Identifying App-Based Meditation Habits and the Associated Mental Health Benefits: Longitudinal Observational Study

Implementing a group-based online mental well-being program for women living with and beyond breast cancer - A mixed methods study

PURPOSE

There is a gap in available mental well-being services in Australia for women diagnosed with breast cancer. This pilot mixed-methods uncontrolled study aimed to assess the feasibility of an online mental health and well-being intervention, the Be Well Plan (BWP), which enables participants to create a personalized, flexible well-being strategy.

METHODS

Women diagnosed with stages I-IV breast cancer were recruited into 4 asynchronous groups to participate in the BWP, a 5-week facilitator-led group-based mental health and well-being program. Psychological measures used at baseline and post-intervention included: the Warwick Edinburgh Mental Well-Being Scale, Brief Resilience Scale, Self-compassion Scale, 9-item Patient Health Questionnaire, 7-item General Anxiety Disorder scale, and EORC QLQ-C30. Multivariate analysis of variance and effect sizes were calculated on pre- and post-psychological measures, followed by qualitative content analysis on post-completion interviews with participants.

RESULTS

Nineteen women (mean age 45.7, standard deviation = 7.74) were included in the study. Large effect sizes were reported for mental well-being, depressive symptoms, and anxiety (partial ω2 = 0.28, 0.21, and 0.20, respectively). Self-compassion, resilience, and quality of life results were not statistically significant. Qualitative content analysis provided insight into experiences with Program Delivery Experience, Application of the BWP, Mental Health Improvements, Supporter Involvement, Adopted Interventions, and Recruitment. Participants reported benefits in mindfulness, grounding techniques, and physical activities.

CONCLUSION

The BWP has the potential to be an effective intervention to support the mental health and well-being of breast cancer survivors.

IMPLICATIONS FOR CANCER SURVIVORS

This study highlights flexible interventions that accommodate the diverse needs of breast cancer survivors to improve mental well-being and alleviate psychological distress.

Ramifications of behavioural complexity for habit conceptualisation, promotion, and measurement

Behavioural theories, predictions, and interventions should be relevant to complex, real-world health behaviours and conditions. Habit theory and habit formation interventions show promise for predicting and promoting, respectively, longer-term behaviour change and maintenance than has been attained with theories and interventions focused only on deliberative behavioural factors. However, the concept of habit has largely been treated as uniform across different types of behaviours. In this conceptual review, we contend that the definitional aspects of habit differ at a conceptual level for simple versus more complex behaviours, with ramifications for prediction, promotion, and measurement of habits. Specifically, habits are defined as direct context-response associations learned through repeatedly rewarded responding - but what is meant by 'response' and 'reward' depends upon the complexity of the behaviour. We review literature that suggests (1) responses in complex habits have separable and substitutable components (vs a single and static, unitary component) and (2) rewards for complex habits are necessarily continued and intrinsic (vs temporary and extrinsic, respectively). We discuss some empirical and theoretical questions raised by these issues around behavioural complexity and habit. Lastly, we outline the ramifications of these issues for habit measurement (habit strength and habit formation) via self-report and objective measures.

Evaluating Objective Metrics of habit strength for taking medications

Habit strength for taking medication is associated with medication adherence. However, habit strength is typically measured via self-reports, which have limitations. Objective sensors may provide advantages to self-reports. To evaluate whether habit-strength metrics derived from objective sensor data (MEMS® Caps; AARDEX Group) are associated with self-reported habit strength and adherence (objective and self-reported) and whether objective and self-reported habit strength are independently associated with adherence. Patients (N = 79) on oral medications for type 2 diabetes completed self-reports of habit strength and medication adherence and used MEMS® Caps to take their prescribed medication for one month. MEMS® Caps data were used to create five objective metrics of habit strength (e.g., individual-level variance in pill timing) and quantify medication adherence (% days correct dosing). Consistency in behavior from week to week (versus across each day) had the greatest association with self-reported habit strength (r(78) = 0.29, p = 0.01), self-reported adherence (r(78) = 0.32, p = 0.005), and objective adherence (r(78) = 0.61, p < 0.001). Objective and self-reported habit strength were independently associated with adherence. Weekly pill-timing consistency may be more useful than daily pill-timing consistency for predicting adherence and understanding patients' medication-taking habits. Self-reports and objective metrics of habit strength may be measuring different constructs, warranting further research.

