ORCID Profile
0000-0003-3285-3174
Current Organisation
University of California, San Diego
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Publisher: Informa UK Limited
Date: 14-03-2022
DOI: 10.1080/17437199.2022.2046482
Abstract: In this White Paper, we outline recommendations from the perspective of health psychology and behavioural science, addressing three research gaps: (1) What methods in the health psychology research toolkit can be best used for developing and evaluating digital health tools? (2) What are the most feasible strategies to reuse digital health tools across populations and settings? (3) What are the main advantages and challenges of sharing (openly publishing) data, code, intervention content and design features of digital health tools? We provide actionable suggestions for researchers joining the continuously growing Open Digital Health movement, poised to revolutionise health psychology research and practice in the coming years. This White Paper is positioned in the current context of the COVID-19 pandemic, exploring how digital health tools have rapidly gained popularity in 2020-2022, when world-wide health promotion and treatment efforts rapidly shifted from face-to-face to remote delivery. This statement is written by the Directors of the not-for-profit Open Digital Health initiative (n = 6), Experts attending the European Health Psychology Society Synergy Expert Meeting (n = 17), and the initiative consultant, following a two-day meeting (19-20th August 2021).
Publisher: Center for Open Science
Date: 05-08-2022
Abstract: Background and Aims: When attempting to stop smoking, discrete smoking events (‘lapses’) are strongly associated with a return to regular smoking (‘relapse’). No study has yet pooled the psychological and contextual antecedents of lapse incidence, captured in Ecological Momentary Assessment (EMA) studies. This systematic review and meta-analysis aimed to synthesise within-person psychological and contextual predictor-lapse associations in smokers attempting to quit.Design: We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science. A narrative synthesis and multilevel, random-effects meta-analyses were conducted.Setting: No restrictions on setting.Participants: Adult, non-clinical populations attempting to stop smoking.Measurements: Outcomes were the association between a psychological (e.g., stress, cravings) or contextual (e.g., cigarette availability) antecedent and smoking lapse incidence definitions of ‘lapse’ and ‘relapse’ the theoretical underpinning of EMA study designs and the proportion of studies with pre-registered study protocols/analysis plans and open data.Findings: We included 56 studies, with 18 studies contributing 1+ effect sizes to the meta-analyses. We found positive relationships between lapse incidence and ‘environmental and social cues’ (k = 12, OR = 4.19, 95% CI = 1.77, 9.91, p = 0.001) and ‘cravings’ (k = 10, OR = 1.73, 95% CI = 1.25, 2.40, p = 0.001). ‘Negative feeling states’ was not significantly associated with lapse incidence (k =15, OR = 1.10, 95% CI = 0.97, 1.24, p = 0.14). In the narrative synthesis, negative relationships with lapse incidence were found for ‘behavioural regulation’, ‘motivation not to smoke’, and ‘beliefs about capabilities’ positive relationships with lapse incidence were found for ‘positive feeling states’ and ‘positive outcome expectancies’. Although lapse definitions were comparable, relapse definitions varied widely across studies. Few studies explicitly drew on psychological theory to inform EMA study designs. None of the included studies drew on Open Science principles.Conclusions: In smokers attempting to stop, environmental and social cues and cravings appear to be key within-person antecedents of smoking lapse incidence.
Publisher: JMIR Publications Inc.
