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Dick S, O'Connor Y, Thompson MJ, O'Donoghue J, Hardy V, Wu TSJ, O'Sullivan T, Chirambo GB, Heavin C. Considerations for Improved Mobile Health Evaluation: Retrospective Qualitative Investigation. JMIR Mhealth Uhealth 2020; 8:e12424. [PMID: 32012085 PMCID: PMC7003121 DOI: 10.2196/12424] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 07/12/2019] [Accepted: 08/31/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Mobile phone use and, consequently, mobile health (mHealth) interventions have seen an exponential increase in the last decade. There is an excess of 318,000 health-related apps available free of cost for consumers to download. However, many of these interventions are not evaluated and are lacking appropriate regulations. Randomized controlled trials are often considered the gold standard study design in determining the effectiveness of interventions, but recent literature has identified limitations in the methodology when used to evaluate mHealth. OBJECTIVE The objective of this study was to investigate the system developers' experiences of evaluating mHealth interventions in the context of a developing country. METHODS We employed a qualitative exploratory approach, conducting semistructured interviews with multidisciplinary members of an mHealth project consortium. A conventional content analysis approach was used to allow codes and themes to be identified directly from the data. RESULTS The findings from this study identified the system developers' perceptions of mHealth evaluation, providing an insight into the requirements of an effective mHealth evaluation. This study identified social and technical factors which should be taken into account when evaluating an mHealth intervention. CONCLUSIONS Contextual issues represented one of the most recurrent challenges of mHealth evaluation in the context of a developing country, highlighting the importance of a mixed method evaluation. There is a myriad of social, technical, and regulatory variables, which may impact the effectiveness of an mHealth intervention. Failure to account for these variables in an evaluation may limit the ability of the intervention to achieve long-term implementation and scale.
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Affiliation(s)
- Samantha Dick
- Health Information Systems Research Centre, Cork University Business School, Cork, Ireland
| | - Yvonne O'Connor
- Health Information Systems Research Centre, Cork University Business School, Cork, Ireland
| | - Matthew J Thompson
- Department of Family Medicine, University of Washington, Seattle, WA, United States
| | - John O'Donoghue
- Global eHealth Unit, Department of Primary Care and Public Health, Imperial College London, London, United Kingdom
| | - Victoria Hardy
- Department of Family Medicine, University of Washington, Seattle, WA, United States
| | | | - Timothy O'Sullivan
- Health Information Systems Research Centre, Cork University Business School, Cork, Ireland
| | | | - Ciara Heavin
- Health Information Systems Research Centre, Cork University Business School, Cork, Ireland
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Cottrell D, Wright-Hughes A, Eisler I, Fortune S, Green J, House A, Kerfoot M, Owens D, Simic M, McLellan V, Tubeuf S, Farrin A. Longer-term effectiveness of systemic family therapy compared with treatment as usual for young people after self-harm: An extended follow up of pragmatic randomised controlled trial. EClinicalMedicine 2020; 18:100246. [PMID: 31956857 PMCID: PMC6956753 DOI: 10.1016/j.eclinm.2019.100246] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 11/22/2019] [Accepted: 12/12/2019] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Self-harm in adolescents is common and repetition frequent. Evidence for effective interventions to reduce self-harm is limited. Long term follow-up of existing studies is rare. METHODS Extended follow up, from 18 to at least 36-months, of the SHIFT trial: a pragmatic, multi-centre, individually-randomised, controlled trial involving young people (11-17) who had self-harmed at least twice and presented to Child & Adolescent Mental Health Services (CAMHS). SHIFT evaluated manualised family therapy (FT) versus treatment as usual (TAU) in reducing repetition of self-harm leading to hospital attendance 18 months post-randomisation.We obtained ONS mortality data, adult mental health data, and further details of hospital attendance from routine Hospital Episode Statistics (HES) data plus researcher follow-up. We assessed longer-term differences in outcome using multivariable Cox Proportional Hazards regression analysis, and assessed all-cause mortality and morbidity relating to hospital attendances for reasons other than self-harm. STUDY REGISTRATION ISRCTN 59793150. OUTCOMES The original sample of 832 were randomised between April 2010 and December 2013. Extended follow-up continued until February 2017 for a median 55·4 months (range 0-82·5 months), providing post 18-month data for 804 (96·6%) participants, of whom 785 (94·4%) had a minimum of 36-months follow-up.There was no evidence of a between-group difference in the primary outcome during the extended follow-up period (Hazard Ratio (HR) 1·03; 95% CI: 0·83, 1·28; p-value=0·78), consistent with our findings in the original trial with 18 months follow-up (HR 1·14, 95% CI 0·87, 1·49; p-value 0·33). There was a reduced rate of self-harm in older participants aged 15-17 (HR 0·7, 95% CI 0·56, 0·88), as compared with those aged 11-14; and significantly increased rates of self-harm in participants whose index episode combined self-injury and poisoning (HR 1·8, 95% CI 1·2, 2·7). Two deaths were reported during the extended follow up period. INTERPRETATION For adolescents referred to CAMHS after self-harm, having self-harmed at least once before, trial FT confers no benefits over TAU in reducing subsequent hospitalisation for self-harm over 18 months or 36 months. FUNDING NIHR HTA Reference: 07/33/01.
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Affiliation(s)
- D.J. Cottrell
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
- Corresponding author.
| | | | - I. Eisler
- Institute of Psychiatry, Psychology and Neurological Science Kings College London, London, UK
| | - S. Fortune
- Department of Psychological Medicine, University of Otago, New Zealand
| | - J. Green
- Division of Neuroscience & Experimental Psychology, School of Biological Science, University of Manchester, Manchester, UK
- Royal Manchester Children's Hospital, Manchester, UK
| | - A.O. House
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - M. Kerfoot
- University of Manchester, Manchester, UK
| | - D.W. Owens
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - M. Simic
- South London and Maudsley NHS Foundation Trust, London, UK
| | - V. McLellan
- Clinical Trials Research Unit, University of Leeds, Leeds, UK
| | - S. Tubeuf
- Institute of Health and Society (IRSS), Université Catholique de Louvain, Louvain-La-Neuve, Belgium
| | - A.J. Farrin
- Clinical Trials Research Unit, University of Leeds, Leeds, UK
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203
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An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing. ACTA ACUST UNITED AC 2019; 2:268-281. [PMID: 34308270 DOI: 10.1123/jmpb.2018-0068] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Background Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. Purpose Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. Methods We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X. Results During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. Conclusions Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.
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204
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Kowatsch T, Otto L, Harperink S, Cotti A, Schlieter H. A design and evaluation framework for digital health interventions. IT - INFORMATION TECHNOLOGY 2019. [DOI: 10.1515/itit-2019-0019] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Abstract
Digital health interventions (DHIs) have the potential to help the growing number of chronic disease patients better manage their everyday lives. However, guidelines for the systematic development of DHIs are still scarce. The current work has, therefore, the objective to propose a framework for the design and evaluation of DHIs (DEDHI). The DEDHI framework is meant to support both researchers and practitioners alike from early conceptual DHI models to large-scale implementations of DHIs in the healthcare market.
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Affiliation(s)
- Tobias Kowatsch
- Center for Digital Health Interventions, Institute of Technology Management , 27215 University of St. Gallen , Dufourstrasse 40a , St. Gallen , Switzerland
- Department of Management, Technology, and Economics , ETH Zurich , Weinbergstrasse 56/58 , Zurich , Switzerland
| | - Lena Otto
- Chair of Wirtschaftsinformatik, esp. Systems Development , Technische Universität Dresden , Dresden , Germany
| | - Samira Harperink
- Center for Digital Health Interventions, Institute of Technology Management , 27215 University of Saint Gallen (ITEM-HSG) , Dufourstrasse 40a , St. Gallen , Switzerland
| | - Amanda Cotti
- Center for Digital Health Interventions, Institute of Technology Management , 27215 University of Saint Gallen (ITEM-HSG) , Dufourstrasse 40a , St. Gallen , Switzerland
| | - Hannes Schlieter
- Chair of Wirtschaftsinformatik, esp. Systems Development , Technische Universität Dresden , Dresden , Germany
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205
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Is there a right control condition in mHealth trials? A critical view on pain medicine. NPJ Digit Med 2019; 2:107. [PMID: 31701021 PMCID: PMC6831563 DOI: 10.1038/s41746-019-0184-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 10/11/2019] [Indexed: 01/31/2023] Open
Abstract
When mobile health (mHealth) applications (apps) are investigated, the question of the proper control condition arises. Normally, the randomized controlled trial (RCT) is seen as the gold standard when testing efficacy of clinical interventions. Yet, mHealth apps rarely comprise innovative treatments but rather provide established treatments digitally. The classical RCT utilizing a placebo or waiting group condition may not always be the suitable methodology, since non-treatment is not appropriate if a disease urges treatment and the development of chronic disease needs to be prevented. The present commentary discusses conceivable control conditions in mHealth trials and illustrates their limitations.
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206
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Engaging Adolescents With Sexual Health Messaging: A Qualitative Analysis. J Adolesc Health 2019; 65:660-666. [PMID: 31495641 PMCID: PMC6814537 DOI: 10.1016/j.jadohealth.2019.05.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 04/22/2019] [Accepted: 05/30/2019] [Indexed: 12/31/2022]
Abstract
PURPOSE Personalized and interactive text messaging interventions may increase participant engagement; yet, how to design messages that retain adolescent attention and positively affect sexual health behaviors remains unclear. The purpose of this study was to identify the characteristics of sexual health text messages perceived as engaging by sexually active adolescent females. METHODS We conducted semistructured, open-ended interviews with sexually active females aged 14-19 in one urban emergency department. Participants received automated sexual health information sent via an interactive, two-way texting format. The 343 messages viewed by participants were based on key stakeholder input, relevant theoretical models, and existing evidence-based guidelines. Interviews elicited feedback. Enrollment continued until saturation of themes. Interviews were recorded, transcribed, and coded based on thematic analysis using NVivo 10. RESULTS Participants (n = 31) were predominantly Hispanic (28; 90%), insured (29; 94%), and recently sexually active (24; 77%). Themes were as follows: (1) Tone: messages should be direct, factual, entertaining, and respect adolescent autonomy; messages should not be intrusive, presumptive, or preachy. (2) Emotion evoked: participants preferred messages that provoked thought, validated feelings, and empowered. Messages from a reliable source felt comforting, making participants feel cared for and special. (3) Interactivity: participants favored messages that offered choices, such as a mini-conversation. (4) Personalization: messages should look similar to adolescent digital preferences but be individually tailored with relatable characters. CONCLUSIONS This study informs the tone, structure, and style of sexual health text messages directed toward adolescent females in the emergency department. Future work should consider these characteristics when designing digital interventions to engage adolescent females.
