Using Cluster Analysis to Explore Engagement and e-Attainment as Emergent Behavior in Electronic Mental Health

Sanatkar, Samineh; Baldwin, Peter; Huckvale, Kit; Clarke, Janine; Christensen, Helen; Harvey, Samuel B; Proudfoot, Judy

doi:10.2196/14728

Cited by 31 publications

(36 citation statements)

References 19 publications

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“…This work will help to elucidate whether reduced use reflects disengagement or e-attainment, and what constitutes a sufficient 'dose' of the mobile app for people to internalise the skill of slowing down, as well as potential detrimental patterns of use, such as excessive engagement. 111,115,139 Technical problems were assessed, consistent with reporting recommendations for digital therapeutic trials, and were infrequent. 135 They were mainly attributable to connectivity issues, emphasising the infrastructure challenges to scaling up digital therapeutics in the NHS.…”

Section: Discussionmentioning

confidence: 99%

“…[112][113][114] Poor user experience has been highlighted as a critical barrier to engagement with digital therapeutics, particularly for marginalised groups. 115,116 Digital designs are often 'skeuomorphic', replicating analogue versions of therapy artefacts and, therefore, failing to address barriers to use. 117 For example, a commonly used tool for identifying and modifying distressing cognitions in CBT, a thought record, is often digitally reproduced with the same interface as paper versions: usually text prompts and response boxes presented as a form.…”

Section: Digital Therapeutics For Psychosismentioning

confidence: 99%

“…technology-assisted achievement of goal(s)] of personal goals if skills acquisition has been sufficiently supported. 115,134 Therefore, adopting multiple metrices of engagement, reflecting the goals of both the technology and the individual, is recommended.…”

Section: Evaluating User Experience: Validation Of the Slowmo Therapy Designmentioning

confidence: 99%

“…The adherence criteria were based on the assumption that engagement with the mobile app would be indicative of its usefulness, usability and appeal; however, sustained use throughout therapy was not necessary given that the aim was to support internalisation of the skill of slowing down in response to fast thinking. 15,115 Home screen views were selected as the target interaction given that slowing down with the mobile app is undertaken through viewing the home screen (to access safer thoughts) or subsequent screens that provide multiple routes to slowing down.…”

Section: Self-reported and System Analytics Of Adherence To The Slowmo Mobile Appmentioning

confidence: 99%

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Digitally supported CBT to reduce paranoia and improve reasoning for people with schizophrenia-spectrum psychosis: the SlowMo RCT