Date: 07-08-2020
DOI: 10.2196/18690
Abstract: Drink Less is a behavior change app that aims to help users in the general adult population reduce hazardous and harmful alcohol consumption. The app includes a daily push notification, delivered at 11 am, asking users to “Please complete your mood and drinking diaries.” Previous analysis of Drink Less engagement data suggests the current notification strongly influences how users engage with the app in the subsequent hour. To exploit a potential increase of vulnerability of excess drinking and opportunity to engage with the app in the evenings, we changed the delivery time from 11 am to 8 pm. We now aim to further optimise the content and sequence of notifications, testing 30 new evidence-informed notifications targeting the user’s perceived usefulness of the app. The primary objective is to assess whether sending a notification at 8 pm increases behavioral engagement (opening the app) in the subsequent hour. Secondary objectives include comparing the effect of the new bank of messages with the standard message and effect moderation over time. We also aim to more generally understand the role notifications have on the overall duration, depth, and frequency of engagement with Drink Less over the first 30 days after download. This is a protocol for a micro-randomized trial with two additional parallel arms. Inclusion criteria are Drink Less users who (1) consent to participate in the trial (2) self-report a baseline Alcohol Use Disorders Identification Test score of 8 or above (3) reside in the United Kingdom (4) age ≥18 years and (5) report interest in drinking less alcohol. In the micro-randomized trial, participants will be randomized daily at 8 pm to receive no notification, a notification with text from the new message bank, or the standard message. The primary outcome is the time-varying, binary outcome of “Did the user open the app in the hour from 8 pm to 9 pm?”. The primary analysis will estimate the marginal relative risk for the notifications using an estimator developed for micro-randomized trials with binary outcomes. Participants randomized to the parallel arms will receive no notifications (Secondary Arm A), or the standard notification delivered daily at 11 am (Secondary Arm B) over 30 days, allowing the comparison of overall engagement between different notification delivery strategies. Approval was granted by the University College of London’s Departmental Research Ethics Committee (CEHP/2016/556) on October 11, 2019, and The London School of Hygiene and Tropical Medicine Interventions Research Ethics Committee (17929) on November 27, 2019. Recruitment began on January 2, 2020, and is ongoing. Understanding how push notifications may impact engagement with a behavior change app can lead to further improvements in engagement, and ultimately help users reduce their alcohol consumption. This understanding may also be generalizable to other apps that target a variety of behavior changes. DERR1-10.2196/18690
Publisher: Informa UK Limited
Date: 15-09-2022
Publisher: Oxford University Press (OUP)
Date: 08-05-2021
DOI: 10.1093/TBM/IBAB048
Abstract: Acceptability is a core concept in digital health. Available frameworks have not clearly articulated why and how researchers, practitioners and policy makers may wish to study the concept of acceptability. Here, we aim to discuss (i) the ways in which acceptability might differ from closely related concepts, including user engagement (ii) the utility of the concept of acceptability in digital health research and practice (iii) social and cultural norms that influence acceptability and (iv) pragmatic means of measuring acceptability, within and beyond the research process. Our intention is not to offer solutions to these open questions but to initiate a debate within the digital health community. We conducted a narrative review of theoretical and empirical ex les from the literature. First, we argue that acceptability may usefully be considered an emergent property of a complex, adaptive system of interacting components (e.g., affective attitude, beliefs), which in turn influences (and is influenced by) user engagement. Second, acceptability is important due to its ability to predict and explain key outcomes of interest, including user engagement and intervention effectiveness. Third, precisely what people find acceptable is deeply contextualized and interlinked with prevailing social and cultural norms. Understanding and designing for such norms (e.g., through drawing on principles of user centered design) is therefore key. Finally, there is a lack of standard acceptability measures and thresholds. Star ratings coupled with free-text responses may provide a pragmatic means of capturing acceptability. Acceptability is a multifaceted concept, which may usefully be studied with a complexity science lens.
Publisher: JMIR Publications Inc.
Date: 29-03-2022
Abstract: lt i Drink Less /i is a behavior change app to help higher-risk drinkers in the United Kingdom reduce their alcohol consumption. The app includes a daily notification asking users to i “ /i Please complete your drinks and mood diary, i ” /i yet we did not understand the causal effect of the notification on engagement nor how to improve this component of i Drink Less /i . We developed a new bank of 30 new messages to increase users’ reflective motivation to engage with i Drink Less /i . This study aimed to determine how standard and new notifications affect engagement. ur objective was to estimate the causal effect of the notification on near-term engagement, to explore whether this effect changed over time, and to create an evidence base to further inform the optimization of the notification policy. e conducted a micro-randomized trial (MRT) with 2 additional parallel arms. Inclusion criteria were i Drink Less /i users who consented to participate in the trial, self-reported a baseline Alcohol Use Disorders Identification Test score of ≥8, resided in the United Kingdom, were aged ≥18 years, and reported interest in drinking less alcohol. Our MRT