The Open Public Health Journal




ISSN: 1874-9445 ― Volume 12, 2019
RESEARCH ARTICLE

The Influence of Social Media Lifestyle Interventions on Health Behaviour: A Study on Patients with Major Depressive Disorders and Family Caregivers



A. Jattamart1, A. Leelasantitham1, *
1 Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhonpathom, Thailand

Abstract

Background:

The World Health Organisation (WHO) predicts that depression will be the second leading cause of diseases by 2020. If depression is not properly treated, it can develop into a depressive disorder and increase the risk of suicide.Besides biopsychosocial factors, lifestyle is said to be a major cause of this disease and has led to an increase in its prevalence.

Objective:

The objective of this study was to study the intentions of patients with major depressive disorders and family caregivers to change their health behaviour and lifestyle through social media influences.

Methods:

This was a cross-sectional study. Participants were invited to take part in the research and give their informed consent. The sample consisted of 157 patients diagnosed with major depressive disorders, aged 18 years and over, and 110 family caregivers. Data were collected from the questionnaires designed according to the I-Change Model (ICM). Statistical results to confirm causal relationships were analysed based on Structural Equation Modelling (SEM) and by using the SmartPLS 3 software.

Results:

Patients and family caregivers were questioned about their perspectives on health matters and the influence on their motivations and intentions to change patients’ health behaviour and lifestyle, particularly social media interventions. The patients received information and counselling about health matters, health awareness, motivation and their intentions to change their health behaviour. The family caregivers were presented with the same information and counselling to motivate them to influence the depressive patients’ intentions to change their health behaviour.

Conclusion:

It is possible that lifestyle interventions on social media can influence the intention to change health behaviour in both patients and caregivers. However, if the patient lacks interest in participating in the treatment and does not have a good relationship with the clinician or provide relevant information to the experts; this can be an obstacle to changing their health behaviour. Therefore, future research should be conducted to ascertain which interventions are appropriate for patients and to study the long-term effects of any risks from using social media in patients with major depressive disorders.

Keywords: Health behaviour change, Social media, Depressive patients, Family caregivers, Lifestyle intervention, I-Change model.


Article Information


Identifiers and Pagination:

Year: 2019
Volume: 12
First Page: 387
Last Page: 405
Publisher Id: TOPHJ-12-387
DOI: 10.2174/1874944501912010387

Article History:

Received Date: 08/06/2019
Revision Received Date: 23/08/2019
Acceptance Date: 13/09/2019
Electronic publication date: 30/09/2019
Collection year: 2019

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© 2019 Jattamart & Leelasantitham.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence this author at the Division of Technology of Information System Management, Faculty of Engineering, Mahidol University, Nakhonpathom, Thailand; E-mail: adisorn.lee@mahidol.ac.th





1. INTRODUCTION

The World Health Organisation (WHO) predicts that by 2020, depression will be the second most common illness in the world and a major cause of disability-adjusted life year (DALY) – one DALY can be thought of as one year loss of the healthy life. Depression is predicted to be a major mental health problem in the population of Thailand [1Kongsuk T, Supanya S, Kenbubpha K, Phimtra S, Sukhawaha S, Leejongpermpoon J. Services for depression and suicide in Thailand. WHO South-East Asia J Public Health 2017; 6(1): 34-8.
[http://dx.doi.org/10.4103/2224-3151.206162] [PMID: 28597857]
]. According to a report of Adjaye-Gbewonyo, Rebok [2Adjaye-Gbewonyo D, Rebok GW, Gallo JJ, Gross AL, Underwood CR. Urbanicity of residence and depression among adults 50 years and older in Ghana and South Africa: an analysis of the WHO Study on Global AGEing and Adult Health (SAGE). Aging Ment Health 2019; 23(6): 660-9.
[http://dx.doi.org/10.1080/13607863.2018.1450839] [PMID: 29634295]
] that confirms the impact of depression on the DALY of the older adult population. If it is not properly treated, it can develop into a depressive disorder and patients can suffer from depressive moods, which can lead to chronic impairments [3Chen S-K, Lin SS. A latent growth curve analysis of initial depression level and changing rate as predictors of problematic Internet use among college students. Comput Human Behav 2016; 54: 380-7.
[http://dx.doi.org/10.1016/j.chb.2015.08.018]
]; it can also increase the risk of suicide [4Merikangas KR, He JP, Burstein M, et al. Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication--Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry 2010; 49(10): 980-9.
[http://dx.doi.org/10.1016/j.jaac.2010.05.017] [PMID: 20855043]
].

Clinical Practice Guidelines of Major Depressive Disorders begin with screening and case-finding (as shown in Section 1.1 Pre-Diagnosis) in order to isolate people at risk or in the early stages of treatment as soon as possible. Once found, the risk population undergoes diagnosis by a psychiatrist (as shown in Section 1.2 Diagnosis) to find the cause and explain the abnormalities of the disease in patients. After that, it describes the treatment process which aims to make no residual symptoms through medication and various forms of therapy including health behaviour interventions (Section 1.3 Lifestyle Intervention with Major Depressive Disorder (Post-Diagnosis)), which is a tool used to reduce problems and obstacles to the treatment of diseases. However, another important issue is the change in health behaviour that is described in Section 1.4. This can help to reduce the health risk behaviours of those people who are in the risk groups identified in Section 1.1, and who can be supported by the treatment procedures described in Section 1.3. Therefore, the research is interested in conceptual modelling of behaviour modification to obtain a new model to explain the acceptance of lifestyle interventions using social media, with the intention of changing health behaviours from the perspective of depressed patients and family caregivers.

1.1. Pre-Diagnosis

Pre-Diagnosis explains the screening and evaluation of clinical results from risk populations to proceed with disease diagnosis process which uses tools to screen for depression. The currently depression screening is used in conjunction with digital platforms and online media especially in social media that found relationship between using social media and health has received considerable attention. People with health problems and general users use social media to promote health and increase their knowledge of particular conditions. This is known as Health Information-Seeking Behaviour (HISB) [5Nagelhout ES, Linder LA, Austin T, et al. Social Media Use Among Parents and Caregivers of Children With Cancer. J Pediatr Oncol Nurs 2018; 35(6): 399-405.
[http://dx.doi.org/10.1177/1043454218795091] [PMID: 30168367]
]. Instead of direct inquiries from experts, young users tend to show HISB [6Basch CH, MacLean SA, Romero R-A, Ethan D. Health Information Seeking Behavior Among College Students. J Community Health 2018; 43(6): 1094-9.
[http://dx.doi.org/10.1007/s10900-018-0526-9] [PMID: 29779074]
]. They also share their experience of certain diseases and promote mental health through sharing advice on mental health problems [7Onrust S, Bubera A, Lazic A. Use of social network Facebook for mental health prevention and counselling. Eur Psychiatry 2015; 30: 222.
[http://dx.doi.org/10.1016/S0924-9338(15)30182-6]
], increasing their social support network [8Antheunis ML, Tates K, Nieboer TE. Patients’ and health professionals’ use of social media in health care: motives, barriers and expectations. Patient Educ Couns 2013; 92(3): 426-31.
[http://dx.doi.org/10.1016/j.pec.2013.06.020] [PMID: 23899831]
]. HISB is influenced by motivation, awareness, and perceptions of health and behaviour [9Weaver JB III, Mays D, Weaver SS, Hopkins GL, Eroğlu D, Bernhardt JM. Health information-seeking behaviors, health indicators, and health risks. Am J Public Health 2010; 100(8): 1520-5.
[http://dx.doi.org/10.2105/AJPH.2009.180521] [PMID: 20558794]
] and is indicative of how personal characteristics play a role in promoting health.