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207
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Hiremath SV, Amiri AM, Thapa-Chhetry B, Snethen G, Schmidt-Read M, Ramos-Lamboy M, Coffman DL, Intille SS. Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study. PLoS One 2019; 14:e0223762. [PMID: 31613909 PMCID: PMC6793862 DOI: 10.1371/journal.pone.0223762] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 09/26/2019] [Indexed: 01/05/2023] Open
Abstract
Low levels of physical activity (PA) and high levels of sedentary behavior in individuals with spinal cord injury (SCI) have been associated with secondary conditions such as pain, fatigue, weight gain, and deconditioning. One strategy for promoting regular PA is to provide people with an accurate estimate of everyday PA level. The objective of this research was to use a mobile health-based PA measurement system to track PA levels of individuals with SCI in the community and provide them with a behavior-sensitive, just-in-time-adaptive intervention (JITAI) to improve their PA levels. The first, second, and third phases of the study, each with a duration of one month, involved collecting baseline PA levels, providing near-real-time feedback on PA level (PA Feedback), and providing PA Feedback with JITAI, respectively. PA levels in terms of energy expenditure in kilocalories, and minutes of light- and moderate- or vigorous-intensity PA were assessed by an activity monitor during the study. Twenty participants with SCI took part in this research study with a mean (SD) age of 39.4 (12.8) years and 12.4 (12.5) years since injury. Sixteen participants completed the study. Sixteen were male, 16 had paraplegia, and 12 had complete injury. Within-participant comparisons indicated that only two participants had higher energy expenditure (>10%) or lower energy expenditure (<-10%) during PA Feedback with JITAI compared to the baseline. However, eleven participants (69.0%) had higher light- and/or moderate-intensity PA during PA Feedback with JITAI compared to the baseline. To our knowledge, this is the first study to test a PA JITAI for individuals with SCI that responds automatically to monitored PA levels. The results of this pilot study suggest that a sensor-enabled mobile JITAI has potential to improve PA levels of individuals with SCI. Future research should investigate the efficacy of JITAI through a clinical trial.
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Affiliation(s)
- Shivayogi V. Hiremath
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, Pennsylvania, United States of America
- Department of Electrical and Computer Engineering, Temple University, Philadelphia, Pennsylvania, United States of America
- * E-mail:
| | - Amir Mohammad Amiri
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, Pennsylvania, United States of America
- Department of Biomedical Engineering, Widener University, Chester, Pennsylvania, United States of America
| | - Binod Thapa-Chhetry
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America
- Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts, United States of America
| | - Gretchen Snethen
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, Pennsylvania, United States of America
| | - Mary Schmidt-Read
- Magee Rehabilitation Hospital, Jefferson Health, Philadelphia, Pennsylvania, United States of America
| | - Marlyn Ramos-Lamboy
- MossRehab, Einstein Healthcare Network, Philadelphia, Pennsylvania, United States of America
| | - Donna L. Coffman
- Department of Epidemiology and Biostatistics, Temple University, Philadelphia, Pennsylvania, United States of America
| | - Stephen S. Intille
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America
- Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts, United States of America
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208
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Forman EM, Goldstein SP, Crochiere RJ, Butryn ML, Juarascio AS, Zhang F, Foster GD. Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss. Transl Behav Med 2019; 9:989-1001. [DOI: 10.1093/tbm/ibz137] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
This randomized trial demonstrated qualified support for the ability of a machine learning-powered, smartphone-based just-in-time, adaptive intervention to enhance weight loss over and above a commercial weight loss program.
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Affiliation(s)
- Evan M Forman
- Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, USA
| | - Stephanie P Goldstein
- Weight Control & Diabetes Research Center, Warren Alpert Medical School of Brown University, Providence, USA
| | - Rebecca J Crochiere
- Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, USA
| | - Meghan L Butryn
- Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, USA
| | - Adrienne S Juarascio
- Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, USA
| | - Fengqing Zhang
- Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, USA
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209
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Boutelle KN, Kang Sim DE, Manzano M, Rhee KE, Crow SJ, Strong DR. Role of appetitive phenotype trajectory groups on child body weight during a family-based treatment for children with overweight or obesity. Int J Obes (Lond) 2019; 43:2302-2308. [PMID: 31591483 PMCID: PMC6858531 DOI: 10.1038/s41366-019-0463-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 07/10/2019] [Accepted: 07/16/2019] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Emerging evidence suggests that individual appetitive traits may usefully explain patterns of weight loss in behavioral weight loss treatments for children. The objective of this study was to identify trajectories of child appetitive traits and the impact on child weight changes over time. METHODS Secondary data analyses of a randomized noninferiority trial conducted between 2011 and 2015 evaluated children's appetitive traits and weight loss. Children with overweight and obesity (mean age = 10.4; mean BMI z = 2.0; 67% girls; 32% Hispanic) and their parent (mean age = 42.9; mean BMI = 31.9; 87% women; 31% Hispanic) participated in weight loss programs and completed assessments at baseline, 3, 6,12, and 24 months. Repeated assessments of child appetitive traits, including satiety responsiveness, food responsiveness and emotional eating, were used to identify parsimonious grouping of change trajectories. Linear mixed-effects models were used to identify the impact of group trajectory on child BMIz change over time. RESULTS One hundred fifty children and their parent enrolled in the study. The three-group trajectory model was the most parsimonious and included a high satiety responsive group (HighSR; 47.4%), a high food responsive group (HighFR; 34.6%), and a high emotional eating group (HighEE; 18.0%). Children in all trajectories lost weight at approximately the same rate during treatment, however, only the HighSR group maintained their weight loss during follow-ups, while the HighFR and HighEE groups regained weight (adjusted p-value < 0.05). CONCLUSIONS Distinct trajectories of child appetitive traits were associated with differential weight loss maintenance. Identified high-risk subgroups may suggest opportunities for targeted intervention and maintenance programs.
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Affiliation(s)
- Kerri N Boutelle
- Department of Pediatrics, UC San Diego, La Jolla, CA, USA. .,Department of Family Medicine and Public Health, UC San Diego, La Jolla, CA, USA. .,Department of Psychiatry, UC San Diego, La Jolla, CA, USA.
| | | | - Michael Manzano
- Department of Pediatrics, UC San Diego, La Jolla, CA, USA.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, La Jolla, CA, USA
| | - Kyung E Rhee
- Department of Pediatrics, UC San Diego, La Jolla, CA, USA
| | - Scott J Crow
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - David R Strong
- Department of Family Medicine and Public Health, UC San Diego, La Jolla, CA, USA
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210
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Smith KE, Mason TB, Juarascio A, Schaefer LM, Crosby RD, Engel SG, Wonderlich SA. Moving beyond self-report data collection in the natural environment: A review of the past and future directions for ambulatory assessment in eating disorders. Int J Eat Disord 2019; 52:1157-1175. [PMID: 31313348 PMCID: PMC6942694 DOI: 10.1002/eat.23124] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 05/31/2019] [Accepted: 06/04/2019] [Indexed: 12/14/2022]
Abstract
OBJECTIVE In recent years, ecological momentary assessment (EMA) has been used to repeatedly assess eating disorder (ED) symptoms in naturalistic settings, which has allowed for increased understanding of temporal processes that potentiate ED behaviors. However, there remain notable limitations of self-report EMA, and with the rapid proliferation of technology there are ever-increasing possibilities to improve ambulatory assessment methods to further the understanding and treatment of EDs. Therefore, the purpose of this review was to (a) systematically review the studies in EDs that have utilized ambulatory assessment methods other than self-report, and (b) provide directions for future research and clinical applications. METHOD A systematic literature search of electronic databases was conducted, and data regarding study characteristics and methodological quality were extracted. RESULTS The search identified 17 studies that used ambulatory assessment methods to gather objective data, and focused primarily on autonomic functioning, physical activity, and cognitive processes in ED and control groups. DISCUSSION Together the literature demonstrates the promise of using a range of ecologically valid ambulatory assessment approaches in EDs, though there remains limited research that has utilized methods other than self-report (e.g., wearable sensors), particularly in recent years. Going forward, there are several technology-enhanced momentary assessment methods that have potential to improve the understanding and treatment of EDs.
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Affiliation(s)
- Kathryn E Smith
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota
- Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota
| | - Tyler B Mason
- Department of Preventive Medicine, University of Southern California, Los Angeles, California
| | | | - Lauren M Schaefer
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota
| | - Ross D Crosby
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota
- Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota
| | - Scott G Engel
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota
- Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota
| | - Stephen A Wonderlich
- Center for Bio-behavioral Research, Sanford Research, Fargo, North Dakota
- Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota
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Linardon J, Cuijpers P, Carlbring P, Messer M, Fuller‐Tyszkiewicz M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry 2019; 18:325-336. [PMID: 31496095 PMCID: PMC6732686 DOI: 10.1002/wps.20673] [Citation(s) in RCA: 330] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Although impressive progress has been made toward developing empirically-supported psychological treatments, the reality remains that a significant proportion of people with mental health problems do not receive these treatments. Finding ways to reduce this treatment gap is crucial. Since app-supported smartphone interventions are touted as a possible solution, access to up-to-date guidance around the evidence base and clinical utility of these interventions is needed. We conducted a meta-analysis of 66 randomized controlled trials of app-supported smartphone interventions for mental health problems. Smartphone interventions significantly outperformed control conditions in improving depressive (g=0.28, n=54) and generalized anxiety (g=0.30, n=39) symptoms, stress levels (g=0.35, n=27), quality of life (g=0.35, n=43), general psychiatric distress (g=0.40, n=12), social anxiety symptoms (g=0.58, n=6), and positive affect (g=0.44, n=6), with most effects being robust even after adjusting for various possible biasing factors (type of control condition, risk of bias rating). Smartphone interventions conferred no significant benefit over control conditions on panic symptoms (g=-0.05, n=3), post-traumatic stress symptoms (g=0.18, n=4), and negative affect (g=-0.08, n=5). Studies that delivered a cognitive behavior therapy (CBT)-based app and offered professional guidance and reminders to engage produced larger effects on multiple outcomes. Smartphone interventions did not differ significantly from active interventions (face-to-face, computerized treatment), although the number of studies was low (n≤13). The efficacy of app-supported smartphone interventions for common mental health problems was thus confirmed. Although mental health apps are not intended to replace professional clinical services, the present findings highlight the potential of apps to serve as a cost-effective, easily accessible, and low intensity intervention for those who cannot receive standard psychological treatment.
