countries. Added value of this studyFindings from this research contributes to the literature by providing a synthesis of how technology is being used to support public health authorities in reducing the spread of COVID-19. The results of this global landscape analysis of COVID 19 mobile apps, included both COVID-19 Exposure Notification Apps and non-Exposure Notification Apps. Implications of all the available evidenceThe findings of this research can provide the foundation for future studies to assess adoption rates and, subsequently identify app features that lead to increased adoption of both Exposure Notification and non-Exposure Notification Apps. Only when a product meets the needs of consumers will it be adopted and utilized, and in the case of exposure notification tools, save lives. Eligibility CriteriaThe study eligibility criteria included any COVID-19 mobile applications and COVID-19 Exposure Notification (EN) systems that were downloadable or activated on a mobile device. The search process involved using queries "(*country name* COVID app) OR (COVID app *country name*), OR (COVID app *country name*+) OR (*country name*+ COVID app)" in Google search engine to identify COVID-19 apps in each country. The data included active and inactive apps discovered during the data collection period of June 2021 - July 2021.

Objective: We examined the link between cohabitation with a partner and nighttime smartphone use through the social control of health behavior theory. Background: Nighttime smartphone use is a behavioral risk factor for sleep problems. While previous research has predominantly focused on individual-level risks of sleep disturbances, the role of social context remains underexplored. Theoretical frameworks, specifically the Social Control of Health Behavior, suggest that social relationships regulate health-related behaviors; however, it is unclear how far this regulation extends to modern digital behaviors among couples. Method: We analyzed survey data from three waves of the SmartSleep Study (2018, 2020, and 2023; total N = 25,028), including a longitudinal follow-up subset (N = 1,003). We tested multivariate associations between living with a partner, changes in cohabitation status and frequent nighttime smartphone use by fitting generalized linear mixed-effects models. Additionally, we mapped the complex interplay between indicators of social integration, social support, smartphone use, and sleep quality using hierarchical clustering of non-linear correlations. Results: Cohabiting participants had lower odds of frequent nighttime smartphone use compared to those living alone (OR = 0.66; 95% CI: 0.61, 0.72). This lower risk was driven primarily by cohabitation with a partner (OR = 0.49; 95% CI: 0.36, 0.66). Longitudinal analysis supported these findings, showing that sustained cohabitation was associated with less frequent nighttime use (OR = 0.56; 95% CI: 0.38, 0.82). Clustering analysis revealed that indicators of social integration and support clustered with favorable sleep quality. Conclusion: Our findings suggest that the health-protective effects of cohabitation with a partner extend to digital behaviors. Consistent with social control of health behavior theory, the presence of a partner appears to reduce frequent nighttime smartphone use, highlighting the critical importance of considering social context when addressing digital health hygiene and promoting sleep.

Mobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for personalisation, which has been proven effective in promoting behaviour change. In this work, we propose a theoretically driven and personalised behavioural intervention delivered through an adaptive knowledge-based system. The behavioural system design is guided by the Behavioural Change Wheel and the Capability-Opportunity-Motivation behavioural model. The system exploits the ever-increasing availability of health data from wearable devices, point-of-care tests and consumer genetic tests to issue highly personalised physical activity and sedentary behaviour recommendations. To provide the personalised recommendations, the system firstly classifies the user into one of four diabetes clusters based on their cardiometabolic profile. Secondly, it recommends activity levels based on their genotype and past activity history, and finally, it presents the user with their current risk of developing cardiovascular disease. In addition, leptin, a hormone involved in metabolism, is included as a feedback biosignal to personalise the recommendations further. As a case study, we designed and demonstrated the system on people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes, such as physical activity increase and sedentary behaviour reduction. We trained and simulated the system using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrate that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention.

BackgroundMost adults fail to meet the moderate to vigorous physical activity-based recommendations needed to maintain or improve health. Vigorous Intermittent Lifestyle Physical Activity (VILPA) refers to short (1-2 mins) high-intensity activities that are integrated into activities of daily living. VILPA has shown strong potential to improve health and addresses commonly reported barriers to physical activity. However, it is unknown how VILPA can best be promoted among the adult population. This study aimed to evaluate the usability, user engagement, and satisfaction of a mobile application (MovSnax) designed to promote VILPA. MethodsA concurrent mixed methods design was used. It comprised four parts. Part A was a survey with n=8 mHealth and physical activity experts who had used the app over 7-10 days. Part B was think- aloud interviews with n=5 end-users aged 40-65 years old. Part C was a survey with a new group of 40-65-year-old end-users (n=35) who had used the MovSnax app over 7-10 days. Part D was semi- structured interviews with n=18 participants who took part in Part C. Directed content analysis was used to analyze the results from Parts A, B, and D, and descriptive statistics were used to analyze findings from Part C. ResultsParticipants reported positive views on the MovSnax app for promoting VILPA but also identified usability issues such as unclear purpose, difficulties in manual data entry, and limited customization options. Across the different data collections, they consistently emphasized the need for more motivational features, clearer feedback, and gamification elements to enhance engagement. Quantitative assessment showed satisfactory scores on objective measures but lower ratings on subjective aspects, possibly due to unfamiliarity with the VILPA concept and/or technical barriers. ConclusionsThe MovSnax app, tested in the present study, is the worlds first digital tool aimed specifically at increasing VILPA. The findings of the present study underscore the need for further app refinement, focusing on clarifying its purpose and instructions, boosting user engagement through personalization and added motivational elements, enhancing accuracy in detecting VILPA bouts, implementing clearer feedback mechanisms, expanding customization choices (such as font size and comparative data), and ensuring transparent and meaningful activity tracking.