With regard to people at risk of mental health problems, there is evidence that social media and information technology can be used to quickly assess and screen high-risk patients for treatment and follow-up. The evaluation and screening of high-risk patients is done by a group of medical personnel who assess symptoms based on user-generated content (UGC) obtained from patients [10Aldarwish MM, Ahmad HF. Predicting depression levels using social media posts. 2017 IEEE 13th international Symposium on Autonomous decentralized system (ISADS) 2017. In: IEEE; 2017.]. Such content helps the professionals predict levels of mental illness and its prevalence by using artificial intelligence to develop a user-level mental model [11Aldarwish MM, Ahmad HF. Predicting depression levels using social media posts. Autonomous Decentralized System (ISADS), 2017 IEEE 13th International Symposium on 2017. In: IEEE; 2017.
[http://dx.doi.org/10.1109/ISADS.2017.41]
]. This model uses text-mining technology to examine the text on web posts, web blogs, micro-blogs, forums, bulletin board systems or other media [12Tung C, Lu W. Analyzing depression tendency of web posts using an event-driven depression tendency warning model. Artif Intell Med 2016; 66: 53-62.
[http://dx.doi.org/10.1016/j.artmed.2015.10.003] [PMID: 26616420]
] and can serve as a screening channel for depression [13Cavazos-Rehg PA, Krauss MJ, Sowles S, et al. A content analysis of depression-related Tweets. Comput Human Behav 2016; 54: 351-7.
[http://dx.doi.org/10.1016/j.chb.2015.08.023] [PMID: 26392678]
]. This also helps to assess the risk of depression in pregnant women who are at risk by examining the content posted by users on platforms and testing it with a tool designed for depression screening (The Edinburgh Postnatal Depression Scale (EPDS)). Women with an EPDS score of 13+ are more likely to post words related to mental health conditions [14Bradley D, Landau E, Wolfberg A, Baron A. 870: Posts to an anonymous digital health platform may help assess depression risk among pregnant women. Am J Obstet Gynecol 2019; 220(1): S567.
[http://dx.doi.org/10.1016/j.ajog.2018.11.894]
]. Therefore, this shows that digital platforms and social media may be used to assess the risk of depression.

1.2. Diagnosis

From the effects of the depressive disorder described above when explaining the cause of the disease, it is found that there are complex causes that can help to understand the risk factors for the disease. This will support those involved in treatment with regard to understanding and explaining the patient’s disorder. That is why specialists develop biopsychosocial models to help in the search for factors related to the treatment process [15Engel GL, Engel L. The clinical application of the biopsychosocial model. Am J Psychiatry 1980; 137(5): 535-44.
[http://dx.doi.org/10.1176/ajp.137.5.535] [PMID: 7369396]
] which are caused by biological issues such as stress, gene abnormalities, abnormal neurotransmitters [16Shyn SI, Hamilton SP. The genetics of major depression: Moving beyond the monoamine hypothesis. Psychiatr Clin North Am 2010; 33(1): 125-40.
[http://dx.doi.org/10.1016/j.psc.2009.10.004] [PMID: 20159343]
] [17aan het Rot M, Mathew SJ, Charney DS. Neurobiological mechanisms in major depressive disorder. CMAJ 2009; 180(3): 305-13.
[http://dx.doi.org/10.1503/cmaj.080697] [PMID: 19188629]
], psychological influences (personality, behaviour) [18Lewinsohn PM. A behavioral approach to depression. Essential papers on depression 1974; 150-72., 19Pantic I. Online social networking and mental health. Cyberpsychol Behav Soc Netw 2014; 17(10): 652-7.
[http://dx.doi.org/10.1089/cyber.2014.0070] [PMID: 25192305]
] and social factors (social and environmental support) [20Chapman DP, Whitfield CL, Felitti VJ, Dube SR, Edwards VJ, Anda RF. Adverse childhood experiences and the risk of depressive disorders in adulthood. J Affect Disord 2004; 82(2): 217-25.
[http://dx.doi.org/10.1016/j.jad.2003.12.013] [PMID: 15488250]
-22Björkenstam E, Vinnerljung B, Hjern A. Impact of childhood adversities on depression in early adulthood: A longitudinal cohort study of 478,141 individuals in Sweden. J Affect Disord 2017; 223: 95-100.
[http://dx.doi.org/10.1016/j.jad.2017.07.030] [PMID: 28735168]
]. Lifestyle can also be a major cause of the disease. Treatment [23Lopresti AL, Hood SD, Drummond PD. A review of lifestyle factors that contribute to important pathways associated with major depression: diet, sleep and exercise. J Affect Disord 2013; 148(1): 12-27.
[http://dx.doi.org/10.1016/j.jad.2013.01.014] [PMID: 23415826]
] includes: 1) improving the quality of diet which can help to reduce the risk of depression by 35% [24Jacka FN, Pasco JA, Mykletun A, et al. Association of Western and traditional diets with depression and anxiety in women. Am J Psychiatry 2010; 167(3): 305-11.
[http://dx.doi.org/10.1176/appi.ajp.2009.09060881] [PMID: 20048020]
], 2) introducing an exercise programme after a CES-D assessment [25Nabkasorn C, Miyai N, Sootmongkol A, et al. Effects of physical exercise on depression, neuroendocrine stress hormones and physiological fitness in adolescent females with depressive symptoms. Eur J Public Health 2006; 16(2): 179-84.
[http://dx.doi.org/10.1093/eurpub/cki159] [PMID: 16126743]
] and 3) improving sleeping patterns, because insomnia is a major problem for depressed patients as it affects the brain structure and neurogenesis [26Lucassen PJ, Meerlo P, Naylor AS, et al. Regulation of adult neurogenesis by stress, sleep disruption, exercise and inflammation: Implications for depression and antidepressant action. Eur Neuropsychopharmacol 2010; 20(1): 1-17.
[http://dx.doi.org/10.1016/j.euroneuro.2009.08.003] [PMID: 19748235]
].

In addition, there are reports that confirm the relationship between depression and physical illness. This includes a longitudinal study of the relationship between depressive symptoms and Coronary Heart Disease (CHD), which is the number one cause of death in the world population, which confirms that depression can predict CHD [27Pössel P, Mitchell AM, Ronkainen K, Kaplan GA, Kauhanen J, Valtonen M. Do depressive symptoms predict the incidence of myocardial infarction independent of hopelessness? J Health Psychol 2015; 20(1): 60-8.
[http://dx.doi.org/10.1177/1359105313498109] [PMID: 23988677]
], or a report that shows that in the case of patients with depression, 18-27% have or will have prostate cancer [28Qan’ir Y, Song L. Systematic review of technology-based interventions to improve anxiety, depression, and health-related quality of life among patients with prostate cancer. Psychooncology 2019; 28(8): 1601-13.
[http://dx.doi.org/10.1002/pon.5158] [PMID: 31222956]
]. However, depression can be prevented and cured. For this reason, the mental health service should pay attention to the treatment and prevention of depression by educating and adjusting behaviours in such a way as to reduce risk factors for disease and disease severity.

Previous research has found that the relationship between the use of social media in a patient group helped the patients to participate in the treatment process [29Youn SJ, Trinh N-H, Shyu I, Chang T, Fava M, Kvedar J, et al. Using online social media, Facebook, in screening for major depressive disorder among college students. Int J Clin Health Psychol 2013; 13(1): 74-80.
[http://dx.doi.org/10.1016/S1697-2600(13)70010-3]
], worked as a medium to share information and allowed them to comment on mental health service providers [30Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: Systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res 2013; 15(4)e85
[http://dx.doi.org/10.2196/jmir.1933] [PMID: 23615206]
, 31Shepherd A, Sanders C, Doyle M, Shaw J. Using social media for support and feedback by mental health service users: Thematic analysis of a twitter conversation. BMC Psychiatry 2015; 15(1): 29.
[http://dx.doi.org/10.1186/s12888-015-0408-y] [PMID: 25881089]
] and to develop a health service system through social media [32Korda H, Itani Z. Harnessing social media for health promotion and behavior change. Health Promot Pract 2013; 14(1): 15-23.
[http://dx.doi.org/10.1177/1524839911405850] [PMID: 21558472]
, 33Chou W-YS, Hunt YM, Beckjord EB, Moser RP, Hesse BW. Social media use in the United States: Implications for health communication. J Med Internet Res 2009; 11(4)e48
[http://dx.doi.org/10.2196/jmir.1249] [PMID: 19945947]
]. It is also used as a tool to communicate and exchange useful information between different medical groups [34Thackeray R, Neiger BL, Smith AK, Van Wagenen SB. Adoption and use of social media among public health departments. BMC Public Health 2012; 12(1): 242.
[http://dx.doi.org/10.1186/1471-2458-12-242] [PMID: 22449137]
, 35Heldman AB, Schindelar J, Weaver JB. Social media engagement and public health communication: Implications for public health organizations being truly “social”. Public Health Rev 2013; 35(1): 13.
[http://dx.doi.org/10.1007/BF03391698]
] based on ethical requirements, privacy laws, confidentiality and professional codes of practice [36Grajales FJ III, Sheps S, Ho K, Novak-Lauscher H, Eysenbach G. Social media: A review and tutorial of applications in medicine and health care. J Med Internet Res 2014; 16(2)e13
[http://dx.doi.org/10.2196/jmir.2912] [PMID: 24518354]
]. It also includes the use of Behavioural Intervention Technologies (BITs) comprising web-based interventions, mobile technologies, social media (discussion groups, social networking), virtual reality and web-based games [37Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. Behavioral intervention technologies: Evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry 2013; 35(4): 332-8.
[http://dx.doi.org/10.1016/j.genhosppsych.2013.03.008] [PMID: 23664503]
], all of which are used in the process of behavioural and psychological intervention. This includes cognitive and affective methods that support behavioural, physical and mental health while also tracking their long-term effects. It has also been found to be effective in face-to-face therapy [38Andersson G. Internet interventions: Past, present and future. Internet Interv 2018; 12: 181-8.
[http://dx.doi.org/10.1016/j.invent.2018.03.008] [PMID: 30135782]
].