Mindfulness Meditation App Abandonment During the COVID-19 Pandemic: An Observational Study

Objectives

Mindfulness meditation apps are used by millions of adults in the USA to improve mental health. However, many new app subscribers quickly abandon their use. The purpose of this study was to determine the behavioral, demographic, and socioeconomic factors associated with the abandonment of meditation apps during the COVID-19 pandemic.

Method

A survey was distributed to subscribers of a popular meditation app, Calm, at the start of the COVID-19 pandemic in March 2020 that assessed meditation app behavior and meditation habit strength, as well as demographic and socioeconomic information. App usage data were also collected from the start of each participant's subscription until May 2021. A total of 3275 respondents were included in the analyses. Participants were divided into three cohorts according to their subscription start date: (1) long-term subscribers (> 1 year before pandemic start), (2) pre-pandemic subscribers (< 4 months before pandemic start), and (3) pandemic subscribers (joined during the pandemic).

Results

Meditating after an existing routine was associated with a lower risk of app abandonment for pre-pandemic subscribers (hazard ratio = 0.607, 95% CI: 0.422, 0.874; p = 0.007) and for pandemic subscribers (hazard ratio = 0.434, 95% CI: 0.285, 0.66; p < 0.001). Additionally, meditating "whenever I can" was associated with lower risk of abandonment among pandemic subscribers (hazard ratio = 0.437, 95% CI: 0.271, 0.706; p < 0.001), and no behavioral factors were significant predictors of app abandonment among the long-term subscribers.

Conclusions

These results show that combining meditation with an existing daily routine was a commonly utilized strategy for promoting persistent meditation app use during the COVID-19 pandemic for many subscribers. This finding supports existing evidence that pairing new behaviors with an existing routine is an effective method for establishing new health habits.

Preregistration

This study is not pre-registered.

Using mobile meditation app data to predict future app engagement: an observational study

OBJECTIVE

Meditation with mobile apps has been shown to improve mental and physical health. However, regular, long-term meditation app use is needed to maintain these health benefits, and many people have a difficult time maintaining engagement with meditation apps over time. Our goal was to determine the length of the timeframe over which usage data must be collected before future app abandonment can be predicted accurately in order to better target additional behavioral support to those who are most likely to stop using the app.

METHODS

Data were collected from a randomly drawn sample of 2600 new subscribers to a 1-year membership of the mobile app Calm, who started using the app between July and November of 2018. App usage data contained the duration and start time of all meditation sessions with the app over 365 days. We used these data to construct the following predictive model features: total daily sessions, total daily duration, and a measure of temporal similarity between consecutive days based on the dynamic time warping (DTW) distance measure. We then fit random forest models using increasingly longer periods of data after users subscribed to Calm to predict whether they performed any meditation sessions over 2-week intervals in the future. Model fit was assessed using the area under the receiver operator characteristic curve (AUC), and an exponential growth model was used to determine the minimal amount of data needed to reach an accurate prediction (95% of max AUC) of future engagement.

RESULTS

After first subscribing to Calm, 83.1% of the sample used the Calm app on at least 1 more day. However, by day 350 after subscribing, 58.0% of users abandoned their use of the app. For the persistent users, the average number of daily sessions was 0.33 (SD = 0.02), the average daily duration of meditating was 3.93 minutes (SD = 0.25), and the average DTW distance to the previous day was 1.50 (SD = 0.17). The exponential growth models revealed that an average of 64 days of observations after subscribing to Calm are needed to reach an accurate prediction of future app engagement.

DISCUSSION

Our results are consistent with existing estimates of the time required to develop a new habit. Additionally, this research demonstrates how to use app usage data to quickly and accurately predict the likelihood of users' future app abandonment. This research allows future researchers to better target just-in-time interventions towards users at risk of abandonment.