randomized 350 new users to test whether receiving a notification, compared with receiving no notification, increased the probability of opening the app in the subsequent hour, over the first 30 days since downloading i Drink Less /i . Each day at 8 PM, users were randomized with a 30% probability of receiving the standard message, a 30% probability of receiving a new message, or a 40% probability of receiving no message. We additionally explored time to disengagement, with the allocation of 60% of eligible users randomized to the MRT (n=350) and 40% of eligible users randomized in equal number to the 2 parallel arms, either receiving the no notification policy (n=98) or the standard notification policy (n=121). Ancillary analyses explored effect moderation by recent states of habituation and engagement. eceiving a notification, compared with not receiving a notification, increased the probability of opening the app in the next hour by 3.5-fold (95% CI 2.91-4.25). Both types of messages were similarly effective. The effect of the notification did not change significantly over time. A user being in a state of i already engaged /i lowered the new notification effect by 0.80 (95% CI 0.55-1.16), although not significantly. Across the 3 arms, time to disengagement was not significantly different. e found a strong near-term effect of engagement on the notification, but no overall difference in time to disengagement between users receiving the standard fixed notification, no notification at all, or the random sequence of notifications within the MRT. The strong near-term effect of the notification presents an opportunity to target notifications to increase “in-the-moment” engagement. Further optimization is required to improve the long-term engagement. R2-10.2196/18690
Publisher: F1000 Research Ltd
Date: 11-11-2020
DOI: 10.12688/WELLCOMEOPENRES.16348.1
Abstract: Background/Aim : We have a limited understanding of the broader determinants of health of international migrants and how these change over time since migration to the United Kingdom (UK). To address this knowledge gap, we aim to conduct a prospective cohort study with data acquisition via a smartphone application (app). In this pilot study, we aim to 1) determine the feasibility of the use of an app for data collection in international migrants, 2) optimise app engagement by quantifying the impact of specific design features on the completion rates of survey questionnaires and on study retention, 3) gather preliminary profile health status data, to begin to examine how risk factors for health are distributed among migrants. Methods : We will recruit 275 participants through a social media c aign and through third sector organisations that work with or support migrants in the UK. Following consent and registration, data will be collected via surveys. To optimise app engagement and study retention, we will quantify the impact of specific design features (i.e. the frequency of survey requests, the time of day for app notifications, the frequency of notifications, and the wording of notifications) via micro-randomised process evaluations. The primary outcome for this study is survey completion rates with numerator as the number of surveys completed and denominator as the total number of available surveys. Secondary outcomes are study retention rates and ratings of interest after app usage. Ethics and dissemination : We have obtained approval to use consented patient identifiable data from the University College London Ethics Committee. Improving engagement with the app and gathering preliminary health profile data will help us identify accessibility and usability issues and other barriers to app and study engagement prior to moving to a larger study.
Publisher: Wiley
Date: 10-03-2023
DOI: 10.1111/ADD.16173
Abstract: When attempting to stop smoking, discrete smoking events (‘lapses’) are strongly associated with a return to regular smoking (‘relapse’). No study has yet pooled the psychological and contextual antecedents of lapse incidence, captured in ecological momentary assessment (EMA) studies. This systematic review and meta‐analysis aimed to synthesize within‐person psychological and contextual predictor–lapse associations in smokers attempting to quit. We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science. A narrative synthesis and multi‐level, random‐effects meta‐analyses were conducted, focusing on studies of adult, non‐clinical populations attempting to stop smoking, with no restrictions on setting. Outcomes were the association between a psychological (e.g. stress, cravings) or contextual (e.g. cigarette availability) antecedent and smoking lapse incidence definitions of ‘lapse’ and ‘relapse’ the theoretical underpinning of EMA study designs and the proportion of studies with pre‐registered study protocols/analysis plans and open data. We included 61 studies, with 19 studies contributing ≥ 1 effect size(s) to the meta‐analyses. We found positive relationships between lapse incidence and ‘environmental and social cues’ [ k = 12, odds ratio (OR) = 4.53, 95% confidence interval (CI) = 2.02, 10.16, P = 0.001] and ‘cravings’ ( k = 10, OR = 1.71, 95% CI = 1.34, 2.18, P 0.001). ‘Negative feeling states’ was not significantly associated with lapse incidence ( k = 16, OR = 1.10, 95% CI = 0.98, 1.24, P = 0.12). In the narrative synthesis, negative relationships with lapse incidence were found for ‘behavioural regulation’, ‘motivation not to smoke’ and ‘beliefs about capabilities’ positive relationships with lapse incidence were found for ‘positive feeling states’ and ‘positive outcome expectancies’. Although lapse definitions were comparable, relapse definitions varied widely across studies. Few studies explicitly drew upon psychological theory to inform EMA study designs. One of the included studies drew upon Open Science principles. In smokers attempting to stop, environmental and social cues and cravings appear to be key within‐person antecedents of smoking lapse incidence. Due to low study quality, the confidence in these estimates is reduced.