Online intervention in health behaviour is a strategy for improving the sustainable treatment of patients with the goal of correcting problematic behaviour that is a barrier to the treatment of disease and optimal health and well-being [39Maher CA, Lewis LK, Ferrar K, Marshall S, De Bourdeaudhuij I, Vandelanotte C. Are health behavior change interventions that use online social networks effective? A systematic review. J Med Internet Res 2014; 16(2)e40
[http://dx.doi.org/10.2196/jmir.2952] [PMID: 24550083]
]. This is important for the development of public health. It also helps to reduce travel restrictions, such as the cost of transporting medical supplies and access to people in remote areas [40Patel R, Chang T, Greysen SR, Chopra V. Social media use in chronic disease: A systematic review and novel taxonomy. Am J Med 2015; 128(12): 1335-50.
[http://dx.doi.org/10.1016/j.amjmed.2015.06.015] [PMID: 26159633]
, 41Williams G, Hamm MP, Shulhan J, Vandermeer B, Hartling L. Social media interventions for diet and exercise behaviours: A systematic review and meta-analysis of randomised controlled trials. BMJ Open 2014; 4(2)e003926
[http://dx.doi.org/10.1136/bmjopen-2013-003926] [PMID: 24525388]
]. Studying online intervention in health behaviour is challenging for researchers and those involved in learning about the social psychology factors, behaviour, and the process of behavioural change in individuals and various groups because it is difficult to know when online health behaviour interventions have occurred.

1.3. Lifestyle Intervention with Major Depressive Disorder (Post-Diagnosis)

Online health behaviour interventions are used as a tool to prevent problems and reduce the obstacles to the treatment of diseases in people with mental illnesses. Internet-based studies on the effect of depression in adolescent mothers tested the intervention behaviour using variables based on the Theory of Planned Behaviour (TPB) and found that it affects the attitudes and perceptions, and controls the intention to improve health behaviour [42Cynthia Logsdon M, Myers J, Rushton J, et al. Efficacy of an Internet-based depression intervention to improve rates of treatment in adolescent mothers. Arch Women Ment Health 2018; 21(3): 273-85.
[http://dx.doi.org/10.1007/s00737-017-0804-z] [PMID: 29260321]
]. Compared to controlled interventions, internet interventions including psychoeducation websites and websites offering cognitive behaviour therapy, have been shown to help reduce levels of depression using the Centre for Epidemiologic Studies Depression Scale (CES-D) [43Christensen H, Griffiths KM, Jorm AF. Delivering interventions for depression by using the internet: Randomised controlled trial. BMJ 2004; 328(7434): 265.
[http://dx.doi.org/10.1136/bmj.37945.566632.EE] [PMID: 14742346]
]. Evaluating the characteristics of Technology-Based Interventions (TBIs) effect on anxiety, depression, and health-related quality of life in patient groups with prostate cancer, the results indicated that the patient group controlled by the TBI platform had a reduced level of depression [28Qan’ir Y, Song L. Systematic review of technology-based interventions to improve anxiety, depression, and health-related quality of life among patients with prostate cancer. Psychooncology 2019; 28(8): 1601-13.
[http://dx.doi.org/10.1002/pon.5158] [PMID: 31222956]
]. Facebook supports changing the health behaviour of depressed patients [44Naslund JA, Aschbrenner KA, Marsch LA, McHugo GJ, Bartels SJ. Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: An exploratory study. Psychiatr Q 2018; 89(1): 81-94.
[http://dx.doi.org/10.1007/s11126-017-9512-0] [PMID: 28470468]
] and has used interventions including Facebook groups to support lifestyle interventions for weight loss and physical activity in patients with serious mental illnesses. Various studies confirm the use of Facebook as a tool to support lifestyle interventions for weight loss in those who are obese and have mental illnesses [44Naslund JA, Aschbrenner KA, Marsch LA, McHugo GJ, Bartels SJ. Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: An exploratory study. Psychiatr Q 2018; 89(1): 81-94.
[http://dx.doi.org/10.1007/s11126-017-9512-0] [PMID: 28470468]
]. Therefore, we can conclude that social media in the form of Internet Support Groups (ISGs), resources and discussion groups can be used to promote health behaviour [37Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. Behavioral intervention technologies: Evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry 2013; 35(4): 332-8.
[http://dx.doi.org/10.1016/j.genhosppsych.2013.03.008] [PMID: 23664503]
].

These findings show that changes in health behaviour that focus on the lifestyle of patients with mental health disorders are integrated within social media, and can influence depressed patients’ attitudes, perceptions, and intentions to access health care. However, there is little evidence to show that social media has an impact on changes in health behaviour and there are no studies on the factors that are directly related to the behavioural modification process of a person's health. In addition, there are no studies on interpersonal relationships which have an important role in interventions to change personal health behaviour both in face-to-face and online therapy [45Santarossa S, Kane D, Senn CY, Woodruff SJ. Exploring the role of in-person components for online health behavior change interventions: Can a digital person-to-person component suffice? J Med Internet Res 2018; 20(4)e144
[http://dx.doi.org/10.2196/jmir.8480] [PMID: 29643048]
]. Any evaluation of the treatment processes requires information from the family caregiver, which has also been lacking.

Table 1
Summary of the cognitive perspective of health behaviour.


Currently, there are many concepts and theories that try to explain the health behaviour and give importance to the cognitive perspective of the people who are affecting the behavioural change. Therefore, it is important to consider using these concepts and theories in accordance with the objectives being studied. The cognitive perspective for describing health behaviour is shown in Table 1. However, there are limitations to using these concepts and theories in cases where the behaviour is complex, as can be seen from using the Health Belief Model (HBM) [46Rosenstock IM. Historical origins of the health belief model. Health Educ Monogr 1974; 2(4): 328-35.
[http://dx.doi.org/10.1177/109019817400200403]
] and Social-cognitive Theory (SCT) [47Bandura A. Social cognitive theory: An agentic perspective. Annu Rev Psychol 2001; 52(1): 1-26.
[http://dx.doi.org/10.1146/annurev.psych.52.1.1] [PMID: 11148297]
]. They do not include a study on the factors that cause motivation (social influence, self-efficacy/behavioural skills, attitude), or the information and intention that affect behaviour. Protection Motivation Theory (PMT) [48Rogers RW. A protection motivation theory of fear appeals and attitude change1. J Psychol 1975; 91(1): 93-114.
[http://dx.doi.org/10.1080/00223980.1975.9915803] [PMID: 28136248]
], the Theory of Reasoned Action (TRA) [49Fishbein M. A theory of reasoned action: Some applications and implications 1979.], the Theory of Planned Behaviour (TPB) [50Ajzen I. From intentions to actions: A theory of planned behavior Action control 1985; 11-39.] and the Attitude-Social influences-Self-Efficacy model (ASE-model) [51de Vries H, Dijkstra M, Kuhlman P. Self-efficacy: The third factor besides attitude and subjective norm as a predictor of behavioural intentions. Health Educ Res 1988; 3(3): 273-82.
[http://dx.doi.org/10.1093/her/3.3.273]
] do not explain the information, environment and barriers that affect the intention and behaviour of individuals. Therefore, when studying health behaviour it is important to consider the factors that influence the cognitive perspective of the person under study.