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Affiliation(s)
- Jake Linardon
- School of PsychologyDeakin UniversityGeelongVictoriaAustralia
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Per Carlbring
- Department of PsychologyStockholm UniversityStockholmSweden
| | - Mariel Messer
- School of PsychologyDeakin UniversityGeelongVictoriaAustralia
| | - Matthew Fuller‐Tyszkiewicz
- School of PsychologyDeakin UniversityGeelongVictoriaAustralia,Center for Social and Early Emotional DevelopmentDeakin UniversityBurwoodVictoriaAustralia
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Comer JS, Conroy K, Timmons AC. Ensuring wearable devices don’t wear out their welcome: Cautions for the mental health care road ahead. CLINICAL PSYCHOLOGY-SCIENCE AND PRACTICE 2019. [DOI: 10.1111/cpsp.12297] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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213
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Yin AL, Hachuel D, Pollak JP, Scherl EJ, Estrin D. Digital Health Apps in the Clinical Care of Inflammatory Bowel Disease: Scoping Review. J Med Internet Res 2019; 21:e14630. [PMID: 31429410 PMCID: PMC6718080 DOI: 10.2196/14630] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 07/02/2019] [Accepted: 07/04/2019] [Indexed: 12/12/2022] Open
Abstract
Background Digital health is poised to transform health care and redefine personalized health. As Internet and mobile phone usage increases, as technology develops new ways to collect data, and as clinical guidelines change, all areas of medicine face new challenges and opportunities. Inflammatory bowel disease (IBD) is one of many chronic diseases that may benefit from these advances in digital health. This review intends to lay a foundation for clinicians and technologists to understand future directions and opportunities together. Objective This review covers mobile health apps that have been used in IBD, how they have fit into a clinical care framework, and the challenges that clinicians and technologists face in approaching future opportunities. Methods We searched PubMed, Scopus, and ClinicalTrials.gov to identify mobile apps that have been studied and were published in the literature from January 1, 2010, to April 19, 2019. The search terms were (“mobile health” OR “eHealth” OR “digital health” OR “smart phone” OR “mobile app” OR “mobile applications” OR “mHealth” OR “smartphones”) AND (“IBD” OR “Inflammatory bowel disease” OR “Crohn's Disease” (CD) OR “Ulcerative Colitis” (UC) OR “UC” OR “CD”), followed by further analysis of citations from the results. We searched the Apple iTunes app store to identify a limited selection of commercial apps to include for discussion. Results A total of 68 articles met the inclusion criteria. A total of 11 digital health apps were identified in the literature and 4 commercial apps were selected to be described in this review. While most apps have some educational component, the majority of apps focus on eliciting patient-reported outcomes related to disease activity, and a few are for treatment management. Significant benefits have been seen in trials relating to education, quality of life, quality of care, treatment adherence, and medication management. No studies have reported a negative impact on any of the above. There are mixed results in terms of effects on office visits and follow-up. Conclusions While studies have shown that digital health can fit into, complement, and improve the standard clinical care of patients with IBD, there is a need for further validation and improvement, from both a clinical and patient perspective. Exploring new research methods, like microrandomized trials, may allow for more implementation of technology and rapid advancement of knowledge. New technologies that can objectively and seamlessly capture remote data, as well as complement the clinical shift from symptom-based to inflammation-based care, will help the clinical and health technology communities to understand the full potential of digital health in the care of IBD and other chronic illnesses.
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Affiliation(s)
- Andrew Lukas Yin
- Medical College, Weill Cornell Medicine, New York, NY, United States.,Cornell Tech, New York, NY, United States
| | - David Hachuel
- Cornell Tech, New York, NY, United States.,augGI Technologies, New York, NY, United States
| | | | - Ellen J Scherl
- Jill Roberts Center for Inflammatory Bowel Disease, Weill Cornell Medicine, New York, NY, United States
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214
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Kim J, Marcusson-Clavertz D, Yoshiuchi K, Smyth JM. Potential benefits of integrating ecological momentary assessment data into mHealth care systems. Biopsychosoc Med 2019; 13:19. [PMID: 31413726 PMCID: PMC6688314 DOI: 10.1186/s13030-019-0160-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 07/28/2019] [Indexed: 01/03/2023] Open
Abstract
The advancement of wearable/ambulatory technologies has brought a huge change to data collection frameworks in recent decades. Mobile health (mHealth) care platforms, which utilize ambulatory devices to collect naturalistic and often intensively sampled data, produce innovative information of potential clinical relevance. For example, such data can inform clinical study design, recruitment approach, data analysis, and delivery of both "traditional" and novel (e.g., mHealth) interventions. We provide a conceptual overview of how data measured continuously or repeatedly via mobile devices (e.g., smartphone and body sensors) in daily life could be fruitfully used within a mHealth care system. We highlight the potential benefits of integrating ecological momentary assessment (EMA) into mHealth platforms for collecting, processing, and modeling data, and delivering and evaluating novel interventions in everyday life. Although the data obtained from EMA and related approaches may hold great potential benefits for mHealth care system, there are also implementation challenges; we briefly discuss the challenges to integrating EMA into mHealth care system.
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Affiliation(s)
- Jinhyuk Kim
- Department of Informatics, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, Shizuoka, 432-8011 Japan
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA USA
| | - David Marcusson-Clavertz
- Department of Psychology, Lund University, Lund, Sweden
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Kazuhiro Yoshiuchi
- Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Joshua M. Smyth
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA USA
- Department of Medicine, Hershey Medical Center and The Pennsylvania State University, Hershey, PA USA
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215
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Blake MJ, Latham MD, Blake LM, Allen NB. Adolescent-Sleep-Intervention Research: Current State and Future Directions. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2019. [DOI: 10.1177/0963721419850169] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Emerging evidence suggests that cognitive-behavioral interventions should be considered as a first-line treatment for adolescent sleep problems and can also address associated mental health concerns. This article’s aim is to prioritize future directions for this research. Studies are needed that (a) leverage innovative technology, which may allow for a more personalized-medicine approach to care; (b) use alternative research designs, such as treatment-optimization strategies; (c) evaluate mechanisms of therapeutic improvements, including mediators and moderators of change, predictors of treatment adherence, dose–response relationships, and specificity of effects of different treatment components; (d) “scale up” the interventions so that a large proportion of adolescents can access them; and (e) develop flexible approaches to delivery (e.g., online platforms) and stepped-care guidelines. National and international standards of secondary school start times of 8:30 a.m. or later are also needed. The field is now mature enough to take these next steps, which will enhance the effectiveness and public health impact of these interventions.
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Affiliation(s)
- Matthew J. Blake
- Melbourne School of Psychological Sciences, The University of Melbourne
| | | | - Laura M. Blake
- Melbourne School of Psychological Sciences, The University of Melbourne
| | - Nicholas B. Allen
- Melbourne School of Psychological Sciences, The University of Melbourne
- Department of Psychology, University of Oregon
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216
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Seewald NJ, Smith SN, Lee AJ, Klasnja P, Murphy SA. Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials. STATISTICS IN BIOSCIENCES 2019; 11:355-370. [PMID: 31462937 PMCID: PMC6713230 DOI: 10.1007/s12561-018-09228-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 10/10/2018] [Accepted: 12/14/2018] [Indexed: 11/24/2022]
Abstract
There is a growing interest in leveraging the prevalence of mobile technology to improve health by delivering momentary, contextualized interventions to individuals' smartphones. A just-in-time adaptive intervention (JITAI) adjusts to an individual's changing state and/or context to provide the right treatment, at the right time, in the right place. Micro-randomized trials (MRTs) allow for the collection of data which aid in the construction of an optimized JITAI by sequentially randomizing participants to different treatment options at each of many decision points throughout the study. Often, this data is collected passively using a mobile phone. To assess the causal effect of treatment on a near-term outcome, care must be taken when designing the data collection system to ensure it is of appropriately high quality. Here, we make several recommendations for collecting and managing data from an MRT. We provide advice on selecting which features to collect and when, choosing between "agents" to implement randomization, identifying sources of missing data, and overcoming other novel challenges. The recommendations are informed by our experience with HeartSteps, an MRT designed to test the effects of an intervention aimed at increasing physical activity in sedentary adults. We also provide a checklist which can be used in designing a data collection system so that scientists can focus more on their questions of interest, and less on cleaning data.
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Affiliation(s)
- Nicholas J Seewald
- University of Michigan, Department of Statistics, 311 West Hall, 1085 South University Ave, Ann Arbor, MI, 48109,
| | - Shawna N Smith
- University of Michigan, Departments of Psychiatry and General Medicine
| | | | | | - Susan A Murphy
- Harvard University, Departments of Statistics and Computer Science
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217
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Murray LK, Haroz E, Dorsey S, Kane J, Bolton PA, Pullmann MD. Understanding mechanisms of change: An unpacking study of the evidence-based common-elements treatment approach (CETA) in low and middle income countries. Behav Res Ther 2019; 130:103430. [PMID: 31780251 DOI: 10.1016/j.brat.2019.103430] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 06/15/2019] [Accepted: 06/17/2019] [Indexed: 12/16/2022]
Abstract
There is a recognized need to better understand "essential ingredients" of psychological treatments, and refine interventions to be more scalable and sustainable. The goal of the present study was to look within a specific modular, flexible, multi-problem transdiagnostic psychological intervention -the Common Elements Treatment Approach (CETA) - and examine questions that would lead to optimizing CETA for scale up and sustainment. Utilizing data from two trials of CETA in two different countries (Thailand and Iraq), this manuscript aims to: 1) determine the "active treatment dose" or how many sessions are needed to achieve clinically meaningful change overall, in CETA); and 2) test how trajectories of client symptom change varied based on client characteristics and/or on delivery of certain elements. Results showed that overall 50% of CETA clients show some improvement after 4-6 sessions (1 SD) and large improvement (2 SD) after 7-10 sessions. Trajectories of change show steady symptom decline over time. Results support gradual exposure as one of the "active ingredients". Findings suggest that modular, flexible transdiagnostic models may allow for more efficient, targeted treatment as we gain more knowledge about key ingredients, their timing within treatment, and client outcomes.
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Affiliation(s)
- Laura K Murray
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th Floor, Baltimore, MD 21205, USA.
| | - Emily Haroz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th Floor, Baltimore, MD 21205, USA.
| | - Shannon Dorsey
- Department of Psychology, Guthrie Hall, University of Washington, Seattle, WA 98195, USA.
| | - Jeremy Kane
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th Floor, Baltimore, MD 21205, USA.
| | - Paul A Bolton
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th Floor, Baltimore, MD 21205, USA; Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, 8th Floor, Baltimore, MD 21205, USA.
| | - Michael D Pullmann
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, 6200 74th Street, Building 29, 98115, USA.