Publisher: JMIR Publications Inc.
Date: 12-03-2020
Abstract: lt i Drink Less /i is a behavior change app that aims to help users in the general adult population reduce hazardous and harmful alcohol consumption. The app includes a daily push notification, delivered at 11 am, asking users to “Please complete your mood and drinking diaries.” Previous analysis of i Drink Less /i engagement data suggests the current notification strongly influences how users engage with the app in the subsequent hour. To exploit a potential increase of vulnerability of excess drinking and opportunity to engage with the app in the evenings, we changed the delivery time from 11 am to 8 pm. We now aim to further optimise the content and sequence of notifications, testing 30 new evidence-informed notifications targeting the user’s perceived usefulness of the app. he primary objective is to assess whether sending a notification at 8 pm increases behavioral engagement (opening the app) in the subsequent hour. Secondary objectives include comparing the effect of the new bank of messages with the standard message and effect moderation over time. We also aim to more generally understand the role notifications have on the overall duration, depth, and frequency of engagement with i Drink Less /i over the first 30 days after download. his is a protocol for a micro-randomized trial with two additional parallel arms. Inclusion criteria are i Drink Less /i users who (1) consent to participate in the trial (2) self-report a baseline Alcohol Use Disorders Identification Test score of 8 or above (3) reside in the United Kingdom (4) age ≥18 years and (5) report interest in drinking less alcohol. In the micro-randomized trial, participants will be randomized daily at 8 pm to receive no notification, a notification with text from the new message bank, or the standard message. The primary outcome is the time-varying, binary outcome of i “Did the user open the app in the hour from 8 pm to 9 pm?” /i . The primary analysis will estimate the marginal relative risk for the notifications using an estimator developed for micro-randomized trials with binary outcomes. Participants randomized to the parallel arms will receive no notifications (Secondary Arm A), or the standard notification delivered daily at 11 am (Secondary Arm B) over 30 days, allowing the comparison of overall engagement between different notification delivery strategies. pproval was granted by the University College of London’s Departmental Research Ethics Committee (CEHP/2016/556) on October 11, 2019, and The London School of Hygiene and Tropical Medicine Interventions Research Ethics Committee (17929) on November 27, 2019. Recruitment began on January 2, 2020, and is ongoing. nderstanding how push notifications may impact engagement with a behavior change app can lead to further improvements in engagement, and ultimately help users reduce their alcohol consumption. This understanding may also be generalizable to other apps that target a variety of behavior changes. ERR1-10.2196/18690
Publisher: JMIR Publications Inc.
Date: 09-06-2023
DOI: 10.2196/38342
Abstract: Drink Less is a behavior change app to help higher-risk drinkers in the United Kingdom reduce their alcohol consumption. The app includes a daily notification asking users to “Please complete your drinks and mood diary,” yet we did not understand the causal effect of the notification on engagement nor how to improve this component of Drink Less. We developed a new bank of 30 new messages to increase users’ reflective motivation to engage with Drink Less. This study aimed to determine how standard and new notifications affect engagement. Our objective was to estimate the causal effect of the notification on near-term engagement, to explore whether this effect changed over time, and to create an evidence base to further inform the optimization of the notification policy. We conducted a micro-randomized trial (MRT) with 2 additional parallel arms. Inclusion criteria were Drink Less users who consented to participate in the trial, self-reported a baseline Alcohol Use Disorders Identification Test score of ≥8, resided in the United Kingdom, were aged ≥18 years, and reported interest in drinking less alcohol. Our MRT randomized 350 new users to test whether receiving a notification, compared with receiving no notification, increased the probability of opening the app in the subsequent hour, over the first 30 days since downloading Drink Less. Each day at 8 PM, users were randomized with a 30% probability of receiving the standard message, a 30% probability of receiving a new message, or a 40% probability of receiving no message. We additionally explored time to disengagement, with the allocation of 60% of eligible users randomized to the MRT (n=350) and 40% of eligible users randomized in equal number to the 2 parallel arms, either receiving the no notification policy (n=98) or the standard notification policy (n=121). Ancillary analyses explored effect moderation by recent states of habituation and engagement. Receiving a notification, compared with not receiving a notification, increased the probability of opening the app in the next hour by 3.5-fold (95% CI 2.91-4.25). Both types of messages were similarly effective. The effect of the notification did not change significantly over time. A user being in a state of already engaged lowered the new notification effect by 0.80 (95% CI 0.55-1.16), although not significantly. Across the 3 arms, time to disengagement was not significantly different. We found a strong near-term effect of engagement on the notification, but no overall difference in time to disengagement between users receiving the standard fixed notification, no notification at all, or the random sequence of notifications within the MRT. The strong near-term effect of the notification presents an opportunity to target notifications to increase “in-the-moment” engagement. Further optimization is required to improve the long-term engagement. RR2-10.2196/18690
Publisher: JMIR Publications Inc.