1.4. I-Change Model

The I-Change Model (ICM) addresses the limitations of the behavioural models (Theory of Planned Behaviour (TPB), Attitude-Social Influence-Self-efficacy (ASE)) [50Ajzen I. From intentions to actions: A theory of planned behavior Action control 1985; 11-39.] and improves the ability to explain the factors that influence the health behaviour modifications which are a result of compre- hensive intentions to predict future changes in health behav- iour. The factors related to health behaviour modification include: 1) Predisposing factors related to demographic characteristics such as behavioural, psychological, biological, social and cultural (in which previous research found differences in the demographic characteristics which affected the variability in the use of social media) [53Vries Hd, Mesters I, van de Steeg H, Honing C. The general public’s information needs and perceptions regarding hereditary cancer: An application of the Integrated Change Model. Patient Educ Couns 2005; 56(2): 154-65.
[http://dx.doi.org/10.1016/j.pec.2004.01.002] [PMID: 15653244]
], 2) Information factors related to health counselling and information, 3) Awareness factors related to awareness of good health and appropriate practices of individuals based on knowledge, action cues and risk perceptions, 4) Motivation factors concerning attitudes, social influences and self-efficacy to practice various behaviours, 5) The intention to support behavioural change which is the main factor in predicting future behaviour modification, 6) Ability factors involved in planning for future behavioural changes 7) Barriers that restrict behavioural change and 8) Behavioural state which modifies that behaviour (Fig. 1).

The ICM was introduced as a conceptual framework to predict smoking cessation behaviour which was influenced by helpful tailored advice, personal motivation and psychological intervention [54de Vries H, Eggers SM, Bolman C. The role of action planning and plan enactment for smoking cessation. BMC Public Health 2013; 13(1): 393.
[http://dx.doi.org/10.1186/1471-2458-13-393] [PMID: 23622256]
-56Candel M, Segaar D, Cremers H-P, de Vries H. Efficacy of a Web-based computer-tailored smoking prevention intervention for Dutch adolescents: Randomized controlled trial. J Med Internet Res 2014; 16(3)]. It has been used to study the psychosocial factors that predict eating in moderation, resulting from an awareness of the risk behaviour of individuals [57Walthouwer MJL, Oenema A, Candel M, Lechner L, de Vries H. Eating in moderation and the essential role of awareness. A Dutch longitudinal study identifying psychosocial predictors. Appetite 2015; 87: 152-9.
[http://dx.doi.org/10.1016/j.appet.2014.12.214] [PMID: 25544317]
]. It has also been used to study the skills and knowledge of nurses and provide them to pregnant women and their partners who are concerned about alcohol consumption during pregnancy [58van der Wulp NY, Hoving C, de Vries H. A qualitative investigation of alcohol use advice during pregnancy: Experiences of Dutch midwives, pregnant women and their partners. Midwifery 2013; 29(11): e89-98.
[http://dx.doi.org/10.1016/j.midw.2012.11.014] [PMID: 23434309]
]. In addition, ICM has been used to study behavioural interventions that explore motivation and changes in the risk behaviour of people with Familial Hypercholesterolemia (FH) based on a control group and an intervention group. The researchers found that it was helpful to improve awareness of the risk of disease by increasing knowledge and risk perception, and also by increasing motivation to encourage health behaviours which help reduce the risk of cardiovascular diseases [59Broekhuizen K, van Poppel MN, Koppes LL, Brug J, van Mechelen W. A tailored lifestyle intervention to reduce the cardiovascular disease risk of individuals with Familial Hypercholesterolemia (FH): Design of the PRO-FIT randomised controlled trial. BMC Public Health 2010; 10(1): 69.
[http://dx.doi.org/10.1186/1471-2458-10-69] [PMID: 20156339]
]. It has also been used in web-based interventions to promote Physical Activity (PA) in adults with Type 2 diabetes by integrating it with the factors that influence behavioural change. A study was carried out to assess the effectiveness and acceptance of its use and it was found that it is important to develop and tailor web-based interventions and evaluate the ability to use it before implementation [60Moreau M, Gagnon M-P, Boudreau F. Development of a fully automated, web-based, tailored intervention promoting regular physical activity among insufficiently active adults with type 2 diabetes: Integrating the I-change model, self-determination theory, and motivational interviewing components. JMIR Res Protoc 2015; 4(1)e25
[http://dx.doi.org/10.2196/resprot.4099] [PMID: 25691346]
]. Although the ICM has been used in extensive study on health behaviours, it was only used to explain the opinions and the direction of the relationship between the variables studied (as shown in Table 2). Therefore, there is a need to explain the relationships more clearly. This led to the study on the intention ‘to go to bed’, for example which resulted from personal awareness and motivation. It explains the causal relationship between external and internal variables (or between latent variables) to test the consistency of the model and the theory applied. ICM was previously introduced to study the awareness and motivation to predict the intention of ‘bedtime’ in the form of a structural equation model based on the red frame [61Cassoff J, Gruber R, Sadikaj G, Knäuper B. What motivational and awareness variables are associated with adolescents’ intentions to go to bed earlier? Curr Psychol 2014; 33(2): 113-29.
[http://dx.doi.org/10.1007/s12144-013-9201-6]
] (Fig. 1). It has also been used to integrate with the information–motivation–behavioural skills model (IMB) to predict the motivation to use condoms during sexual intercourse in the form of a structural equation model based on the blue frame [62Eggers SM, Aarø LE, Bos AE, Mathews C, de Vries H. Predicting condom use in South Africa: A test of two integrative models. AIDS Behav 2014; 18(1): 135-45.
[http://dx.doi.org/10.1007/s10461-013-0423-2] [PMID: 23392911]
] (Fig. 1).

Fig. (1)
I-Change Model [50Ajzen I. From intentions to actions: A theory of planned behavior Action control 1985; 11-39.].


Table 2
Summary of previous research using ICM.


Fig. (2)
Proposed SMLI model based on the I-change model.


It can be seen that the ICM has been used to describe the online health behaviour interventions of patients. It can help us to understand the psychosocial factors that influence comprehensive behavioural changes. However, the ICM was not used in previous research to study social media and the health behaviour interventions of depressed patients. There was no acknowledgement of interpersonal relationships or how social media interventions changed health behaviour. Therefore, it is necessary to ascertain if there is any consistency between the ICM variables and the social media health behaviour interventions in the form of structural equations. This can help us to understand the psychosocial factors that influence appropriate behavioural changes.

1.5. The Research Study

The purpose of this study is to ascertain the influence of Social Media Lifestyle Interventions (SMLI) on health behaviour from a study on patients with major depressive disorders and family caregivers and to confirm any consistencies between the ICM and SMLI in the form of a structural equation model.

2. MATERIALS AND METHODS

2.1. Conceptual Model

The main objective of this research is to predict the intentions of patients to change their health behaviour resulting from the acceptance of SMLI. It uses the social media classification criteria of Grajales III and Sheps [36Grajales FJ III, Sheps S, Ho K, Novak-Lauscher H, Eysenbach G. Social media: A review and tutorial of applications in medicine and health care. J Med Internet Res 2014; 16(2)e13
[http://dx.doi.org/10.2196/jmir.2912] [PMID: 24518354]
] which focuses on the popular social networking and media sharing sites and Microblogs (Facebook, Instagram, YouTube, Twitter and Line) that use health care interventions. The ICM framework is used to explain the intentions that lead to the most predictable behaviour [63Smit ES, Hoving C, Schelleman-Offermans K, West R, de Vries H. Predictors of successful and unsuccessful quit attempts among smokers motivated to quit. Addict Behav 2014; 39(9): 1318-24.
[http://dx.doi.org/10.1016/j.addbeh.2014.04.017] [PMID: 24837754]
] and to focus on the factors that influence cognitive perspectives and changes in health behaviour. Factors tested include Information Factors (INF), awareness factors (AWN), motivation factors (MOT), barriers (BAR), and Intention to Change Health Behaviours (INT). Fig. (1) shows that intention is primarily responsible for ability factors and behavioural state to predict future behaviour changes. Therefore, in order to monitor future behaviour changes, it is first necessary to understand the intentions of the person. As this is a cross-sectional study, it does not explain the ability factors and behavioural states. In addition, further studies based on the ICM include the relationship between INF and MOT, INF and INT, AWN and INT and the relationship between BAR and INT. The proposed SMLI model based on the I-change model is shown in Fig. (2).