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218
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A randomized trial exploring mindfulness and gratitude exercises as eHealth-based micro-interventions for improving body satisfaction. COMPUTERS IN HUMAN BEHAVIOR 2019. [DOI: 10.1016/j.chb.2019.01.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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219
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Mofsen AM, Rodebaugh TL, Nicol GE, Depp CA, Miller JP, Lenze EJ. When All Else Fails, Listen to the Patient: A Viewpoint on the Use of Ecological Momentary Assessment in Clinical Trials. JMIR Ment Health 2019; 6:e11845. [PMID: 31066701 PMCID: PMC6524455 DOI: 10.2196/11845] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 02/05/2019] [Accepted: 04/03/2019] [Indexed: 01/09/2023] Open
Abstract
A major problem in mental health clinical trials, such as depression, is low assay sensitivity in primary outcome measures. This has contributed to clinical trial failures, resulting in the exodus of the pharmaceutical industry from the Central Nervous System space. This reduced assay sensitivity in psychiatry outcome measures stems from inappropriately broad measures, recall bias, and poor interrater reliability. Limitations in the ability of traditional measures to differentiate between the trait versus state-like nature of individual depressive symptoms also contributes to measurement error in clinical trials. In this viewpoint, we argue that ecological momentary assessment (EMA)-frequent, real time, in-the-moment assessments of outcomes, delivered via smartphone-can both overcome these psychometric challenges and reduce clinical trial failures by increasing assay sensitivity and minimizing recall and rater bias. Used in this manner, EMA has the potential to further our understanding of treatment response by allowing for the assessment of dynamic interactions between treatment and distinct symptom response.
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Affiliation(s)
- Aaron M Mofsen
- Department of Psychiatry, School of Medicine, Washington University in St Louis, St Louis, MO, United States
| | - Thomas L Rodebaugh
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, United States
| | - Ginger E Nicol
- Department of Psychiatry, School of Medicine, Washington University in St Louis, St Louis, MO, United States
| | - Colin A Depp
- Department of Psychiatry, University of California - San Diego, San Diego, CA, United States
| | - J Philip Miller
- Division of Biostatistics, School of Medicine, Washington University in St Louis, St Louis, MO, United States
| | - Eric J Lenze
- Department of Psychiatry, School of Medicine, Washington University in St Louis, St Louis, MO, United States
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220
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Psihogios AM, Li Y, Butler E, Hamilton J, Daniel LC, Barakat LP, Bonafide CP, Schwartz LA. Text Message Responsivity in a 2-Way Short Message Service Pilot Intervention With Adolescent and Young Adult Survivors of Cancer. JMIR Mhealth Uhealth 2019; 7:e12547. [PMID: 30998225 PMCID: PMC6495290 DOI: 10.2196/12547] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 01/15/2019] [Accepted: 02/05/2019] [Indexed: 12/29/2022] Open
Abstract
Background: Text message interventions hold promise for adolescents and young adults (AYAs) with chronic health conditions, including childhood cancer survivors; however, engagement is often suboptimal. Limited research has studied mobile health intervention outcomes beyond efficacy. Understanding responsivity to different types of text messages (ie, when a participant texts back) can provide practical, actionable information to optimize engagement in future projects.
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Affiliation(s)
| | - Yimei Li
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Eliana Butler
- The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | | | - Lauren C Daniel
- Rutgers University Camden, The Children's Hospital of Philadelphia, Camden, NJ, United States
| | - Lamia P Barakat
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher P Bonafide
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Lisa A Schwartz
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
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221
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Luckett DJ, Laber EB, Kahkoska AR, Maahs DM, Mayer-Davis E, Kosorok MR. Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning. J Am Stat Assoc 2019; 115:692-706. [PMID: 32952236 DOI: 10.1080/01621459.2018.1537919] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible health-care for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment. However, most existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale. We propose a new reinforcement learning method for estimating an optimal treatment regime that is applicable to data collected using mobile technologies in an out-patient setting. The proposed method accommodates an indefinite time horizon and minute-by-minute decision making that are common in mobile health applications. We show that the proposed estimators are consistent and asymptotically normal under mild conditions. The proposed methods are applied to estimate an optimal dynamic treatment regime for controlling blood glucose levels in patients with type 1 diabetes.
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Affiliation(s)
- Daniel J Luckett
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Eric B Laber
- Department of Statistics, North Carolina State University
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill
| | | | | | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill
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222
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Rothman AJ. It's About Time: Answering the Call for Greater Precision in Research and Practice. Appl Psychol Health Well Being 2019; 11:191-197. [PMID: 30972957 DOI: 10.1111/aphw.12158] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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223
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Ford JA, Jones AP, Wong G, Barton G, Clark A, Sims E, Swart AM, Steel N. Improving primary care Access in Context and Theory (I-ACT trial): a theory-informed randomised cluster feasibility trial using a realist perspective. Trials 2019; 20:193. [PMID: 30947737 PMCID: PMC6449944 DOI: 10.1186/s13063-019-3299-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 03/18/2019] [Indexed: 11/30/2022] Open
Abstract
Background Primary care access can be challenging for older, rural, socio-economically disadvantaged populations. Here we report the I-ACT cluster feasibility trial which aims to assess the feasibility of trial design and context-sensitive intervention to improve primary care access for this group and so expand existing theory. Methods Four general practices were recruited; three randomised to intervention and one to usual care. Intervention practices received £1500, a support manual and four meetings to develop local, innovative solutions to improve the booking system and transport. Patients aged over 64 years old and without household car access were recruited to complete questionnaires when booking an appointment or attending the surgery. Outcome measures at 6 months included: self-reported ease of booking an appointment and transport; health care use; patient activation; capability; and quality of life. A process evaluation involved observations and interviews with staff and participants. Results Thirty-four patients were recruited (26 female, eight male, mean age 81.6 years for the intervention group and 79.4 for usual care) of 1143 invited (3% response rate). Most were ineligible because of car access. Twenty-nine participants belonged to intervention practices and five to usual care. Practice-level data was available for all participants, but participant self-reported data was unavailable for three. Fifty-six appointment questionnaires were received based on 150 appointments (37.3%). Practices successfully designed and implemented the following context-sensitive interventions: Practice A: a stacked telephone system and promoting community transport; Practice B: signposting to community transport, appointment flexibility, mobility scooter charging point and promoting the role of receptionists; and Practice C: local taxi firm partnership and training receptionists. Practices found the process acceptable because it gave freedom, time and resource to be innovative or provided an opportunity to implement existing ideas. Data collection methods were acceptable to participants, but some found it difficult remembering to complete booking and appointment questionnaires. Expanded theory highlighted important mechanisms, such as reassurance, confidence, trust and flexibility. Conclusions Recruiting older participants without access to a car proved challenging. Retention of participants and practices was good but only about a third of appointment questionnaires were returned. This study design may facilitate a shift from one-size-fits-all interventions to more context-sensitive interventions. Trial registration ISRCTN18321951, Registered on 6 March 2017. Electronic supplementary material The online version of this article (10.1186/s13063-019-3299-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- John A Ford
- Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Norwich Medical School, University of East Anglia, Chancellors Drive, Norwich, NR4 7TJ, UK.
| | - Andy P Jones
- Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Norwich Medical School, University of East Anglia, Chancellors Drive, Norwich, NR4 7TJ, UK
| | - Geoff Wong
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Garry Barton
- Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Norwich Medical School, University of East Anglia, Chancellors Drive, Norwich, NR4 7TJ, UK.,Norwich Clinical Trials Unit, University of East Anglia, Norwich, UK
| | - Allan Clark
- Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Norwich Medical School, University of East Anglia, Chancellors Drive, Norwich, NR4 7TJ, UK.,Norwich Clinical Trials Unit, University of East Anglia, Norwich, UK
| | - Erika Sims
- Norwich Clinical Trials Unit, University of East Anglia, Norwich, UK
| | - Ann Marie Swart
- Norwich Clinical Trials Unit, University of East Anglia, Norwich, UK
| | - Nick Steel
- Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Norwich Medical School, University of East Anglia, Chancellors Drive, Norwich, NR4 7TJ, UK
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224
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Riley WT, Oh A, Aklin WM, Sherrill JT, Wolff-Hughes DL, Diana A, Griffin JA, Campo RA. Commentary: Pediatric Digital Health Supported by the National Institutes of Health. J Pediatr Psychol 2019; 44:263-268. [PMID: 30597095 PMCID: PMC6657444 DOI: 10.1093/jpepsy/jsy108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/20/2018] [Accepted: 12/03/2018] [Indexed: 12/18/2022] Open
Affiliation(s)
- William T Riley
- Office of Behavioral and Social Sciences Research, National Institutes of Health
| | | | | | | | - Dana L Wolff-Hughes
- Office of Behavioral and Social Sciences Research, National Institutes of Health
| | | | - James A Griffin
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
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225
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Affiliation(s)
- Jessica R Golbus
- Division of Cardiovascular Diseases, Department of Internal Medicine (J.R.G., B.K.N.), University of Michigan, Ann Arbor
| | - Predrag Klasna
- School of Information (P.K.), University of Michigan, Ann Arbor
- Kaiser Permanente Washington Health Research Institute, Seattle (P.K.)
| | - Brahmajee K Nallamothu
- Division of Cardiovascular Diseases, Department of Internal Medicine (J.R.G., B.K.N.), University of Michigan, Ann Arbor
- Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP) and Department of Internal Medicine (B.K.N.), University of Michigan, Ann Arbor
- The Center for Clinical Management and Research, Ann Arbor VA Medical Center, MI (B.K.N.)
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226
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Phillips SM, Courneya KS, Welch WA, Gavin KL, Cottrell A, Nielsen A, Solk P, Blanch-Hartigan D, Cella D, Ackermann RT, Spring B, Penedo F. Breast cancer survivors' preferences for mHealth physical activity interventions: findings from a mixed methods study. J Cancer Surviv 2019; 13:292-305. [PMID: 30912011 PMCID: PMC6499383 DOI: 10.1007/s11764-019-00751-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 03/13/2019] [Indexed: 10/27/2022]
Abstract
PURPOSE Despite the benefits of physical activity for breast cancer survivors, the majority remain insufficiently active. Mobile health (mHealth) physical activity interventions may be a more scalable strategy to increase activity among survivors. However, little is known about their preferences for mHealth intervention features. This study explored survivors' preferences for these features. METHODS Survivors (N = 96; Mage = 55.8 (SD = 10.2)) self-reported demographic and disease characteristics, physical activity. A subset (n = 28) completed a semi-structured phone interview. Transcribed interviews were evaluated using a thematic content analysis approach and consensus review. Following interviews, the full sample self-reported interests and preferences for intervention features via online questionnaires. Quantitative data were analyzed using descriptive statistics. RESULTS Five themes emerged from interview data: (1) importance of relevance to breast cancer survivors; (2) easy to use; (3) integration with wearable activity trackers; (4) provide sense of accomplishment; and (5) variability in desired level of structure and personalization. The highest ranked intervention features were: daily and weekly progress feedback (87.5%), newsfeed (86.6%), activity challenges (81.3%), and scheduling tool (79.2%). Survivors were interested in receiving progress feedback (80.2%) and motivational (78.1%) and reminder (75.0%) messages. CONCLUSIONS Breast cancer survivors are interested in mHealth physical activity promotion interventions, but preferences varied around themes of relevance, ease of use, and enhancing personal motivation. IMPLICATIONS FOR CANCER SURVIVORS Engaging survivors in developing and implementing remotely delivered mHealth activity promotion interventions may enhance their effectiveness.