et al. 2021

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Background Reasoning may play a causal role in paranoid delusions in psychosis. SlowMo, a new digitally supported cognitive–behavioural therapy, targets reasoning to reduce paranoia. Objectives To examine the effectiveness of SlowMo therapy in reducing paranoia and in improving reasoning, quality of life and well-being, and to examine its mechanisms of action, moderators of effects and acceptability. Design A parallel-arm, assessor-blind, randomised controlled trial comparing SlowMo plus treatment as usual with treatment as usual alone. An online independent system randomised eligible participants (1 : 1) using randomly varying permuted blocks, stratified by site and paranoia severity. Setting Community mental health services in three NHS mental health trusts in England, plus patient identification centres. Participants A total of 362 participants with schizophrenia-spectrum psychosis. Eligibility criteria comprised distressing and persistent (≥ 3 months) paranoia. Interventions Eight face-to-face SlowMo sessions over 12 weeks plus treatment as usual, or treatment as usual alone (control group). Main outcome measures The primary outcome measure was paranoia measured by the Green Paranoid Thoughts Scale and its revised version, together with observer-rated measures of persecutory delusions (The Psychotic Symptom Rating Scales delusion scale and delusion items from the Scale for the Assessment of Positive Symptoms). The secondary outcome measures were reasoning (measures of belief flexibility, jumping to conclusions, and fast and slow thinking), well-being, quality of life, schemas, service use and worry. Results A total of 362 participants were recruited between 1 May 2017 and 14 May 2019: 181 in the SlowMo intervention group and 181 in the treatment-as-usual (control) group. One control participant subsequently withdrew. In total, 325 (90%) participants provided primary Green Paranoid Thoughts Scale outcome data at 12 weeks (SlowMo, n = 162; treatment as usual, n = 163). A total of 145 (80%) participants in the SlowMo group completed all eight therapy sessions. SlowMo was superior to treatment as usual in reducing paranoia on all three measures used: Green Paranoid Thoughts Scale total at 12 weeks (Cohen’s d = 0.30, 95% confidence interval 0.09 to 0.51; p = 0.005) and 24 weeks (Cohen’s d = 0.20, 95% confidence interval –0.02 to 0.40; p = 0.063); Psychotic Symptom Rating Scales delusions at 12 weeks (Cohen’s d = 0.47, 95% confidence interval 0.17 to 0.78; p = 0.002) and 24 weeks (Cohen’s d = 0.50, 95% confidence interval 0.20 to 0.80; p = 0.001); and Scale for the Assessment of Positive Symptoms persecutory delusions at 12 weeks (Cohen’s d = 0.43, 95% confidence interval 0.03 to 0.84; p = 0.035) and 24 weeks (Cohen’s d = 0.54, 95% confidence interval 0.14 to 0.94; p = 0.009). Reasoning (belief flexibility, possibility of being mistaken and Fast and Slow Thinking Questionnaire measure) improved, but jumping to conclusions did not improve. Worry, quality of life, well-being and self-concept also improved, improving most strongly at 24 weeks. Baseline characteristics did not moderate treatment effects. Changes in belief flexibility and worry mediated changes in paranoia. Peer researcher-led qualitative interviews confirmed positive experiences of the therapy and technology. Nineteen participants in the SlowMo group and 21 participants in the treatment-as-usual group reported 54 adverse events (51 serious events, no deaths). Limitations The trial included treatment as usual as the comparator and, thus, the trial design did not control for the effects of time with a therapist. Conclusions To the best of our knowledge, this is the largest trial of a psychological therapy for paranoia in people with psychosis and the first trial using a brief targeted digitally supported therapy. High rates of therapy uptake demonstrated acceptability. It was effective for paranoia, comparable to longer therapy, and equally effective for people with different levels of negative symptoms and working memory. Mediators were improvements in belief flexibility and worry. Our results suggest that targeting reasoning helps paranoia. Future work Further examination of SlowMo mechanisms of action and implementation. Trial registration Current Controlled Trials ISRCTN32448671. Funding This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and National Institute for Health Research (NIHR) partnership. This will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 11. See the NIHR Journals Library website for further project information.

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Section: Discussionmentioning

confidence: 99%

Section: Digital Therapeutics For Psychosismentioning

confidence: 99%

Section: Evaluating User Experience: Validation Of the Slowmo Therapy Designmentioning

confidence: 99%

Section: Self-reported and System Analytics Of Adherence To The Slowmo Mobile Appmentioning

confidence: 99%

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Digitally supported CBT to reduce paranoia and improve reasoning for people with schizophrenia-spectrum psychosis: the SlowMo RCT

et al. 2021

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“…They then used a K-means clustering algorithm to find groups of patients as indicated by PE because the PE pattern reflects the behavioral characteristics of patients using the app. Sanatkar et al [ 27 ] also used a clustering technique to analyze the mHealth app usage data so as to distinguish PE with the app in an e-mental health community. They measured 5 usage features of PE: number of user logins, number of daily trackers used, number of learning activities started and completed, and number of reminders received.…”