2.2. Hypotheses

The relevant factors of ICM are described as follows:

The predisposing factors related to demographic characteristics are 1) behavioural factors (lifestyle, eating behaviour, health status, smoking behaviour) [64Bronner K, Mesters I, Weiss-Meilik A, et al. Determinants of adherence to screening by colonoscopy in individuals with a family history of colorectal cancer. Patient Educ Couns 2013; 93(2): 272-81.
[http://dx.doi.org/10.1016/j.pec.2013.06.029] [PMID: 23916675]
], 2) psychological factors (personality), 3) biological factors (gender, genetic), and 4) Social and cultural factors (price of cigarettes, policies) [65Fransen GA, Mesters I, Janssen MJ, Knottnerus JA, Muris JW. Which patient-related factors determine self-perceived patient adherence to prescribed dyspepsia medication? Health Educ Res 2009; 24(5): 788-98.
[http://dx.doi.org/10.1093/her/cyp014] [PMID: 19304927]
]. Previous studies found that differences in age, gender, status, relationship, and health problems affect the perception and behaviour of individuals using social media both positively and negatively [66Keating RT, Hendy HM, Can SH. Demographic and psychosocial variables associated with good and bad perceptions of social media use. Comput Human Behav 2016; 57: 93-8.
[http://dx.doi.org/10.1016/j.chb.2015.12.002]
, 67Mcandrew FT, Jeong HS. Who does what on Facebook? Age, sex, and relationship status as predictors of Facebook use. Comput Human Behav 2012; 28(6): 2359-65.
[http://dx.doi.org/10.1016/j.chb.2012.07.007]
].

Information factors are 1) Messages (content suggestion, content type and the quality of information that affects feelings and awareness of health), 2) Channels (this relates to the channel used to receive health information) and, 3) Sources, (relates to the sources from which the health information is obtained). Regarding the relationship between the content on social media, it was previously found that when the content creators and readers have a close relationship, the emotional responses and feelings are more effective [68Lin R, Utz S. The emotional responses of browsing Facebook: Happiness, envy, and the role of tie strength. Comput Human Behav 2015; 52: 29-38.
[http://dx.doi.org/10.1016/j.chb.2015.04.064] [PMID: 26877584]
]. This includes information from health professionals which is acceptable to most readers [69Eastin MS. Credibility assessments of online health information: The effects of source expertise and knowledge of content. J Comput Mediat Commun 2001; 6(4)
[http://dx.doi.org/10.1111/j.1083-6101.2001.tb00126.x]
]. The form of the content (likes, comments/poll votes, and views) also influences participation of the readers [70Hales SB, Davidson C, Turner-McGrievy GM. Varying social media post types differentially impacts engagement in a behavioral weight loss intervention. Transl Behav Med 2014; 4(4): 355-62.
[http://dx.doi.org/10.1007/s13142-014-0274-z] [PMID: 25584084]
]. Therefore, this study examines the information and advice about participating in secret groups and seeking health information from social media lifestyle interventions. For this reason, the following hypotheses are proposed:

  • H1a: Information factors have a positive influence on awareness factors in changing the behaviour of patients' health as a result of accepting social media lifestyle interventions.
  • H1b: Information factors have a positive influence on motivation factors in changing the behaviour of patients' health as a result of accepting social media lifestyle interventions.
  • H1c: Information factors have a positive influence on intentions to use social media to change the behaviour of patients' health as a result of accepting social media lifestyle interventions.

Awareness factors are related to awareness of good health and appropriate practices and are evaluated from 1) knowledge related to behaviour that causes health hazards [71van der Wulp NY, Hoving C, de Vries H. Correlates of partner support to abstain from prenatal alcohol use: A cross-sectional survey among Dutch partners of pregnant women. Health Soc Care Community 2016; 24(5): 614-22.
[http://dx.doi.org/10.1111/hsc.12235] [PMID: 25944241]
], 2) cues to actions related to what induces people to change their behaviour (price, personal satisfaction) [61Cassoff J, Gruber R, Sadikaj G, Knäuper B. What motivational and awareness variables are associated with adolescents’ intentions to go to bed earlier? Curr Psychol 2014; 33(2): 113-29.
[http://dx.doi.org/10.1007/s12144-013-9201-6]
, 72Pajor EM, Eggers SM, Curfs KCJ, Oenema A, de Vries H. Why do Dutch people use dietary supplements? Exploring the role of socio-cognitive and psychosocial determinants. Appetite 2017; 114: 161-8.
[http://dx.doi.org/10.1016/j.appet.2017.03.036] [PMID: 28359781]
] and 3) risk perceptions from experiencing unwanted side effects and the severity of danger or illness when performing inappropriate behaviour [57Walthouwer MJL, Oenema A, Candel M, Lechner L, de Vries H. Eating in moderation and the essential role of awareness. A Dutch longitudinal study identifying psychosocial predictors. Appetite 2015; 87: 152-9.
[http://dx.doi.org/10.1016/j.appet.2014.12.214] [PMID: 25544317]
]. Therefore, this study examines the information and advice given about the awareness of a proper lifestyle after joining a secret group, and about seeking health information from social media lifestyle interventions. Therefore, its hypotheses are as follows:

  • H2a: Awareness factors have a positive influence on motivation factors in changing the health behaviour of patients as a result of accepting social media lifestyle interventions.
  • H2b: Awareness factors have a positive influence on patients' intention to change health behaviour as a result of accepting social media lifestyle interventions.

Motivation factors assess the advantages and disadvantages of behaviour change and consist of 1) attitude, which is the measure of a person's attitude toward behaviour change, 2) social influence, this is whether a person in a society or social support encourages the change in behaviour and, 3) self-efficacy, which is an assessment of the perception of difficulty or ease in changing the behaviour [73Cheung KL, Evers SM, Hiligsmann M, et al. Understanding the stakeholders’ intention to use economic decision-support tools: A cross-sectional study with the tobacco return on investment tool. Health Policy 2016; 120(1): 46-54.
[http://dx.doi.org/10.1016/j.healthpol.2015.11.004] [PMID: 26718686]
, 74van der Wulp NY, Hoving C, de Vries H. Partner’s influences and other correlates of prenatal alcohol use. Matern Child Health J 2015; 19(4): 908-16.
[http://dx.doi.org/10.1007/s10995-014-1592-y] [PMID: 25087003]
]. Therefore, this study explains the motivation to change health behaviour when joining a secret group and when seeking health information from social media lifestyle interventions. Therefore, its hypothesis is as follows:

  • H3: Motivation factors have a positive influence on patients' intentions to change health behaviour as a result of accepting social media lifestyle interventions.

In this study, barriers refer to the behaviour modification limitations assessed by participation in treatment and communication relationships and by providing useful information to experts regarding the treatment [75Street RL Jr. How clinician-patient communication contributes to health improvement: Modeling pathways from talk to outcome. Patient Educ Couns 2013; 92(3): 286-91.
[http://dx.doi.org/10.1016/j.pec.2013.05.004] [PMID: 23746769]
].

  • H4a: Barriers influence motivation factors in changing patient’s health behaviour as a result of accepting social media lifestyle interventions.
  • H4b: Barriers influence patients' intentions to change health behaviour as a result of accepting social media lifestyle interventions.

Intention factors refer to the intention to support that behavioural change. This is measured by the current response of the person to changing the behaviour in various ways, including giving advice to others, the intention to act on their own or the intention to use the advice given [64Bronner K, Mesters I, Weiss-Meilik A, et al. Determinants of adherence to screening by colonoscopy in individuals with a family history of colorectal cancer. Patient Educ Couns 2013; 93(2): 272-81.
[http://dx.doi.org/10.1016/j.pec.2013.06.029] [PMID: 23916675]
, 76Rüther T, Wissen F, Linhardt A, Aichert DS, Pogarell O, de Vries H. Electronic cigarettes—attitudes and use in Germany. Nicotine Tob Res 2016; 18(5): 660-9.
[http://dx.doi.org/10.1093/ntr/ntv188] [PMID: 26385930]
]. This study focuses on the intention to change the health behaviour of patients by using SMLI. The proposed hypotheses of the study are shown in Table 3.

The proposed hypotheses of the study are shown in Table 3.

2.3. Participants

This is a cross-sectional study using purposive sampling based on the characteristics of the sample of patients who met the criteria and objectives of the research. It consisted of patients diagnosed with mild and moderate levels of a major depressive disorder. Patients with severe levels of a depressive disorder were eliminated from the study. All the patients were assessed by a nine-question assessment tool (9Q scale) [77Kongsuk TAS, Loiha S, Maneeton N, Wannasawek K, Leejongpermpoon J, Eds. Development and validity of 9 questions xsfor assessment of depressive symptom in Thai I-san community. 6th Annual International Mental Health Conference 2007.Bangkok, Thailand. 2007.]. 157 patients over the age of 18 years and who were able to read and write in Thai language and 110 family caregivers were selected. Data were collected from a hospital in Thailand during the period July 2018 to January 2019. The research process was approved by The Centre of Ethical Reinforcement for Human Research, Mahidol University, Thailand (MU-CIRB 2018/058.0503). Individuals were invited to participate in the research. The purpose of the study was clarified by a research assistant who gave out information sheets to the participants and assured them that any information collected would be confidential. The questionnaire issued to the respondents did not contain any identifiable information. Participants were told that they could withdraw from the research at any time and that the questionnaires would be destroyed after the project was completed [78Megan A, Moreno NG, Peter S. Moreno, and Douglas Diekema. Ethics of Social Media Research: Common Concerns and Practical Considerations. Cyberpsychol Behav Soc Netw 2013; 16(9)]. All the participants in the research project gave their informed consent.