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Affiliation(s)
- Siobhan M Phillips
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - Kerry S Courneya
- Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, Alberta, Canada
| | - Whitney A Welch
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Kara L Gavin
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alison Cottrell
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Anne Nielsen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Payton Solk
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - David Cella
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ronald T Ackermann
- Department of Medicine, Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Bonnie Spring
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Frank Penedo
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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227
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O'Cathain A, Croot L, Sworn K, Duncan E, Rousseau N, Turner K, Yardley L, Hoddinott P. Taxonomy of approaches to developing interventions to improve health: a systematic methods overview. Pilot Feasibility Stud 2019; 5:41. [PMID: 30923626 PMCID: PMC6419435 DOI: 10.1186/s40814-019-0425-6] [Citation(s) in RCA: 189] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 02/24/2019] [Indexed: 12/04/2022] Open
Abstract
Background Interventions need to be developed prior to the feasibility and piloting phase of a study. There are a variety of published approaches to developing interventions, programmes or innovations to improve health. Identifying different types of approach, and synthesising the range of actions taken within this endeavour, can inform future intervention development. Methods This study is a systematic methods overview of approaches to intervention development. Approaches were considered for inclusion if they described how to develop or adapt an intervention in a book, website or journal article published after 2007, or were cited in a primary research study reporting the development of a specific intervention published in 2015 or 2016. Approaches were read, a taxonomy of approaches was developed and the range of actions taken across different approaches were synthesised. Results Eight categories of approach to intervention development were identified. (1) Partnership, where people who will use the intervention participate equally with the research team in decision-making about the intervention throughout the development process. (2) Target population-centred, where the intervention is based on the views and actions of the people who will use it. (3) Evidence and theory-based, where the intervention is based on published research evidence and existing theories. (4) Implementation-based, where the intervention is developed with attention to ensuring it will be used in the real world. (5) Efficiency-based, where components of an intervention are tested using experimental designs to select components which will optimise efficiency. (6) Stepped or phased, where interventions are developed with an emphasis on following a systematic set of processes. (7) Intervention-specific, where an approach is constructed for a specific type of intervention. (8) Combination, where existing approaches to intervention development are formally combined. The actions from approaches in all eight categories were synthesised to identify 18 actions to consider when developing interventions. Conclusions This overview of approaches to intervention development can help researchers to understand the variety of existing approaches, and to understand the range of possible actions involved in intervention development, prior to assessing feasibility or piloting the intervention. Findings from this overview will contribute to future guidance on intervention development. Trial registration PROSPERO CRD42017080553. Electronic supplementary material The online version of this article (10.1186/s40814-019-0425-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alicia O'Cathain
- 1Medical Care Research Unit, Health Services Research, School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
| | - Liz Croot
- 1Medical Care Research Unit, Health Services Research, School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
| | - Katie Sworn
- 1Medical Care Research Unit, Health Services Research, School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA UK
| | - Edward Duncan
- 2NMAHP Research Unit, University of Stirling, Stirling, FK9 4NF UK
| | - Nikki Rousseau
- 2NMAHP Research Unit, University of Stirling, Stirling, FK9 4NF UK
| | - Katrina Turner
- Population Health Sciences, Canynge Hall, 39 Whatley Road, University of Bristol, Bristol, BS8 2PS UK
| | - Lucy Yardley
- Population Health Sciences, Canynge Hall, 39 Whatley Road, University of Bristol, Bristol, BS8 2PS UK
| | - Pat Hoddinott
- 2NMAHP Research Unit, University of Stirling, Stirling, FK9 4NF UK
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228
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Biroscak BJ, Pantalon MV, Dziura JD, Hersey DP, Vaca FE. Use of non-face-to-face modalities for emergency department screening, brief intervention, and referral to treatment (ED-SBIRT) for high-risk alcohol use: A scoping review. Subst Abus 2019; 40:20-32. [PMID: 30829126 PMCID: PMC6579646 DOI: 10.1080/08897077.2018.1550465] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Background: The purpose of this review was to examine and chart the "scope" of strategies reported in ED-SBIRT (emergency department-based screening, brief intervention and referral to treatment) studies that employ non-face-to-face (nFtF) modalities for high-risk alcohol use (i.e., risk for alcohol-related injury, medical condition, use disorder) and to identify research gaps in the scientific literature. Methods: The scoping review population included study participants with high-risk alcohol use patterns as well as study participants targeted for primary public health prevention (e.g., adolescent ED patients). Core concepts included SBIRT components among intervention studies that incorporated some form of nFtF modality (e.g., computer-assisted brief intervention). The context encompassed ED-based studies or trauma center studies, regardless of geographic location. After screening a total of 1526 unique references, reviewers independently assessed 58 full-text articles for eligibility. Results: A total of 30 full-text articles were included. Articles covered a period of 14 years (2003-2016) and 19 journal titles. Authors reported the use of a wide range of nFtF modalities across all 3 ED-SBIRT components: "screening" (e.g., computer tablet screening), "brief intervention" (e.g., text message-based brief interventions), and "referral to treatment" (e.g., computer-generated feedback with information about alcohol treatment services). The most frequently used nFtF modality was computerized screening and/or baseline assessment. The main results were mixed with respect to showing evidence of ED-SBIRT intervention effects. Conclusions: There is an opportunity for substance use disorder researchers to explore the specific needs of several populations (e.g., ED patients with co-occurring problems such as substance use disorder and violence victimization) and on several methodological issues (e.g., ED-SBIRT theory of change). Substance use disorder researchers should take the lead on establishing guidelines for the reporting of ED-SBIRT studies-including categorization schemes for various nFtF modalities. This would facilitate both secondary research (e.g., meta-analyses) and primary research design.
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Affiliation(s)
- Brian J Biroscak
- a Department of Emergency Medicine , Yale University School of Medicine , New Haven , Connecticut , USA
| | - Michael V Pantalon
- a Department of Emergency Medicine , Yale University School of Medicine , New Haven , Connecticut , USA
| | - James D Dziura
- a Department of Emergency Medicine , Yale University School of Medicine , New Haven , Connecticut , USA
| | - Denise P Hersey
- b Department of Clinical Information Services, Cushing/Whitney Medical Library , Yale University , New Haven , Connecticut , USA
| | - Federico E Vaca
- a Department of Emergency Medicine , Yale University School of Medicine , New Haven , Connecticut , USA
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229
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Gonul S, Namli T, Huisman S, Laleci Erturkmen GB, Toroslu IH, Cosar A. An expandable approach for design and personalization of digital, just-in-time adaptive interventions. J Am Med Inform Assoc 2019; 26:198-210. [PMID: 30590757 PMCID: PMC6351973 DOI: 10.1093/jamia/ocy160] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 08/17/2018] [Accepted: 11/15/2018] [Indexed: 11/12/2022] Open
Abstract
Objective We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables. Materials and Methods We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions. Results We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns. Conclusion While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.
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Affiliation(s)
- Suat Gonul
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
- SRDC Software Research & Development and Consultancy Corp., Ankara, Turkey
| | - Tuncay Namli
- SRDC Software Research & Development and Consultancy Corp., Ankara, Turkey
| | - Sasja Huisman
- Department of Internal Medicine (Endocrinology), Leiden University Medical Center, Leiden, the Netherlands
| | | | - Ismail Hakki Toroslu
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Ahmet Cosar
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
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230
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Forman EM, Goldstein SP, Zhang F, Evans BC, Manasse SM, Butryn ML, Juarascio AS, Abichandani P, Martin GJ, Foster GD. OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses. Transl Behav Med 2019; 9:236-245. [PMID: 29617911 PMCID: PMC6610167 DOI: 10.1093/tbm/iby016] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Given that the overarching goal of weight loss programs is to remain adherent to a dietary prescription, specific moments of nonadherence known as "dietary lapses" can threaten weight control via the excess energy intake they represent and by provoking future lapses. Just-in-time adaptive interventions could be particularly useful in preventing dietary lapses because they use real-time data to generate interventions that are tailored and delivered at a moment computed to be of high risk for a lapse. To this end, we developed a smartphone application (app) called OnTrack that utilizes machine learning to predict dietary lapses and deliver a targeted intervention designed to prevent the lapse from occurring. This study evaluated the feasibility, acceptability, and preliminary effectiveness of OnTrack among weight loss program participants. An open trial was conducted to investigate subjective satisfaction, objective usage, algorithm performance, and changes in lapse frequency and weight loss among individuals (N = 43; 86% female; body mass index = 35.6 kg/m2) attempting to follow a structured online weight management plan for 8 weeks. Participants were adherent with app prompts to submit data, engaged with interventions, and reported high levels of satisfaction. Over the course of the study, participants averaged a 3.13% weight loss and experienced a reduction in unplanned lapses. OnTrack, the first Just-in-time adaptive intervention for dietary lapses was shown to be feasible and acceptable, and OnTrack users experienced weight loss and lapse reduction over the study period. These data provide the basis for further development and evaluation.
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Affiliation(s)
- Evan M Forman
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Stephanie P Goldstein
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Fengqing Zhang
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Brittney C Evans
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Stephanie M Manasse
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Meghan L Butryn
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Adrienne S Juarascio
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Pramod Abichandani
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Gerald J Martin
- Center for Weight, Eating, and Lifestyle Science (WELL Center), Drexel University, Philadelphia, PA, USA
| | - Gary D Foster
- Weight Watchers International, New York, NY, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
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231
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Abstract
Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.
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Affiliation(s)
- Michael R Kosorok
- Department of Biostatistics and Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, U.S.A.;
| | - Eric B Laber
- Department of Statistics, North Carolina State University, Raleight, North Carolina, 27695, U.S.A.;
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232
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Conroy DE, Hojjatinia S, Lagoa CM, Yang CH, Lanza ST, Smyth JM. Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods. PSYCHOLOGY OF SPORT AND EXERCISE 2019; 41:172-180. [PMID: 30853855 PMCID: PMC6404972 DOI: 10.1016/j.psychsport.2018.06.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
OBJECTIVES The conceptual models underlying physical activity interventions have been based largely on differences between more and less active people. Yet physical activity is a dynamic behavior, and such models are not sensitive to factors that regulate behavior at a momentary level or how people respond to individual attempts at intervening. We demonstrate how a control systems engineering approach can be applied to develop personalized models of behavioral responses to an intensive text message-based intervention. DESIGN & METHOD To establish proof-of-concept for this approach, 10 adults wore activity monitors for 16 weeks and received five text messages daily at random times. Message content was randomly selected from three types of messages designed to target (1) social-cognitive processes associated with increasing physical activity, (2) social-cognitive processes associated with reducing sedentary behavior, or (3) general facts unrelated to either physical activity or sedentary behavior. A dynamical systems model was estimated for each participant to examine the magnitude and timing of responses to each type of text message. RESULTS Models revealed heterogeneous responses to different message types that varied between people and between weekdays and weekends. CONCLUSIONS This proof-of-concept demonstration suggests that parameters from this model can be used to develop personalized algorithms for intervention delivery. More generally, these results demonstrate the potential utility of control systems engineering models for optimizing physical activity interventions.