Section: Introductionmentioning

confidence: 99%

Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster Analysis

Wu¹,

An²,

Yu³

et al. 2021

J Med Internet Res

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Background Hypertension is a long-term medical condition. Electronic and mobile health care services can help patients to self-manage this condition. However, not all management is effective, possibly due to different levels of patient engagement (PE) with health care services. Health care provider follow-up is an intervention to promote PE and blood pressure (BP) control. Objective This study aimed to discover and characterize patterns of PE with a hypertension self-management app, investigate the effects of health care provider follow-up on PE, and identify the follow-up effects on BP in each PE pattern. Methods PE was represented as the number of days that a patient recorded self-measured BP per week. The study period was the first 4 weeks for a patient to engage in the hypertension management service. K-means algorithm was used to group patients by PE. There was compliance follow-up, regular follow-up, and abnormal follow-up in management. The follow-up effect was calculated by the change in PE (CPE) and the change in systolic blood pressure (CSBP, SBP) before and after each follow-up. Chi-square tests and z scores were used to ascertain the distribution of gender, age, education level, SBP, and the number of follow-ups in each cluster. The follow-up effect was identified by analysis of variances. Once a significant effect was detected, Bonferroni multiple comparisons were further conducted to identify the difference between 2 clusters. Results Patients were grouped into 4 clusters according to PE: (1) PE started low and dropped even lower (PELL), (2) PE started high and remained high (PEHH), (3) PE started high and dropped to low (PEHL), and (4) PE started low and rose to high (PELH). Significantly more patients over 60 years old were found in the PEHH cluster (P≤.05). Abnormal follow-up was significantly less frequent (P≤.05) in the PELL cluster. Compliance follow-up and regular follow-up can improve PE. In the clusters of PEHH and PELH, the improvement in PE in the first 3 weeks and the decrease in SBP in all 4 weeks were significant after follow-up. The SBP of the clusters of PELL and PELH decreased more (–6.1 mmHg and –8.4 mmHg) after follow-up in the first week. Conclusions Four distinct PE patterns were identified for patients engaging in the hypertension self-management app. Patients aged over 60 years had higher PE in terms of recording self-measured BP using the app. Once SBP reduced, patients with low PE tended to stop using the app, and a continued decline in PE occurred simultaneously with the increase in SBP. The duration and depth of the effect of health care provider follow-up were more significant in patients with high or increased engagement after follow-up.

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A Framework of Environmental, Personal, and Behavioral Factors of Adult Learning in Online Communities of Practice

2023

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Adult learning is a complex phenomenon that takes place over an adult’s lifetime and is not limited to a particular age. It includes a set of activities to enhance life through improving skills, knowledge and capabilities. The foundational theories of adult learning, such as andragogy theory, place the individual adult centre stage and differentiate adult learning from formal learning. They also shift the focus from the individuals to the environment in which adult learning takes place. In line with this movement, online communities of practice (OCOPs) have evolved from being considered as online environments for learning to specialised forums that allow practitioners to collaborate around a project of mutual interest. The principles of adult learning are directly applicable to engagement in OCOPs because they include practical methods founded on the belief that adults are self-directed, autonomous learners and that learning is most effective when the environment plays the role of a facilitator, rather than being just a supportive and traditional setting for learning. However, how individual adults engage in OCOPs and benefit from them is not well understood. This paper draws on social cognitive theory to examine: how environmental, personal and behavioural factors shape engagement in OCOPs. To answer this question, twenty-one interviews were conducted with members of GitHub, a large online community of practice for IT professionals. The findings revealed that adults’ engagement in OCOPs involves project-based activities on mutual interests and willingness to help others. The findings also show that engaging in online communities does not only satisfy intrinsic, well-defined, expected outcomes and shape adults’ engagement, but also has an impact on adults’ lifelong learning achievements, such as professional experience and credit recognition. Based on these findings, a revised framework for adults’ engagement in OCOPs is presented and discussed.

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Using Cluster Analysis to Explore Engagement and e-Attainment as Emergent Behavior in Electronic Mental Health

Cited by 31 publications

References 19 publications

Digitally supported CBT to reduce paranoia and improve reasoning for people with schizophrenia-spectrum psychosis: the SlowMo RCT

Digitally supported CBT to reduce paranoia and improve reasoning for people with schizophrenia-spectrum psychosis: the SlowMo RCT

Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster Analysis

A Framework of Environmental, Personal, and Behavioral Factors of Adult Learning in Online Communities of Practice

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