2.4. Measurement Instrument

The questionnaire was designed based on the ICM theory framework and consisted of six parts: Part 1: predisposing factors of the participants; Part 2: information factors; Part 3: awareness factors; Part 4: motivation factors; Part 5: barriers factor and Part 6: factors related to the intention to change the behaviour. It was measured and evaluated according to the Likert 5 criteria: Level 1 (strongly disagree) to 5 (strongly agree) and was divided into two sets. Questionnaire series 1: Collect data from the patients before starting the questionnaire. The clinical psychologist or psychiatric nurse assessed their readiness to complete the questionnaire using the Thai Mental State Examination (TMSE) form [79(Thailand) TTBF. Thai Mental State Examination (TMSE). Siriraj Hosp Gaz 1993; 45(6): 359-74.]. After talking with the patients, the clinical psychologist or psychiatric nurse wrote out their responses to assess them and prevent them from being asked any questions that may stimulate their condition and to assess the situation prior to the questionnaire phase by interviewing one person at a time for no more than 15 minutes at a time. Questionnaire series 2: Collect information from related individuals with experience of caring for patients for at least six months. Both the patients and family caregivers were asked the same questions, as shown in Table 4.

2.5. Data Analysis

The analysis of causal relationships was carried out using the partial least squares structural equation model (PLS-SEM) which uses a variance-based structural equation model (variance-based SEM). Testing was carried out using the SmartPLS software V.3.2.8 [82Ringle CM, Wende S, Becker J-M. SmartPLS 3 2015.] to test the concept of education in each factor. PLS-SEM in the SmartPLS software does not operate under restrictive assumptions about data distribution since it is non-parametric and is effective in predicting the relevant elements to be used to test the theory [83Zhang S, Leidner D. From improper to acceptable: How perpetrators neutralize workplace bullying behaviors in the cyber world. Inf Manage 2018; 55(7): 850-65.
[http://dx.doi.org/10.1016/j.im.2018.03.012]
, 84Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a Silver Bullet. J Mark Theory Pract 2011; 19(2): 139-52.
[http://dx.doi.org/10.2753/MTP1069-6679190202]
]. For example, it has been used to study the following; the behaviour of social media users [85Hassan L, Dias A, Hamari J. How motivational feedback increases user’s benefits and continued use: A study on gamification, quantified-self and social networking. Int J Inf Manage 2019; 46: 151-62.
[http://dx.doi.org/10.1016/j.ijinfomgt.2018.12.004]
], the leadership behavior of employees for information systems security [86Guhr N, Lebek B, Breitner MH. The impact of leadership on employees’ intended information security behaviour: An examination of the full‐range leadership theory. Inf Syst J 2019; 29(2): 340-62.
[http://dx.doi.org/10.1111/isj.12202]
], education [87Kurt ÖE. Examining an e-learning system through the lens of the information systems success model: Empirical evidence from Italy. Educ Inf Technol 2018; 1-12.] and business studies [88Koay KY. Understanding consumers’ purchase intention towards counterfeit luxury goods: An integrated model of neutralisation techniques and perceived risk theory. Asia Pac J Mark Log 2018; 30(2): 495-516.
[http://dx.doi.org/10.1108/APJML-05-2017-0100]
] including the study on health behaviours with a view to prevent diseases [89Tweneboah‐Koduah EY. Social marketing: Using the health belief model to understand breast cancer protective behaviours among women. Int J Nonprofit Volunt Sect Mark 2018; 23(2)e1613
[http://dx.doi.org/10.1002/nvsm.1613]
] and the impact on mental health problems [90Chou W-P, Lee K-H, Ko C-H, et al. Relationship between psychological inflexibility and experiential avoidance and internet addiction: Mediating effects of mental health problems. Psychiatry Res 2017; 257: 40-4.
[http://dx.doi.org/10.1016/j.psychres.2017.07.021] [PMID: 28719830]
]. PLS-SEM can analyse the results of measurement models and structural models simultaneously. It is suitable for testing small and medium samples [91Chin WW. How to Write Up and Report PLS Analyses.Handbook of Partial Least Squares: Concepts, Methods and Applications 2010; 655-90.
[http://dx.doi.org/10.1007/978-3-540-32827-8_29]
] and can provide results for the accuracy of the content and the accuracy of the classification with Composite Reliability (CR) and average variance (AVE) statistics [92Salehan M, Kim DJ, Koo C. A study of the effect of social trust, trust in social networking services, and sharing attitude, on two dimensions of personal information sharing behavior. J Supercomput 2018; 74(8): 3596-619.
[http://dx.doi.org/10.1007/s11227-016-1790-z]
]. For these reasons, PLS-SEM is the most suitable model for this study. The results of the PLS-SEM analysis of the patient and family caregiver information are presented in the form of measurement and structural models.

3. RESULTS

3.1. Descriptive Statistics

The random sampling of 186 depressive patients produced 157 complete data sets (14 were screened out, nine were unqualified, and six were incomplete). Of 139 family caregivers, 110 had complete data (15 were unqualified and 14 were incomplete). The demographic characteristics of the participants are listed in Table 5.

3.2. Measurement Model

After the examination of the quality of variables according to the criteria described in a study [93Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 2nd ed. 2017.] and the list of questions in the first set of questionnaires (patients), it was found that all the Composite Reliability (CR) values were between 0.875-0.896 (with the value not less than 0.7). Cronbach's α values were between 0.757-0.848, and these values were in accordance with the value which was not less than 0.7. The Average Variance Extracted (AVE) values were between 0.568-0.780 (with the value not less than 0.5). The results of the quality of the variables and the questions from the list of questionnaires in set 2 (family caregivers) found that the Composite Reliability (CR) value was between 0.879-0.919, and Cronbach's α value was between 0.770-0.897. The Average Variance Extracted values (AVE) were between 0.647-0.813 as shown in Table 6.

The questions used in the measurement of quality questionnaires passed all the weight values criteria. According to the criteria, the values not less than 0.7 showed reliability of the questions used in the measurement. The first set of questionnaires (patients) had a weight between 0.730-0.968 and the second questionnaire (family caregiver) had a weight between 0.72-0.907. Fornell-Larcker [94Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 1981; 18(1): 39-50.
[http://dx.doi.org/10.1177/002224378101800104]
] criteria were also used to evaluate the relationship between the variables in the form of a diagonal matrix, which found that the square roots of the AVEs in each construct (bold letters) were greater than the values in the horizontal row and corresponding rows. This indicated that the variables were categorical (Discriminant Validity), as shown in Tables 7 and 8.

3.3. Structural Model

This study examined the fit of the model before testing with path coefficient significance based on the structural model and criteria set by Hair Jr and Hult [93Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 2nd ed. 2017.], and Henseler and Hubona [95Henseler J, Hubona G, Ray PA. Using PLS path modeling in new technology research: Updated guidelines. Ind Manage Data Syst 2016; 116(1): 2-20.
[http://dx.doi.org/10.1108/IMDS-09-2015-0382]
]. It was evaluated using the Stone-Geisser Q2 process of blindfolding. For the Awareness Factors (AWN) (Q2=0.026), Motivation Factors (MOT) (Q2=0.088) and Intention to Change Health Behaviours (INT) (Q2=0.245), the result was greater than 0, indicating that the construct is relevant in accordance with the predictive relevance of the model. The value of the standardised root mean square residual (SRMR) was measured - the acceptable criteria must be lower than 0.08 (and was 0.077 for our model). The suitability of the model was assessed based on the value of Goodness-of-Fit (GoF) which was 0.37. This passed the acceptable criteria (which must be higher than 0.36) [96Wetzels M, Odekerken-Schröder G, Van Oppen C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. Manage Inf Syst Q 2009; 177-95.
[http://dx.doi.org/10.2307/20650284]
]. All this indicates that the model is a good fit.