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Affiliation(s)
- David E Conroy
- Department of Kinesiology and Human Development and Family Studies, The Pennsylvania State University; Department of Preventive Medicine, Northwestern University
| | - Sarah Hojjatinia
- Department of Electrical Engineering, The Pennsylvania State University
| | | | - Chih-Hsiang Yang
- Department of Kinesiology, The Pennsylvania State University; Department of Preventive Medicine, University of Southern California
| | - Stephanie T Lanza
- Department of Biobehavioral Health, The Pennsylvania State University
| | - Joshua M Smyth
- Department of Biobehavioral Health, The Pennsylvania State University
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233
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Candlish J, Teare MD, Cohen J, Bywater T. Statistical design and analysis in trials of proportionate interventions: a systematic review. Trials 2019; 20:151. [PMID: 30819224 PMCID: PMC6396459 DOI: 10.1186/s13063-019-3206-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 01/17/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND In proportionate or adaptive interventions, the dose or intensity can be adjusted based on individual need at predefined decision stages during the delivery of the intervention. The development of such interventions may require an evaluation of the effectiveness of the individual stages in addition to the whole intervention. However, evaluating individual stages of an intervention has various challenges, particularly the statistical design and analysis. This review aimed to identify the use of trials of proportionate interventions and how they are being designed and analysed in current practice. METHODS We searched MEDLINE, Web of Science and PsycINFO for articles published between 2010 and 2015 inclusive. We considered trials of proportionate interventions in all fields of research. For each trial, its aims, design and analysis were extracted. The data synthesis was conducted using summary statistics and a narrative format. RESULTS Our review identified 44 proportionate intervention trials, comprising 28 trial results, 13 protocols and three secondary analyses. These were mostly described as stepped care (n=37) and mainly focussed on mental health research (n=30). The other studies were aimed at finding an optimal adaptive treatment strategy (n=7) in a variety of therapeutic areas. Further terminology used included adaptive intervention, staged intervention, sequentially multiple assignment trial or a two-phase design. The median number of decision stages in the interventions was two and only one study explicitly evaluated the effect of the individual stages. CONCLUSIONS Trials of proportionate staged interventions are being used predominantly within the mental health field. However, few studies consider the different stages of the interventions, either at the design or the analysis phase, and how they may interact with one another. There is a need for further guidance on the design, analyses and reporting across trials of proportionate interventions. TRIAL REGISTRATION Prospero, CRD42016033781. Registered on 2 February 2016.
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Affiliation(s)
- Jane Candlish
- ScHARR, University of Sheffield, 30 Regent Court, Sheffield, S1 4DA, UK.
| | - M Dawn Teare
- ScHARR, University of Sheffield, 30 Regent Court, Sheffield, S1 4DA, UK
| | - Judith Cohen
- ScHARR, University of Sheffield, 30 Regent Court, Sheffield, S1 4DA, UK
- Hull Health Trials Unit, University of Hull/Hull York Medical School, York, UK
| | - Tracey Bywater
- Department of Health Sciences, University of York, Area 2, Seebohm, Rowntree Building, York, Y010 5DD, UK
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234
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Liu N, Liu Y, Logan B, Xu Z, Tang J, Wang Y. Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network. Sci Rep 2019; 9:1495. [PMID: 30728403 PMCID: PMC6365640 DOI: 10.1038/s41598-018-37142-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 10/06/2018] [Indexed: 01/16/2023] Open
Abstract
This paper presents the deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctors and patients the data-driven personalized decision recommendations. The proposed DRL framework comprises (i) a supervised learning step to predict expert actions, and (ii) a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes. Both steps depend on deep neural networks. As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation. In the experimental results, we have demonstrated promising accuracy in predicting human experts' decisions, as well as the high expected reward function in the DRL-based dynamic treatment regimes.
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Affiliation(s)
- Ning Liu
- Department of Electrical Engineering and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Ying Liu
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
| | - Brent Logan
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Zhiyuan Xu
- Department of Electrical Engineering and Computer Engineering, Syracuse University, Syracuse, NY, 13244, USA
| | - Jian Tang
- Department of Electrical Engineering and Computer Engineering, Syracuse University, Syracuse, NY, 13244, USA
- DiDi AI Labs, Beijing, China
| | - Yanzhi Wang
- Department of Electrical Engineering and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
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Kramer JN, Künzler F, Mishra V, Presset B, Kotz D, Smith S, Scholz U, Kowatsch T. Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial. JMIR Res Protoc 2019; 8:e11540. [PMID: 30702430 PMCID: PMC6374735 DOI: 10.2196/11540] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 10/26/2018] [Accepted: 10/31/2018] [Indexed: 11/27/2022] Open
Abstract
Background Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring. Objective The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. Methods In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. Results Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. Conclusions This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. Trial Registration ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d) International Registered Report Identifier (IRRID) DERR1-10.2196/11540
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Affiliation(s)
- Jan-Niklas Kramer
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Florian Künzler
- Center for Digital Health Interventions, Department of Management, Technology and Economics, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Varun Mishra
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Bastien Presset
- Institute of Sports Studies, University of Lausanne, Lausanne, Switzerland
| | - David Kotz
- Department of Computer Science, Dartmouth College, Hanover, NH, United States.,Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
| | - Shawna Smith
- Institute for Social Research, University of Michigan - Ann Arbor, Ann Arbor, MI, United States.,Medical School, University of Michigan - Ann Arbor, Ann Arbor, MI, United States
| | - Urte Scholz
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Tobias Kowatsch
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
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236
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Goetz LH, Schork NJ. Personalized medicine: motivation, challenges, and progress. Fertil Steril 2019; 109:952-963. [PMID: 29935653 DOI: 10.1016/j.fertnstert.2018.05.006] [Citation(s) in RCA: 247] [Impact Index Per Article: 49.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/04/2018] [Accepted: 05/04/2018] [Indexed: 01/07/2023]
Abstract
There is a great deal of hype surrounding the concept of personalized medicine. Personalized medicine is rooted in the belief that since individuals possess nuanced and unique characteristics at the molecular, physiological, environmental exposure, and behavioral levels, they may need to have interventions provided to them for diseases they possess that are tailored to these nuanced and unique characteristics. This belief has been verified to some degree through the application of emerging technologies such as DNA sequencing, proteomics, imaging protocols, and wireless health monitoring devices, which have revealed great inter-individual variation in disease processes. In this review, we consider the motivation for personalized medicine, its historical precedents, the emerging technologies that are enabling it, some recent experiences including successes and setbacks, ways of vetting and deploying personalized medicines, and future directions, including potential ways of treating individuals with fertility and sterility issues. We also consider current limitations of personalized medicine. We ultimately argue that since aspects of personalized medicine are rooted in biological realities, personalized medicine practices in certain contexts are likely to be inevitable, especially as relevant assays and deployment strategies become more efficient and cost-effective.
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Affiliation(s)
| | - Nicholas J Schork
- The Translational Genomics Research Institute, Phoenix, Arizona; The City of Hope/TGen IMPACT Center, Duarte, California; J. Craig Venter Institute, La Jolla, California; The University of California, San Diego, La Jolla, California.
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237
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Warnick JL, Pfammatter A, Champion K, Galluzzi T, Spring B. Perceptions of Health Behaviors and Mobile Health Applications in an Academically Elite College Population to Inform a Targeted Health Promotion Program. Int J Behav Med 2019; 26:165-174. [DOI: 10.1007/s12529-018-09767-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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238
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Pham Q, Graham G, Lalloo C, Morita PP, Seto E, Stinson JN, Cafazzo JA. An Analytics Platform to Evaluate Effective Engagement With Pediatric Mobile Health Apps: Design, Development, and Formative Evaluation. JMIR Mhealth Uhealth 2018; 6:e11447. [PMID: 30578179 PMCID: PMC6320392 DOI: 10.2196/11447] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 10/12/2018] [Accepted: 10/29/2018] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Mobile health (mHealth) apps for pediatric chronic conditions are growing in availability and challenge investigators to conduct rigorous evaluations that keep pace with mHealth innovation. Traditional research methods are poorly suited to operationalize the agile, iterative trials required to evidence and optimize these digitally mediated interventions. OBJECTIVE We sought to contribute a resource to support the quantification, analysis, and visualization of analytic indicators of effective engagement with mHealth apps for chronic conditions. METHODS We applied user-centered design methods to design and develop an Analytics Platform to Evaluate Effective Engagement (APEEE) with consumer mHealth apps for chronic conditions and implemented the platform to analyze both retrospective and prospective data generated from a smartphone-based pain self-management app called iCanCope for young people with chronic pain. RESULTS Through APEEE, we were able to automate the process of defining, operationalizing, and evaluating effective engagement with iCanCope. Configuring the platform to integrate with the app was feasible and provided investigators with a resource to consolidate, analyze, and visualize engagement data generated by participants in real time. Preliminary efforts to evaluate APEEE showed that investigators perceived the platform to be an acceptable evaluative resource and were satisfied with its design, functionality, and performance. Investigators saw potential in APEEE to accelerate and augment evidence generation and expressed enthusiasm for adopting the platform to support their evaluative practice once fully implemented. CONCLUSIONS Dynamic, real-time analytic platforms may provide investigators with a powerful means to characterize the breadth and depth of mHealth app engagement required to achieve intended health outcomes. Successful implementation of APEEE into evaluative practice may contribute to the realization of effective and evidence-based mHealth care.