Date: 10-08-2020
Abstract: ehavior change apps can develop iteratively, where the app evolves into a complex, dynamic, or personalized intervention through cycles of research, development, and implementation. Understanding how existing users engage with an app (eg, frequency, amount, depth, and duration of use) can help guide further incremental improvements. We aim to explore how simple visualizations can provide a good understanding of temporal patterns of engagement, as usage data are often longitudinal and rich. his study aims to visualize behavioral engagement with i Drink Less /i , a behavior change app to help reduce hazardous and harmful alcohol consumption in the general adult population of the United Kingdom. e explored behavioral engagement among 19,233 existing users of i Drink Less /i . Users were included in the s le if they were from the United Kingdom were 18 years or older were interested in reducing their alcohol consumption had a baseline Alcohol Use Disorders Identification Test score of 8 or above, indicative of excessive drinking and had downloaded the app between May 17, 2017, and January 22, 2019 (615 days). Measures of when sessions begin, length of sessions, time to disengagement, and patterns of use were visualized with heat maps, timeline plots, k-modes clustering analyses, and Kaplan-Meier plots. he daily 11 AM notification is strongly associated with a change in engagement in the following hour reduction in behavioral engagement over time, with 50.00% (9617/19,233) of users disengaging (defined as no use for 7 or more consecutive days) 22 days after download identification of 3 distinct trajectories of use, namely engagers (4651/19,233, 24.18% of users), slow disengagers (3679/19,233, 19.13% of users), and fast disengagers (10,903/19,233, 56.68% of users) and limited depth of engagement with 85.076% (7,095,348/8,340,005) of screen views occurring within the i Self-monitoring and Feedback /i module. In addition, a peak of both frequency and amount of time spent per session was observed in the evenings. isualizations play an important role in understanding engagement with behavior change apps. Here, we discuss how simple visualizations helped identify important patterns of engagement with i Drink Less /i . Our visualizations of behavioral engagement suggest that the daily notification substantially impacts engagement. Furthermore, the visualizations suggest that a fixed notification policy can be effective for maintaining engagement for some users but ineffective for others. We conclude that optimizing the notification policy to target both effectiveness and engagement is a worthwhile investment. Our future goal is to both understand the causal effect of the notification on engagement and further optimize the notification policy within i Drink Less /i by tailoring to contextual circumstances of in iduals over time. Such tailoring will be informed from the findings of our micro-randomized trial (MRT), and these visualizations were useful in both gaining a better understanding of engagement and designing the MRT.
Publisher: BMJ
Date: 07-2021
DOI: 10.1136/BMJOPEN-2020-046435
Abstract: Ecological momentary assessment (EMA) involves repeated, real-time assessments of phenomena (eg, cognitions, emotions, behaviours) over a period of time in naturalistic settings. EMA is increasingly used to study both within-person and between-person processes. We will review EMA studies investigating key health behaviours and synthesise: (1) study characteristics (eg, frequency of assessments, adherence, incentives), (2) associations between psychological predictors and behaviours and (3) moderators of adherence to EMA protocols. This review will focus on EMA studies conducted across five public health behaviours in adult, non-clinical populations: movement behaviour (including physical activity and sedentary behaviour), dietary behaviour, alcohol consumption, tobacco smoking and preventive sexual health behaviours. Studies need to have assessed at least one psychological or contextual predictor of these behaviours. Studies reporting exclusively on physiological outcomes (eg, cortisol) or those not conducted under free-living conditions will be excluded. We will search OVID MEDLINE, Embase, PsycINFO and Web of Science using terms relevant to EMA and the selected health behaviours. Reference lists of existing systematic reviews of EMA studies will be hand searched. Identified articles will be screened by two reviewers. This review is expected to provide a comprehensive summary of EMA studies assessing psychological or contextual predictors of five public health behaviours. The results will be disseminated through peer-reviewed publications and presentations. Data from included studies will be made available to other researchers. No ethics are required. CRD42020168314.