The testing of the structural model from the resampling of patient data using the bootstrap method for the 5,000 list generated an approximate estimation for increasing confidence by analysing the relationship between the constructs [93Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 2nd ed. 2017.]. The multicollinearity tests with the VIF values found that the causal variables do not correlate above the threshold of 3.3 [93Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 2nd ed. 2017.]. When considering the value of the path coefficients, the p-value and t-value corresponded to the t-value which was higher than 1.96 (significance level=5%), 2.58 (significance level=1%), and 3.29 (significance level =0.1%) [93Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 2nd ed. 2017.]. This means that hypothesis H1a is accepted: Information Factors (INF) have a positive influence on Awareness Factors (AWN) at a significance level of 0.01 (β = 0.224, t=2.899); H1b is accepted: Information Factors (INF) have a positive influence on the Motivation Factors (MOT) at a significance level of 0.05 (β = 0.178, t=2.318); H1c is accepted: Information Factors

Table 3
Proposed hypotheses.


Table 4
Measurement items in the patients questionnaire.


Table 5
Baseline demographic characteristics of participants.


Table 6
Internal consistency, Reliability and convergent validity of the measurement model.


Table 7
Loadings and Cross-loadings.


Table 8
Fornell–Larcker criterion analysis.


(INF) have a positive influence on Intention to Change Health Behaviours (INT) at a significance level of 0.001 (β = 0.248, t=3.532); H2a is accepted: Awareness Factors (AWN) have a positive influence on Motivation Factors (MOT) at a significance level of 0.001 (β = 0.333, t=5.029); H2b is accepted: Awareness Factors (AWN) have a positive influence on Intention to Change Health Behaviours (INT) at a significance level of 0.01 (β = 0.233, t=3.212); H3 is accepted: Motivation Factors (MOT) have a positive influence on Intention to Change Health Behaviours (INT) at a significance level of 0.001 (β = 0.288, t=3.632); and H4b is accepted: Barrier (BAR) has a positive influence on the Intention to Change Health Behaviours (INT) at a significance level of 0.05 (β = 0.169, t=2.498). However, there was no statistical significance in the relation between the Barrier (BAR) and Motivation Factors (MOT). Accordingly, H4a is rejected. The results are shown in Table 9 and Fig. (3).

The sampling of the data from the family caregivers used the 5,000 bootstrap method and a multicollinearity examination with VIF values. This found that the causal variables do not correlate above the threshold of 3.3 [93Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 2nd ed. 2017.]. The results show that the following hypotheses are accepted H1b: Information Factors (INF) have a positive influence on Motivation Factors (MOT) at a significance level of 0.01 (β = 0.257, t=2.707); H3: Motivation Factors (MOT) have a positive influence on Intention to Change Health Behaviours (INT) at a significance level of 0.001 (β = 0.419, t=4.450); and, H4a: Barriers (BAR) have a positive influence on Motivation Factors (MOT) at a significance level of 0.01 (β = 0.253, t=2.792). The results reject Hypotheses H1a, H1c, H2a, H2b and H4b because INF does not have a positive relationship with AWN and INT, AWN does not have a positive relation with MOT and INT and BAR does not have a positive relation with INT. The results are shown in Table 8 and Fig. (3). The results of checking the model fit according to the criteria of Hair Jr and Hult [93Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 2nd ed. 2017.], and Henseler and Hubona [95Henseler J, Hubona G, Ray PA. Using PLS path modeling in new technology research: Updated guidelines. Ind Manage Data Syst 2016; 116(1): 2-20.
[http://dx.doi.org/10.1108/IMDS-09-2015-0382]
] found that the value of Stone-Geisser Q2 for AWN (Q2=0.024), MOT (Q2=0.121) and INT (Q2=0.162) was relevant and predictive of the model. The value of the standardised root mean square residual (SRMR) was lower than 0.08 (0.076 for our model) and the value of the goodness-of-fit (GoF) was equal to 0.70 which indicates that the model is a good fit [96Wetzels M, Odekerken-Schröder G, Van Oppen C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. Manage Inf Syst Q 2009; 177-95.
[http://dx.doi.org/10.2307/20650284]
]. The results are shown in Table 9 and Fig. (4).

The linear relationship between the exogenous and endogenous variables is presented in the form of a measure- ment model and shows the relationship between the variables and the structural model, which describes the causal relation- ship between the variables and also examines the predictive relevance of the models with the Stone-Geisser Q2 values, a standardised root mean square residual (SRMR) and goodness-of-fit (GoF) values. This confirms that the models presented in this study can be applied to the data studied. In addition, in this study, a new relationship is found between Information Factors (INF) and Motivation Factors (MOT) and between Information Factors (INF) and Intention to Change Health Behaviours (INT). The results are shown in Tables 3 to 8.

4. DISCUSSION

This study is the first to predict that the intention to change health behaviour is influenced by social media lifestyle interventions from the perspective of depressed patients and family caregivers by analysing PLS-SEM. The psychosocial factors relate to the intention of the person based on the ICM. The majority of patients were female (77.1%), and Facebook was the most popular social media platform (96.8%) followed by Line (96.2%). The results of the study (shown in Fig. 3) indicate that the views of the depressed patients are mostly consistent with the hypotheses. Only hypothesis H4a does not find a relationship between Barrier and Motivation Factors. This explains that participation in treatment and communication relationships between clinician and patient does not affect the motivation to change health behaviour arising from social media interventions.

The relationship between Information Factors (INF) has a significant positive influence on Awareness Factors (AWN), Motivation Factors (MOT) and Intention to Change Health Behaviours (INT), as shown in Fig. (3) which is in agreement with previous studies. This explains how individuals receive information and advice about health and how it affects their motivation [97Opie RS, Itsiopoulos C, Parletta N, et al. Dietary recommendations for the prevention of depression. Nutr Neurosci 2017; 20(3): 161-71.
[http://dx.doi.org/10.1179/1476830515Y.0000000043] [PMID: 26317148]
] to participate in activities [44Naslund JA, Aschbrenner KA, Marsch LA, McHugo GJ, Bartels SJ. Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: An exploratory study. Psychiatr Q 2018; 89(1): 81-94.
[http://dx.doi.org/10.1007/s11126-017-9512-0] [PMID: 28470468]
]. Additionally, the source of information or a close relationship with experts affects the users' acceptance [68Lin R, Utz S. The emotional responses of browsing Facebook: Happiness, envy, and the role of tie strength. Comput Human Behav 2015; 52: 29-38.
[http://dx.doi.org/10.1016/j.chb.2015.04.064] [PMID: 26877584]
, 69Eastin MS. Credibility assessments of online health information: The effects of source expertise and knowledge of content. J Comput Mediat Commun 2001; 6(4)
[http://dx.doi.org/10.1111/j.1083-6101.2001.tb00126.x]
]. When a person is more aware of and is motivated in accessing information about health, it is more beneficial to health [98Dominic Agyei Dankwah GCY. Health Information Literacy among Malaria Patients in Ghana: Sustainable Development Goals (SDG) 3 in Focus. Open Public Health J 2019; 12.]. Therefore, information plays an important role in raising awareness and motivation and can also predict the intention to change health behaviour. However, to be clear, elements such as information, factors, messages, channels, and sources of factors, all affect awareness, motivation, and predictions of Intention to Change the Health of patients with major depressive disorders who accept social media interventions.

With regard to the Awareness Factors (AWN) in Fig. (3), there is a significant positive influence on Motivation Factors (MOT) and Intention to change health behaviours (INT). This finding is supported by a previous study that explains that people should be aware that being healthy is an important factor in terms of increasing motivation when it comes to adjusting their health behaviour and that awareness can relate to knowledge and perceptions risk [57Walthouwer MJL, Oenema A, Candel M, Lechner L, de Vries H. Eating in moderation and the essential role of awareness. A Dutch longitudinal study identifying psychosocial predictors. Appetite 2015; 87: 152-9.
[http://dx.doi.org/10.1016/j.appet.2014.12.214] [PMID: 25544317]
]. It shows that awareness of good health and the negative health effects of social media interventions can greatly affect the motivation to change health behaviour and also contribute to the intention to change the health behaviour of depressed patients who accept interventions from social media.

From Fig. (3), it can be seen that Motivation Factors (MOT) have a significant positive influence on Intention to Change Health Behaviours (INT) of depressive patients in accepting intervention from social media.

Table 9
Hypothesis testing.