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Affiliation(s)
- Quynh Pham
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Gary Graham
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Chitra Lalloo
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Child Health Evaluative Sciences Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Plinio P Morita
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,School of Public Health and Health Systems, Faculty of Applied Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Emily Seto
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Jennifer N Stinson
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Child Health Evaluative Sciences Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, ON, Canada.,Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada
| | - Joseph A Cafazzo
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada
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239
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Middelweerd A, Te Velde SJ, Mollee JS, Klein MC, Brug J. App-Based Intervention Combining Evidence-Based Behavior Change Techniques With a Model-Based Reasoning System to Promote Physical Activity Among Young Adults (Active2Gether): Descriptive Study of the Development and Content. JMIR Res Protoc 2018; 7:e185. [PMID: 30578198 PMCID: PMC6320419 DOI: 10.2196/resprot.7169] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 05/07/2017] [Accepted: 07/02/2018] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The Active2Gether intervention is an app-based intervention designed to help and encourage young adults to become and remain physically active by means of personalized, real-time activity tracking and context-specific feedback. OBJECTIVE The objective of our study was to describe the development and content of the Active2Gether intervention for physical activity promotion. METHODS A systematic and stepwise approach was used to develop the Active2Gether intervention. This included formulating objectives and a theoretical framework, selecting behavior change techniques, specifying the tailoring, pilot testing, and describing an evaluation protocol. RESULTS The development of the Active2Gether intervention comprised seven steps: analyzing the (health) problem, developing a program framework, writing (tailored) messages, developing tailoring assessments, developing the Active2Gether intervention, pilot testing, and testing and evaluating the intervention. The primary objective of the intervention was to increase the total time spent in moderate-vigorous physical activity for those who do not meet the Dutch guideline, maintain physical activity levels of those who meet the guideline, or further increase physical activity levels if they so indicated. The theoretical framework is informed by the social cognitive theory, and insights from other theories and evidence were added for specific topics. Development of the intervention content and communication channel resulted in the development of an app that provides highly tailored coaching messages that are framed in an autonomy-supportive style. These coaching messages include behavior change techniques aiming to address relevant behavioral determinants (eg, self-efficacy and outcome expectations) and are partly context specific. A model-based reasoning engine has been developed to tailor the intervention with respect to the type of support provided by the app, send relevant and context-specific messages to the user, and tailor the graphs displayed in the app. For the input of the tailoring, different instruments and sensors are used, such as an activity monitor (Fitbit One), Web-based and mobile questionnaires, and the location services on the user's mobile phone. CONCLUSIONS The systematic and stepwise approach resulted in an intervention that is based on theory and input from end users. The use of a model-based reasoning system to provide context-specific coaching messages goes beyond many existing eHealth and mHealth interventions.
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Affiliation(s)
- Anouk Middelweerd
- VU Medical Center Amsterdam, Department of Epidemiology & Biostatistics, Amsterdam, Netherlands
| | - Saskia J Te Velde
- VU Medical Center Amsterdam, Department of Epidemiology & Biostatistics, Amsterdam, Netherlands.,te Velde Research & Consultancy, Bunnik, Netherlands
| | - Julia S Mollee
- VU University Amsterdam, Department of Computer Science, Amsterdam, Netherlands
| | - Michel Ca Klein
- VU University Amsterdam, Department of Computer Science, Amsterdam, Netherlands
| | - Johannes Brug
- VU Medical Center Amsterdam, Department of Epidemiology & Biostatistics, Amsterdam, Netherlands.,University of Amsterdam, Faculty of Social and Behavioural Sciences, Amsterdam, Netherlands
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240
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Liao P, Dempsey W, Sarker H, Hossain SM, Al'absi M, Klasnja P, Murphy S. Just-in-Time but Not Too Much: Determining Treatment Timing in Mobile Health. PROCEEDINGS OF THE ACM ON INTERACTIVE, MOBILE, WEARABLE AND UBIQUITOUS TECHNOLOGIES 2018; 2:179. [PMID: 30801052 PMCID: PMC6380673 DOI: 10.1145/3287057] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 10/01/2018] [Indexed: 10/27/2022]
Abstract
There is a growing scientific interest in the use and development of just-in-time adaptive interventions in mobile health. These mobile interventions typically involve treatments, such as reminders, activity suggestions and motivational messages, delivered via notifications on a smartphone or a wearable to help users make healthy decisions in the moment. To be effective in influencing health, the combination of the right treatment and right delivery time is likely critical. A variety of prediction/detection algorithms have been developed with the goal of pinpointing the best delivery times. The best delivery times might be times of greatest risk and/or times at which the user might be most receptive to the treatment notifications. In addition, to avoid over burdening users, there is often a constraint on the number of treatments that should be provided per time interval (e.g., day or week). Yet there may be many more times at which the user is predicted or detected to be at risk and/or receptive. The goal then is to spread treatment uniformly across all of these times. In this paper, we introduce a method that spreads the treatment uniformly across the delivery times. This method can also be used to provide data for learning whether the treatments are effective at the delivery times. This work is motivated by our work on two mobile health studies, a smoking cessation study and a physical activity study.
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Affiliation(s)
- Peng Liao
- University of Michigan, Ann Arbor, MI
| | | | | | | | | | - Predrag Klasnja
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
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241
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Bidargaddi N, Almirall D, Murphy S, Nahum-Shani I, Kovalcik M, Pituch T, Maaieh H, Strecher V. To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App. JMIR Mhealth Uhealth 2018; 6:e10123. [PMID: 30497999 PMCID: PMC6293241 DOI: 10.2196/10123] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 06/20/2018] [Accepted: 07/10/2018] [Indexed: 11/19/2022] Open
Abstract
Background Mobile health (mHealth) apps provide an opportunity for easy, just-in-time access to health promotion and self-management support. However, poor user engagement with these apps remains a significant unresolved challenge. Objective This study aimed to assess the effect of sending versus not sending a push notification containing a contextually tailored health message on proximal engagement, measured here as self-monitoring via the app. Secondary aims were to examine whether this effect varies by the number of weeks enrolled in the program or by weekday versus weekend. An exploratory aim was to describe how the effect on proximal engagement differs between weekday versus weekend by the time of day. Methods The study analyzes the causal effects of push notifications on proximal engagement in 1255 users of a commercial workplace well-being intervention app over 89 days. The app employs a microrandomized trial (MRT) design to send push notifications. At 1 of 6 times per day (8:30 am, 12:30 pm, 5:30 pm, 6:30 pm, 7:30 pm, and 8:30 pm; selected randomly), available users were randomized with equal probability to be sent or not sent a push notification containing a tailored health message. The primary outcome of interest was whether the user self-monitored behaviors and feelings at some time during the next 24 hours via the app. A generalization of log-linear regression analysis, adapted for use with data arising from an MRT, was used to examine the effect of sending a push notification versus not sending a push notification on the probability of engagement over the next 24 hours. Results Users were estimated to be 3.9% more likely to engage with the app in the next 24 hours when a tailored health message was sent versus when it was not sent (risk ratio 1.039; 95% CI 1.01 to 1.08; P<.05). The effect of sending the message attenuated over the course of the study, but this effect was not statistically significant (P=.84). The effect of sending the message was greater on weekends than on weekdays, but the difference between these effects was not statistically significant (P=.18). When sent a tailored health message on weekends, the users were 8.7% more likely to engage with the app (95% CI 1.01 to 1.17), whereas on weekdays, the users were 2.5% more likely to engage with the app (95% CI 0.98 to 1.07). The effect of sending a tailored health message was greatest at 12:30 pm on weekends, when the users were 11.8% more likely to engage (90% CI 1.02 to 1.13). Conclusions Sending a push notification containing a tailored health message was associated with greater engagement in an mHealth app. Results suggested that users are more likely to engage with the app within 24 hours when push notifications are sent at mid-day on weekends.
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Affiliation(s)
- Niranjan Bidargaddi
- Personal Health Informatics, College of Medicine & Public Health, Adelaide, Australia
| | - Daniel Almirall
- Insitute for Social Research, Michigan University, Ann Arbor, MI, United States
| | - Susan Murphy
- Department of Statistics, Harvard University, Boston, MA, United States
| | - Inbal Nahum-Shani
- Insitute for Social Research, Michigan University, Ann Arbor, MI, United States
| | - Michael Kovalcik
- Insitute for Social Research, Michigan University, Ann Arbor, MI, United States
| | | | | | - Victor Strecher
- Jool Health, Ann Arbor, MI, United States.,School of Public Health, University of Michigan, Ann Arbor, MI, United States
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242
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Kasari C, Sturm A, Shih W. SMARTer Approach to Personalizing Intervention for Children With Autism Spectrum Disorder. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2018; 61:2629-2640. [PMID: 30418492 PMCID: PMC6693574 DOI: 10.1044/2018_jslhr-l-rsaut-18-0029] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 08/12/2018] [Accepted: 09/05/2018] [Indexed: 06/09/2023]
Abstract
PURPOSE This review article introduces research methods for personalization of intervention. Our goals are to review evidence-based practices for improving social communication impairment in children with autism spectrum disorder generally and then how these practices can be systematized in ways that personalize intervention, especially for children who respond slowly to an initial evidence-based practice. METHOD The narrative reflects on the current status of modular and targeted interventions on social communication outcomes in the field of autism research. Questions are introduced regarding personalization of interventions that can be addressed through research methods. These research methods include adaptive treatment designs and the Sequential Multiple Assignment Randomized Trial. Examples of empirical studies using research designs are presented to answer questions of personalization. CONCLUSION Bridging the gap between research studies and clinical practice can be advanced by research that attempts to answer questions pertinent to the broad heterogeneity in children with autism spectrum disorder, their response to interventions, and the fact that a single intervention is not effective for all children. PRESENTATION VIDEO https://doi.org/10.23641/asha.7298021.
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Affiliation(s)
- Connie Kasari
- Graduate School of Education, University of California, Los Angeles
| | - Alexandra Sturm
- Department of Psychiatry, University of California, Los Angeles
| | - Wendy Shih
- Department of Psychiatry, University of California, Los Angeles
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243
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Hamaker EL, Asparouhov T, Brose A, Schmiedek F, Muthén B. At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study. MULTIVARIATE BEHAVIORAL RESEARCH 2018; 53:820-841. [PMID: 29624092 DOI: 10.1080/00273171.2018.1446819] [Citation(s) in RCA: 158] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.