Publisher: JMIR Publications Inc.
Date: 11-12-2020
DOI: 10.2196/23369
Abstract: Behavior change apps can develop iteratively, where the app evolves into a complex, dynamic, or personalized intervention through cycles of research, development, and implementation. Understanding how existing users engage with an app (eg, frequency, amount, depth, and duration of use) can help guide further incremental improvements. We aim to explore how simple visualizations can provide a good understanding of temporal patterns of engagement, as usage data are often longitudinal and rich. This study aims to visualize behavioral engagement with Drink Less, a behavior change app to help reduce hazardous and harmful alcohol consumption in the general adult population of the United Kingdom. We explored behavioral engagement among 19,233 existing users of Drink Less. Users were included in the s le if they were from the United Kingdom were 18 years or older were interested in reducing their alcohol consumption had a baseline Alcohol Use Disorders Identification Test score of 8 or above, indicative of excessive drinking and had downloaded the app between May 17, 2017, and January 22, 2019 (615 days). Measures of when sessions begin, length of sessions, time to disengagement, and patterns of use were visualized with heat maps, timeline plots, k-modes clustering analyses, and Kaplan-Meier plots. The daily 11 AM notification is strongly associated with a change in engagement in the following hour reduction in behavioral engagement over time, with 50.00% (9617/19,233) of users disengaging (defined as no use for 7 or more consecutive days) 22 days after download identification of 3 distinct trajectories of use, namely engagers (4651/19,233, 24.18% of users), slow disengagers (3679/19,233, 19.13% of users), and fast disengagers (10,903/19,233, 56.68% of users) and limited depth of engagement with 85.076% (7,095,348/8,340,005) of screen views occurring within the Self-monitoring and Feedback module. In addition, a peak of both frequency and amount of time spent per session was observed in the evenings. Visualizations play an important role in understanding engagement with behavior change apps. Here, we discuss how simple visualizations helped identify important patterns of engagement with Drink Less. Our visualizations of behavioral engagement suggest that the daily notification substantially impacts engagement. Furthermore, the visualizations suggest that a fixed notification policy can be effective for maintaining engagement for some users but ineffective for others. We conclude that optimizing the notification policy to target both effectiveness and engagement is a worthwhile investment. Our future goal is to both understand the causal effect of the notification on engagement and further optimize the notification policy within Drink Less by tailoring to contextual circumstances of in iduals over time. Such tailoring will be informed from the findings of our micro-randomized trial (MRT), and these visualizations were useful in both gaining a better understanding of engagement and designing the MRT.
Publisher: Center for Open Science
Date: 10-03-2022
Abstract: Ecological Momentary Assessment (EMA) involves repeated, real-time s ling of health behaviours in context. We present the state-of-knowledge in EMA research focused on five key health behaviours (physical activity and sedentary behaviour, dietary behaviour, alcohol consumption, tobacco smoking, sexual health), summarising theoretical (e.g., psychological and contextual predictors) and methodological aspects (e.g., study characteristics, EMA adherence). We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science until February 2021. We included studies focused on any of the aforementioned health behaviours in adult, non-clinical populations that assessed ≥1 psychological/contextual predictor and reported a predictor-behaviour association. A narrative synthesis and random-effects meta-analyses of EMA adherence were conducted. We included 633 studies. The median study duration was 14 days. The most frequently assessed predictors were ‘negative feeling states’ (21%) and ‘motivation and goals’ (16.5%). The pooled percentage of EMA adherence was high at 81.4% (95% CI = 80.0%, 82.8%, k=348) and did not differ by target behaviour but was somewhat higher in student (vs. general) s les, when EMAs were delivered via mobile phones (vs. handheld devices), and when event contingent (vs. fixed) s ling was used. This review showcases how the EMA method has been applied to improve understanding and prediction of health behaviours in context.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Olga Perski.