According to the results of studies on health behaviours, people who are aware and have healthy attitudes will have an increased level of motivation which plays an important role in the Intention to Change Health Behaviour [13Cavazos-Rehg PA, Krauss MJ, Sowles S, et al. A content analysis of depression-related Tweets. Comput Human Behav 2016; 54: 351-7.
[http://dx.doi.org/10.1016/j.chb.2015.08.023] [PMID: 26392678]
, 79(Thailand) TTBF. Thai Mental State Examination (TMSE). Siriraj Hosp Gaz 1993; 45(6): 359-74., 99Bartels SJ, Pratt SI, Aschbrenner KA, et al. Pragmatic replication trial of health promotion coaching for obesity in serious mental illness and maintenance of outcomes. Am J Psychiatry 2015; 172(4): 344-52.
[http://dx.doi.org/10.1176/appi.ajp.2014.14030357] [PMID: 25827032]
]. On the other hand, if a person lacks motivation to have good health behaviour, it is an important factor when it comes to changing such poor health behaviour habits. The results show that the level of motivation plays an important role in an individual’s intention to change their health behaviour. Therefore, we suggest that the awareness and attitude of patients towards good health should be promoted in order to create incentives for changing their health behaviours. This will lead to the intention to change health behaviours by accepting intervention in living with regard to social media. In addition, when a patient does not participate in treatment and lacks a good relationship with experts, it can affect the treatments given [75Street RL Jr. How clinician-patient communication contributes to health improvement: Modeling pathways from talk to outcome. Patient Educ Couns 2013; 92(3): 286-91.
[http://dx.doi.org/10.1016/j.pec.2013.05.004] [PMID: 23746769]
] and can hinder the intention to change health behaviours caused by accepting interventions from social media.

Fig. (4) provides the views of the family caregivers and indicates how they play an important role in promoting the patient's well-being [100Suchitporn Lersilp SP. Correlation between the Well-being of Children and Caregivers: A Study of a Northern-Thai Suburban Community. Open Public Health J 2018; 11.]. Information Factors (INF) have a significant positive correlation with the statistics for Motivation Factors (MOT) which is consistent with the study by Walthouwer and Oenema [57Walthouwer MJL, Oenema A, Candel M, Lechner L, de Vries H. Eating in moderation and the essential role of awareness. A Dutch longitudinal study identifying psychosocial predictors. Appetite 2015; 87: 152-9.
[http://dx.doi.org/10.1016/j.appet.2014.12.214] [PMID: 25544317]
]. This explains how information and health advice are important for behaviour modification and how obtaining information or advice about the benefits of accepting and interacting with social media tends to motivate patients to have good health behaviour. The more the patients accept to have a better attitude towards health behaviour, the greater the incentive to change the behaviour by accepting social media interventions [61Cassoff J, Gruber R, Sadikaj G, Knäuper B. What motivational and awareness variables are associated with adolescents’ intentions to go to bed earlier? Curr Psychol 2014; 33(2): 113-29.
[http://dx.doi.org/10.1007/s12144-013-9201-6]
]. Motivation Factors (MOT) have a significant positive relationship with the Intention to Change Health Behaviours (INT) of patients with depression who accept social media interventions.

The main obstacle to motivating people to change health behaviour is when they are not interested in participating in the treatment and lack good relationships with the experts. Therefore, family caregivers can help by encouraging patients to become interested in participating in treatment and act as mediators in communication with experts and patients. On the other hand, the assumptions in H5 are not supported in the study results from the caregiver's perspective, which may be due to the caregiver working with patients who lack awareness of good health [57Walthouwer MJL, Oenema A, Candel M, Lechner L, de Vries H. Eating in moderation and the essential role of awareness. A Dutch longitudinal study identifying psychosocial predictors. Appetite 2015; 87: 152-9.
[http://dx.doi.org/10.1016/j.appet.2014.12.214] [PMID: 25544317]
]. The hypothesis does not relate to the Intention to Change Health behaviour of patients with depression who accept social media intervention.

Finally, the results show that accepting social media interventions has benefits for patients in supporting their participation in treatment by creating guidelines for online self-help in clinical practice [101Walsh S, Szymczynska P, Taylor SJC, Priebe S. The acceptability of an online intervention using positive psychology for depression: A qualitative study. Internet Interv 2018; 13: 60-6.
[http://dx.doi.org/10.1016/j.invent.2018.07.003] [PMID: 30206520]
]. This also benefits the experts in reducing the limitations of face-to-face therapy, such as access to travel information, costs, transportation time, medical supplies and getting access to people in remote areas [40Patel R, Chang T, Greysen SR, Chopra V. Social media use in chronic disease: A systematic review and novel taxonomy. Am J Med 2015; 128(12): 1335-50.
[http://dx.doi.org/10.1016/j.amjmed.2015.06.015] [PMID: 26159633]
] [41Williams G, Hamm MP, Shulhan J, Vandermeer B, Hartling L. Social media interventions for diet and exercise behaviours: A systematic review and meta-analysis of randomised controlled trials. BMJ Open 2014; 4(2)e003926
[http://dx.doi.org/10.1136/bmjopen-2013-003926] [PMID: 24525388]
]. It also helps to solve the problems of psychiatrists and psychosocial staff in Thailand [1Kongsuk T, Supanya S, Kenbubpha K, Phimtra S, Sukhawaha S, Leejongpermpoon J. Services for depression and suicide in Thailand. WHO South-East Asia J Public Health 2017; 6(1): 34-8.
[http://dx.doi.org/10.4103/2224-3151.206162] [PMID: 28597857]
] and improve the treatment processes. Therefore, if in future there is a lifestyle-based social media intervention programme developed based on this model, it should focus on providing information and advice to patients about the benefits of accepting social media lifestyle interventions. It should raise awareness and promote the results of living a proper lifestyle and use incentives to encourage participants to change their health behaviour through interactions on social media. Increasing the intention to change is important for predicting the changing behaviour of the patient and improving health [8Antheunis ML, Tates K, Nieboer TE. Patients’ and health professionals’ use of social media in health care: motives, barriers and expectations. Patient Educ Couns 2013; 92(3): 426-31.
[http://dx.doi.org/10.1016/j.pec.2013.06.020] [PMID: 23899831]
, 45Santarossa S, Kane D, Senn CY, Woodruff SJ. Exploring the role of in-person components for online health behavior change interventions: Can a digital person-to-person component suffice? J Med Internet Res 2018; 20(4)e144
[http://dx.doi.org/10.2196/jmir.8480] [PMID: 29643048]
].

5. LIMITATIONS

This study had several limitations. It was based on a sample group from a hospital who were invited to take part and therefore lacked diversity. The demography was also limited to one area. In addition, the depressed patients were not separated into groups of various severity (mild, moderate and severe depression). This could reflect the differences in the patients' responses. Therefore, the results of this study cannot be said to explain the intentions of complicated depressed patients. In addition, there should be a study on the intention to change health behaviours in various forms (sleep, eating or exercise) because the results of this study only explain the overall health behaviour.

Future studies should also include a long-term follow-up of the patients who show an intention to change their health habits. It can show how their plans develop and how their behaviour changes. Moreover, the long-term effects of the risks of using social media by depressed patients both in terms of their privacy and the unreliability of information should be studied [8Antheunis ML, Tates K, Nieboer TE. Patients’ and health professionals’ use of social media in health care: motives, barriers and expectations. Patient Educ Couns 2013; 92(3): 426-31.
[http://dx.doi.org/10.1016/j.pec.2013.06.020] [PMID: 23899831]
]. In particular, personal comments on social media are a cause for concern, especially for people with mental health disorders.

Fig. (3)
Results of the proposed SMLI model for patients.


Fig. (4)
Results of the proposed SMLI model for family caregivers.


CONCLUSION

The findings from this study show how it is possible to use social media to intervene in lifestyle and create a willingness to change health behaviour from the perspective of depressed patients and family caregivers which covers the psychosocial factors of the I-Change Model. Information and awareness are important factors that increase the patient’s motivation level and can predict that persons intention to change their health behaviours. However, if the patient lacks interest in participating in treatment and does not have a good relationship with a clinician, he/ she tends not to change health behaviours by accepting social media interventions. Therefore, interventions in patients who use social media should focus on providing advice and raising awareness of a proper lifestyle. They can also focus on organizing activities for patients to engage in the long term, as well as encouraging good relationships between experts and patients.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

This research was approved by the Centre of Ethical Reinforcement for Human Research, Mahidol University, Thailand (MU-CIRB 2018/058.0503).

HUMAN AND ANIMAL RIGHTS

No animals/humans were used for studies that are the basis of this research.

CONSENT FOR PUBLICATION

Written consent from all the participants in this study was obtained before publication.

AVAILABILITY OF DATA AND MATERIALS

Not applicable.

FUNDING

The study is supported by the Rajamangala University of Technology Rattanakosin for PhD scholarship. (Contract number 001/59)

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

The authors would like to thank all the participants in this study; psychiatrist, clinical psychologist and staffs at the hospital; The Centre of Ethical Reinforcement for Human Research, Mahidol University, Thailand.

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