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Affiliation(s)
- E L Hamaker
- a Methodology and Statistics, Faculty of Social and Behavioural Sciences , Utrecht University
- b KU Leuven
| | | | - A Brose
- b KU Leuven
- d Humboldt University Berlin
- e Max Planck Institute for Human Development, Center for Lifespan Psychology
| | - F Schmiedek
- e Max Planck Institute for Human Development, Center for Lifespan Psychology
- f German Institute for International Educational Research (DIPF), Center for Education and Lifespan Development
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244
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Rabbi M, Aung MS, Gay G, Reid MC, Choudhury T. Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults. J Med Internet Res 2018; 20:e10147. [PMID: 30368433 PMCID: PMC6229514 DOI: 10.2196/10147] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 05/12/2018] [Accepted: 06/19/2018] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Chronic pain is a globally prevalent condition. It is closely linked with psychological well-being, and it is often concomitant with anxiety, negative affect, and in some cases even depressive disorders. In the case of musculoskeletal chronic pain, frequent physical activity is beneficial. However, reluctance to engage in physical activity is common due to negative psychological associations (eg, fear) between movement and pain. It is known that encouragement, self-efficacy, and positive beliefs are effective to bolster physical activity. However, given that the majority of time is spent away from personnel who can give such encouragement, there is a great need for an automated ubiquitous solution. OBJECTIVE MyBehaviorCBP is a mobile phone app that uses machine learning on sensor-based and self-reported physical activity data to find routine behaviors and automatically generate physical activity recommendations that are similar to existing behaviors. Since the recommendations are based on routine behavior, they are likely to be perceived as familiar and therefore likely to be actualized even in the presence of negative beliefs. In this paper, we report the preliminary efficacy of MyBehaviorCBP based on a pilot trial on individuals with chronic back pain. METHODS A 5-week pilot study was conducted on people with chronic back pain (N=10). After a week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, MyBehaviorCBP recommendations were issued. An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities. RESULTS In all, 90% (9/10) of participants felt positive about trying the MyBehaviorCBP recommendations, and no participant found the recommendations unhelpful. Several significant differences were observed in other outcome measures. Participants found MyBehaviorCBP recommendations easier to adopt compared to the control (βint=0.42, P<.001) on a 5-point Likert scale. The MyBehaviorCBP recommendations were actualized more (βint=0.46, P<.001) with an increase in approximately 5 minutes of further walking per day (βint=4.9 minutes, P=.02) compared to the control. For future improvement opportunities, participants wanted push notifications and adaptation for weather, pain level, or weekend/weekday. CONCLUSIONS In the pilot study, MyBehaviorCBP's automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow. To the best of our knowledge, this is the first time an automated approach has achieved preliminary success to promote physical activity in a chronic pain context. Further studies are needed to examine MyBehaviorCBP's efficacy on a larger cohort and over a longer period of time.
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Affiliation(s)
- Mashfiqui Rabbi
- Department of Statistics, Harvard University, Cambridge, MA, United States
| | - Min Sh Aung
- Information Science Department, Cornell University, Ithaca, NY, United States
| | - Geri Gay
- Information Science Department, Cornell University, Ithaca, NY, United States
| | - M Cary Reid
- Department of Medicine, Weill Cornell Medical College, New York, NY, United States
| | - Tanzeem Choudhury
- Information Science Department, Cornell University, Ithaca, NY, United States
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245
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Nelson BW, Allen NB. Extending the Passive-Sensing Toolbox: Using Smart-Home Technology in Psychological Science. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2018; 13:718-733. [PMID: 30217132 DOI: 10.1177/1745691618776008] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
New smart-home devices provide the opportunity to advance psychological science and theory through novel research opportunities in home environments. These technologies extend the in vivo research and intervention capabilities afforded by other assessment techniques such as Ecological Momentary Assessment methods as well as mobile and wearable devices. Smart-home devices contain a multitude of sensors capable of continuously and unobtrusively collecting multimodal data within home contexts. These devices have some complementary strengths and limitations compared with other assessment methods. This article (a) briefly reviews data collection methods in home environments, (b) discusses the unique advantages of smart-home devices, (c) describes the extant smart-home literature, (d) explores how these devices may advance evaluation and refinement of psychological theories, (e) describes examples of psychological processes that are potential targets for smart-home assessment and intervention, (f) considers methodological challenges and barriers, (g) discusses ethical considerations, and (h) concludes with a discussion of future directions for research and the merging of passive-sensing technologies with active self-report methods. This article aims to highlight the potential utility of smart-home devices within psychological research to evaluate psychological theories related to behavior within the home context.
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Affiliation(s)
- Benjamin W Nelson
- Department of Psychology and The Center for Digital Mental Health, University of Oregon
| | - Nicholas B Allen
- Department of Psychology and The Center for Digital Mental Health, University of Oregon
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246
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Cheung YK, Hsueh PYS, Ensari I, Willey JZ, Diaz KM. Quantile Coarsening Analysis of High-Volume Wearable Activity Data in a Longitudinal Observational Study. SENSORS (BASEL, SWITZERLAND) 2018; 18:E3056. [PMID: 30213093 PMCID: PMC6164779 DOI: 10.3390/s18093056] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 09/04/2018] [Accepted: 09/06/2018] [Indexed: 11/17/2022]
Abstract
Owing to advances in sensor technologies on wearable devices, it is feasible to measure physical activity of an individual continuously over a long period. These devices afford opportunities to understand individual behaviors, which may then provide a basis for tailored behavior interventions. The large volume of data however poses challenges in data management and analysis. We propose a novel quantile coarsening analysis (QCA) of daily physical activity data, with a goal to reduce the volume of data while preserving key information. We applied QCA to a longitudinal study of 79 healthy participants whose step counts were monitored for up to 1 year by a Fitbit device, performed cluster analysis of daily activity, and identified individual activity signature or pattern in terms of the clusters identified. Using 21,393 time series of daily physical activity, we identified eight clusters. Employment and partner status were each associated with 5 of the 8 clusters. Using less than 2% of the original data, QCA provides accurate approximation of the mean physical activity, forms meaningful activity patterns associated with individual characteristics, and is a versatile tool for dimension reduction of densely sampled data.
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Affiliation(s)
- Ying Kuen Cheung
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
| | | | - Ipek Ensari
- Center for Behavioral Cardiovascular Health, Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA.
| | - Joshua Z Willey
- Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA.
| | - Keith M Diaz
- Center for Behavioral Cardiovascular Health, Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA.
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247
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Klasnja P, Hekler EB. Rethinking Evaluations Of mHealth Systems For Behavior Change. ACTA ACUST UNITED AC 2018; 22:11-14. [PMID: 30680312 DOI: 10.1145/3276145.3276149] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Much of the recent research in mobile health (mHealth) has focused on the development of apps and wearables for promoting healthy behavior changes, such as losing weight, increasing physical activity, or adhering to a medication regimen. These interactive systems help users make changes in their behavior by, for instance, tracking healthrelated activities and states, providing feedback, helping users set and track goals, and facilitating supportive social interactions. We refer to the features that implement such functionality as the system's "intervention components," as they are designed to actuate psychosocial mechanisms (e.g., modeling, selfefficacy, positive reinforcement, etc.) thought to mediate the behavior change process. As with any other type of behavioral intervention, mHealth systems are only effective for some users and some of the time, but insofar as they do work, they do so mainly through the mechanisms of change that are activated via users' interactions with the system's intervention components.
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248
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Newton RL, Carter LA, Johnson W, Zhang D, Larrivee S, Kennedy BM, Harris M, Hsia DS. A Church-Based Weight Loss Intervention in African American Adults using Text Messages (LEAN Study): Cluster Randomized Controlled Trial. J Med Internet Res 2018; 20:e256. [PMID: 30143478 PMCID: PMC6128956 DOI: 10.2196/jmir.9816] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 05/17/2018] [Accepted: 06/18/2018] [Indexed: 12/23/2022] Open
Abstract
Background African American adults experience a high prevalence of obesity and its associated comorbidities, including diabetes. Church-based interventions have been shown to be effective in decreasing weight in this population. mHealth interventions can address two needs for obesity treatment in this community, including enhancing weight loss and providing wide dissemination. Objective This study aimed to assess the feasibility and efficacy of a church-based weight loss intervention that incorporates mHealth technology. Methods In this study, 8 churches (n=97) were randomly assigned to the intervention or delayed intervention condition (control group). We recruited participants through their respective church. Volunteer church members were trained by study staff to deliver the 10-session, 6-month intervention. Participants in the intervention group attended group sessions and received automated short message service (SMS) text messages designed to reinforce behavioral strategies. Conversely, participants in the delayed intervention condition received SMS text messages related to health conditions relevant for African American adults. We obtained measures of body composition, blood pressure, blood glucose, and cholesterol. Results We successfully recruited 97 African American adults, with a mean age of 56.0 (SE 10.3) years and a mean body mass index of 38.6 (SE 6.4) kg/m2 (89/97, 91.8% females), who attended the churches that were randomized to the intervention (n=68) or control (n=29) condition. Of these, 74.2% (72/97) of the participants (47/68, 69.1% intervention; 25/29, 86.2% delayed intervention) completed the 6-month assessment. The average intervention group attendance was 55%. There was a significant difference in weight loss (P=.04) between participants in the intervention (–1.5 (SE 0.5) kg) and control (0.11 (SE 0.6) kg) groups. Among participants in the intervention group, the correlation between the number of SMS text messages sent and the percent body fat loss was r=.3 with P=.04. The participants reported high satisfaction with the automated SMS text messages. Conclusions Automated SMS text messages were well-received by participants, suggesting that more enhanced mHealth technologies are a viable option for interventions targeting African American adults. Trial Registration ClinicalTrials.gov NCT02863887; https://clinicaltrials.gov/ct2/show/NCT02863887 (Archived by WebCite at http://www.webcitation.org/71JiYzizO)
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Affiliation(s)
- Robert L Newton
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Leah A Carter
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - William Johnson
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Dachuan Zhang
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Sandra Larrivee
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Betty M Kennedy
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Melissa Harris
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Daniel S Hsia
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
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249
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Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research. Addict Behav 2018; 83:5-17. [PMID: 29174666 DOI: 10.1016/j.addbeh.2017.11.027] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 11/02/2017] [Accepted: 11/02/2017] [Indexed: 02/06/2023]
Abstract
Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants' daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants' behavior and mood in their natural environments. Data from portable/wearable electronic sensors measuring participants' internal and external environments can be combined with EMA (e.g., by timestamps recorded on questionnaires) to provide objective information useful in determining the momentary context of behavior and mood and/or validating participants' self-reports. Here, we review three objective ambulatory monitoring techniques that have been combined with EMA, with a focus on detecting drug use and/or measuring the behavioral or physiological correlates of mental events (i.e., emotions, cognitions): (1) collection and processing of biological samples in the field to measure drug use or participants' physiological activity (e.g., hypothalamic-pituitary-adrenal axis activity); (2) global positioning system (GPS) location information to link environmental characteristics (disorder/disadvantage, retail drug outlets) to drug use and affect; (3) ambulatory electronic physiological monitoring (e.g., electrocardiography) to detect drug use and mental events, as advances in machine learning algorithms make it possible to distinguish target changes from confounds (e.g., physical activity). Finally, we consider several other mobile/wearable technologies that hold promise to be combined with EMA, as well as potential challenges faced by researchers working with multiple mobile/wearable technologies simultaneously in the field.
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250
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Kim S, Wong WK. Discussion on Optimal treatment allocations in space and time for on-line control of an emerging infectious disease. J R Stat Soc Ser C Appl Stat 2018. [PMID: 30270943 DOI: 10.1111/rssc.12266] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Seongho Kim
- Biostatistics Core, Karmanos Cancer Institute, Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201
| | - Weng Kee Wong
- Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA 90095
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